diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..0ea2899e94557a5afd06043128b3b9151c35d050 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +*.ipynb filter=lfs diff=lfs merge=lfs -text diff --git a/alphagenome/source b/alphagenome/source deleted file mode 160000 index b7d3963ce241c2390ea18bb99fa0722e1c169952..0000000000000000000000000000000000000000 --- a/alphagenome/source +++ /dev/null @@ -1 +0,0 @@ -Subproject commit b7d3963ce241c2390ea18bb99fa0722e1c169952 diff --git a/alphagenome/source/.gitattributes b/alphagenome/source/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..2f77e919cdc5d76d29bfc56dbc96e814aee0dc9a --- /dev/null +++ b/alphagenome/source/.gitattributes @@ -0,0 +1 @@ +*.ipynb linguist-documentation diff --git a/alphagenome/source/.github/ISSUE_TEMPLATE/bug_report.yml b/alphagenome/source/.github/ISSUE_TEMPLATE/bug_report.yml new file mode 100644 index 0000000000000000000000000000000000000000..700f70cba71d867f9b7d9b32a14746c430add089 --- /dev/null +++ b/alphagenome/source/.github/ISSUE_TEMPLATE/bug_report.yml @@ -0,0 +1,48 @@ +--- +name: Bug report +description: >- + Report a bug or unexpected behavior to help us improve AlphaGenome. +labels: +- bug + +body: +- type: markdown + attributes: + value: > + ## Thank you for helping us improve AlphaGenome! + + * Please verify that your issue has not been reported using + [Issue search][issue search]. + + * If you have a question about usage, please + consider [starting a discussion][Discussions]. + + * If you prefer a non-templated issue report, click [here][Raw report]. + + [Discussions]: https://www.alphagenomecommunity.com/ + + [issue search]: https://github.com/google-deepmind/alphagenome/search?q=is%3Aissue&type=issues + + [Raw report]: https://github.com/google-deepmind/alphagenome/issues/new?template=none +- type: textarea + attributes: + label: Description + description: A concise description of the bug. + placeholder: | + Text may use markdown formatting. + ```python + # for codeblocks, use triple backticks + ``` + validations: + required: true +- type: textarea + attributes: + label: System info (python version, alphagenome version, etc.) + description: >- + Include the output of `import alphagenome; alphagenome.__version__` + placeholder: | + ``` + ... + ``` + validations: + required: true \ No newline at end of file diff --git a/alphagenome/source/.github/ISSUE_TEMPLATE/config.yml b/alphagenome/source/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 0000000000000000000000000000000000000000..ed24bc0a217cb86fdae77e80afce5d36efd5893b --- /dev/null +++ b/alphagenome/source/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,5 @@ +blank_issues_enabled: false +contact_links: + - name: Have questions or need support? + url: https://www.alphagenomecommunity.com/ + about: Please ask questions on our community forums. diff --git a/alphagenome/source/.github/workflows/presubmit_checks.yml b/alphagenome/source/.github/workflows/presubmit_checks.yml new file mode 100644 index 0000000000000000000000000000000000000000..69ae8f1d4cd9b25a8ddd7f6e26274b0f3beeffd1 --- /dev/null +++ b/alphagenome/source/.github/workflows/presubmit_checks.yml @@ -0,0 +1,37 @@ +# Copyright 2025 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +name: CI +on: [push, pull_request] +jobs: + test: + runs-on: ${{ matrix.os }} + strategy: + fail-fast: false + matrix: + python-version: ["3.10", "3.11", "3.12", "3.13"] + os: [ubuntu-latest] + steps: + - uses: actions/checkout@v4 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v5 + with: + python-version: ${{ matrix.python-version }} + - name: Install alphagenome with dependencies + run: | + python -m pip install -U pip hatch + - name: Check + run: python -m hatch run check:all + - name: Unit tests + run: python -m hatch test diff --git a/alphagenome/source/.github/workflows/release.yaml b/alphagenome/source/.github/workflows/release.yaml new file mode 100644 index 0000000000000000000000000000000000000000..af29b1cf9eceb63baeced7856824fa953f0245a9 --- /dev/null +++ b/alphagenome/source/.github/workflows/release.yaml @@ -0,0 +1,45 @@ +# Copyright 2025 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +name: Release + +on: + release: + types: [published] + +# Use "trusted publishing", see https://docs.pypi.org/trusted-publishers/ +jobs: + release: + name: Upload release to PyPI + runs-on: ubuntu-latest + environment: + name: pypi + url: https://pypi.org/p/alphagenome + permissions: + id-token: write + steps: + - uses: actions/checkout@v4 + with: + filter: blob:none + fetch-depth: 0 + - name: Set up Python 3.12 + uses: actions/setup-python@v5 + with: + python-version: 3.12 + - name: Install hatch + run: python -m pip install -U pip hatch + - name: Build package + run: python -m hatch build + - name: Publish package distributions to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 diff --git a/alphagenome/source/.pylintrc b/alphagenome/source/.pylintrc new file mode 100644 index 0000000000000000000000000000000000000000..057ac4ca973c370dfdb93c2ff16362ed48270c5a --- /dev/null +++ b/alphagenome/source/.pylintrc @@ -0,0 +1,458 @@ +# This Pylint rcfile contains a best-effort configuration to uphold the +# best-practices and style described in the Google Python style guide: +# https://google.github.io/styleguide/pyguide.html +# +# Its original canonical open-source location is: +# https://google.github.io/styleguide/pylintrc +# +# Also includes some modifications specific to this repository. + +[MASTER] + +# Add files or directories to the ignore list. They should be base names, not +# paths. +ignore=third_party, + ./src/alphagenome/protos + +# Add files or directories matching the regex patterns to the ignore list. The +# regex matches against base names, not paths. +ignore-patterns= + +# Pickle collected data for later comparisons. +persistent=no + +# List of plugins (as comma separated values of python modules names) to load, +# usually to register additional checkers. +load-plugins= + +# Use multiple processes to speed up Pylint. +jobs=4 + +# Allow loading of arbitrary C extensions. Extensions are imported into the +# active Python interpreter and may run arbitrary code. +unsafe-load-any-extension=no + +# A comma-separated list of package or module names from where C extensions may +# be loaded. Extensions are loading into the active Python interpreter and may +# run arbitrary code. +extension-pkg-allow-list= + +# Minimum Python version to use for version dependent checks. +py-version=3.10 + +[MESSAGES CONTROL] + +# Only show warnings with the listed confidence levels. Leave empty to show +# all. Valid levels: HIGH, INFERENCE, INFERENCE_FAILURE, UNDEFINED +confidence= + +# Enable the message, report, category or checker with the given id(s). You can +# either give multiple identifier separated by comma (,) or put this option +# multiple time (only on the command line, not in the configuration file where +# it should appear only once). See also the "--disable" option for examples. +#enable= + +# Disable the message, report, category or checker with the given id(s). You +# can either give multiple identifiers separated by comma (,) or put this +# option multiple times (only on the command line, not in the configuration +# file where it should appear only once).You can also use "--disable=all" to +# disable everything first and then reenable specific checks. For example, if +# you want to run only the similarities checker, you can use "--disable=all +# --enable=similarities". If you want to run only the classes checker, but have +# no Warning level messages displayed, use"--disable=all --enable=classes +# --disable=W" +disable=abstract-method, + apply-builtin, + arguments-differ, + attribute-defined-outside-init, + backtick, + bad-option-value, + basestring-builtin, + buffer-builtin, + c-extension-no-member, + chained-comparison, + cmp-builtin, + cmp-method, + coerce-builtin, + coerce-method, + consider-iterating-dictionary, + consider-using-enumerate, + consider-using-in, + delslice-method, + div-method, + duplicate-code, + eq-without-hash, + execfile-builtin, + file-builtin, + filter-builtin-not-iterating, + fixme, + getslice-method, + global-statement, + hex-method, + idiv-method, + implicit-str-concat-in-sequence, + import-error, + import-self, + import-star-module-level, + inconsistent-return-statements, + input-builtin, + intern-builtin, + invalid-field-call, + invalid-str-codec, + locally-disabled, + long-builtin, + long-suffix, + map-builtin-not-iterating, + metaclass-assignment, + misplaced-comparison-constant, + missing-function-docstring, + missing-module-docstring, + next-method-called, + next-method-defined, + no-absolute-import, + no-else-break, + no-else-continue, + no-else-raise, + no-else-return, + no-init, # added + no-member, + no-name-in-module, + no-self-use, + nonzero-method, + not-an-iterable, # false positives around dataclasses + not-callable, # false positives for jax.jit + oct-method, + old-division, + old-ne-operator, + old-octal-literal, + old-raise-syntax, + parameter-unpacking, + print-statement, + raising-string, + range-builtin-not-iterating, + raw_input-builtin, + rdiv-method, + reduce-builtin, + relative-import, + reload-builtin, + round-builtin, + setslice-method, + signature-differs, + standarderror-builtin, + suppressed-message, + sys-max-int, + too-few-public-methods, + too-many-ancestors, + too-many-arguments, + too-many-boolean-expressions, + too-many-branches, + too-many-instance-attributes, + too-many-locals, + too-many-nested-blocks, + too-many-positional-arguments, + too-many-public-methods, + too-many-return-statements, + too-many-statements, + trailing-newlines, + unichr-builtin, + unicode-builtin, + unnecessary-comprehension, + unnecessary-lambda-assignment, + unnecessary-pass, + unpacking-in-except, + use-dict-literal, + useless-else-on-loop, + useless-object-inheritance, + useless-suppression, + using-cmp-argument, + wrong-import-order, + xrange-builtin, + zip-builtin-not-iterating, + +[REPORTS] + +# Set the output format. Available formats are text, parseable, colorized, msvs +# (visual studio) and html. You can also give a reporter class, eg +# mypackage.mymodule.MyReporterClass. +output-format=text + +# Tells whether to display a full report or only the messages +reports=no + +# Python expression which should return a note less than 10 (10 is the highest +# note). You have access to the variables errors warning, statement which +# respectively contain the number of errors / warnings messages and the total +# number of statements analyzed. This is used by the global evaluation report +# (RP0004). +evaluation=10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10) + +# Template used to display messages. This is a python new-style format string +# used to format the message information. See doc for all details +#msg-template= + + +[BASIC] + +# Good variable names which should always be accepted, separated by a comma +good-names=main,_ + +# Bad variable names which should always be refused, separated by a comma +bad-names= + +# Colon-delimited sets of names that determine each other's naming style when +# the name regexes allow several styles. +name-group= + +# Include a hint for the correct naming format with invalid-name +include-naming-hint=no + +# List of decorators that produce properties, such as abc.abstractproperty. Add +# to this list to register other decorators that produce valid properties. +property-classes=abc.abstractproperty,cached_property.cached_property,cached_property.threaded_cached_property,cached_property.cached_property_with_ttl,cached_property.threaded_cached_property_with_ttl + +# Regular expression matching correct function names +function-rgx=^(?:(?PsetUp|tearDown|setUpModule|tearDownModule)|(?P_?[A-Z][a-zA-Z0-9]*)|(?P_?[a-z][a-z0-9_]*))$ + +# Regular expression matching correct variable names +variable-rgx=^[a-z][a-z0-9_]*$ + +# Regular expression matching correct constant names +const-rgx=^(_?[A-Z][A-Z0-9_]*|__[a-z0-9_]+__|_?[a-z][a-z0-9_]*)$ + +# Regular expression matching correct attribute names +attr-rgx=^_{0,2}[a-z][a-z0-9_]*$ + +# Regular expression matching correct argument names +argument-rgx=^[a-z][a-z0-9_]*$ + +# Regular expression matching correct class attribute names +class-attribute-rgx=^(_?[A-Z][A-Z0-9_]*|__[a-z0-9_]+__|_?[a-z][a-z0-9_]*)$ + +# Regular expression matching correct inline iteration names +inlinevar-rgx=^[a-z][a-z0-9_]*$ + +# Regular expression matching correct class names +class-rgx=^_?[A-Z][a-zA-Z0-9]*$ + +# Regular expression matching correct module names +module-rgx=^(_?[a-z][a-z0-9_]*|__init__)$ + +# Regular expression matching correct method names +method-rgx=(?x)^(?:(?P_[a-z0-9_]+__|runTest|setUp|tearDown|setUpTestCase|tearDownTestCase|setupSelf|tearDownClass|setUpClass|(test|assert)_*[A-Z0-9][a-zA-Z0-9_]*|next)|(?P_{0,2}[A-Z][a-zA-Z0-9_]*)|(?P_{0,2}[a-z][a-z0-9_]*))$ + +# Regular expression which should only match function or class names that do +# not require a docstring. +no-docstring-rgx=(__.*__|main|test.*|.*test|.*Test)$ + +# Minimum line length for functions/classes that require docstrings, shorter +# ones are exempt. +docstring-min-length=10 + + +[TYPECHECK] + +# List of decorators that produce context managers, such as +# contextlib.contextmanager. Add to this list to register other decorators that +# produce valid context managers. +contextmanager-decorators=contextlib.contextmanager,contextlib2.contextmanager + +# Tells whether missing members accessed in mixin class should be ignored. A +# mixin class is detected if its name ends with "mixin" (case insensitive). +ignore-mixin-members=yes + +# List of module names for which member attributes should not be checked +# (useful for modules/projects where namespaces are manipulated during runtime +# and thus existing member attributes cannot be deduced by static analysis. It +# supports qualified module names, as well as Unix pattern matching. +ignored-modules= + +# List of class names for which member attributes should not be checked (useful +# for classes with dynamically set attributes). This supports the use of +# qualified names. +ignored-classes=optparse.Values,thread._local,_thread._local + +# List of members which are set dynamically and missed by pylint inference +# system, and so shouldn't trigger E1101 when accessed. Python regular +# expressions are accepted. +generated-members= + + +[FORMAT] + +# Maximum number of characters on a single line. +max-line-length=80 + +# lines made too long by directives to pytype. + +# Regexp for a line that is allowed to be longer than the limit. +ignore-long-lines=(?x) + (^\s*(import|from)\s + |\$Id:\s\/\/depot\/.+#\d+\s\$ + |^[a-zA-Z_][a-zA-Z0-9_]*\s*=\s*("[^"]\S+"|'[^']\S+') + |^\s*\#\ LINT\.ThenChange + |^[^#]*\#\ type:\ [a-zA-Z_][a-zA-Z0-9_.,[\] ]*$ + |pylint + |""" + |\# + |lambda + |(https?|ftp):) + +# Allow the body of an if to be on the same line as the test if there is no +# else. +single-line-if-stmt=yes + +# Maximum number of lines in a module +max-module-lines=99999 + +# String used as indentation unit. The internal Google style guide mandates 2 +# spaces. Google's externaly-published style guide says 4, consistent with +# PEP 8. Here, we use 2 spaces, for conformity with many open-sourced Google +# projects (like TensorFlow). +indent-string=' ' + +# Number of spaces of indent required inside a hanging or continued line. +indent-after-paren=4 + +# Expected format of line ending, e.g., empty (any line ending), LF or CRLF. +expected-line-ending-format= + + +[MISCELLANEOUS] + +# List of note tags to take in consideration, separated by a comma. +notes=TODO + + +[STRING] + +# This flag controls whether inconsistent-quotes generates a warning when the +# character used as a quote delimiter is used inconsistently within a module. +check-quote-consistency=yes + + +[VARIABLES] + +# Tells whether we should check for unused import in __init__ files. +init-import=no + +# A regular expression matching the name of dummy variables (i.e., expectedly +# not used). +dummy-variables-rgx=^\*{0,2}(_$|unused_|dummy_) + +# List of additional names supposed to be defined in builtins. Remember that +# you should avoid to define new builtins when possible. +additional-builtins= + +# List of strings which can identify a callback function by name. A callback +# name must start or end with one of those strings. +callbacks=cb_,_cb + +# List of qualified module names which can have objects that can redefine +# builtins. +redefining-builtins-modules=six,six.moves,past.builtins,future.builtins,functools + + +[LOGGING] + +# Logging modules to check that the string format arguments are in logging +# function parameter format +logging-modules=logging,absl.logging,tensorflow.io.logging + + +[SIMILARITIES] + +# Minimum lines number of a similarity. +min-similarity-lines=4 + +# Ignore comments when computing similarities. +ignore-comments=yes + +# Ignore docstrings when computing similarities. +ignore-docstrings=yes + +# Ignore imports when computing similarities. +ignore-imports=no + + +[SPELLING] + +# Spelling dictionary name. Available dictionaries: none. To make it working +# install python-enchant package. +spelling-dict= + +# List of comma separated words that should not be checked. +spelling-ignore-words= + +# A path to a file that contains private dictionary; one word per line. +spelling-private-dict-file= + +# Tells whether to store unknown words to indicated private dictionary in +# --spelling-private-dict-file option instead of raising a message. +spelling-store-unknown-words=no + + +[IMPORTS] + +# Deprecated modules which should not be used, separated by a comma +deprecated-modules=regsub, + TERMIOS, + Bastion, + rexec, + sets + +# Create a graph of every (i.e., internal and external) dependencies in the +# given file (report RP0402 must not be disabled) +import-graph= + +# Create a graph of external dependencies in the given file (report RP0402 must +# not be disabled) +ext-import-graph= + +# Create a graph of internal dependencies in the given file (report RP0402 must +# not be disabled) +int-import-graph= + +# Force import order to recognize a module as part of the standard +# compatibility libraries. +known-standard-library= + +# Force import order to recognize a module as part of a third party library. +known-third-party=enchant, absl + +# Analyse import fallback blocks. This can be used to support both Python 2 and +# 3 compatible code, which means that the block might have code that exists +# only in one or another interpreter, leading to false positives when analysed. +analyse-fallback-blocks=no + + +[CLASSES] + +# List of method names used to declare (i.e., assign) instance attributes. +defining-attr-methods=__init__, + __new__, + setUp + +# List of member names, which should be excluded from the protected access +# warning. +exclude-protected=_asdict, + _fields, + _replace, + _source, + _make + +# List of valid names for the first argument in a class method. +valid-classmethod-first-arg=cls, + class_ + +# List of valid names for the first argument in a metaclass class method. +valid-metaclass-classmethod-first-arg=mcs + + +[EXCEPTIONS] + +# Exceptions that will emit a warning when being caught. Defaults to +# "Exception" +overgeneral-exceptions=builtins.StandardError, + builtins.Exception, + builtins.BaseException + diff --git a/alphagenome/source/.readthedocs.yaml b/alphagenome/source/.readthedocs.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c1b00d4c21a0f8f8f1e32c6f901a806312f36a05 --- /dev/null +++ b/alphagenome/source/.readthedocs.yaml @@ -0,0 +1,42 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Read the Docs configuration file for Sphinx projects +# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details + +# Required +version: 2 + +# Set the OS, Python version and other tools you might need +build: + os: ubuntu-22.04 + tools: + python: "3.10" + jobs: + pre_build: + # Copy colabs into docs/source so they can be included in the documentation. + - cp -r colabs docs/source/ + +# Build documentation in the "docs/" directory with Sphinx +sphinx: + builder: html + configuration: docs/source/conf.py + fail_on_warning: false + +python: + install: + - method: pip + path: . + extra_requirements: + - docs diff --git a/alphagenome/source/CHANGELOG.md b/alphagenome/source/CHANGELOG.md new file mode 100644 index 0000000000000000000000000000000000000000..c8d72032cf46547181fb4652bebaf55fadf6eb95 --- /dev/null +++ b/alphagenome/source/CHANGELOG.md @@ -0,0 +1,42 @@ +# Changelog + +All notable changes to this project will be documented in this file. + +The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/), +and this project adheres to +[Semantic Versioning](https://semver.org/spec/v2.0.0.html). + +## [0.2.0] + +### Added + +- Add `is_insertion` and `is_deletion` properties to `Variant`. +- Add `DnaModel` abstract base class. +- Add support for center mask scoring over the entire sequence by passing + `None` for width. + +### Changed + +- Move RPC requests and responses to `dna_model_service.proto`. +- Move functionality to convert `TrackData` to/from protocol buffers to + utility module. + +## [0.1.0] + +### Added + +- Add `L2_DIFF_LOG1P` variant scoring aggregation type. +- Add `is_snv` property to `Variant`. +- Add non-zero mean track metadata field to model output metadata. +- Add optional interval argument to `predict_sequence`. + +## [0.0.2] + +### Added + +- `colab_utils` module to wrap reading API keys from environment variables or + Google Colab secrets. + +## [0.0.1] + +Initial release. diff --git a/alphagenome/source/CONTRIBUTING.md b/alphagenome/source/CONTRIBUTING.md new file mode 100644 index 0000000000000000000000000000000000000000..e85d9451a8b06aef26eecd751141c6737e1ae2f9 --- /dev/null +++ b/alphagenome/source/CONTRIBUTING.md @@ -0,0 +1,25 @@ +# How to Contribute + +## Contributor License Agreement + +Contributions to this project must be accompanied by a Contributor License +Agreement. You (or your employer) retain the copyright to your contribution, +this simply gives us permission to use and redistribute your contributions as +part of the project. Head over to to see +your current agreements on file or to sign a new one. + +You generally only need to submit a CLA once, so if you've already submitted one +(even if it was for a different project), you probably don't need to do it +again. + +## Code reviews + +All submissions, including submissions by project members, require review. We +use GitHub pull requests for this purpose. Consult +[GitHub Help](https://help.github.com/articles/about-pull-requests/) for more +information on using pull requests. + +## Community Guidelines + +This project follows [Google's Open Source Community +Guidelines](https://opensource.google/conduct/). diff --git a/alphagenome/source/LICENSE b/alphagenome/source/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..d645695673349e3947e8e5ae42332d0ac3164cd7 --- /dev/null +++ b/alphagenome/source/LICENSE @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/alphagenome/source/README.md b/alphagenome/source/README.md new file mode 100644 index 0000000000000000000000000000000000000000..9138ce5611c2acbe041ca6c70d2eb6f3c008be1a --- /dev/null +++ b/alphagenome/source/README.md @@ -0,0 +1,235 @@ +![AlphaGenome header image](docs/source/_static/header.png) + +# AlphaGenome + +![PyPI Python version](https://img.shields.io/pypi/pyversions/AlphaGenome) +![Presubmit Checks](https://github.com/google-deepmind/alphagenome/actions/workflows/presubmit_checks.yml/badge.svg) + +[**Get API key**](https://deepmind.google.com/science/alphagenome) | +[**Quick start**](#quick-start) | [**Installation**](#installation) | +[**Documentation**](https://www.alphagenomedocs.com/) | +[**Community**](https://www.alphagenomecommunity.com) | +[**Terms of Use**](https://deepmind.google.com/science/alphagenome/terms) + +The AlphaGenome API provides access to AlphaGenome, Google DeepMind’s unifying +model for deciphering the regulatory code within DNA sequences. This repository +contains client-side code, examples and documentation to help you use the +AlphaGenome API. + +AlphaGenome offers multimodal predictions, encompassing diverse functional +outputs such as gene expression, splicing patterns, chromatin features, and +contact maps (see [diagram below](#model_overview)). The model analyzes DNA +sequences of up to 1 million base pairs in length and can deliver predictions at +single base-pair resolution for most outputs. AlphaGenome achieves +state-of-the-art performance across a range of genomic prediction benchmarks, +including numerous diverse variant effect prediction tasks (detailed in +[Avsec et al. 2025](https://doi.org/10.1101/2025.06.25.661532)). + +The API is offered free of charge for +[non-commercial use](https://deepmind.google.com/science/alphagenome/terms) +(subject to the terms of use). Query rates vary based on demand – it is well +suited for smaller to medium-scale analyses such as analysing a limited number +of genomic regions or variants requiring 1000s of predictions, but is likely not +suitable for large scale analyses requiring more than 1 million predictions. +Once you obtain your API key, you can easily get started by following our +[Quick Start Guide](#quick-start), or watching our +[AlphaGenome 101 tutorial](https://youtu.be/Xbvloe13nak). + + + +![Model overview](docs/source/_static/model_overview.png) + + + +The documentation also covers a set of comprehensive tutorials, variant scoring +strategies to efficiently score variant effects, and a visualization library to +generate `matplotlib` figures for the different output modalities. + +We cover additional details of the capabilities and limitations in our +documentation. For support and feedback: + +- Please submit bugs and any code-related issues on + [GitHub](https://github.com/google-deepmind/alphagenome/issues). +- For general feedback, questions about usage, and/or feature requests, please + use the [community forum](https://www.alphagenomecommunity.com) – it’s + actively monitored by our team so you're likely to find answers and insights + faster. +- If you can't find what you're looking for, please get in touch with the + AlphaGenome team on alphagenome@google.com and we will be happy to assist + you with questions. We’re working hard to answer all inquiries but there may + be a short delay in our response due to the high volume we are receiving. + +## Quick start + +The quickest way to get started is to run our example notebooks in +[Google Colab](https://colab.research.google.com/). Here are some starter +notebooks: + +- [Quick start](https://colab.research.google.com/github/google-deepmind/alphagenome/blob/main/colabs/quick_start.ipynb): + An introduction to quickly get you started with using the model and making + predictions. +- [Visualizing predictions](https://colab.research.google.com/github/google-deepmind/alphagenome/blob/main/colabs/visualization_modality_tour.ipynb): + Learn how to visualize different model predictions using the visualization + libraries. + +Alternatively, you can dive straight in by following the +[installation guide](#installation) and start writing code! Here's an example of +making a variant prediction: + +```python +from alphagenome.data import genome +from alphagenome.models import dna_client +from alphagenome.visualization import plot_components +import matplotlib.pyplot as plt + + +API_KEY = 'MyAPIKey' +model = dna_client.create(API_KEY) + +interval = genome.Interval(chromosome='chr22', start=35677410, end=36725986) +variant = genome.Variant( + chromosome='chr22', + position=36201698, + reference_bases='A', + alternate_bases='C', +) + +outputs = model.predict_variant( + interval=interval, + variant=variant, + ontology_terms=['UBERON:0001157'], + requested_outputs=[dna_client.OutputType.RNA_SEQ], +) + +plot_components.plot( + [ + plot_components.OverlaidTracks( + tdata={ + 'REF': outputs.reference.rna_seq, + 'ALT': outputs.alternate.rna_seq, + }, + colors={'REF': 'dimgrey', 'ALT': 'red'}, + ), + ], + interval=outputs.reference.rna_seq.interval.resize(2**15), + # Annotate the location of the variant as a vertical line. + annotations=[plot_components.VariantAnnotation([variant], alpha=0.8)], +) +plt.show() +``` + +## Installation + + + +> [!TIP] +> You may optionally wish to create a +> [Python Virtual Environment](https://docs.python.org/3/tutorial/venv.html) to +> prevent conflicts with your system's Python environment. + + + +To install `alphagenome`, clone a local copy of the repository and run `pip +install`: + +```bash +$ git clone https://github.com/google-deepmind/alphagenome.git +$ pip install ./alphagenome +``` + +See [the documentation](https://www.alphagenomedocs.com/installation.html) for +information on alternative installation strategies. + +## Citing `alphagenome` + +If you use AlphaGenome in your research, please cite using: + + + +```bibtex +@article{alphagenome, + title={{AlphaGenome}: advancing regulatory variant effect prediction with a unified {DNA} sequence model}, + author={Avsec, {\v Z}iga and Latysheva, Natasha and Cheng, Jun and Novati, Guido and Taylor, Kyle R. and Ward, Tom and Bycroft, Clare and Nicolaisen, Lauren and Arvaniti, Eirini and Pan, Joshua and Thomas, Raina and Dutordoir, Vincent and Perino, Matteo and De, Soham and Karollus, Alexander and Gayoso, Adam and Sargeant, Toby and Mottram, Anne and Wong, Lai Hong and Drot{\'a}r, Pavol and Kosiorek, Adam and Senior, Andrew and Tanburn, Richard and Applebaum, Taylor and Basu, Souradeep and Hassabis, Demis and Kohli, Pushmeet}, + year={2025}, + doi={https://doi.org/10.1101/2025.06.25.661532}, + publisher={Cold Spring Harbor Laboratory}, + journal={bioRxiv} +} +``` + + + +## Acknowledgements + +AlphaGenome communicates with and/or references the following separate libraries +and packages: + +* [Abseil](https://github.com/abseil/abseil-py) +* [anndata](https://github.com/scverse/anndata) +* [gRPC](https://github.com/grpc/grpc) +* [immutabledict](https://github.com/corenting/immutabledict) +* [intervaltree](https://github.com/chaimleib/intervaltree) +* [jaxtyping](https://github.com/patrick-kidger/jaxtyping) +* [matplotlib](https://matplotlib.org/) +* [ml_dtypes](https://github.com/jax-ml/ml_dtypes) +* [NumPy](https://numpy.org/) +* [pandas](https://pandas.pydata.org/) +* [protobuf](https://developers.google.com/protocol-buffers/) +* [pyarrow](https://arrow.apache.org/) +* [SciPy](https://scipy.org/) +* [seaborn](https://seaborn.pydata.org/) +* [tqdm](https://github.com/tqdm/tqdm) +* [typeguard](https://github.com/agronholm/typeguard) +* [typing_extensions](https://github.com/python/typing_extensions) +* [zstandard](https://github.com/indygreg/python-zstandard) + +We thank all their contributors and maintainers! + +## License and Disclaimer + +Copyright 2024 Google LLC + +All software in this repository is licensed under the Apache License, Version +2.0 (Apache 2.0); you may not use this except in compliance with the Apache 2.0 +license. You may obtain a copy of the Apache 2.0 license at: +https://www.apache.org/licenses/LICENSE-2.0. + +Examples and documentation to help you use the AlphaGenome API are licensed +under the Creative Commons Attribution 4.0 International License (CC-BY). You +may obtain a copy of the CC-BY license at: +https://creativecommons.org/licenses/by/4.0/legalcode. + +Unless required by applicable law or agreed to in writing, all software and +materials distributed here under the Apache 2.0 or CC-BY licenses are +distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, +either express or implied. See the licenses for the specific language governing +permissions and limitations under those licenses. + +This is not an official Google product. + +### Third-party software + +Your use of any third-party software, libraries or code referenced in the +materials in this repository (including the libraries listed in the +[Acknowledgments](#acknowledgements) section) may be governed by separate terms +and conditions or license provisions. Your use of the third-party software, +libraries or code is subject to any such terms and you should check that you can +comply with any applicable restrictions or terms and conditions before use. + +### Reference Datasets + +A modified version of the GENCODE dataset (which can be found here: +https://www.gencodegenes.org/human/releases.html) is released with the client +code package for illustrative purposes, and is available with reference to the +following: + +- Copyright © 2024 EMBL-EBI +- The GENCODE dataset is subject to the EMBL-EBI terms of use, available at + https://www.ebi.ac.uk/about/terms-of-use. +- Citation: Frankish A, et al (2018) GENCODE reference annotation for the + human and mouse genome. +- Further details about GENCODE can be found at + https://www.gencodegenes.org/human/releases.html, with additional citation + information at https://www.gencodegenes.org/pages/publications.html and + further acknowledgements can be found at + https://www.gencodegenes.org/pages/gencode.html. diff --git a/alphagenome/source/__init__.py b/alphagenome/source/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f7928201d4859b256307b7c2501ea4b7f17313ae --- /dev/null +++ b/alphagenome/source/__init__.py @@ -0,0 +1,4 @@ +# -*- coding: utf-8 -*- +""" +alphagenome Project Package Initialization File +""" diff --git a/alphagenome/source/colabs/batch_variant_scoring.ipynb b/alphagenome/source/colabs/batch_variant_scoring.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..cfb2b32ef961a967ab9ebbb5440d109bbc11b1e3 --- /dev/null +++ b/alphagenome/source/colabs/batch_variant_scoring.ipynb @@ -0,0 +1,1422 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "Vjnah732ZKsd" + }, + "source": [ + "# Batch variant scoring" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8DVMMApBqEGT" + }, + "source": [ + "In this notebook, we demonstrate how to score many variants using AlphaGenome.\n", + "\n", + "To prepare numerous variants for scoring, provide the following information as\n", + "columns in a tab-separated Variant Call Format (VCF) file: - `variant_id`: A\n", + "unique identifier for each variant. - `CHROM`: Chromosome, specified as a string\n", + "beginning with 'chr' (e.g., 'chr1'). - `POS`: Integer representing the base pair\n", + "position (1-based; build hg38 (human) or mm10 (mouse) - see\n", + "[FAQ](https://www.alphagenomedocs.com/faqs.html#how-do-i-define-a-variant)). -\n", + "`REF`: The reference nucleotide sequence at the specified position. - `ALT`: The\n", + "alternate nucleotide sequence at the specified position." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "H-coxSuBNqDc" + }, + "source": [ + "``` {tip}\n", + "Open this tutorial in Google colab for interactive viewing.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "cellView": "form", + "executionInfo": { + "elapsed": 55, + "status": "ok", + "timestamp": 1753113298483, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "WA7A7CAEJFmZ" + }, + "outputs": [], + "source": [ + "# @title Install AlphaGenome\n", + "\n", + "# @markdown Run this cell to install AlphaGenome.\n", + "from IPython.display import clear_output\n", + "! pip install alphagenome\n", + "clear_output()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "executionInfo": { + "elapsed": 2531, + "status": "ok", + "timestamp": 1753113301166, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "nt721WCQBSyH" + }, + "outputs": [], + "source": [ + "# @title Setup and imports.\n", + "\n", + "from io import StringIO\n", + "from alphagenome import colab_utils\n", + "from alphagenome.data import genome\n", + "from alphagenome.models import dna_client, variant_scorers\n", + "from google.colab import data_table, files\n", + "import pandas as pd\n", + "from tqdm import tqdm\n", + "\n", + "data_table.enable_dataframe_formatter()\n", + "\n", + "# Load the model.\n", + "dna_model = dna_client.create(colab_utils.get_api_key())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "height": 843 + }, + "executionInfo": { + "elapsed": 10501, + "status": "ok", + "timestamp": 1753113311836, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "fNX9Mg80HuPZ", + "outputId": "1707defc-2066-48c7-fc24-cfd3abc562cd" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:09\u003c00:00, 2.39s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: total number of rows (121956) exceeds max_rows (20000). 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ATAC-seq\u003c/td\u003e\n", + " \u003ctd\u003e.\u003c/td\u003e\n", + " \u003ctd\u003eATAC-seq\u003c/td\u003e\n", + " \u003ctd\u003eCL:0000624\u003c/td\u003e\n", + " \u003ctd\u003eCD4-positive, alpha-beta T cell\u003c/td\u003e\n", + " \u003ctd\u003eprimary_cell\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003e-0.010510\u003c/td\u003e\n", + " \u003ctd\u003e-0.440600\u003c/td\u003e\n", + " \u003c/tr\u003e\n", + " \u003ctr\u003e\n", + " \u003cth\u003e...\u003c/th\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " 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\u003ctd\u003eGeneMaskLFCScorer(requested_output=RNA_SEQ)\u003c/td\u003e\n", + " \u003ctd\u003eUBERON:0036149 gtex Skin_Not_Sun_Exposed_Supra...\u003c/td\u003e\n", + " \u003ctd\u003e.\u003c/td\u003e\n", + " \u003ctd\u003epolyA plus RNA-seq\u003c/td\u003e\n", + " \u003ctd\u003eUBERON:0036149\u003c/td\u003e\n", + " \u003ctd\u003esuprapubic skin\u003c/td\u003e\n", + " \u003ctd\u003etissue\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eSkin_Not_Sun_Exposed_Suprapubic\u003c/td\u003e\n", + " \u003ctd\u003e0.000052\u003c/td\u003e\n", + " \u003ctd\u003e0.069106\u003c/td\u003e\n", + " \u003c/tr\u003e\n", + " \u003ctr\u003e\n", + " \u003cth\u003e121952\u003c/th\u003e\n", + " \u003ctd\u003echr16:1135446:G\u0026gt;T\u003c/td\u003e\n", + " \u003ctd\u003echr16:611158-1659734:.\u003c/td\u003e\n", + " \u003ctd\u003eENSG00000292430\u003c/td\u003e\n", + " \u003ctd\u003eENSG00000292430\u003c/td\u003e\n", + " \u003ctd\u003elncRNA\u003c/td\u003e\n", + " \u003ctd\u003e+\u003c/td\u003e\n", + " \u003ctd\u003eNone\u003c/td\u003e\n", + " \u003ctd\u003eNone\u003c/td\u003e\n", + " \u003ctd\u003eRNA_SEQ\u003c/td\u003e\n", + " \u003ctd\u003eGeneMaskLFCScorer(requested_output=RNA_SEQ)\u003c/td\u003e\n", + " \u003ctd\u003eUBERON:0036149 gtex Skin_Not_Sun_Exposed_Supra...\u003c/td\u003e\n", + " \u003ctd\u003e.\u003c/td\u003e\n", + " \u003ctd\u003epolyA plus RNA-seq\u003c/td\u003e\n", + " \u003ctd\u003eUBERON:0036149\u003c/td\u003e\n", + " \u003ctd\u003esuprapubic skin\u003c/td\u003e\n", + " \u003ctd\u003etissue\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eSkin_Not_Sun_Exposed_Suprapubic\u003c/td\u003e\n", + " \u003ctd\u003e0.004865\u003c/td\u003e\n", + " \u003ctd\u003e0.963724\u003c/td\u003e\n", + " \u003c/tr\u003e\n", + " \u003ctr\u003e\n", + " \u003cth\u003e121953\u003c/th\u003e\n", + " 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\u003ctd\u003etissue\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eSkin_Not_Sun_Exposed_Suprapubic\u003c/td\u003e\n", + " \u003ctd\u003e0.000508\u003c/td\u003e\n", + " \u003ctd\u003e0.363304\u003c/td\u003e\n", + " \u003c/tr\u003e\n", + " \u003ctr\u003e\n", + " \u003cth\u003e121955\u003c/th\u003e\n", + " \u003ctd\u003echr16:1135446:G\u0026gt;T\u003c/td\u003e\n", + " \u003ctd\u003echr16:611158-1659734:.\u003c/td\u003e\n", + " \u003ctd\u003eENSG00000292433\u003c/td\u003e\n", + " \u003ctd\u003eENSG00000292433\u003c/td\u003e\n", + " \u003ctd\u003elncRNA\u003c/td\u003e\n", + " \u003ctd\u003e+\u003c/td\u003e\n", + " \u003ctd\u003eNone\u003c/td\u003e\n", + " \u003ctd\u003eNone\u003c/td\u003e\n", + " \u003ctd\u003eRNA_SEQ\u003c/td\u003e\n", + " \u003ctd\u003eGeneMaskLFCScorer(requested_output=RNA_SEQ)\u003c/td\u003e\n", + " \u003ctd\u003eUBERON:0036149 gtex Skin_Not_Sun_Exposed_Supra...\u003c/td\u003e\n", + " 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.colab-df-buttons div {\n", + " margin-bottom: 4px;\n", + " }\n", + "\n", + " [theme=dark] .colab-df-convert {\n", + " background-color: #3B4455;\n", + " fill: #D2E3FC;\n", + " }\n", + "\n", + " [theme=dark] .colab-df-convert:hover {\n", + " background-color: #434B5C;\n", + " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", + " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", + " fill: #FFFFFF;\n", + " }\n", + " \u003c/style\u003e\n", + "\n", + " \u003cscript\u003e\n", + " const buttonEl =\n", + " document.querySelector('#df-6bf06b5c-580c-4a30-836c-54fbf5012d99 button.colab-df-convert');\n", + " buttonEl.style.display =\n", + " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", + "\n", + " async function convertToInteractive(key) {\n", + " const element = document.querySelector('#df-6bf06b5c-580c-4a30-836c-54fbf5012d99');\n", + " const dataTable =\n", + " await google.colab.kernel.invokeFunction('convertToInteractive',\n", + " [key], {});\n", + " if (!dataTable) return;\n", + "\n", + " const docLinkHtml = 'Like what you see? Visit the ' +\n", + " '\u003ca target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb\u003edata table notebook\u003c/a\u003e'\n", + " + ' to learn more about interactive tables.';\n", + " element.innerHTML = '';\n", + " dataTable['output_type'] = 'display_data';\n", + " await google.colab.output.renderOutput(dataTable, element);\n", + " const docLink = document.createElement('div');\n", + " docLink.innerHTML = docLinkHtml;\n", + " element.appendChild(docLink);\n", + " }\n", + " \u003c/script\u003e\n", + " \u003c/div\u003e\n", + "\n", + "\n", + " \u003cdiv id=\"df-63afa612-55f4-422d-bd4d-87c93e448a44\"\u003e\n", + " \u003cbutton class=\"colab-df-quickchart\" onclick=\"quickchart('df-63afa612-55f4-422d-bd4d-87c93e448a44')\"\n", + " title=\"Suggest charts\"\n", + " style=\"display:none;\"\u003e\n", + "\n", + "\u003csvg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", + " width=\"24px\"\u003e\n", + " \u003cg\u003e\n", + " \u003cpath d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/\u003e\n", + " \u003c/g\u003e\n", + "\u003c/svg\u003e\n", + " \u003c/button\u003e\n", + "\n", + "\u003cstyle\u003e\n", + " .colab-df-quickchart {\n", + " --bg-color: #E8F0FE;\n", + " --fill-color: #1967D2;\n", + " --hover-bg-color: #E2EBFA;\n", + " --hover-fill-color: #174EA6;\n", + " --disabled-fill-color: #AAA;\n", + " --disabled-bg-color: #DDD;\n", + " }\n", + "\n", + " [theme=dark] .colab-df-quickchart {\n", + " --bg-color: #3B4455;\n", + " --fill-color: #D2E3FC;\n", + " --hover-bg-color: #434B5C;\n", + " --hover-fill-color: #FFFFFF;\n", + " --disabled-bg-color: #3B4455;\n", + " --disabled-fill-color: #666;\n", + " }\n", + "\n", + " .colab-df-quickchart {\n", + " background-color: var(--bg-color);\n", + " border: none;\n", + " border-radius: 50%;\n", + " cursor: pointer;\n", + " display: none;\n", + " fill: var(--fill-color);\n", + " height: 32px;\n", + " padding: 0;\n", + " width: 32px;\n", + " }\n", + "\n", + " .colab-df-quickchart:hover {\n", + " background-color: var(--hover-bg-color);\n", + " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n", + " fill: var(--button-hover-fill-color);\n", + " }\n", + "\n", + " .colab-df-quickchart-complete:disabled,\n", + " .colab-df-quickchart-complete:disabled:hover {\n", + " background-color: var(--disabled-bg-color);\n", + " fill: var(--disabled-fill-color);\n", + " box-shadow: none;\n", + " }\n", + "\n", + " .colab-df-spinner {\n", + " border: 2px solid var(--fill-color);\n", + " border-color: transparent;\n", + " border-bottom-color: var(--fill-color);\n", + " animation:\n", + " spin 1s steps(1) infinite;\n", + " }\n", + "\n", + " @keyframes spin {\n", + " 0% {\n", + " border-color: transparent;\n", + " border-bottom-color: var(--fill-color);\n", + " border-left-color: var(--fill-color);\n", + " }\n", + " 20% {\n", + " border-color: transparent;\n", + " border-left-color: var(--fill-color);\n", + " border-top-color: var(--fill-color);\n", + " }\n", + " 30% {\n", + " border-color: transparent;\n", + " border-left-color: var(--fill-color);\n", + " border-top-color: var(--fill-color);\n", + " border-right-color: var(--fill-color);\n", + " }\n", + " 40% {\n", + " border-color: transparent;\n", + " border-right-color: var(--fill-color);\n", + " border-top-color: var(--fill-color);\n", + " }\n", + " 60% {\n", + " border-color: transparent;\n", + " border-right-color: var(--fill-color);\n", + " }\n", + " 80% {\n", + " border-color: transparent;\n", + " border-right-color: var(--fill-color);\n", + " border-bottom-color: var(--fill-color);\n", + " }\n", + " 90% {\n", + " border-color: transparent;\n", + " border-bottom-color: var(--fill-color);\n", + " }\n", + " }\n", + "\u003c/style\u003e\n", + "\n", + " \u003cscript\u003e\n", + " async function quickchart(key) {\n", + " const quickchartButtonEl =\n", + " document.querySelector('#' + key + ' button');\n", + " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n", + " quickchartButtonEl.classList.add('colab-df-spinner');\n", + " try {\n", + " const charts = await google.colab.kernel.invokeFunction(\n", + " 'suggestCharts', [key], {});\n", + " } catch (error) {\n", + " console.error('Error during call to suggestCharts:', error);\n", + " }\n", + " quickchartButtonEl.classList.remove('colab-df-spinner');\n", + " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n", + " }\n", + " (() =\u003e {\n", + " let quickchartButtonEl =\n", + " document.querySelector('#df-63afa612-55f4-422d-bd4d-87c93e448a44 button');\n", + " quickchartButtonEl.style.display =\n", + " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", + " })();\n", + " \u003c/script\u003e\n", + " \u003c/div\u003e\n", + " \u003c/div\u003e\n", + " \u003c/div\u003e\n" + ], + "text/plain": [ + " variant_id scored_interval ... raw_score quantile_score\n", + "0 chr3:58394738:A\u003eT chr3:57870450-58919026:. ... -0.003778 -0.215740\n", + "1 chr3:58394738:A\u003eT chr3:57870450-58919026:. ... 0.012303 0.280661\n", + "2 chr3:58394738:A\u003eT chr3:57870450-58919026:. ... -0.000334 -0.080577\n", + "3 chr3:58394738:A\u003eT chr3:57870450-58919026:. ... -0.009233 -0.440600\n", + "4 chr3:58394738:A\u003eT chr3:57870450-58919026:. ... -0.010510 -0.440600\n", + "... ... ... ... ... ...\n", + "121951 chr16:1135446:G\u003eT chr16:611158-1659734:. ... 0.000052 0.069106\n", + "121952 chr16:1135446:G\u003eT chr16:611158-1659734:. ... 0.004865 0.963724\n", + "121953 chr16:1135446:G\u003eT chr16:611158-1659734:. ... -0.000141 -0.137554\n", + "121954 chr16:1135446:G\u003eT chr16:611158-1659734:. ... 0.000508 0.363304\n", + "121955 chr16:1135446:G\u003eT chr16:611158-1659734:. ... -0.001741 -0.737944\n", + "\n", + "[121956 rows x 21 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# @title Score batch of variants.\n", + "\n", + "# Load VCF file containing variants.\n", + "vcf_file = 'placeholder' # @param\n", + "\n", + "# We provide an example list of variants to illustrate.\n", + "vcf_file = \"\"\"variant_id\\tCHROM\\tPOS\\tREF\\tALT\n", + "chr3_58394738_A_T_b38\\tchr3\\t58394738\\tA\\tT\n", + "chr8_28520_G_C_b38\\tchr8\\t28520\\tG\\tC\n", + "chr16_636337_G_A_b38\\tchr16\\t636337\\tG\\tA\n", + "chr16_1135446_G_T_b38\\tchr16\\t1135446\\tG\\tT\n", + "\"\"\"\n", + "\n", + "vcf = pd.read_csv(StringIO(vcf_file), sep='\\t')\n", + "\n", + "required_columns = ['variant_id', 'CHROM', 'POS', 'REF', 'ALT']\n", + "for column in required_columns:\n", + " if column not in vcf.columns:\n", + " raise ValueError(f'VCF file is missing required column: {column}.')\n", + "\n", + "organism = 'human' # @param [\"human\", \"mouse\"] {type:\"string\"}\n", + "\n", + "# @markdown Specify length of sequence around variants to predict:\n", + "sequence_length = '1MB' # @param [\"2KB\", \"16KB\", \"100KB\", \"500KB\", \"1MB\"] { type:\"string\" }\n", + "sequence_length = dna_client.SUPPORTED_SEQUENCE_LENGTHS[\n", + " f'SEQUENCE_LENGTH_{sequence_length}'\n", + "]\n", + "\n", + "# @markdown Specify which scorers to use to score your variants:\n", + "score_rna_seq = True # @param { type: \"boolean\"}\n", + "score_cage = True # @param { type: \"boolean\" }\n", + "score_procap = True # @param { type: \"boolean\" }\n", + "score_atac = True # @param { type: \"boolean\" }\n", + "score_dnase = True # @param { type: \"boolean\" }\n", + "score_chip_histone = True # @param { type: \"boolean\" }\n", + "score_chip_tf = True # @param { type: \"boolean\" }\n", + "score_polyadenylation = True # @param { type: \"boolean\" }\n", + "score_splice_sites = True # @param { type: \"boolean\" }\n", + "score_splice_site_usage = True # @param { type: \"boolean\" }\n", + "score_splice_junctions = True # @param { type: \"boolean\" }\n", + "\n", + "# @markdown Other settings:\n", + "download_predictions = False # @param { type: \"boolean\" }\n", + "\n", + "# Parse organism specification.\n", + "organism_map = {\n", + " 'human': dna_client.Organism.HOMO_SAPIENS,\n", + " 'mouse': dna_client.Organism.MUS_MUSCULUS,\n", + "}\n", + "organism = organism_map[organism]\n", + "\n", + "# Parse scorer specification.\n", + "scorer_selections = {\n", + " 'rna_seq': score_rna_seq,\n", + " 'cage': score_cage,\n", + " 'procap': score_procap,\n", + " 'atac': score_atac,\n", + " 'dnase': score_dnase,\n", + " 'chip_histone': score_chip_histone,\n", + " 'chip_tf': score_chip_tf,\n", + " 'polyadenylation': score_polyadenylation,\n", + " 'splice_sites': score_splice_sites,\n", + " 'splice_site_usage': score_splice_site_usage,\n", + " 'splice_junctions': score_splice_junctions,\n", + "}\n", + "\n", + "all_scorers = variant_scorers.RECOMMENDED_VARIANT_SCORERS\n", + "selected_scorers = [\n", + " all_scorers[key]\n", + " for key in all_scorers\n", + " if scorer_selections.get(key.lower(), False)\n", + "]\n", + "\n", + "# Remove any scorers or output types that are not supported for the chosen organism.\n", + "unsupported_scorers = [\n", + " scorer\n", + " for scorer in selected_scorers\n", + " if (\n", + " organism.value\n", + " not in variant_scorers.SUPPORTED_ORGANISMS[scorer.base_variant_scorer]\n", + " )\n", + " | (\n", + " (scorer.requested_output == dna_client.OutputType.PROCAP)\n", + " \u0026 (organism == dna_client.Organism.MUS_MUSCULUS)\n", + " )\n", + "]\n", + "if len(unsupported_scorers) \u003e 0:\n", + " print(\n", + " f'Excluding {unsupported_scorers} scorers as they are not supported for'\n", + " f' {organism}.'\n", + " )\n", + " for unsupported_scorer in unsupported_scorers:\n", + " selected_scorers.remove(unsupported_scorer)\n", + "\n", + "\n", + "# Score variants in the VCF file.\n", + "results = []\n", + "\n", + "for i, vcf_row in tqdm(vcf.iterrows(), total=len(vcf)):\n", + " variant = genome.Variant(\n", + " chromosome=str(vcf_row.CHROM),\n", + " position=int(vcf_row.POS),\n", + " reference_bases=vcf_row.REF,\n", + " alternate_bases=vcf_row.ALT,\n", + " name=vcf_row.variant_id,\n", + " )\n", + " interval = variant.reference_interval.resize(sequence_length)\n", + "\n", + " variant_scores = dna_model.score_variant(\n", + " interval=interval,\n", + " variant=variant,\n", + " variant_scorers=selected_scorers,\n", + " organism=organism,\n", + " )\n", + " results.append(variant_scores)\n", + "\n", + "df_scores = variant_scorers.tidy_scores(results)\n", + "\n", + "if download_predictions:\n", + " df_scores.to_csv('variant_scores.csv', index=False)\n", + " files.download('variant_scores.csv')\n", + "\n", + "df_scores" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "H_B5J8HDrA-h" + }, + "source": [ + "Note that the resulting output dataframe could be quite large, especially if you\n", + "use many scorers for scoring. Very large dataframes can't be filtered\n", + "interactively, but you can interact with them using the standard `pandas`\n", + "commands:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "height": 1574 + }, + "executionInfo": { + "elapsed": 474, + "status": "ok", + "timestamp": 1753113312546, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "69iH8Chkr5XD", + "outputId": "9287ddeb-ee32-4ff9-e35b-27a7b2b4fe31" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "repr_error": "Out of range float values are not JSON compliant: nan", + "type": "dataframe" + }, + "application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/9e65f7085e7ffcb7/data_table.js\";\n\n const table = window.createDataTable({\n data: [[{\n 'v': 0,\n 'f': \"0\",\n 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RNA-seq\u003c/td\u003e\n", + " \u003ctd\u003eT-cell\u003c/td\u003e\n", + " \u003ctd\u003eprimary_cell\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003e\u003c/td\u003e\n", + " \u003ctd\u003e-0.000237\u003c/td\u003e\n", + " \u003ctd\u003e-0.312224\u003c/td\u003e\n", + " \u003c/tr\u003e\n", + " \u003ctr\u003e\n", + " \u003cth\u003e95034\u003c/th\u003e\n", + " \u003ctd\u003echr16:1135446:G\u0026gt;T\u003c/td\u003e\n", + " \u003ctd\u003echr16:611158-1659734:.\u003c/td\u003e\n", + " \u003ctd\u003eENSG00000292400\u003c/td\u003e\n", + " \u003ctd\u003eENSG00000292400\u003c/td\u003e\n", + " \u003ctd\u003elncRNA\u003c/td\u003e\n", + " \u003ctd\u003e-\u003c/td\u003e\n", + " \u003ctd\u003eNone\u003c/td\u003e\n", + " \u003ctd\u003eNone\u003c/td\u003e\n", + " \u003ctd\u003eRNA_SEQ\u003c/td\u003e\n", + " \u003ctd\u003eGeneMaskLFCScorer(requested_output=RNA_SEQ)\u003c/td\u003e\n", + " \u003ctd\u003eCL:0000084 total 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\u003ctd\u003eGeneMaskLFCScorer(requested_output=RNA_SEQ)\u003c/td\u003e\n", + " \u003ctd\u003eCL:0000084 total RNA-seq\u003c/td\u003e\n", + " \u003ctd\u003e-\u003c/td\u003e\n", + " \u003ctd\u003etotal RNA-seq\u003c/td\u003e\n", + " \u003ctd\u003eT-cell\u003c/td\u003e\n", + " \u003ctd\u003eprimary_cell\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003e\u003c/td\u003e\n", + " \u003ctd\u003e0.000173\u003c/td\u003e\n", + " \u003ctd\u003e0.280661\u003c/td\u003e\n", + " \u003c/tr\u003e\n", + " \u003ctr\u003e\n", + " \u003cth\u003e95036\u003c/th\u003e\n", + " \u003ctd\u003echr16:1135446:G\u0026gt;T\u003c/td\u003e\n", + " \u003ctd\u003echr16:611158-1659734:.\u003c/td\u003e\n", + " \u003ctd\u003eENSG00000292431\u003c/td\u003e\n", + " \u003ctd\u003eENSG00000292431\u003c/td\u003e\n", + " \u003ctd\u003elncRNA\u003c/td\u003e\n", + " \u003ctd\u003e-\u003c/td\u003e\n", + " \u003ctd\u003eNone\u003c/td\u003e\n", + " 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Visit the ' +\n", + " '\u003ca target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb\u003edata table notebook\u003c/a\u003e'\n", + " + ' to learn more about interactive tables.';\n", + " element.innerHTML = '';\n", + " dataTable['output_type'] = 'display_data';\n", + " await google.colab.output.renderOutput(dataTable, element);\n", + " const docLink = document.createElement('div');\n", + " docLink.innerHTML = docLinkHtml;\n", + " element.appendChild(docLink);\n", + " }\n", + " \u003c/script\u003e\n", + " \u003c/div\u003e\n", + "\n", + "\n", + " \u003cdiv id=\"df-044efd3c-dcfe-404a-afa2-5b4a6503aca2\"\u003e\n", + " \u003cbutton class=\"colab-df-quickchart\" onclick=\"quickchart('df-044efd3c-dcfe-404a-afa2-5b4a6503aca2')\"\n", + " title=\"Suggest charts\"\n", + " style=\"display:none;\"\u003e\n", + "\n", + "\u003csvg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", + " width=\"24px\"\u003e\n", + " \u003cg\u003e\n", + " \u003cpath d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/\u003e\n", + " \u003c/g\u003e\n", + "\u003c/svg\u003e\n", + " \u003c/button\u003e\n", + "\n", + "\u003cstyle\u003e\n", + " .colab-df-quickchart {\n", + " --bg-color: #E8F0FE;\n", + " --fill-color: #1967D2;\n", + " --hover-bg-color: #E2EBFA;\n", + " --hover-fill-color: #174EA6;\n", + " --disabled-fill-color: #AAA;\n", + " --disabled-bg-color: #DDD;\n", + " }\n", + "\n", + " [theme=dark] .colab-df-quickchart {\n", + " --bg-color: #3B4455;\n", + " --fill-color: #D2E3FC;\n", + " --hover-bg-color: #434B5C;\n", + " --hover-fill-color: #FFFFFF;\n", + " --disabled-bg-color: #3B4455;\n", + " --disabled-fill-color: #666;\n", + " }\n", + "\n", + " .colab-df-quickchart {\n", + " background-color: var(--bg-color);\n", + " border: none;\n", + " border-radius: 50%;\n", + " cursor: pointer;\n", + " display: none;\n", + " fill: var(--fill-color);\n", + " height: 32px;\n", + " padding: 0;\n", + " width: 32px;\n", + " }\n", + "\n", + " .colab-df-quickchart:hover {\n", + " background-color: var(--hover-bg-color);\n", + " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n", + " fill: var(--button-hover-fill-color);\n", + " }\n", + "\n", + " .colab-df-quickchart-complete:disabled,\n", + " .colab-df-quickchart-complete:disabled:hover {\n", + " background-color: var(--disabled-bg-color);\n", + " fill: var(--disabled-fill-color);\n", + " box-shadow: none;\n", + " }\n", + "\n", + " .colab-df-spinner {\n", + " border: 2px solid var(--fill-color);\n", + " border-color: transparent;\n", + " border-bottom-color: var(--fill-color);\n", + " animation:\n", + " spin 1s steps(1) infinite;\n", + " }\n", + "\n", + " @keyframes spin {\n", + " 0% {\n", + " border-color: transparent;\n", + " border-bottom-color: var(--fill-color);\n", + " border-left-color: var(--fill-color);\n", + " }\n", + " 20% {\n", + " border-color: transparent;\n", + " border-left-color: var(--fill-color);\n", + " border-top-color: var(--fill-color);\n", + " }\n", + " 30% {\n", + " border-color: transparent;\n", + " border-left-color: var(--fill-color);\n", + " border-top-color: var(--fill-color);\n", + " border-right-color: var(--fill-color);\n", + " }\n", + " 40% {\n", + " border-color: transparent;\n", + " border-right-color: var(--fill-color);\n", + " border-top-color: var(--fill-color);\n", + " }\n", + " 60% {\n", + " border-color: transparent;\n", + " border-right-color: var(--fill-color);\n", + " }\n", + " 80% {\n", + " border-color: transparent;\n", + " border-right-color: var(--fill-color);\n", + " border-bottom-color: var(--fill-color);\n", + " }\n", + " 90% {\n", + " border-color: transparent;\n", + " border-bottom-color: var(--fill-color);\n", + " }\n", + " }\n", + "\u003c/style\u003e\n", + "\n", + " \u003cscript\u003e\n", + " async function quickchart(key) {\n", + " const quickchartButtonEl =\n", + " document.querySelector('#' + key + ' button');\n", + " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n", + " quickchartButtonEl.classList.add('colab-df-spinner');\n", + " try {\n", + " const charts = await google.colab.kernel.invokeFunction(\n", + " 'suggestCharts', [key], {});\n", + " } catch (error) {\n", + " console.error('Error during call to suggestCharts:', error);\n", + " }\n", + " quickchartButtonEl.classList.remove('colab-df-spinner');\n", + " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n", + " }\n", + " (() =\u003e {\n", + " let quickchartButtonEl =\n", + " document.querySelector('#df-044efd3c-dcfe-404a-afa2-5b4a6503aca2 button');\n", + " quickchartButtonEl.style.display =\n", + " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", + " })();\n", + " \u003c/script\u003e\n", + " \u003c/div\u003e\n", + " \u003c/div\u003e\n", + " \u003c/div\u003e\n" + ], + "text/plain": [ + " variant_id scored_interval ... raw_score quantile_score\n", + "0 chr3:58394738:A\u003eT chr3:57870450-58919026:. ... -0.003778 -0.215740\n", + "168 chr3:58394738:A\u003eT chr3:57870450-58919026:. ... -0.030633 -0.680793\n", + "2109 chr3:58394738:A\u003eT chr3:57870450-58919026:. ... -0.001469 -0.373276\n", + "2110 chr3:58394738:A\u003eT chr3:57870450-58919026:. ... 0.001324 0.171333\n", + "2111 chr3:58394738:A\u003eT chr3:57870450-58919026:. ... -0.002098 -0.383161\n", + "... ... ... ... ... ...\n", + "95033 chr16:1135446:G\u003eT chr16:611158-1659734:. ... -0.000237 -0.312224\n", + "95034 chr16:1135446:G\u003eT chr16:611158-1659734:. ... 0.000030 0.126219\n", + "95035 chr16:1135446:G\u003eT chr16:611158-1659734:. ... 0.000173 0.280661\n", + "95036 chr16:1135446:G\u003eT chr16:611158-1659734:. ... -0.000035 -0.126219\n", + "95037 chr16:1135446:G\u003eT chr16:611158-1659734:. ... 0.000978 0.635053\n", + "\n", + "[524 rows x 20 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Examine just the effects of the variants on T-cells.\n", + "columns = [c for c in df_scores.columns if c != 'ontology_curie']\n", + "df_scores[(df_scores['ontology_curie'] == 'CL:0000084')][columns]" + ] + } + ], + "metadata": { + "colab": { + "last_runtime": {}, + "name": "batch_variant_scoring.ipynb", + "provenance": [ + { + "file_id": "1RzAW0xmRFe7UkS0HKCOFFV6tVkc_elUh", + "timestamp": 1729614262382 + }, + { + "file_id": "", + "timestamp": 1729177232324 + }, + { + "file_id": "1UcU771Brz691HgrtQBrA41Qrq55_GgJS", + "timestamp": 1728927969951 + } + ], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/alphagenome/source/colabs/essential_commands.ipynb b/alphagenome/source/colabs/essential_commands.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..e1e17895ce507ec62ca5b4e4b5681a87d886d89e --- /dev/null +++ b/alphagenome/source/colabs/essential_commands.ipynb @@ -0,0 +1,1405 @@ +{ + "cells": [ + { + "metadata": { + "id": "LIIksEJ7fbxF" + }, + "cell_type": "markdown", + "source": [ + "# Essential commands\n", + "The following describes essential commands for interacting with the AlphaGenome API. It is broken into two sections: data and methods.\n", + "\n" + ] + }, + { + "metadata": { + "id": "gcms9aHWNnqs" + }, + "cell_type": "markdown", + "source": [ + "```{tip}\n", + "Open this tutorial in Google colab for interactive viewing.\n", + "```" + ] + }, + { + "metadata": { + "executionInfo": { + "elapsed": 13, + "status": "ok", + "timestamp": 1749822645556, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "iEs6z4rGe3lk" + }, + "cell_type": "code", + "source": [ + "# @title Install AlphaGenome\n", + "\n", + "# @markdown Run this cell to install AlphaGenome.\n", + "from IPython.display import clear_output\n", + "! pip install alphagenome\n", + "clear_output()" + ], + "outputs": [], + "execution_count": 1 + }, + { + "metadata": { + "id": "rKyGK083Wwh7" + }, + "cell_type": "markdown", + "source": [ + "# Imports" + ] + }, + { + "metadata": { + "executionInfo": { + "elapsed": 1070, + "status": "ok", + "timestamp": 1749822646891, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "V7MD3DBEfJwf" + }, + "cell_type": "code", + "source": [ + "from alphagenome.data import genome\n", + "from alphagenome.models import dna_client\n", + "import numpy as np\n", + "import pandas as pd\n", + "from google.colab import userdata" + ], + "outputs": [], + "execution_count": 2 + }, + { + "metadata": { + "id": "dzkwq2tyfj0q" + }, + "cell_type": "markdown", + "source": [ + "##  Data: model inputs" + ] + }, + { + "metadata": { + "id": "3qR6e2XtW5IZ" + }, + "cell_type": "markdown", + "source": [ + "### Genomic interval\n", + "\n", + "A genomic interval is specified using `genome.Interval`:" + ] + }, + { + "metadata": { + "executionInfo": { + "elapsed": 57, + "status": "ok", + "timestamp": 1749822647220, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "XIZHnO32W4Hn" + }, + "cell_type": "code", + "source": [ + "interval = genome.Interval(chromosome='chr1', start=1_000, end=1_010)" + ], + "outputs": [], + "execution_count": 3 + }, + { + "metadata": { + "id": "Qn9x1ArcXLVI" + }, + "cell_type": "markdown", + "source": [ + "By default, these are human hg38 intervals. See the\n", + "[FAQ](https://www.alphagenomedocs.com/faqs.html#what-are-the-reference-genome-versions-used-by-the-model) for more\n", + "details on organisms and genome versions.\n" + ] + }, + { + "metadata": { + "id": "PCGNRUfHXOL1" + }, + "cell_type": "markdown", + "source": [ + "#### Interval properties\n", + "\n", + "Access some handy properties of the interval:\n" + ] + }, + { + "metadata": { + "executionInfo": { + "elapsed": 66, + "status": "ok", + "timestamp": 1749822647565, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "8bn73Lm3XL1C", + "outputId": "f872b5f0-51bd-4455-9ec6-96c3d19a5c1d" + }, + "cell_type": "code", + "source": [ + "interval.center()" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "1005" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 4 + }, + { + "metadata": { + "executionInfo": { + "elapsed": 56, + "status": "ok", + "timestamp": 1749822647918, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "fJVk-ocQXWhm", + "outputId": "d0203696-36b6-4ca5-a204-f417284f2ca7" + }, + "cell_type": "code", + "source": [ + "interval.width" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "10" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 5 + }, + { + "metadata": { + "id": "BvlmfYgBXXig" + }, + "cell_type": "markdown", + "source": [ + "#### Resize\n", + "\n", + "Use `genome.Interval.resize` to resize the interval\n", + "around its center point:" + ] + }, + { + "metadata": { + "executionInfo": { + "elapsed": 64, + "status": "ok", + "timestamp": 1749822648252, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "y72ZqANrXehY", + "outputId": "5c001c60-01db-4807-8508-a795a88dc629" + }, + "cell_type": "code", + "source": [ + "interval.resize(100)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "Interval(chromosome='chr1', start=955, end=1055, strand='.', name='')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 6 + }, + { + "metadata": { + "id": "ZrM4rMJDXkNF" + }, + "cell_type": "markdown", + "source": [ + "#### Compare intervals\n", + "\n", + "We can also check the interval's relationship to other intervals:" + ] + }, + { + "metadata": { + "executionInfo": { + "elapsed": 68, + "status": "ok", + "timestamp": 1749822648622, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "Ye04nJETXmBL" + }, + "cell_type": "code", + "source": [ + "second_interval = genome.Interval(chromosome='chr1', start=1_005, end=1_015)" + ], + "outputs": [], + "execution_count": 7 + }, + { + "metadata": { + "executionInfo": { + "elapsed": 62, + "status": "ok", + "timestamp": 1749822648949, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "9yecEDzAXpIS", + "outputId": "d5a0d62c-4d96-4945-c33b-9566f5c9d1b0" + }, + "cell_type": "code", + "source": [ + "interval.overlaps(second_interval)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 8 + }, + { + "metadata": { + "executionInfo": { + "elapsed": 61, + "status": "ok", + "timestamp": 1749822649266, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "tMN-FGXZXsqr", + "outputId": "32d4de71-3d1c-4965-91c2-8f82b09d9aab" + }, + "cell_type": "code", + "source": [ + "interval.contains(second_interval)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 9 + }, + { + "metadata": { + "executionInfo": { + "elapsed": 330, + "status": "ok", + "timestamp": 1749822649862, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "sDjWXjJYXuPB", + "outputId": "f43f4e1b-e319-44c7-d6d9-68b5384d579c" + }, + "cell_type": "code", + "source": [ + "interval.intersect(second_interval)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "Interval(chromosome='chr1', start=1005, end=1010, strand='.', name='')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 10 + }, + { + "metadata": { + "id": "X0U15RKjXwZL" + }, + "cell_type": "markdown", + "source": [ + "As a subtle point, AlphaGenome classes use 0-based indexing, meaning that the\n", + "interval includes the base pair at the `start` position up to the base pair at\n", + "the `end-1` position. See the [FAQ](https://www.alphagenomedocs.com/faqs.html#how-do-i-specify-a-genomic-region)\n", + "for more on this topic." + ] + }, + { + "metadata": { + "id": "dIDUCQKOX1Vj" + }, + "cell_type": "markdown", + "source": [ + "### Genomic variant\n", + "\n", + "A `genome.Variant` specifies a genetic variant:\n" + ] + }, + { + "metadata": { + "executionInfo": { + "elapsed": 54, + "status": "ok", + "timestamp": 1749822650248, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "R_D6AoKFXyBJ" + }, + "cell_type": "code", + "source": [ + "variant = genome.Variant(\n", + " chromosome='chr3', position=10_000, reference_bases='A', alternate_bases='C'\n", + ")" + ], + "outputs": [], + "execution_count": 11 + }, + { + "metadata": { + "id": "L9QcFhogX693" + }, + "cell_type": "markdown", + "source": [ + "This variant changes the base `A` to a `C` at position 10\\_000 on chromosome 3\\.\n", + "Note that the `position` attribute is 1-based to maintain compatibility with\n", + "common public variant formats (see [FAQ](https://www.alphagenomedocs.com/faqs.html#how-do-i-define-a-variant) for more\n", + "info.)" + ] + }, + { + "metadata": { + "id": "l6SRhPTrYKY3" + }, + "cell_type": "markdown", + "source": [ + "#### Insertions or deletions (indels)\n", + "\n", + "Variants can also be larger than a single base, such as insertions or deletions:" + ] + }, + { + "metadata": { + "executionInfo": { + "elapsed": 56, + "status": "ok", + "timestamp": 1749822650560, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "PMmSYhSfX9K9" + }, + "cell_type": "code", + "source": [ + "# Insertion variant.\n", + "variant = genome.Variant(\n", + " chromosome='chr3',\n", + " position=10_000,\n", + " reference_bases='T',\n", + " alternate_bases='CGTCAAT',\n", + ")\n", + "\n", + "# Deletion variant.\n", + "variant = genome.Variant(\n", + " chromosome='chr3',\n", + " position=10_000,\n", + " reference_bases='AGGGATC',\n", + " alternate_bases='C',\n", + ")" + ], + "outputs": [], + "execution_count": 12 + }, + { + "metadata": { + "id": "_v9VY9kqYRoP" + }, + "cell_type": "markdown", + "source": [ + "The sequence we pass for the `reference_bases` argument could differ from what\n", + "is actually at that location in the hg38 reference genome. The model will insert\n", + "whatever is passed as the reference and alternate bases into the sequence and\n", + "make predictions on them." + ] + }, + { + "metadata": { + "id": "za3OTKasYYlb" + }, + "cell_type": "markdown", + "source": [ + "#### Reference interval\n", + "\n", + "We can get the `genome.Interval` corresponding to the\n", + "reference bases of the variant using `genome.Variant.reference_interval`:" + ] + }, + { + "metadata": { + "executionInfo": { + "elapsed": 62, + "status": "ok", + "timestamp": 1749822650873, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "UUyBaqWtYfc0", + "outputId": "cb3fc331-604d-4d58-9802-f4491dfe9bb2" + }, + "cell_type": "code", + "source": [ + "variant = genome.Variant(\n", + " chromosome='chr3', position=10_000, reference_bases='A', alternate_bases='T'\n", + ")\n", + "\n", + "variant.reference_interval" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "Interval(chromosome='chr3', start=9999, end=10000, strand='.', name='')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 13 + }, + { + "metadata": { + "id": "DATTTBK6YiEZ" + }, + "cell_type": "markdown", + "source": [ + "A common use-case is to make predictions in a genome region around a variant,\n", + "which involves resizing the\n", + "`genome.Variant.reference_interval` to a sequence\n", + "length compatible with AlphaGenome:" + ] + }, + { + "metadata": { + "executionInfo": { + "elapsed": 81, + "status": "ok", + "timestamp": 1749822651223, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "YijroJwOYnfU", + "outputId": "f923811e-b6c8-4aca-bc5a-d821377557d3" + }, + "cell_type": "code", + "source": [ + "input_interval = variant.reference_interval.resize(\n", + " dna_client.SEQUENCE_LENGTH_1MB\n", + ")\n", + "input_interval.width" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "1048576" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 14 + }, + { + "metadata": { + "id": "qGIpFPd_YmYc" + }, + "cell_type": "markdown", + "source": [ + "#### Overlap with interval\n", + "\n", + "We can also check if a variant’s reference or alternate alleles overlap an `genome.Interval`:" + ] + }, + { + "metadata": { + "executionInfo": { + "elapsed": 128, + "status": "ok", + "timestamp": 1749822651618, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "HmiX9TELYxTD", + "outputId": "3e267c68-30dd-4aaf-e316-2a87df03d96e" + }, + "cell_type": "code", + "source": [ + "variant = genome.Variant(\n", + " chromosome='chr3',\n", + " position=10_000,\n", + " reference_bases='T',\n", + " alternate_bases='CGTCAAT',\n", + ")\n", + "\n", + "interval = genome.Interval(chromosome='chr3', start=10_005, end=10_010)\n", + "\n", + "print('Reference overlaps:', variant.reference_overlaps(interval))\n", + "print('Alternative overlaps:', variant.alternate_overlaps(interval))" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reference overlaps: False\n", + "Alternative overlaps: True\n" + ] + } + ], + "execution_count": 15 + }, + { + "metadata": { + "id": "Kr0329VfZAZo" + }, + "cell_type": "markdown", + "source": [ + "##  Data: model outputs\n", + "\n", + "### Track data\n", + "\n", + "\u003ca href=\"https://services.google.com/fh/files/misc/trackdata.png\"\u003e\u003cimg src=\"https://services.google.com/fh/files/misc/trackdata.png\" alt=\"anndata\" border=\"0\" height=500\u003e\u003c/a\u003e\n", + "\n", + "`track_data.TrackData` objects store model predictions.\n", + "They have the following properties (using `tdata` as an example of a\n", + "`track_data.TrackData` object):\n", + "\n", + "* `tdata.values` store track predictions as a `numpy.ndarray` .\n", + "* `tdata.metadata` stores track metadata as a `pandas.DataFrame`. For\n", + " each track in the predicted values, there will be a corresponding row in the\n", + " track metadata describing its origin.\n", + "* `tdata.uns` contains additional unstructured metadata as a `dict`." + ] + }, + { + "metadata": { + "id": "I3HTL-NTZR6n" + }, + "cell_type": "markdown", + "source": [ + "#### From scratch\n", + "\n", + "You can create your own `track_data.TrackData` object\n", + "from scratch by specifying the values and metadata manually. The metadata must\n", + "contain at least the columns name (the names of the tracks) and strand (the\n", + "strands of DNA that the tracks are on):\n" + ] + }, + { + "metadata": { + "executionInfo": { + "elapsed": 58, + "status": "ok", + "timestamp": 1749822651961, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "uwsbveEvZBbt" + }, + "cell_type": "code", + "source": [ + "from alphagenome.data import track_data\n", + "\n", + "# Array has shape (4,3) -\u003e sequence is length 4 and there are 3 tracks.\n", + "values = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11]]).astype(\n", + " np.float32\n", + ")\n", + "\n", + "# We have both the positive and negative strand values for track1, while track2\n", + "# contains unstranded data.\n", + "metadata = pd.DataFrame({\n", + " 'name': ['track1', 'track1', 'track2'],\n", + " 'strand': ['+', '-', '.'],\n", + "})\n", + "\n", + "tdata = track_data.TrackData(values=values, metadata=metadata)" + ], + "outputs": [], + "execution_count": 16 + }, + { + "metadata": { + "id": "qEc4ioZVZgR0" + }, + "cell_type": "markdown", + "source": [ + "#### Resolution\n", + "\n", + "It’s also useful to specify the resolution of the tracks and the genomic\n", + "interval that they come from, if you have this information available:\n" + ] + }, + { + "metadata": { + "executionInfo": { + "elapsed": 55, + "status": "ok", + "timestamp": 1749822652304, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "JGnuY6g_ZaBL" + }, + "cell_type": "code", + "source": [ + "interval = genome.Interval(chromosome='chr1', start=1_000, end=1_004)\n", + "\n", + "tdata = track_data.TrackData(\n", + " values=values, metadata=metadata, resolution=1, interval=interval\n", + ")" + ], + "outputs": [], + "execution_count": 17 + }, + { + "metadata": { + "id": "QeN8D_yGZlVB" + }, + "cell_type": "markdown", + "source": [ + "Note that the length of the values has to match up with the interval width and\n", + "resolution. Here is an example specifying that the values actually represent\n", + "128bp resolution tracks (i.e., each number is a summary over 128 base pairs of\n", + "DNA):\n" + ] + }, + { + "metadata": { + "executionInfo": { + "elapsed": 56, + "status": "ok", + "timestamp": 1749822652621, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "I-iPD_ZGZjMI" + }, + "cell_type": "code", + "source": [ + "interval = genome.Interval(chromosome='chr1', start=1_000, end=1_512)\n", + "\n", + "tdata = track_data.TrackData(\n", + " values=values, metadata=metadata, resolution=128, interval=interval\n", + ")" + ], + "outputs": [], + "execution_count": 18 + }, + { + "metadata": { + "id": "HhVG6w41ZqEp" + }, + "cell_type": "markdown", + "source": [ + "####  Converting between resolutions\n", + "\n", + "We can also interconvert between resolutions. For example, given 1bp resolution\n", + "predictions, we can downsample the resolution (by summing adjacent values) and\n", + "return a sequence of length 2:" + ] + }, + { + "metadata": { + "executionInfo": { + "elapsed": 64, + "status": "ok", + "timestamp": 1749822652955, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "Ce41uPkcZoWd", + "outputId": "38adb4cb-7a38-4cc3-8f12-af6ea95dfc0b" + }, + "cell_type": "code", + "source": [ + "interval = genome.Interval(chromosome='chr1', start=1_000, end=1_004)\n", + "\n", + "tdata = track_data.TrackData(\n", + " values=values, metadata=metadata, resolution=1, interval=interval\n", + ")\n", + "\n", + "tdata = tdata.change_resolution(resolution=2)\n", + "tdata.values" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 3., 5., 7.],\n", + " [15., 17., 19.]], dtype=float32)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 19 + }, + { + "metadata": { + "id": "23Ehe3duZqC3" + }, + "cell_type": "markdown", + "source": [ + "We can also upsample track data to get back to 1bp resolution and a sequence of\n", + "length 4 by repeating values while preserving the sum:" + ] + }, + { + "metadata": { + "executionInfo": { + "elapsed": 65, + "status": "ok", + "timestamp": 1749822653280, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "dDi9OWWWZzq_", + "outputId": "51f3878b-d71e-48f7-f6c7-7a87830704ed" + }, + "cell_type": "code", + "source": [ + "tdata = tdata.change_resolution(resolution=1)\n", + "tdata.values" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1.5, 2.5, 3.5],\n", + " [1.5, 2.5, 3.5],\n", + " [7.5, 8.5, 9.5],\n", + " [7.5, 8.5, 9.5]], dtype=float32)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 20 + }, + { + "metadata": { + "id": "Rkne2GEwZ82L" + }, + "cell_type": "markdown", + "source": [ + "####  Filtering\n", + "\n", + "`track_data.TrackData` objects can be filtered by the\n", + "type of DNA strand the tracks are on:\n" + ] + }, + { + "metadata": { + "executionInfo": { + "elapsed": 71, + "status": "ok", + "timestamp": 1749822653638, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "RoIaGXLtaBbz", + "outputId": "75db5fc1-a5a6-4798-e26f-9d62443abbeb" + }, + "cell_type": "code", + "source": [ + "print(\n", + " 'Positive strand tracks:',\n", + " tdata.filter_to_positive_strand().metadata.name.values,\n", + ")\n", + "print(\n", + " 'Negative strand tracks:',\n", + " tdata.filter_to_negative_strand().metadata.name.values,\n", + ")\n", + "print('Unstranded tracks:', tdata.filter_to_unstranded().metadata.name.values)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Positive strand tracks: ['track1']\n", + "Negative strand tracks: ['track1']\n", + "Unstranded tracks: ['track2']\n" + ] + } + ], + "execution_count": 21 + }, + { + "metadata": { + "id": "xogO-bWVaatH" + }, + "cell_type": "markdown", + "source": [ + "#### Resizing\n", + "\n", + "We can resize the `track_data.TrackData` to be either\n", + "smaller (by cropping):" + ] + }, + { + "metadata": { + "executionInfo": { + "elapsed": 61, + "status": "ok", + "timestamp": 1749822654145, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "clj536M9abBp", + "outputId": "f7819f36-70c7-4b78-cb04-b0e2e030ff6e" + }, + "cell_type": "code", + "source": [ + "# Re-instantiating the original trackdata.\n", + "tdata = track_data.TrackData(\n", + " values=values, metadata=metadata, resolution=1, interval=interval\n", + ")\n", + "\n", + "# Resize from width (sequence length) of 4 down to 2.\n", + "tdata.resize(width=2).values" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[3., 4., 5.],\n", + " [6., 7., 8.]], dtype=float32)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 22 + }, + { + "metadata": { + "id": "wl3aNKu-akaO" + }, + "cell_type": "markdown", + "source": [ + "Or bigger (by padding with zeros):\n" + ] + }, + { + "metadata": { + "executionInfo": { + "elapsed": 61, + "status": "ok", + "timestamp": 1749822654456, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "pHWZRppBaeC1", + "outputId": "087a212d-ca6f-4490-fe59-408e9a157ee2" + }, + "cell_type": "code", + "source": [ + "tdata.resize(width=8).values" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 1., 2.],\n", + " [ 3., 4., 5.],\n", + " [ 6., 7., 8.],\n", + " [ 9., 10., 11.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.]], dtype=float32)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 23 + }, + { + "metadata": { + "id": "SRkggBayar2r" + }, + "cell_type": "markdown", + "source": [ + "#### Slicing\n", + "\n", + "We can slice into specific positions of the\n", + "`track_data.TrackData`:" + ] + }, + { + "metadata": { + "executionInfo": { + "elapsed": 56, + "status": "ok", + "timestamp": 1749822654784, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "yHcMdzN1amMz", + "outputId": "73a293d2-279c-4709-b79c-cfd24235e19d" + }, + "cell_type": "code", + "source": [ + "# Get the final 2 positions only.\n", + "print('slice by position: ', tdata.slice_by_positions(start=2, end=4).values)\n", + "# Same, but using slice_interval:\n", + "print(\n", + " 'slice by interval: ',\n", + " tdata.slice_by_interval(\n", + " genome.Interval(chromosome='chr1', start=1_002, end=1_004)\n", + " ).values,\n", + ")" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "slice by position: [[ 6. 7. 8.]\n", + " [ 9. 10. 11.]]\n", + "slice by interval: [[ 6. 7. 8.]\n", + " [ 9. 10. 11.]]\n" + ] + } + ], + "execution_count": 24 + }, + { + "metadata": { + "id": "p1OiFpXGa-Ja" + }, + "cell_type": "markdown", + "source": [ + "#### Subsetting tracks\n", + "\n", + "Subset (and reorder) to specific track names:" + ] + }, + { + "metadata": { + "executionInfo": { + "elapsed": 63, + "status": "ok", + "timestamp": 1749822655084, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "_l9bKsuvaxtu", + "outputId": "abed781e-42d7-4c55-85fe-754ac8351905" + }, + "cell_type": "code", + "source": [ + "# Get only tracks with the name 'track1'.\n", + "track1_tdata = tdata.select_tracks_by_name(names='track1')\n", + "track1_tdata.values" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0., 1.],\n", + " [ 3., 4.],\n", + " [ 6., 7.],\n", + " [ 9., 10.]], dtype=float32)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 25 + }, + { + "metadata": { + "id": "bdTrjm4pbLIC" + }, + "cell_type": "markdown", + "source": [ + "The metadata gets automatically filtered to `track1` too:\n" + ] + }, + { + "metadata": { + "executionInfo": { + "elapsed": 58, + "status": "ok", + "timestamp": 1749822655417, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "aErYz_nRbCID", + "outputId": "59679dd0-7f08-44d9-dfb8-3f5a34e7380c" + }, + "cell_type": "code", + "source": [ + "track1_tdata.metadata.name.values" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "array(['track1', 'track1'], dtype=object)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 26 + }, + { + "metadata": { + "id": "lgprEnpIbPtR" + }, + "cell_type": "markdown", + "source": [ + "Finally, if we pass in a stranded `genome.Interval` or\n", + "leave unspecified as `None` when constructing a\n", + "`track_data.TrackData`, we can reverse complement\n", + "transform our track values in a strand-aware manner:\n" + ] + }, + { + "metadata": { + "executionInfo": { + "elapsed": 58, + "status": "ok", + "timestamp": 1749822655727, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "HeZXB4K3bMyJ", + "outputId": "1d6250e5-f1c3-471a-b347-c73664d056a5" + }, + "cell_type": "code", + "source": [ + "interval = genome.Interval(\n", + " chromosome='chr1', start=1_000, end=1_004, strand='+'\n", + ")\n", + "\n", + "tdata = track_data.TrackData(\n", + " values=values, metadata=metadata, resolution=1, interval=interval\n", + ")\n", + "\n", + "tdata.reverse_complement().values" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "array([[10., 9., 11.],\n", + " [ 7., 6., 8.],\n", + " [ 4., 3., 5.],\n", + " [ 1., 0., 2.]], dtype=float32)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 27 + }, + { + "metadata": { + "id": "Hb8duvxibcXW" + }, + "cell_type": "markdown", + "source": [ + "### Variant scoring output\n", + "\n", + "# \u003ca href=\"https://services.google.com/fh/files/misc/anndata.png\"\u003e\u003cimg src=\"https://services.google.com/fh/files/misc/anndata.png\" alt=\"anndata\" border=\"0\" height=500\u003e\u003c/a\u003e\n", + "\n", + "The output of variant scoring is in `anndata.AnnData` format, which is a\n", + "way of scoring data together with annotation metadata. Originally developed in\n", + "the single-cell RNA-seq field, `anndata.AnnData` is useful when you have\n", + "metadata associated with an array of data.\n", + "\n", + "`anndata.AnnData` objects have the following properties (using\n", + "`variant_scores` as an example `anndata.AnnData` object):\n", + "\n", + "* `variant_scores.X` contains a `numpy.ndarray` containing the variant\n", + " scores per each gene in the region. This matrix has shape (`num_genes`,\n", + " `num_tracks`), where `num_tracks` is the number of output tracks in your\n", + " requested OutputType (such as `RNA_SEQ`, `DNASE`, etc.). Note that if you\n", + " did not use a gene-centric scorer, then `variant_scores.X` will have shape\n", + " (1, `num_tracks`), reflecting the fact that the variant has a single global\n", + " score and not per-gene score.\n", + "* `variant_scores.var` contains the track metadata as a\n", + " `pandas.DataFrame`. For every track in the scores (`num_genes`,\n", + " `num_tracks`), there will be a row in the track metadata explaining the\n", + " track (its cell type, strand, etc.).\n", + "* `variant_scores.obs` contains the gene metadata as a\n", + " `pandas.DataFrame`. Note that the gene metadata is None in the case\n", + " of non gene-centric variant scorers.\n", + "* `variant_scores.uns` contains some additional unstructured metadata that\n", + " logs the origin of the variant scores, namely:\n", + " * The `genome.Variant` that was scored\n", + " (variant\\_scores.uns\\[‘variant’\\])\n", + " * The `genome.Interval` containing the interval\n", + " (variant\\_scores.uns\\[‘interval’\\])\n", + " * The [`variant scorer`](https://www.alphagenomedocs.com/api/models.html#variant-scorers) that was used to\n", + " generate the scores (variant\\_scores.uns\\[‘scorer’\\])" + ] + }, + { + "metadata": { + "id": "wxew4DE0bmLO" + }, + "cell_type": "markdown", + "source": [ + "#### From scratch\n", + "\n", + "You are unlikely to need to create an `anndata.AnnData` object from\n", + "scratch, but just for reference, here is how it would be done:" + ] + }, + { + "metadata": { + "id": "e3jeE3HVbpof" + }, + "cell_type": "code", + "source": [ + "import anndata\n", + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "# Creating a small matrix of variant scores (3 genes x 2 tracks).\n", + "scores = np.array([[0.1, 0.2], [0.3, 0.4], [0.5, 0.6]])\n", + "\n", + "gene_metadata = pd.DataFrame({'gene_id': ['ENSG0001', 'ENSG0002', 'ENSG0003']})\n", + "\n", + "track_metadata = pd.DataFrame(\n", + " {'name': ['track1', 'track2'], 'strand': ['+', '-']}\n", + ")\n", + "\n", + "variant_scores = anndata.AnnData(\n", + " X=scores, obs=gene_metadata, var=track_metadata\n", + ")" + ], + "outputs": [], + "execution_count": null + }, + { + "metadata": { + "id": "7IHNdc_BbttW" + }, + "cell_type": "markdown", + "source": [ + "## Methods: making predictions\n", + "\n", + "The main commands for making model predictions are:\n", + "\n", + "* `dna_client.DnaClient.predict_sequence` to predict\n", + " from a raw DNA string\n", + "* `dna_client.DnaClient.predict_interval` to predict\n", + " from a genome interval (a `genome.Interval`)\n", + "* `dna_client.DnaClient.predict_variant` to make\n", + " predictions for ref and alt sequences of a variant (a\n", + " `genome.Variant` object)\n", + "* `dna_client.DnaClient.score_variant` to score the\n", + " effects of a variant by comparing ref and alt predictions.\n", + "* `dna_client.DnaClient.score_variants` the same as\n", + " the above, but for scoring a list of multiple variants.\n" + ] + }, + { + "metadata": { + "id": "lJ17YLKRQ6nq" + }, + "cell_type": "code", + "source": [], + "outputs": [], + "execution_count": null + }, + { + "metadata": { + "id": "X10M3ojgbw4a" + }, + "cell_type": "markdown", + "source": [ + "## Methods: visualization\n", + "\n", + "The main command for visualizing model predictions is:\n", + "\n", + "* `alphagenome.visualization.plot_components.plot`, to turn a list of\n", + " of [plot components](https://www.alphagenomedocs.com/api/visualization.html#plot-components) into a\n", + " `matplotlib.figure.Figure`.\n", + "\n", + "See the [visualization basics guide](https://www.alphagenomedocs.com/visualization_library_basics.html) and [visualizing predictions tutorial](https://www.alphagenomedocs.com/colabs/visualization_modality_tour.html) for more details." + ] + } + ], + "metadata": { + "colab": { + "last_runtime": {}, + "provenance": [ + { + "file_id": "1hJ2uMZ3sA8pu_UvSNikENLECC-X5XrR8", + "timestamp": 1749822158925 + } + ] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/alphagenome/source/colabs/example_analysis_workflow.ipynb b/alphagenome/source/colabs/example_analysis_workflow.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..4bd60bb4f501a405522449c00c690f88e13a28c5 --- /dev/null +++ b/alphagenome/source/colabs/example_analysis_workflow.ipynb @@ -0,0 +1,1369 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "_" + }, + "source": [ + "# Example analysis workflow: TAL1 locus" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "on5cvb0VoK_D" + }, + "source": [ + "Goal: We will explore non-coding T-cell acute lymphoblastic leukemia associated\n", + "mutations near the *TAL1* locus.\n", + "\n", + "T-cell acute lymphoblastic leukemia (T-ALL) is a cancer of the blood and bone\n", + "marrow. A large fraction of T-ALL cases arise due to aberrant upregulation of\n", + "the *TAL1* oncogene. In particular, the locus harbors positions in which\n", + "non-coding variants can occur that activate *TAL1* gene expression from several\n", + "kilobases away.\n", + "\n", + "Key activities:\n", + " 1. Visualize the genomic context of variants of interest.\n", + " 1. Predict the functional impact of a specific variant on gene expression,\n", + " accessibility, and histone marks.\n", + " 1. Systematically compare the predicted effects of known disease-associated\n", + " variants to a set of background variants." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "d3eXVn1XNl3F" + }, + "source": [ + "```{tip}\n", + "Open this tutorial in Google colab for interactive viewing.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "id": "W28j15ApoK_D" + }, + "outputs": [], + "source": [ + "# @title Install AlphaGenome\n", + "\n", + "# @markdown Run this cell to install AlphaGenome.\n", + "from IPython.display import clear_output\n", + "! pip install alphagenome\n", + "clear_output()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-lcTVQLvqB6F" + }, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "KuLPy_awXaP_" + }, + "outputs": [], + "source": [ + "import io\n", + "import itertools\n", + "\n", + "from alphagenome import colab_utils\n", + "from alphagenome.data import gene_annotation\n", + "from alphagenome.data import genome\n", + "from alphagenome.data import transcript as transcript_utils\n", + "from alphagenome.models import dna_client\n", + "from alphagenome.models import variant_scorers\n", + "from alphagenome.visualization import plot_components\n", + "from IPython.display import clear_output\n", + "import numpy as np\n", + "import pandas as pd\n", + "import plotnine as gg" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "toBpbthy93gr" + }, + "outputs": [], + "source": [ + "dna_model = dna_client.create(colab_utils.get_api_key())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "q16KB0jNLSh2" + }, + "source": [ + "We load gene annotations from GENCODE, a public repository of gene information,\n", + "using annotations with the highest level of experimental support (Transcript\n", + "Support Level '1')." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "AI5q4j1GT8xt" + }, + "outputs": [], + "source": [ + "# Load gene annotations (from GENCODE).\n", + "gtf = pd.read_feather(\n", + " 'https://storage.googleapis.com/alphagenome/reference/gencode/'\n", + " 'hg38/gencode.v46.annotation.gtf.gz.feather'\n", + ")\n", + "\n", + "# Filter to protein-coding genes and highly supported transcripts.\n", + "gtf_transcript = gene_annotation.filter_transcript_support_level(\n", + " gene_annotation.filter_protein_coding(gtf), ['1']\n", + ")\n", + "\n", + "# Define an extractor that fetches only the longest transcript per gene.\n", + "gtf_longest_transcript = gene_annotation.filter_to_longest_transcript(\n", + " gtf_transcript\n", + ")\n", + "longest_transcript_extractor = transcript_utils.TranscriptExtractor(\n", + " gtf_longest_transcript\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "D3qpCQqpLf2S" + }, + "source": [ + "We further define a few utility functions that we'll use throughout the\n", + "notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6kql5u1bYHjZ" + }, + "outputs": [], + "source": [ + "# @title Utilities\n", + "def generate_background_variants(\n", + " variant: genome.Variant, max_number: int = 100\n", + ") -\u003e pd.DataFrame:\n", + " \"\"\"Generates a dataframe of background variants for a given variant.\n", + "\n", + " This is done by creating new sequences of the same length as the alternate\n", + " allele.\n", + "\n", + " This allows us to test if the specific sequence of the oncogenic variant has a\n", + " greater effect than a random sequence of the same length at the same location.\n", + "\n", + " Args:\n", + " variant: The variant to generate ism variants for.\n", + " max_number: The maximum number of ism variants to generate.\n", + "\n", + " Returns:\n", + " A dataframe of variants.\n", + " \"\"\"\n", + " nucleotides = np.array(list('ACGT'), dtype='\u003cU1')\n", + "\n", + " def generate_unique_strings(n, max_number, random_seed=42):\n", + " \"\"\"Generates unique random strings of length n.\"\"\"\n", + " rng = np.random.default_rng(random_seed)\n", + "\n", + " if 4**n \u003c max_number:\n", + " raise ValueError(\n", + " 'Cannot generate that many unique strings for the given length.'\n", + " )\n", + "\n", + " generated_strings = set()\n", + " while len(generated_strings) \u003c max_number:\n", + " indices = rng.integers(0, 4, size=n)\n", + " new_string = ''.join(nucleotides[indices])\n", + " if new_string != variant.alternate_bases:\n", + " generated_strings.add(new_string)\n", + " return list(generated_strings)\n", + "\n", + " permutations = []\n", + " if 4 ** len(variant.alternate_bases) \u003c max_number:\n", + " # Get all\n", + " for p in itertools.product(\n", + " nucleotides, repeat=len(variant.alternate_bases)\n", + " ):\n", + " permutations.append(''.join(p))\n", + " else:\n", + " # Sample some\n", + " permutations = generate_unique_strings(\n", + " len(variant.alternate_bases), max_number\n", + " )\n", + " ism_candidates = pd.DataFrame({\n", + " 'ID': ['mut_' + str(variant.position) + '_' + x for x in permutations],\n", + " 'CHROM': variant.chromosome,\n", + " 'POS': variant.position,\n", + " 'REF': variant.reference_bases,\n", + " 'ALT': permutations,\n", + " 'output': 0.0,\n", + " 'original_variant': variant.name,\n", + " })\n", + " return ism_candidates\n", + "\n", + "\n", + "def oncogenic_and_background_variants(\n", + " input_sequence_length: int, number_of_background_variants: int = 20\n", + ") -\u003e pd.DataFrame:\n", + " \"\"\"Generates a dataframe of all variants for this evaluation.\"\"\"\n", + " oncogenic_variants = oncogenic_tal1_variants()\n", + "\n", + " variants = []\n", + " for vcf_row in oncogenic_variants.itertuples():\n", + " variants.append(\n", + " genome.Variant(\n", + " chromosome=str(vcf_row.CHROM),\n", + " position=int(vcf_row.POS),\n", + " reference_bases=vcf_row.REF,\n", + " alternate_bases=vcf_row.ALT,\n", + " name=vcf_row.ID,\n", + " )\n", + " )\n", + "\n", + " background_variants = pd.concat([\n", + " generate_background_variants(variant, number_of_background_variants)\n", + " for variant in variants\n", + " ])\n", + " all_variants = pd.concat([oncogenic_variants, background_variants])\n", + " return inference_df(all_variants, input_sequence_length=input_sequence_length)\n", + "\n", + "\n", + "def vcf_row_to_variant(vcf_row: pd.Series) -\u003e genome.Variant:\n", + " \"\"\"Parse a row of a vcf df into a genome.Variant.\"\"\"\n", + " variant = genome.Variant(\n", + " chromosome=str(vcf_row.CHROM),\n", + " position=int(vcf_row.POS),\n", + " reference_bases=vcf_row.REF,\n", + " alternate_bases=vcf_row.ALT,\n", + " name=vcf_row.ID,\n", + " )\n", + " return variant\n", + "\n", + "\n", + "def inference_df(\n", + " qtl_df: pd.DataFrame,\n", + " input_sequence_length: int,\n", + ") -\u003e pd.DataFrame:\n", + " \"\"\"Returns a pd.DataFrame with variants and intervals ready for inference.\"\"\"\n", + " df = []\n", + " for _, row in qtl_df.iterrows():\n", + " variant = vcf_row_to_variant(row)\n", + "\n", + " interval = genome.Interval(\n", + " chromosome=row['CHROM'], start=row['POS'], end=row['POS']\n", + " ).resize(input_sequence_length)\n", + "\n", + " df.append({\n", + " 'interval': interval,\n", + " 'variant': variant,\n", + " 'output': row['output'],\n", + " 'variant_id': row['ID'],\n", + " 'POS': row['POS'],\n", + " 'REF': row['REF'],\n", + " 'ALT': row['ALT'],\n", + " 'CHROM': row['CHROM'],\n", + " })\n", + " return pd.DataFrame(df)\n", + "\n", + "\n", + "def coarse_grained_mute_groups(eval_df):\n", + " grp = []\n", + " for row in eval_df.itertuples():\n", + " if row.POS \u003e= 47239290: # MUTE site.\n", + " if row.ALT_len \u003e 4:\n", + " grp.append('MUTE' + '_other')\n", + " else:\n", + " grp.append('MUTE' + '_' + str(row.ALT_len))\n", + " else:\n", + " grp.append(str(row.POS) + '_' + str(row.ALT_len))\n", + "\n", + " grp = pd.Series(grp)\n", + " return pd.Categorical(grp, categories=sorted(grp.unique()), ordered=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jEGP__L4oK_E" + }, + "source": [ + "# Assembling variant data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "height": 206 + }, + "executionInfo": { + "elapsed": 106, + "status": "ok", + "timestamp": 1753113373280, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "U2Q-amDjkYVt", + "outputId": "d5b919e7-4d4d-4459-dd4c-ac59a882b5e1" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "repr_error": "Out of range float values are not JSON compliant: nan", + "type": "dataframe" + }, + "text/html": [ + "\n", + " \u003cdiv id=\"df-5e1d8b6f-c327-4e87-a340-bba4fb53ac61\" class=\"colab-df-container\"\u003e\n", + " \u003cdiv\u003e\n", + "\u003cstyle scoped\u003e\n", + " .dataframe tbody tr th:only-of-type {\n", + 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Visit the ' +\n", + " '\u003ca target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb\u003edata table notebook\u003c/a\u003e'\n", + " + ' to learn more about interactive tables.';\n", + " element.innerHTML = '';\n", + " dataTable['output_type'] = 'display_data';\n", + " await google.colab.output.renderOutput(dataTable, element);\n", + " const docLink = document.createElement('div');\n", + " docLink.innerHTML = docLinkHtml;\n", + " element.appendChild(docLink);\n", + " }\n", + " \u003c/script\u003e\n", + " \u003c/div\u003e\n", + "\n", + "\n", + " \u003cdiv id=\"df-3180aa97-91f2-4d21-ae03-c97b65d3c6af\"\u003e\n", + " \u003cbutton class=\"colab-df-quickchart\" onclick=\"quickchart('df-3180aa97-91f2-4d21-ae03-c97b65d3c6af')\"\n", + " title=\"Suggest charts\"\n", + " style=\"display:none;\"\u003e\n", + "\n", + "\u003csvg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", + " width=\"24px\"\u003e\n", + " \u003cg\u003e\n", + " \u003cpath d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/\u003e\n", + " \u003c/g\u003e\n", + "\u003c/svg\u003e\n", + " \u003c/button\u003e\n", + "\n", + "\u003cstyle\u003e\n", + " .colab-df-quickchart {\n", + " --bg-color: #E8F0FE;\n", + " --fill-color: #1967D2;\n", + " --hover-bg-color: #E2EBFA;\n", + " --hover-fill-color: #174EA6;\n", + " --disabled-fill-color: #AAA;\n", + " --disabled-bg-color: #DDD;\n", + " }\n", + "\n", + " [theme=dark] .colab-df-quickchart {\n", + " --bg-color: #3B4455;\n", + " --fill-color: #D2E3FC;\n", + " --hover-bg-color: #434B5C;\n", + " --hover-fill-color: #FFFFFF;\n", + " --disabled-bg-color: #3B4455;\n", + " --disabled-fill-color: #666;\n", + " }\n", + "\n", + " .colab-df-quickchart {\n", + " background-color: var(--bg-color);\n", + " border: none;\n", + " border-radius: 50%;\n", + " cursor: pointer;\n", + " display: none;\n", + " fill: var(--fill-color);\n", + " height: 32px;\n", + " padding: 0;\n", + " width: 32px;\n", + " }\n", + "\n", + " .colab-df-quickchart:hover {\n", + " background-color: var(--hover-bg-color);\n", + " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n", + " fill: var(--button-hover-fill-color);\n", + " }\n", + "\n", + " .colab-df-quickchart-complete:disabled,\n", + " .colab-df-quickchart-complete:disabled:hover {\n", + " background-color: var(--disabled-bg-color);\n", + " fill: var(--disabled-fill-color);\n", + " box-shadow: none;\n", + " }\n", + "\n", + " .colab-df-spinner {\n", + " border: 2px solid var(--fill-color);\n", + " border-color: transparent;\n", + " border-bottom-color: var(--fill-color);\n", + " animation:\n", + " spin 1s steps(1) infinite;\n", + " }\n", + "\n", + " @keyframes spin {\n", + " 0% {\n", + " border-color: transparent;\n", + " border-bottom-color: var(--fill-color);\n", + " border-left-color: var(--fill-color);\n", + " }\n", + " 20% {\n", + " border-color: transparent;\n", + " border-left-color: var(--fill-color);\n", + " border-top-color: var(--fill-color);\n", + " }\n", + " 30% {\n", + " border-color: transparent;\n", + " border-left-color: var(--fill-color);\n", + " border-top-color: var(--fill-color);\n", + " border-right-color: var(--fill-color);\n", + " }\n", + " 40% {\n", + " border-color: transparent;\n", + " border-right-color: var(--fill-color);\n", + " border-top-color: var(--fill-color);\n", + " }\n", + " 60% {\n", + " border-color: transparent;\n", + " border-right-color: var(--fill-color);\n", + " }\n", + " 80% {\n", + " border-color: transparent;\n", + " border-right-color: var(--fill-color);\n", + " border-bottom-color: var(--fill-color);\n", + " }\n", + " 90% {\n", + " border-color: transparent;\n", + " border-bottom-color: var(--fill-color);\n", + " }\n", + " }\n", + "\u003c/style\u003e\n", + "\n", + " \u003cscript\u003e\n", + " async function quickchart(key) {\n", + " const quickchartButtonEl =\n", + " document.querySelector('#' + key + ' button');\n", + " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n", + " quickchartButtonEl.classList.add('colab-df-spinner');\n", + " try {\n", + " const charts = await google.colab.kernel.invokeFunction(\n", + " 'suggestCharts', [key], {});\n", + " } catch (error) {\n", + " console.error('Error during call to suggestCharts:', error);\n", + " }\n", + " quickchartButtonEl.classList.remove('colab-df-spinner');\n", + " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n", + " }\n", + " (() =\u003e {\n", + " let quickchartButtonEl =\n", + " document.querySelector('#df-3180aa97-91f2-4d21-ae03-c97b65d3c6af button');\n", + " quickchartButtonEl.style.display =\n", + " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", + " })();\n", + " \u003c/script\u003e\n", + " \u003c/div\u003e\n", + " \u003c/div\u003e\n", + " \u003c/div\u003e\n" + ], + "text/plain": [ + " ID CHROM POS ... output Study ID Study Variant ID\n", + "0 Jurkat chr1 47239296 ... 1 Mansour_2014 NaN\n", + "1 MOLT-3 chr1 47239296 ... 1 Mansour_2014 NaN\n", + "2 Patient_1 chr1 47239296 ... 1 Mansour_2014 NaN\n", + "3 Patient_2 chr1 47239291 ... 1 Mansour_2014 NaN\n", + "4 Patient_3-5 chr1 47239296 ... 1 Mansour_2014 NaN\n", + "\n", + "[5 rows x 8 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# @title Define variants of interest\n", + "\n", + "\n", + "def oncogenic_tal1_variants() -\u003e pd.DataFrame:\n", + " \"\"\"Returns a dataframe of oncogenic T-ALL variants that affect TAL1.\"\"\"\n", + " variant_data = \"\"\"\n", + "ID\tCHROM\tPOS\tREF\tALT\toutput\tStudy ID\tStudy Variant ID\n", + "Jurkat\tchr1\t47239296\tC\tCCGTTTCCTAACC\t1\tMansour_2014\n", + "MOLT-3\tchr1\t47239296\tC\tACC\t1\tMansour_2014\n", + "Patient_1\tchr1\t47239296\tC\tAACG\t1\tMansour_2014\n", + "Patient_2\tchr1\t47239291\tCTAACC\tTTTACCGTCTGTTAACGGC\t1\tMansour_2014\n", + "Patient_3-5\tchr1\t47239296\tC\tACG\t1\tMansour_2014\n", + "Patient_6\tchr1\t47239296\tC\tACC\t1\tMansour_2014\n", + "Patient_7\tchr1\t47239295\tAC\tTCAAACTGGTAACC\t1\tMansour_2014\n", + "Patient_8\tchr1\t47239296\tC\tAACC\t1\tMansour_2014\n", + "new 3' enhancer 1\tchr1\t47212072\tT\tTGGGTAAACCGTCTGTTCAGCG\t1\tSmith_2023\tUPNT802\n", + "new 3' enhancer 2\tchr1\t47212074\tG\tGAACGTT\t1\tSmith_2023\tUPNT613\n", + "intergenic SNV 1\tchr1\t47230639\tC\tT\t1\tLiu_2020\tSJALL043861_D1\n", + "intergenic SNV 2\tchr1\t47230639\tC\tT\t1\tLiu_2020\tSJALL018373_D1\n", + "SJALL040467_D1\tchr1\t47239296\tC\tAACC\t1\tLiu_2020\tSJALL040467_D1\n", + "PATBGC\tchr1\t47239296\tC\tAACC\t1\tLiu_2017\tPATBGC\n", + "PATBTX\tchr1\t47239296\tC\tACGGATATAACC\t1\tLiu_2017\tPATBTX\n", + "PARJAY\tchr1\t47239296\tC\tACGGAATTTCTAACC\t1\tLiu_2017\tPARJAY\n", + "PARSJG\tchr1\t47239296\tC\tAACC\t1\tLiu_2017\tPARSJG\n", + "PASYAJ\tchr1\t47239296\tC\tAACC\t1\tLiu_2017\tPASYAJ\n", + "PATRAB\tchr1\t47239293\tTTA\tCTAACGG\t1\tLiu_2017\tPATRAB\n", + "PAUBXP\tchr1\t47239296\tC\tACC\t1\tLiu_2017\tPAUBXP\n", + "PATENL\tchr1\t47239296\tC\tAACC\t1\tLiu_2017\tPATENL\n", + "PARNXJ\tchr1\t47239296\tC\tACG\t1\tLiu_2017\tPARNXJ\n", + "PASXSI\tchr1\t47239296\tC\tAACC\t1\tLiu_2017\tPASXSI\n", + "PASNEH\tchr1\t47239296\tC\tACC\t1\tLiu_2017\tPASNEH\n", + "PAUAFN\tchr1\t47239296\tC\tAACC\t1\tLiu_2017\tPAUAFN\n", + "PARASZ\tchr1\t47239296\tC\tACC\t1\tLiu_2017\tPARASZ\n", + "PARWNW\tchr1\t47239296\tC\tACC\t1\tLiu_2017\tPARWNW\n", + "PASFKA\tchr1\t47239293\tTTA\tACCGTTAATCAA\t1\tLiu_2017\tPASFKA\n", + "PATEIT\tchr1\t47239296\tC\tAC\t1\tLiu_2017\tPATEIT\n", + "PASMHF\tchr1\t47239296\tC\tAC\t1\tLiu_2017\tPASMHF\n", + "PARJNX\tchr1\t47239296\tC\tAC\t1\tLiu_2017\tPARJNX\n", + "PASYWF\tchr1\t47239296\tC\tAC\t1\tLiu_2017\tPASYWF\n", + "\"\"\"\n", + "\n", + " return pd.read_table(io.StringIO(variant_data), sep='\\t')\n", + "\n", + "\n", + "oncogenic_tal1_variants().head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BFIdSrssL33x" + }, + "source": [ + "To get a sense of where these variants lie, we will visualize the variants in\n", + "the table above in a ~30kb interval centered around the *TAL1* gene." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "height": 185 + }, + "executionInfo": { + "elapsed": 682, + "status": "ok", + "timestamp": 1753113374147, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "dKLY3YaOT0GP", + "outputId": "4b08a36d-afde-414a-86a8-083783d9a1c4" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "\u003cFigure size 1440x144 with 2 Axes\u003e" + ] + }, + "metadata": { + "image/png": { + "height": 168, + "width": 1147 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# @title Visualise variant positions.\n", + "\n", + "# Define TAL1 interval.\n", + "tal1_interval = genome.Interval(\n", + " chromosome='chr1', start=47209255, end=47242023, strand='-'\n", + ")\n", + "\n", + "# Gather unique variant positions and plot labels.\n", + "unique_positions = oncogenic_tal1_variants()['POS'].unique()\n", + "unique_positions.sort()\n", + "\n", + "# Manually define labels to avoid overplotting.\n", + "labels = [\n", + " '47212072, 47212074',\n", + " '',\n", + " '47230639',\n", + " '47239291 - 47239296',\n", + " '',\n", + " '',\n", + " '',\n", + "]\n", + "\n", + "# Build plot.\n", + "_ = plot_components.plot(\n", + " [\n", + " plot_components.TranscriptAnnotation(\n", + " longest_transcript_extractor.extract(tal1_interval)\n", + " ),\n", + " ],\n", + " annotations=[\n", + " plot_components.VariantAnnotation(\n", + " [\n", + " genome.Variant(\n", + " chromosome='chr1',\n", + " position=x,\n", + " reference_bases='N',\n", + " alternate_bases='N',\n", + " )\n", + " for x in unique_positions\n", + " ],\n", + " labels=labels,\n", + " use_default_labels=False,\n", + " )\n", + " ],\n", + " interval=tal1_interval,\n", + " title='Positions of variants near TAL1',\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dpFi65J3kYVt" + }, + "source": [ + "From the plot, we can see that the *TAL1* gene is transcribed from right to left\n", + "as it is on the (-) strand.\n", + "\n", + "From right to left, we see three positional groups:\n", + "\n", + "1. a cluster of several 5’ variants upstream of *TAL1*\n", + " ([Mansour et al. 2014](https://pubmed.ncbi.nlm.nih.gov/25394790/),\n", + " [Liu et al. 2017](https://pubmed.ncbi.nlm.nih.gov/28671688/)). This is also\n", + " called the MuTE (“mutation of the TAL1 enhancer”) site.\n", + "1. an intronic variant\n", + " ([Liu et al. 2020](https://pubmed.ncbi.nlm.nih.gov/32632335/)).\n", + "1. a 3’ cluster of two variants downstream of *TAL1*\n", + " ([Smith et al. 2023](https://pubmed.ncbi.nlm.nih.gov/36632736/)).\n", + "\n", + "In the original studies, all three variant groups were shown to converge on a\n", + "common mechanism: upregulation of the *TAL1* oncogene." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "27D5FSnDX1pw" + }, + "source": [ + "# Exploring individual variant outcomes\n", + "\n", + "Next, let's plot the predicted effect of one of these variants." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "executionInfo": { + "elapsed": 52, + "status": "ok", + "timestamp": 1753113374381, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "AenSItCfX34z", + "outputId": "26ab698e-6ec5-416d-8f7e-cb9df4c6bc63" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Variant(chromosome='chr1', position=47239296, reference_bases='C', alternate_bases='CCGTTTCCTAACC', name='Jurkat')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Define the variant of interest.\n", + "variant = vcf_row_to_variant(oncogenic_tal1_variants().iloc[0])\n", + "variant" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "usoOQGaFYXXW" + }, + "outputs": [], + "source": [ + "# Define the ontology of interest\n", + "ontology_terms = ['CL:0001059']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LFKfK8INYZpA" + }, + "source": [ + "To make predictions relevant to a specific biological context, we provide the\n", + "model with an ontology term. Here, we use `CL:0001059'` which corresponds to\n", + "[\"common myeloid progenitor, CD34-positive\"](https://www.ebi.ac.uk/ols4/ontologies/cl/classes/http%3A%2F%2Fpurl.obolibrary.org%2Fobo%2FCL_0001059).\n", + "This is a close match to the tissue-of-origin reported in\n", + "[Mansour et al. 2014](https://pubmed.ncbi.nlm.nih.gov/25394790/) (\"purified\n", + "normal hematopoietic stem cell samples (CD34)\"), which used purified\n", + "hematopoietic stem cells based on CD34 marker expression.\n", + "\n", + "To make predictions, we'll specify the `interval` (resized to an input sequence\n", + "length of 2^20 = 1,048,576 bp). We'll also pass in our `requested_outputs`\n", + "field, which indicates to the model to predict three types of outputs: RNA-seq\n", + "(a measure of gene expression), DNAse-seq (a measure of DNA accessibility), and\n", + "ChIP-Histone (a measure of histone modifications, which can indicate active or\n", + "repressed chromatin states)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "pRv5MT9hkYVt" + }, + "outputs": [], + "source": [ + "# Make predictions for sequences containing the REF and ALT alleles.\n", + "output = dna_model.predict_variant(\n", + " interval=tal1_interval.resize(2**20),\n", + " variant=variant,\n", + " requested_outputs={\n", + " dna_client.OutputType.RNA_SEQ,\n", + " dna_client.OutputType.CHIP_HISTONE,\n", + " dna_client.OutputType.DNASE,\n", + " },\n", + " ontology_terms=ontology_terms,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "76xYbH_mNFqV" + }, + "source": [ + "We will use some of the plotting tools provided with the\n", + "`alphagenome.visualization` package to build our plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "height": 634 + }, + "executionInfo": { + "elapsed": 2864, + "status": "ok", + "timestamp": 1753113379794, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "KmQMwe1MkYVt", + "outputId": "b7f73a34-1aba-4a72-8dda-7aaa3693834e" + }, + "outputs": [ + { + "data": { + "image/png": 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WaQ6+GWn8l5rn1yBNrJu0pioForF46pkL7YduQ2qf3kXj0PPoaNS3qAzi88/S6sTpfaZ3rMbMC0jvs3LrwD4kn4bi86Xn5CKkHY+bFcirdcUfQ+bzGDeXlSuI9O/QHthoPiwMlLk7ehJp/SU2DyDtpNsW4apQ6IwctVt0uwb6pedf88CIfMXEaU14ENJcprlQc5XmrBfQeWjVmIfzWs7je24g1yozq6EF5W+NzZ3oCzQB6RnQWrPB2lx5ia+KZYZrzdd6dj5DmvdeR0ci7VpcMpDeqs8c5WnuVF9r8pWGeKWp7fMhjff4rL/HuZ6HmfP5GrsmT6uP+1J18rx8TtrWaCzSe/DvpWzLxD8T0go/w8R89GsLzrdE/6PeW8uUV1ES5dUi8df8Jf4XI807Gi+Rf59yhWE3CP035NM41Zx3Fyr3uaLiZ0d1U5bap3bWxrbEOK7jWu6oYFP3XGVs6z0bmXjN8bI/oJE+qj+yOyNvR5z6orn9YaS1ihi8j7RWauDYnc/fhtdLZcrW/4BRMjCW0j+KyIWtuF4txO2DzfN5g+w16e9z3WB9Vi6P00zABEzABEzABEzABEzABNqIAAt0/cWoZQYeAx4DHgMeAx4DHgMeAx4DHgMeAx4DHgMeAx4DHgNNHAN8XVODfkYjKv1+BVv9UCrHrK9D3h/DteKkmK5zpal82cb0G7N1EX9ysJGdNC6TT9dypOmabx9x+nH/nkzenzgfncs7VPkIh6BS7VF8vTbl68q1Vw5fckaK7Z0c6o3X6vN+uTw3hvr1Y7Xs5AQXeXxWrr7Qfu3aNz7k3aCUPelnBxs5WdQ9D8SdFuJjG+XwoXbHazmCLJ0vl7iqjM2vOZfTq/KMQRPRWaF9dXYFZWyWKUN51Y/IQdcqc9d8vlBubci7C8d4rvw/hHjlfw/1i/k51w//YlvIWmlFdZWKw35vFMex6huNsuz+w3W3fH7iapHsK257voyia8qrCuWqbDmqxHuidsVzpd2EuufLIK425Fe/vgjnYqoxOTLDUQ4g0VblqWzZ6FzSM62d/RrMu8TL2TnaiZ3apudTcb9HteG8OpufuKEhvnA+Iu1AlGWv9nwX8qjsNB9BzvEaA7G9Ggt1z1s4nzfTVzk//RfFNuuoZyR7fUqJvq6FXXw2Y74JIe9jHE8I5zVF+UvFkac6U39VkR3p5wWb8SXSa0L61RxnQbFPe+XtSYv9L9lObJYI5X3JsUdgfVKIk5Nmg7FQSRz5a0IZhfe9XBnkGx7yvsBxuqwt13Hu0dhbJ5c2LOTTXH5DONcYHxPOdS811rbP109cdbCp5fincB7fP8pzcMzD+QEoO39oTGbH8ANc98q1bSBxn4RyYzv0vMVnSHH75vIcn7FXut6j2Xm2wbyXsW8wvkgTu2z+MVxrzo/PxD2cz1jAZkSw0XP+XDjXfJ19Tr/iesF83pZcU15VqEvtq26sLGwOytgvkmNZE9I69LmhDZqjI28d4zydjeuZ7yt2zVpT5ctpyjV1Xp9r62ius+P+ba4H5Mskrjrkq+U4FOnZ0DgfE+JjX8/K59U1QQ6zb2VsNWa/Cdda1/wmnNcW5S8VR57DQz7NCXOXsisXT75hoYwajheHc/Uv+24Ro23y5RBXFewLx3MmbWg+b+CyccZmtSKbojjyDI35itKzceVsM2n15nTiu6MRsQ6O2Xkz3uurYz2kP4009ylNrPLv8br5mbQmr80Dq6pQvurYAMW5akyoM7br5lJMsGv1Z44yh4V21eTrDfFq1xCk+VTnmvM1XmN7xS59T1cq7Ftj3Fdn2lDVWN3Ynhns1fYG75RS+bHtgh4NeW8vYzcjNh8g3df5wz2vDfmGlcpXLp68Mf9vOdc8I+aad7LvTM1LcxWVQ7zm9uz8OJrr7LqgwecK0pv07IR+VpNPbaub/ziv+DMStlUhP8XV+0x5TIh/rKh/oe7ZsYl9WiGXX/P2yFg2R7HQ+I1jVxy3LlV2qXjyjAhlNHhm8nlK2RK/XaYdTV6nkPfukP/VfJ2+rnwuMiuz8hjwGPAY8BjwGPAY8BjwGOiIMaAvFRxMwARMwARMwARMwARMwARMwARMwARMwATaiQBfAF2D5qS6rUOVH+o6ozQ92HwYbPRDcrSJ+bQ720Gk/wV9gfZHciSemaMcBn6NPkU7oENRPlxBxHpIP1SrnFnI24/jDGgppB/I9aO+nC9OK9MetauuTbJvJKi9u6lYdCRSm1XvAHQd0vdV59K3tTimQeWH+q8JUWl7oqJdqSN2clocHtJ3LLKjvm7Ei5nClTmbj7mWo+hyaCbK68NxerQCugvNhq6kDDl0lAqXkHALkgNHX4690FmljDPxavulaF3Un7xyMNE9Ghjyy6lCuxTPV6asc0jTvZQjlcZGbzQEjUFV6K8oDaTLIULjs5B1SIvmZY+0SY4a/0S6p9cjOS3rXvdFRyCNgV1QXf2c50PFbc9nrOBa9+RetECmXRqfcuraEum8VDidBD1fciLXPRHTbWVMvzU+bke6RzcjjZsZgs38nMs5Rhy0o35fjnWB6525+F2IOI3jrKFtc3F+OVKcxluTAuXKKeRspHGue7F4aI/Gw9xI9+FZFUp8OidxqroU6uYkxQfFuUnppyC1uxbthOIzMhPn+6Bx6M+0od6zx7UY6JnXs/AcWpayxU4sd0fLIM1rrR6ouweFDg4Fv9tYBbRLTnxnBjvt5qrnv6lBfVK4lvImhXPNwwo7UOZ04bw9D7+hss/R0ui4WDFt0Ri7OFyfSXvvL9GoIcRLf0QzY9eX40LoHqSxdillLcixKMxB5MnofCRHM40H3XuNTz1HKlfPv95Rh6M5sFG65r8N0BuoGsX7wmkajkJ6Xt5Geo/IsX4Wjsqnd5v6KUfMNFBPFYfDwuWJHGfDXn2Rvdqo5/q2kN7oIfT3agx7ogfRooFLfB4mELce+keZwo4mTTw2QnpG1W/15SOkvqidHRnuyFS+ZqmG0O8OeW64B5pTNEcr/BvNR1viPD0r1xujq9BPv5j88i/5DuJM8/4XSHNPU9ZU2aKaev4WGf6GlkB6V+jea/xUo6eRnqELUKmg51Xp/0R6lvpyVBl6fhR+T99Udj5cRoSe1y+Rnjcx0jjVWNO8HRnm8zV2PTgYPEt5nzRm3Ei62rUz2g/puezDcQH0EOqKzqFv3Rspo6nJ82UyjGlq5ja01/t1bfQd2hXpj290n6dHA9EB6AWUBtJW5LB1uMx/1tC7/Jpoy1HjfjdlQ0eiRtfmmbzxVOUNR3GdOzPnfw1lDuE+bZLP04HPnJpSg0aipWChtmqe1TtRc/QKaC/UlNCa477SeuNcrGdg1cYywbsPWgk73avVgv25ZfIdS9q86AQYvVfGrjlJp5FpLFozzDt6122JNB9pXtL8VC/QdrVZ85ye/etRpZ8rmvTs5OuN17SzNT4jxXXfqvSnqkRd2xHfDb1Fnc9EG+x7cH4LWgZpDtRcrXeGxq/W7Zqz9e74D7Z6b7R3eDZTof5HBr2bKgqhb6sH41srymQjEzABEzABEzABEzABEzCBzkOADyb6IsIyA48BjwGPAY8BjwGPAY8BjwGPAY8BjwGPAY8BjwGPgSaOAb7hqUFy1piI5NBWTnKcqWOMbXXIW1vquxnSa4NNdd6G+L7oGzQJrZRP1zVhFSQHq6+RnPDS+glyAtGJ0jYqylsUh21tyNegPUX2+TjyyrlAzgaq+8SCdP3Y/nBIf6ggvSakDcunNXZNvi1CXtXfM29PnBwC1a7xqCm7CMrx55WQd+1sucRVhXiV+whqsBO97LN2+XY1dk3eS0IdR+Vtia8NaRqXcmSu94wT96eQ/m5BWk1IazLrWBb57wtlqO/dCuo4IaRrHOefj9qQ1uS25+vJXlNmVShX4F9G0+ftiRsWbDRW6o0FrmtD2miOcoitx1TXhOOCzc0cu5SwuS3YHJLh1YW4t0J8TT6fykL3hHRVVO855HpoSBuR67McVj4MaVfmyy11jf2wkKdBWzJtXhibH5F4yJG/iMevQzkv59olJzf1Q85G+uOK/PjcJaTLpmQb8vl0TajO5K3K1bsAaddm0g8tUUZNsLk6lCkHn69C3EG5Mv8b4gvbSZrmNjliqnFr5PK+GOIb7ORb1K58HHlrQv4mv4dCvzYP+XUf1w5xN4W4lziWe0bUnyMK2iQHqNdDGRfn+lsd4pW3cDwSL161wW6rfPmhjfOT/i3SO1BOt+n4Ibwa8tXt7luUP2Mfx+dr5ezyaaEOVZgfX3FOfpu0eruzh/btHfLq/btQtlyuR4S07/JpIe82If0HjnXv9HzbmnpNWVWhXPWnurH82GguUhtkf3yuDzUhvsOeG+qXg6XapvHR4N1T1D/s+iK9i5q8pioqr7XiaM8s6HOk/sixt26e5Lo6xCvtoqI6iY/zy99zedfM5B2cz0vaQije49p8erlr8n0Uyr6gnF25NPIPy7Rv57wtaXMhOQqr72vl+laVydtgPGfShhaUq7H9ZLAZw7Gi8aNyCEMzZWvtUk5aXygTWRu8+2I5I3L9Oj/k+Wc+T6lr7KtDnpL3kPRmr83JWxXKV1/uRg3WPMQNDzb/zvWnL/Ft8sxR7rBQZ02eTYhXe0utAc8JNvfn85a7Jk9rjPvqTPuqytWnNIKeg3QcoQb/O0uwWSNjE2111Bj8Tak6SBuEJqM3UPYzZG0ob1ipvOXiyRvzf895vXdgaO/gUL7amF8vNetzBeW06rNDeTWhjSUZkF4V+5HnQfxzIe2v+bTA4JGi8on7bYh/imODtVnIG/t6blHZpeIob0QoW/el3NylNK03dX+Knq/LQprSNUffi45DQ5D+wE+TXgORpndOHJ87Ftk4riE3MzETjwGPAY8BjwGPAY8BjwGPgc4yBrqyoHcwARMwARMwARMwARMwARMwARMwARMwARNoGQE5eM7RiFr7exg5ovVGj/BFk36IbhCIf4LId1E/tHzGYLdwfhc2dzbI2HYRG1C0nDj1w/Up+Wpoixwgjw3xa7Ib2px5mxZca5fAr5Hq36SgnB1D3C20Q07oFQVs9eO6nIIVVi+T6XRs5XDY2kGOPQrl6r6QuuU4mw83h4j5YS0HpFYLlCenOTmRKJwY7m2+/JOJkIObxnHRPZF9W7Zd90T3Lx/OIELt0lhZP58Yri8n76gSabuH+DOxkTNFUbgqRGbLX5Y4OWAonBiOdYdQ1gn5+Aqu18VmANLz9ecK7JtiorlEc9vNtO/dEhlvJF6cl2BczJWx2Tacy3Hyy4K8VxD3fkF8U6Oept7PgvRsv4O2C4XcxfEflRRIG8dhF+etw5v4zGjuU9/Vn0dz9amfCnHc5JIrvuyBZZPfQ/RLc8iFSPfxcvr1B45bIs3Tu5Be9IyQlAY5Sp8VL+KRPHp+Tg/X21Bml7xNuD61RHw18QNRLWXdVGRD/HvE6x3XHck+Bt0nhexY+//UhmfRXjvD9mqYXHlM6KfezQpn0kbxyYeLifgYiUl8BvI215P37Xwk17cizSn6w6c4VxSYtW0UbVMbxoRaNNeXDJh2xHMT76meiVlLNq5+QkvWVBVW0XQz+Gnd8ljIuWqZEhq8M4LtLeG4ZC5vHHtPUMcD+XLD+LsmH1/hdWSutrc0fEABV+YLoX2fEvdUiM/3LW/e6DXP7gxoOQxvRiuFDOdTj96bzQmNzcVaXzQ1NHVuq7T81lqbnxTmhny9N4eI/H3q6GfuDNpb9H4r1d58v/LXrTnu82WXuh6dSSg1F+tdPioojme9m45AcS1ar3yeha5EXIC6oQPgpDJaO+h/g2nwngvzUZzz6t6RtEn9Gxwa0dTPFW317DSXSZzTdswXQD/nI261EJ+f++I68bwSY1fZYp7182VXeN0Tu8bmL71bS4W9SDgDacxMh9ZFGms3o8/p31No57BeIqouZN/VrfHuyJbtcxMwARMwARMwARMwARMwgTYmoA+RDiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAi0j8CA/BGvXv3Ia07IqGuSOP06vzI+40bmywZFc+iFbYd5MCauE89szce1xKucehRdglXWayNb9EBeTQ0S0z6Y365z6JpHxhpB5p2wh8JMz39YhLv9jfxqNzaLoXPQiGod+Qj9LJB8U8s6dLTd3/niZtLJJ1DELOhI9hr5CkzN131RB3U+XqEBOkDH0LWHT3OhBZJSDpfg8WFQI92Qs8c+GtFL3ui3bPqJEu+So8nwj7Sq8n9wXPWcDQt7ruG7wTCqO9LODTfa5jAw+h80bRW0jTk458fkoYdIgOj7veu6y97yBYTMi4jy0bZm+fkS50Vkl7S+2ckpZItRXanxo7Gg+aGnoTwHRmaZXprCjOd8EJnKWrjScg6EcuWZHv680E3bRaegq6lO/skEOYIrbGC6zNaHMvGlL3kN/pLC3kN4XchxSOJKmvhDOSx2ewWZ8icR4X/uSPn+BzffElSo/jqu5S42r8BytHsrNPkfxvXYyNuehwWiGgvpj1JOcyNlpLvQ4tnujovaWKaIuaQHO+oSrB4oywOsn4keEtCbNe+TVe+zzkLdfUfkdEJcfz0VNaO/nRmNZ0jyje/oHpHd4l6LGhbiWrKnKFFtZEk1bCf0bvY6+Ren6QqKEIaGUUmuMrxkb75aoKc75+fESx158Touyl0srsm+LOM0xpcZYqb5V2o5LM4y/I5PWI1uEzLdyHFZpQXk7mlzu84DG4R75PBVc3xFshtDuW9HWKOu0WUERhSZxLLR0bd7Yei0/Bjv0mYNEU9tbCK+DI7PPRuFzwlh8Cs0p0VY5Fw9C9yLNy5of5ynow++IWxHJSfyegvTWiBpRppA492TfkWq3nh31s3Buoq1jSSv6XNFWz06ZLpRN0rpPa4Gl4J//www5paufz9KfN2Mp2OmP7VYK12dwXerzxU3BJrsuisVUcrysgvmrkL8KJ+9E9CdOVf++SH3V+ziOT42r/6Jr6ENXjg4mYAImYAImYAImYAImYAJTAQEv7qeCm+gumIAJmIAJmIAJmIAJmIAJmIAJmIAJTJME5CynIMe66FxZdIyOn1nHS9kpfBCO7XWIzpXRaahBvfxoLWfQr0JCtG9g18yI6Fy+KT96z5wpY2PO+yI5Id6VLxvbHYh7EckhYyk0I5KTw6ig8SGP4kuFL0ollIun7sVJfxUdg1ZFsyA5SskJUfWPRgrl6v4m2NQ7BNYxLo6TItPmxMV7N5Z6vi1TgJyTFUrd67Zse8lxSHtiWql2lbqf8bmMfSp6JhXXL/Q7+1zGukq2C5YTyPdlyFvpoS2f99jf3jSmVF8VH7+Hjv3VOO4WOvBJmY6UZFEmTz5pfjnThDbMw/EP6Hv0V7RR3rjcNeXo2Tsh2PyZ57NPOXulBZshwS7OQXXZKFPz8MNIzkU7NVZeW6TThvGUe0Cm7Cc4P62Cusrdn2xa0XP0FfXKAasoxHE1HYnlxpWc6RSyz9HJXN+KlHd/dD/SHw3pD3h0z/oqQwy0QXPormgMWhpdgN7F7lN0GVq7zrjxk2w/y7Fp1rwXqtc7UqFH481pGwuY6HnqG0qP76CSlbX3c0N9P9IYPUu6BwugM9Br6Euarj8M2iL0IdvmOOZmILLcmIvcs2MuW06Tz2nLIWTSM7cHWgRpXIvrqKB4z0u95wvfk6EhpcZLHKttMf/GNZzm+ZaG5vSt0jrHZRhrrLyMNEcPYQxJE1UQ92c1VOjsSXxznTwrbWOdHe2R0+ffkf4IbXN0A9KYfg2dhhZucqG/ZIhjoeScRd0aR2XX5tiUulelxmCHPXOBU2Pt1Tu5KaE1x32l9WafsUrm4sncp5Ea31Sg8aN33vnZyhhHc3N9HNLa/Y+VNqQZdiXHG2XFtDg2VXw8b/LnijZ8dprR7dRJW/3Tuk9hx1wh8Tq/XtS91rpGQeel3lP9g43eZR0W6KP+mPYCtBP6FQ3R874X+jA0ajuOB2YaGJ8fRbXGuyNTtE9NwARMwARMwARMwARMwATamkDXtq7A5ZuACZiACZiACZiACZiACZiACZiACZiACbQJgfi9zpn8sFt2p8WQXtMmrWheodM3L1uLc8l5R45/cu7aKlNa/LH/OlhNytaCI4YcHi5CPdA1aAXlx64fijsKnhnyyCmvMGD7Y2FC45GXYiIng+fQRmgmypoZzaH6udYP+Aol6w7pHXXoqHvd0v42xrPU/cx+39qHe9TYs1nVjIY21rZ8kU21z+cvdx37e1AFfRWLEeUKK0hrtbZTt8In6Czq+S2SI89/ecbllN6UcAHGcqDph/5UQcbtsdGco6D/QaFuZ+N4TvxaIX33cOyIwx6ZShfmXPNOS0Jj967UM6Q647i6ifvV2DOk9GGxoZxPQHKuWxWdgp5AP2eu34T7MtFeR+xv51CF9kbXok+Q5tfd0AjsL+TY1DClzn2V9FPjI/bv3UoyYNOuzw339BnqVDt3QZcjtVNObduiW9Bt3NduHGPokDUVbViCBpyM9Lyci3Q9Pe2fBcU1xvWhkY09U3WdaaWT5tb3Wqi/3nPWSm1qzWIOiow5DkBLoZ3RrblK9K7QfFik7BhqzbYVlkXbjiVBzpx/RXehcWhRpHfRq4wnzVnNDe09Z3XIM9dcOBXk64hxv1SmXZXOxTGL5hsF/UHOrJlyTuR8ZqR5aSxpvbMiLs4L08X4TN7WOi039zRrnLbxs9Ocfl8ZMu0YM8NzMc41b/6Ers4VGp8XRS9DfxpdG+Xyd+gl7R2FLqYRy6FRoTF7Zhr1PuffhevO/u7oUJau3ARMwARMwARMwARMwAQ6I4HsB5bO2D63yQRMwARMwARMwARMwARMwARMwARMwARMoJhA/PF28eLksrEx78CyVq2fGHeNLlkvP77LUTM6QkT7VmkJP3zLCfGaUFj6g7+cJzhsFuKiM0C2vo25kM2rSLu46b9Er+ekTrycklo90Lb5KHQlJEfNLaj3LqQdCbOhTepuhc7EezcD/ZATf6kwICS06r0uVVkuXrs8lgpzNbNd8dlS9qY+m5FByXbBUo5wWUehUu3Pxn8WLko+d5UUUsKmufPQ15QXHZBL9hebeB9KVN+8aJ4jPesPo37ouKaUQt4J2MsJUOFg7kn/RvLv3kh6NnkQ5WUdypqQtfmm1LkzuXdAk9EbSGPs3xWUWOm9a+rz3dxxVddk7tMT6FC0KpG6zzuiD5DmIzlB1QvYaVfVi9D2aB4Sl0AXBaO9YLRpPk/Bdbaf5Z63jpz3Cprd5KiNMzn0HDUaOuK5oc7v0RVod7QgjVwAyblSawH1Yd9Mw1s85hqFUGywDdH6nU7v9wPRqyj/xxlt8Z5v9H1Dm+YqbnKjsQ8Ei+V5bsrNEY0W1BkMuB8jUClnz9r2biNteQ+dhDai7lnQYPQQ6o7Oh/nsTWxTHAsl5yzKbIu1eUc9c03EU7F5R4z7OBfr3f1ExS39xfDjjL3mxxjiONA655sC6bOJwl8zaSGqSYdyc0Oce7Lv1Hje7M8VbfDsNKnDOePruJ6I5uf5WiWk7RiOD9LWT3L2X3Ed3w1N/XzRkna2al769SUF3hIK/VUsnPhJnD8arjdv1UpdmAmYgAmYgAmYgAmYgAmYQJsTsAN6myN2BSZgAiZgAiZgAiZgAiZgAiZgAiZgAibQJgQeD6WuzQ/XchZsSohOCps0JRO22pFNodzOdOWKfC4kLkyb5ylhuBbxcqJRiPYlTJsVHZ3M1wtOOkMopRf6EBU50kVHwRf5cTz2v65iyhCLdZrVksYz1TkpUnfWUSSbc73Gi2mWRUvv9fPUKic/BTlHNQig60Pk8iGhLe51gzpzEWsXGdCumYhfrjntknML+aJT1dZF5ZeJiwzmoA11Thk5+9W4js9HmaLqJcXnfekyz11RWZWMgTgPbU7ZPYoKKYqDk5xuXglpeuYbhPBsFaY1MG5ehJyrFHYrw7tUyZeS8A7SWDmslBHlLkSa7pnCskiO0KU0PNjtHo7tcqCN81LRuaGyYzhuib5HG5G2fyONWBEbzZ9FIT5fY0jUc9GUEMfVIpS/RFMyFtky3sajq0nbO6TLMXbGItsYh70cgWUfn5/C+SJXxrtcjwlxpeY9/SZTHWw6Yt7LNblpl3DrTY6DQ66nYfRmE0ro0OdG8zM6nPZeE9qcvactWVM1AUED0/ief75BChFhnEbnxCKT5sbFsVdujq1kzBfVX0Ok5ni9q0rOj/mM9LW5a8t8UdPMNeP5RzSCDm+G5MCpeW2FDIBK3uNxLLT32ryjnrm2Gh81FNxu457HZW7q2yN05mbGwfgmdmz+jP23TczbGubl5peYln1Hao78OVRc6v3ah/SKPldU8OyU62Mlz1W5/PqfV0ZjcFcw2ikcdwjH+Fm1rgzs9Xw/EyKa+vmibFs6IDGOVT0v2XBhuFic8V1RH/3e6IC75ypNwARMwARMwARMwARMoICAHdALoDjKBEzABEzABEzABEzABEzABEzABEzABKYAAto5TT/g9kSnlmsvP87K4TEbLg8XG5C2Ubm8ubRx4bpvE/JkTe/mQmXIUfXP+TJoSzfijgzxD/Nj+2d5m5ZeU6acGV5Hqms7FH/0v5q06NiQrWZsuFiyxI/ce5G+YEvbVSJ/rFsOyQ12tCRuqUz7SxTR7OgW3WtQfk3ND4TaD6WtRd9DHkq6xq8cX25vdkubn/FPtGu6guwHh3aJgcZsU0NNyLA/5S9WKrPGE0HOMjGM5OTtcCE29YKMiajYmS+T+T7OP0Ya82XnilyVlYyBy8gjRxw5Qmk3zJKB5ufnIc1hCtpdepaCjHLEqSqIb5Uoxug9FDQSaWwe3pRCyTsZ+2Ehz/4c1f+isHuIfIE80phSwi7y2BkeuldtHsKYqqGivugJdALt0/wYx9+p2JT6Ywi1rxc6SCfZQJ7puf5jiLueMovm1ny27LXG7Ach4sxyPPLjiuuiZzqWLcd6BT1LqV0j9jKJedSnsiH088ZgdBBli08+/JaIeZCYXJ9P7MzX9EfO53KMqwrt/3tT2ttez00z72lL1lRNwZC3je95vc+LwhFE6g9dWjvE+WZVeK2VL5y4BYjbPh9fyTX3+RPszgq2B1BWnAcLs+s5QZeSOLDQwJEpgUbG9URMfgyosnNVfI9n1xp5olrnyK691+Yd9czl+98q1+057hkL+kygd43uq+79MdlOkF72DxVJ17onvqO/4Fzv/TTQj2pUasd/vTvfD6ZHR7uYt4nH7cM8Uy8bcZqPVg+RcZ5Su5r9uaKZz0657sTnqm85owrS9D5V+DVtXIXjwkj384YQnz/UhIhtsB+cT8xek55fc5czb5U06pwflf1MTLrWRVuGCkfmKtaY1lpU4UJsB+XS612SrndG/IOycqZOMwETMAETMAETMAETMAETaGMC+pDpYAImYAImYAImYAImYAImYAImYAImYAImMIUR4If4r2hydPjcgx9hr0VLxm5w3hOtgc4j7tFc9+7gWpIjwQ3YHIj6yobjdGgpdDraMpfvlXC9I2lyHG5SoM3jyXBCyPR7yjgCyaFN9coh7yq0BpJD69+CXVscVI/CPmj9cB6dAPL13UuEHAXF9mza2VcGHGdGf+ZUfHUv2iK8RqEfqTp0DfUtpEo49kBbc3oPaqtdC+O91i7IczWzc0eST/dyOXQ15QwI7e/N+eGcR2fqkxgb0ZmjmVU1K9t85LqJtlSFdskJTg45R4XSTqZd3zWj5JPI8y6aET1ImbujdJwrcD4v2ovTZ9FWMZ66NM6Ghes9sTkZ9dU1xzk4/Butg5rUJoqdRJ4/hXL17GquWDRcq+y51B50dowLxzgGNI8snEtLLylbY/SskHY0duehBaIt57rX66P/EFfnSBTS9ex8jvqju7BZWvEcNb534fQiNDbYttXhtFCwnL7nb2IlmjNeRTOgBo5AlKfndtdQppxqGgvDMdC9mhNt2JhxK6X/gXI0psajXbmf0YHxXK41v8hR6D90pZQzm+7PsaTL2VocdP90/29B+uOLH5CehyaFMGYPJJOeifXR3ZS7cmCqOroj7WIen7Vs+S8TfwLS7uzTKUH5CCtxek4w1M7do8P5fqRp/O2E6uY6zvsizVPVwe6ucGzsoHeceM6NbqOMRZSB4/RIz318zi6hDW83VlhT0in/56BhTclXzpbyFBZDB2P3Eto82P+d9t9ZLm+JtPZ4bjahvY8jzWtyUEsD55rjdQ92DlF195S+tGRNpbJrkPjXhrIrPeg5U9iUvIerjbrgOBs6lVOt81p9jUF/H6HcWPf11LUZ6hrqXp2j7u0EXTczHEG++5DmwUsp+0q0OqqbSzhfCB1C+jtoaDPrmZayXQ4vsdwQzRQ7znkV55ehnuh79HAGylucT0J9sNsmE193yljQfKV5S6Hd1uYtfeaK+tIJ4tps3HP/uqFl0ZH0U3Pxykhr7L1gqets0LrtfiTH5tljAud6d67G9e0orjOOI7/Kae8wkQrvCO3RnNcV6f1yfWjIPbTr0Vyj1PfmfK5ozrNTjscrIbEln5FUxK1In+O0xj8vlHkH/Y7rk3wbLiHiCaS5+n/w0tprlmike420zh9B3EH5zO1wvQR1vEH9NyKNveyaakaudX81P80f2vKPbJvCOPw1cR+hWdEj5DkeaT2ZBs71HKyEzuTydaS1XV0gvhrFtVB1Ns3nJmACJmACJmACJmACJmACbUiABb1+KLDMwGPAY8BjwGPAY8BjwGPAY8BjwGPAY8BjwGPAY8BjoIljgK9sapAc5PQj+meN6B/Z72CwrQ55a0t9N0N6bbCpLmPzN2z0Y7zaIcmR5Gv0YybuvXx+0vqiERkb2efzDc21WY6KsR45Jn2IapF2D69o/GDbDclRJpYzmXPVG/ugduxfVB7xNSHfsKL0SuMoY6FM/WrHq+Xykn5Gzl7tVbuVV05ax4XzmhyvqpivkfJL2pF/K5S9l+O4FnvV/T6So67OG4wjxYW0cuMn3oeqXNv7k/erkF/1fxrKa1BPI33bh3yx/brHWXaq+7+oW76MUJfSm9z2fFml7gllyxlLjlmqZ3TmXNc3o+75soirDfYl26U8BI2xV4OtIsRAPL/LxCl+94I6zs3Y5J+P35NWG9LrtYG4oSF+RL7M0KY/kp4dS99wnW1PvXyk9UBykFU7de/kLF4bNCDWwbWe6fODnWwljVMxjc+14h7It4u4tVG2DWO4/gHJ/jF0Yjivyectd02e6pBP5dQb29l8pHVHtcH2glxaTYgvOb+RrjEU+6xjXTs5H5xJW6JcezMsNZ+onGsqsZeN6gx5mvQeIs+SKLLeJ18fafMgPa+qZFiOzbAQr7n8xnCu+keHc+XR2N2hoNzqYFObT8tfY7cHivOdypRz5Zeh7DruubaNCeXHNui5U9ui/RecLx3zcH5wJk0236JsPxRXb2wE7rG8BuML+82R2hptVF62DfdyPWNBf0eEPEPzaZn21gab6rxNpr569ytvl78mX1Umr+55XM+IXXzXxb58Qtx2+TIy7asJZXXYc0P9W2b6o3ZrjlG/svPRbVwXzfF/y9lVuqaqCXU2Oq7z7Mh3Q8irtqqN2bZewnUsO/8cVjdWJ+lDg82IgnrlHCgH5XhvxUnvBV1rvv9NOG9yn1QXYTqkd0N2DMV3YfZ5kPFw1DszjoaFumvy7S4Ya3kuVSGvyi33nAwtVXZz4qlraKy3sfzlbDNp9e4Z8TfH8jlqnIxGGp/x/omz/pAoyYq47Jp7DNe1QdtmWDZrbU45VSitP19vpuzqYFM4jkhr1jNXqj7FE4aFOhuMnxAvo6qiMhQfbYrSG4sjb0vGfXWmfXpXxblY9zq7flP79ew2GN+h/9lyZKv3msrLPncq7/jG+pNPJ08tUpnD8mmVXGfy/5ZzzTMqK78eVd/mKiqP+CZ/riDPzaEe1VXRs4NddcjTYNwS3+hnJGyqYp1F/Yhx2OhzkNoV9etG7GfH9pGMfXxnxLk7lnNUuXLyaZQ3IpTZ4Jmp1Jb8+qOGbF90rvfKmFy85qrD8+VmmOjd9EAuj8au1iTZ50Bx+gMKTUCpCNWZfIXPR6l6HV/ZdznmZE4eAx4DHgMeAx4DHgMeAx4DRWNAfyXrYAImYAImYAImYAImYAImYAImYAImYAIm0DICctTU7mXl1KdlVRTn5guf40hZBl2I9IN9FzQj+hTdgfZDK6N6gXxjiJBD+e5IznByeOqNlO9BdDC6FdUF8tzPxVZI6d+jedBANOf/W5U/o4wfkercFt2NxqBY71Wcr0S6HJbaLFD+2xT+VKaCK8tVhr0cd/dGzyM5Q3ZHI9HBaFOkH9LbJFD3TRSs+3QPknOBxtr76DQ0CH2EWj1Q75cUOhjdiOS0MhvSvZYqDpRzAcYrIjHW2NK9HovUHzkx7oLkTNDugXrl9Kc+yhFRbdB9fAEdiLYmvdn3lbwaY7o/+6MHkJ6vmZHKfBGdg9ZG/0H1AnkPIGIX9CTSeOuCHkSbkXZ2fevKr8h7BtZq06WoFmksyQFZ7fkH+gOqC9hP4mJdpDZ+jPqhOAb0DKRB9w+pn2sgOdK8j+SANQP6AGkM7462RPUC+dQvtekapHE2PapFw5DGvfrfZoH6dT/ODBUMZdfEAU2sTM/HcyXyqM8Kb1LPKyVs8tEakwpDaEvffGIj1xW/hyhb90f3Srxvo316TusF4nTPfxci9b9VrJS34fpntB3SHPkaUrmj0f/QapRxNcdmB/JrrC6CzkJiqPvVB8kJ6QF0CKpC2TCEixPRo+gT1BvJSUnj/CSkPwbQeQyam/ZCGoPqg8a98mi+uhUNwX4fjhUH7IdjvBS6CNWiXug79AjaG22IzXiOrRa4P3NlCnu6BQXrOZ8jSM+wnkuV9y+0NZqPtl/XgvKVta2fG61VdkWXoZeQ2M+ENG7uRXo2N6cfGk/1AnHHEbEMatKaCvvIvznstyf/YSiOvy6ca/zuTnt+U6+BrXhB2RrjKyK9GzRvd0Nj0SVoOfQOanag/IlI74Yl0SnoGRTfhbonz6Oz0QrY6X582+zKpo2MGiN/QXeid5HmW90z3SfNlcvBsMGagvh9kebEN9D0aGCQ5rk0kK/D1ubU3dxnLja/Ux1bcdz3p2NxLu7B+WdI84KemQ3Qr6hrRInOP0u85rnL0cvoe9QX6bkbic5Bg8h/BMeOCm9T8Qro30jzjsZyLTpd8bRN81ODQLzWK5q3rkSy0ThW/ntQqc8VzX12GtSvCNrwJYfBSO8yvSNnQ/G5KsxTJvKKTJrmQK0fSgbq/pzEtdHO6HakazHQe+N1pPl7E3QCatdA2+6iwkXQIehmpHusoPaNQVovn4WWwbZk+3Tvkfiujy5G6pfG7sxI7/EH0ZFoQez+xtHBBEzABEzABEzABEzABEyggwl0YXHewU1w9SZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAibQ9gRw0qyilvdUE9+LylnDwQRMoIkEeI6GkeUodBnP0dAmZrd5GxDgnuxAsVehZ7kncupzaCcCsO9OVaPRjGhZ+Gf/wKGdWuFqTMAETKDzE2C+rKWVctYezFw5ovO32C00ARMwARMwARMwARMwARMwARNojEDXxgycbgImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAIm0GkJaEdUhaM7bQun3oYtT9e0w+tNdj6fem+ye2YCJmACJmACJmACJmACJmACJmACJmACJtCQgB3QGzJxjAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAlMKQTWoqHP4wA9fEpp8FTUTrFXOHYq6pO7YgImYAImYAImYAImYAImYAImYAImYAImYAKNErADeqOIbGACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACnZMAjudLoOU6Z+um7lbB/VTUBY2cunvq3pmACZiACZiACZiACZiACZiACZiACZiACZhAfQJ2QPeIMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETqIiAdmWoyNBGJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACnY9AFwKtkhL8w3/SUXGct7qzuHdA73z33y0yARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwgUYJBMdzOZ0r/CQR1wPN2hbO52qQd0Bv9LbYwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwAQ6F4HsDuecL0frtkaDUS80Dn2FzsYRfURrtrx7axbmskzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABFqHQNbJPF+idjgnfVniD0Obou/QK+gLNCNaHX2IzfOYjuW8VULXVinFhZiACZiACZiACZiACZiACZiACUzTBPiwWov0wbZ6mgbRiTvPvRkR7tHQTtzMsk2j/Y+gyWihsoYVJFLG0MBjRAXmNjGBKYIAY7oqjOufp4gGT4ON5P4MC/eopjnd99zVHGrOYwJNJ8CzpnAAGom+Q1rnSlVNL805WoMA7KvDPahtjfJchgmIAGOqJoyrYSbSeQn4+e+898YtMwETMAETaB8CvAtb9F1C+7Ty/2uhvTuHNdap7V236zMBEzABEzCBqYkA79OuUuyTnMxL9Q877Xr+b6Sdz69FO6Bd0DZkW5/j0ugfSI7prRbsgN5qKF2QCZiACZiACZiACZiACZiACZhAZyHAh+xlwxfzQ9ujTdT1YPhS/fZK68P+1JDnY47T3Odz+ixHWf14cnAlzLDbAjv9df7VfFHydiV5bGMCUwIBxvaIMBdUTwntVRtp7wCkHxPPQo+i6Jz5WWN9mBL721ifWisdNgcjzYtVrVXm1FIOTDTmvkHRCbi6qG+k12Zsom2p4+7ZMsjXE22DLkYvom/RBPQBugYV1hnLIH039E/0JPoI/YBUxivobLRwUZsz+afDZn+kZ2oM+h69gU5Ds5XKqzS0D7oOvYNU73j0GjoXNfpHW9gsgS5F7yP1+Qt0F9qyVL35eGy3QDci9V1lfIYeQ8ejku3Pl9OJrg+nLeegZVAXNCrox07URjfFBFqdAM/rUKR5s6Zc4aT3Q4ege1Cc8zRPv4muQFuiev8LM9dVSGXXliu7s6TRTr2Tpb6dpU3t1Q76vGzo+9D2qtP1dDwB7nmbPf/53lHXIuhE9BQahSai0eg5dA5aJZ8nf43NWkhrHa3bvkSTkMp4AV2ChqDpYj7Oa1GpdWG5+ObmK1dmY2k12f7SbgXNq5chzbNjkdZbn6K7kebjOfKMcmX0wmY/NBxpfavPcFozvoeuR7ugGZSHYzVqrI2l0ofl20FZN2fK2ymfXsl1S8ogb2uMt3Uo5wL0KhqNNGY/Rw+io9D8+X4QV4PEaWg+zdedl0C4Zw0cu3QfS6V13t40v2X0tQoNQweXK4X0EYFLdc7uaq71/a3+qHdAuTKcZgImYAImYAImUJoAv4f+JPE+7Y5WRVuixdFM2Vxc9+b6BLQs2pc8v0EPoE/Qj6R35TgKvYsmla6x6Sn1vvxpenbnMAETMAETMAETMAETMAETMAETMIGUwDvoB9SqfzXdArbLkvco9CCqaUE5lWZVHWuhDfgQPycf3ss6YWLTDdv4o9t/sf+p0opaYPcBed9AY1tQRmtmraIw3aP30VnlCtYXI6TrixP9AHR8OVunmYAJtAuBQ6jloHapaeqr5Eu6pLn404KuHUzcQDQC1RakK0pzuPJrTp+Wwrl0Vl+iNxb034n2LGOk/2o0lvNczm441+tl4iZwri/j5w36Ne+jf/DOPjiXL15eyMn04ULvdd2rPmjxoL3Jvwf5r8rnJ74vcbeh1ULaRI5aU/0K/Qntis265H05pGcPn3CR/Z7/W67lbLVo0G/Iu2dRvSqENO2Cc0nIo6gxSO3eQCL9fPL+TglFgXQ5KqlPQ0K63tVjkJzO5Qi1KroD6d5MSSHOcX+k0WfBQP1yMAETgADP/W85nIY0V8QwjhPNRfpjG0mfdeQkuR2Pz4v/bzZFnR0VWlvDccwU1fKWN3ZZilD/2+vzdMtb7BLahUBLn3/y96Chp6P9kb4XUYjrJq3RBgXJYfEezrVT3zfBLj0QPyeH/6Dsuk1/IKa1l9Z62tVP2hPpD2T2oYzbOS+1TtRcpvWj1n1fo3wYSIT+EC0ftN7qFyK1xs//kZrWQoorKlNp+p5jPNLaLR/qvreh/VoPypFTbGLQWlF5tdYSj/XRMdgeTl/PqrMKJ8RvzqnWqrKNQfnFvipoG44nY7srxx9QUZ+VNzq6a97/XhG5UK8/lNef9E0yNrtzfmU+U7nr5pZBvtYab/+lfetm2jiZc43LWdFaQUdS35nw/3O5vjjNBKYgAlW0VWuB99FZTW03z4Ic3U4i38WhnL2aWobtTcAETMAETGBaJcA7VM7icjqfHQZay2sNvSLSOlSfBfQ5ZCTpQ7D7MHDSd7D6Plffw/6HtC4cJUzSoLW/Pk910UXI0yoHfbBxMAETMAETMAETMAETMAETMAETMIEWEeCzqpyiFkVPtaigKTfz9TT9O6QfUOVs0VjQD6VzB6PLGjNujXTuzW7hHt3UGuW1cxkbUt8S6BH68Fo71+3qTMAEGhLQF5TvoGvQIeiMhiaOKSLAHHZumIv/WpTeWBx5bwr5d2vMdmpJ5zvxLejLEPRkY32CzYpIfwhWKPLfH8p4DpuXcuXJQeUt9Be0GOk9kRyhFkLXBduDaI8cporCBURqDVCFpifvLDqiNdET4fxS8qu8fPg3EashOQLJ6ac3+fVDwqJoBNKPDf8j7wwc86E7EQ8h/RAxF/lm4tgLrYFGIjlUXU7epTnWC8QtT4TqlgPVbWiBUK/K2AdNQPtjt1+9jOGCeP2IoTWQ7s8oNBTNHPquevXuPhJ9haaYQLfEe7bQ4IvoT6v+KDPFgHBDTaCAAM+HnumLUB+kz35bIz33fZAcP/X86B31PPoVWg45mIAJTAUEWvr8k19rlv+hA5G+O9FnCa2TtOaK66aFudZa7DO0PpKDb12gjHm5eBrpOxV9B3Mq0jwzHWXMirT+mAftih5EA9AGSF4XhetEktQOhcewKVpDykGjQTz2mv9iKCq7G/nUrqK80UnktKJ04g5SwfR3GQ5aRw5Co9FhaEHS41pT/R2MLkVaz22J6gXKGErEzWhO9AYSm/6UofXmzJz3RduiEUjfU61FfCkWKiOGg0q0/bSMjU61PtY6W5zHovVok+5RU0KTy2il8abx8yRaF8nZ/kS0JNJ91ZgV81XQP9AktB1yMAET+H8CV3Gqz7j638L6G4wJmIAJmIAJmEBlBFhrRufzc8khzRCOh3I8BO2B3kJzZEpUnMJV5NfaVJ+BtHt6ve8189eZ/M0+7drsnM5oAiZgAiZgAiZgAiZgAiZgAiZgAiaQEuAD+zccbgw49GNeY0FOGQrPkPfVxoydnvw2MLjaLEzABDoFgUOYuxZCO6DTadFLnaJVbsRUR4AfqeUAri/Zv0V/akkHKUsOxRuHMi4rKOsI4uR4fip6PaZz/g7n26PovK4v+RsE7A5C+oL/fTRZBjqiRzjdCKkPckjfMZuZdi3L9VYh7s/Y639GiT8SyEloCPoSDUQHZPOG87Wxly5HnymO44/oUU7lcPU56o7+UJD3b8TJIegDtC153pMNxwnoQk6PCXm0o6ac2vNB7+dNkNZBcla6DKmfKmMSehUdh17LZ+zk1/pRJw2xP528vW6eCbQLAeYBzWVHh8ou5rgqz8hNSHNAGjj/Av2H0+WRHCgnhiQfTMAEpmACrfT8HwsCrU3kADGUuUKfJfRH5nHdw+nPb6NTSV8Q1WSR0Qb5NVyH5BSs9c1q2P4FPY9+iracf4K0nqomTk7stTFtSjrS3xlp7/WoH3oXLUefTkY6TwPnE9EItCeXmnflhFIXKGNpLv6FxO52NCiw+SoacT0W3YAGE6c1b92cXldQy052D9n/zfFGpLbs0sQim1NGa4w3Oc3Ph8RrdRhph/lXUOrEw0Fr7ifRwVwuip5SvIMJmMAvBHg29IdCtyL9sUZTn3tjNAETMAETMIGpigBr8y6VdghTfY99L9oSnYf0Hj2Md+v56FKk72C3R8+Ez0kqWu9bhR/DseL6gn2zD3ZAbzY6ZzQBEzABEzABEzABEzABEzCB9iHAh8d5kf7a+We0ZKlaSZsejQ12W0Q7rqdDm6KL0AvoS/QDeh9dgfQjVWEgrRap3mo0DzofvYsmoJGZOurs8gVhtzI6ET2BPkYT0efoTrRt3j5TZg3pqnsY6oYORi+g79DXSDuBrpDPT5x+CLo0xK+t65yq83la6fqyUM6y1LdUqTJJm4m0LUN6moc49W8w+gd6Fo1C4vQJugmtU6a8EaSrj0NRX6T/Mvl1JE5jYj7O6+zyZZG2JNJ/F/ww+gDp/n4V8vyWY7d8ntDuYaSp7ppwvTvnT6Jv0Dj0ANIPvvUCcbVEPBAiB4YysvdpaMxA2qycb450X/Vjc8kgW3Q0EsMxSAzeRFejISUzkkD65kjtVb5vkcZrPSfBmJ/4aqT21iqO48boDqRxrWf14GxdXG+NNN6/QGKr/wZcz95yRW0ivgqlPEL5K3F+C1J+sX0MyeEvDZzrGT8UvYzUZ42fC9AsReVn8jWpXaGu2Sn31FDXeI6aSz5EapMcFAcW1Un8IPTfYCsGmofuQtsU2Ye6akkXB/GeC/0r5P+e42voD6juuy3Ot0Maw7qHGn+3oZJzZqhjNmw0P72EdN/VJ3E8HpXlV6rdrR1PO3qgvdF9KI6h9zm/O8TLMaEwkK5nW+P/M6R79TrSsx6/jKyXj/j4HGoMLoYuQ7q/k9DN0ZgvNuOXmIX1tnckbcs/M6sTp3eEeOmZGIkOQGW/CyW9zZ8J6hiGxLkmcopxXMfnR3NRdk4ckbEdGtLq4vK8m9oP7PP8mjRu8vW38rUcR+ZFw9DHLSx7J/LL2VpOTlfmy2JcP1pqbBOv+fjykGd+mDVpfiD7WPJGh6C5c3XLoVNBjtsX5dLk2DKOuEtC/G4F6Q/l4+I1eb/gXM5GCvXWe/RB73Y5gSn8E9sfwnn2cBYXP6H+KDrvp+nk148Yh6UXSXIS+d8M521+oOoRSM+Inoe+6GSk+U3P+5hsA7jWO1LPv94PWj/qHfQ++jdaLGdbTZzudW2M13VGw3L2XUnbFd2DNN/Etds1nK+ctc2UVzcHYKP8attTaAxSXcvm6tD65BakeTyuoYdzvmGJ8sVE5YxQOseK1zeZNpKty/ZI71HVK2Yfo4fQH5DWZg0C8WsgvXO0zlEerSXvRTuiVv/RiyJj30Zzrnf440jPeWEgTWuJ/ZD69RbSeNF64Xl0NOpbmJFI0uZH/0RaV2odorzvoxHor0jPSINAfBU6B72BlOcb9CzSuq3w/U38TEjvatnJPo6rZzjXGqzs2qZBI1o34hThQM+j/XjuNT8UBs2bhLNJvKrQoBUjYdINDUb/QOI2CkVuN3G+TlOqw74GpevwEN7TdUY1+fJIa/Kakjy1ocxqjvOg81G9z9pcDw02I1Qn5016prHXO13jSXPgByg+mxq7v0UNPucRp75fGvq4tq5zqi7ov5//HBSY+fnPPP/wmAtEfwyYzmN+iN+f5IdTek36d2gPTj/IGOj7o/h+/Q3pLxRmzkRicy86qzG7Tpq+L+1aCGmulbN+bbl2Bh5752yO51p/AKl19E7YfN9IGdeSfkY5m6akcd/1zloOjUL3oStC/t0rLac5ZbTSeNuKNq4W2rkP7PTuKxlIf59EOfC3ecjMyZpn5kMXobj+0jvrNDRzUUOInwXtjm5AWj9rraHvQV5FZ6D8Z5W0GOJVV/o+CNcrcX4L0hpYZTyGNol1cq41uNY7+n5FayC9my9AsxS1K5NP7y2t09UPfYcxBj2K9kX6LNcpA22rQeIzrFQDSdO7VzZDsza6DvEjFM/5zuhB9FWI37JUmblytCZV+eI2JKZxvgD6E7oPZbk+EeLr/vg2k6eW8wfC9cBQbnr/g4ZW0qZgo3lFQXO6gwmYgAmYgAlMswRYL+o9qrAymq0REH8iXWvps8imzYDeQOlvMuTV94ldw7n+tyb9JqnvmMaEMqvCsf0ONEIf4iwz8BjwGPAY8BjwGPAY8BjwGPAY8BjwGOjEY4BPiQ8hfcl/QqnPcKRtHWy+5qj/Dja9p4TNQrwupPFIPzrFazli7VpULvG1wU4/YsmJKeaXk9TITB3RrjpbDja9Q55Y10Sux+XiLihRd02wO47jHeFc+b/J5Fc/tPNe3fjlWrt/js3Y6zor7ZLV6uOdOvSB/8NQr3ZPLayDdH3hLh7qi/6LaA7pFwmRkY5yAhPjbJx2WmpQJjYjgt2fOb4TzpVfnMdk7lG0G5ovB7svQz7VN1n5MteKuw11L8g3LNjVcLw4nCt/5K+8+lJkm9w9epo4jdOYnr9H+sv9tK8E/cisE33B0qD/Gbs1scn2YwLX2XZgWm+cDA3ljuB4ZDhXW8eE88j+4Hy9pFcHm1qO+iJItvpReDRS/9M8BI2Jy0K6IpQmm1i26pPzTr1+EVeVsdmCc40VlT8mE6+826Ge6IEQr+fhu4zNc5zXzQUZVs1t10DK+yRTvvqj+6i2xT7tW9AfzR9qb7QRA+WN1//hXP89eZ5DbbDRM/NpONc9zeY9J7A+KaQrLTvHqK6F82WHPGuQ9lXIp7ZozGT5fcD1IkV5WzOOOkaENlTnyyV+HvR8SFcbxVF9yjKvy0d8VcZWjqWxP2NC3sj85nxdgUlM3xV7vSt0LZ4aW4V5Qr6hwfazonKzcdiNCLYN+ttY3lLplFcVylR7t0F6r+lcrOK5rm9CRXNZuz0T1D8stK0m9ofrQ5Dmwfic6LnKzos3ZmyHhvwj8jyIb24/qkKZYtTkcZNvR2td05ZBSM/0S6g7yrazyeOH/JoT03HQnDaSb7OQX2XM1pQysJ8Vxfe6dupMMvf0n6HckaXKJH1opu45StkVxZPvtJBXuzVm650jU+aWRXkVR6gNdnJSz+aXQ44MpLlL5W+LeOobEeptbO0jZ7eRmXbqGcu+IzS3bR3byLn6pGcvrnnVt+yzqB98UgaEmdA9KDLQvJxf/xyQ7z82w0IerQ9uDuca56PD+bKhfDnY/DfExTrqrWtIO6Wg/KEhzwiOR4Zz9XtMOI9lHZzPG+rtg12+X2pbnJ+Uf2g+L3En58oX52yeq7jums/XlGvyV4c6ajkeFM7FPd++dG2QF3bXhzyRQT7f26QPKMi3HPHZcaN1mfLGcnTcqCDf1sRrjEU7vZO11ojXL3Je73nmug96JWMjhnonZFnqDz4a9K+14qhraKi/Jlsmcdln/tfNqY8yqkLZtc3JXyoPZS4Zyo1s9Vmo0s9SNSHvsFg+1/9AevZjeZoTsnPBP3JsmrWmpMzaUIfWynHe0fpLbR+pOghDg80Ijs15pr8M+VWY5poxmWvFNficR5z6Gucbjfds33Ve7/M0137+c88kTPz8N2RyeBh7Wps3a91AvntDGS+Umg+aE0+ZNaHcEU3JT57qkE/PUlUT89aGvHVzTz4/6W8Gm9vzaZVck3ceFD831lt/VpK/lE2mz0NL2cR4bE8N9um8SeiKPg5xKzaWP+RpchmU3xrj7e7QzlcraWeRDflrQhmNsirKXyoulCmgQ9BX4VprFT1f8d31NOc98mUQd1rGRraa7/V+iPk+53zpgnxVGZstONf7QeNrTCZe65VmfU8V7vUB5M+uefQ+zLbtAa575dvW2texP/lyiR9aJq0mpJV7pkcEm3rjIVOu0s8ONtk14JaBz7CQVlPQtvguFrN1s+lcPxPy6R7HtXOcGxSnsTJTLo/itAZVutqSXwtkv7cdEeyq8+0K7Z4tpKusOYtsHNd2a3uzNVuPAY8Bj4HOMwZ4Dw5C+oyu9/C/UP/wrpQTeRJF/NzoNfQhStdlhAa/aeTyTIfN8UhlX9LYfcemXp2N2TeWrkW+gwmYgAmYgAmYgAmYgAmYgAmYQOcncGVo4g5lmrpjSLueD4P6ISAGffl8KVoX6QPtjGgGzgeis5Ccui7kL6Tn41gqnE6CHED1X84qf2/Oty1lnInXh93bkdqmH796kndmjv3QgUht25u69QNFqfA7ElZC26Pe5J+J4zLoZdQTyUGgLpA+JxcHhYjHdJ3TY1n71jqnDvX1P6G8neJfoBeUv1uI+x959EORgu7XdWhzpPbPEBjLOexIpC/7j9NfxnMsFf5OQg+0MdIPMuK8QinjXPxDXO+FNCZ0j/py1D3eFelHhk3QH1CpMISEndF+aGby9+G4AFK5+u5BOz9qnKWB9BU5bB0uP8zdH92va6Itx9XD+bOZuHqnlL0gEf9DcvAbidZBYqB2zII2RDeioqCxdBQSZ/1BQF+OugfXB2Ptjq0yioLuj37kOR/NRV6Na3GLef/Cue73z0jl9ws2AzjX/Rabcyl/LY6lwuUkSCq/L8fZ0S1Iec9Ep6FF0WZIdc+EdD++QfpC67coH5rbLnGaC72N1GY5t4uN5pOl0HFI46Uu0LfVuPgnUnvFZd7AoC/nRyCx2QX9FZUK6ud7aJlwTzW2xVPhd9RxOMc/ooNRH2yUrva8gfoiffFWL5BnIBHDkdp/MRJD9WNGtCS6E82LbsS2G8d2D9Q7PZXeipZFX6LdkZ4vjTO1U8/RWeh7VBT0HKmP84exIy7iLOZDKF/PdamgMf00Wirw7MX5n0oZd7L4S2jPvWiBzFjTmP8JbYl0ng/t9kzkK9Y17TwNad75MKTLITb77tq6KF9BXHP7kS2qJeOmoEnNi2J8as64AOn52x8ek5tX0i+5KE9zguZEhcuaWdbaId8ojnomywbqVJgdbYrh3UjPrebmfP16JhXKzTV171Dslgj2lR5iu7VuyoZYr+IqqTtf76qhMM3P2vlQOxy+grQ7tHYavx/tgrrk6m3Ny79TWOHah2oVr/flMughpPeW1leaC/W8nY56ov9guyBHPYvpupFTza9pyD2Lp8V4jpej9dCLSPdY6+M+HDVH672kMfsPyl6dY1HQc70R2h/FuV3rineD8Skcd0a1aCckpxCVr/f8Pmgc+jPl78ixKKjfRyG9L5uyvrkCe/VL75aD0CzUqz7pHann6Bg0GtUF2iA7zT9fIPVH6x1x1pj/NfoU7YAORa0RZqOQU5HuQVx/9edc91ThANokZvnwFhF/QxrLGgvql8ZANXoaaRxo3skH3XdxfxItRz6tf5RX/Yvv4rHZTNSv+KuRxuHJaGCw1/t0FaSyxFN9yAaxXByJ5WZoeuqahaPa+St0GHoHdUQYHCr9kePtHdGAMnVOJO06tDnSM9ycz1J1xcP8IKRyYpCTZPa9rPuUBu617u1wpPt0MVoU6XnR+FgS3YnmReXWlBq7ek7KfdZehvSj0JGoKc/0Q9jvhdTOij7nhb7HPpb9PO3nP12L+/kHQgUhziHPMsY+qcC+ngljbToiVguRtzU1/5RmT3/noc0Lh3bf2sz2V5OvSwvLaGbV6fyo9eXOoQCtL7Su0mcyvR8Vdg/HkocWlNHS8ab39+qhYc3lX7JfrZhQQ1kjUfzc3pvz36AJaAWk+T8fPibiJLQciutLffcg+7vQbOhK2Mexk8+va61fpFb7norqhlDeOUhr0MOR/khP/dE7dQP0BqpGZ6KpNSxPxw5Aet/rXa+1RT/0WKkOw007oP6LdK3Fx6D1yXdfzv55rg9GCyGtBVSmuG6B3kS69xoTdQGbFbnYOkQ09r1tNmuDc8rSuvaDkLBGAwNHmIAJmIAJmMAUSEBrJb2HK2m6DIPdII56B49Hu6I9Fc+78mcdM3YLcLkI+oSkF4mX8/lk2ZQKpOt7kdfQOLQiedLvOovaSFw31akj0vdULQ/qg2UGHgMeAx4DHgMeAx4DHgMeAx4DHgMeA517DPDpb1akD5D6ILpq/n4RNxP6LqRX59PLXZPnkpDvqLwd8bUhbTTHkjtuZuyaWrc+ZKtPDxTUXRPSlL5GQbq+mFeaNDCbzvXQED8iny9nV5MpI5ZV6XFovmzK0pcCMf8GBenzka4f3GQzJJ9e6hpbORooz6V5G+JGhDSNjyXz6fE6Y9eg3aXyKJ6wZij/vbwd8cNCmgx3Lkifi3j98KX0tbLpXFeH+Np8vpzdo8HusFJ2pF8bbPSDVL1de8rkGRryqG1H5O2I64k+Dza7lWi78l6Zz6trgpxOxob8J+ZtiNePsQ+H9Idy5VeFeBV0f0HebNmyWbvAJo6ZevmxbUm7Xg3tqtvpKF9v/hp7/fClNj6C9MVWve+hiDshpH/DUQ54delc14a0rzn2Lcgby1b5fy9Ij2P3B9LlLJYt+7+h7HQntrxkj0YGm21zeYeFeNXbVA3L11XqmrL3D+Wr/UuXssu1rSrTprs5b7CTBXHDg82/82Vm8r7DuZy3GrApisN2aMj7WVF6W8dRd1Wm7S9zLoe9/FgbFmz0XMpRNE3XOWrus9qcZyK2o6agjbWhjdX5tEx7hwabEVmbFvajKpQpIM0ZNzWZ/E19JoYW9ZXyfh/KrOPEdbadJRmVKO/0UJ5+/G6wE2BRnhzfecineUr9O6mcPel/C3Z5FnIsXjmflzg55cpW67gGY1f2hDMyZe6SL6PUNXmGZPJtlOuT3kPfhvQji8ogrQ+K65a3c/nl6KDG6V02Ipz/yFFztn4Uif2/jvMG839RfZXGZeorufbB5rehDU9xLMX1/GBzbq5vVbH9RW0ibb2Q/h5HOWjXm290TZATiE70B4d16VwPC/FK27tE3oVJE8vRSH9MU1T+r0M5L+fKH5opv6nrm01CXt3zeuOlqA2hn32x1bMxCa1UZEf8KkhlamzUex8X2ZeKI281iuOq1FxVE2ze4tjgHVim7Fmwj+s+/eFW9p7p2VS9DZ7fMuVp3aM8fyiyIV6OP3IAk80K0Ybz20PcoUX5iuKwrwp5IpumHGuLyiyKo464dnqjKL0zx9H2uC6+NN9O0moCv2EFaZFlVT4tXme4NGdNWRvq1rM+R1EdxA8NNmpLk57povKycZQX18rv5W0z9Y7Ip2X63hc7P/+5ORomfv6L31sfhbF8QakxVS6evHo3xmdyh3K2TU2j3JpQdsnxXlQmeaozbSo5T5TIWxvyNph7ZE+Iaw1dNPgOsKjMfBz5jgt16PNkxe/EfDn561Cm2jU0n5a9Jn3jYJtfQy4X4r/iWHZd0NwyyNfS8bZQpp87lutnR6Rl2vYy5w3WucSdE2wafJ9Urr0qC70S8q6du59VmXoblEtaS76n0ueS2lD+VkVtJG1+9C3SmlOO70kU1yNC3qasgaJtdVF9RXHUMTTWk08nviakFT7TsieMCDb1nh3ihoZ4GZ2QLzvTz2HBriaU14Prq0LcKI7LlMpbKp48cnATUznC1dtdnuvqUHZtqfyVxlPOraGsUyvNY7uGnwHNxEw8BjwGPAamzDHAOzD9HwEJ+r1H3/ddhJ5Dn6JNQlrdep24+D3aC5zL+bxu3VN0jk2al7Aoug/pO7iDo63KiDaZuP7E1aBBRWU2Na4rBTmYgAmYgAmYgAmYgAmYgAmYgAl0cgJ82NMPM3K0UNixoLlbEqe/nJYjw0MF6eWihofE1csYXU4b9GV2a4dY9yr6a+sShT9M3Y/k04h7ljj9qKSwRD69wms5HapfzZF25akXaJMcoJ8Mkbvl07neFXVBXyI5mFQaKrlHd1C/fnxq1UCZD1PgGFTFPZq7ROEfEH9lPo28+gJFjl8KS+bTK7yeK9iJWYNAm3oTuVVIkBPyNw2Mykfox9iz8iaUo/i7Qny5tp+azxuuN+A4M5qITsnbUL6+aDo2xK9JP+bM24Trk/Lx5NUPQ0+E+Me4fjBvw7W+aFLIt70l7RoXyoz3pKDa/4+iT3LmGhxiTgx9zuc5mQix1n3cJJ8Yrv9F3jEFafeGODE+oyD9UeJUtn5E1Q/IaaBdM3DYLlwW5dMXeirz+mCzfjjGg370bM6coTzKW2mIc8iltOfFSjNl7OQkm37zmAs3h+v82MiayRmzwRyXL6iTXp9O2ycUtE33WuNBz2X2nrbbM1HQptaMakk/su1ozrhp1XdpeNccR6PGoD+3FFJYX+wcytEfDekH9ooD+btjfAXSPKX33YmNZI5zxJcZu1rOf0/dTxbkje8azU0H5tOpvz9xe2biZ8rbFF2Tbx7iLwxpt1L3nVk7rvUeivOo/jcJ9S8f/kKE1i0K+Xr7hnitH9dG/0RyoNTcPyuKnLbl/LBg29qHcmuf3UNl55WYE5Qc1y75eb6xdsay5fzxdQnjWPbgMAbzZl8R8e98ZLjW/N8V3Uz575awuZF4zXVLUP5cBTaa787Kx1Oe4uOYy78H4nvnrvx4yZeTud6Gc42dR8gT13z1zInXmkX96IeWL1NWU5K0rih6xx0fCtF7f5lKCwz38bFgv2ouX1PXPwuSX8+F3qP/KmoD9Y0m/o6Qlh1/Taor5NezPKqZ+qKofSXi9FwrlBrzJbJ1iujhoRW6L60WePY0b7dkTRnbUsln7eY802X7yjh8GIMxqNznvHJl+PnP0WFM+PkvPWJaOodofRGD5tAGAf4Loc+K1MC480dEXmppc+fdWMboEu/MtqYwNFSgdXRdoC3PcfE60j3dLJtWcD40xDW1jJaOt9bgX9CdVo86A55aD+bDzSEiv9bL29W7DmXdEyLLvTNPyhdE3vHEac2n0NTvqarJMxDJ0fmmUEa9A/HvEaHy9dlM9tmgZ6S5ayF999NZgtZ0Z1TSmLAGEasd0IdoTRi9UEnerA15tEZ/BfVCyzY1fxPs42fjos8tTSjGpiZgAiZgAibQfgR43yp0K6qR+MWR/nfjg4rSs3G8b3/CTt/zaf2r76WvQoejOdCZpPXF5udMHn0H+znqgeZXvBqSSS88pQitseP3UL8ny1YyJH5yLJ+43ki/I1yL9D3gdIWFNTFSCzQHEzABEzABEzABEzABEzABEzCBKYOAHFk2Rb/mA6J209MX0zHsFE6u1ofZfHew1wfb36GN0SKoD8p/cJ47ny9z/XiZtLJJ1K3Pnrsj/Ti/DFJb8h9qexInp5Sss1Ys9+l4UnD8mLgBIW9BcvkoWOnLgUa/IChfSoPUy4hZGW2lD/PUIUe0GHYNJ1cRry8a6gK2cmDYFw1BiyPxyH9ub5N7pEZQvxzEdkHLodmQ7kk+qP5P8pFcPxO/wChI0z1SUH+aE/qHTKNLZF6BeHHSFzR3lrApF/0qbdcPZUWhsbbLsajUDzziqPAC5Zdq+0OkT0Zqv+xv/yVLvX9fKohTlL6AUni5RLp+fFPIc29Ju9Q+je2TGS8Lc7wePUH/xKEoDCKyC9K9ebDIgLxjKetZ0vTjptp2dYFdYwz0I2X2OUuLIE5f7GlOyc8RGjNxDnoSm4Iq0yg9kwrzhmN6oNzTOEhtFmiTvlxcPlRQNC4qqbvU3NnYuFbZzZ7zK2lYG9uMKCqf+zYOrs+TtirSWLs52LXnM1HUtNaKa0k/sm1o8riBbWu/S8+hQfqifX/K/qIVAG1EGfpCX+GyZpSn9qyNJqKdNG+VK4P0s0iX9G7txUF5T0bDudYctxs2dWsAzkcSfyvxW6DjOdc67j9oHFoJ/QPNjLTu09qtwTqPuHqBMnoTcTOaHb2PfpMziZfHc7IpEp87yfcnjnpOZgl5DuWotmpOytfblTgFTaIj6Mf+4VrzpBgdTnl6V2h9cQjn2u1ODFszFM5V1KX3qtgpnMG1+BcF8VSoN88XGebiVgvXf6Ds/Rqx1xiYFcX3djTX2klrgKIQy9+W8rV2LxV0XxTU/k9zRs1Z36wSymjKeye2dWXa+lmuDdlLjSkFtbXwvpXJm0/SmHw0H6lrmL5FO8RiLqR5cWTWjjSNC6231W6tD2bMpofzuXNx4rEHupz853O8GT2bfY5z9pHJdMS/R55cct2lnlOF7PhTXdsj/VCocXMlknP/N7+YNvyXtA+JnbNhyrQVAy+t23Rvh6DFUT+kuSAb8vc2l9zkyxatKTO1VfJMNOeZTquAjebhXZCeidlQz0zd8VRsPimILxfl578hHT//DZm0Z4yeea1pHDqYAPNOX5qgta2C3mX5cAURxyI5vdyYT9R1a5RRVO5UFtfkz26B7aIcD0BroSqkNUl+wVLunflSCY6fh/iXS6SPCvF6R2dDnDvn5r6XW0/2CZny39FsXaK+KS1a/1vAlxU0Wp+V70D6nPkWWo98H5TLB9f1Sd8TaS2sdbLWTflQ7p7nbZt6PTpk6N/UjLY3ARMwARMwgfYiwPtS6yF93/kT79Y0cK7vY7U27cnlD+Fc3yddilZEX5P2PWkXctRO5/nvT5Ul/lal97C+K/oEu9ex/zfnej8P4/w44uI6QN+Hf4zmR0shve/VNrWnQQjtjPHXc3IaOgRdSbkXc3wYTY+0BtP3GFpDTEDbkvdJji0O+S9fWlygCzABEzABEzABEzABEzABEzABE2gzArdQ8nikH9TWQfeoJj5A6svb9UKtDX7YIX1x0u4P+YJZ+l9lf4/0gVUfePXBs8gBI9rrA2+TA3XrR4y7UPwxQWWoXpUXP4jHHwhVf9EX7SUdLrBPP/ATogNOuOzQw9XUfibqhbZBl6k1sFiZwyKhZWlcDKTpy/8R6FeZaN1rfUEvTt2Q7nNb3CN9N3At2ipTt7580L1Iv1whyFFBX7yUqr8t75G+GFGYGI75Qxw/Y/myZGw+sYLrlrT9q1JfKFGvmCnoi6LCQN4fuPdfkag+RPt6tth8Wpj5/+9NY+n5735a0q6TacvyaAu0f9Bk+vA05zehi2jvmEx7Y126N99m4vOnH4WIQgakNdbHUukqNo7hHplK9bzFEMdPJqrBqZ7l9g6zUGG8d2V/SCzVMJiXGtuVzJvNmvNLtaWd40s+c7QjpmXHWns+E22JoiX9qGtXC8dNi/vHfLIZhWyNnkEXtLjAXwrYPZTzEv17rill0p4TsJdDo+aSncn/aFPyY/8d9ndQziMcX0Q7oKeQ1gnZsAcXt6FV0OlBMV1rtcPQ0UjrgTExoehIXT2J15pxBaRneUPaUbS+0o8fT2P/W2wuQqujJ3Jlvs317ej3aEwuLTuv/yOXFi/P4GRb1Bfp/fF4CbvmRpeaqzSHan2roPPGwgyNGeTS43ukD/FSY6HoPVKq7Sorlq91tNRYKCq/1DtAZZV6D8R3YlPeO7GtYlgJx6K2Nta/fPqXjN1S60LZaq5Xu7JzvdbihxB3CuoSCtRzrbV2LEv3Us9Pfr37Z+IWQauhQ4O0htN4vg7V0J7vOcYQmeh5bdI6g3Lk5K5ncW+0S5D+oE7zx3D0T2w+raupfU+0ZlWo5Jlq15bBR8xHoJZ8lmpOm+O9Vt4m3etcZeXmg2ja5GcaLlpLXota8jmvHBc//37+y42PfJrmkHlQc+eQrzMF9ssXrmvmx9c5xDle8/56XN9TZDsFxMU5V01tLrNYRj9YdIGP1pTtFbanIr1Tn6PaNwoqvZK4Y9EmNG02bIrmwZaU0dLx1hr8C7rd6lGl3g1xraf3QL0A7x2IuBz1CAn6znEsmhCutfbUWii/HgrJ6bNWai2itZVCY+n5dsX3idbvLXmf1rVxCj0peg6KurJ1iJzMcSPuR9m1O/f8bOwOzBQ0iXPNqToqaI7ReCh5z4NdSw5xTM7QkkKc1wRMwARMwARak4DWyJSXivfpj2G9nK5nSNKaZGOk7zUXRcejS5VG0NpJ3z+8huZBJ2B/O/k/+iW5/r+kRcd0fU+k76FmChanc1Q9+t5V66eTQ/yHHPUd9iC0EboR1X3OkU1sO3XqOyOtpfqj17nW+/0vxL2oI/od2hfpuy+9h7XOvRnVoMdRq4SurVKKCzEBEzABEzABEzABEzABEzABE2hzAnxwHE8lt4aKdspUuB3n+vL+DWz0oTQf9KFYH2KVpg+rM2E3M5oDzcm18ivU+wAb4uIh/oiQi2708kgs5Kwhx6fdkershWYPdevDeQzl6s+Yde5T+qUvEOJ92i3T2nj+CjbP5npxFtf6wuJdtA2aBRvtnh45rZKzL7ps7j3ai8K2Qt+hg9C81Ku/5tePgHOG+/RJqLAj7tHXoe6+RZ0mriPaFJtSCfPpS7S7o6Ob3C7GwgQ0hIaviuTA9QTSj9jx+k2+2FqmoGNNrqugjNaMit+HjaY/+nKxMVW3ZuUVltWR41pNrGRsV9iVTmVWjmuTx2kLnom2hNLkfrRlY5pR9nnk0byiL8h7MafovwVNpetMeTOE+LI/YGPTlzxbhHyXZfI3ekreIzD6K1J79uJ+X99ophIG5JVzSKx/z7wZ6XrXraV60F1I64G30Y1oHfRf1BMpvBWODQ60WU4baqfyjEEbUPYbDQwzEaSrXUsjsX8B6UeOZ9AwtByKjPP1xrUBJkmpOrLx88qwlUOpuSrO86pumQrm+XJzQ1GTY/lDKikbm9qCQkq1Xaax/IMqLH9EQfntFRXbemaFba1ph4Y1uJ88G0tQ78lIaeciXU9Pm7Xmjuvd+IzXy0/6V9iugdZHZ6PnkZ61weh89DLlD+AYQ2TyfIVMhmbyyqlrH66XRMegEWgCWhYdibTDu9rREeG1UOmCtGHGjmhAmTrPIq2ln6XKFF8yKd7r0RXe6+oSJZWbD0pkqSi6rT/n+fn381/RQAxGcQ4p+rxYSTnvY/R9MNS6ZWoPkZf62VxmsQx9PliknYHtHupbjnfGz3mR9k5I78FxxxJta0kZrTHe9B2ZQnP5l+hWx0VzH2aj9ouQuF+DVkD6/q9fZj10Zmhhg/VUG7Y8vk9uqvB9OqwN29KRRVe6HniIRuqzmH4LuJj7Gj+vNWg7aRsTeSBS2cPQQkhr4Fkz9/zJkLEt73m/UIfW1Q4mYAImYAIm0CkI8C5U0G7nP/LOnB5tgM5BtTTwU3QBmg89iF7NNHoOzuVMfjvSd0wzIf0PjANlwzGubdIsqoM4fW80F1K578iGeK1Zj0J6T8tpfOVgr/el/phc8b8lfgm1UWmcp+/rX5pet9u62vA7JSstpOu7ZH2XJef5PdFv0ZLk0++++p77UbXrF+uW/6tFiYMJmIAJmIAJmIAJmIAJmIAJmMCUQ+BKmqofZ7bic+a+fECcEK7VA6XVC9jow/FKSB9Ot8D+47wN1/qw3FZhu1DwgdR9dUElbVl3QXX1o+DzD2K2b9Sw2OAg+qQfbIrCZUSq79XUoS8WPkexHqXVBdKn42JIiNDuqnLqzYe25BTv0bHUfXa+YtqnL1L65+Pb8fpL6tIfSsQfK/JVfxYi+tDWPvRhbN6gg66/CPWmXzoVBdorZ8JZQ1q0LzJtzbgWtyuM0XSc0gc5IG2OTkaaby5GK4YGx7rkKFpqVzOZRqet9mIwKrSvH+2S01kcQyG6/IE8h2AhNSecRn2nVZBRXzJORvruTmPohQry2OQXAnNzqC0BQ1/yKmTHWns+EyWa1SrRLe5Hc1vBM9Ga71LNIwr3N9IefcGv8CCqLmOrNdP0SOugK8rY1UuiT38g4rgQeRDP7aWV5i1jF9dgCxbZUMck4jWHSvUC7fl1iPiO40v5dF1jo/niKrQp+hZtQpkji2zzcdi9TtwB+XhdU+4aIf7xXPormeufi/ISl40vZVMia4uiNYfqnmsNszh6sUWlNcys94jGqsq+tWFyi2NU/iKh/BYX1oQCVK/eOSXXLgVlxXeqWLRX6M+4nI5xO7FEhUVz/TbY6gfAu8h3YIl8Jdfb5NH4vTdIz4XWpVpDn4gWQGeGaxUdmSys55Ksep83KZBHz9dRyqS+ctgAnYCWQpcRNxAbzRlKn5fD0zpvRviQcuK6rbHsDwQDPVeaZ/RjaIeHwGdIaEh7f5aK97of7WjymrId4LX15zw//37+mzKMNYesh5bneZmbueeTpmTGfiL5HiPPukhz0OFNyT+l2dLfj+nvW7R7YbQF+lcz+qB1st5fXUIZWu+1eaDdavOqTahod2zrfRfVCmW0dLxNog2P0q71kb7vOKwJ/enMphvTuN7oVbQT46zI6ajkeqgNO9ai9wn36kbatloz27c1HDS3tDTE9Z6+5ysV+pRKaGL8e9jvg0agwehmGOg7/wkF5cS1wMWkH12Qrqj2uOdaOyt8WaINjjYBEzABEzCBNiHAO1JrYW1AlK57uIy7ket8ZaK2RJsgfd8Sw/OcnI/0feI76BPyj8+k6zswram+I/4oypmN873QB+iQEmssvaf1Xd8o0r+OZXH+HPmHcS3J+V1rk4/QPZxfSdyuqIbzE4m7EemPO1X3nEjf2e6HVkSHklTvezKuxxEvvYnaNOgLNwcTMAETMAETMAETMAETMAETMIEph8BdNPUrpC+tN+WD5gCO+pCpcFU4Zg9KV/iCD5sfF6QrSj8CtlWI9esDe1Foq7rjjyj6cqFcEEd90d4czVCmYN0n/YCiz927IP1AOiv6EV2Ry9ef6+lDXHtzUrWN3aPVsSn3A0quOxVfVnqP3gglzl+i5GeIn4x0rzcuYdMR0c+FSuV8NE+JBqxFvBwGFaJ9CdNWi27VdjGvjEdX07q9QwvlUDBjONd4/jmc60exBgFbPYPLh4T2YhDHjKrdukGjGo/ojUlz5gzlUd5GA0wnYfRsMNQXoA6VE1i7yJSxNhPxy4W07Fhrz2eiqGnZuErnxaJyWrUfRRWUiWurd2mZKitO2j1Y3slz9VkluRgr+2J3RrD9K/nOqSRfBTbxPSbn8KaGPUKGa8P8UC8/bdZ64zKkOe17JAeEx5taSd6eclcmbjGkuVxzfTbcz0Ucs4vm0uKl8sbwfgmbVo8OjDTXKzRnnm+sTZGtnJrbIsTyN+ce9GiLCkqU+USIb8p7J7Z1bdqqtW57BDFZtagi2rAQ8XOHtOxcX3a9Sz6tXVYpKrMojjE2Gl1I2uEhPfvuiUz0zt+gKH9T4qhnIvofebYL+ebiuHCmjG6ca43RHM1WaVtog5yzXgr2h8EsrmHLFoFdY5/HyuavILEtP0vFdWypPrR0TVlB91pkUnbcU3K5z3mVrEn8/P9ye/z8lximuee/BjM5RGjuOKxElgbRuTI07yosTfxmDYynvojY3w3pr5xKGg1ZXszbH5Hh9pDpQNJmbrQADFph3o7r70cprl8ZaW2s78i0S/qSuba1tIwaymut8bY47atoPdkK7HIYWv0yvhdeZHzEeb6uktD+dVq91sYLjO+TRWjDEo2bN7CYhZjmrIOUZ7oGpTUvYkzIFhnXK4V+aa2Z/WzUvFpCLu7f65yui75EWm/eQB1FfSm7FiDPQPJq/VwUKlkLFOUriqsKkWq3gwmYgAmYgAm0KQGtaRRUCe9MhfSdRpT+55d4fjdRWoPsiT5Hf0Pxt9unOP8Htvo++S00PuTX96/paThOH44ncXwP7UsdQ0Jc/qC1gNa+j2KjDZuWQccg1XVMSFuB4x+J6xsyH87xLKTfz64j/iE0nPOL0DXoH0jl/pY2nsqxw0IE02ENcMUmYAImYAImYAImYAImYAImYAKVE+BD5CSsrw85duQo6cPu0/ogXFDS2BA3Bx9MZ8+nE7cUcTvl41vxOtaveuoF6u5NxBH5+Fa6HhfK6VuuPJgNRfrr9+aoplTZlDeZtPhlxa6c7xZs7yHtk1w+tTU6NxRxkpPJgaXqaoX4cvdIPw4f1wp1FBUR71GfosRMnH60VNCXLw0CPL8l8qaQcDTjSk6mnSHoCyz1sQf6c75BtFPOQkeG+Ifpx2d5mza6bna7aHPRj1mxmd+HE81HqR19+prDAyH+UPIXfQ91KOk9ke5j/HE8ZGmbA+36hpJvCKX/jXbpR8fCQFr3MFfVpZN/WDPnDM0zwworKo68PEQPpQ1LF5s4toDAn0qM1YOx1VjTc6nnIIZ2eyYydZY6rejdVSJzs/tRoryKoxnXrfYuLfds0aD5M40aHGyrSzWUcbAIaSuH9MtK2WXjySNnl/ND3DHUoR8QGg2aK8oZkd6f9D2CzcPlbPNp5N2OuI2Q1oCnFaRr3r0QaT03EWmnnDj35s0rvqbeXhifGzJcTZn6IaUucD2Ki3tDxEHZtMz5H8K5bJ8rYdNW0TWh4G3oy+BylZDer1x6QVosewXyxjVegVn641ZTy1Y5Gq/6QWxu9NfCgkNkM8svVWR87+i/HNaYqyRch5F+iNP8WvbHrlZu618pT2M/HyIvfS56IZM4Npw3WGuHeH0mabCGpIquqNzzHdc/8UdHrX9ep6wnQrknk3/GfCPjNWn60bEuL+eVrLWUPVtfbbm5s5G0qlJtKxF/GPH63DIInS8+JexSB0aCPsPo82pbBr071SaFBveXNrTks1TZ9zJsW7SmbEsooeyS4x4uGtfHlWlD2b6HfH7+fwHh5z83kIqef54XfQ9yVjA9AButuUoG0nuhSzEYmDHSd2FPhutLSF+mZAFTR8K/6Ma7SHPt1fS3qly3SNdnxgtyNnKkmYAGoCux0fu6ZCD91yT+saRBIwnk17t512B2Pfd9TBnVYvdgsK0bD61RRiuNtxtpW3yfX0i79O4rGUjXWL2mpEHnSIjvhSUD53yr9iJiwXxkO1zfRx0fhHrOpG36vqwwkNYvn8D9rm5kvVPuO98R+fKaef1SyKd1dNFz9gfS694XzayjXjb6/AoR66PRaFN0DXXn160l1wKhsBM46rktCnEt0KcosdK40KZlg32TPgtXWoftTMAETMAETCBLgHdkGngH9UU7Iu0grt+eLuZ4GlqIc62T90Obo90x1zvxn+gHpHVfuubAtu57D2x+4lrvTa2tf0bPy4b49zjoN0DF/QuTBRSvEOx1ugLSWmAj9CVSXn3Hou+MLkVnoJeR1mN7I5X7MQfZrIduRT3QomglNAodojTs/s2xQ0MdpA5thSs3ARMwARMwARMwARMwARMwARNoCoErg/FmHPcI5zEuX85rRHyE9KFYX0Trg7U+9PZAW3N6D/o2n6kVr1W+whnUp50R0y+1OazIQT8w9P8ludX/1ZfwCtqpaOVWL72yAi+LbeC4RTiPcXUl8OWA+Mcftf5Ne5dVIkc5vKzL6YO6rMvQ+ifxHh1JfUNQ+kMPR32RMRzpy4zxrV9t8hZlTkJ9qGubMuU/EtIGxbYV2B5O3DfoV0i7AAxG6XceHPsi/W8BtxXka7Mo7quY6Usrhd9T/xGoty44zsPhKrQGkoOZvuxql9DCdr1M209AK6Lp1GCOChoj54QO6I9h9ONXDEdyoj4uh/Sjub6cU77eSPdNX6ApnES++ONWiGrTg+r9Gskp6THashWq+zGQ84XQwaRpDl2hTVtSuvBLSBqJ1K77aM+uSA6h4ieHtZXQRajd5zjq1DukfxRNSsf2L037/3jS+4X4ig7Yj0D6cnhERRmKjeYj+ibKqFIyRzmv/JHTo4L5yYy172LWDngmilv9S2x8d+mL8Z7lDPNpLexHvrip5Xr30BHNSfqSvmyAud5Feu70zj0VpnHMlM0XErUbcA1aF8XnQeNvRqT11mNoTjQZxXdDXbnYbIv2RgNQXCvNyanm0CuC4VG0KY6Ruryc6AeK34Syf43NndnExs6pQ/+96+oozi/dOF+HfFp/aP7TOvKgEuX8NdRbTZ7z0Kyy4zgzOp7TbUO+Y2mX+l4XSB+K9LxLVdm0VjrXvdT6SuuB/1HHQWiWWDbnsyM9ayOIK9W/wqYExjeGRK3djkZ6n6SB835Ia6pbuNT9aVKgfL17zgqZVLbYLhAL4Vzv0PXRf4i7rkmFlze+g2RJY1C7KGq31L7KwnE6tBQ6HW0Zi6GtX3GucaCwB2nXoiVjOuc90RroPOIejfEtPGoO1xiV8+HsKotjX3Qyp3uGsvXHYj9n6onrXa0JD0dxvM/G+anYqQ/qSz7MTMTb2Ggdp/53kwHHuE7XOFe4Kxzj4UBOJiCxeBj79VB3JYa8S3DU+u8dVDd2OL+X+LPRWmgG2StwvgSHmnD5KceXwnm7HkB6OxUeGyrdi6PWUFui7LwnpruS9iw6G6VrxsYCeapQnBOGNmYf02lTW36WinPubrQtvfcFoTOvKVvyOS/2veTnaT//iZ//pj//R/AM3Yf0nrmU5+pKpDVIOj8qcK7PYYdwqvlxaIhOD4w5fa7cDmltovlfc9ApaBDS+z4NnPdDW3I6LERNkYcwv2ktNQZpHfAs/dIfds8fO8S53s9ro38T9xz6VbazlDGS698hvRM3Rc9juwuaJdpx3gdtjR4g7ho0U0xrxnEwefR5TCGulcoVE212pv44z7ZGGaqzNcbbrylH401r3Edo4/FosdghtRnpc/mZxL2OVoppjR3JU4P03qttzLYV0++lLI0FrU+03uirsjlq7f5nTs9DReshmbVZYJxOonCtndS29dHdtGdlFD8Xded0eXQSae+2WUNaVvBwsn+PZkOX09a4RtXzpbE4DI1tWRUNc4dnXMxU9pboCurLrlniWmAf4vdE6bqM43zoMk53RKMVVxDeIk73Rn3YpiC90qilMNT3G+PR85Vmsp0JmIAJmIAJNJdAeM+dRf430KVIv59oPbci+iPS7uZPoQvC8RPiFGSvzyF6d1UpInwG0anen/qjNq1Xltcl+iLET0+01rVaq8yBhmE6QGmEHuGozys613dF/0NHoa3RFmhv8uu3sn2Qvgs6mvzLcVT9+l/x7kdbcTkErc35gmgTdBH6THYdHmiIGmuZgceAx4DHgMeAx4DHgMeAx4DHgMeAx8AUMgb4IKkPth8gfdCVfkRzlfpsR5o+mMom2o/jXB9ydf0+2iWc1+bLIL42pFXn07LXpeyIXwDpQ3isW1/Gy0lA19+hDTJpVbkya0LasFJ1kz4i2AzN2xD/YKZs/YBSG7RK3ratrqnv+UwbxnA+Q1FdxOsLEPGInMQoXqvtQ2JaPj/xI0JaAwY5noV25J0FvR3L5zgR6YcLtWUyGopqw3W9cUDcsBBfk29XvCa9Jtg0uI/E68eO2OcxnNcGbZvJr/GuL31kt26ZegaTPjrYyfYHNCZzTdb///6D+KEhbUSZMocFm3r9I646xNeWyqt4gn70yfZRPL9GP4X8ei73z5dBXFVIr9fmXPtrgk0DrqHukmWQr7ntGhPbxVF90djUeIn3UM/60gX90RdncQ5S38VA+WO+/3LerSBfbbCpN+6iHWlDQ3q5e1iyDPLqC8ePQxlqi35Y+xJp7MS26bh2vm3tdU3d86KXMu3JjyG1r44P51XRtlQbZR9sGozfTD1VpfIrPlNGllPReYM6Gil3RGhDyXtalJ88VZm264dR3Uu1Z3TmXNc3o+75Mohrt2eCuoYhtaWmoB3rhDSl6z39IapF2n06CeyHBpsGjIhvbj+qQplUUfw9MenVwaZJ97RUec2Jp/66dqo95cogvSuKa6Xzy9lm2MqZIY5jfXlfTqtly8R2WCav5rkxSHNknPtUrt6t2p08vZdl8msOkm1si8o4Op9H14T5Mnaaj8u1+bMSZcR6dByNsvO6fnhZqChfjCN990wetVX9zs7x5xXlx2YoinVXFdmUiiPfiJB3aCmbwEeOH49k6onvoG8ycWrDUdlyuK6K6aXKJ31GdFOunDFcZ++dyr40V/awkKemVNmh7Xqez8+VP45r3aO4jlD5D+TKHxryNJgjoh3pw4JNgzYQ3xeNCOkqX/dU7+3sWG7AnfS/oWy75GCSz/deuT43lkZ51UhtqkUHh/N4T7PtO7eoLOxvCHlURswX23wJcTUhfViGVd9MHuXT85Ef4+8QNyBfJ3EbozGZ/JrXtc7IPmMqc2CmvpEZ+8hen59kJ4lryfVwvg1tdU0btLbLj/UxoX2xrTq+hJaopB3YVaGYt8EYK1cG+VryWaom1Ft33zP3Y49Mm3Qf3ke16LRse7hu1poylKU+V5fqH2lDkWya/EyTZxb0dsivMir+nKf2EB7M5NW4rw2q93maOD//Dd/rfv5LPP+MFzk/6v2WXStovtMYy8+Pw4nrnX8+iJsT3YOy843KUxn5uUnrwZ3zZeSe4ZpQVsnnrCg/eaozbagqsikVR77akLfB3JPPg92iaGSmLvVb60W9Z7PvXq1vGny/EJ7nLUkblStD9lpbZDnWcr1Wvg05XiXnavJeFsrTH6YnjQnbuVHsw8ahrS0uI9ZL2a0x3uainAdCv2Lf43pAYzcbd1xjfc60rSbkra00TyZvrLNw3FFuVWxXvmziz8j1ReMoPo93cn5cSK/J5i1XZkGfCsd1Y2WQrvdu/L5afdS7V2un7HxBdY2PrbawoR1DUcq+qHzifx/Tw3E0xzhG/s75iBA/NMd2aIgfUVRuhu+wYFfv3iidsAqKz/N/OO8a4vUMPB7yyVAs1a44ho7kfES4rteukD8+j7Ifg2qD6r63LdfmUIbWCcp/WWO2Tu+YsW3u5u4x4DHQ2ccA75AuqJuOjbUVm4HoVqR33u1oB7QEmjW8lxbgfEgsh3OVnSqk692n9em+KH2fZmzj+7Um2KycS9daRu9ZrbPl5M6hrnytE1Tu8WjGbL5gl/5mQdBaTXZnoz75uvP5Ost1V7XcwQRMwARMwARMwARMwARMwARMYMohwAdKfQq9KtPiB4j6tFQPSLuJtHWQfqDTB1/9lfX76DQ0CGk3nzYJ1P0uBa+E5GD6OdKXBGPQFWhF0u/m2FZhawrWD5vvod5IXzxIPduqwoJy9WVFDNfRX/140iAQ/ySRq6Kbkb6g0D0SrwvQsugF1CaBur+mYP1Q8U8Ux4LaeTNam/SaNqn4l0L1Jc6J6A00PYr3SPcrDdSv8f7vcKkviwoDZg+QsAg6Gb2M9AVTd/Qm0vOyBWrXQJt+RLtT6bZIY30MUt/0vKpNK5GuMdquoQXtGkJDdb8eRZ8g9UU/vL6ITkJyMNJ5vUCcxrEcc65E6rvyjUWak7YjfRe1qV6mdrigzqepZlF0KHoMaX7si75HzyCNJc1TD3LskEDdH1LxCkhfUD6C1MZe6AN0F9oLPYWmljBX6IjuTbMCzG4g42B0G9K40lygOfRAJOdfXdcLGn+oOc9qs56JfP3xmjbcz/lWSGNO43AepHlxzlJ5svEt6EclxU9pNlr3zBsanX0Xl+tH9rvyOTAsJ/2Qnw16Tx2MbkVvoy5oZqR3rJ5d/bC/CPfoRo5FQfk0V+r9pXs/PXoPqVzNQ0cVZSIu22atHcq1WWlF4VAi9Y7SGmAGNA5pnj8I6Y+K1J+SgXTx1VpPa7tP0UxoNLodbUb670pkjs/7xyFfCbPmR1O31lJro51De3Std5Duz+voErQJOgE1KVD2eKTndTOk+6p+iJ/Ghpjpnaf3//6oyYGyNS8p7xrov+h9pLJVh94BN6Hd0Zao1QJ1jqEwPT8q+16kMSxmureamw5GGq/1AvmOI2IZdCF6C4nxjEj57kD7oZVRqwTqO4uCtkBqk56DH9ATSGuKA0pUsj3xh6HX0CSkNmqs706e35TIo+dB9/gspPftF0hjfDzSu+oItCz59fzUC8Sp379CYvMcUhv7IpX5GPo7Wgw73dsYfsuJnvcHkO6z7reCxuu5aEns7wtxHXagDZqv5keHovuR7rPa+jPS/deY3RyJzSscKwlxTlAZz1aSIdpQx5Ocr4puRqOR5kM972rnsugF1ORAuZeSaS+kez8Z6b0yEPXPFoadxkKnW1PSLj2/q6B/ojhGv+f8ZrQ26TUcy4WtSTwfvYc0D6jvUs9sJsrx85+jCBM//yWef9hoBz+935ZEp6BnkMbqzOg79Dw6G62A3eboW87rBeL0R3XrE7k20hh9GY1FKuPHcK3nd0u0ALZXcJxiA+3XO2AQ0jOp+fUdpPeY3rOj0D3oj2h+bMWjQSD+ZiIXQL9DtyPNCd2Dajlej3ZCWq8+xLHJgV0aNU9sEzLeWEkB1PUJdo8H291bo4xsvZTfGuPtU8oZTLkacxcj3Q+NVY23r5DWIkci7UT5N46Vhvje0zuk3QJt1FjZG+lZm4A0Dkaig9GmSO+7Dgm0Tc/tIugspPWD2tIHifMD6BBUhToqTBcq1ru0QaD9mru2R08gjZGu6FG0FWnHNMjQihGUrzo3QePRLkj/W54c6SZyvh46Cb2LfkLiqnlDc+yxHMuFfUk8Eb2BpkdaB0h63isNOwTDSyrNYDsTMAETMAETyBLgfaWg78n0v8fof4xcEc0TbfTO0zkHfRcwDG2GDsdcu4RrY5dXkNYT+r3xXXSLzsO1iv2Zc723FfT9goK+Y5ghnKcHzH6ijlk5HYA+RvofRf6KHkdan+tdF9cA+l8F1+ZaTusqfzmk8DCX40nT/6Sj/10vbTtBn2MU9G7ui83vkT7jpEF1x/POeIze+52xbW6TCZiACZiACZiACZiACZiACZiACZiACeiLo7nBUIvkfDs3X7boRzIHEzCBqYgAz/mcdOdTpB9z5TwhZ4qKAnmrMHxPxuSLX9pWlNdGJmACHUOA5/ZOat4QHchze27HtMK1moAJdBYCzAmH0RY5ON3AnLBtZ2mX22ECJmACJmACrU2Ad56cvkcj/RGB/ljrxdauw+W1PgHum/7QQI7kH3LP5mv9Gqa+EmG2FL3S+H4DZotOfT10j0zABEzABFqLAO+M1AGc90XqaM21fJrlcK4/AFsd7Yz0PaLWT/r9QOFzTPSHmmnAVn80eB/SH65p46OvQ7kqSk7gaYhlx+sQJ0dxOZgvz/W96DO0EXHv5+zklK7fIWYP8WrvW+hZ9DySA/ta6FCkvLtRxkjKvZZzfdehDXJuLmoDaVNs0OLWwQRMwARMwARMwARMwARMwARMwARMwAQ6LQG+kPmEL2QuoIEHoD3QvzptY90wEzCB5hLQjiAK/+SZr9j5vLmVOZ8JmEDHEeCd3o3aV0Pa9fLijmuJazYBE+hEBLQO0A/Cje3E2Yma7KaYgAmYgAmYQLMIyLFJO0jfyGdfO583C2GHZFoz1PpCh9Q+ZVaqXesVhk2ZzXerTcAETMAE2oqAHLApW07f6c7fcv7O1sW1nM/1v2+cinZFY5H+ZxE5e+t7Rf0vH7/FRv9T3MucK2wTjtcTlzqf58tVusoOdnWHjJ3+F5bX0MpoIVTPAZ3rmdAP6Ct0KXoYvYHeowztgi4Hd7WzD/ozOpnrv3KcGck5XmUXtkHxU2qwA/qUeufcbhMwARMwARMwARMwARMwARMwAROYtgjIGWUoOpQvbC7myxz9l7EOJmACUw8B7QyiL2/1pbKDCZjA1E1A/+2sfrA5kve5nnsHEzCBaZgAa/v4Rym3MCfYqWsaHgvuugmYgAlMIwT02VfBf3Q1Bdxw1im9aObv0PqhudrB1KERAnCbH5OdkHaDvaYRcyebgAmYgAlM5QR4L6QO5+gnOYArcJ46n5O0FIfN0dboaJKGE9efc21asQW6Hp2HXiBtDMc0YHMe11+EsuUDrXd2mhSOTTqoHMr7gcOzZFwFrcD5COJ+DGlq8zJoLnQH8X/JVhDa0Y34yZyfS9riSA7n2oBjM8Vn7aemczugT013030xARMwARMwARMwARMwARMwARMwgamUAF/OfM6XNrvRPX3BMwDVTqVddbdMYJokwDOuH3QlBxMwgamcAM/703SxWT8GTeVo3D0TmCYJ6MdcOq7dwRxMwARMwARMYKonwHtPf3TtP7yeAu4030Pqf2eTA5wc5hTuR1eEcx/KE5iH5OPRbYx5Oew5mIAJmIAJTEMEgjN2+t0fr4HU6ZzT6HAuB+510fZIf5inTSq+Rh+i6KT9a87lfH4ZWffgWBcoO30vE/9FOGq39J6cz4wmoZ9CfHpsQlC5auPjSL9TrIr0v9aMRTFtAuc9UBqoV+eT1b/Qx7T9nKsvcqqfJoId0KeJ2+xOmoAJmIAJmIAJmIAJmIAJmIAJmMCUT4AvbW6iF5KDCZiACZiACZiACZjAFEiAH+f+QbP1I2Ol4UPWgCtWamw7EzCBzkvAz3/nvTdumQmYgAmYQCGB2Yn9Dr2NtIv3GaxLm+rMVljw1B4Jp0foo+RgAiZgAiYwjRDIOIZHh/P0D5CIn5GD/ieRLcNRDugKX6F/ozfRi0jf/3yA/Zyc74/k+J3+0R5x3UmLzt0N3sWkfYPNaEzlED4f572I0zu8MJCu3c7zfyAVy32aTJ+i5dEcaCymqfM8YclwfE/9JV4O79N8sAP6ND8EDMAETMAETMAETMAETMAETMAETMAETMAETMAEplwCfNFbS+u9m/KUewvdchMwgWmLgHa61g94lYYfKjW0nQmYQKcn4Oe/098iN9AETMAETCAS4LsGf8/g4WACJmACJmACJQjIiZskOWGnztkc6xzDSVqdqCFoQ7RUKKKW470yRbuhkeT5Q0jLHrTr+OLodfSaErCLO6M3MA+O4Kr7+ZC4HMcB6M0iR/OiuFBH6pBOXW9hM5LTjZEczuUgH4PqOQldkO1vJn2aPLUD+jR5291pEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABE2hfAvxAN5QaJQcTMIFpjICf/2nshru7JmACJmACJmACJmACJmACUw2B6HBOh9IdzhU4T53PSVuIw+ZoG7QCmg59gT5AF6Pb0LvoLdQLyQF9VZQGOZFziGXK+XwcGoVmQ5+nRo2HRzF5CalcOY/LcTw0+5fMsd1Eqn1Xoj2I++aX1F/awbWczN8JcQti24246GT/r2jr4/8T0Hby5mECJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJjANEwie2+n/BhKcsutokKb/2W4jtBVaA80SEh/iOBI9i7SD+Sfo04L8rxC/GFqJtGeyDuicq8zL0AvoN6QX7mROWhrUTmx+5tCDy9+hM9BHaH2i3/jF6pd/Q58W4epgtHewuS/aqB1qK8d5iJOj/afZ/D4vJuAd0Iu5ONYETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAEpmoCwUFbDt3pLud0Nt3ZmvjpOayLtkDro/nRZCQH71vRjkg2B5JNu5DXC6FcxXUnfRJH7YguB/QN0TNIO6Cnu4wT3kZjkeoYgN4MbfklNfdvTAvlnkVdq2GyLXqA89M43o/6ov5oEBoSzg8lz31qW6YM7X4uh/uPdXSojIB3QK+Mk61MwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMYIongP91Nxyuo/N3XX+IX44LOXJvgpYOCZ9z/ADdgrTL+fvkfQ1bOZRvjDbl+g6utSl2WmZ07tY58XGH8Wou5Rj+BOmrxfhgo53M5dS+AToaHV+ifWm7ydsHm5k4167nqkOO5ruiQ9BcSI7y36IZUTf0ILoADSfP9xwdWkjADugtBOjsJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJtBZCeCgXbfjd7aNRC/I9WZIO4SviOSwLcftR9FzQbUcP8Fx+1OOdYG8+3NxLrqUtN9wrZ3O5fhdGEiXk/mEkDhjdASPbeO4H2nHo3FoO9KfJk67pKvt9ZzliVe9A4jfUuVy1A7rckRfgMO8aHUkx3PtzP4A6dpd3aEVCdgBvRVhuigTMAETMAETMIF2I5D+Vz8OJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJtBJCXTpqHYFx23tRP5TURtIP4P4fdAMSDuCa2dz7SK+KDoTaSfx6cgfHca5TB281SftQj6Z04U4fxN9zLWcvksGbOPO5Q9htAbakDz3KJ7znzj/mfPZOD8B/Qbdjo4k+vlYKOlqn3Zol6O6HOYvI32PTHqhk31M97F1CXRv3eJcmgmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYQEcRwDE7dTwPDt4rcDof+gK9hT5Dn6N70d3oDfQUWhfdgJYIDuETOU+dznWt83BMdznn/G2SVM48HAdy/X7WVjaZEJ3xbyNODuibontQtuwvyH80cXI0l4P5OlxfwXE8Up1yeF8eqS1yTtdu6XUhtjEb5/O2I+Ad0NuOrUs2ARMwARMwARNoOwLeAb3t2LpkEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzCBlhNo1x3QcdbuihP2Txznoumbo13QimgSkjP5LGgUOgG7c7Dry3EM12nguheHb5GcvWcg7ceQ1OCAbXfStQv65SSqnr24voTrdKfzfIZM25YhTbuav4ndosQ32LWcqJlI3w7ti+ZGPZE23Fbb5cB+FXqG/No1vUF+0hzagYAd0NsBsqswARMwARMwARNodQJ2QG91pC7QBEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEygFQm0mgO6HK1plyS/65J+M5gtgc3ZaBX0MnoEvYfGBS3J8RuK+AfHNISy5Tguh/JniRqE1uD6Ma5Tp/ZoG4/Ep47mHLcnTg7ht3K9ZYzP22evsRnLtZzM5yTP56qfY9qn7Hm4np/jrOhtTMYozqFzENBfBDiYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAl0EgJyxqYpcs7+KTho13M8L3DWXgP7G9D06BQkx/B3yasdzWO4OXOenqrsX6pKL7XDuBzQN0aPoa6ogQN6Ju5+ZSKso38oqsHu5yFdzuVxd/S7iNs25LmaYzeUtjH0M2bRtZznJYdORkADw8EETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETKCTEJAzNiF1/sZ5ey30N3Q6OhhtlHXW5roXZn9Gs6EDSDsavYm0q7lCd6lM16KT+R3BZpNwLHI+j07rco7/Ars3UG/KX1Z5ODbmm/xgKHvzcCzptB7SfeiEBMoNpk7YXDfJBEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABKZuAjhyL0UPf4u2R7Ojz5B2Qe+HepK+Hg7gcQfywcTJoXs4uhbV7TgeHNXrdkEnn5zV+xP/gewUOE8dzTk+TrpsB3GcmetxwaToEHcul9P6Iki7po9Eje2afgU2qvtuFRrap1OHKYhAY39lMAV1xU01ARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwgc5HAIduha5SbF32XHEyCEc5dJ+D9kFPIDmi/xrJ0XxupN3OszuHL8C1wvs4dE+kmO4c69K5ngethlTOiehSzuWIXhe4lkO5wgPhuLaOmfgQXXeQM7zC/8Jxw3CM8XWGOomO5hzHoOFoQj0DX0xRBLwD+hR1u9xYEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzCBKYUADtxd5HwdHLBT52yipuOgXcg/if2IdhznIe4qtCzaC12O3aRoF46nZ/L14HwupLK/Uzz22R3PVdeZaFM0A5qIFLceulUO5tjLWT11fifIoXx9JHvtqB7jf0kN/4Y8unosRK2lfhGv8h2mcgLeAX0qv8HungmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYQPsTiE7lqpnzZdHx6EkuX0A3c34/+l1oWdxUenuul0Vn48x9SXQ+V1kKwVblpT7AIV3nSutJfO9oE9LlEP4W+ieqQnuH9LhjeSwz7ph+TzY968we4usOagPpPxCxL1rPzud5QlPvtf6qYurtnXtmAiZgAiZgAiYwtRLwAmZqvbPulwmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAlMHQRSx26ctOflcBzaEslZ+yX0BZoRzY1mwpd3EY5xZ/RXOK1CixMvx/GSITiA/8TxNxhdhO5F+5LvXTmrc2zgY0P0Qti8iWpJXqDIjrhPSZ8DzYfNR0U2sVHl0qKNj1MfgfjXElNfz9wjEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABE+ggAjhny7H8ErQKugZdil5Gn+PYLcdx7Vy+TKZ5s3A+PRqDflJ8hQ7eT2Nai5ZGK6B3lRXVOaBTjsr9iXrf5lxO8EtxnIfrj2MdHLtzPZm0p9DmQdo5vV5ZXNeFIif3bLrPp04C6fb7DiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAq1DAGfubpR0CFpNRxy1d0b3os+i87mO6PlMjfNzLgdw7ZDeNxNfeKr8IeE1jrej/mg/6p4h1iHnctlwPQFNCvYfhuPK4Zg/nEPEaeh/IW+sJ2/n62mUgB3Qp9Eb726bgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAm0GYFVKXkoegT9V7Voh/GMQ3idU3eMw+RLJCfxWZGcyeU4XreLua6zgXxdlTc4lp9NmhzR10bHEP0rOaErP+e90EJoJ3QP6Rsj7caunc7r6sBUzu+6lqP8X1B0VFe0gwnUEehuFiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAi0n8NNPPyVdu6b7Q8sBXbugP4Yj95c4fneLDt75WuQkHuLe4fge2gAtSZ77SYq7luezyVE8dWLHTk7ob3I4mMuT0J/QLlw/wFFO5Sp/CbQQ+hT9GV1AnvEcHUygyQS8A3qTkTmDCZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZjAlEpATuJSWwQ5n0+enG4kPksoP+4q/mO5+oITuRo1AinP5mgB5SEt9feVjYLOw7Wc1LVTuXY578rhPuKHoCOQdi9fCa0b9BXHv+gcuzPsfA4Jh2YTsAN6s9E5owmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYwJRAAIfrumbKSVxS3AcffJD88MMPjXYhm//HH//fl1yO7Nk0FdS9e3cdZgqF/oRz+HSNVoB/ebC5naN2Ll8L7ak4yk+95TmmQeeUOZDDMUFZm08wORHJ+XwTtDrnA9CG6EL0ifI7mEBLCKQj3MEETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAEpkYCchKXw7nCW2+9ldxwww3Jbbfdlnz88cdJr169kumnnz5ZY401kuOPPz7p3bt3uoP52LFjU8fycePGJX379k1mmWWWZOLEiUm3bt1SxRDLjdeZuj4KcQtx7IdG1WUqOKGu6GT+Is7lR2Oincv/zPmsHE9Dn6K50DxoDbQLmhkdiBoE7ZROmW82SHCECbQCAQ2uVijGRZiACZiACZiACZhAuxLwAqZdcbsyEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABE5iyCch5XA7mF110Ubrj+ZJLLpnMP//8qbP56NGj053Qa2pqkhVWWCHt6Omnn5789a9/TR3P55577tRujjnmSPP06NEjmWmmmZLFF188dV5X2jLLLJPayV4B/+8NOFyD5Hi+L/66DxLXNTqap0YE4roR9yPH6TlOyMSvx/lRaHUk5/TP0TdoTtQLPYLOJs9NHB1MoF0J2AG9XXG7MhMwARMwARMwgVYiYAf0VgLpYkzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABExgaifw7bffJgcccEByxRVXJCuvvHLyhz/8IVlppZWSOeecM3UmV3j33XdTB3I5lOMMnrzwwgvJlVdembzyyivJdNNNl4waNSr58MMPU9vvv/8++frrr9Md0uWIPuOMM6bXyy+/fPLoo4+mNpShncsvQZujS7HdK8SnW7EXOKI/SPThxD9K3u4cJ3Ocjbi10JJocTQRPYtuJ927mwPCoWMI2AG9Y7i7VhMwARMwARMwgZYRsAN6y/g5twmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAlMUQTk7C117Zr6bzcpaNfzffbZJ9luu+2Sq6++OnUwj0Fl/vTTT0m3bt0alKm0aPvVV18l/fr1S3dPf/XVV5MZZpghtb/sssuS0047LT1fc801kwcflB95Gsia7oJ+FeqHdkK3UuZ3vySnBjNxvjw6GG2B9iS9Rhk52j8mgvKx0xGwA3qnuyVukAmYgAmYgAmYQAUEvMCuAJJNTMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETGBKJhB9sLMO4+rP5MmTk+7duzfaNeXXbuWrrbZa8vbbbye33357stZaayUTJ05Mdz7Pl1tUoJzTZVdkW1NTk5xwwglp2iGHHJLstVe6yXnqKC8ncp1zOIjDCUge6zeh4UjbrvdGS6NqJF+YE8l3kfJlnc/z19g5mECHE7ADeoffAjfABEzABEzABEygGQTsgN4MaM5iAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAlMKgeDEnTb3iy++SJ3HH3744WTMmDHJFltskWy77bZJr1696rojezmmy7E8G+69995kgw02SKqrq5P7778/3e28Obuo//jjj2k+1fPkk08mJ510UjJ8+PBkmWWWSQ4//PBks802S3dFj+3GcbyrHMk5ylN+dfQntBmajCah6dBYJIf0GvQY5op3MIFOT6DxP//o9F1wA03ABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABKYmAtpA/IcffkhOP/305Oyzz06++eabZKGFFkq+++67pF+/fsk666xTzwFd9lnn8+hoPt108vNOkvHjx6fH5jifK1+3bt3S/P/73/+Sww47LHnnnXeSI444InU+l+N5DGqHQtzFnKMczh8k/hmOM6LVkPx3Xybt9dTYwQSmMAJ2QJ/CbpibawImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAJTIgHtDi7H8OjMXa4P2s38xBNPTE444YRk/fXXT/bcc89kscUWS2afffZkttlma5B14sSJyY477pg8++yzSW1tbZ2j+bfffpv07t070Q7mn3/+eZq/OWHkyJFpe6677rpk+eWXT6699tpkww03TKaffvq07HJ9wvm8C32XB7x0c3Pqdx4T6EwE7IDeme6G22ICJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACUykB7Q4eHbW/+uqrZKaZZkq0Q7kc0zM7h6fnciQ/44wzki222CK54YYbGhDJ5pFTu8oZPnx4Isf1l156KVlqqaXSPKpDTudyPn/zzTfT87g7eoNCCyJUj8o9+uijk+effz7ZY489kr/+9a/pbuwxlHM+l03cDb2geEeZwBRJwA7oU+Rtc6NNwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMYMohMGnSpGTEiBFJTU1N8vjjj6cO44MGDUo22GCD1Kk7Bjmfy+n7P//5TzJ+/PjkiCOOSJO+/vrr1IlcDuX9+/dPdx5XiLuqd+3aNfnf//6XaMfz+eefv668+eabL905/fbbb08eeuihZI011kjz5EPWKV3O8bPOOmtqcvfddyc77LBDonJU/iabbJLP6msTmOYIaEv/aa7T7rAJmIAJmIAJmMAUT8ALmCn+FroDJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACUwOB6IcadzAv1aezzz47Ofzww1PH7hVXXDH57rvvkieffDIZN25cctRRRyWHHHJI0rNnz+THH39Md0lfaaWVkrfeeiupra1NHc9PPPHE1H7MmDFJ9+7dk3322SfZfffdk3nmmacuT1Hd2hH9kksuSfbbb7/UMf3VV19Nndej43p+9/K//e1vSe/evZODDjoomWGGGZL77rsvufPOO9Od2Ndcc820CuVtrL+YdSlqj+NMYGogYAf0qeEuug8mYAImYAImMO0RsAP6tHfP3WMTMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMIFOQqBCB+y61l5//fXJjjvumCy11FLJmWeemSyzzDJJ3759kxdeeCHZbbfdkpdeeim57rrrkm222SbNM2rUqGTbbbdNdzPfc889k2HDhqVO59otXTun33LLLemO6NqNXLuSNxZ++OGHdOfz5557LnVc/8tf/lJvl3SlyzH90ksvTc4777zkgAMOSM4666xEu6o3ta+ZttgBvbEb4/QploAd0KfYW+eGm4AJmIAJmMA0TcAO6NP07XfnTcAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAEOgOBDz/8MLn99ttTB3LtFr7RRhulu5vPPPPMafN++umn1IlbzufXXHNN6ji++eabp2lxp/P//Oc/6U7m66+/fnLZZZclc845Z/Lpp58mu+66a/LUU08l8803X7pjuXYhn2222dK87733XlJdXZ2ofjmub7XVVmk9RSHW88ADD6Q7sGsXdTnAb7fddkmfPn2SiRMnpjutP/TQQ8kXX3yRtuWEE05IHd6zoRmO6HZAL7ohjpsqCNR/OqaKLrkTJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACzSVQztl68uTJySOPPJLuFH7XXXelO4TLWfv7779PTj/99NSx+8gjj0yWXHLJ1Cn8q6++Sncrn3XWWZMZZ5wxbVJ0TNf54MGD0zg5mz/66KPpLuhzzTVXMvfcc6dlamfye++9N3U+V91dunRJdy/fb7/9Uody7YC+zjrrJP369UvLyYdu3bqlUarn6quvTv71r38l559/fnLSSSclkyZNStuvXdVVxrHHHps6wuedz5Vf9TqYgAn8QsAO6B4JJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJjCNE8g6hZdzttaO59olXE7bv/3tb1OH7XnnnTf5+OOPk1NOOSXdkVw7ll9++eUpUTmA63r8+PHJqFGj6ijHOrTruHZPHzt2bPL000+n5WkH9aWWWiq57bbbknHjxqVlKGR3OV999dWT3r17J2+88Ua6m3o2vP/+++nu6GussUa9+IEDByYnnnhictRRR6V1yTF+gQUWSOtyMAETqJyAHdArZ2VLEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABE5gqCUTn7ldeeSUZOXJkMs888yTLL798MtNMM6X9lZO3HMEnTJiQ7k5+/fXXJ4sttlgdC+14vuCCCya/+tWvkltvvbUuvm/fvsmgQYOSO+64I7niiiuSDTbYIN0NXUFO6XIGlwO6HOBfeumlZMyYMakDupzH5dj+1ltvJW+++Way9tprp22Q47raOt9886U7pMvRvH///nX16aS6ujqRE7qc4rWben5H9549eyZrrrlmvTy+MAETqJxA18pNbWkCJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJjC1EZAj9+mnn546kGs3cO1svs4666RO2sOHD0+7G3cs33zzzZNrr722zvlczt2SnMO1m7jK0Pk777xTh2mnnXZKHcq1e/oOO+yQnHDCCelu6VtssUXy8ssvJ7/73e+SqqqqNI+cwxWWXnrpZKuttkqdzKNDe48ePZLu3X/Ze/nBBx9M65FDe9wBffLkyWle7XSuIOd0hXI7uqcGDiZgAk0i0EUPvYMJmIAJmIAJmIAJTGEEvICZwm6Ym2sCJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJtC+BLSjuELc2Vzn+Z3AY4vOOOOM5Oijj053Ff/Nb36TzDHHHMnnn3+eHHbYYemO5/fdd1+6q3gpR+5Y7pNPPplsvPHGyQorrJA6jU8//fR1eZ555pm0jieeeCL56quv0qrldH744YenTumLLLJIMnbs2OSLL75Id0RXkN1qq62W7oL+t7/9Ldl2222TSZMmJY899lhy/PHHJ3I4v+2225KVV145tVef1d9vvvmmbuf2NKFjQpeOqda1mkDbE7ADetszdg0mYAImYAImYAKtT8AO6K3P1CWagAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAlMpQTefvvtdGfxAQMGNOjhe++9l+5mPs888yR33313uoN5DBdddFFy6KGHpukXXnhhssQSS9Q5eWcLig7o55xzTnLQQQelu5sfcsghDepSxFNPPZV8+eWXaZnzzz9/aqMdzBdeeOHU8Vw7m/fv3z+N69atW/LQQw+lbZBz+8wzz5z06tUr+eyzz1L7U089Nd1FvZMGO6B30hvjZrWcwC//D0HLy3EJJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACnYDARx99lPzvf/9LrrvuuuSNN95Idw2fd955Uydz7XaedTK/8sork4kTJyY777xzGi/HbzmUd+/ePVlvvfXS3cXvuuuu5JZbbkkd0JWWD9oZ/euvv06OO+641DF8l112yZvUXa+00koN0lR2bW1t8sc//jF1Plcdcj5XWGuttdLd1OWArt3Otav6Ouusk2ywwQapU72DCZhA+xOwA3r7M3eNJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJtAkAnLKjs7fXbt2Tc/l+J0NijvvvPOSCy64IBk1alSywAILJJtttlm6a/mrr76aDB8+PJkwYUJy2mmnJUsuuWSatXfv3unx22+/TY9Z5+9+/fqlO44r3HDDDcnhhx9e5xieRmaCdiP/4osvktNPPz2Zc84588klr1955ZXkhBNOSGacccZkp512Su3y/ZptttnSfkgOJmACHU/ADugdfw/cAhMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMoJCDncTmcyyk7OmbHuHwGpd97773pjuennHJKssYaa6S7ns8wwwzJ2LFjkwMPPDC5+uqrk6effrrOAX3QoEFpMffcc0961M7nMfTt2zd54okn0suXXnopef/995OBAwc2cH5XmnZW32STTZJtttkmtddO6nEX81ienNtl+/bbbyfjx49Pd2e/8cYbU2f5iy++OFluueWiqY8mYAKdmIAd0DvxzXHTTMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAEpm0Ccj5XePbZZ5Nbb701deDu06dPUl1dnay++urJQgstlO5wHu0uueSSZNZZZ20ATXnmm2++1DFcu43HsNZaayXzzjtvop3Ijz/++OSggw5KbV5//fW6nckXWWSR1Fn8mWeeaeCArrqPO+641On9sMMOS53dFfLO54qTg/wxxxyTjBgxIvnuu++SiRMnJquuumoybNiwOsf1Bg13hAmYQKcj0CX+dwydrmVukAmYgAmYgAmYgAmUJvBz6SSnmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmMCUQUA+nNGPMzqQZ1sup27tTH7uuecmjzzySOrc3bNnz3THcDlvr7TSSsnZZ5+dHvMh65QuWzmYb7fddsm4ceOSkSNHJnPPPXcyefLkdMfzSy+9NHUC//DDD5MFF1ww6d+/f/LFF18k33zzTXLeeeclH330UfLHP/4x3eX84IMPrrcD+l133ZVsvPHGyRFHHJEce+yxqfP6559/ntTW1iYvvPBCsuGGGybzzz9/nZO86lb8HHPMkay22mrJzDPPnG/61HLdZWrpiPthAnkCdkDPE/G1CZiACZiACZjAlEDADuhTwl1yG03ABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABOoRKOdwPnr06OS5555LdzQfOHBgmu/ee+9Nttpqq6RXr17J3nvvnWywwQbJPPPMk/To0SPZa6+9krvvvjtNv+qqq5LpppuuAe233norefzxx1OH78svvzx1ONdx8803T22zTuqyu+6665JHH300mTBhQrLccsslu+22W7LKKqskRx99dHLyyScn119/fbL11lvX1fPDDz8kgwYNSh3U5cQuJ/qnn346eeqpp1KHdzmin3baaanz+jQY7IA+Dd70aaXL3aeVjrqfJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJtCRBLp06ZJICnIGv//++5ObbropdTR/55130viXXnqpronauVy7i59yyilJVVVVXbxOTj311HSXcTmOv/nmm8mSSy5ZtzO5HN1Vz5133pkcdNBB6c7p8803XzLbbLMl++67b3LhhRemjuGLLLJIXZ5VV101kb799tukd+/e9eqSA7uCdkdXiI7r2n39jTfeSHdT/9Of/pTuoK56l1122bQetX3llVeuV5YvTMAEpnwCdkCf8u+he2ACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJmACJtBJCMj5WyE6mmebpZ3B77jjjmT48OHJ888/nzp/DxgwIN1F/JNPPkmv+/fvX5dlpZVWSlZYYYVk9tlnT9MUdNRO4/369UtmnnnmRE7qkyZNKqxzyy23TBZeeOFkvfXWS7p37568/fbbyV133ZUceOCBybvvvps8++yzSc+ePevqk1N83vl85MiRqd3gwYPrHNBj3+SsrjDLLLMka6yxRjJkyJBk3XXXTetyMAETmHoJdIkT0tTbRffMBEzABEzABExgKiTwyyeqqbBj7pIJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmMDUS6BPnz7p7uFyLJeWWWaZZLHFFkt++OGHZNddd0169eqV3HLLLcnAgQNLQoi7j7/22mvJcsstl+5iLifxuOt5qYzZ9M033zy57bbb0h3SN9hgg7odzWNe1fHpp58mTz/9dHLkkUcm7733XnLppZcm2223Xb3iv/7669TRXs7wDg0I/LLVfYNoR5jAlE/Af2Iy5d9D98AETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETKATEJgwYUJy9913J1deeWVy7LHHJgsttFDqGC6H7m7duiU33XRTuju4diXXruaKU3jiiSfS3ck33HDD1Pk8OpkXdUm7nyvIgVz17bbbbul10Y7rio+O50r/7rvvUif31VZbLc3/3HPPpQ7oMe/jjz+e/OUvf0l+9atfJbW1tckLL7yQzDTTTMm//vWvBs7nKls7nzuYgAlMewTsgD7t3XP32ARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMoBUJRIfxk046KTn66KPTkpdffvnkkEMOqXM+V9w666xTr9aYb/LkyWm8nMUVopN5PePMxahRo5ILLrggGTBgQLLTTjuVMkvrVogO5nI+V3j44YeTGWaYIVl66aXrpfft2zcZN25c8sADD6QO8gceeGCy/fbbJ4suumjJOpxgAiYw7RGwA/q0d8/dYxMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwgVYkIIfxb7/9Nnn++eeT/v37J19++WVy1113pQ7o+RCdzOUUHh3N33zzzfR8lVVWSeSMrl3SywXtSP7OO+8kl1xySTLnnHOWNM06sk+cODFRPf/85z+TO++8M1l55ZWTwYMH18u72GKLJXfccUcy22yzJT169ChZrhNMwASmbQLlZ6hpm417bwImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAImYAIVEXj//feT++67Lzn33HOTv/zlL8kjjzxSb/fzWEjcjVzXcQf0119/PT2fa665UufzGF9U8XPPPZecdtppyZprrpnsscceyY8//pg6r2fLVT45w7/77rvJ6NGjk6+//jpR+9SmV155Jdl2223TdmoX9HyYe+6581G+NgETMIF6BOyA7gFhAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiZgAiYwzRGQk7dCdpfwlkC48sork+mnnz7Zfffdk5tuuim59dZbkyeffDJZddVVyzqUq87oPB53Ps87k8d2/fDDD6nz+fjx45Ozzjorje7WrVtds7W7esz76quvJn/+85+TcePGpXHSiiuumPz73/9ONt1002TWWWdNsvZ1hfjEBEzABBohYAf0RgA52QRMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMYOojkHU81+7gAwcOLOmQLUdt7TQeHcSzNGLa9ddfn2y33XZp0iqrrJI6oN9xxx2pA7psioLaIGfyF198MenVq1ey9NJLp2Z5B/ToKH7XXXclV199dXL44YcngwYNSvOOGjUqeeedd5IXXnghWWGFFZLq6uq0jE022STdUV27ny+wwALJcsst16AJ+XoaGDjCBEzABAoI2AG9AIqjTMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAEpl4C2hX8scceSy6++OLk9ttvT+abb77UgVs7mBcFOWqXcj5X2n333Zd89tlnydprr51mHzx4cHpU2cccc0zqvJ7dqTxbx4wzzpjIAf67775L5pxzzjQpvzO56vjyyy+TE044IenZs2fqUH7ppZemO6w//fTTyWuvvZZod/R99923zgG9X79+yTrrrJOtyucmYAIm0CoE7IDeKhhdiAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYwJRAQLuNn3HGGck111yTTJw4MXUMl0O6nLiXXXbZ5Keffkqyu6OrT0o/++yzk48++ig59thjk9lmmy3tatxB/P77708dw+MO6CpHTu3PPfdcMmHChJKO7SpDO5j36NEjWWihhZLvv/++XrnpRQg33XRT6myueo444og0X+/evZM11lgj2XvvvZNNN900GTBgQDaLz03ABEygTQh0bZNSXagJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJdCICcixXePzxx5O777473R38nnvuSfbcc8909/KnnnoqTdfu4/nwzTffJH//+9+TCy+8MHVCzwbtbn7RRRcl22+/fZ1DunZSX3/99VOzG264IbnuuuuSa6+9tl6+WI92P3/11VfT3c9/9atfpQ7w+aC48ePHp47xK6+8crLffvslzz//fOoYr13W99lnHzuf56H52gRMoM0IeAf0NkPrgk3ABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABEzABDqSgJy8JTlux13NN99882SllVZKBg0alDZNu47Lsfyxxx5LdxLP734um3nmmSc599xz053K559//npdkmO5dkLfd99907xyFNdu6u+++25qt9tuu6VO5UOGDEm22GKLdAdzhbh7+kwzzZTIiV07sSsU1a+4oUOHJgcddFBdvtTYwQRMwAQ6gIAd0DsAuqs0ARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARNoGwJy9o5O3HLyjo7eTz/9dLLiiismc801VzL33HOnlct2mWWWSWaYYYbkmWeeScaOHZv06dOnsGH7779/YfwDDzyQTJo0KTn//PPTMlSPnN579eqV2svB/Oyzz04WW2yxRDuj54PsFdSOyZMnJ927F7t29u3bN5/V1yZgAibQIQS6dkitrtQETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAETMAEGiEwcuTI5JBDDkmeeOKJ1FIO442F6Hz+3XffJd9++23y5z//OZl99tmTlVdeOfniiy/q7SAu2wEDBiTLL7988sYbbyQvvvhixfXIULuny2Fdx//85z/JuHHjkp133jk544wzkttvvz1ZdNFFU6d27bq+wgor1Ks79uXzzz9P1lprrWSHHXYo6XzeWJ+dbgImYALtSaD4z2TaswWuywRMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwAQKCDz44IOpM/ccc8yRrLLKKunO4tolXI7j0dE8n01O65tsskmy3XbbpUnDhw9Pdtxxx2TmmWdOJkyYkDdPr9dZZ53kkUceSR5//PFkzTXXLLQpitTO6apLTuiLL754uov5wIED6xzJN9544+T1119PrrvuumSvvfaqt8N5bL8c7CUHEzABE5hSCNgBfUq5U26nCZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACUxjBDbaaKN0B/NXXnkl+fHHH5Nu3brVEZAzuXY579evXxqnHcXl1N2jR4807pZbbkl69uyZ/O1vf0t23333ennzGAcPHpwcc8wxqQO6Qinn9nw+Xa+99tqJ8mfDpEmT0nYst9xyafTLL7+cHrt3t9tmPVC+MAETmCIJdJ0iW+1Gm4AJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJmIAJTJUE5Eiunc4VFlxwwVTPPfdc8v7776c7mJ922mnJ8ssvn8w999zJVlttldTU1CQTJ06scxpXvHY0/+CDD5IBAwYke+65Z53zucouCksvvXTSv3//RLunf/LJJ6lJKdtvvvkmLVs7sStEZ/Vsu+V8rrDtttsmX331VfKPf/wjvXYwARMwgamBgB3Qp4a76D6YgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYgAmYwBRKQM7m2t08Op3LobtLly5pb7RjuHYYf/PNN5NXX301ufDCC5Mzzzwz3eF8xRVXTJ599tnUwVxxMfTp0ydZYokl0suFF164Ll4npXY2V3mrrLJK6uT+zDPPpHmKbD/99NNkyJAhSXV1dfLOO+80KDu2OyZMP/30dTu01zP2hQmYgAlMwQTsgD4F3zw33QRMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwASmdAJy2u7WrVvqdP7ll18mt912W3LVVVclr732Wtq1wYMHpzucn3/++ckxxxyTHHnkkemu57J7+umn053LTznllGTMmDGpvRzHF1tssWSWWWZJxo8fXxdfxCk6vyttww03TE2027qCdi5/+eWX0/PoHK+6amtrU40ePTpNczABEzCBaY1A92mtw+6vCZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZiACZhA5yEwYcKE5Morr0z++c9/ps7fM844YzLddNMlyy+/fHLnnXcmq6++etKzZ8/0/Ljjjkv23XffusYvuuiiyU477ZScffbZyT333JNst912aVpVVVXyq1/9Knn99ddTR/G+ffumTuT5Hcqj87vyqD6FW265JVGb1JbHH388effdd1Mnd+3S3qNHj+Suu+5K5ptvvkS7mzuYgAmYwLRIwA7o0+Jdd59NwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMoJMQ0G7mf/zjH5OFFlooOeqoo5KBAwcmffr0SWafffZk0qRJyYABA5JVVlklGTFiRLLyyiunrdaO6No1Xdpggw1SB3Q5jkcHdDmMr7jiismll16avPfee8n888/fwPlc5Xz//ffJww8/nO6k/swzz6Rlv/DCC6lmm222ZNCgQcm3336bOqCrLoWFF144PTqYgAmYwLRKwA7o0+qdd79NwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMoIMJPPjgg8kBBxyQOovLEV27lheFNdZYI3VAlzO5Qvfu3escyuUkLgfz++67ry6rdlFXmeeee27y8ssvJ9XV1UnXrl0bFF1bW5scfvjh6W7n2tFcdltssUWy4YYbJosttlgDe0eYgAmYgAkkScPZ1FRMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMwARMoB0I3HzzzcmPP/6Y7L///nXO5z///HPy008/JTrGIMdwhTfffDM9ypm8S5cu6flcc82VLL/88smoUaOSd999N41TWGSRRZI555wz3dl87NixdfHZE+1y/qc//Sm5//7/Y+8+wOSqygYAnxR6C02KCKioiApI+VFqUDqCFEGaNEEEkSbSRJqIiPQOUgICgnSR3kKXEnqvoYYaQgkJqf93hpl1Mzu7O7vZMrPznuf5uDPnnHvuOe+9M2F3v7lzW+Fu6Hm7xx57SD6vqKWSAAECXwhIQHclECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAj0uMH78+PTcc8+lL33pS2mJJZZoOn5OLG+eYJ4b8l3OBw0alIYNG5ZGjhzZ1Dcnqufygx/8oLC94YYbmtoWWGCBtOiii6abbropvf766031zR/MNddcafPNNy/c+VwhQIAAgeoEJKBX56QXAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0IUC00wzTeGu4++++26aYYYZ2hx59tlnT8stt1x66qmnmu6CXko+zzvmBPQ8xtVXX900Tt7nRz/6UVp55ZXTHHPM0eb4GgkQIECgegEJ6NVb6UmAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAh0ocDaa69dGO3BBx+cYtTJkyeniRMnTlG3+uqrF5LVH3/88UJ96U7p+fF3vvOdQpL5vffe27TP9NNPn/7whz+kf/3rXynfDV0hQIAAga4RkIDeNY5GIUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBDoosO666xb2uOCCC9KIESOa9s7J5QMGDCg8z3c9z2WVVVYpbO+///7CNvcpldlmmy3deuut6aOPPmqq84AAAQIEukdAAnr3uBqVAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQaEfgm9/8Zlp//fXT9ddfX7hb+fjx49OECRPSW2+9la699tq08cYbp2233TZ9/PHH6Xvf+16aZZZZ0jvvvJNGjRrVYuQ8Vv/+0iJbwKggQIBAFwv0y19ToRAgQIAAAQIE6kzA/8DU2QkzXQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0JrAG2+8kTbaaKP00EMPpXnmmScttthiaeLEien5559P00wzTdprr73SjjvumGacccb0/vvvp7nmmqu1odQTqCWB/92iv5ZmZS4EukBAAnoXIBqCAAECBAgQ6HEBCeg9Tu6ABAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECg+wRyEvqVV16ZbrvttkLi+WyzzZbWXnvt9LOf/Sx9+9vf7r4DG5lA9wlIQO8+WyP3soAE9F4+AQ5PgAABAgQIdEpAAnqn2OxEgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRqW2D06NFp+umnTwMGDKjtiZodgfYFJKC3b6RHnQpIQK/TE2faBAgQIECgwQUkoDf4BWD5BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIND3BCZPnpz69ZOz2/fObMOuyMXcsKe+7y+8f99fohUSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECtS4g+bzWz5D5ESBA4AsBCeiuBAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoSmBgVb10IkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhMpcBbb73VNML8888/laPZnQABAgR6Q8Ad0HtD3TEJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQhwIS0OvwpJkyAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEekNAAnpvqDsmAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE6lBAAnodnjRTJkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQG8ISEDvDXXHJECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQB0KSECvw5NmygQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoB4F5npo7ZRDIUCAAIH6FZCAXr/nzswJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQowIS0HuU28EIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQvwID63fqZk6AAAECBAg0sEC/Bl67pRMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgboVmPbTxx/Kk59//vmXqdtFmDgBAgQaXMAd0Bv8ArB8AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIVCsgAb1aKf0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQ4AIS0Bv8ArB8AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIVCsgAb1aKf0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQ4AIS0Bv8ArB8AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIVCsgAb1aKf0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQ4AIS0Bv8ArB8AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIVCsgAb1aKf0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQ4AIS0Bv8ArB8AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIVCsgAb1aKf0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQ4AIS0Bv8ArB8AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIVCsgAb1aKf0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQ4AIS0Bv8ArB8AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIVCsgAb1aKf0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQ4AIS0Bv8ArB8AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIVCsgAb1aKf0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQ4AIS0Bv8ArB8AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIVCsgAb1aKf0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQ4AIS0Bv8ArB8AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIVCsgAb1aKf0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQ4AIS0Bv8ArB8AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIVCsgAb1aKf0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQ4AIS0Bv8ArB8AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIVCsgAb1aKf0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQ4AIS0Bv8ArB8AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIVCswsNqO+hEgQIAAAQIEakhgcg3NxVQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBagXmWLrU09/+qzXTr14F+tXrxM2bQHsC7oDenpB2AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECgISEB3IRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAVQIS0Kti0okAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEJKC7BggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgKgEJ6FUx6USAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECEtBdAwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQlYAE9KqYdCJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABCeiuAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoSkACelVMOhEgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQICABHTXAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhUJSABvSomnQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBAArprgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSqEpCAXhWTTgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAgAd01QIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJVCUhAr4pJJwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCQgO4aIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGqBCSgV8WkEwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhIQHcNECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBVAhLQq2LSiQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQkoLsGCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAqAQnoVTHpRIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIS0F0DBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFCVgAT0qph0IkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEJ6K4BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKhKQAJ6VUw6ESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgIAEdNcAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECFQlIAG9KiadCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQEACumuAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBKoSkIBeFZNOBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQICAB3TVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAlUJSECvikknAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEJCA7hogQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgaoEJKBXxaQTAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECAxEQIAAAQIECBAgQIAAAQIECBAgQIBAfQv069ev1xYwefLkXju2AxMgUFsCbb0Xea/ouXPV1nno7lk4z90tbHwCBAgQIECAAAECBAgQIECAAAECtSHgDui1cR7MggABAgQIECBAgAABAgT6sEBOAupIDBkyZAqNHXfcsbD/jDPOmEaNGtWq1CGHHFLol7ftlTzO3/72t7TlllumxRZbLA0cOLCw7y233NLertoJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoIEF3AG9gU++pRMgQIAAAQIECBAgQIBAzwgcfPDBLQ50/PHHp48++ijtvvvuadCgQVO0L7nkkk3PP/nkk3TxxRcXksPHjBmTLrjggrTrrru2GK+jFcOHD0/77LNPYbcFFlggzTXXXOmdd97p6DD6EyBAgEA7AkceeWT64x//mF555ZXC+63SmAJPPfVUWnXVVdM666yTyj9o1pgifWPVu+yySzr33HPT6NGjU//+7vfTN85q51fx0ksvpZVWWin94Ac/SFdccUXnB7Jnrwkcc8wxab/99ksvvvhiWmihhXptHg5cvwKvvvpqWmGFFVL+mf4///lP/S7EzAkQIECAAAECBAgQIECAQBUCEtCrQNKFAAECBAgQIECAAAECBAhMjUClO5Ln5LOcgL7HHnukhRdeuNXhL7roovTpp5+mPffcM51yyinp73//e5ckoOeEiny38+9///tpjjnmSNtuu20677zzWp2HBgIECBDomMC4cePSaqutlu65557CN05IPu+YX1/qfcIJJ6S99tqrkJR6+umn96WlNexaRo4cmZZffvn08ssvp/z/apLPG/ZSaFr42WefnXbaaae0+OKLp/PPPx9InQnkf7PXXHPNdOedd6a//OUvks/r7PzVynTzz9M77LBD+va3v134t0EhQIAAAQIECBAgQIAAAQJ9XUACel8/w9ZHgAABAgQIECBAgAABAnUtkBPOc1JTTkDPd1PLd1O8//7703LLLTdV65p99tnTj3/846rGyHdfVwgQIECgeoGHH344/ehHPyq8fz/22GPpu9/9bvU769lnBCZMmJDWXnvtdNttt6VDDz00HXjggX1mbY28kOuuuy5tvPHGaZ555kn5G2Xmn3/+RuZo+LVPmjQpbbjhhumaa65J++67byF5WakvgccffzwNHjw4TZ48OQ0bNqxw5+p6Lh352S2vWZl6gfw+sMkmm6Qrr7yy8HN7vpO+Ulmg/Pp0DVZ2UkuAAAECBAgQIECAAIF6EfCdkPVypsyTAAECBAgQIECAAAECBBpO4JFHHikkQeRE8a985SuFu5TncuaZZzachQUTIECgXgSOPPLItOyyy6allloqvf3225LP6+XEdfE8n3rqqUKC8gMPPFD44Jjk8y4G7qXhdtlll/STn/ykkGiY734u+byXTkSNHPall15KX/7yl9Ott96ahg4dKvm8Rs5LR6aRv6EkfyPU9773vfTOO+/UffJ5R9aub9cI5A+J52+5uf766wvfMCb5vGtcjUKAAAECBAgQIECAAAEC9SEgAb0+zpNZEiBAgAABAgQIECBAgEADCpxxxhmFVW+33XaFbb6Lak5mu+SSS9LHH3/cgCKWTIAAgdoVGDduXFp55ZXTH/7wh3TUUUcV7no97bTT1u6EzazbBE444YS0+OKLp0UXXbSQ0LjMMst027EM3DMCI0eOLJzPs846K1188cXp/PPPL3zDgdK4AmeffXb61re+leabb77Ch43y+79SPwL53+xVV1017bfffoUPDtxxxx3+za6f01czMz3vvPPSIosskuaaa67C+0D+9huFAAECBAgQIECAAAECBAg0koDfkDbS2bZWAgQIECBAgAABAgQIEKgbgdGjR6d//vOfabbZZksbbrhhYd4DBw5MW265ZcptF110Ud2sxUQJECDQlwUmTZqUHnzwwfSlL30pPfnkkyl/e8Xvfve7vrxka2tFYOzYsWm11VZLe+21Vzr00EPTPffck6affvpWequuB4H8+v73v/9duMt1Pr/Dhw9Pm266aT1M3Ry7SSAnLq+33nppxx13TL///e/Tww8/nGaeeeZuOpphu1ogv6bzOZt33nnTo48+Wvi2qX322aerD2O8Pi6Q3wc22mijwgfFd9ttt/T444+nWWedtY+v2vIIECBAgAABAgQIECBAgEBLgYEtq9QQIECAAAECBAgQIECAAAECvS2Q766Z73K+0047TZG8lv/Ifeyxx6Yzzzwz/frXv+7taTo+AQIEUr9+/Sg0E1hiiSV4EEh//OMfC6H8T6De3yteffXVQiK60rZAvZ/ntlc3ZeuRRx6Zcij1K/D973+/fiffBTNvpNdrF3BVHCL/bJ5DIUCAAAECBAgQIECAAAECjSjgDuiNeNatmQABAgQIECBAgAABAgRqXiAnmOey7bbbTjHX7373u2nppZcu3GH3oYceqvl1mCABAn1fYPLkyanR44knnkhzzDFH4Vsr8t1UG92jUdc/fvz4tM4666T+/fsXks8b0aGtd7x69bjlllvSDDPMkBZYYIGUk9DrYR1tnYfubqsHn6mZ48SJE9PPfvazwoev9txzz7q4HqZmvX1x3/xtJXPOOWfhjtUPPPBAnzuHHXmN98Xz2xNryu8Dm2++eeF9YNddd+1z11B3Gnbk+tSXAAECBAgQIECAAAECBGpfQAJ67Z8jMyRAgAABAgQIECBAgACBBhPIX+GdkyFy+eEPf1j4w3bzyMmNuZSS1BuMx3IJECBQcwL5w0EjRowofEBo2WWXTX/5y19qbo4m1P0CAwcOTNdee206+eST0xFHHFG4Fj777LPuP7AjdKvAj3/84/TWW2+lQYMGpa9//evpwgsv7NbjGby2BfIHTC699NJ0/vnnp5NOOiktvvjiadSoUbU9abObQuA73/lOevvtt9Nyyy2XfvCDH6TDDz+cEIEOCeT3gYsuuqjw78EZZ5yRFltssTRy5MgOjaEzAQIECBAgQIAAAQIECBDoCwIS0PvCWbQGAgQIECBAgAABAgQIEOhTAqXE8sGDB6df/vKXFSPfifOf//xn+vTTT/vU2i2GAAEC9Sow7bTTpltvvTUdc8wx6cADD0wrrbRSGjt2bL0ux7ynQmDnnXdOzzzzTOFu2fPMM0+67777pmI0u9aCQE4+z990kO90+4tf/CJtttlmadKkSbUwNXPoJYGtttoqvfTSS4Wk0/nnn7/w/q/Uj0D+wNBNN92Ujj/++HTIIYcUPvTr3+z6OX+1MtN8F/SXX3658DP5l7/85XTjjTfWytTMgwABAgQIECBAgAABAgQI9IiABPQeYXYQAgQIECBAgAABAgQIECBQncCYMWMKd1IbMGBAYXvWWWdVjI033rjwh+6chK4QIECAQO0I7LHHHumxxx5LTz/9dJp33nnTww8/XDuTM5MeE/jGN75RuGv2yiuvnFZYYYV00EEH9dixHaj7BI477rh08803p3//+99poYUWSq+99lr3HczINS+w4IILFq6BddddN62++uppr732qvk5m+CUAr/97W8L/2a/8MILhQ8MPfjgg4gIdEhggQUWSMOHD08bbrhhWnvttVO+phQCBAgQIECAAAECBAgQINAoAv0mT57cKGu1TgIECBAgQKDvCPgfmL5zLq2EAAECDSuw8MILF+6M+sorr6T8uFSGDBmStttuu7TeeusVkptaK3fccUfKd0hfZpllmhIl8t37Dj300LTEEkukJZdcsuKua6yxRtpiiy0KbXvvvXd6//33C4/vvvvuwl0cc/t8881XqNtggw0KoRAgUPsC/fr167VJ+v1iZfpx48YVEpGGDh2aDj/88LT//vtX7qi2zwucdtpphYS073//+yn/+z3jjDP22TW39V7Ul94rRo0aVfiWg2effTbl/3fbcssta+qctnUeunuifek8d8TqggsuKPw//KKLLpruuuuulO+ar9SPwIQJE9I666xTuJP9wQcf7END9XPqamqm+cPh22yzTfr617+e7rnnnjTHHHPU1PxMhgABAgQIECBQcwI3LPPFlNZ6qOamZkIEulig935538ULMRyBcgEJ6OUinhMgQIAAAQL1ICABvR7OkjkSIECAQJsCrSWg57uk3nvvvenqq69O66+/fptjfOtb30rPP/984e66OamtlIDe1k6777574avmcynNobX+Ofkij6kQIFD7ApINa/cc5ffcfffdt/Ahn3yXTKUxBfLddfPd0Ndaa6107rnn9lmEtt6L+mJi8p577pnOOOOMwrfS9O9fO18429Z56O6Lry+e52rN8t3QV1xxxbTsssumyy+/vNrd9KshgZNOOqnwId38M1b+lgOFQEcF3njjjcL7QP5QeP6ZXiFAgAABAgQIEGhDQAJ6Gzia+piABPQ+dkIt538CEtBdDQQIECBAgEA9CkhAr8ezZs4ECBAgQIAAAQLdJiDZsNtoDUyAQAcE2novauTE5A4QdknXts5DlxygjUGc5zZwNBEgQIAAAQIECBAgQIDA/wQkoLsaGkdAAnrjnOuGW2nt3JKj4egtmAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAvUlMLC+pmu2BAgQIECAAAECBAgQIECAAAECBAiUC7jjbLmI5wQI9IaA96LeUG95TOehpYkaAgQIECBAgAABAgQIECBAgAABAgS6VsAd0LvW02gECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDoswIS0PvsqbUwAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIdK2ABPSu9TQaAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE+qyABPQ+e2otjAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAl0rIAG9az2NRoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgT4rIAG9z55aCyNAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEDXCkhA71pPoxEgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKDPCkhA77On1sIIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQtQIS0LvW02gECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDoswIS0PvsqbUwAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIdK2ABPSu9TQaAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE+qyABPQ+e2otjAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAl0rIAG9az2NRoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgT4rIAG9z55aCyNAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEDXCkhA71pPoxEgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKDPCkhA77On1sIIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQtQIS0LvW02gECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDoswIS0PvsqbUwAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIdK2ABPSu9TQaAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE+qyABPQ+e2otjAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAl0rIAG9az2NRoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgT4rIAG9z55aCyNAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEDXCkhA71pPoxEgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKDPCkhA77On1sIIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQtQIS0LvW02gECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDoswIS0PvsqbUwAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIdK2ABPSu9TQaAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE+qyABPQ+e2otjAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAl0rIAG9az2NRoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgT4rIAG9z55aCyNAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEDXCgzs2uGMRoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBoReCjZ1ppUE2AAAEC9SLgDuj1cqbMkwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAr0sIAG9l0+AwxMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKBeBAbWy0TNkwABAgQIECDQTKAfDQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAOBSZ+9lBx1svU4exNmQABAgRCwB3QXQYECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUJWABPSqmHQiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQnorgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqEpAAnpVTDoRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAgAR01wABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIVCUgAb0qJp0IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAQAK6a4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEqhKQgF4Vk04ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgIAHdNUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECVQlIQK+KSScCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQkIDuGiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBqgQkoFfFpBMBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQISEB3DRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAVQIS0Kti0okAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEJKC7BggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgKgEJ6FUx6USAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECEtBdAwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQlYAE9KqYdCJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABCeiuAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoSkACelVMOhEgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQICABHTXAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhUJSABvSomnQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBAArprgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSqEpCAXhWTTgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAgAd01QIAAAQIECNSVQL9+/S7MUVeTNlkCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAj0EYGBfWQdlkGAAAECBAg0jsCiS0WJ5W7ROEu2UgIECBAgQKAkcPM/7kiP3f5U2vPvO6X+/fun68+6Nb3w8Mtpt1N3TPEhNVB1KjB58uTCzPM5XL3/Jk2ruGniv7r9vOZju3bq9MIxbQIECBAgQIAAAQIECBAgQIAAAQIE6lNgjqVL8/7il8P1uQqzJlCNgD9eVaOkT10KSECvy9Nm0gQIECBAgAABAgQIECBAoDEFjtrm5MLCbxxye/rq9xZMrzzxWuH5ar9YJX1n+W81Jkqdr3r8uPFpozm3S2NHf95iJVt9dZd05uPHpJlmnbFFW1dUPHbHU+nQjY9Oe5+zS1p+/WW7YkhjECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOjzAv37/AotkAABAgQIECBAgAABAgQIEOiTAqXk87y4kSM+7JNrbIRFDX/y9YrJ53nt7772ftpg0Dbpibue6RaKwzc9Nn0y8tN08AZHpQsPv7zbjtMtkzcoAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgV4SkIDeS/AOS4AAAQIECBAgQIAAAQIECHRMYNKkSa3ucOyOp7fapqF2BSZPnpymm3G6die41yoHpdX7b5Jy/+4qQw66OO296sHdNbxxCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECPQZAQnofeZUWggBAgQIECBAgAABAgQIEOi7Aq88+Vra4Tt7trrAT0eNTrdccGer7Rp6T+CDuDv9R+9/3GIC7772Xvr5/Dumo7c/pUVbaxVrDNg0deWHDUa9N+W8Jk3qvgT31takngABAn1VYMzosemNF0akV59+vVs/QNRX/ayLAAECBAgQIECAAAECBAgQIECAAAECtSwgAb2Wz465ESBAgAABAgQIECBAgAABAunVZ95Iv1r8d+n1595qU+OvW5/UZrvGrhfIdyS/ccjtKX9AoFIZP2582uzLv0o/+9IvWzRf9Ocr0ofvfJSe+e8LLdraqrj+7Fvbaq6qbeKEiema026s2Pem84Y21f/3P8PSo7c/WbGfSgIECBBoW2D9WX6RtvvWbmmH7+4V77k3td1ZKwECBAgQIECAAAECBAgQIECAAAECBAjUlYAE9Lo6XSZLgAABAgQIECBAgAABAgQaT+A3y+xb9aInTpzY6l1Wc7J0LZcP3xmVVu+/SSHy3WKrKXlNzaN8n7bayvt25vnDtzwedzA/tfABgUpl9EefVaqe6rpstP1ie6QJ4ycUxsrrfPq+59Kffn5suv+6h9MhG/8tvfbsm60e57qzbk0n/uasiu0X/vny9PHIT9K+a/4p/XH9I9Pvf3xoxX658tkHXigc8/03P2i1T2l+bXbQSIAAgT4mUP5v7km7Vn7P7WPLthwCBAgQIECAAAECBAgQIECAAAECBAg0jMDAhlmphRIgQIAAAQIECBAgQIAAAQJ1J5AT2D4fM67qea81zWZpxllnSBcOPy3NPGimwn5jPh2T8t228526jxl6aPrKt75c9Xg92fG0vYY0HS7fLfbmSZe2OPzItz9Mrz3zZho/bkIa++nYdNgmxzT16d+/X1p1ixXTnmfsFInRL6axoz9Plx/3n5Tv9v3UPc+lhRZbIG13+OZp6TUWT9NMO02LsTtascsy+6QXHn6labc8tznmnT298+p76YHrH0kLLvrl1DwB/Y3n30oLfHP+lD8k8Mx9z6f33xrZ0UNO0f/1SDBfe7rNYy0DCx6lcuel9xUe3nPlA2nnY7dNG+2xbovjnLXfBS3qShVvvfh22niu7adov/3ie9Kqm63QYp/f/uCAQt0DkfT+15sPSl9acK6U98/X2rix49Ndl/03Lfmj76YXH3457X3Ob9IPfrJ00xjZ4d6rH0rLrbtUmna6qT8fLSanggABAr0k8PR/n0+7L/+HXjq6wxIgQIAAAQIECBAgQIAAAQIECBAgQIBATwj0K78TSU8c1DEIECBAgAABAp0V6Nev37ClogwbNqyzQ9iPAAECBAgQqHGBTz78NN3+z3vS4M2WLyTwHv/rMzs1440j8XjzAzZKu69wYHrzhRFNYwx5/sT05UXm69SY3bnTHisdWEgUL5WrRp2XZpp1xqbn+Xc4awzYtEumcNqwo9Ii3//qVI2V70LevCy12vfSj7ZYqXBH9NbKd1b41hRrbK1fV9Yfcf0f0rJrLtk05A7f3TPuMP9Ghw9R6QMB5QbtDZrHGDd2XLr65BvSmfv8o6l7pbHbG0s7AQIEalWgtfdG73W1esbMiwABAgQIECBAgAABAgQI9ILADct8cdC1HuqFgzskgR4V6NejR3MwAj0o0L8Hj+VQBAgQIECAAAECBAgQIECAAIF2Bf68+fHppF3PKtyFurPJ5/kglx9/bfrZl345RfJ5rt/2m7ulES+/0+48urrDcw++mN588X+J8OXjN08+z20bDNomPXjDI4U7hucyadKk8l06/XznpfdJLz02PP11m5PSPVc90OY4zw97KR226THp3dfeS0/c9Uz68+bHpVHvfdRin4dveaLN5PO8Q/kaWwzSDRUHrP3ndO6B/yyM/FrcNb0zyeeV3PbCHQABAABJREFUpnXrhXdVqm6z7t3X30/rzrjlFMnnbe6gkQABAnUm0Na/2zkx/dNRo+tsRaZLgAABAgQIECBAgAABAgQIECBAgAABApUEJKBXUlFHgAABAgQIECBAgAABAgQI9LhAvjP0P/9yZRp202PdfuytF9k13X/dw91+nNIBcsL2rsvtX0h+r1T+c8bNlarTAesckf59yo2FtkkTuy4BPY/36+//Pt3yjzvTIRv9rZCE/petTkgjXvkiMf/BGx9NOy25d8rzOnTjowt3ov/Tz49Le61yUBp6yb1pk3l2qDjfWq286Igr0htxF/zT9xrS6Snef+2U38Bz5C9O7PBYv1xsj4r73Hv1g+ns/S8sfChAIUCAQD0LXHtm5X/PSms6Y+/z63l55k6AAAECBAgQIECAAAECBAgQIECAAAECRYF++eubFQIECBAgQIBAvQj069dv2FJRhg2bMgGoXuZvngQIECBAgEDrAvnOqL1RNtxtnbTiRsulxVderEsPn+9cvtXCu6QvLTR3evre55rG3mDXtdMHI0amXU/6ZZpj3tnThPET0trTbd7msWecdYZ09lPHpc2/8us2+01t4xzzDkrbHb55OmaH06Z2qD65/6XvnBV37/0sPRAfXjhtzyHdssabJ12anrz7mfTJh6PTD9crfg1ttxzJoAQIEOh6gfb+Lf/a4gulMx49uusPbEQCBAgQIECAAAECBAgQIECgvgRuKP7uc62H6mveZkug4wL9Or6LPQjUh4AE9Po4T2ZJgAABAgQIFAUkoLsUCBAgQIBAfQvkD8LHv+cVF9Fe0lrFnbq48tCr9knLr79sl4x69PanphuH3N4lYxmkcQT+evNBad/VDyss+PL3zkmzzjlL4yzeSgkQqHuB9v4t/9KCc6ULh/uQU92faAsgQIAAAQIECBAgQIAAAQJTKyABfWoF7V8/ApX/KFY/8zdTAq0K9G+1RQMBAgQIECBAgAABAgQIECBAoAsFdl1uv7TGgE3Ta8++2YWjdu1QB29wVPp8zOdVDTri5XfS2M9a7yv5vCpGncoESsnnuXr0x5/xIUCAQN0ITJo0qd25jv98fLt9dCBAgAABAgQIECBAgAABAgQIECBAgACB2heQgF7758gMCRAgQIAAAQIECBAgQIBA3Qu8/9bI9NyDLxXWcfpeQ6ZYz7CbH0sP3vBIzazxJzNt1e5cXn3mjbT1IrumXy62R8W++U7vCoGpFdj667umfDfhS4+5ps0PO0ztcexPgACBnhKYPMm/jz1l7TgECBAgQIAAAQIECBAgQIAAAQIECBDoTgEJ6N2pa2wCBAgQIECAAAECBAgQIECgIHDbhXc1Sbz02KuFxy8++ko6aIO/pv3WPDwdsM4RNSX14iOvVJzPFSdcm/ZZ/bC0w3f2LLS/+9r76fqzb23Rd/Ov7NSiTgWBzgqc+fvz03ozb5U+/uCTzg5hPwIECNSEgA9o1cRpMAkCBAgk78cuAgIECBAgQIAAAQIECBAgQGBqBSSgT62g/QkQIECAAAECBAgQIECAAIF2BWaefeamPiNHfJgevPHRtPNS+6T7/v1Qu/v2Roedl94njfl0TItDn7bnkPTIrU9MUX/sjqcX7lJ95NYnpg/fGZVGf/xZ+uCtD1vsq4LA1ApsPPf26Y5L75vaYTq8/63xAZLDNj2mcJ2XYuLEiR0exw4ECBDwBSGuAQIECPS+QP6ZZauv7pIuOerq3p+MGRAgQIAAAQIECBAgQIAAAQJ1K9DPJ9zr9tyZOAECBAgQaEiBfv36DVsqyrBhwxpy/RZNgAABAgTqVWCD2bdJoz/6rO6mf/OkS5vm/Nqzb6ZfLrZH3a3BhPueQPPrsrtXN2H8hLT2dJtXPMxxdx6Wvrvityu2qSRAoPEE8gdT1ppmszYXPsscM6cr3j+3zT4aCRAgQKB7BbZeZNc04uV3Cgfpyf+v7N5VGZ0AAQIECBAgQKDuBG5Y5ospr1WbN6mpO08TrmWBfrU8OXMjMDUC7oA+NXr2JUCAAAECBAgQIECAAAECBKoSqMfk8+YLy0m4ks+rOtU69TGBTz4c3eqK9lz5oFbbNBAgQKCSwORJkytVqyNAgACBHhIY9/n4puTzHjqkwxAgQIAAAQIECBAgQIAAAQJ9VEACeh89sZZFgAABAgQIECBAgAABAgQIdJ3AZcdc03WDGYlAHQns+N0962i2pkqAQK0LLL/BsrU+RfMjQIBAXQtMmjQp5W9uyttSeeP5t1L+QG0u686wxRTr803ZdX26TZ4AAQIECBAgQIAAAQIECPSqwMBePbqDEyBAgAABAgQIECBAgAABAg0hMN/X5qnrO+2dfcBFDXGeLJJAucBH739SXjXF849HfpJmHjRT6t/ffS7ahNJIoBEEqri5+aL/941GkLBGAgQI9JrA0dufmm4+/44Wx1/w219OZz91fIv6e69+MK2wwf+1qFdBgAABAgQIECBAgAABAgQIEGhPQAJ6e0LaCRAgQIAAAQIECBAgQIAAgQ4LvP/WyLTXygelsaPHppU2/kEa88mYDo9hBwIEKgu88cKItMA35qvc2EW1+a6ZR/7ixHZH23iu7VP/Af3TjeMvabevDgQIEEiTq8hSx0SAAAECnRaolHyeB3vtmTfTU/c+12LckSM+bFGnggABAgQIECBAgAABAgQIECBQjYBbE1WjpA8BAgQIECBAgAABAgQIECBQtcDECRPT5gvsVLjj+YfvfJT+feqNadR7H1e9fy11fOfV92ppOuZCoCCw3bd2S/lDHt1VLj7yyrTmwJ+n2/95T1WHmDRxUlX9dCJAgAABAgQIEOg+gTHx4d+2yh4rHtiiuZ9vsWlhooIAAQIECBAgQIAAAQIECBCoTkACenVOejWIQL9+/baNmJy3U7Pk2H9IcZyFqx0n+g/PUW1//SoLhOHgov0hlXuoJdC2QPH6Gdp2rylbO/Oa78j4ne0b85ojYmTEKdWMEf1+FzE+YtFq+utDgAABAgQIEGgukJPOf/N/+6XV+2+S1pp2sz6Dc8lRV8fNWt2ttc+c0D60kPwhj/x6ax77rXX4VK9w9Eej09kHXDTV4xiAAAECBAgQIECgZwUu/NNlHT5g//79OryPHQgQIECAAAECBAgQIECAAAECWUACuuuAAIGGEIiE2qERdZE1EvPsH/GziMsjXo8YGzE64pmIMyNWaH7S4nnpgxP5wxOl+DQevxFxS8RhEd9o7URH218jbo3IxxoTkROWH4k4OGLO1vYrm8PZ0bd07EWq2aee+sTaDimub3A9zTvmeljEDBFHVDnvU6PfuxFHV9lfNwIECBAgQIBAk8AB6x6Rnn/opT4ncs1pN6bNv7JTn1uXBfVNgWE3PVZISJ+aD03cdtHdncJp746bnRrUTgQI1JVANe89PtNVV6e0qsnmb4u5998Ppifvfqaq/joRINB9Am88/1bHB+8nAb3jaPYgQIAAAQIECBAgQIAAAQIEsoAEdNcBge4R2D+G/XbEm90zvFHbEHigaH9yG31qtimSnOeNyd0ZcWnE6hF5PSdG5MTg5yI2j7g7+v02tuXlsag4tBj5jtc3R8wf8ceIZ2Of4yMGlu8Uz/eMmKnY/4TYXhgxIeKQiMdjn6/EttUS7etF4/YRn7baqb4a8mt36w5OueZe83FeFow15Eypf8QfgKt6L4p+Y6J/vgbWjf2X76CB7gQIECBAgEADC+TEo4dvfrzPCnzw1od9dm0W1jcFbr3wrk4v7Ovf/2qn9p00cVKn9rMTAQIECNSXwNP/fb7wYacz9j6/MPGtvrpLOniDo9KeKx9UqP/DT6q9D0J9rdtsCdSDQP8B/uxbD+fJHAkQIECAAAECBAgQIECAQF8RqJSI2FfWZh0Eek0gkjhHxMFzKD0sEPafxSGf7eHDdsnhIuF3xhjohoglIi6O2CXWM0WmS/SZNer3jsjb8vJo9D+kvDL2WTXqhkTsHjF9xK/L+swa+42tsN+fo+6AiJxcvUt5e34eY88dm79HXBKRk+dXqdSvnurCosPXT42+5nPyef53fkgH/S+I/vkvhfmc39vBfXUnQIAAAQIEGlTggk581XuDUlk2gR4ReOD6h9NqW63cqWPFz3md2i9uu965/exFgAABAnUlsPvyfyjM97JjrylEeXngukfSxIkT04ABA8qbPCdAoJsFRr49qpuPYHgCBAgQIECAAAECBAgQIECAwP8EGu6j8PFHtP+LuCTizYjPI0ZE3BSxafmFkesi7oz4KGJMxBMR+0dMV6Hv8KjPMXPEcRGvF/d5NLYb5P6xHRhxQMQLEWMjXorYtcJYg6N+csQhEctE3BDxUcSHEZdHFO5GHNuvRVwc8V5Ent/tETlxtUWJ+vkiTonIcxxX3OeK2C5d3jnqto3Ix8/bVSOGRnwS8XHEtRH57sBVlejbZWuJsfJa87wq/gU16n9WbD+p+eSibo6Iv0Q8E5GdPoq4NWKNqhZR7BT9l47I/u9G5Gvn1YhTI+YrHyfqhkTkuS5cNpeo6rdrxFMR+RrI1+HJEbOVj9He89gnj5/PzfwR/4jI88rrGxaxRfn+Udf8XOTXQT6XIyOa5hmPp4vYLyLf9fqziHzO74po8frI40d9LrtHPB0xxXri+fAc5fMo7rd5tOXr9cPifvncHBhR6bVVWudc0X5mxIiI7J8NtysfP+qa1lk81sJRl/8KXkiMzo+bxdDm+0d9Z85xfh3+NiKbZf8pxiyfXzvP94z2/Bq+J2LL8uTzvG/UfRxxUDw8up2xmpqj/+3xZM2IcRG/ijku1XzfSsnnxfZ/FbffaONYZxbbftNGnzabYj7bFs9J3uY7b98bMToiXx+XRVQ8ftR35H1t2ui/W8TDxXHz9Z2v0asjVms+wXheuOZKdblfPD64+Dxft03XULM+Q4r1C+e6ePzD4vMrSn3Kt9Ger/t8Lc/RvC2erxlxXcT7xfb8b8XfIgaVj9Ha8+ibM0by6+P1OL8dSiKP/m/Ffvl2ifk9tdIHHVo7rHoCBAgQIECgAQWeuf+Fwp0ubzjntgZcvSUTqF2BqckFnzxpUqcWNmmSBPROwdmJQIMJxO8dGmzFjbnc5x54sTEXbtUEelngpUeH9/IMHJ4AAQIECBAgQIAAAQIECBBoJIGGSkCPRLod4+TmRLwNittjYnttxJcipri7b/Q9IuryHX1zsvVFESdH5IS+XH9jtE8T2/KS626OWCfi6oh/RHw9Iict/zi2ebx8nKERZ0XMHHFStP08tpXKslGZkwBzyXcYfiBio4icPL1o8fkCsT0/Iq8jJ9jeHG153KYSz78aTx6KyMd+KSKv+8aIdSNyoudPmjpP+SDX3xTxccTpEXkueW13xD5ztbJPa9VdsZZTi4Pnu/pWKr8qVpYSYnMS6EJRNyxiv4j3IvI6Suc1J/bna6LdUjTK1856EbdEHBvxXMTOEQ9F+8LtDvJFh+NjkxPkZ4/I87w4Yq2IPOa0ER0teZw8r+9FnBuRr4WvRVwYc/p9K4P9MOrzuZw+4pyI8yLyhxLy8fN18ZeIfC2fEpGv4W9GXBLt+dovL7nP8RGzReT1/DNijYj8OshjtCgxztlReVHEIhE5OTePMTLiTxH5nFT6ZoZB0XZPRJ77ZRF5nfNHnBP9t4ltW2VUNB4a8WqxU35ciiGlHafiHJ8QY+S5PxGRH+d5drb8qrjjn+KPcW3+xT/aP+/IQaJ/vqv3vyL6RWxe5b75es/l8Ur9w2zbqN8g4tcx/geV+nSwLr+/XRXxRkS2vC9i44j/xrG+1XyseP7VeN6R97UhxTHzdZmvnxMj7ozIr538GmyrHB+NdxQ7nBfb0vWTtxVLeOS55/eIn8Rc5yzvFHX/F3WLRlwTffP1XyhRf1BsbohYLuLaiDzPFyP2jrgn2meNbTXlO9FpvrxPNZ0r9Mn7TRexcoU2VQQIECBAgACBJoHdfngADQIEalCgs0nkeSmTJrb542irq50sAb1VGw0EGkVAcnmjnOn21/nR+5+030kPAgS6XGCBb+Y/m3SsdPbLbzp2FL0JECBAgAABAgQIECBAgACBvihQKdGzL64zJ/UtFgs7NeLjiJXil+FPNV9otC9Qeh6Pc5Lr/hGvR/xf9H07t0V9rrsyIidm/z6iPCE3/2bn4YjBpeTQ2Ccn8OYkx0sjXor4brSNim0e79jY5KTQ/SIuyXVlZZ14vlX0v7BUH/vk5N3tI+6NOCba/tys7Y/x+LCIX0bk5M1SOT0e5LkdWNY/e+S5nRfjLhRtnzbbJz/cIGLNqL+1VB/9cnJynm+ew1Gl+iq2U72WmEe+G30+bxvHdvd4/n6zeeVk1NUi7o36nAhcKjlZNCehbx71F5cqY/9B8XhoxInx+N/R9k6prXwb7TNH3ZCI/HrJ5/auUp9o2zceHxlxZkROvG61RN/lo3G3iHwd5OuqkHQa9X+Ize0ROVn01VzXgbJ49M3X1mYxXuEvxDFens+wiD/H48uj/uWy8fI8c8LwGc3ro2++vvOHGK6PWD/aJ+T2qM9Jtg9E7B+P/xP1+drL9SvFZueI5yOWi/pRxfoDYntLRL7mplhP7LNt1OVrJ7+O8h2+x+R9com2Q2JzcMRvIppfv7l5iYh87e8U+0zMFdH/uNg8HpHPQT7PFUtxXodE/8HRYaF4fkh5x2ibmnO8VIz3/Rj3lfJxO/I85vCV6L9gRHa/oyP7dqDv0Oi7VUROfm5RYg45yTlbzBaxTMSKEdk4X1NTlOi7UFTk83RBrP2q8vZOPl8v9lsvxvtPaf84zu7x+PiI/H7142bjVv2+FmPk9WwWkV8X+VotXEOlEu1zNn9e/jj6Hx99BkX9KhFD4vnQ8j6tPM/XZf53YvOIk8v6bFN83nTtxjFWjbpDI3Ly+jpxnFGlfaJt23h8brF9z1J9G9t87nJ5qI0+bTU9WGxcObZN56OtHbQRIECAAAECjSfw4bsfNd6irZhAnQgsuer3mmb6+ZjP03Qz5M+XVlf++5/8o1PHy6RO3jm940eyBwECBAj0lsALD5f/qrnyTM498J/ph+vlXy8qBAj0pED/Af07fLj43XOH97EDAQIECBAgQIAAAQIECBAgQCALdPw3EfXrlhNlcwJxvrPwFMnneUlR90azpeUE2VwOj/q3S/XxOCeG/i4iJ/ruUKov2+4R/T5vtk9OVs6JqbNH7Ns8oTAe59/W5rvMfi9+wTOgwnh3R5+m5PNieylZMf+lvzwp9PxinyVLY8W4C8TjnHD8WsQUCeMxdk4k/mfEHBEblfZptr04+txaVp8TrXOpmMBaYYxS1VSvpTjQabHNfzUtJW+Wxv9VPMi/JWtKqo61LxHPc8JoTsJuSj7POxTPw8HxcPqIjXNdG+Wn0ZYTVC+J/fL5bF6OiSfDI1aP4+Xk4bbKdsXGP8c4I0sd4/HYeJyTvztTciJtvq7yNVko8ThfbydGTBPxi2J1882j0WeK5PNiY77uJ0fsFe35Wi+UePxubP5UfNr8ui+dg7yeUcX23H9cPG5tPbtHWx57++jXlHxe3Dcf44OILUtjNdt+Fo/zvJoSh+Px01GXXz/fDvtZKuzTkaqpOcdHxVyy+dSW+YoDfBDj5WuiO8qbxUHnbmXwvaM+vy72iMgJzDdErBHzea95//DO/3bk96JPI3Zr3jaVj2+LY5UnO58cY74U8aM47kJ5/E68r+XrOr8/5PfmptdKaa5xzHzddUf5R/F4pddK4Rgx/2ljkxPi82vr+mYHLlnuGHMa1aw+v66GxPNHIyq9Ppp3LT0uvR+NqNRYRV3p37723teqGEoXAgQIECBAoK8KbPGVnfrq0qyLQN0LnLDzmWn0x5+ly469Jv1kpq3Szf+4o+o1XXLU1VX3bd7RHdA7xWYnAgQI1JXALsvsW9V8p5sh//pLIUCgpwUW/PaXe/qQjkeAAAECBAgQIECAAAECBAg0sEBOyG6U8oPiQpsn+7W29qWKDbeVd4gkwOcjeTAnq381toPKkgRHxfOcKFle3sr9I4aVN8TznBCak8/nLT5u3qXSnWvzWLnkJOKmZNxiXSm5NCedl8r3iw/uiv7jm9WXHuY1bhWR+5US2EttlY7/erFx9gpjtVVVaayOriWPn+d4ZEROOD8mV8R5yInW20Z8GPGvXFcsPyxuZ4s+hzSrLz0sJeF+u0Jb86q2rocJMfad0XnhiGz4Whtjlcap9FffnNjelPTdxhjlTa/Fea2U/Dw0OuZE4tL5b77fA+WDxBpyAvciEW/GeM+Wt8fz0muh+Xilx3dX6P/fqJtiPXGMGaNuiYj3I/aI5xV2KyQIVzofL8S8Pq6wQ+l6HBRtn1Ror7Zqas5xC89qD1rWrwSSk6W7q7R5jDDO70P5NTVPbJaPyK+1R+L5T6Lt4WaT2jMerxKxbtTn112bJfYfHB1yNC/DY98hZXV3lD3PidcTY/98jX09Il9zrxa3uWtV72v52okxron+60U8Go8vz/tG3B9t+cMN3VJi7DfiWLfG4PkDKovF8/yhiVzyPOaIOC7qmr9O8ntWfp/eJPpv8kXXKf6b/3I3d7TNGft9UKG9edWcxSftnp9WxhlZrJ+rlXbVBAgQIECAQIMLTJwwMU0YX/4jaYOjWD6BGhPYYNA2TTM6apuT08gRo9LGe66bBk7T+q8DH7+z9GNLxxcTP6d0fCd7ECDQcALeKxrjlK/2i/yrQ4UAgZ4WWGq1xdMt/8h/slIIECBAgAABAgQIECBAgAABAt0v0PpfnLr/2D19hEHFA5aStNs6/mzFxtbuHJvr811hc79RzQb6qJVBCwmG8cv1Su2l5MOcRF1e2urfoi0nMkZiYh6j+VjVrCXvM6j84PF8VHlds2MMKG9r53mL+Ub/0tpbtLWylmz4Sazxgtj317FdNZ7fHo9/GpETZ4+P52ObzaOUgLl61OVorczcWkOxfmoMmw9dGued8uPFvHOSbXsJpeW75ectxip2erts7s33LbVVmltb13zuP6jZTh1dz+yxb75Ac+L/wc0PXsXjUa30KV1DHb0ey4ebmnNcybN8/Gqelz6QMVdcC9OXXcvV7F9Nn/mLnaa4o3n5jnHsfF1dGfPISefPR5wf8d3cL+q+EZs/R5wb/a7LdVWUwdGn/JzfEXVDyvat9nruzPn6eRwr36Jpi4hDi8cdG+u5LB7vXVxz2XS65OmQGCW//2wTUbpFVH6cy3nFbWmT37Pyv8vlVmXdUn7Pau/9Ykxxp+nLd67y+QzFfqVxqtzti27h+rN4tErEkhFLRMwScWE4b9WhgXQmQIAAAQIEalbgnqu66nOYNbtEEyPQ5wTO2u+C9PAtj6W/3nRQq2v73eD2fhxpddc0adLk1hu1ECDQEAI+h1I7p3nSpEkpf2Bwmmkr/dmh++c5cJqp/XVt98/REQgQKApUvlkQHgIECBAgQIAAAQIECBAgQIBAuwL92+3RdzqMKi6lmu+fKyVEF+4GXKHMV6xrkThdoW9vV/WltZQsTys+2Km4zXdDz+XMUofitrT23SPpsV8bsV3ZfuVPu8qwNM485QeIZM38G/lSwnx5c1vPW4xV7Fy6ditdo5X+ItyZNZbuSN5iDq2sp3SMR9o4F4Xz1NaCu6mtM+svTaWSZ4enGet+PXZ6LSInIK/c4QGq22HVYrf7q+kec3o1+uXb330nzmnpTtjfiefTRWwXdZObR9TlhONcXijWb5CfxDiHVDjng7/oOsV/W1xLxdby67nD5yuOP6Y4j2/GmPkDRDkR+u7iNiehd1e5MgbOr5Wt8usiIn8AY+2Ix2I+j5UdNK/rwwpW5e9f+by0V94tdujM+0retbRfaZz2jlfefmBU7BqxZMSb5Y2eEyBAgAABAvUvcPeVVf0vZf0v1AoI9DGBh295otUVvfdGe59zbXXXQsNkCehtA2klQIBADwps/+090jrTb5E+H/N5Dx7VoQgQqEeBAQMa6U/F9XiGzJkAAQIECBAgQIAAAQIECNSuQCP9VuG/xdOQE//aK48UOwwu7xjJg4tE3QIRr0SS4Kjy9hp8XlrLijH3Sne8LyWk5jsd10UJ98djovdEbBhrWi62q0XcGfXPlC2gdM5XmsqFtXU9ZNMVi+O3Z1hqLyXpNp9WnmOl89Pe1BcMg4UrdBpcrCvNvUKX/1WF3Sfx7KWIL8d4+Q7X5aXSddJ0bZV3juc/iJhiPXGMT6PuqYiczDxHhX26u2piPkAcu9Ltd7rqHE/tGkofojgw5tnm+3O05yTwqkv0XzQ6bxIxOeKiqndMqXTX9IJflOERZ7cSbxf7XFpsz307Ulq8Nornq/QaK52npmsv2iu9bipdr03ziGvx9YgLo2LNiBci8vtje4napfVXun5aXWMcZ0w0/isiO+b3qi0j8pzPq7BTfs+aPeaSk/yntuT3yVzyee9MKe33aGd2jn32jMjJ/rNG7NzJMexGgAABAgQI1LDA7f/MP5IpBAj0JYG9f3TIVC1nctxtVyFAgEC7Avk3U0q3C7z5wojCMX4y01bpF1/bJY145Z1uP2bzA7gbfo9yOxiBqRKY72tfmqr97UyAAAECBAgQIECAAAECBAg0rkCbCY59jOW0WM+EiD9Gct9i5WuLupxUXirnFB/kJNC5S5XFRMij43l2ywmYNV8i+fGNmOTNEQtH7NF8wrGenLy9RcSHEVc2b6uDx/l8ThtxeUS/iNPL5xxrfyjq7orYKNa6fXl7fh7134to77drV0XXkRGbR9+cWN287BFPvhZxSxzvtbK28qdDihV/iHGaErDj8fRR/5fyzlU+z8mwf40xml7L8firUbdbRL7eL6hynNwtX/fZ8m8xRlOSbTyeK+r+WByn9NrIT88v1uX1zFZ8nE3zeTmi9Lxse2w8z+3nRL9B5X2ibvaIpcrru+j5B8VxFqww3lVR1xXneIqhYy1DI/JdwgdXOGalquOi8rGIlSLOb8Vo5qg/KNr3rjRApbronxO7b4jI9qfFtZqPUSjRtmjEvKXnzer7R/2f43l+fdwb++T3iXw380cjdqgU0fxccf8Diu2PlsarcvujOOZPyvruGs+/HnF7jPlqbotth97XYsy5I/L7XXmZKSpmicivlXHljWXP27p+2tk1DSl22Dq2OfLxcgJ8ecnnP5e/x3xLif9NfaJupojy96DyMUrP83tfTpqv2D/GmS0in/v5WhmgtN/trbS3WR3nKJ+vFyImt9lRIwECBAgQIECAAAECNSPwychPa2YuJkKAAAECXSfw9vD30pA/Xtx1AxqJAIHaFejMb2P75T8LKQQIECBAgAABAgQIECBAgACBjgvku7A2RIkcuKcj0W6XWOzpEY/E46tjm+98m+96u0zEJxGrZozoe2+0HxUP94l4Mh5fFtvREfnu6d+NuDvibxH1Un4dE823p8uJxWvENidmfyVik4hJEdvFmvP666nkOyznZM0vR7wfcUUrk88J9rdFnB1r3y22+XviR0UsELF4RD6fP4x4N6JiCZtPY9+cwJ6PeUc8ztucbL50RPZ8O2Knijs3q4xx7ol9T4qq30aUrqvx8finETm594vb0rQ30JTtj8fTnFg7LMa+KbazRfw8YlDEPnHMl6bs3uazo6M1X+N5Po/FeNfFdsaIfJ3kJOSjYrx87RdKPM4WZ8bDX0U8FY8vj21ez3oRH0W8FZGvr6YS++TE8+yWX4svxeMbY5stc0L+VyNWjjg3Il+zXV1ujQHzWq4orm1MPH415vSPrjrHFSZc+mBATjhut8Q8Pou5rRUd83vOlhHrxfObY/tiRB5rkYgfR8wasWtEeVky+h9SrJwutvNE5OtjsYh8Lo6NyO9rzUs+Xn5vuDO2+Xr5ICLvt0rE1yLy9b1jRE+Ua+IgV8ZcroxtXvMSEetEjIzI10zz0pH3tfw+8d8Y95nYPhzxekQ2/EnEvBEnhn1774G3R79s+JcYJ79v5Ndsfh0cnrdtleiTX/t5Pfn6mybimqhr8Z4TdbdGv/2i/S8RL8Tj/Bp8JWLmiIUi8jnJr8F8ztosMdZHsX++5gfHdvZ4Xphvs7JhPD434ryIbZs3RP98reXr7LnY78nmbR4TIECAAAECBLLAcb86HQQBAn1QoH9/yUd98LRaEoGeFfA59J717sDRbrvo7rT/Bbt3YA9dCRBoGAHv3Q1zqi2UAAECBAgQIECAAAECBAh0tUDDJKBnuEiky3eVzcl0e0cMjtgg4v2InMR7VkRTib77Rt9HoiIneW4dkZMGc3LmgRHHRPu4//Wu7Ucx15djLcsU556TOQdHfBxxQ8Sfo/3B2l5By9ll/1jThdGyR8SQeP55y15f3Ck5+uWE599GbByRk3oHROSk2qcjTop4IqLNEuNfHeOsEJ0OiFgzYraIPEbOvPhTtOdk62pK/i3/8xG/idgpIif7XhmRx32smgHK+uSk0pw0flTEdhE5qTav6+iY00UdGa9ounrss1fEFhHZbEJEntce0f7PCuPtHHXPRuS1/Dqi+XreiOf5NTNFiXF+E5bXF/uvFttBETnB+LWIv0VcMMUOXfckv8YXitgsIidh5/e/OyL+kQ/Rhec4D5fvLJ7/cv+diOER/y1UVlFiHm/HritH13y9bh7xg4ifROTk52x0acQ50e/e2JaXJaIiRy6fReTrI5+fyyJyov2LXzRN8d9b4tmZEfn6zvsOihgdka/TbJOTs/P56YlyRRwkz+UPEetGjI/IdfvHHPJ8mko878j72vDY8eCIwRGrRswVkdf0XMR+ERdHtFnieM/EedkmOuV/P3aJmL64w+Ft7vi/xvPi4Z+KT/PjiiWOk7/R4J5o3C1ixYifRnwU8WZEtunI6/rU6L9GRL7mT4uotuTX5Zcj9qx2h+7qFxbDWhl70VbqVRMgQIAAAQI9IHDdWflzbgoBAvUqMPrjz9JMs87YYvofvf9Ji7qOVMhd6oiWvgQaVyB+99G4i+8DK59jvtnTyBH5V45tF+e5bR+tBAgQIECAAAECBAgQIECAAAECBPqCQD+/COwLp7Ex1xCJiUNj5TlR91txHb/QaAqx/vzXmjti7YNrbe0xt2/EnHLC8MUxv5xE3XAlDBaPRT8W8ZswyInASisCYbVtNJ0bkb+NYUgr3VR3UCBc84dt8gdsxkV8P2yr+gtv7Hd59F8l4uuxS05+n6oS4w2OAW6PuDDG26ojg8W+rSagL7XUUjMOG9Zac0eOoi8BAgQIECDQUYHV+2/S0V30J0CghgS+t9K307F3HNZiRlP72r7glVPTPAvN3WJcFQQINI7AuLHj0rozbtnmgnc5fru04W7rtNlH49QLVHpPv2H8xWnAgPzros6V1597M23/7T2q2nm3U3dM6/063xdBIUCgJwVuOm9o+tt2p3TokPn/C/P/HyoECBAgQIAAAQIEelzghmW+OORaD/X4oR2QQA8L9Ovh4zkcgR4T6N9jR3IgAl0oEEmJ/xfD5QTJGyOhseGSz7uQcqqGivMwb8QU7yPxPN9G7fjiwFdO1QHqe+d8fb4TcU59L8Ps61Ug3hsnxtzzHduXiNiomnXE63fJ6LdhxCFdkXxezTHb6hNzWLpSxD75zv4KAQIECBAg0AsCE8ZP6IWjOiQBAl0p8MRdz7QYrite21V+5rXFsVUQIECAQM8IrDXNZmnSpEmdPli1yeeFA1R3H4ROz8WOBAgQIECAAAECBAgQIECAAAECBAj0voAE9N4/B2bQAYFIjtw54vDYJSc259+WH9yB3XXteoE9YshX4pycF3FkxJB4/lxEvo3R9RGXdv0h62PE+MP7SRHzRoytjxmbZV8UiOvvuljX7hHTV7m++aLfHyNOr7K/bgQIECBAgECDCdx8/h0NtmLLJdAYAqftOaQxFmqVBAh0q4APonQrb5cMvum8O3RqnOvPvrVT+9mJAAECBAgQIECAAAECBAgQIECAAIG+KzCw7y7NyvqowL6xrgUiXo74RfxR44E+us56WdbNMdF8d+X8fapzROTbIT4fcWLE8XF+JtfLQsyTQF8ViJdhfj1WVaJv/uBIDoUAAQIECBAgUFHgwRseqVivkgCB+hZ48IZH63sBZk+AQN0I+HVhz5yquFFI3IS85a9mP3r/kw5PIN81/dgd3augw3B2INALApVe9+1NozP7tDem9p4XGD9ufJpm2ml6/sCOSIAAAQIECBAgQIAAAQINLeAO6A19+utv8fGLsIUjBkZ8M+Ki+ltB18041t8vYnDXjdjxkeL4t0asHTFfxHQRM0V8P+JvEeM7PqI9GlEgrpUhEfl6HtKI67dmAgQIECBAgEA9CYz/PH/mVCFAoK8JDP758lO9pPiZbqrHMAABAgQIdI1AW+/JOUmxI+WmIUM70r3Q1z8JHSazAwECBDot8M6r76V1Z9gyHf/rMzs9hh0JECBAgAABAgQIECBAgEBnBCSgd0bNPgQIECBAgAABAgQIECBAoAEF5llo7gZctSUT6HsCzZMPc5LiP/9yZd9bpBURINDjApKOe5y8Uwfc4iu/Ltwdva0k9eYDn3/ovzp1HDsRIFAfAtW+F9THahpzlvmDQvk8Xntm/tJihQABAgQIECBAgAABAgQI9JzAwJ47lCMRIECAAAECjSAQX/O8QawzRy7zFrc/jPohxcfvxy/E9y4+tiFAgAABAgTqSGCuBeaso9maKgECrQmMePndtOCiXy4033PVA611U0+AAIGuF/BlCV1v2sERR733cdpu0d3TwGkGpL8/cWyK39e0OcJ7r3/QZnulRgmtlVTUEeh+AR8E6n7jWjzCNNNPU4vTMicCBAgQIECAAAECBAgQaAABd0BvgJNsiQRqQSAnnkZMjli4FubT2TnE/A8prmNwZ8ewH4EGEFgy1rhNMdYsrvdrzep+1gAGlkiAAAECBPqkwMgRH/bJdVkUgUYTOOeAC5uW/NANj3bN8iWVdo2jUQgQINADAm++MCK9+vQb6ZFbn2jzaJcde02b7RoJECBAoPcF+vdv+4NEvT9DMyBAgAABAgQIECBAgACBviogAb2vntmpWFck1y4acVLEkxEfRYyLeCvi2ohfRkzffPh4npOKu+TPjDHOYhH/ing3YmzEcxGHRszQ2pKi7ScRQyM+ivg04v6InPhYsUTbgIg9Ih6PGBMxMuK6iOUr7RD1q0ccE3FrsW9e792V+pa5VL2WGG9wRMGxlTiy0vGi7xIRl0S8HZHP0+sRf4/44jZmlXbqQ3XFtWazzyIG9aGlVbWUWPO2EeXXzOdR92rEhRFLVBoo6ktJ9HnfXVrpUxr78ErtuS72nTFiVEQe56LW+qlvPIG4y9UhEf3aiIUbT8WKCRAgQIBA3xC48sTr+sZCrIJAgwvcc9WDTQKffTqmwTUsnwABAo0rsO8af0qn7zUkHb/TGSnftTzHjUNuT9t8Y9c04uV30hl7n9+4OFZOoFEEuuSve42CVZvr7D+gb/y5f+xnn6cTdj4zvfbsm7UJbVYECBAgQIAAAQIECBAg0EJgYIsaFQ0tEEmkBwXAwRH5txX/jTgv4tOIeSIGR5wVsXPEMhFdWuLYy8WAt0Xk74q7LOL1iB9F5Dn9ONp/HL8A/7z5QaNu13h+UkT+HtALIsZF5DvrDom270X/vcv659sAXFzs81xsT46YI+LnEXfGPhvHPlc33yce/ybipxFjI16MmL2svcXTzqylOMgdsR3aYsCU7i6vi2OsE3VXRmSvfCua5yO+FfHLiPWjfYVYS55vnyyxvlliYZtF5F+P5g8obBWRz2cjlsdi0VcVFz5rbFeI2CJi4+Lr5p42UHIy+gVxrXzcRp/WmvLrZraIfA42inHmjHE6/p28rY2ungABAgQIECBAgAABAgS6XWDC+IldcoyctKgQINDYAtW8D1TTp7EVe371lx9/beGgX1po7nTp0f9On44aXXi+9SL5V++dK85z59zsRWCqBfz/2FQT1uMAvZmAPmH8hDRwmoFTsI2JD7heccJ1adXNVkjzf33eKdrGjxufJk+anO649L607FpLpkFzz5aevu+59J8zb043n5f/RJrSf86Ix5MurcdTYc4ECBAgQIAAAQIECBBoOIEpfyJsuOVbcHOBSB49IJ4fGpETvzeJXxLfXy4UfX4Sdb8rr5/a5zHugBjj3IgZI34ax/53HjPqcyL8vyI2jtgzoulO4NG2cDw/OmJkxDKxz/DY5n0Oi02+ldfv4vHlUX9fri+WnLCcE9TvjcgJ7TmpPO9zemxykne+e/htUf9JsX/e/DXiDxHPRnwl4pVmbS0edmYtzQYZGsc+pMWgZRVxjOmj6uyIaSNy0vwVpS7Rtkk8zmZnRQwu1ffBbU6wnjniuIj8IYEdIxo1Af3R8uumeE3vFCaHR6zayvnPH1BYJGLfiHyNd7T8KnaYFHFMxO8jtok4tqOD6E+AAAECBAgQIFA/AvN+9Uvp7VferZ8JmykBAu0KTJrQNQno7R5IBwIECBDoEYH4PWGnjnPugf/s1H52IkCAQF8QePaBF9Jftz4p/e6sndN3V/x2XS2pf//euQP6UdudXEga/+cbZ6S55p8jPf3f59P7b3yQ/rTpF38mGvLHi9t1XGPbwemmIUPb7acDAQIECBAgQIAAAQIECNSmQO/8RFqbFg09q0hWXTgADokYH7FO/JK6RfJ5Bor6/8Rmrfy4i8sqMV7+jc6dcYxC8nku8Tgnt+5TfPrrmGe/Ultst4+YLuLk6De8VB+PP4zHR5T2adY/P9y5+PzA6FdIPs8lHueE9Usi5o7ICepNJdrui3gqotq/yHZmLc0PWc3j5aPTvBEPxbyaks/zjvE83xZgWMQqwfW9agaLfvmO8ZMjvhaxV8SzEWMj3og4LiLfVbtFifqlIy6PeDfi84hXI06NmK9F57KK6LNoRD7mba31jbYnIsZH5LWWl5xwnq+PnICer8vFo99y5Z3aeh79BxfnkO8C/sOIWyI+ivgk4saIZdrav9QW/RaOyGsZUql/1A/N7c3b4nku20TcG/FeRPZ+PSIfN99ZfGpL/oBCLsu2MdBJ0fZWxJ5xzAXa6NeiKfp/Nyp/EHFrRP6QxriIfE46VGKcXKp2yPOMODni5Yh8zX0Q8e+IiuuM+nkizo54J2JMxKMR+XhN575DE9aZAAECBAgQINDgAvlOZQoBAn1LYNznE/rWgqyGAAECBHpfwP8y9v45MAMCVQrE35Sq7Nl3u73+3Jvptz84IL3x/Ii058oH1d1Cm98B/d5/5z939kwp3bF88wV2ShvPvX3affk/NCWfVzsDyefVSulHgAABAgQIECBAgACB2hSQgF6b56U3ZrVdHHSaiHzH8CfbmkC0f95We1ttkfSZE31zou4hZf1+VHx+Q/n+cbyXo+75iIUivtasvdV9os/1xX6lPimOmZPVc+L2ZxF3NRun9LDFPhX6VFPV6rzaWEtp3EVinrtGHBCxfcQ3WjlgKSE721QqpfofV2psoy4nc/8x4o6IEyLej9gj4raYy/TN94vnP4nn+U7y60XcEnFsxHMROcn/oWhfOLatlrB4Nhpvj1g1+n6zvGPU5XOVk5yvjr5vN2+Ptu/H86Ujbo2212M7pNie78jdmZIT14dG5Gv7lIh8LWS7u+JYK3VmwCr2+XP0GRKRz2W+Y332y45fjtgkYmpL6cMa+UMlrZX8Wsjne4aIPJ+OlJL1kDgHH8SO+UMA+UMFHfWq2iHGXiqO8WjELhH5WssJ9NdErBxxd7SvE9umEs/njCf5Gs0fVsnvIcdHPBpxesSeEQoBAgQIECBAgEAHBSZNzJ8BVQgQ6EsCCy6afwyd+hI/G079IEYgQIAAgakW8H481YQGIFC3Al7/HT91Yz4dk7b/9h5T7Pjcgy92fKBe3KN5AvrBGxzVKzP5+INPuvS4Z+x9fnrjhRFdOqbBCBAgQIAAAQIECBAgQKDrBQZ2/ZBGrFOBFYvzzncz7o3yreJBc5JopfJCVOYk5RwvFTu0uk/8km1EJJ+Ojn75bskzxvOcaLtIxICIl+N5pdt75WPk0iIZulhf7abVeRUHqLSW0thbxoMcTSXmf3k82THm/GGz6pwYnstXm9U1f1hK1F+0lfbWqleIhiXjWK/mDnHs/WNzacRGEb+P+FOxfubYDonI7yGDo/9duT6X2Gff2BwZcWbEGsXq1janRsOqETmZee+yTrkulzPK6vPTnYp15xa3OWH8nYifx/H3jPl8XGGftqrWisbfxn4nlzrFOD+Nx1dFnBOPvxVtXZ1pk9fwZsR3i9dn0/zieHM1Pen8gx2Lu97dzhBDon33iK3iuMfFXB5tp38+x/nDCFtFfBRxZbH/kNjm6ySft6brodjW1qYqhzhmvtZyon6+9laNed5RGjTa5o/HD0bkO50vHG35gwS5/CUivxaOj7qmhPPok8/zfcU+NgQIECBAgAABAh0QmCgBvQNauhKobYH4OSn/fJc+Htm1ySK1vWqzI0CgWwWq+CBKfu9RCBAgQKB2BBr5ffm6v9+Sjtup5Z+gdl1u/3TzpPynsfoo/fuX7kf0xXw/H/N5mm6G6epj8q3M8rJjr0m3/OOOdOk7Z7fSQzUBAgQIECBAgAABAgQI1IJA/1qYhDnUhMB8xVm80c2zyYmf345oSvQtHm+24jYntFYqpfpBzRqr3afUr9r+zY9RaS7t1XXmOO/FoPtFfC9iloi5I9aOeCRi44hr4o/CzV+v90TdhxHLRv1PY9tU4nlOAl66WDF787YqHp8Qv2x8tdQvHuek65x4nrfbN9s/H3POiEuiT3my8TFRPzxi9ZjLgs32qfTwqqh8K2Lb6Nv027B4PCjqNo3IHzbIdwVvKtE2UzzZPKIp+TnmkD9QcGFEbtuiWfdqH74YHXMyfFOJMa+OJ3dELBKxUvO2Lnw8PsaaWD5eHLv0AYPyptaeLxkuhxTj2NjmZOwdIrLt71rbKdcXz/E+8TBfX0e31bdZWz43+drK539Msb70IYCfxfE7et1V47BuHOfrESfFMfN5aSrxPK8z39Yj303+x7kh5jBNbLaMyJkUh+S6Uon+D8XjfL0oBAgQIECAAAECHRQYOSL/GKIQINAXBMaPyz9Kx6d8Z8s/SisECBAgQKDrBOL3b103mJEIECDQDQLDbn6sYvJ56VCr998kvf/mBx0+8iO3PZHyvkdufWLq6ruCtzaZ8g+K/2SmrdIpu5+TRr2X/4zWPeXd1zv6Z6yOz2PUex93fCd7ECBAgAABAgQIECBAgECPCuQ7yioEskC/IkO3/ma4mFjbmd9KdGZ+Hd2no/07e+W0OE64PBWD5SiVT+PBDZFEe29sH41YIWK9iKtzh+g/Otp+Gw/Pj7giHl8T23z3+G9GrB/xeMTiES2Sm6OurTJFYm/uGMd6OcZ/PR4uHNtB8XxUPF6qOMht5YNF+4Tod2fuH/H9iNfK+5SeF/ueFc8Pitg44qJi2y9iO0PEmdGn/JrcLOpnjTgjmsYW++fNuRF7Rfwq4vRm9dU8vCvGykn25WVoVKwSkdfRwqa8cwef5wTofA6fCq9Li+PfF/P4qIPj5O5LFKP5rtl9pRivVf9S5+hzY8zhpni+RmzXiefXtTOHHYvt2bxQYp983vOa8jnI5+/EXB91S8Zmg/y4WRkV/Y8vPq/W4YfF/gvFmIdMOVzh2TeKdfkDLnn+i0bMGJHPbSXTodG2TXEfGwIECBAgQIAAgSoEJk7o6I8XVQyqCwECvSYwedIXPwZP6qJvNmjx03uvrcyBCRAg0NgC8buwxgawegINLNCZl/+9Vz2Yvv+j7/U5tfxemH+GHTjNlH8Kz3VrTZv/zNR+2fwrv27q9LO91kvL/3TZ9L2V8p8gCn8TSUMvuTct9sNvpgHTDEiP3f5UWuBb86d9Vjus0H7rBXelTz8cnQ6/Zv/2DzSVPWYe1PIDpVeddH165YnX0pE3HpgGDBxQ+Oajrihn739huvivV3XFUMYgQIAAAQIECBAgQIAAgT4g0L8PrMESukYg30E4lwW6ZrgOj1JKEJ2tlT1zwnEuzRNJq93n47J9O3KMVqbTZnW186qUFDvFwPELrDz3UlL2ys0boy0n7q4acUNEbts9Iifh5qTmUgL2u833qeLxO630ebtYX7IrbUe00r9UP6iV9ubVZ8aTfNu1nZpV5iTycRFNCc5lbfnpkGZ1+Zd9T8bzYRHfj1+kLdO8rYrH1a67iqGq7rJn9NwjYnTEfhH5DuLvx9yvjsh3Xe9IOS/Wn397mN/T5434Q8RXIvKd83MSdjVl7+iUsw+Oin0GtLZDtOXfrq4Y8Wwc879l/Urnq5SgnpuXjDi4LPZotl+1DnMW99mkwnh5/C2K7TMXt6VrtL1z22wqHhIgQIAAAQIECLQl8Pbwjv540dZo2ggQ6G2BSZMmF6ZQ2vb2fByfAIHGEOhMcmRjyFglAQIEekfgqpPznya6tzx933Ppjkvv696DlI1+xJYnpLWn23yKu5iPGT226uTz8sleduw1aa9VDkp3X3l/4Q7n15x2Uzpii+PTVl/dJW2+wE7pyF+cmHb9v/ynnv+VB294tHyYbnk+zXSV7zf32NCnCgZrDNi0y47b08nn2frfp97YZfM3EAECBAgQIECAAAECBAh0rYAE9K71rOfR7i5O/se9tIjnisf9ZivHL93d+Plm7a3uE0my80W//JH/NyJJ9rPiPi/GNt+y72vRXum3MZWO0cp02qxudV7FvTp6nPeK+7W4hUGs7c6IdSPmiJgu4jsRp0T/HxT3ebDNmbZsnKdlVaEmJzXnUkqaL21L9cXmpk32b96/vL3pecz3zXiS7+C+ck5ujlg+Hn834spoK6290D/aFo/N/xWepHRfPJ/cPKJu6WJbTmDvSKl23a2NWbp7eqXrKu8zqHzHWNvEiBMiloi2fPx8B/grI9aPyHe/n658n/aex1i5vBNxRPQ9JiJ7Hd7efrk99nkiNudFfCdi+zb2KdkuWsE/j5HLd4vnMY87JKJfWSxcGj/qq3UoXXM/rTBe8/EPLY5d6t/euS1NxZYAAQIECBAgQKAdgXFjx7fTQzMBAvUkMLmUgN5Fd0Cvp7WbKwEC3SMgubx7XDs86hefL+rwbl25Q/z+riuHMxYBAt0s0N3fdrX7Cgemw39+bPrpbFunjz/4pJtXk9KbL45IQy++p3CcfBfznDQ95tMxaYtmdzTv7CQO3fjowq4n7Zq/3Lftkr9pKCdQl+KWC+5se4dmrZ+P+TydtueQlJP32yul/69vq9+T9zzbVnNVba8+/XpV/bq6UzXWXX1M4xEgQIAAAQIECBAgQIBAdQIS0KtzaoRe58YiczbBxpE4ulhbC+5MYmxb4xXbbitu1yrvG8f7WtTlxPRXI15u1t7qPtFn7bJxcyLs51F3b0S+I/RKzcYpPWyxT4U+1VS1Oq821tLWuKVk8uZrb7V/HGPOaNwwIv8W75pWO1ZuWKW8ujjnfDft4WE4qtj+SHE7uEL/nISd75Cdy8Pl7a08P7VYn5ObSwnOZ1ToW2obGm1ntxJjon7zmHfpTtgVhmlRtWL0r/R+OLjYs7TeFjsWKz4sbrPTFCXGnTUq8vXbagnXdyOuiNg0OuXr5+sROQl/asphsXNO4N815vDVKgc6MPp9FpH3bfGBhxgnJ8X/ImJSxDkRlc7BjcVjNb8LerGq7U07DqW7rVd67VYaOP82Na9lyZj3bBU6DK5Qp4oAAQIECBAgQKANgfGfS0Bvg0cTgboTyAkxuUye9MV2qhcg2XCqCQ1AgAABAgQIEJgqgU7+/9ha0242VYetdufPPhmTtvnGb6vt3tTvuQdfTDsuvlfKd/SuJoH96O1Lf3L6Yoiz978wrT/r1unTUaM7fOyu3OGvW5+U/1ZZ1ZD//MuV6YoTrk05eb+18sLDL6fNFvhVGnbz4611aaq/9Oh/pxydNcjz3uG7e7V7nO7qcNtFd3XX0MYlQIAAAQIECBAgQIAAgakQqJRwORXD2bVeBeIXB8Nj7odETBtxbSRsLlNpLVGfE8Svr9RWTV3sP1dEvnPyXGX974jnz0Tku2CvX2qLx/ka/Wvx+ekxz+a/mclJ8zmpPCfYLtxsn9nj8QGlfUr1xe1pxe3hsc/0zfZZNh7/PCIn7F5etk9Hn3Z4LTGXFYprneJYUbdVcV7jYvuv5o3RNkv5xKIuJ11fEJHbDguuj8v7tPN89xhjoVKf4pz+Fs/zecjepXJVPBgZkRO9SwnypbY94sHXIm6J479Wqmxne2u057vbbxORk7Cfj31vb75PHGeGeL5lxMS8jfYdKkW05fOXHTZvvn87j/Nd6Xdp3ieO99N4vkrEixFt/mYr5pGT/XPCcz6PTR/giMcDou7YiDz3phL100X8OKJfWf008XyOYl1Onu50Kc4pv3bymIdUM1Ds81b0OyZi3og9KuyzcdTNGXFj9P1lK/6bRHv+Le6msbzZKozRVNVBh6tjx5cifhP7rVNp3Kj/YUT+gEn+JW7OjrowIr8WDmneP/rk97d8LSkECBAgQIAAAQIdEBjz6dgO9NaVAIFaF5hUTDwvJaLX+nzNjwCBPiJQZeJdH1lt4y5jcuMu3coJEJhSYNzY/Oet/5WOJkDn5PNdl9s/DX/y9bT3jw5JG8+9fXrwxkfbZH7y7vznmtosawzYNJ26R/M/t30xzzGjx6YPRnyY/rLVCYU7pl94eP5T1xfljz89Mj1002Np+8X2SP849NJCMv4vvv6btMsy+6YP3vowXXvmze0u9t6rH0xn7vOPdNa++U+IHS9/3vy4ju/UhXv8ZasTu3A0QxEgQIAAAQIECBAgQIBAVwnkOxUrBAoCkbB5RCRm5mvi4IgH4/G9sX0o4tOIeSJWjsiJurmuRYn+Q1pU/q9ilxg/J9TuGpHHPzTikFJztE2M/beL57dFXBaPL4ttTl7+cUROFr0nYorfbsQ+r0S/30d9/q3DQ/H4ktjm32T9LGKBiGOiz32xbV4ujicbFfs8EvtcE49zQm1OPs/JwjvGPh833yH6rBjPdyjW5cTmXL7RfL2xz7bF+uzY4bXEvjlRtn+Mmc3fiMjJ8ctG/F/EhIidYtzhsW1eton+v4uKoREjIuaOWC8in6u/R+TE546W7Pxo0fKjeLxmxBIRwyKOKg0Wc/k0+mwfzy+NuCMe520+X0tHrBHxdsROpf7tbWO8yTHG6dGvNOczKuyTz9GgiGuie06Ubq2cFQ1bRfwqIjtUU26ITsfEHNaO7WMRi0Tk6yRn2ORE62puB/e36Ht2xD1Fj7zvqhHTROQxs2Op5IT0WyKGR9/7Y/tqRD7nq0d8O+LfccxnSp2nYntq7Juvka3iOH+NMZ+uYqx8nrNdNigvuT6XbFyxxDE+Ka5/2+iQz8MpFTt+UVm1Q4w7PsbN5+TGiPwhmXtj+2jEZxFficivl69FzFesi03hgyj5PWSP6J/fR+4utudr6bqIpg+75M4KAQIECBAgQIBA2wKj3sk/IigECPQVgVLi+djPPu+SJcXPbV0yjkEIECBAYOoEvB9PnZ+9CTSqQE563uC3a6dfH7tNGjAg/7ms82XixInp5cdeTV9bYqG035qHtxjooA3+mg67at8W9c0rPnz3o7TpvKU/zU3Z9ejtTkmXvFX5zz/3X/dwm+PWQuOVJ16XBk4zIG196M/T9DNOV7gr+pYL/jp98uHoitP77zXDUo5czj/0XxX7VFt57d9vSXucUfWf7wrDjh83Pt3xr/uqPUS39Zs4YWIaMHDqrs1um5yBCRAgQIAAAQIECBAg0KACAxt03ZbdikD8kuOwYvLoLtElJ8/mpPCcGPtBxKMRf424IKJS2aZSZbFuj9jmRNFWSxz7/jh2TiI9NGKNiHzn4pyYe1jEkdHe4i+iUXdS7DM82veO2Dqif0ROsj0w2s6L7RQl6nKi8+ZRmZNXcwL1byNyovCdEYdHc64vLzkRt3xtXyqr27b5Tp1Yy2mx/2oRK0TMFdEv4s2IIRHHx3iPxba8PBQVOUl5rYg5Iz6JeDDitOh/dXnnKp/vGf02jNgxYuGIfN5PiDgoxsxOTSUfIyzzfHOS75oRs0W8HXF6xJ+i/a3/9a7q0ZDodXTE+IgW5y7q8pxyaTX5OTfGce+IeT0fD5eJ7ffj+SPF/dra3B+N+Tr7U8SuEdk/fxjiD7F/Nm23RL9z4nh5v70i8vXyYUQ+D9nn8rIB8m8R829XV41YPmKDiHz+XorYOeKciKkuMacxMaW/xEAnRuS1bdzeoLFP/nDBwdEvn8emEnXfiCerRLwbcU3ztgqP829+t43I5+yUCu2lqg45xNwej3ksETtn459EbBeRPxwwIiKf5zzv9yMKJfq/H/3zNXpExHoRy0Q8F5GNh0dIQA8EhQABAgQIECBQrUBrfwyvdn/9CBCoLYGXHh2ell59ifTWS+/U1sTMhgCBuhWI38XU7dxNnAABAn1BYGrfh6866fr0rWUXSatttXKnOUa991HafIGd0oTxE9M3lv5aemHYyy3Guu/fD6XRH3+WZpp1xhZtpYrWks9z+8i3R6XjdzojvfzEq2mHI7dKi6+8WGG3/A0/B/4k/0mk9sulx1yTcsw4ywzps0/G9OiE84cNbhh/cdUfNLji+Ot6dH6tHeyFh19Oi/5f/lOVQoAAAQIECBAgQIAAAQK1IiABvVbORA3NI35BlZOac2J2VSX656Tbqkr0PSQ65qhYoj0nj29SsbGVytgnJ8O2lxDbtHf0nxBPjitGK6P+rzr6D4lnOTpUOrKW6JsT+3NUXWKf/0bndareobqOk2LcY6JrjnZL9M3J2Ru22zE6RN9tY5OjtZITi/MHCC6NvjnxfYoSdTmRuKoSfb9VVcdmnWKf++Jp/hBAmyX6HRIdcrQo0XZ2VOYoL4ObV0S/nGR/VDHK+1b9PMYZEp1ztFqiz0nRmKOptLWG3Cnaz4hNjub7vBBPqnqtx/75gxzt9u2MQ+yTE+D3K0bzKVZ8HP3zhyK2L2+MxPTB5XWeEyBAgAABAgQItC2Q7zamECDQdwRGvftxYTFjR0/xefO+s0ArIUCAAIFeE4jfyfXasR2YAIGpE3jv9RZ/nql6wMfvfDr9bnC+T8wXpVLyealtg0HbpJsnXVpx7GreQ/KdvHPJxyuNc9Q2J1ccr5Yrezr5vGTxp02OSYdcsU9VNKPibvS1UH77gwNavWZqYX7mQIAAAQIECBAgQIAAgUYUyMmeCgECBGpBoPSbrvr7DWEt6JkDAQIECBAgQIAAgW4WmDQxf/mMQoBAXxE4dY9zC0vJd6fsiiLXsCsUjUGg7wtUk1TY9xW6d4WNYpzvdDx+XL7PR+GGGt2LWmOjN9p6e4L/gxEfFq6jHA/d9Fh65cnXeuKwNXmMfFfx/Pp68MZH08SJHfv/xObJ59Us7p9/ubJitzUGbFqxvrXKfEfvJ+56Jj1w3cOtdVFfJnDPVfn+UtWVWeaYubqOPdCr9L4/tYcaEx/C7a3k/6mdu/0JECBAgAABAgQIECBQSwIS0GvpbJgLgQYTiLtQfy9i/4jLYulrR/wnfsF7f4MxWC4BAgQIECBAgACBuhBwB/S6OE0mSaBqgSVW/U6h78QuSkCv+sA6EiBAgECfF+juBOnhT72e1hz487TO9Fukt156O+Vk1ZyAetZ+F/TpZPT8/+N/2/6Uwnr3XeOw1Bf+//ydV99Loz8a3WOviU8+/DT9fd8L0hvPv1U4Zk5mvem8oWmzL/8qnXvgP9Mpu52T9l/r8PSrxX9XuKbGjR3XY3PrigN1xWcxhl58T/r5/L9KB6z957TWNJulUe9Vd/frWy64s8NLOOcPF7XYZ9znX3ywpEVDOxV7rXJQ+uTDnruW2plOXTTn99JqyneW7/CXDlczbKf6nLn3Pzq1X/lO68/yi/TT2bbu8IcsysfxnAABAgQIECBAgAABAo0uIAG90a8A6yfQuwJLx+GPiFg9In/X4na9Ox1HJ0CAAAECBAgQIECgNYG3X3m3tSb1BAjUocBsc85SmHVO/FIIECDQJQJdkfnYJRNp7EHq/TQ89+CL6fidzkjvvzWycCLzHZj/9PNjC8nAV518fSHhfMfv7dV0krf5xm+bHl9y1NWpM0mw9XLFrDXtZummIUML0334lidSfv7Uvc/16PS74sMFOeH8vIMvSY8NfSpt9dVd0gazb5v++59hU72Ojz/4pJCgf8C6R6Sbz7+jkKB/1UnXp+0X2yOdsff56dozb04bzbld+tffrk7bLbp74ZrKH2L423anFI6d78Z99Sk3TDGPdWfcsseNpxqiCwYY9e7/ks73We2wdkcc+9nn6a9bn9Ruv2o6rDvDFtV006cLBA7Z6G9VjdIVr/uqDlRFp/zvwKvPvFFFz+q6jBtTXx8yqW5VehEgQIAAAQIECBAgQKDnBAb23KEciQCBWhWIXx5tG3PL0aMljjskDpijV0ocf2gcuF+vHNxBe1XAue9VfgcnQIAAAQIE6lSgPBmjTpdh2gQIFAUmxJ3PJ4yf0GUetZSY0mWLMhABAl0uUO/J0V0OYsApBPK/Jbsut3+h7tq/39JCJ9+d+otU4RZNTRVHbXNyyjH/IvOms548Nk0z7TStd66BlnwH7jGfjE2zFj8Y1tEp7bHigem7Ky6a/nLDgWn6Gadrd/dHbnsizTxopjTXl+dI004/TZpptpkq7pOTt/sP6J/i20ub2nMy9ym7n5MWi7shPx2J77uf9qv0k53yvWWqL3ncnHCeywV/yl+M+kX54/pHFh7cOOGS1L9//8KHDu689L70i4M2ST/acqW0wDfma+pb6cGno0anjefevqnpwesfSUdte3LT89effbPSblXVZePL3j075Q/kLvL9r6YBAwdUtV9f6fTKE6+1u5S7r+j8F9vm/x8dOI0/V7eL3A0d3nxhRFWjTpo0uap+PdVph+/smW6elO9pNfXF/5dMvaERCBAgQIAAAQIECBBobAF3QG/s82/1BAgQIECAAAECBAgQIECAAAECDSgwbuy4NObTsQ24cksmQIAAge4W6ExCX04+X2PApl02tbdefLtwd+tPPvy0qjHfe+ODdNKuZxXusN6V5d5/P5hGvPxOYcicbH709qems/a7IJU+uLXzUvsUEqd3WnLvTh/7ybufTevNvFUk7+9XuGN8Tui9/7qH0+XH/Sddesw1hWPlu47nO37nu1nvssy+adP5diwkguc7V5fK+29+ULhr+HWR/J/vrp7Px54r/zENveSedMwOpxWSz3PJyee5nLDzmenDuFN2Nh5282NpswV+lf596o2FOeQ7kf/2hwcUxmte8rhtlTUH/jyNeu+jQvJ5Lv847NK03bd2S+M+H9/qbg9EsvmGc2zbantXNPzsS78sfDji4r9eNcVw2fXM35+fnrzn2TRp0qRWD1XpOsxrGjO6a/9frLc+EHjTeUNbXXt7DTedd0dTl672aO/Y2qsTmNzGtV3dCLXbq7deM7UrYmYECBAgQIAAAQIECBDomICPlHfMS28CBAgQIECAAAECBAgQIECAAAECdS8w8u1R6fNmSWd1vyALIECg1wU6k3Tc65PuixOowxORk7+3WPDX3XI2Nppzu3T208enV596PX1zma+neRaaO7372nvpqXufT4N/vny6+uQb0jJrLZl+E0nZn30yJl175i3pgldOSXPOP0fK87r/P8PS7PMOSgsttkD64K0P09eWWCjtv9af0/MPvVS48/jvzto5XXbsf+Lu49OmmeLO4j/f56dxZ/FpC2s5cZe/p2tOv6niui456uop6l9+/NW0zTd+m8574aS089L7pM8+HpP2GbJr+s8ZN6UVNvi/9KMtVqw4TvPK5x58Ka01TcsE75wg3Vr582bHpT/9e7/0/LCX0m+W3a9Ft5zcnqO1sum8O0zRlJP4c5TK+Yf8K213+Oat7V6xfpN5phwzd1p3hi0KfS967fQ09wJzpiEHXVw4PzcNGVpxjO6qHPLHi9NKG/8g3XX5f1N+XCo50T+XnY/bNm20+7pN9R+P/CRtPNcXd2bfbN8N0hN3P5OW+vHiaetDNk3bL7p7YQ3XfHpBeuiGR9Osc82SvrvCot019aka97W4g/yCi3654hg5gfeRW5+o2FZN5XG/Oj2ts8OPC11/OuvW1eyiTxcKPBUfKPlOfKtBW2W/NQ9vq7lX2p7+7/NpsR98s1PHbp50PrnG7u7eqQXZiQABAgQIECBAgAABAr0oIAG9F/EdmgABAgQIECBAgAABAgQIECBAgEBvCIx656M0/vMJXXfoOkx47LrFG4kAgWoF3Gm0Wqk679fGvwn57thP3PlMOuaXp6alV18i7XHGTt2WfF5S/OVie1QEPWKL41vU5zt2b/6V6pLhc2L2dpFE3LzkhOv5F5k35Tuwd6bkJPRSOWrbkwsPn77v+fT3fS/ozHDt7vPfSLBfc5qfp0kTW797d7uDtNHhoiOuKCTwn7LbF3dPb6NrVU3d9UGFqg5e7NTa9ZSbT9tzSFp1sxXS7PMMKvQ++bdnNw1dunv6U/c8V7ize6mUkuvz891P+1UaG3dF/97Ki6VvxQcmaqXkNd886X9zbj6v+655aKqnme8kP9NsMzV9M8BUD2iAqgVujrvXt5WAXv4tBlUP3M0dd1/+D61ek+0duvn/i7T1zQXtjaOdAAECBAgQIECAAAECBFKSgO4qIECAAAECBAgQIECAAAECBNoUePHRV9ps10iAQP0JjI27n4967+P6m7gZEyBAgEDNC1x27DVpk73XbzHPWy64M/1165Oa6q/9+y1pljlnadGv3is6m3zeW+vuruTz0nq6Kvm8t3w6etxN59uxo7s09T9h5zObHreW8N3m4G18+KPN/aai8eANjpqKvb/Y9ZHbnkwrbrjcVI9jgI4L5Pfh/EGg1spz8W0Pfa24A3pfO6PWQ4AAAQIECBAgQIBAbwr0782DOzYBAgQIECBAgAABAgQIECBQ+wIPXPdI7U/SDAkQ6JDA26+8m155/NUO7dNW517Id2prOtoIEKhRgRvPva1GZ9Z3plULd5kf+faoJtCb4u66q/ffJN179YNTJJ+XOlx85JV9B99KCHShwOdjPu/C0bpnqPffGtklAx+68dGpVu+03SULrPFBxo0d1+oMp51+mlbberNhuhmm7fzhJ/9v1/Gfj+/8OPYkQIAAAQIECBAgQIAAgSQB3UVAgAABAgQIECBAgAABAgQItCmQE4cUAgT6nsBxO53R9xZlRQQI9JpANYnPbzw/ouL8Jk2alIbd/Fga/dHoQntORMxJy81jyEEXF54f/+szUzXHqngglT0mcMru5xTO19+2O6VwzIM3nPq7JPfY5B2IQA0I/GSmrVL+xppqSn4P7e7y4TujWhzi6Xufa1HX2Yqjtj25s7vabyoFDv3Z0a2OMHlSs2ztVnv1fMPnY8alzn5Io/nr5fqz2v9g3F1X3J/efLHy/7/0/ModkQABAgQIECBAgAABArUlIAG9ts6H2RAgQIAAAQIECBAgQIAAgZoSyAleb77gj601dVJMhgABAgQI9DGBcw64KO235uFpg9m3Ta8/92Y68/f/aLHCCw+/vFB37Zk3pweue7hFe09V5MS1/P9HOSEt39X7yXueLRy6PCl+/Ljx6dkHXmhR393zrJVvpLjqpOu7e6nGJ9DnBR68vv1vorrwz5enNQf+PO263H5p7OjqEtY7A3fYJse02O1Pmx7boq6zFbdddHdnd7XfVAq09Y1nk2o0AT0vedf/2z915sMXzf+dPP/Qf7Wql/9dzx+kOiwS9Lf95m49/u95qxPTQIAAAQIECBAgQIAAgRoSGFhDczEVAgQIECBAgAABAgQIECBAoMYEqr3rXo1N23QIEOhhgfLEyx4+vMMRIFBHArdffE9adbMVCjP+7JMx6bJjrkmXHHV10wq2//Ye7a7mwPWOTH+96Y9pqdUWr9i39J7Ur1+/iu0drZwwfkL6+z4XpP4D+qfLjr2mzd2PGXpoGvHyO+no7U8t9Pv6kgunI288sPB46CX3ph9tvmKadc5Z2hxDIwECBLJATvo++PK904obLtcqyJA/Xlxoe+7BlwrRXeXJu7/4sE1p/PffGtldhzJuDQlM7oG763d2ucOfer3w4YvL3z8nzTpH9f+ulq9p3Nhxadrpp20xjfIP4l985FVp8/03bNFPBQECBAgQIECAAAECBBpZwB3QG/nsWzsBAgQIECBAgAABAgQIEGhHoFa/crudaWsmQKAKgfm/Pk8VvXQhQIBA1wocscXxhTuKXvf3W9JPZ9s6/eOwSzt1gH3X+FPaY6UvErsnTphYuHP6gev9Jb302PC0xoBNC3HxX69KU/thusfvfDqtPd3m6YoTrm03+Twv5HeDD25KPs/PX3p0eNpknh0Kccpu56SN594+Hf/rMzu1ZjsRINB4AodufHThPfPqU26YYvH52yByfU+WEa+803S4zRfYqScP7VjdLPDxB59UPEIt3wG9NOGN59q+4txbqyz/ppAbhwxtresU9ef84aKq+ulEgAABAgQIECBAgACBRhKQgN5IZ9taCRAgQIAAAQIECBAgQIBABwXK7w7Wwd11J0CghgXeeul/SUQ1PE1TI0CgTgQ6+k0Ix+10xlSv7Kl7niskYK417WYp3zn9/msfTr/+/u+bxj17/wvTejNvlcZ8OqZTx9rhu3sWEsq7upQSR//7n2FTDP35mM/TxIkT03/OuDm9+swbnTpsR89Dpw5iJwIEelzg5N+ene656oHCcSfFXal744MsW3991x5ftwP2jMDftj+l8oHKs7Ur9+r12rb+7dv7R4cU/l/hkdueKMzzln/cOcV8T9zl7xXnP37chBb1Lz/+aos6FQQIECBAgAABAgQIEGhkgYGNvHhrJ0CAAAECBAgQIECAAAECBNoWqIc7nrW9Aq0ECPSIQJ0kp/SIhYMQIFBzAuvPunU66LK900obLVf13J6465n06tOdSwKv9iB/XP/INrsecf0f0mI/+EaaabaZ2uynkQCBxhA4ZKO/pZsnXZpefOSVXl3wh++M6tXjO3jXC8w57+wVBx35dn2c6/ytJ/m10bz8/seHpEdvf6qpap/VDqu4xlyZE9j79es3RXulb4Pbacm9Wxyn1UE1ECBAgAABAgQIECBAoAEE3AG9AU6yJRIgQIAAAQIECBAgQIAAgc4KTJo4qbO72o8AAQIECBAgUDMCh/3s6Krnct7Bl6S9Vjmo6v7d1fGAtf+cNph928KdWxUCBAhkgfyNDr9Zdr9exdhyoZ179fgO3vUC1/79loqD/uOwKZO6K3aqkcqrTr6+MJN/n3pj4d/N5snn7U3x5vPvaNElf9NApdLZb1WpNJY6AgQIECBAgAABAgQI1LuABPR6P4PmT4AAAQIECBAgQIAAAQIEulHAHdC7EdfQBAgQIECAQI8K3HDObe0e76qTrk8X/Omydvv1dIecTJfjiuOvTR+++1Hrh/eNFK3baCHQBwS2+upvenUV+e7n48dN6NU5OHjPCbz82Ks9d7CpPNIpu52TXnz0lXTSrmd1eKTjdzqjxT6V7oCeO+VvVXn58fpxabEwFQQIECBAgAABAgQIEOhCAQnoXYhpKAIECBAgQIAAAQIECBAg0NcExn8+vq8tyXoIEOgGgfJ8x3xnwHdefa8bjmRIAgRqUWD8uPFp/Oe1n5B4zA6ntcl37oH/TKfsfk6bfXq78bS9hqRN590hTS5/4+3tiTk+AQI9IvDxB5/0yHFaO8h5B13SWpP6Ohdo7Y7f9bSsnZfap1PTrfShirY+jL/TknuniRMndupYdiJAgAABAgQIECBAgEBfEhjYlxZjLQQIECBAgAABAgQIECBAgEDXCtz/n2FdO6DRCBDokwKjP/qskAzZr1+/wvo2W2Cn9NnHY9IFr5ya5llo7j65Zosi0BsC+XX25otvp9GjRqcn7nomzTjLDKlf/37peysvlr70lTnTmE/HplnnnCWNePmd9NknY9IC35w/jR09Ng2ae7bCdF96bHh69ek30le+NX+6/qxb029O3D4NGDigxVLGfvZ5mn7G6VJORsvHfOnR4emj9z9JX1pwrrTQtxdo0X/D2bdNn48Z16K+FivyXcRLJa9nljlmToN/vkL67grfShcdcUUtTrninNYYsGmadvpp0lWjzkvTTDtNUx956RW5VBIg0EUC1/79li4ayTC1JvDcgy+lby/3jaZpNVqC9Zj4/6UZZpq+af2T4/+B2iprTbNZumniv5p+/mmrrzYCBAgQIECAAAECBAj0VQEJ6H31zFoXAQIECBDoZYFIPMl/kT4sYq2IOSNGRFwVcWj88frDXp6ew3ejQE5OeOre59LXFl+okPQw21yzpNnnHZQGDGiZ1NCN0zA0AQIECHSRwMyzz9xFIxmGAIG+LLDXKgdVXN7zD70Ud0Uen343+OA08u1RTX2++r0F0+KRMJsT1t9/a2SaZrqB6fZ/3hNJtN9Ou574y6hP6eMPPk3vvv5+mmnWGdNSqy+echLIwRv+LT1y6xNN46z9yx+nuReYMw2aZ7a04KJfTtPNOG36+pILNyVj5v83ffyOp9Nlx16TxsU8Xn3q9TToS7NF+8D0k1+vkV575s205narppvPG1rY3nfNsHTX5felr31vofTLI7dMs1T5HpiP816e62wzRsxU0aIzlTn5d+zoz9N0M0yb+g/oX/B697X30vQzT59mnWOWzgzZq/tMnDBximTn5h9aKE2sVNe8LT9+7dk30wLfmK9isnR3Lmr0x5+l4U++nhb74TebEoxyQtbECZPSu3GX/5yUffcV96eXHx+efrLTGoVz89ANj6ZNfr9+mnO+2Qv7fDDiwzR7XKOTJk5KA6eZ8lfyeW1P3/d8yq+J/HPUZ/Fhjrdeeiettf2qaXi+XueeNT1y25PptD2HdMsyrzn9pqZxp59purTp3j9N5x/6r6qOtdupOxYS1fJdQ+sl+bx8Ye++9n68pt4vJNjXYxk3dnxaZ/ot0s2TLq3H6ZszAQIECNSQwG4/PGCKf0/+vs8FNTS77p/KZcdck35x0P8+pFbNB7ryh8FunHBJ6t+/c186f8SWxxd+BsplpZ/9IP3qqF+keRf+Uvcv1hEIECBAgAABAgQIECDQRQL98i+4FQIECBAgQIBAVwrEH9i/HuPdG5F/W3p1xLMR/xexasRzESvE/4N8ENsOlxh72FJRhg2r3buxXnr0v9OZ+/yjaW0b7b5uWjT+KP/obU+kH6y3TPphRC4fvvtRIUFl2um+uFPZuLHj0oTxEwuJJflOc7ncf+2wQvL2N5fOpJVL6Y50Nw0ZmibEH/6XWWvJwi+q83j5K9DzMUp3oqw8whe1haSZNz5IN55ze8xz6ZTvYjlbJDvMOf/s6S9bnlBIuphx1hkKSTpdUXKCel5bTqhYevUl0upbr9IVw3b7GB++M6qQVFK6I05OPsnOpXPW7RNwgD4jMObTMenSo68pJNqdsPPfC6/fry2xUPrgrQ8LyXN//Nfv0t6rHlx4zc2/yLzp18dsU0g+y3c5fOWJ1wp1H77zUVpmzSXSzINmKiRj5ddxZ//oVWuwOUnwrbi7Z15n6X2yVub45osj0jG/PK1w19G2yuXvnVO4A2lXlHxuc3LXxPET4hqYtin57vMxnxeS2XJMH3fqyndA7eprICdtnrH3+V2xDGMQIECg4QV+d9bOhf8HnuvLc0Ry+0xpxCvvpufjbpMvPvpKIZE+J9zfc+UD8f/y4ztstfEe66ZN9/lpJC2/kfZdPX8W+IsyQ/y/6x5n7BT/hkxMd13x38LYK2zwf5Hg/0naYNe10hsvvJ12/b/9mvr/9DdrFZLz5/3qF8kv+635p/TUPfnHuC9Knuca2wxOq26+Qjr7gIvSff9+qKmtlh8ccf0f0rJrLhmJ4q+ma067Mf4/Kv6/Pv7t3HjPnxQ+tFD+7+cVx1+bTttrSC0vydwITCFQSkLPd77/6Wxb0yFAgAABAh0WuGH8xU03Efn9aofG77Of7PAY9bpD/n/gXU/6ZdP0n77vubT7CgdWtZxNfrdeWnuHHxf+/7n5t5KUds6/0/k0vr3m2jNuTo/f+XTa4oCN0uvPvZWO3fH0Vsdvfi5a7aSBAAECBAjUu8ANX/zNPK1VH79bqndu8+9VgS++NrRXp+DgBLpHQAJ697galQABAgQINLRAJDvfGABrROwWv1w9qYQR9cfG4z0jzoj6X3cGKSegf2nmeZdafPSKbe6eExZz4mJO1Fx1sxXT0Evu6fAd2fId6NbfZa1ISpguxlih1Tvt5UTEbb+5W5vz0dgxgZzUcvTthxR2ykm18yw0d9MAObF+mkjaryapvpqjjnjlnfT0vc8XrpX7r304jf1sbLrjX/dVs2uH+gycZkDhq9Gnm+GLDxd0aOca7Jw/+PDph6PjQxMT0uvPvpVO/u3ZhTs0VlvyXUd/9bet0y0X3JHmiDtDfnmR+dIC35wvPXTTY+mj9z5uNbH3R1usmLb648+++LBGJNp+EnPIr9VPRn5aOIf5rkEXHn5Zob618q1lv55W2WT59NJjw1O+o+XCi30lLT74OynfoXXpuLtqPv6zD7yY5ojksDkjOezjuKvlgDh/+cMjOdG7rZL/oPTk3c+mS466qpAMno/z4I2Pxt0wV09f/e6CcT33TxvMvm1bQ/RYW36f3Pf838bdZCenZ/77fPrSgnMVXlv5wybn/OGipnks8v2vFu5YO+scM4frp4UE+eZlwW9/Oe1/we6Fa+H6s25N10X0dMnnfvlIpJv/a/OkOy+7L73xfP7SjcplseW/lf52y0Hp/TdHFs5n8wTxnPS35UI7V9zx+z/+XnoyEs5zEnhHy/Z/3iIS+VYvOB61zcmFa60Wy+rbrJJ+/vufFu60O+bTsYVvsch3Cb736gfTDefcVotTNicCBAgQIECAAIGiwO/O3iWtFd/kkBPcNpxjWy4ECBAgQKDDAscMPbTwLUW5bDD7NoUblDRSaf6NIk/e82zac6U/dmr5+fdAi3x/4TTq3Y/TUdue3Kkx8k4rbrRcOviyvQu/Y+yq34V3ejJ2JECAAAEC3SFwSfFvbj9v/W963XFYYxLoBQEJ6L2A7pA9IyABvWecHYUAAQIECDSMQPwi9Gux2JxZNzzi6/HL0UmlxUdbvg1szgrM/4P9pWjr8E+TOQF9ljRoqeX6rVYa1raBBQbHBwNW+OmyhWTh/pGMfGl8VWq15RtLfTW98PAr1Xbvln450T7fdb5Ufrj+MpHIu1uaYeYZOnS8nAxeumNj/oPEi4+8Urgbcr67Zk58n32eQU1/pMjt//3PsHTugf9Ms801S3r09qc6dCyd/yew7q9WT5v+fv3032uGuTumC4MAAQIECBAgQIAAgV4XyIlzN59/x1Qlu/X6IkyAAAECBHpV4PrP/5l+udge6a2X3unVefTGwa8cOaTp5hP5w/gHb3hUb0yj3WNuuNs6aetDNk0D4wYf00w3sOmu9e3uqAMBAgQIEKg1AQnotXZGzKf7BCSgd5+tkXtZQAJ6L58AhydAgAABAn1NIBLEd4g1/T3izEh03al8fdFeujv6atHe4dvkSkAvF/WcAAECBAgQIECAAAECBAgQIECAAAECBAgQmBqB/G2IZz11XLr8uP/ENyxePjVD9eq+eR35W/iqKcutu1R66dHhhW8KzHdZ/97K307LrbNU4RtJ813gJ46fmJZZa8nCN2G+/cq76eMPPklLrbZ44VsjJ8a3IeZvChwwoH+afubpC9+yOCH652+AzN+ymL9ZdswnY1O/uHHMDNH+2cdj0syzz1T4xtP+sU8cLo3/fEJhG/dMKfT74kG/wlgzzjJ96h998w1W8s1W8vwK+xX6peIYcrmqOc+N1GfihImFb+rM12K+nvIHNfr1j2sytrnk66iWSr5hUKVSa/OsNEd1BLpEQAJ6lzAapC4EausfoLogM8l6EZCAXi9nyjwJECBAgECdCMQvRf4WU907R/zi5JjyaUd7/s7J30TsEu2nlbeXnudE81baFo07oM/oDuit6KgmQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQI1LHDN2VcWZrfeLzes4VmaGoEuEXg7vrVuvi4ZySAEakzgi4/51dikTIcAAQIECBCoa4HZirP/qJVVlOoHtdLeXvXYmdIsM7bXSTsBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQewKvvTlr7U3KjAh0j8C83TOsUQn0voAE9N4/B2ZAgAABAgQaTaD09UKVv1euqBF3R1+60WCslwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI9HmBi/o9lNcYd4Zeps+v1QIJECDQRwX699F1WRYBAgQIECDQewKlO5yX7oRePpPSR5lbu0N6eX/PCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKBGBCSg18iJMA0CBAgQINCHBJ4rruWbrazpG8X651tpV02AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECNSogAb1GT4xpESBAgACBOha4vTj3Nfr16zfF/2vE81mibYWIMRH/reM1mjoBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQaUkACekOedosmQIAAAQLdJzB58uSXYvSbIhaO+E3ZkQ6N5zNFnB/9RnffLIxMgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAt0hMLA7BjUmAQIECBAg0PACu4TAvREnxl3PfxzbZyKWi1g14vmIPzS8EAACBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAjUoYA7oNfhSTNlAgQIECBQ6wLFu6AvE/McEpETz38X8fWIEyN+GO0f1PoazI8AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEWgq4A3pLEzUECBAgQIBAFwhEkvnrMcx2XTCUIQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgRgTcAb1GToRpECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoNYFJKDX+hkyPwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECNSIgAT0GjkRpkGAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFaF5CAXutnyPwIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQIwIS0GvkRJgGAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEal1AAnqtnyHzI0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQI0ISECvkRNhGgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEKh1AQnotX6GzI8AAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQI1IiABvUZOhGkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECg1gUkoNf6GTI/AgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI1IiABPQaORGmQYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgVoXkIBe62fI/AgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFAjAhLQa+REmAYBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRqXUACeq2fIfMjQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAjQhIQK+RE2EaBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQqHUBCei1fobMjwABAgQIEJhCoF+/fhfmwEKAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECPS8wsOcP6YgECBAgQIAAgakSWHSpKDHCFlM1ip0JECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBnheYY+nSMSdvvciuacTL76Rznz0hLfDN+Xt+Lo5IoHsF+nXv8EYn0HsC7oDee/aOTIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEGlYgJ5/n8ujtT1U0GDN6bNpt+QPS5cf9p2K7SgIECBDoHQEJ6L3j7qgECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAGwI3n3dHeua/L6TTf3deG700ESBAgEBPC0hA72lxxyNAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgSaBCZPnlxRY8L4CRXrVRIgQIBA7wpIQO9df0cnQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBA3QhIQK+bU2WiBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECg7wn069ev7y3KiggQINCHBSSg9+GTa2kECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKDWBSZPnlzrUzQ/AgQIEGgmIAHd5UCAAAECBAi0KRCfMv5ZxEkRd0V8HDE54oK2dor25SOuixgZ8VnE4xF7RAxoaz9tBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFDbAgNre3pmR4AAAQIECNSAwIExhyUiPo14I2LRtuYUSeY/jfbLI8ZGXBIxMmK9iOMiVojYJEIhQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBQEItegsoQbo1d2UUuAAIFeFnAH9F4+AQ5PgAABAgTqQGDPmOM3I2aN2Lmt+cYPhLnP3yMmRgyOr8j6ZcTv4/GSEfdF5Lupb9bWGNoIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBxhKI3ILGWrDVEiBAoM4FJKDX+Qk0fQIECBAg0N0C8UPe7REvRFTz097PYj5zR1wc3R8qzS0e57uh5zup59JmEntpH1sCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgwQVauTF6g6tYPgECBHpdQAJ6r58CEyBAgAABAn1K4EfF1dxQYVV3Rt1nEcvHXdCnq9CuigABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECDwP4FqbpXHiwABAgR6XGBgjx/RAQkQIECAAIG+LPCt4uKeL19k3AV9QiSevxL134n4WsQz5X2aP4++w1ppX7SVetUECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAAwk8P+yl9NaLb6fBP1+hgVZtqQQIEOh9AQnovX8OzIAAAQIECPQlgdmKi/molUWV6ge10q6aAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIPCFQL+2IX6z7H6FDgt95yvpq99dsO3OWgkQIECgywQkoHcZpYEIECBAgACBKgRKPxq2+yVZccf0pSuNV7wz+lKV2tQRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECfUig3eyCL9b63usfSEDvQ6fdUggQqH2B/rU/RTMkQIAAAQIE6kigdIfz0p3Qy6c+a7GitTukl/f3nAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOjjAnEzuj6+QssjQIBA3xKQgN63zqfVECBAgACB3hZ4rjiBb5ZPJH5YzN+88tWICREvl7d7ToAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDSmQHxLemMu3KoJECBQpwIS0Ov0xJk2AQIECBCoUYHbivNaq8L8Vo66GSPujR8cP6/QrooAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBoEIH33xrZICu1TAIECPQ9AQnofe+cWhEBAgQIEOhNgcvi4O9HbBZ3PF+mNJF4PH08Prz4/LTenKBjEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAr0vcOsFd/X+JMyAAAECBDolMLBTe9mJAAECBAgQaBiBSB7fIBabI5d5i9sfRv2Q4uP3447me+fHsf046neMhzkRfWg8vji2+SPL60d8q1h/Se6rECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAo0hEPkE6bBNjkmzzjFz2nOjlCZNmpzO2u+Cxli8VRIgQKAPCkhA74Mn1ZIIECBAgEAXCywZ421TNubX4nmOXF6NKCSg5xI/NF4VieerxMM/RGwcke9+/mLEXhEnRvvk3E8hQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEGkPg4w8+SXdfcX9hsTkBXepAY5x3qyRAoO8KSEDvu+fWyggQIECAQJcIxA99h8RAOaousc890XmdqnfQkQABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEOizAhLO++yptTACBBpUoH+DrtuyCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgV4Q6NcLx3RIAgQIEOg6AQnoXWdppBoT6Nev37YRk/N2aqYW+w8pjrNwteNE/+E5qu2vX2WBMBxctD+kcg+1BNoWKF4/Q9vuNWVrZ17zHRm/s31jXnNEjIw4pZoxot/vIsZHLFpNf30IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEA1AhLQq1HShwCBmhKIhNqhEZNralKtTCbm2T/iZxGXR7weMTZidMQzEWdGrNB813he+uBE/vBEKT6Nx29E3BJxWMQ3Wjlcira/RtwakY81JiInLD8ScXDEnK3tVzaHs6Nv6diLVLNPPfWJtR1SXN/gepp3zPWwiBkijqhy3qdGv3cjjq6yv24ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKBdAQno7RLpQCDtHwbfjniTRY8LPFC0P7nHj9wFB4wk53ljmDsjLo1YPSKv58SInBj8XMTmEXdHv9/Gtrw8FhWHFiPf8frmiPkj/hjxbOxzfMTA8p3i+Z4RMxX7nxDbCyMmRBwS8Xjs85XYtlqifb1o3D7i01Y71VdDfu1u3cEp19xrPs7LgrGGnSL+MXny5Krei6LfmOifr4F1Y//lO2igOwECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgxwRGvfNReu6hl9o83iO3PdFmu0YCBAgQ6DmBSsmLPXd0RyJQBwKRxDkipplD6WGBsP8sDvlsDx+2Sw4XCb8zxkA3RCwRcXHELrGeD5sPHn1mjed7R+RteXk0+h9SXhn7rBp1QyJ2j5g+4tdlfWaN/cZW2O/PUXdARE6u3qW8PT+PseeOzd8jLonIyfOrVOpXT3Vh0eHrp0Zf8zn5PP+bPaSD/hdE/3zH9HzO7+3gvroTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBHpE4PxD/5VynPXksWmhxSrfX3Gf1Q5LN0/K94FUCBAgQKC3Ber6DuiRLPl/EZdEvBnxecSIiJsiNi2HzXURd0Z8FDEm4omI/SOmq9B3eNTnmDniuIjXi/s8GtsNcv/YDow4IOKFiLERL0XsWmGswVE/OeKQiGUiboj4KOLDiMsjCv9axvZrERdHvBeR53d7RE5cbVGifr6IUyLyHMcV97kitkuXd466bSPy8fN21YihEZ9EfBxxbUS+O3BVJfp22VpirLzWPK+VKx086n9WbD+peXvUzRHxl4hnIrLTRxG3RqxRaZzW6qL/0hHZ/92IfO28GnFqxHzl+0TdkIg814XL5hJV/XaNeCoiXwP5Ojw5YrbyMdp7Hvvk8fO5mT/iHxF5Xnl9wyK2KN8/6pqfi/w6yOdyZETTPOPxdBH7ReS7Xn8Wkc/5XREtXh95/KjPZfeIpyOmWE88H56jfB7F/TaPtny9fljcL5+bAyMqvbZK65wr2s+MGBGR/bPhduXjR13TOovHWjjqJsfjQmJ0ftwshjbfP+o7c47z6/C3Edks+08xZvn82nme70SeX8P3RGxZnnye9426jyMOiodHtzNWU3P0vz2erBkxLuJXMcelmu9bKfm82P6v4vYbbRzrzGLbb9ro02ZTzGfb4jnJ23zn7XsjRkfk6+OyiIrHj/qOvK9NG/13i3i4OG6+vvM1enXEas0nGM8L11ypLveLxwcXn+frtukaatZnSLF+4VwXj39YfH5FqU/5NtrzdZ+v5Tmat8XzNSOui3i/2J7/rfhbxKDyMVp7Hn37RVt+fbwe57dDSeTR/63Y766I/J5a6YMOrR1WPQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgxwVefGR4jx/TAQkQIECg4wJ1ewf0SKTbMZZ7WsTEiH9HvBDxpYhlIvKdXkvJljl5MN/9Nd/19/2IiyI+jVg7Itfn5MDVI0lvfDxuXqaJJzdH5GTCqyOmjdg8IictrxHbfIzlIq6P+Dxik4iTou29GCvfPbi8LBsV+0bcEZHvMPy9iI3yNvZZP7Z3R+Q79Z4fsVCx7eZo+1qMl+dbKPH8q7HJfeePuC3inxE5iT0fPyd7bhz9/xOPy8tPouKnEXm+p0csFrFOxLKxz2KxT7aptnTFWk6Ng/08YqeIOysc+FfFulJCbF57dhkasXBETqi8IWKmiLy2nNi/U6wj27ZZol/uf3lETuq8LOLViJy8v3PET6N9hRhneDxurxwfHXaLGBGR55mvoWycr4t8vYyL6EiZPTrfGzEq4tyIQRGbRlwYc/pyzOlv8bi8/DAq8rWdr4lzIuaKyB9KyMe/MWKViHxdnRIxY8TPIi6J9iVjvAPicfOS+2SDtyLyevL887X5fxH59VD+Gsnn5Oyo3z7ijYicnDsq4gcRf4r4cbTn19aEeNy8DIon90Tk8bP/9BF5XudE/0nR/7zmncsej4rnh0ZsG5Gvh/y4VIaXHkzFOT4hxlgp4tqI6yLy+0tnS+ka/lOsaVJbg0R7fg+pukT/Z2ON/4odtorYPOLhKnZer9jn8Up9Y7xto36DiA1j/A/ieaVuHanL729rR1wZMTRiyYiNI1aNsZePYzwXjwslnn81Nh15XxsS/fO6n4zI75ljIuaPWDFirYhbIlorx0fDBhH5tZGvteGtdSzVx1zviznm+f4ktnNmn+b7RF1+jSwacXm0jSy1Rf1B8Thfo7kuvy+/G7F4xN4R60T7D6P/x6X+bWy/E23zRVzcRp+2mvLrbXDEyhF5HgoBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoEcFIk+iR4/nYAQIECDQvQIDu3f47hk9/jFaLEY+NSIn7q0UCXxPNT9StC9Qep4T/OLx/hGvR/xf9H07t0V9rsuJkTkZ+fcRR+T6ZiUnM+akzsGxz+e5Pvb5R2xysvSlES9FfDfaRhXbjo1tTvTdL+KSXFdW1onnW0X/C0v1MV4peffeqDsm2v7crO2P8fiwiF9G5KTYUjk9HuS5HVjWP3vkuZ0X4y4UbZ822yc/3CBizai/tVQf/f4Sj/N8t484qlRfxXaq1xLzyHejz+dt49juHs/fbzavnIy6WsS9Uf9Es/nkZNGcdLx51DclYsb+g6JuaMSJ8fjf0fZOs32meBjtM0fFkIh87edze1epQ7TtG4+PjDgzYo1SfaVt9F0+6neLyNdBvq4KSadR/4fY3B6Rk0VfzXUdKDkxNV9bm8V4k/J+MV6ez7CIP8fjnNz6ctl4eZ6/jvozmtdH33x95wTb/IGD9aN9Qm6P+pwM+0DE/vH4P1Gfr71cv1Jsdo54PmK5qB9VrD8gtjmZN19zU6wn9tk26vK1k19H+Q7fOQm4UKLtkNgcHPGbiObXb25eIiJf+/kDAxNzRfQ/LjaPR+RzkM9zxVKc1yHRf3B0WCieH1LeMdqm5hwvFeN9P8Z9pXzcjjyPOXwl+i8Ykd3v6Mi+Heg7NPpuFZGTn1uUmENOcs4Ws0UsE7FiRDbO19QUJfouFBX5PF0Qa7+qvL2Tz9eL/daL8ZoSnuM4u0fd8RH5/erHzcat+n0txsjr2Swivy7ytVq4hkol2uds/rz8cfQ/PvoMivpVIobE86HlfVp5nq/L/O/E5hEnl/XZpvi86dqNY6wadYdG3BexThxnVGmfaNs2Hp9bbN+zVN/GNp+7XB5qo09bTQ8WG1eObdP5aGsHbQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBqRH47JMxkROU0gwzz9DKMNGoECBAgEDdCvSv05nnRNmcQJzvLDxF8nleT9S90WxdOUE2l8Oj/u1SfTzOiaG/i8iJvjuU6su2e0S/z5vtk5OVc2Lq7BH7Nk8ojMc5MTjfZTbf0XxAhfHujj5NyefF9lKy4kfxvDwp9PxinyVLY8W4C8TjnHD8WsQUCeMxdk4k/mfEHBEblfZptr04+txaVp8TrXOpmMBaYYxS1VSvpTjQabGdLqKUvFka/1fxIP8fRlNSdax9iXieE0ZzEnZT8nneoXgeDo6H00dsnOvaKD+Ntpygeknsl89n83JMPBkesXocLycPt1W2Kzb+OcYZWeoYj8fG45z83ZmSE2nzdVVIPs8lHufr7cSIaSJ+Uaxuvnk0+jQ5NWvI1/3kiL2iPV/rhRKP343Nn4pPm1/3pXOQ1zOq2J77j4vHra1n92jLY28f/ZqSz4v75mN8ELFlaaxm28/icZ5XU+JwPH466vLr59thP0uFfTpSNTXn+KiYSzaf2jJfcYAPYrx8TXRHebM46NytDL531OfXxR4ROYH5hog1Yj7vNe8f3vnfgfxe9On/s3ce8FFUXRS/9C5dQVC6IIoF7CJFFCt2P7uCXUFRsWChiGJDAQEFOyAoIAiIIIr0LoTekd57b6F95ww7cbPZTXaT3WQ3Off3O5mZ9+5r/3kz2XLnLfSid14a98egLd9g526ocwV0Ldotx/pTcV/jvOb9gffmhGvF7Sva5LyLhP3oac+9Vpw20P/c2DAgntcWH/hwzWX5FPq02yud11VPHM+B/F0f3q7uvns/2uQvM4g0939fSve1IKqSiwiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAgkT+DEiRN2e+FH7bbTHk3eMZlcxFfYK/VaW/dXeibjpSwREAEREIGMIsAg7li0Kzyd9g72CzSOmp6MMb4O+Ce1DMGDDFavgG0RnyDB3ThmoKSvbaQ/FOebgWMGhDL4vJRn39vF38q1rIvGIOKEYFxPmhtcyqBz1y727EyE/1GvdHeXY3wYop8bwO7m+Wt/nSezqJ+6kkvyV1eoY2H97ONHEAPOP2MCzgMDrRtDu6ABTPPYlZ5tYfi09Up3d90g3HP95HknJTcfjqHuCXAuD5Hh2mTqcusZ78eHge0JQd9+8gMlrcV59Rf8PA4FGEjsnn/v8v/4VoYxMIC7MrQB9S3xzcexey141+fuT/LjPw1picaDNvIj7UJoO/QSjv0UcwKE/Z2P5ejXXj8F3PlYBHn7/OQHm5SWc5yEZ7CN+vi5QBgsHSlLtg0w5n2I19QZ2FwF8VqbjeNbkTfLq1MvY78udAvSed0layhfDw6Ut61G2Z4+aeN9jhl4fRzlOccqQZxzazxbugZ1X+PcQR3D4N8ImoP9QSwLTUceH26IiKHu9WhrNCrnAyrVccyHJmjsRzGoE9K8rxPes3ifvhf+955yTfSXgeslkVcc5Xb4yfdOKu45SPH8BKhnpye9RIB8JYuACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIhA2AgcO5o4FO7Y0cShVFvWbLOCRQv6be+jR7rYyrmr7c7mN9v8CYuT+MwaPd9qNqjhpMcf5tqaMhGIDgKH9h+y3HlzW46cOaKjQ+qFCESYQKwGoBfxcHGDtJPDVNiTGWjlWKZzVVj67faqaE+ASp3/hggY9Jfv/qdkELWvJeefJI+BjAhMZB3edQUzFpYp4ts4jnf7pnm1EeodL0l/Ubc79iR5AcZChvswxj4o+yy29XE8Fvu3Qwyc7Yzjw159dgMwr0caFcj8vzL5zzstDL3bdOvZ4tsR9JtBtikFlPoW43GSujxOmz1bt03vsm6ev74lN+fpX8SrUKjjKYqynKAM/G/j3XgQ+7sD+LhzKNT56FtdWs6xP56+9Qdz7D6QUQJzIa/PXA6mfDA+Z3qcEq1o7lsQbXNeDUY/GHS+DOKDH+fTD2lVsGkP/QC/EUwLwurBx/ecj0daT5+ywc7n1Jyv+9DWG9CD0Luedg9jPAOx/6pnzD7dCcthT9TC+89jENuncZ/Wy7N1N7xn8X+sLysfN+M9K6X7xSFPoby+hYM8zufxc+sJspjcREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAERCBtBNYv22hNqjW36x+tm1DRnm37jDKr7rfyAZ/+5gSg+7M3rm9no0784mT1bNXPn4vSRCBFAlylnw84VKlV0fIXckNrUiwW0GH/7gN2Z7HGdla1Mvb9os4B/ZQhApmJQPYYHcxuT7/LBNF/NyCaQc3+rLQnMUngtD/nDE7LTGNxUXb37Dzj2XI1dNrXroNn6469OYJLsyWjJj7lfA/DxdCt5wzfBhAEywBqN2DeNzu54yR1eZzduetvjp70U2FqxuiuSJ6kDwHG47YxO5lz4ZwnP/2LdFJqxu/2yR/PkPuLca9DIa6gzwDkOiFXEFyB+h636cG4o09r4MdVu8/DOXVXwj4Px3mgJkg76S2kua/6l3vS72A7qKetn3Nej3k+lmQuefJ953PI5wvtH/L04xzUyQeIHoa4sjq3DEKPlA1GxbxWHuZ1AfEBjJuguejPXJ9GOa5dflj53r94XlKyrR6H1NxXWNQt59aTUnvKFwEREAEREAEREAEREAEREAEREAE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3c55z1XttDHJKvApU7K1yb22aHVH3pu1YFlG7WDZaU7zd/Xruc66yuiqWm2VbCCCAAAIIIIAAAggggAACMRQgAT2GmGwKAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQaI8Cs/JWuZ/PftpV7q1uj4efcMf82voZbm8jNwpYAvjMnava5Nhs5PW/L327TfbNThFAAAEEEEAAAQQQQAABBBoXIAG9cSNaxFggJSXlWsVemzZn01p/XGA7wyLdjtqvsYi0Pe3CC8jwtID9beFbUItAwwKB62diw63qLm3Kz3w0229qW/WrhyJP8Y9ItqF2NysqFKMiaU8bBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgcQSKK+udE+tnuzumP+i+9uSt9y4lRPdK+s/cx9tWeAsSXvl7i1uR9kuV6F2yVJWFW11N896ytmxUxJDYLVGGZ9fsK7Bzs7T8pKqPQ22acmFz6+d6iZtXdSSu2DbCCCAAAIIIIAAAggggAACTRRIa+J6rIYAAgjsI6CE2omqPFWjJaTsszDOKtRXuwHnq4orFMcoeivsOX72SdvHiid0HFM09YraX6vJ4/7rwLRY0wLFEsVUxVNaZ3lIG++l1r9bk7GKkYpeilLFWsWrige13k5NGyzaxqNq8M1AowO0zooGV0iwhTq+29TlWxWn69gmJlD371BfsxV/jLDPD6ndTYp7FOdHuA7NEEAAAQQQQAABBBBAAAEEEEAAAQQQiCuB97fMd3cueNkN7tjLHZoz2B2WM1TTIa5/do59HhpXfaUzCLSmwJLCje52JZ6vVEJ2JOXYnge4H4w8y43uNiiS5nHZpqC82N2c+5Qrrmy7ROW4hEmATr26YYY7rPvQenv66Y5l9S5rrQX3Ln7DHdNzf5edltFau2Q/CCCAAAIIIIAAAggggAACEQiQgB4BEk3iVuBX6tmfFBvjtofJ27HPdGgHKXYk4iHqy49+6veLihMVuxXvKVYq7FuRAxSWlP4dtbtBidAPaD64zNWLVwMVlnTcR3Gs4hbFb7SOtf+Z1gsd4uOnqp+lsH1tU3RSHKe4TfFdrXec1lmv+bBFyy/QAks+L1J0DtsosSrt+imJsstx9zOv8zJEx/A9xeM6fxG9F6ldqdb7m9a5W9MT9NpuXqAggAACCCCAAAIIIIAAAggggAACCCCQMAIlSjK9d9HrXrLpkl0bvfjvuk+9/vfM7KJk9CFeMrpND+w6wGWmpifMsdFRBJoqYKOZP66Rzh9fNdFV7a2OeDPTdy5306ctd6f1He2+f8BZbkTnvhGvGw8N7bh/MfsZt7E0Lx66Qx+iFHhv83x306jzXef0rLBrfroj7LhLYdu2VOWWsgL9XH3kfjjyiy21C7aLAAIIIIAAAggggAACCCDQBAES0JuAxirxIaCkzc3qiQWllQVkb4nDNup3whUl/HZUpycoDlc8r/ihjic/+EDUpqte/0xh09AyR+1vC63UOqerbpziJwr7lO77IW26ar2yMOvdqbpfKyy5+oehy+21tm2jsz+iGK/opzg1XLtEqpNF1NdPnP7MW/K5/S4dF6X/02pvI6bbOScBPUo8miOAAAIIIIAAAggggAACCCCAAAIItK3A06s/djvLbayMfcvOPbvdR1sXemGle0Yn98DY69xIJaJTEEhWgeW7N7vb573olmna1DJx6yI3SXHugCPdd/Y/0w3s2KOpm2q19fS5vfvTwtfc7Pw1rbZPdhRbgT3VFe6dzXPdxUNsrKW6JW9PkW4w2hRa3Sav7ffOefrZGNbZxoWiIIAAAggggAACCCCAAAIIxINAh5bohJIlj1GMV2xU7FFsVryr+Fro/qxOMVlRqLBRYecrfqXIDNN2jeotOiv+qlgfWGeOphdZe03TFL9WLFeUKVYqrg+zrdNUv1dxm2KsYoKiUJGveEkxOLC9EZp/XrFdYf37SGGJq/sU1fdX/ENhfSwPrPOypkeFNlbdtQrbv01PV0xU7FbsUrypsNGBIypqG7Nj0bbsWK1fp4TbueovCSyvMyq06noo7lIsVphToeIDxdnhtlNfndofpTD/bQq7dtYqHlL0D11HdeMU1tdhwcv02sr1ioUKuwbsOnxQ0S10G4291jq2fTs3AxRPKaxfdny5iitD11dd8LmwnwM7l3mK2n5qPlPxS8U8RYnCzvnHin1+Pmz7qrfyE8UiRZ3j0es1FqH9CKx3hZbZ9ZofWM/OzW8V4X62/OPspeX/VmxWmL8ZXhe6fdXVHmdgX8NUt1fzXmK0zQfFxOD1Vd+Uc2w/hz9WmJn519lmaP8aeW0jkdvP8BTFVaHJ57au6nYpfqfZexrZVu1itf9IL2zohXKFjWg+JnjdcMnngeUvBKY28np95d+BBT+qr0Fj9erPtYFzYtMvKaYqihV2fbyoCLt/1Ufzvpah9jcoZgW2a9e3XaOvKb4Q3Ee99q45v87aaf7WwGu7bmuvoaA24wL1w6xO88cHXr/stwmdarld93Yt1/mkXq+/qHhLsSOw3H5X/EWRE7qN+l6rbYqW2c/Hep3fqJLI1d4+sf1YYe+p4W50qG+31COAAAIIIIAAAggggAACCCCAAAIIINCmAtvKCt1TSgSMtOSXF7ufzBznNpXkR7oK7RBIGIHK6ir32MqP3DVT/9Gs5HP/gO2Llrc2zXaXfnyf+/Oi19yOsl2tZrGnqiLqfT27Zop7fWNu1OuxQnwJvLZhRtgOfbZzRdj6tqis1FMF/rzof/YdXlvsnn0igAACCCCAAAIIIIAAAgiEEYj5COhKpPuO9vOwokrxP4U9l8tuRR6rsJFe/WRLSx78o17bqL87FM8qbLiMcxVWb8mBZ+kfkaGfdthzGt9TWDLha4oMxRUKS1o+W1Pbh92i/bZij+JSxQNatl3bstGDQ8vRqviFYpLCRhg+VPFVm2qdL2v6icJG6n1SMTSw7D0tG6Ht1Q7vodfDA21tCI8PFc8pLInd9m/Jnher/RuaDy3nq+JChfX3n4rRivMUR2ud0VrHbCItsTiWh7SzyxTfU0wOs+PvBur8hFg7j+YyUTFMYZ86T1B0UtixWWL/93QcZttgUTtr/5LCkjpfVKxVWPL+DxQXavmJ2s4azTdW7leDGxSbFdZPu4bM2K4Lu17KFdGU7mo8VVGgeFyRo/ia4hn1aaD69BfNh5bjVWHXtl0/jyl6KeymBNv/O4pTFXZd/UPRUXGJYryWH6Ht/VrzwcXamMEmhR2P9d+uzWMU9vMQ+jNi5+RR1X9TsUFhybkFiuMUv1ecqeX2s1Wp+eCSoxdTFLZ9889SWL8eU/tqtX8iuHHIfIFe3664VmHXg837ZY0/04xz/Ddt42TFm4q3FPb+0tTiX8O/1zFVN7QRLbf3kIiL2i/RMb6gFb6uuEIxK4KVLwi0mReurbZ3reovUnxF29+p1+GaRVNn72/nKl5RTFQcobhYcbq2fYL2sVTzXtHr4ZrYNTxAEcn72ji1s+NeoLD3zNLAuidpeo7ifUV95X4tuEhhPxt2ra2pr6Ffr75OUx+tv+dr2tN8gtdRnf2MjFK8pGV5/jLV/07zdo1anb0vb1McpviZ4jwtP17td/ntG5gerGX9Fc830KahRfbzdpriFIX1g4IAAggggAACCCCAAAIIIIAAAggg0M4FPtuxwvXK6uJGdO4btxL/Wv6+sxFzoyk2WvpPch93jxz7fZeTYR+JUxBIfIFVRVu9Uc8X79oY84OxZNsX1013r2+Y5Y1MfWDX/q5besea0M+QzXdOy7LP8Zu1b30W7uZo9PJn1nziPt62xB3X6wB300Hnu6GdejW63U/U/u9L7SsbSqIL2CjnSwo3ulHdBtY5lGk7lsXVoc3MW+Xe2zLPnd3/8LjqF51BAAEEEEAAAQQQQAABBNqrQFosD1wfcozW9h5SWOLeyfrQYmHw9rV8kP/aEvw0/yvFesUxarvFlqne6iwx0pKRf674o9UHFUuEtKTO07TOHqvXOk9pYsnS/1WsVByiZQWBZfdpaom+v1SMt7qQcp5ef13tn/HrtT0/eXeq6u7VsjuDlt2i+TsU31JYUqxf/qkZ69tvQ9qbh/XtCW13qJYVBa1jsxcpvqj6D/x6tbtL89bfbyr+7NdHMG32sagfNhq9nbeLNf2JXu8I6pclo35BMVX184P6Y8milnR8heprEzG1fo7qJir+rvn/adnWoHXqzGp5Z1WMU9g1aef2Y7+Blv1C839S/Ftxtl8fbqq2J6j+BoVdB3ZdeUmnqv+NJh8pLFl0rdVFUSwx1a6ty7W9altP27P+5Cru1Lwlt64K2Z718/uq/1dwvdra9W0Jtm8rvqzllbZc9ZYM+5niV5p/Q/V27Vn9yZr8QLFMcazqCwL1v9bUknntmqtzPFrnWtXZtWM/RzbCtyUBe0XLbtPkVsWPFMHXry22T2vs2rcbBqqsQu3/qsk8hZ0DO89hS6Bft6n9aWowVK9vC22oZc05x2O0vSO13dWh243mtfowWO2HKMx9UjTrRtF2otp+XXFMuHXUB0tyNotuirGKkxRmbNdUnaK2Q1Vh5+lpHfurocub+PoCrXeBtleb8Kz9/ER19yvs/erMoO1G/L6mbdjxXK6wnwu7Vr1ryC9a3jP4dei82t+vNjmqP1UxTq8nhrap57Vdl/Z74grFgyFtrgm8rr12tY/TVXe7YpriPO2nwF9Hy67V/OOB5T/16xuY2rmzMrOBNg0tmhFYeIqmteejoRVYhgACCCCAAAIIIIAAAggggAACCCCQnALV+uj5wWXvuKc1snhWh3T38DHfdgfn2MeZ8VWWKUnxjSaOdry2eIf72awn3YNHf8tlpabH14HRGwSiENDnyu6/6z51f1PydYVGQG/JYjd7PKvk8HAlNaWDEtGzvWT0/tnd3fG9R7qTeo9yAzv2CNe8Tp2N3P7h1gXumdWf1Emgt4TjKz/5m7tq+MnuuhGnuey0jLDbWrl7i/vt3PFub9ilVCaiwGsbZtZJQLffS5/uWB53h3L/krfcCb0P9G7AoCCAAAIIIIAAAggggAACCLStQIcY794SZS2B2EYWrpN8bvtR3Yag/VmCrJU/qH6LX695Swy9WWGJvt/260OmN6rdnqB1LFl5taK74hfBCYWat8TgKQob0Tw1zPY+UZva5PPAcj9ZsVCvQ5NCnwy0OcLflrY7SPOWcLxOUSdhXNu2ROLnFPZpz1f9dYKmz6vNByH1lmhtJWwCa5ht+FXNPpbAhh7WNFPhJ2/62/+uZlIUtUnVOvbD9doSRi0J+3m/oU0D5+FWzdonABcHLwszf6HqLEF1vNaz8xlc7tWLNYqztD9LHm6oXBdYeKe2k+c31HyZ5i35uynFPj2068pLPreiebve/q6wT8m/EagOnsxRmzrJ54GFdt3b53E3abld617R/DZNfh94GXzd++fAjqcgsNzal2u+vuP5iZbZtr+pdrXJ54F1bR87FVf52wqalmje+lX7aanmF6nOfn4Okn2XMOtEU9Wcc/xn9cXMm1v6BzawU9uza6IlysbARnvXs/Gfqd5+Lm5UWALzBMXZ6s/24Pbytvdney8qUtwQvKyZ8x9qX6HJzg9qmysVZ2i/Q237TXhfs+va3h/svbn2Z8Xvq/Zp111LlKcC+/N/Vrx9qP/2qfjlCvvZshs+/OJbfkd9Kgiqt5+rcXo9RxHu5yO4qT/vvx9tDrcwgjr/d19j72sRbIomCCCAAAIIIIAAAggggAACCCCAAAKJKlBRXelun/+il3xupUwJpz/NfcKtL26pj9SaJqXPz5Rw+3azEk7nFaxztyhptarhh1M2rYOshUArCNjP650LX3H3LH69xZPPGzsc+znKKy92q4u3u6lKHL938RvuK5PvcZd/cr/7x9J33Nz8tfv8rBVVlnlJ51+dfK+XQB5u9PYKfU00btVE97VP/uo+3LLAPjuv05V8PdHgpllPuZKq2q9qG+sqyxNAYMLmOa600r5+rCnLdZNBvq6veCs79ux2/17+Qbx1i/4ggAACCCCAAAIIIIAAAu1SwJLFY1mOC2wsONmvvu2PCSz4MLSBPshYpuRBS1YfrmlOSJJggV5bomRo2WTtFbmhC/TaEkIt+bxfYD64SbiRa21bViyJuCq4cdD6lnTulyMDMx+rfUVIe3tpx/h1hbXzE9j9ZuH2vz6wsHuYbTVUFW5b0R6Lbd/6+CeFJZzfaxU6D5Zofa0iX/GC1QXK8YFpN7W5Lajen/WTcA8Ksyy4qqHroVLbnqzGwxRmuK6BbfnbmRSmjX16X5v0HWZ5fVXrdF7DJT9P1AqWSOyf/+D1PwvdmI6hi+r2V2zU9paELtdr/2cheHv+/Cdh2n+qujrHo310VN3hih2KG/U6zGpegnC487Fc/doVZgX/eszRst1hlkda1ZxzvI9npDsNaeeD1P20tIkbq2e1BvchY3sfsp+pvpqcoLCftdl6fb6WzQra5k81f6riS6q3n7sGi9Y/TQ0sgssarTsupG5SyGv78LhK69s1tp/Crrm1gak1jeh9za4dbeN1tb9AMUfzL9m6iulaZjc3tEjRtjdoX/ZJo92gMlqv7aYJK9aPHoq/qi7458Tes+x9+lK1v7SmaZ3/W+J6by3rqfV2hlkeXNUz8KLR81PPdvIC9b3qWU41AggggAACCCCAAAIIIIAAAggggECSCxRX7nG/nP2sm76z7iizBRUl7oaZj7v/HPc91zPTPlpu+2IJrjN2rmx2RyZtW6RE2dfdzw/6sn1O2uD2LMH2nU1z3ZubZrl+WTnu5D4HuWN77l/vqMwNboyFCDRTIG9PkfvFnGe8xO54LquKtjmLJ1ZPcjkaHd1Giz5Bo6MvLtzoXt0ww9n7TiRla1mh++WcZ/Uzd4D72egL3NBOvVy5EvD/b/YzbnNpUz8Wj2TPtGkLAbsuPtCo+OcPrPk6Lx5HP/ddXlg71evnyK7924KKfSKAAAIIIIAAAggggAACCAQEYp2AnhPYrj8CcEPQ3QIL6xs51uptVFhrVxC0ocJ6NuolGCphMNxyP/nQkqhDS0Pt91lmiYyBD0SDtxXJsdh+c0J3rtcFoXVB+0gNXdbI6336q/b+se+zrJ5jMcPdOsante73NT1drz/S/IUKS5y9X6/LgvrhJ2CepTqL+krn+hYE6ptjGLxpfztbQ/enfluSbWMJpaGr2et9thVotCWk78Hr+svC9a2ha97a5wStFO3xdNe6KQpL/L81eOcRzBfU08a/hqK9HkM315xzHM4zdPuRvPZvyOilayEr5FqOZP1I2gwINKozonnoitq3XVevqB+WdL5M8aTiEGunugM0uVPxuNq9ZXURlNPUJvScT1LduJB1I72em3K+LtO+fqG4UnF7YL9lOp4XNf+zwDGHdCcmL8dpK/b+c43C9m/F5q08EZj6E3vPst99oVYhzZy9ZzX2flEaWCkrdOUIX2cH2vnbiXA1miGAAAIIIIAAAggggAACCCCAAAIIJIPATo3i+tPcJ92SXRvDHs7G0jx3k5Y/fMy3Xce0zLBtWquysrrK/X1JpB9VNt6rF9dNd30yu7lr9zstbGN9lugmbVvs/rn8XS+R1i+vb8x1GR3S3FE9RigZfZQ7qfco1y87J+w2qEQglgLLdm12P9Oo31vKCmK52Rbflt3M8tam2V40tdgNMldoVPWrhp3stu/ZFfcJ+E09TtZz7rX1M2oT0Kdtt6+u4rNU61kcf170mvv3sd91HbwHClMQQAABBBBAAAEEEEAAAQTaQiDW/yIrCBzEwAgOxk+I9kYDDlP8W5b3SZwO07atq5LpWHzLhwMz3wtMbTR0K//2GwSm/rH/RB8IpzQQ14WsF/oyVob+dvqG7kBJsJZA7SfMhy5u6PU+2wo09q/dcNfo3jAbbMox+iOS79OHeo7H38fsBs6Fd57C9K+lq5py/H6fwnlG3V8d93qttE5hCcinRL2ByFY4PdBseiTN1ae1amejdh+sc+qPhH2wXts3Stepbm9wqO7UwHaXB+ovstfazm1hzvlpgbbBk32upcDC0Os56vOl/ZcG+jFS27QbiL6usJHVbWpJ6C1VXtGG7Wfl6/ZzobAbMM5VzFV/5obs1I4rP4xV6PuXnZfGiv/NV1PeV2zb/nqff4PW2B5ZjgACCCCAAAIIIIAAAggggAACCCCQFAIbSna673z6r3qTz/2DXKzk9F/Pec5ZAnhblv9tmOlWFzc45kbU3XtIyeVvbAx+KKT3Oaf7bMcKd92nD2uU5afrJJ/7O7ARmKdpNPY/L/qf+/KkP7srp/zdPbzsXbewYL23PgWBWAt8tGWB+/an/0y45PNYOlTqaQQ2onpzEtlj2R+21TICcwvWutW66cdGQ7f5eC7zCtbt8zsknvtL3xBAAAEEEEAAAQQQQACBZBSIdQL6pwEkS/xrrPi32p8W2lDJg/urbpBitT4sLAhdHoev/WM5SX0PN6q8n5Ba95PUODwQv0tyn6f5KYqv6JiO1fQLismqXxzSbf+cn9zMw2noejDTkwLbb8zQX+4n6QZ3y/oY7vw01vUhMhgWptFpgTq/72GafF4lu916Zc8nHajt2QjXoSXcdVJ7bYU21uvjFHWOR/soUt1ChSUz9wizTktXed+CaN/hRkuP1Tlu7jH4N1H8Vv1s8D1Qyy0JPOKi9qPU+FKFfcvxbMQrOjcg0Nb/FmmNXj9aT2wJtP1vYLm1jabs87MROF/+z5h/nmqvPS0P93MT7nqt7YeuxfWKZ1TxRYU9P9jeHxtL1PaPP9z1U+8xaj+lWviCwhztveoqhfX5iTAr2XtWd/XFkvybW+x90oqd96YUf705TVmZdRBAAAEEEEAAAQQQQAABBBBAAAEEElNgceFG9y0ls27QCOeRlKlKtr5r4attllxdVFnm/rXi/Ui6GnWbOxe87PxRducrmfCHMx511898zC0q3BDxtlbs3uIeXzXRS1q/ZtpD7p1Nc9s8YT/iztMwrgX02bP7z4oP3C/mPOvKqiviuq90DoFYCbymG45y81a5Kt10EO/lgaUTXEF5Sbx3k/4hgAACCCCAAAIIIIAAAkkr0GDyZROO+mGtU6m4Rcl9o0PXV50llfvlscCMJYHaaLVeCSRC3qNZ65slYMZ90QdQ9knoe4phihuDO6zjseTtKxX5ileClyXAvJ3PDMVLihTFP0P7rGOfqbqPFV/VsX4zdLm9Vv2hij7hlgXVvap5+7T9CrW1xOrgcqNejFC8r/2tC1kW+nJcoOI32k5tArbms1R/V2jjCF9bMuzd2kbtz4vmh6vuBoVd709HuB1rZte9Wf5F26hNstV8L9XdEtiO/7NhL58M1NnxdAvMm6mdlz/6r0Om9+m1LX9M7XJC26iuu2JMaH2MXu8MbGdImO29qrpYnOM6m9axTFTYKOGnhdlnuKq/qnKu4mTFk/UYdVb977T8Z+E2EK5O7S2xe4LC7B/WtWr78IqWjVL0818H1XdQ/Z16bT8fU7WOvU/YF0lzFN8OF1q8NLD+rwPL5/jbi3B6hvZ5fkjb6/V6P8VH2uZaW6ZpVO9r2mZvhb3fhZZOquiisJ+V8tCFIa8bun4aWdWNCzS4WlML258lwIcWO/9WHlF//cT/2jaq66QIfQ8K3Yb/2t77qhRh22s73RR27v0neoRux1/vo9AFvEYAAQQQQAABBBBAAAEEEEAAAQQQSE6BT3csd9//7N8uv7w4qgN8fWOu+3cLJYE31pGnVk2Our+NbdNfbgmOv1Ry740zx3lJ+Zb02JyyRCPG3zJvvPvK5HvcM6s/dkUVZc3ZHOu2Y4HSynL367nP6efug3aswKG3R4E39WSKydsWJ8ShF1aU6AkY78RFX5ft2uy+Oe1h9/NZTzubpyCAAAIIIIAAAggggAAC7UHARoiNWVHC4iIl2v1QG/ynYrbmX9PURr61UW/HKnYrTrcdqu1ULf+zZv9PsUDzL2pqn7ja6OmHKD5R/EWRKOX76ugU67OO5WxNLTF7sMJGQ65WXKdjtuNPpPJfddaSNQcqdiherqfzlmD/oeJRHfsNmk5XFCgGKQ5T2Pk8XrFNEbbIpkjrWgK77XOS5m1qyeZHKcxzi+J7YVcOqtR2pmjdB1T1Y4V/XVVo/kKFJfc25V/887SeJdbmatvvatpNcZkiR/F/2udKTSMt96ihXePWn7na3luadlTYdWJJyH/W9uza94rmzeLfmv2uYqHmX9LUjucCRaFik8Kur9qidSzx3NzsZ3Gl5u2TF7O0hPzhilMUjyvsmo11sU9i7VheDhxbqebXqk9Pxeoch+mwf2OAJRw3WtSPEvXtHDW095yrFBfo9XuarlDYtvZXnKnoqrheEVqOUPvbApWZmvZV2PUxWmHn4j6Fva8FF9ufvTdM1tSul50KW+9UxQiFXd/fUbRGeV07eUV9eUVTO+bDFecp8hR2zQSXaN7X7H3iU213saazFOsVZni+op/i77Jv7D3wI7Uzw7u0HXvfsJ9Z+zn4g00bKmpjP/t2PHb9pSteV90+7zmq+0DtfqnldymWa95+BlcrOiuGKuyc2M+gnbMGi7ZVqPXtmj9N0+567fU3qHxF848rnlBcG7xA7e1as+tsqdZbELyMeQQQQAABBBBAAAEEEEAAAQQQQACB5BSwpL4/aMTvpo4q++jKj1zvzK7uq0Ps48jWKVtLC9wza2o/sm6RnZZWlTsb5T2WZWtZofvb0rfdIys+dBcOGusuH3aC65/dPZa7YFtJLGDXz89mPeWW7rKvYCgItC8BS+p+Y0Nuwhz0qxtmuAv0Pn9IzuA26bO+43GvrP/M3bvkDVdRXeX1YdK2Re7s/oe57+7/BTekU6826Rc7RQABBBBAAAEEEEAAAQRaQyAt1jvRP7JsVFlLpvuZ4jTFRYodCkvi/Y+itqjtL9R2tiosyfNqhSUNWnLmbxX3ann5563je059XaVjGRvouyVznqbYpZiguFPLZ8T3EezbO/PXMT2jJTcqxun1nn1b1YyUrHaW8PxjxcUKS+pNVVhS7SLFA4r5igaLtv+atnOiGv1a8UVFN4Vt45+K32t5pJ/0/UTt7dPqHym+p7Bk31cUtt25imiLJZVa0vifFdcpLKnWjuse9enZaDYWMD1L69ykuFJhZpUK69eNWv5cmO39QHVLFHYs31cEH88GvbafmTpF2/mRLN8OtP+CpjkKSzBep/iL4uk6K8Tuhf2MD1VcrrAkbHuPmaR4ynYRw3Nsm7ORxVM0OVixRvGpVxlBUT+2aNVT1NSu1ysUxynOV1jysxn9V/GY2k3VNLQcrgoLKyUKuz7s/LyosET7FTWL6vz/fb36t8Kub1s3R1GssOvUbCw5285Pa5SXtRPry28UX1JUKKzuV+qD9ae26HU072trtOKtitMUpyt6KeyYlip+qXhe0WDR/hbrvFyjRvb744eKrMAKf2hwxc8XPqHZ3wde2nzYov3YEw2maOENipMUFyoKFRsVZhPNz/VDan+2wq75hxWRFvu5HKj4aaQr0A4BBBBAAAEEEEAAAQQQQAABBBBAIDEFKqor3d+WvO1eWDet2Qfw50X/cz0zu7hT+45u9rYi2cDDy99z5ep/opaSqj3uubVT3Ph1U92ZfQ9xVw47yR3cRkmKiWrY3vq9qmiru2Hm425bmX3FR0GgfQpUu70Jc+DW0+9O/7fL6GBfTYcvHTQm0Lf3P8P7HRDLUlRZ5v604FX37hZLg6hb3t08z72/Zb778sCx7lv7ne76ZueENuE1AggggAACCCCAAAIIIJDwAilKxEv4g+AAWk5ASZoTtXVL1D1Q18rylttTfG5Zx28/IJN07KfFWw/VtwPUJ0sYfl79syTqdldkcJgOeq7iRzKwRGBKPQKyulaLHlfY0xjG1dOM6igF5GqfaNoNNuWKI2Ub0S9VrfeS2p+q2E+rWPI7JQoB+eWOUcnNzY1iLZoigAACCCCAAAIIIIAAAggggAACrS+wQ0msv5rznJtbsDZmO8/okOb+ecx3Wny01yWFG93V0/4Rs37Hy4ZGdR3oLtEo8jY6bVZqRrx0i37EgcDc/LXuptwn3e7K0jjoDV1AAIFYCnRKy3Svn/YL1znNH/uoeVtfpick2O/39SU2bljDxZLjLx58nLt2v1Nd94zODTdmKQIIIIAAAu1JYMLYmqM9Z2Z7OmqOtX0KpLTPw+ao24NAh/ZwkBxj0wSU4HeM1rQEyXfaY/J509Riv5bOQz9FnZ9Vve6oPd0f2Nsrsd9rwmzRrs+tiscSpsd0NKkE9N5YpQOyEdsPV3w1koPTz+8RavcVxW1tkXyu/a9R7K0ntoQ7BrU9QfGWIk9RopinuFFR75AiWnaN4jNFkaJQMVFxfrjtU4cAAggggAACCCCAAAIIIIAAAggko8Cc/DXuG1MfjGnyuTnZiOR/Wviqq95b3WJsNs7C35a+1WLbb8sNL9m10f1hwcvuSx/9yd23+A23tmh7W3aHfceJwMSti9yPZjxK8nmcnA+6gUCsBYor97jXNzR/UBv7/fjSuunuuk8fjij53I6jvLrKexrHRZPucf/Uk0VWF21r0d/hsbZjewgggAACCCCAAAIIIIBAfQJp9S2gvv0KKEHwBzr6gYrrFPYJ9q3tVyMujvxG9eIKS97UdLOin+JMxSDF24r/Ktpl0Yc8D+jALSgItJmArkNLzP6JOhDpsBn91fYWxT/brNPO2ajr94fZf1FonY7tQtXZiO1livGKPMUFir8qTlRcqqhTtM49qrhZsUHxiMKGkrpc8bqW/VhmD9Zdg1cIIIAAAggggAACCCCAAAIIIIBA8ghYctoL66a5+5e85apaKEl82e7N7r3N890XB9i4CLEt1n9LkMvNWx3bDcfZ1nZXlrnn1071YmyPEe5ijYp+ap/RLk0j1VLal8DLSib986L/6Quxve3rwDlaBNqZgL3fXzrkuCa/zxfp98ZdC15x722xB+NGX0qryt1jKz/yootGYj80Z0hNdB/iDu422Nko7RQEEEAAAQQQQAABBBBAIJEEUuyDRAoCwQJKDlyj15bcvEphI/Q+216FZGE/IJNkcFpbGagPlmxuIywfoeihqFQsU9h5uV99q9CUgkCDArqOrlWDxxXX6ZoZ12BjFia1QOA93uk6GNbYgaptV7VZoeimOFHreM++Ur0l23+oOF5xheqft3orWnaCJlMUKxVHa1l+oH6Ypja8SCfFKNWvsfqmFO0jd4xKbm7zRytpyv5ZBwEEEEAAAQQQQAABBBBAAAEEEKhPoEzJZX9c8KqbsHlOfU1iVj8wu4d74eQbXXqH2I01pM9s3ANLJ7in13wcs34m0oZ6ZXZxp/c9uDYpcEB2d/u8K5EOgb5GIWDX+yMrPnD/WWkfdVIQQKA9CNx1xBXuzH6HRn2oy3Ztdr+c86zbULIz6nUjWaGDS3H7demn3z+D3VG6KeqMfoe41LoPyI5kM7RBAAEEEEAgsQQmjK3p7zleGgIFgWQW4IOFZD677fzYSEBv5xcAh48AAggg0L4EokxA/6Z0HlU8qS9jrgmW0nbO0OsPFJO17FR/meqf1Pw3FN9Uvd30UFu07A69uEVxh5bdGrwsmnkS0KPRoi0CCCCAAAIIIIAAAggggAACCLSWgCWl/d/sZ9yK3Vtaa5fu5wdd4C4damMENL9YMu59S95w49dOa/7GkmQLPTI6u8M0Mu1hOUO9pPRRXQe4zNT0JDm69n0YldVV3qjnr26Y0b4hOHoE2pmAvZc/etz3ozpqG/n80sn3uZ3l+zxENqrtRNP4cP3eueXQi92QTr2iWY22CCCAAAIIJJYACeiJdb7obXMESEBvjh7rxrVA7IbFiOvDpHMIIIAAAgggECSQqSTur+v1EEWxYp7CEsmrQpQsydzKhJB6ezlZUaI4QdvK1Lp7Am0aWudttbEEdGvT5AT0wH6YIIAAAggggAACCCCAAAIIIIAAAq0isKui1E3ettjlK/GsrKrCiz02rQ5M7bXmFxSsd5ak1prFRm7+0sAxrmNaZrN2W7232t2tZNxX1n/WrO0k28p5OucTty7ywkpaSqqXhH6mRqb94oAjnI2YTmm6gN30UFhR4raWFXqxc89u11+jzh/SbbDrnG4PYYy+VFRXut0VZd6NApl6OkBah9R9NmJPKvjt3PHezzUFAQTal8D8gnVuXv467+aiSMtjKz9q1eRz69fcgrXuqil/d9ePPEc3mh3nOjAaeqSni3YIIIAAAggggAACCCDQigIkoLciNrtCAAEEEEAgTgT6qR9PhfRltRLJr9OXPpOC6g8MzC8L7bfaVar9atUfrBihWKzXnTQdqCjS8s2h6+j18kDdyDDL9qmykc73qaypGFVPPdUIIIAAAggggAACCCCAAAIIIIBATARsdOTpO1e4NzbmuslbF7uKfe7bj8lumr2R/PJi99yaKe5b+/tjAkS/ySoln9+54GUd66zoV25na1TqOlhQuN6LB5ZNcMf3GunO1w0AJ/c5yGUo2Zmyr0C5EsJXF21zy3ZtdvaUAD/ZfJsSzi32aHlosaHhRnTuq5Hnh3gjzx/afagb0rGn0+eFdZoWlpe45bs3K7Z421+meduXnSe/2E0DmalpLis1w0tIz1JieklludtSVhC6W14jgEA7EXhu7SdKQL8yoqNdW7zDPa/fs21R7P3xXj2Z5KOtC73R0Ad27NEW3WCfCCCAAAIIIIAAAggggEC9AnwaVi8NC9qbgD64vE3HbCPynq7EyYnt7fhjdbxy3KttTZLhabHaJttBAIGYCjyurX2sWKjYrbDk8esV31W8rZ/h4/XzOzewx26BaWFgGjrx63Oa2D50e7xGAAEEEEAAAQQQQAABBBBAAAEE2lRg5e6t7s1Ns9zbG2e3+minTT3wp1Z/7L465BjXPaNz1JuwRPvb57/o3tnsfxwU9Sba7QrVGr17yvalXnRNy3ZnDzhMyehHuYO6DtwnUbq9IPkJ4ZYIHpwQbjc5RFPsS4aVRVu9eGXDDG/VbukdvWT0YZ16u3UlO7ztR5JEbsnolZVVrrjSf4BjND2hLQIIJKPAR1sWuk0l+W5Ax+6NHt7flrylm1qiew9rdKNRNpiVv9pdqdHQbzjwXPfVwce0298xUbLRHAEEEEAAAQQQQAABBFpBoEMr7KPNdqEkulGKBxQLFIWKcsUmxZuKbynqPL9Pr/daxKLD2s5oxQuKbYoyxVLF7Yrs+ravZecrJioKFUWK6YprGmifquU3KuYpShV5ircUJ4RbR/VnKe5VfBBoa8f7Sbi2wXVqE/GxqO1pCs+xnvhTuP2p7eGK8YotCjtP6xWPKGwk3YiL2t+msH3fVt9KWnZtoM24+to0pV7bHBfY7rCmrB+v6+iYBiv+pMhV5CsqFHZdv6/4icJPUG3SIWj9YQo7Z2uCNxBUH3wt2YjLWxX2M3xuk3YYxyvpmJp8/WrdQxX/UcxWbFfsUdjPkZ2nryrqDg0jB1X51+y1oSxaZu8vjyrMf5aib2gbe636TIW9x1q7DeHatHWd+vVNxauKFYpdimKFjdZt7zH+CN9t3c1W27+Sy29XfKjYqihRLFB8Xx24T2G/o26LojP+dRXt786I2qtfR4UL9W9JFH2kKQIIIIAAAggggAACCCCAAAIIINCgQIFGUf7v2mnumqn/cFdM+Zt7WgndO8uLGlwnnhaWVO1x41ZOirpLlnx+y7zxJJ9HLbfvCrsqS92L66a7a6c95C7XNfTU6skuP4GuoX2PKLKajSV57tX1M9xv5jznLph4tzvrwz+4H8541N2vhM23Ns12KzQyebTJ5/XtubCixH2yfYl7es3HbvK2xREln9e3LeoRQKB9C1S7vW782qmNIkzbvsx734mHUlpV7u5e9Jr78czH3ZbSgnjoEn1AAAEEEEAAAQQQQAABBFzSjoCupMLf6fzeqrAk+08VTyjsE2NLojxN8R/FDxRjFTEt2vex2uCHinTFi4r1Cnv+pfXpTC0/Uwl1dYZaUJ2NPvuAYqfiaUW54hKFJYgeqvY/03xtUZ0l/T0faLNU0wcV9tytyxSTtfhirfPa52t4cz9SXKgoU6xQdA9Zvs/LphxLYCP2affEfTbo3CehddrHeap7RWFeryuWKSwp9FuKL2v5iToW629LFzM003UtvaNE2b7sv62+mkumYq7iOUW+oqfiJMX9ilsUvRQtVQoD+7HtZykOV9g1c5769xNdG39vqR0n2HaPUn8vUtj7nX1qZm79FBcoXlI8rfiGotEiV0tCHq+wdT9QfEXOu+tZ8Y+qH1rPsnip/ro60l8xXbFFYUNVHKy4TnG1jvciHd/b8dLZNuzHP7XvmxWnBPXBriMr3YLqgme7Bl747Rpr72/Hb1fPZqlGAAEEEEAAAQQQQAABBBBAAAEEWlZgV0Wpm7x1kXtvy3z32c4VMUuSbdle17/1F9d96i4fdoLrn929/kZBS8qrK92vlTRsibyU2AqsLtrmHlg6wf1z2XvuzH6HukuGHOuN3K3PIWO7ozbY2s49u93MnavcjLyVbsbOlW5zqX1dQEEAAQQST+C1DTPdd/Y/03VOt68e9y0V+j1535I39l3QxjX2N8vln/zN/Xz0Be5LA8e0cW/YPQIIIIAAAggggAACCLR3gbRkBNCHeL/Wcd2uWK+4VImFlnRYp6jN+aqwRLuYFm03VRt8XNFRcaH2/T/bgeotEf4FxcWKnyr+ZPVWtGyYJvco8hRjtc6aQP0dmtqzBW9Wm5dUP83qA+VyTS9RWKKpJbSXWb3aWQLhJwob2ddGuA1OGr1b9b9RLFEMVqy2deorTTmWoG1N1L5vq2/bfr32Yf+qf1SRobCk+ZeDll2qeTP7j+I0v76lptr3Dm3bgiIBnZsrNXlEYZ8g27l5MxRGbU5U3T9C62P8uiD0WtJ+r9M+HlP8UfP/0fKSGO8zETf3vBzGhXZcPpYg/Kni65p/QG0+C20T/Fpt7EaW1xUnKCwJ/WqtUx5uHbU9TfX2fvZDxcPh2sRJ3Xk6Bu89Mrio/2fp9buKexUkoDu3LeDTKcjJbnAaqxipyA2qt/cI+xtiuKJSscqWydlGl9+o2YGa9tfrzcHraP6AwOtlIfW8RAABBBBAAAEEEEAAAQQQQAABBFpcoKiizE3atsi9r6Tz6TtWuMq9VS2+z9baQYWO5d/LP3C3HmZfGzRcbMT3W+aOd9N3Lm+4IUubJWDnZMLmOV7s36Wfu3jwse6cAUe4Tmk23kv8FRsR30YZ90LXSKFu0vDnt5UVull5q92Koi3x13F6hAACCDRBwJ4eYknoVw238bb2Lf/VjV1ri+Pza2Pr++3zX3QllXvcpUOP37fz1CCAAAIIIIAAAggggAACrSSQdAnoSngbJrvbFBUKSzpcEM5S9W+o7XvhljWz7lStf5BisvbhJZ9b0Xy19vd/mrUE9O9r/m7V7Q0s/qam9omj1a0J1Nk6+Wpnowtbgvb3FcEJ6D8ItPut2tUmVmp+htaxpNFvKOyTZkuG94qW1a6vNn51Q9OmHEtD2wu3zJJc+ylmqn+1yeeB/v5X/bSEx1M1tVHg54fbQKzqtI/btK1bFadrXxP97ar+ZM3buTtS0VthCdlrFG+r3e3WTm38c2kvVwf5rlWbYVZpRfWWfHmL4kyFbcs+uXhf8Xu1q/Npf3B/tLyXwvpwiMLOtyXN3qx1LNGzTtF6lkD8c8VFimEKSx6eqbDry9ZrtGgbXdTogUDDy+tbT/VT1PbYcBtUvfXZrt8LFNanFYp7tE7tNRluvQjrxqmd9c8SZQ9WzGhovcDx3Kg2lymGKOwHwJJtzeXP6pNdZ7UlcExmeJLC+r5V8ZbidrXdFNTUm1X7ozS5U3Giwq6FzxR2nr+o2OeaUl3Mi/q1T4K17UT1u9S/dzRr70t2/Vnfwha1G6QF1na04u+KG7V+8LVdu57aWmL7OMUHavJPvX64dmHIjJatCVTZ9ft7hb032fWxVHGb1n9Vbez3kV3jdnPBYIVd23/Vsgc13aeovdn+RHGMwq7XDQp7D7lT6xQEr6DX9dm8p+1Y2/2D27fj+eMDx+4lkwfKh5pepThH8VxQvc2eouiosN93e4KW2TrfUNg6oT/v5wbaWRsKAggggAACCCCAAAIIIIAAAggg0OICZVUVbuLWhV7S+bTty5wlBSdreWvTLPd1JdLtp2Tn+sq8/LXu13Ofc9vKdtXXhPoWEFixe4u7e9Fr3sjo5w08wktGH9G5r8svL3ZbldxtYUneNfMF3rRAy47oPsxdOewkN7xznxbolfOSK/+w4CW3cvdWV1QZ9mPUFtkvG0UAAQTiQWD82qnuMiVwp3VIrdOdvD1F7pEVH8RDFxvsw18Wv+7SO6S5iwYf3WA7FiKAAAIIIIAAAggggAACLSVgCX/JVix5MV1howEvaOjgQhLmGmq6zzIlLd6mSksstYRUm/fLGYGZCUF13qzardJ6yzQ7UjFCsTLQpt51tPztkDaW7JqpuhMUJYqPA8uDJ7aOJf/ZdkOT/8I0r7eq3n41cCz+xvZXP6/XC0tS3aL4WOvUSbAONPQ/CQ9OeAzukNVbcu+ZivnBC1pjXsdgCZRvKuzT+P8pLCm2h8KSeX+o8BLQA9OLND1c8TdFgVf7+dTOm/3r/32FJcvathYpRiksufNCLbeR7C0hOrTYfr6ssHUmKY5VXKY4XOscEXwd6/VQ1U9UDFPYtWHXoSVpn2/zWv49tX9E840VSxC24/xU7d9tqHHw/oPa5Wh+iqJc8aIiS2HbfEx9qNY6TwS1be5s2ARpf6PanyWbm4P9zExT/EdRqRisOE1hTrkKr6i5vYeYkSXUmvl6hSVuf1txgZYfp/6v07xX9Nq2a+c1Q/GyYoXiCMVERZsn2ap/HdWPMxRW6v0ZUrvRWm7J54MUv9Yx3hVYp77J37Wgu+Jb9TUIqbf35fcUdl29pjCvKxQvad9na2rXuV3b9v5l9pcqHtCy7erLeM3XFtX9Ti9uV+Qp3lBsUxym+JniPC0/Xuvsql2hnhm1O0mLchSz6mmSdNU65oN1UJvlY3a1RfX23vFgoOLpoEX283u34nK1sRH0Z9oyzdvP9B8C7R4Oam+z/1TY76DfqN2rWiffKjU/TJMfKez8Nud3k22OggACCCCAAAIIIIAAAggggAACCDQqsHPPbveTmePcst2bG22bDA3sg9KHlr3r7j3q6n0Op3pvtXt69SfuoeXvOH1Au89yKlpHwEatfXHddC/S9UDdxm6IsARxG6H3xN4HeonoY3uMsM/ZYtLZGTtXul/OftbtriyNyfbYCAIIIJBoAlvKCtxHukntrP72FdPn5eHl77pijS6eCOWuha8oCT3VfWngmEToLn1MUgF9F+j9vW1PG2qodNRTYEZ1HRCzv2VC92VPc7G/b+bkr/FuzOifbV9lUxBAAAEEEEAAAQRaUiCtJTfeRtu2hEIrbXVb8oGB/S+r5/gtCdsS0C38BPR619Ef65v1YWKx2g7StKNeW9L5/opUxSq9tkTa0GL7sGL7aE6pt1+BjYY7Fn9/V2nGorao/y/pxXf8ZMTAgh2B6fDgtkHzIwLzlqgdTTlN+7utnhWOqKc+XPV3VNlBcZr6PTe4gbbfy3+tZbcFkistAf1+vV4T0tY+EX5S0VXxdS1/xl+u9S7T/POKpzU/Wsuqg9fV/DmKo1Vfmzysds+qzpJ3L1S8ENT+Cc1bIukVam/b9Ira52gyUfF3zf9Py7bWLKn3/839OTKHRxWW8F5le9F+/6rJPMUvFNbP5pTrtHInhf1sLGxkQ4douSWJWyLsV4Lbqk92brv5dXptPzP/UqxRnKr2G4OWnaH59xR/U3jbUXs7r48pshUXqf1rVm9Fi36iyf3+6yinTb5+tV97f/i6wt4j+iq+pBiguEv9M/9w5XhV3qfoorhO7caFa+TXaR92/Ncovq226xpqG7TM+mCJ3vaz5H1qp+08pclkxX8V9n54iJYVBJZZf5YofqkYb3WB+tM1vV0xTWFPuSgILLLtXav5xwPLf+rXBy2/RPN2Pdj5snN9niJPcX1o2yR+bYn9v5TVR5quVuxW7Kew6yRL8ZbiHoVX5Gsj6Nv74IuKiZq39xUz+7LCfkdYfe350bytM1Xt7PzdpJineWuTobD3uh6KH6vNGk0pCCCAAAIIIIAAAggggAACCCCAQIsJbCzJcz+e8ZjbUGofZbSf8vH2JV7SjY2c7RcbSfu2ef91U3fU97VF+/GJpyNtLPk8uK9Tti91FiO79HdXaZT7s/odts+IvdEc26vrZ3gjslft83VENFuhLQIIIJD4As+u+cR9od+h9h2TdzCLCze6/+nGn0Qpe9XR389/yWVoJPTQRPpEOQb6mdgCs/JWeTdAziuI7Cvjw3KGuBtHfckdkjM4JgduSecz1Qd72pE99WhXRam33ff0+tHjvue6Z3SOyX7YCAIIIIAAAggggEB4gWRMQO8fONQN4Q85ZrUPakuWiLcjZIt+MmthPXvy63OClkeyjiXbWjtLQI+kvW0+eB9Bu4t4tin72a6tW8Lom4o1CktoHKv4o+JiRT/9A/4UJR9Wa97KFEW+4mjVX6j64ATer6r+qEC77oFppJNT1dAiVqXmXypBW1NfQ899Q/uyBOhRimlarzb53FbQ6/E69us1e1IgLCE3uPxdbWqTzwMLHtH0CsUxCi8BXds4XBM75hfV3q7N2qLXBVp+qypeVdh5eCh4eZj55v4c2XV6k/brJZ9b0fwi9cHO9ymadtFrS3yNpOSo/W2BhnY9HaY4N/DaRure59zUs9F92mlduw7t+vPLDzSTrviJltUmn9tCvf5Q/fifZm0UdL//dl4tCXeyltdeu4GN2XvEjxWW3Bttac71awnodq79Uq6ZnyvubaAT3w0su0XHMa6Bdnad9dXyfyneVlu7ySCacqPW2eOvoPmPtb3Vej1c8Qu9LghaZk+MsOvlZE1Ttcy/lm4ItLGbWWrbW531XW1/otmrFPskoKvOEtAtCdovyzVzpdZLnE8SgzrfxNmPtJ5ds0cqjlfY75YCxSeKpyzksVfT2qKXr8rVrsnfKOz9w34OVygswdzen+q0txVVdbPWmadZe2+z68t+1mYp/qJlb1gbCgIIIIAAAggggAACCCCAAAIIINBSAss1AuMNMx53O8uLWmoXcb3dfyx9x/372O96yXSWjP6bOc+77Xt2xXWf6VxkAja66K26meBBnePLhp7gvqLRPbukZ0e2slpZwvkDSyc4S7ikIIAAAgholKvCDW6+EmcP6z7Uvttw9y1+w+3zpUecQ1Wrx7fMG+/SNO7W6f0OifPe0r1kEVikn52Hl73npu+0r1sjL5ao/s1PH3Zn68kDPxr5xSaNUm5J57PyV7v3N8/3nmJQWGGpCXXLhpKd7ubcp9xDx3zLZaVmhC7mNQIIIIAAAggggECMBNJitJ142kxKoDMt+m9D/QPUko+jSUD2jZrSv2jXibZ9U8/fPvuRy0JtzMIv9gn/BH3QPVXTOYoTFRcoXrMGal+sZZak+6TiZc2/rqkNwzJS8WWFJTBawnFtIrPmIym3a9u3hWuofVyr+sfDLQtTZ8nilgg/XeuN19SSN6do2xvCtG2oakxg4Yf1NLJ6S0C3pNDJIW3CJceuD7TpHtTWkkmtdFNfbwuq92d7B2YOCrMstGqfcxvaoJHXy2UU7hsNv985Wn93I9vwF3fTzK2BF3Yd5CneVjyofbxl9TreizQ5wuaDyhwtf1WvFynmKK5Qu6Ga2rVnn67P1HJLzg4uvuGpant0yDJ72UeRqrDrM1fhn9dJoW217Sptw/azX+iyCF43+frVfido+9p1SrqmQxSWjP1HhR3TxWGO2brznuJMxa/Uxm6S+MAq6ymPqN62/Z16ltdXXaDtrgyzcJPqhivMM7RsVIV591PYvBU7RxWKS9XXSwN1wZMMveitZT21v53BC/T6cr2+XMu6amqfwNl1NUWvbaT+ccFtk3Vex2nX6j7Xa2PHq/WmqI2NGB9x0TpPqLEFBQEEEEAAAQQQQAABBBBAAAEEEGg1gdl5q93Ns55yRZVlrbbPeNvR3IK1zkZCX120zT28/F1Xve/4AfHWZfoTpYDdUPDgsgnu0ZUfeglcFw062o3uNsi76aC+Uly5x90yd7z7RNcGBQEEEEDgcwG7KccS0N/dPM/Z79BELPa7/jdzn3d3d/i6O7nPqEQ8BPqcIAKrira6fy1/30v8bk6xnzfbxpVDT3LX7Heq65xm41/VX+yJPjbS+Wc7VrhJ2xa5fL1urCwoXO9+N+8Fd9cRV7pU78HoFAQQQAABBBBAAIFYCyRjArolM9q/qgbFGivC7RUG2lnSbLhiiY9W/Hb+fC/N2Dp1EiYDbf11/ITepuwjsKmoJjHbjxIRd+mDz2e1dxtB9xTFa35PtOwZLbPE5F8Elp2r6QrFjxXViocU2/z2rTlV3ywp/nzt82bFNxXfs/2rzpJlf6XllrgbSfGvh831NPbrc8IsLwhTVxmos+Rcv/QMzJylqUV9pXN9C4Lq7efISlN/jgrq2Ue4ftfTtLZ6rZyHNdLoIi2/JqSNJb6+qnUtEfwMzf9OcYni7kC73aq3NnYe7UYJK77hzwOv65v4hv553VpPwy311Ld4tY7JkrQt4fsOHacl2t+luEFxT5id28+mWVi8ofaWqP5WaDvVX626CxTXaLmfEB7arL7Xwe95wW28a0LbC7fcv17Sg1awc2S/u/ybEurbn52jcO+nti97L52q47Fjmal4WPPvqz7aG0vq2zf1CCCAAAIIIIAAAggggAACCCCAAAJtIPDxtsXuV3Oec+XV/sdKbdCJONnlL2c/6yo/f0BlnPSKbsRaoLSq3L22YaYX+3fp5y4cNNadO+BI1zVkVPTNpfneKKAritrsI+tYHzrbQwABBGImMHHrIrdy9xb396U2/lXilko95eKXs59x94z5hju+98jEPRB6HpcCG0vy3H9WfOje3jRbCRx7Y9LHCo1k/sTqSe5/G2e67+7/Be/vmLQONekPpZXl3pN8Ptu5ws3YudLZE46aslf7+f7bkrfcTQedH5M+sxEEEEAAAQQQQACBugLJmIBuIw5bsumZikfb4IQvDeyzvn/VHRBYbqN8+8XWsQR0W2daUL0lOvfX606KDUqOLAksW6FplWKElqepPvTT9HD7CN5spPNNOZaGtr09sNCOp07RMUxWhUWdEkgQtroZocta67X69qb29ab6Yv0+VmH/OvmBwhJ1j9TyRRH0xU+u7VdPWzvPVsIl4dazyj7V/ro/UZ/+vs/S6Crs58gS7u3n6JboVm391jrea7VXi7BFy/O14KcWOmf7a3qqwm4muF6Ro/iGwopv2E3rWJJyY8Vv37eehvWd73qat1i1fWJmCeinKe4Jtxcdr90IUqZlzyle0fwVqns5pO2YwOsngn42g5sMVL3/b//uWr8g3L6aWWfmHbTtHs3cjiWil6u/H2g7hyqOU7zY3G2yPgIIIIAAAggggAACCCCAAAIIIIBA2wi8sXGW+8OClxjtO8BP8nnbXIdtudcVSp68d/Eb7oGlE9wZfQ9xFw0e647sPtzZ6J8/01MBIhkptC37z74RQACBthKwZNofzXjM5ZX741W1VU+av98K3Xz289lPu78edY07uud+zd8gW2j3ApYIbk/UeXHdp7q50cYOjH2xv1HuXvSae2HdNHdqn9Fe4vmCgvUxu5ny+bVTXf/s7u6KYSfGvvNsEQEEEEAAAQQQaOcCHZLw+B/XMdnIvxcrsXB0Q8en5ZkNLW/isg8D650Tur72N0J1lmS+VrEqaHm966jNuYF2fhtLmtyjuqmKjoqTg7bjz+6zTpg2kVTV268GjqWh7VqCp5XgY6+3vfZhIx1/RbFb8Xq9DVtpgdyLFR8qbtIu/6jIUPjW1gu7KcBKzW25gReByezA9LS61bWv/PpZ9SyPpPrTQKNw10Qk6we3sUTcPMXxOg9faGjlFvo5amiXzVqm87dC8ag2Ykno9knShUEbjNbQP1+2rTpFLnYdnBRa30avBwb2G3qzSp3uyOUlVdjPnCWRj9cxXBnSX7tBxuzChTW1m2T8ZfY+1RLFzlF39e3gGG08IpsY7YvNIIAAAggggAACCCCAAAIIIIAAAgi0gMAzqz92d8x/keTzFrBlk4knYE8AmLB5jvv+Z/9xl3x8n6aPkHyeeKeRHiOAQCsLJEPyuU9mvwduzn3SvbL+M7e1tKCVJdldMgksKdzorp72oLME7pZKPg/2Wl20zY1bNdFLQI/1zZT3L3nTfbhlQTKdHo4FAQQiFNixZ7dbW7Q9wtY0QwABBBCIViDpEtCVQLlGCLcpLDnYRq0eGw5F9ZYgbqMCN6lo/V6KUTYN2cAkvV6sOEXLvuwv07xZ3x14/U/10x8l2Kosad6SNa9Xu2FB63TX/K/9dfz6wPThwPQPWicraJ2jNX+Zwn57WjJpc0rUx6K+nBg41jr7Vd3XA/0q1/SF4IVa1iW0k6rrrLqnFbbsDnHtCm3TGq/VjzMV2WH21TdQ549Kby93BuqGhGk/RXVLFSdpe5cELw+8PkV1Nir+J2HWjahKRjPV8GPFV7XNb4ZbSfWHKvqEWxZcp21Z0v8NgTpLRP5iuHVUbzcVWFJy3Bb1cbgiXLKy/XxlKkqDOv+g5u0Glr9qHbtZpE5RXYbi5KBKuxHEzqv9vAcnsluT6xWtNrSA9m/XVnrdHjt7ikJv1f0pUG+j+TdYdO6tzfkK+1l9Sutf56+gZeMV3w4XgTb5QcuCXRvcZ5QL/xpo/4j6NiB0XdV1Uvg3u9jx91QcGtrOXqvejtMS7u1GBHu/oyCAAAIIIIAAAggggAACCCCAAAIIJJBAtUZhfFCjPf9t6dsJ1Gu6ikDrCawv2ekqqqtab4fsCQEEEEAgLgTKqivcXQtfdRdM+rO7ePK97k+a/2DLfFdQHvzVdlx0lU7EoYD9jf3kqsnum58+7NYW74jDHkbfJUvOuWXeeDcvf230K7MGAggkrMAn25a4S3VT7tc++av3pCi7SYuCAAIIIBBbgbTYbi4+tqYEyD8qsdCO7VbFDM1bkqgl51qSoSUOW7LvAYE6TeoWtR8XWhf0+ofavv3LzJJLbfu3K27zl2tZlda3hM0PFS9q/kVN1ynOVIxVWCKyn0DpraZ1VqvdzzX7d8VMzY/X1JI/LVF5kOJetQlN8n1e9V8NtJmtdWyE8J6KyxQ28vJ3tE6dpG21OUn131ZY6RyYHhB8vFrn2kC99SvqY9G6zyg6aJtmvkFhyfFHK45R2G/y72m7azQNLteo/c2qmKjYrLCE2QsUdq4eUdynaKtyr3Y8TP2bqOkahZ2XoxRnKOxfJ3Ye/PKBZuw8WmKsnXe73gp0vA8q9qruGr1+T2EJ3a9pukRxoOIixW7F1WpWrWlzypVa2a69R7UPSyCfrihQ2HV0mOIQxfGKbYoGi/ryjLZhyfcPKiZofo6mdl7zFXat2XYOV+xQxHOxPr6i/udqukCxSWHX2IWKdMXdfud1zEvUzpL3H1Ms1PwETe3GAGs3RGHJ53ZzxyiF/YzYef2WZu28vqT5lzVdobB9fkFh65+jaI1i56mf+jBFU3vPsW8VhinOU9h5fFVhx9Vo0WG9r+1Yv99U2LWUrbqHGl2xFRqoHx+oP7/Uru5SLNf8W5quVth72lDFqYpPFL77YM3be+QsTRcqNipyFEcojlPYDQeWVG/XNQUBBBBAAAEEEEAAAQQQQAABBBBAIAEE9FmOm7htkXtkxQduxe4tCdBjuogAAggggAACCLSNgN2MZPGyRkRPURcO7DrAje25nzu25/7eNNUbR4+CQI3A1rJCd9u8/7rcvFVJR2I35d086yn3n+O+74Z26pV0x8cBIYDA5wL2mcGTqye7h5a94/YGqp/S6093LHd3HPY1t18XS0ejIIAAAgjEQiAtFhuJx23ol8kdSjj8r/r2Q8XpCksKz1LsVMxRWNLp04pw5ZpwlYG6GzW1BPR6i/Y9Xfs+Wg0sOf1sRReFJSvfofiTlu8JXVl1D2idNar/meJqhf1Lb5Hit1r2RJj2lvh6heotIdgSZn+sKFNMVvxB61h9aNlfFaHH1iek7trglZpwLA9rfUu8PVFhf7Xbv2Mt4XOc4n5tb66moWWmKhYrLGHUEpstGXuG4mG1fy20cSu//qP29xXFWIUdV7XCknut3o6nNmlV8+/onFgi/XcUP1VkKOy8P6iwZGX/uvitXtq2LlBY8vZzit9r+VJr15yibWxQHyxB3q6HixVXKVIV9i2MXU8PKOYrIira3n+0vXfU+HrFWQrbXidFgWKBwo4zoqRmtWurYteXJStbYrJdY90V2xW5ir/rGN8O7pheP61jtuvUzuXpirMVxYpNihcV40PaT1H7k1V3p+LcwLLpmp6m+KLCT4QOXq0l5u/VRi9SHBnYr11/dn19qHhK8YKOzf/bWi8bLmr6sY7LrtMJin9o3pLQbR9tXtSPu9WfKerIDYqTFBcqChX2XvNvxbNBnbSfQft5PUVh13BPhSWd28/xvxR/0/bs/YeCAAIIIIAAAggggAACCCCAAAIIIBDnAvbx1sfbl3iJ50t32cd1FAQQQAABBBBAAIFIBeyLwiX6G8ri6dUfu/0693XfP+Asd0qfg+zJwZFuhnZJKmCj5P9xwatud2Vpkh6hvlCuKHE3zhznHlUSeo/Mzkl7nBwYAu1ZoKyq3P1h/svu3S3z9mFYvnuzu3rag+6Gkee6S4cep9FVuQlrHyQqEEAAgSgFUqLIR4xy0zRHAAEE2reAPqi5TQK3Kk7Xe+3E9q3B0SMQOwH9bOWOUcnNtftIKAgggAACCCCAAAIIIIAAAgggkOwC9j3GlO1LvcTzxbtsDAIKAggggAACCCCAQKwEDu42yP1g5NnuGI2KTml/AsWVe9x9i99wr29sP9+72TX/pyOvcn2zurW/E84RIxBPAhNsLFCVc2xcyeaXLaUF7uezn47ohnV7EsjvDr3E9c7q2vwdswUEGhfgTr/GjWiRoALcypOgJ45uI4AAAggggAACCCCAAAIIIIAAAggggAACCCCQzAKWeD5Vieff/PRhd9OsJ0k+T+aTzbEhgAACCCCAQJsJLCzc4K6f8Zj74Wf/cfML7CHClPYisLhwo/vGlAfaVfK5nVu75i+dfJ97bOVHbk+VPTybggACsRIoqihzH21Z4P63YaarrK6K1WYb3c7svNXu6qn/iCj53DY2fecKd8WUvzl7+gMFAQQQQKDpAmlNX5U1EUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHYCpRWlrv39SXwS+unu0VKDqEggAACCCCAAAIItLzAzLxV7luf/tOd0ucg9/0DznL7d+nX8jtlD20msKpoq248eNTtrixrsz605Y7LqivcP5e/515TkuyNB57rTut7sNNTmNuyS+wbgYQUqN5braTvze7THcvdtB3LvBuZqlRnxf5d/8fDr3Cd07Na9NheWf+Z+8ui/7nKwH4j3dmuilL3qznPuS8NWOpuHn2+65zWsv2MtF+0QwABBBJJgAT0RDpb9BUBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgSQVsBEYX9sww72zea4rrtyTpEfJYSGAAAIIIIAAAvEtMHnbYvex4twBR7qfjb6AhLz4Pl1N6t32sl3uxpnj2m3yeTDa5tJ894s5z7qjeoxwNx90PjdeNOmKYqX2JpBfXuSm71jhJZxb4nl+eXFYAltmTzS776ir3aCOPcO2aU6ljbB+7+I3vJvXm1Pe3DTLzcpf7W4/7FJ3RPdhzdkU6yKAAALtTiDFHmFJQQABBBBAAAEEEkVAow/kjlHJzc1NlC7TTwQQQAABBBBAAAEEEEAAAQQQqEfAHs89YfMcb+TBpbs21dOKagQQQAABBBBAAIG2EBjRuY+7d8zVbmDHHm2xe/bZAgJFGvH8e9Mfcct3b26BrSf2Jju4FPfVIce47+5/lsvJ6JjYB0PvEYixgN0k/tHWhe7tTbPdzJ0rXTTZhl3Ts93dR1zljuo5om6vJoyteX3OzKh7u7pom7tt3n/d4l0bo163vhXsPeDqEae67+x/hkvvwJi+9TlR3yQBHrHRJDZWSgQBEtAT4SzRRwQQQAABBBCoFSABnYsBAQQQQAABBBBAAAEEEEAAgcQWsIFx7LHcr6yf4T2Se091RWIfEL1HAAEEEEAAAQSSWCAnvaO7+8ir3JE9hifxUbaPQ7PRgn+a+4SbvnNF+zjgJh5ll7Rs990DznQXDz7WpXVIbeJWWA2BxBew94wZSjZ/e9McN1HJ52XN+Ld7akoH93+jv+y+MviYz2GakIBevbfaPbdmqnto+TuuQv1riTKq6wB3x2Ffc8N0ExYFgRgJkIAeI0g2E38CJKDH3zmhRwgggAACCCDQgAAJ6A3gsAgBBBBAAAEEEEAAAQQQQACBOBawEdMm6Itrezz2it1b4rindA0BBBBAAAEEEEAgWCBNiYO/PPgi9+VBgdFq4Uk4AbsJ9I75L7k3N81KuL63VYcHd+zprh/5RXda34Odvp9sq26wXwRaXWDZrs3eSOf27/ed5UUx3f/lQ09wNxx4bs3NHVEmoG8sydP72Itudv6amPYp3MYyNQK69fOSIcfx8x8OiLpoBfglEq0Y7RNGgAT0hDlVdBQBBBBAAAEETIAEdK4DBBBAAAEEEEAAAQQQQAABBBJLYKWSzS3p/K2Nc1xJ1Z7E6jy9RQABBBBAAAEEEKgVuHLYSe7HB57jbCRbSmIJ/Gv5e+7RlR8lVqfjpLeH5wx1N4w61x2aMyROekQ3EGgZgQUF692fF73mluza1DI7CGz1uF4HuD8efoXr/MFJNTXnzGxwf3YDzWsbZrq/LnnTlVaVN9g21gtP6DXS/fbQi12vzC6x3jTba18CJKC3r/Pdro6WBPR2dbo5WAQQQAABBBJfgAT0xD+HHAECCCCAAAIIIIAAAggggEDyC5RXV7qPtiz0Es/ntMLoZMkvyhEigAACCCCAAALxIXBi7wPd7w+/zHVOy4qPDtGLRgVeWf+Zu2vhq422o0HDAmf2O8T9SCOiD9LI6BQEkklgT1WFe2TFB+7p1R+7are3VQ5tWKfe7onCx11GarpLPTe33n1uL9vl7lzwspu6Y1m9bVp6QU56R/frQ76qpyGMbuldsf3kFSABPXnPbbs/MhLQ2/0lAAACCCCAAAKJJUACemKdL3qLAAIIIIAAAggggAACCCDQfgSq91a7+Rox7f0t89y7m+e5/PLi9nPwHCkCCCCAAAIIINCOBIZ37uPuG3O1G9ixRzs66sQ81I+3LXE/n/VUqyWVJqZS5L1O0+j/lww5zn1zvzNcTkbHyFekJQJxKrCocIO7ff6LbnXRtlbvoSWgW/lhj++7PlldFTmub3Y31yezq6Y5rkI3tj+87D23u7K01fsWboff1s/9dw/4QrhF1CHQmAAJ6I0JsTxhBdIStud0HAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBNpUwB6FvaBwvXtv83z3wZb5bvueXW3aH3aOAAIIIIAAAggg0PIClqh47bSH3E9GnecOzRniBisRvYMScyMp9vfjupKdbn7+Wt28uE7zO1xqSqrL6JCmSHXp3rQmauZT3eBOvdxZ/Q51HdMyI9kFbQICllj6mznPkXwewyuiUjfdPr92qntj4yz3rf1Od18berx3zVIQSDQBe2rZI8s/cE+tntzm7xHFVXvc6uLtXsRz+c/KD72f9+v2Oy2eu0nfEEAAgVYV4K+gVuVmZwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIJLaAJQ1ZMsv7Sjj/YMsCt6WsILEPiN4jgAACCCCAAAIIRC1QWFHi7tCouVayUzPc/l36uZFd+ruRXRWa7telr8tSfUnlHu9vR3tSzryCtW6BprZutOX+JW+6CwYe5S7W6NNDlZBOaVhgXfEOd+PMJ1xZdUXDDVnaJIGiyjL3t6Vvu1fWz3A/PehL7sTeBzZpO6wUmYA9bWutrum5unFlTv4at2TXRletf5d+frOKfxNLWu1NLNmp6a6PRtPun9Xd9ddo2v2zu7temV1cmm50ae/F3pPt/XtVG4x6nuj2Dy9/17vurhp+UqIfCv1HAAEEYiKQYh8UUxBAAAEEEEAAgUQRSElJyR2jkpubmyhdpp8IIIAAAggggAACCCCAAAIIJKxAWVWFvujfri+mt7qVu7d6X1Av27XJbWOk84Q9p3QcAQQQQAABBBBoDYEOLsX1zerm/d1YpeTRWJZjex6gkaePcyco6Tc1wpHXY7n/eN/W8t2b3Y9nPO7yyovivatJ0z9LQL/xwPPc0M69k+aY2vJAbHTuJYUblWy+Vknna9xc3byyq6K02V2y94s+mV1dv0BC+iE5g92Fg8a2m1HszfU/Kz50T66e5CXwt3V5ovBxrwvXdLuurbsS9f7/b/SX3SW6IYqCQIQCKRG2oxkCCSdAAnrCnTI6jAACCCCAQPsWIAG9fZ9/jh4BBBBAAAEEEEAAAQQQQKBlBOyLaC/RfPc2t1LJ5pZwvlrJ5htL8tr8cdwtc8RsFQEEEEAAAQQQQCDRBWxE40uGHKuR0ce6nIyOiX44Men/PCXs3pj7hLMRuimtK2DJzZcNPcF9e78zXOf0rHp3vqNsl54IsM57KsDuylI3rFMfN6KzQk8NsORofRda77rJvmB23mr3+KqJbpam9m/U1iiDOvZ0Nxx4rju1z0FJbb9y9xZ3y9wX3IqiLa3BGtE+EjkB3Q7wt4d81X1ZNzBQEIhAoP2+sUeAQ5PEFiABPbHPH71HAAEEEECg3QmQgN7uTjkHjAACCCCAAAIIIIAAAgggEEOBCn2Jv06PLreRzC3J3J9uUKJ5rEemjGG32RQCCCCAAAIIIIAAAvUKZHRIcwd1G+j6Z3UPjGyc4yw5vX9WjuurkY6zUtPrXTeZFkzbvsz93+yn3Z5WStxNJrtYHkv3jE7uhyO/6M4fOMbt1SjTK5T4O89LOF/nTTeX5te7u85pWW64JaMr9uvcV9O+7mCN0t0pLbPedZJhwbz8de7fK953n+1c0WaHc1SP4e6no77kRnYd0GZ9aIkdV+sJFC+sneYeWDbBVVRXtcQumrzNRE9At4zi2w/7mjtnwBFNNmDFdiNAAnq7OdXt70BJQG9/55wjRgABBBBAIKEFSEBP6NNH5xFAAAEEEEAAAQQQQAABBFpRwL5oXqtk84UaWW9+4XpvaqObk2jeiieBXSGAAAIIIIAAAgi0uUAPJQT3VTK6jUqdnZrhOqZmuuw0m2bUvFZyr1evuixvudVneq/9enudqUT3poxOXamkz0WFG90MJddagu3m0gJ3Qu+R7ktKUD6k2+AmbTMU9b3N89yt815wlfo3ACU+BOwmiPzyIldWVdGsDmV1SHdnDzjcXTToaHdwt0ExuV6a1aEYrryocIP79/L33dQdy2K41aZvyjJEzx94lPvBAWe5Xlldm76hOFnTRtu/Y8FL7tMdy+OkR3W7kegJ6HY0HfTEgjsPv9yd2e/QuDSmU3EjQAJ63JwKOhJrARLQYy3K9hBAAAEEEECgRQVIQG9RXjYeAwEbzWJjaZ5buXur93hAS+ywD5ftQ9/KvVWuqrpmaq9txIojug/zRrLooEczxrps1wdL8wrWum2aDunUy9tPP33I3pQPyGPdN7aHAAIIIIAAAggggAACsRfI21PkFirRfIESzRfqi3z7Mr+osiz2O2KLCCCAAAIIIIAAAgi0Q4EOLsX1yuzihnbu7YbqM/ehnWza2w1T9FGyqv85v31PsLZ4u5LNV3oJ57l5q1xx5Z6wYkM69nLnDTzSnTfgSG/09qaUV9fPcHctfMXtbcrKrJNQAvt36ee+okR0G3G5S3p2QvU9uLPLdm3SiOcfuMnbFsflMdiNJ1ePOMVdNezkOk9QsO/88suL3c49u90OhX0PV1hR4iUhp6ekurQOqS5dN6rYfLrmvdeat3Nl7xPdMjq22vFO2rrI3bngZVeg/sVrSYYEdLNN1Xe8fz7yKndyn4PilZp+tb0ACehtfw7oQQsJkIDeQrBsFgEEEEAAAQRaRoAE9JZxZatNF7CRKxYrqcN/fOICPT4xTx8+RVO6pmW7I3oMc0d2H+6O1HRkl/7eh1LRFEtyX7Z7s5uvxxT6j3HcUlawzyZs5JaaxzfaoxvtMY410z5Z3UhM30eLCgQQQAABBBBAAAEE4legqKLMLdq1Qf8e2ej9m8RGVAz3b4D4PQJ6hgACCCCAAAIIIIBA8ghkapRqS0q3JPIlSq7dVlYY9cEd1WOENyr6GX0P1ujrmRGt/+Sqye7BZRMiakuj5BGw6+0LGnH5K4OPdofmDEmY73dWF21z/1r+nvtw68KEOBl99d3ZAfrOzpLNd+zZ5SWfN+eJYj11A0vNd3P+93M139HF8maC0spyd//St9wr6z+Le+NkSUA3aLvR4J4x33DH6+kWlNgL2PfguypK3W4NsrDbpoqa1zZfU5fWoYM7sOsAN1pPiojDAdlIQI/9ZcEW40SABPQ4ORF0AwEEEEAAAQQiEyABPTInWrWMgI1aslUfGtckeK/VdL1bqg+Sm/NhU7ieWpL4Yd2HegnpvTVqSs0I6v7o6f4I6jWvS6rKa5NN9lQ3/TGOafpgxEZe6KZRGLqla6r5nPROQfMd3aHqk32ATkEAAQQQQAABBBBAAIHIBfboptWdGpl8d2XNl2NF9iVZ0Bdmfr39u6JjaqazkdY6pmVomqnXmnrzGd6oaxtK8rxRzS3pfF3Jjsg7QUsEEEAAAQQQQAABBBBIGIEsJRcf02t/b3T1wR17ukEde3hT+74geJT1fyx7xz25enLCHBcdbRkBS2i+fOiJ3kj6GRp9Ox6LJYs+ohHPX1g3zVXruzZKXYE+mV3dfhrd/sCu/b2Ed0uitZ/7aJ+ebJ8V3DJ3fMJ8XpBMCeh2Ri0J/YJBR7krhp3E96kx+iG3pww8tOxdN2HznKi+j++e0ckd1HWgl4x+ULeBikHeE0zasJCA3ob47LplBUhAb1lfto4AAggggAACMRYgAT3GoGyuQYHy6kovwdxLONfI4ja6+TaNcNCei33odXb/w9xZ/Q5r8uNA27Mfx44AAggggAACCCDQNgLVSu62L60sgXtDaZ7bWLJTkafRkspcjm6+tC+mumd0DkyD5jM7uUx9gV9ZXfdG0Eptr3JvlXezaIWmeUow36bt2yiH9m8Gb6rX2zWN58ddt83ZYK8IIIAAAggggAACCCDQFAFLLh4YSEa3G1inbF/alM2wTpIKWHLllUp8/crgY1ynCEfQb2kKu05f35CrBNJ3+LdxlNh2Q7olo49UUvpIfTe3v55obJ42Crt9zlBgUy80X1HsCjVdvGtjVEm6UXYp5s2TLQHdB7JM41P6jHZXDT/JHZ4zNGGeUBDzE9yMDdoTyJ9d84l7YtUkV6rB2GJR7Gngg7J7OHviQFdF57Qsbxr8unN6lqvQZ322z5LKPYFpuaZ7nA0KZ08YsM8Bbd0uattFTzn3t9HFq6vZnn3OmJrSIbjbJKDH4iSyjbgUIAE9Lk8LnUIAAQQQQACB+gRIQK9PhvrmCvijm9toggs0srklndsHNZaETgkvYB+aWDL6Gf0OcfbYQAoCCCCAAAIIIIAAAm0pYH/T79RjsVcXb3dr9FjvtcU73EYlm29QsvmmknzvCyIKAggggAACCCCAAAIIIIAAAsksYEmQlw493l2msBut26rMzV/r7ln8ujfQEwWBcALJmoAefKyHdBvsrlQi+mlKSE/rkBqOgbogAfts7/0t890DSye4LWUFCWtjA7o9cfwPg59iQAJ6wp5NOt6YAAnojQmxHAEEEEAAAQTiSoAE9Lg6HQndGUtMWaRH0S22x9cr0dymeRopgBK9QAeX4o7uuZ87s9+hbpT+QT2kUy/XMU5G16jvaOwDDPsv2scX1re9+urtTvgS3RVvd7nbvmyampISmNbU1bdue663kUR36ZGchYFRPAoD8zaCh9V5oXkbgcBGLO2Z0cW7CaJnZufPp6rrpmUhIwy0Z1aOHQEEEEAAgSYL2N9Oe6or9Du41Nljs4sqyzQt01Tzmu7Wa6uz+WJNM1PTvdHWbDSgTl748zXTLI3i1UF/EzWn2CO77Yuo1V6iuSWcKzS1flAQQAABBBBAAAEEEEAAAQQQaO8CmR3S3ZcHHeW+Pvxk1z+7e6tx2BPBHlz6jpuweU6r7ZMdJaZAe0hA98+M/QxeOfREd3Kfg7zvCTUsurfI/3QsRXPevOrtJhL7bC1WJfhzvV36fs0+27PP+IpthG+FN7K3N+J3zUjfNd9tlnuDtA1Qv/fr0lcj8Pfzpjka2bulig0Sd9/iN9w8DRKXDGXyWbc5+ww0UJr3QWgygHAMSStAAnrSnloODAEEEEAAgeQUIAE9NufV/qFpj2mzx8Zb4oclEHvTuo+Cis3O2ngrlgSzrmSHW6fRDy1WFG1RsvlGt1UfgFFaTqB3ZlclovdU9HZDOvbyktKHKnooQbi8qtLZo9MsicmmZfpQY48+xPDqFJawbYlD9sGHJTAVVWhaVfPazqd9CJKWkuoyUtOcPXLUPkTN1Hym5jM0b/VpupZtfduWfWBij0kr1rxXF4hqS0DXtW8jDlh7m1qysm3bf20fDHRWwlRo4pS9tnr7AMgSpfPKi7xHHdqjD73YU+Ty9chDO6bGiu0zS9uxx7RZkpY93s0+XLKp91phj2zLSe+ohOpOXsK1PbotJ72Tl8yl98V9dmE/47Zv+yDJPkSyqfXTYrcSxezDJZs3z13Br+Vt7w2NFTt+75Fygb55j6dT/2seLZflsuVmfTBjSw6r1jb9ee/9R6/sA6yaBPO6SeVWF6vEMTu/9tjRAXosrX1IZo+n9acD9Zg9S1hPxve9xs4fyxFAAAEEkkfA+x2r3632d32lRve2m7jsMbFeeK8ra+etzv7Wsi+PLGze/gbz521qXzj5fzfY7+Tavx/0dwNPBkqe64YjQQABBBBAAAEEEEAAAQQQaD8C9v3fyb0Pcr2zunjfp9j3EbVTzfuvTcRLRG0gITVd36N0TM3UIEQZganm9X2ADUqUrTq7QfzJVZO973EoCDQmMCnvL16TU3v8vLGm7W65fTdo32H10FMM7LtVG4jJpva6m76Ts8/0vARyL3G85vvP2vnA96HB3w3G6nM9Gwxq/859lYzer3Y6vHMf732kqcVuWnlo2bvurU2zm7qJuFyPBPS4PC10qgUE0lpgm2wSAQQQQAABBBBoUwFLbrRkCy+xQomuFZZ0odcaa/jzkYc72Cu91jTVnwZGJ25K5y3xw5I+LKnDkjeDEy69JMyg5EtbVqX+VHnT6kD7munnrz9f5teFLrPkXS+xNpBgW6qpf0eyN2/JJTp+a2f9Mg+zKA/M2+jP4YolbFpCqyXF2r3Wdhe2va5JUrcRm2sS1Wvb2f3YYRJgQ7edrqRe+2DKwhJ8a+bTausyUtJclj6cytKHX5a8mpWmD706ZGherwP1jT2azI5zY2mePuD6POHcEoMprS+wfc8uZ5Gbt7r1dx7FHi0p2nuvsHWqolgxhk3tZ7sm2X5P1Fu1nyVLSLdkdPs5scRtL1FMo4Pb+15LFdvH5tL8ltp8zLZr53ebrkOLOflr9tmu3cBgCemWTG83E9Tc0KCbGQLzwTc42PtPut6n9n0fs3p7T0vz3rvsPHhTP/Talvs3CtjvA3t/tpsr/PNu8/4HhPa7xIo3Rr9+T9g7dZ151dUkC9oNG/Yeb+/rNfPeVK8juYnArh3vvdb7giHwXlvndYZuygjckOHfpGE3Z9TeqFEzgn9NsqP9DlOSo/0u1PuwnwRp/bBj+PxGp5rfITW/W/RbxPt9kuK913eyLyz0JYV9eWE3VjT0fu//nrffeWV2I4umdtzmFHrDg3lbvf3Otd9n3k0q3u+imhtY7BzXzOumFs17v/si+J22z8UUqKj9G0TnJvT37uevra81I5t4o5rYr9HA715v6m3Lv7Hk89/W/pztw4p/XH7yqff3h3es9ndIzd8i/u9/+1vArhf/WvGvG+uT2Xhb08S2YJ3zLAN11j9zs5t87Gafz+dr6szNv4a8qfflk11bgZ+DwM+E9SX4uv/8xqCanwX7m8b6bTeo1PztFP4mFq+NXWuB4wz9OynA4xk1p9iNSKn+tR/0M2B/O9rPj127dr1ZX/0+m6X1uuY6rPl7tOZvr5oE4NBrwn5e7D3FvvDb96aqmvck+1lo7Jjt56ZPVldFN9dXYVMLu0moOdez+dm+d+ipMZtLC7yk5MaKXcFhb+zy309k593sFfr+Enhvae2nVtg5sSdq2M1jBbpxrHZery352kb49t9b/cRt7+cnKHHbzrd3vSjqPu2k5jqJ5H2l9r3LrqfA+2nNvxlqfp73vd4//7fC5+8SjZ0dliOAAAIIIIAAAggggAACCCCAQHsUsM8YJm1b1B4PnWNGIGEFvCcNKuw773gq9oRxi+k7V9R2y3IGbCCy/ZWUbqOkH9Clvzdiev/snDqfT9t3izVPMNzmPcVwbeBphhtL8rzvIygIIJCYAiSgJ+Z5o9cIIIAAAgggEBCw5L0bZj7uNpTs9JKtvcSeZiRdWtJMTYJ0zWjI3kjIlqjmTTt4CUWWLFSufXiJ7Zq3sAS8ZCneP/C85JfGR0FOlmPmOBBIVAF777HEQAtK9AL2O8M+5GrpYsmH/qjwNhI/pXEBS2q2JGYbbd+S5C2B2hL37YkJNrp/S/2Osr8DLEH988T0mpsO/NeWZBp6k1tNUnHgRq8k+nug8bNEi3gXsJ+dPpmWjN5VT9DoWPNEDT2tolN6Zu0TNmqespGlBOo0PR2mwG3SzUVelOR7NxptUZ1d461V7GfQT0IPTqz+/MaLmp7YDQCf39Do/60euMFRx2J/t9tXFlXVQTfI+Dcv6G93ew+xv+VtVC8KAggggAACCCCAAAIIIIAAAggggAACCCCAQNMELLfAvmuzeH/L/NqN2GBDlohun/euKd6mp0kXN20HrIUAAnEtkOJ/gRPXvaRzCCCAAAIIIIBAQECjOD5js/ob5iqbHjPh16doMgkgBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEIh/gScKH/c6eU236+K/s/QQgegFOn12zh8bf6xo9NtlDQTiSoAE9Lg6HXQGAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgSQWeDZlpnd0V+4dm8RHyaEhgAACSS3QIamPjoNDAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiJkACegxo2RDCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAcguQgJ7c55ejQwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEYiZAAnrMKNkQAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQ3AIkoCf3+eXoEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBmAmQgB4zSjaEAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggktwAJ6Ml9fjk6BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgZgIkoMeMkg0hgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALJLUACenKfX44OAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCImQAJ6DGjZEMIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEByC5CAntznl6NDAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRiJkACeswo2RACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIJDcAiSgJ/f55egQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGYCZCAHjNKNoQAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCS3AAnoyX1+OToEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCBmAiSgx4ySDSGAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAsktQAJ6cp9fjg4BBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEIiZAAnoMaNkQwgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQHILkICe3OeXo0MAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBGImQAJ6zCjZEAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkNwCJKAn9/nl6BBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgZgJkIAeM0o2hAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIJLcACejJfX45OgQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIGYCJKDHjJINIYAAAggggEBrCKSkpDxj0Rr7Yh8IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAJ1BdIAQQABBBBAAAEEEkxg1BgV9flK6/fGknnuxXU3Jdgh0F0EEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTap8DlHbt4B/78ki/sbZ8CHHUyC/xw5BsuvUOWf4gpyXysHFv7FiABvX2ff44eAQQQQAABBBBAAAEEEEAAgbgWGJB9qMtO7ebyy9e5gvKNrtpVxXV/6RwCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQ7AIkoCf7Geb4EEAAAQQQQAABBBBoBYEU18EdnHOekkS7utKqQldaqbCposyb7lIvGMCgFU4Fu0AgqQSO6nGZO6H3N12HlFTvuKr2VrrC8k0ur3yty9uzTtN1Ll/TneVrtKwiqY6dg0GgpQWyU3Ncj4whrkfmELerYotbWzyzpXfJ9hFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBJJEgAT0JDmRHAYCCCCAAAIIIIBA/QKWHJ3ZoZMec9VRidAFrnLvnvobsyRqgUEdj3Cn9b3e9cwcVu+61XurlIi+220pXeSW7vrQrSqaxnmoVyv6BR1S0r1r3K5vCgLJIJCWkum+0P9n7sCup9c5nNSUNC9Z1sLVPJ3TW27J59vLVrqtZUv1PrPYbSlbotHSNyQDBceAQEwEBmYf5vpkHVDz85Mx1Jtm6aYxv+zdu9fNL3jDfbztn/x+jok4G0EAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEkluABPTkPr8cHQIIIIAAAgggkDQCI5WEeGAXS0RMqf+YUlJcRkqWy0jt5DKUbG4J55mK1JQMl6JlVso0EvfCgglubv5rbnfl1vq3xZJGBTqn9Xan9Pm+27/LKbW+9a1koxd3TMtxI7qc4EV5dalbtXuql4y+TiOuVruq+lalPkggvUN2zWi1geRBG7m2u5IIu6X390aILqrY4SXeWgLuVk23li51FXvLMEQgoQS6pPV1Fwy63fXO2j/ifqfqJox+2aO8OLz7hd569n5vPwP2M7GhZK5iTsTboyECySQwpNNR7qJBd+l3dYd6D8v+Tjqs+wVucKcj3Tub/uT9DqEggAACCCS2gD3pghtUE/sc0nsEEEAAgdYTGNH5eG/ADAoCCCCAAAIIIIAAAggggEDkAik2whEFAQQQQAABBBBIFAElx+SOUcnNzfW6vLFknntx3U2J0n362QSBnpnDvdG1B3U8vAlr17+Kjci9uuhTNyf/FZIS62cKu8QSPY/q8TU3tuflSvLPDtsmmsrSqkK3YvfHbmnhh25j6Xytyr9RfL/uGYPdkI5jlBA4RiPXjnSd03o1muwfbG/Xed6etbVJ6Tv2rHb55et0A0BJNKeItgi0moA9UeG8Abe47LRuMd/nxpL5bsr2/7jNpQtjvm022LBAB41cn+rSdUNMacMNWRpzgazUbu7rw//tOqX1jHjbVXsr3Yydz7rPdjyt38jVEa9HQwQQQACB+BK4bOiDrqK6zH2w5T5XWLEpvjpHbxBAAAEEEIgjgUNzzncna5CNJ1dd54oqt8dRz+gKAggggAACyS1w+ZLPvAN8ftQxyX2gHF27FPjhyDf0PXqWf+wNjLDXLnk46CQSYAT0JDqZHAoCCCCAAAIIIJBMApkdOrvje1/rDs25wBvZOdbFtrlflxO92FG2ys0teM0tKXzfVe7dE+tdxc32UlwHb2T42kitmbeR4qv2Vmj07O36kmVHg6Pk2WhAp/T5geuWMSBmx5Wt5Dj7osfCRvBeWfSJEtI/8W4waUrimyXZ9crcz+2q2OIKyjc0aRsxO7goN2SjFNros0M6HuWGKOm8S3qfKLdQt7ld572yRnhxSM553kK7Cbm4cqfbWb7W5e9Z5/I0zfOm6xo8983qCCsjEIHAEd2/qi98v9ci7/m2+4EdD3WXDrlfI5pNdVO3P+Zd+5SWF7AnNJw78BbXM2OYnniR691wZKPK7ane3fI7Zw/urP4/jyr53MhSdcPAcb2udsM6He2Nhl5QsRFJBBBAAIEEExjd7Yvek2GsfH34I+7THU+6WXn/Tah/GyUYOd1FAAEEEEhQAfsc8cTe3/aSg+wzibc3/SFBj4RuI4AAAggggAACCCCAAAKtL8AI6K1vzh4RQAABBBBAoBkCjIDuvOThgRoNfO/eam8k40Il2SbXiNEp7pBu5yr5/JuuY1pOM66W6Fctq9rlFhW+640YXVFdquS4Eo0UXezN24jR5VXF3rTaVUW/8ZivkaJkvqGuT/aBrlNqd5fhJZN3qptgbsnmqVaX7dWnpWRFNHp2ZXW5K6nMc7s14k+xEtItKbykqsBLjB7aaWzMj6S+DZZUFnhJiiuVrLiueJbcK8M2TU/J9kbIH9LpKG+k8B4ZQ2qP0xLrC8o3eefUfl4+T7per6T78rDba+3K7uqvJYiYbS+N+J+S0qG1u1C7PxuN3pLRPauAmb3eXbmtzfrU0jvuoHGZR3Q5Qdd8vncDhiXn23UTi5Kun7lO6b00cn1vJYD2cJs0AncyWfbQe1AskrjtqQpn9vupO6jb2bFgj2gb9nSAxYXvKRnribgc3Wxg9mH6HVSkn8M1CZ0otn+Xk90X+t3sMlM71zkvNsL2hpK53vv7yt1T9DsmP6LzRqPoBA7LudCd3u/H0a0U0tr+Bvp427/c/II3mrUdVkYAAQQQaD0B+7ff1SOe0N+f3evsdFvZcvf+5nvc9j0rW68z7AkBBBBAAIE4F7hg4B3e50JWbNCGl9bdrCc0zovzXtM9BBBAAAEE/p+9s4C3olrb+Drd3X1IQUBpRZEQVFQUsLDF7rp2XMW8tl5br4Ed1+77WZjXFr12ohh0d37Ps5k57rPP7O5znpffw8ysXv+JM3vmXWvaBwHNgN4+9qN64UxAM6A7c1Fo+yMgB/T2t0/VIxEQAREQARFo1wQ6ogM6nSOrc3q6nGupquxNWs0Ou3b9KjiL/tZqFmPOakzH9HQ49W10QM7FLC50Qs4zWXgZba/T0fLnZe/DwXp5Qhw31dk9zIiq400VnKoT1fgiYu2GlRudmt1mjiZz7gdvTtLh9ic3rdQ1g51L2T1NZXZ3OPTlhVts0uRftW6p+Xnp+67Z0em0WJJZbxqsWcJ5fnC21mCMAzjo3LmagwusgQWuQQZuYp38VP1Gh+wZEZ4dP8V0yt/SbF4yHrOd9w9oYEAw/Yt0WnLaOFs6j/MZrkEJdNbe6LA918UtGS0Vx83OtefjReOQlubzHKcjPj+5vBTXyGXoK6+V/mbjpxM1Z83Kz6iAwzmdzstd11z83Wopm07PHFTx2YInXcdxslpBehUcxk8yTfmDzJyVP6I/T5lvFr8a9KAO8uFXFfqV7o5rWre44ODf0M8WPG0+nPeg65oQb6vJ6eWaeY2ztdN47s1e+Z2ZueIb6Gsza+U3rvMu0Y3n1jB8vpwO0O7ngFO7eV78ueIrMx3X+Jkr0ccV35o1+DsbT+uSP9Q1+53rOuf6Osg819/+SBqvEbyvW7xmNvbxtxG/jnLG+b2bbzLpqVkRafYvyz4yH859UI4YEaGpQkRABEQgugS2qTjS9C/b07ESDgLjTOjvY0b0SA26dKxIgSIgAiIgAiKQBAS6FQwzO9Wd16qlfM7x0PSj/T4HSoLuqYkiIAIiIAIikPAE5ICe8LtIDQyDgBzQw4CnrElFQA7oSbW71FgREAEREAEREIH4OqCnuBwKszjLNGabtp24ObvYX8qBo1Wazx1FR6uWGbVbnF0xs/a6v5xfWQdnm26AY1I9ZkFlfdEyznY9Y/kn5oclb8Mx8l3DWcBjZQXplaY0q8k1YzWd7roWDI3r7M/h9pv7dhFn28bs0UvgUJYO5zV79vGNs5NjEIBrRvKNx0yKScGxsNJyet44u3qLA7R1PHDW5Co4nZOVPye+cNuv/N4J0GGd+5T7lrOpu5au9V/htLrEe0aPGO77Xpjhf7OScaY4szbgfImecBWc+F2z5Vsz5q/0xwRO3qvwdYGNTt4bZ9lnXn5xIFaWnpJpxtZfGNNZ/d37NmflT+ZzOG5/vfiVoB23Y8WobT0ppm/JBHyh4mDXtc3dVqxdZL5Y9AL69Ixrv3qznLRi06Vga9MVDr71eX0xeCTDW9KYhnPAyXdLprquyb6M1wHO2L1k7SxfyYKO42zyW1ccioEpQ/xe6zkAhI7avyz7GG15GwMmFgZdXzQzFGZUm51q/x7yYDL+LeV1diac7el0zyW3/Q0CiUSf2PZtObjC42sfHJjC65PryyDgvwTXq/mYnZ5t42yygX5RoyijBvc627jEAYX2Fy/YZw7usfvLAQfzVv0ccp95Xk1suslUZHeOBJZWZcxZ+YOZhmvXt65BJ5H5WkTEG6kCRUAERKADEyjJbDD7dbrd7z0WB0+/+uc1GljUgY8VdV0EREAEOjqBrNR8c0Dnu9t8MYRcXpt5nb4C1dEPEPVfBERABEQgJgTkgB4TzHGthM/EF635M65tiFflckCPF3nVG2sCckCPNXHVJwIiIAIiIAIiEBYBTwf0BatmmHt/PjisMn1l5o+ihrz+mKF4AJZ9TXZaoa/kSR9HB6jfl3/uckbnTNOc9Tcc2+iknwun/XzMWN0AZ/NGl7N5CZbc9nRgDKcu5RWBeBCgU+JyOH9udEr/pWWWcDqnu58/JTjuN4fTec+i7XXc+9hRnO15o3PnHBfTLxY+b+atnu4jR2hR6SnZZlzDJaY+d/PQCohgLs62/uXCl+C4/TScWmdHsOTIFsXZlEfXnOL6Iocv498ROmhzVnT7k9X56RWuAUZ0uuVgo1Q/A7V8lZ8IcTzv56z6AX8r33KJjsOhGtlsWX4Qrg3bhcTFnj2c7aAzeryPIc4cvl3NqfhCR36oSBzzuWaCx8zo3y15w3y96P8i/EUKg+FgqdbgikmuAYbBGGeSnbvqp40z1HOmejilux8TPHe6uJzOh5ryrM5+BxjYdXOw4izMfs9Zx39d/nEwTTLDK481fUsnBJUn2MQbB508j2vXsz4HnQRbrtKLgAiIgAiER2Bc/aWmOX9wQIXwnua7Ja+bt2bfFvZv/4AqVCIREAEREIGkIGAPFG/vA05HV59qehWPcdwn/L1zz08HJcSX0hwbqEAREAEREAERaCcE5IDeTnakl27QP2Bcw6Xm0V9O8JKifQfLAb1971/17i8CckDX0SACIiACIiACIpBUBDwd0Nl4zhLJ2RhdM+9iZkp7Bt5QZtPNSi1wzTzemAunczieF7WjGYqD3dGc8XnWym/B078TeoZrpm/O6m3P7p3jcuBKdifDYJkpvQi4E+CMynREp1Vn9wzY6VAU/yJAp5jflk9zOTP/iC80GMwHHK7xWjW+4TI4Qm8ablERzU9HYs4e/tac28x6OLQmiqWadDO4fD8zsGxvvzNperaZgwjWblhtKrK6tuvjn/3c6Iz+tssxPRDjg9fBZfu5voaQnpoZSBa/aXi+zIbD8g8YQMb2LMTMprEyHifbVB6JgTbjo76vV65bYr5a9B/XdWHxmplhd7EsC4Mr4HhQja+NRMrYRn62PT+jHAPu6sMqlvv168Uvmzdn3RyQ80NT3mAzrv6SqO8Hu1N0wP8Jg06mLXjS/LHii7D6qswiIAIiIALhEWjO28I1yDJY40CvD+c+YD6Z/5hZbxLnPjTYfii9CIiACIhAZAhwQOuM5Z+6vpTZXq0Bz/4nNFzu83fTtPlPmjdm39ReEahfIiACIiACIpAQBOSAnhC7IWqN2KbiSNOvdHdz2/e7B/U16ag1KMYFywE9xsBVXdwIyAE9buhVsQiIgAiIgAiIQCgEnBzQfZVjz6a7bN18QwceX5aVlueamVJO074oKU4EREAE4kNg0eo/zecLn8Fs4S8G5ITp1EoOMprQeLmpyu7uFJ0QYbMwy/MLf1wUEcfacDtEJ/1R1aeYsqymcIvqMPkXr56Jz0n6dopOAY2K7G6YJTwvalx4z7MAA2A4CGbpGg7Qs7SGA/XmuQbsrcPggNAtxZRlNpkqOG3TcZsD92I9aI+DNqYvfd/l+EzniGAtNQWDK8r2x+CKiUEPrgi2rkikX7Z2AZzQb8JMtVO9FpeTVmz27/Qvk5te4jVNNCPmrZqOQUOfmVkOM8FHs16VLQIiIAIiYPAtj3Szf+c7whr4tACD197AgKdfln0gpCIgAiIgAh2UQDMGtO6KAa3fYBDs//15RbukkJ6S5frd5O83LH9zPvjzkVH5Ml+7BKtOiYAIiIAIiEAIBOSAHgK0JMlSkF5lDux8t2sCnqdmnIVnDR8mScsj10w5oEeOpUpKbAJyQE/s/aPWiYAIiIAIiIAIeBAI1gFdAEVABERABNoXgTWYofGbRa+6nE7nr/4l4M7lpBVhdqsr4fjbOeA88UrI2fNfmXm1axbrWFteepnpkr+16VIw1DTk9sVsYKmxboLqiwEBOqivXL8YzunzzDL7CzLujur8ogziVq1f4moNj4vq7B5wON/E9UWHSgziiKYDfbAI6PjMGdG/XvQyZt1f5Td7TU4vzHr+N1OahIMrflryrnlt1vWu/eZp4+ovNc35gz2D47bNmeD5NR1+rWjmiq+x/o1ZsW5R3NqjikVABESgPRMYULqXGVp5RNhd5D3Cz0vfM2/OvhmD6v4MuzwVIAIiIAIikDwEctNKzH6dbncNaOW9/L++3wNfxliXPB0IsKWcibN/2Z4Bpf512SfmyRmnB5RWiURABERABERABIInIAf04JklS44das4yPYpGuZr73tx7zftQRzM5oHe0Pd5x+5vecbuunouACIiACIiACIiACIiACIiACCQbgYzUHNOnZKzpXbyzaxYqOjbOgmPjTDg20gl1A16PelpeWqnZrfHKpHE2zUrLNzvVnudyqH1r9m3o0VrPLkV0uzCj2nQt2AaO50NNTU5POZ1HlG5iFoYBfYaDMqgK431Qxpr1Kw0HfXBmbeZJVCvLajbbVp9ktqk80qxY69vBOSUlzeSnlyd0f3xx7lywlanL3cy8M+cO87+Fz7Uk7VuyW0I5n7Nh2WkFpilvoEs0OjUuwwz86zf4vqbNXfUTBhk9hZntP/GFQnEiIAIiIAIWgVzc6w4q2y8iPPj3vnPBENOYN8B8Mv/f5v159/m9bkekYhUiAiIgAiIQdwLb1ZzW8jUl3svX5/U1vy77OO7timQDOJi6b+luARfZmNff9azkx6VvB5xHCUVABESgoxLgFya2LD/QNXEMv8YoEwER+IsAn1835g7ABEldXF8eW7V+abvGU5nVzWxSOLKljzWY1EYmAiLQfgnIAb397lv1TAREQAREQAREQAREQAREQATaLQE6x5RndXKpd/GOrn6uhqPs7JXfwSEds+3CIZ3O6XRY37X+IlOcWZdULNi/vqUTTDUcwl/842KzeM1Mn+3PTM0znfOHuGYur8zuZpavXYAH/ZzFeq7rgT/FGZOXYJsOoIUZVXA6H+pKX5HVNWmdcX1CUWTYBDJSs3EOZYddTqwK4PmekZkTq+riVg8HqdDhfpPCbV0vLOgcsnXFYXFrT6AV87qWn1HuN3lhZjWcH7dyDSoKZmZ7vwUrgQiIgAi0UwJbVx4W8S+T8BPZg8v3gyNisXl15rXtlJy6JQIiIAIiYBPoV7J7mwGtXfO3aVcO6KkmDV/COsWkYlByMMaBztOXvW/WbVgTTDalFQEREIEORaA+t58ZVX0ynsHX4hn9SvMBBrLKRKAjE+AXRRtz+7sGdzfk9XN9YdS24sx688SvpwX0Jc9kZcgvtLl/Xbcqp0eydkXtFgERCIBACmcfkomACIiACIiACIhAshCA48rH/WEff9y+Zl9JFv5qpwiIgAiIQOwJrFq31Lz851VtZtzirNRd8rdyOZHzIWZaSkbsG6caRUAERCDKBFauW2K+WviS+Wzh034H40S5KSpeBERABBKOQFV2DzOx6fqofcGG749emXmV+WrRfxKu72qQCIiACPgiQGfj5vwtTEFGpZm/6lezYPWvmo3VC7DyrC74W3KD4eAjd1u2dr6544eJCGofvgRDyg/B4Kp9vVDwHfzfOXfDmfIB34kUKwIiIAIJTqBbwTDDL+h5sw243v+54ivz3eLXzJxVP3pL1io8KzXf9UXCTYvGtExwsnjNLHP3j/sjXfv4+xEQCCUKmcDe33zgyvtwj8Ehl5FIGTvlbWm2rjzclGY2+pz05xd8ZeaZGedE/eu38WDTlDfYjG+4tE3V9/50iOuevCPZMd2fc5/gJ3E/MduRdor6GhUCmgE9KlhVqAiIgAiIgAiIgAiIgAiIgAiIgAhEhgBnO9657nzXTMBfLnrJ1OX2wezl25janN5Bz9wVmRapFBEQARGIHQHO8N6/bE98FWI38/PS98znC58xC1f/HlYD6FC5ZO2ssMpQZhEQARFIBAIjqo6NmvM5+8evV4ysOsHMWfkjnFB+SIQuqw0iIAIi4JNAeVZnOMHt4PpaUG56Sau0HNy9YPUMl0P6fDi/zF/1i+vraSvWLfRZZnuOTE/JNGNqz27jfM4+56WXup47/LHif0mNIMWkmuH4e7l5ybiQ+zGwbG/X8xh+UU4mAiIgAslIgLMvb4sZyvmMxZfxufPAsomuv5HfLn7dpUVrnJ/B8Pn0iKrjWs3szLL55c2mvEHml2UbHYt91ac4EWhPBCqzupkd685xfZXXnzVhZnTeg734x0UYqtF+BmvwvmsoHPCdjF/77WgO6E4cFCYC7ZGAHNDb415Vn0RABERABERABERABERABERABNoVATr/9C2d4JJMBERABDoigdSUNHzxYWuXwjU6oL/wx4XmhyVvhVuU8ouACIhA3Ah0Lxxp+AI32paemoXBkOeZh6YfY1atXxrt6lS+CIiACARNIDutyPQoHAXH8+1NRXZXr/k5uJvXTfdr55r1K12DvT+c95BZvX6Z17ztNWKbyqNNWVaT1+51xRfXktkBPSMlB45g55pOmA0/HKMj2eYl4827c+4MpxjlFQEREIG4ERhdfYpf53P3xpXib8OQiklmy/KDzOyV37kc0b9b8rprIE5eWqkZUX0CJkgZ6rU/vYt3kgO6VzqKaI8EOMhjl/qLAnI+t/vfrXAYfmP/zbw68+p2g6Qn7sfLszo59qcmu6f5Wl9Xc2SjQBFIdgJyQE/2Paj2i4AIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIBEzAntH3t+WfmZXrFgecTwlFQAREIJEIdM7fKmbNKcqsNTvUnmme+e3cmNWpikRABETAH4GanF6mf+meLufitJQMf8kd4zNSszHT696GjnIfzXvYTFvwpFm3YY1j2vYWyL8jfYrH+uxWFzgXvjn7Fp9pEjUyL73cjKu/2OeghGDa3qtojHlv7j1m/Ya1wWRTWhEQARGIO4HeRTuZ5vzBIbWDz0+qcjZxibMa80sinOHc3wzPnfK3NLlwVF++bn5I9SqTCCQTgfSULLMr7jnyM8qDbnbv4h3Nanyl5605twWUNwcDLweX7e86J1/8/WJ84XF2QPlikYgchpRP8lpVdc6mXuMUIQIikNwEUpO7+Wq9CIiACIiACIiACIiACIiACIiACIiACIiACIiACARHIDe9xIysOj64TAmcOjetxBRn1CVwC9U0ERCBSBOoheNlLI1OJHzRLRMBERCB+BNIwfVoP7NH4zWu2VdDdT5370d2WiEc644wB3W+BzOpjzEpJrKv0AeV7Ws4+2xWan788aEFnL2W7aFjoS+jk2FldndfSRIyriKrq9m76caIOZ+zk/z90K1geEL2V40SAREQAW8ECtKrzDaVR3mLDiqcX6Yry2r263zOQtNS0s2mxTsEVb4Si0ByEkgxY2rPxv1St5Cb379sT8N7RV/Gr7psUXaAmdT5PtdXcmvgzL13882mPndzX9liGtevdHefTvj86k5GSnZM26TKREAEYkNAM6DHhrNqEQEREAEREAEREAEREAEREAEREAEREAEREAERSCAC3QpGmO/y3zA/Ln07gVoVXFMyUnPNwNKJpl/pbq6XwCvWLTKzVnxrZq742sxc+Q3WvzEr1y8JrlClFgERSHgC+ekVpiCjMubt3KL8AFxbvja/Lvs45nWrQhEQAREgAc76uEPtWaYpb2BUgPDaul3Nqa6Z1d+dc6f5aem7YdezWfGumA3yYJezN2egnTrrRvPDkrfCLjfUArJTC8zO9ZNNTnpRQEXQyX/2yu8CSpsIiTrlbWnG1J1jMnFvHGnjvvx28auRLlbliYAIiEDUCGxXc5rJTMuNWvm+Cu5dtCO+LvKQrySKE4GkJzC04jDTpWDrsPvBe8VVmAn984XPtCor1aSbPiVjXYMvORjO3XLTi834hsvN27Nvc33FJ56WjXv0AXg+6cs4iKUqp4f5bfk0X8kUJwIikIQE5ICehDtNTRYBERABERABERABERABERABERABERABERABEQiPAJ2ARlafaH7/+XOzct3i8AqLce5UzCa2efE41wxJ7s5DdMqiY5P9ee0NGzaYhWt+dzmlT1vwhJm18tsYt1TViYAIRINArGc/t/vAF8Zjas42D00/OqKf+uZMw31LdjNfL34Z1+NF0UCmMkVABNoBgbqczVwzTOZnlEe9N5yhcZf6C80fy78w//fnlWYR7qdCsU0KtzUjqo5rmWk8L73M7Fx3PhzQ3zZTZ15vlq2bH0qxIecpwhdzxjVcYkoy6wMuo2v+NnDGvyvg9PFM2LdkgmumX/69ioZxttHyrC5m7qofo1G8yhQBERCBiBLYvGS8acjrG9EygymsKLPWNOT2NzOWfxJMNqUVgaQh0AuDLPqX7hWR9vIZJe8ZV61fisFur7nK5H3kkPJJhueSN+PXBoZXHWv49ZfXZl1n1m1Y4y1pVMO3xGD1rLQ8v3VU5/SUA7pfSkogAslHILLfD0u+/qvFCUoAf1wnQRu4DKeJyD/FKqc50HKQfjoVaHqlcyYAhiMs9pOdUyhUBHwTsI6fqb5TtY4N5ZwPpvxQ06JdpdB86KZAykC6U6A1UI9A0iuNCIiACIiACIiACIiACIiACIhAaATyMHvQiKrjQ8scl1wppmfhduagzveYYVVH+525Er8rXQ5GPYpGmT2brnM5eMpEQASSn0BNTq+4dYKDXnaqOw/OfZGb34if6h5WdZQ5pMv9Zljl0YYOmjIREAER+ItAimvQ3W6NV8bE+dydfG1ub7NP882mW8HwoHdIc94WmE39dDift30dz1nFD+h8l6HjUqysNqePmdh8fVDO52xbSVaDKc1silUzQ6onxaSY4ZXHuhywouV8zobx3nqzkl1CaqMyiUA0CKSnZEWjWJXZDggUY8DR1piZOd7Wp3jneDdB9YtAVAjU5/bFpBYntAwyjEQlvGfkvSO1b/NtroGXvpzP3evctHgHs0fjtfgtHf2Bmp59rczqZnoXj/UMdtyuye7pGK5AERCB5CbQ9hdvcvdHrRcBEUhwAng4MxXakODNdDUP7UyF9oAeh2ZAK6Fl0NfQ7VCrb+lgexLEgRPuWort36BXoAuhbt76jrjLoVch1rUCosPyp9D5UEBvXZDuTsiuv6u3upI1HH2bbPVvRJL14UK0l997vDTAdt+MdLOhqwJMr2QiIAIiIAIiIAIiIAIiIAIiIAIhEuheMMJ0yR8aYu7YZaMT0354AbV97RmmMKMq6IrTUjLglHMMZt2cbDJT/c9KFHQFyiACIhAzAnSIjKdV47PZIyqPi0gTijJqzJblB7nKykjNMXRGn9T5PrNt9Um41lVHpA4VIgIikLwEsvF1l/H1l5qtKg6JqmOxL0KczXHH2nPNyKoTAh58w9nad8ZgHc5K6c2y0vLN6JpTzG4NV5miDO8zW3rLH0z4JoWjzISGyw2/lhOKdS3YJpRsMcvTmDfI9C2dEJP6yDIzNTcmdakSEfBHoAeOxy75rV5X+8ui+A5AgINy6MCakZod9952LtgKf3uK494ONSDyBPgVq45qxZjogV+04XO2SBvvHTct2t5UZHcJumj+TufAyVgOWE816bjenObznte9I1WYAV0mAiLQ/gh03L8I7W9fqkfOBM5CMP+ChfZtPOcyFRoYgQ8s9jcGljyxUsHJmW833oT+DW0HsT/XQ3QM5veq94HeRjqnadI+Q9wFljjj9csQnx7+HfoGea6DnJ46nox4vgFm+n9CD0BrocnQ58jTgKVXQzynXTgEWuo1UXJF8Nw9MMgmJ9w5j/3SiD4cCd2HT58HdC1CuhVIz2NgZ+TfKkgGSi4CIiACIiACIiACIiACIiACIhAEAfzuwqxFJ5rs1IIgcsUmKV/o0eFnz8brzLiGS0x5duewK+aMm/s232Iqs7uHXVY0CshNKzV9indxOZrRmUIzIUeDsspMZgIZKdmmPCv8a0G4DHpjNsOeRTuEW4zL0dzTOSY9NRPXgbGurz1sX3MGZuvl4zWZCIhARyPAGbs5+K4pf1Dcu877xc1KdjUTm27w6yzOWSB3qb/IpKcGNjNxQ15fs1+n2wyd1qNhW5QdYHaoORPtyQy5eN4/JrI1wwE9VpaJwVI94RgmE4FEINCjaDszsIyvrGWJSiAeztf9S/cytbm9EgIJHXTpTCtrfwRGVB1nOuJXGPjscNf6i012WuI9Q+RRlpdeanZvvArPEofF5KDbonz/oJ5V8kuUGmgek12jSkQgpgScHCBj2gBVJgLRJAAnzj9RPiWLMQGwX44qv4lxtRGpDg8SOXXBS9Dm0MPQMejPAvfCkaYQ26dCXHraNKSf7BmIPCMRNgU6EeKQ46M80hQi30qHfJcg7GyIztXHeMZzG2VXYPEv6BGIzvPBfw/SqeA4hoFF0MdPgp7zdD7n39spQeK8H+k5Yzr3+btB5lVyERABERABERABERABERABERCBIAjwBciI6uPNS3/wZ1j8jS+06NzZp2SXkGY799cDfsKXTu3vzPmXmbbgSX/Jox7Pl0+chZ7OTTU5m+I5x1/zpuC3vlm6do6ZueIbM3Pl12bWim/NrJXfmbVtH6FEvZ2qQAQSgUA1ZgxLTUmLe1PojLlt1Ylm6ZrZZsbyT0Nqz6ZwYG/MG+A1L/vZE45VmxRuaz6c96B5b+49XtMqQgSCJVCQXmmWrJ0dbDaljxEB3huMb/hHmwEqMareazWV2d0ws+Qt5tWZV5vvl3AOo9ZWktmAQYP/MJw1PRjjFyB2qb/QPPHraWb2qu+Dyeo1LWek5AzrvI6Ga+VZXVzOQovXzAy3qKjkj/Ughc0wWPKzBU9FpS8qVAQCJcBzsjanl+u3U2PuAPPr8o8Dzap0MSQwuuZU8/rM6/Cbdm5Mai3NbGr5ulBMKgygkl7FO5mP5z8aQEolSRYCvI/mM6vFa2Zh39I9pWNYcUad6+sCJZgBPZGNAz9GYaD3H8v/Z5ava+XmFNFmc9DlgLK9gy6TzzQS9Z4y6M4ogwiIgIvAX0/yYwwEDycHQ49Av0OroD+h/4P28mwKw6A3oUXQCuh/0FlQm6HjCJtuKR/La6EZVp5pWI5n2VimQ2dD30MroR+hNt+LRNgIaAM0GRoIvQQtghZAj0Ou2Yix7Aw9DM2B2L7XITqutjGE10A3QWznaivPE1i2ecqKsEkQ6+dyJDQVWgIthp6HODtwQIa0EesLymJf2S7HIVMI38OKv8G9cQgrhf4BfQ2R0yLoVSioIY9IPwAi/9kQj51foJuhGk8YCJsCsa3NHm1BUMpx0JcQjwEehzdCRZ5l+NtGHpbPfVML3QexXezfx9C+nvkR5r4veB5wX86HWtqJ9SzoTIizXi+HuM/fgtqcHywf4bQToa+gVv3B9nTKsx1Wvn0Qx+N1gZWP++ZcyOncsvtZjvjboT8h8ifDgz3LR1hLP626mhG2Aesux2iuu2mqe36Eh7KPeR4eD5EZ+bcq07N9frZPRjzP4Xeg/Tydz5kXYYuh87B6lZ+yWqKR/nVscFqg1dARaGN/97xOzudWvP2LrJuPum634o71kcZnFNozydonXHLm7XehZRCPj8cgx/oRHsx1LRPpT4A+scrl8c1j9GlotHsDse065uwwpsP6+dY2j9uWY8gtzRQrvJlhWB9ibT9hp/FcIp7HPY/lUvc4bO8AvQDNteL5t+JKqNizDG/bSJuCOJ4fM7B/g3IiR/o/kO8tiNdUp4EO3qpVuAiIgAiIgAiIgAiIgAiIgAiIQAgEuheMdDlBx9M4q/Go6lPMoV0fNltXHhYV53O7f5wFc3jVsa7PBmemBuckFQlGnNF4UNm+LieuSZ3vM8OqjsIsbb3xW771I2v+tC7IqDTdCoeZbSqPNHs0XWOO7v602an2vA4521Yk2KuM5CZQk9M7YTrA68hYOExWZ/cIuk25aSWuczoQoyP6FuWcwfcsON9rXqVAmCmNbwKbFe9qDux8txlQOtHwayOyxCMwsuqEhHM+tynRuXzH2r8bttH9mkRnrAkNl5vc9OKQgGal5buc1+nEHq5lpeabCY2XR8T5nG3h/Ri/ypOIVpRRY+gQFksrzWoy9bl9Y1ml6hKBNgR6FI5u+e00SLOgt+GTCAGc/ZxfaIjVLPW8p9m+9oywvngRDW501q3PdXSfikZ1KjMGBDhZAn+jDSjbC/druVGqMcX0KtrR9byI17t4WhPO43H1l7p+PyTK1wX88chOKzScpT5axoGO29WcZtJC+H1ek71ptJqlckVABOJEIC5PVfAj9XD0l454463l1Vg+D1VCx7izQFpOO8QhU3S2fhC6EaJDH8P/g/gM9/TWOsNehnaCnobug7pAdFoehSXLYz1ToTugfOgGxE3E0skGIZBOgDTOMPwBtBtE52k+WeU2hzjdC7EfdLB9GXEst8Ww3QkbH0Gs+0eI/f4PtDNER8+xLYlbrzD8/6DF0K0Q28K+vYE85V7yeAuORF9utgr39nT4CCv+drsRaGcT1jns9kxoDsR+2PuVjv08JvyaxYjHzi7QK9A10LfQ0dBHiG/2W8jGBNdhQQf5EojtfBgaA7HMTChYYzlsVx/obojHQmfoAbTpNC+FDUE492U2dBd0D8RBCayfx8U/IB7LN0E8hrtDjyCex76nMc11UBHE/jwEbQ/xPGAZbQzl3InAB6GuEJ1zWcZ86CKI+8TpSX4x4t6B2PbHIPazFroL6Q/C0pctROQF0C9WIq7bmmJnDGMf/xNlsO3/g7jOdoZqR1gZL4IT8HpfhSB+la94zzik/wZhj0Ip0D6e8V62ebzTPneKB7NJCB8PHYXy5zmlCTKM17enoN8gsvwvtDv0HuraxL0sbHfCdjDXtSlWmTwuefxcD70J8dzhOejLrkPkG1aCe7C0jx8uHQ082HZeI8airWWeiRA2GGE9oGeRlse/yxB+HhYvQVtAz0Ns5w/QqdA7iA/UIbwX0tcwDxSKMV8WNCyUzOHmQT8zoBOhu6FpEK9RdPw/zF/ZSHMQ9AG0FFoETYW8/Z3zV5ziRUAEREAEREAEREAEREAERCDqBPCbxYysPsFw9vFYGl8S06Fnj8ZrzL7Nt2EWqR3xwpg/BWNjrHtfOIFXZvPRU2yMDlsHdr7LbFVxCOrt5nJqCsb4opMO6Xs0XWvy0kqDyaq0IpD0BBLthXcmZu0d13CpKctsDootX4bzpXgw1qNolNmt4cqg8wVTh9K2fwKcMXbrysNdf2uHYrl3802mArM7yxKHQLeC4aY5n4/uE9d477JZya5mYtMNphiOdfnpFXD4vsI1aC4co/P6+IbLXOWFakVwxp7YfGPEnf0S1QGdTmHB3kuGytY9HweyyEQgngR6FP3lkFkH515+SUqWWAT4hS/+dqUTbTjX9UB7Nbh8f1MVw9/1gbaL6XoV0b1J1h4IcHbt3tb+zEkrMv1L6EYSWeMA572bbnR9yaUos8ZsX3OGa7KGNJcrVWwsIyXHbF4y3hzY6W7XV3l4b+o5YUJsWhJ6LV0LhpnO+XTtirxtgetNeXbnkArmDOgyERCB9kXAycE0qj3ED0De+d4MLYa2gcPfl+4VIr7e3sY6r4RnQTOgwUg7k3EIZ9iTEB3ZToMuZbib0SH2E2iE7RyKPPdhm06O/4Z+hHojbiHzIO4aLOgUeib0CMM8jHdD+yP9A3Y48tB59xDoXehqxF3iFvd3rF8IHQrRedO2W7HCtp3rkZ482LZ7UG4T4pa65eHqeGgHhL9qhyMdnZPZXrbhCjs8gGXYfUE7OBs999vuWJ6I7blu7aIzKn/tvItwOgLbRmdROqHvg/CH7UDkL8b6VOh6rD+DuFl2nOcS8fkImwLxuOW+fctOg7gzsH4ZdDu0vR3utETarRB+AsTjgMfVfKZD+DlYvA7RWfQXhgVhmyEtj629Ud565kN5bM/H0CVYfxzhP3mUx3bSYfg293Ck5fHNQQwvQrsifi3jEU4n2w+gs7D+HMJ57DF8GyyOhr6DtkD4Qiv8bCxfgXjMteoP8kxCGI8dnkec4XsF89AQNxmL86FjIffjl9GbQzz2j0SedQxA+mux+BziPuB+djSrXZORfgQSNGF7smdCxIWzj/ujvH4o92fPcoPZRhsakL4RIvc3gskbRNqpSLs/NNgpD9pAJ2eyKIIGQkMhMuYx1cqQtgkB3E/3o+9PecaHuL0L8u2C8p6z86OeE7F+HcTr1Si3cgO+rqEM9mdviOcFj1XXMWQb4svctz3Xkf46pClG+HBoCraneqbxss3jkn8n9oFu9EhzkLXdcuyijpEIuwD6L7QT6llo50HcJKzfbcWfbIf7WHLf0T7ykcZX1IdW5DAsW/aHrwwRjstDeddZZfL6PBPiOeLTwOkqJDgF+g36F5QJcd8/i7jjwdRzP/gsT5EiIAIiIAIiIAIiIAIiIAIiECsCeemlZkT18ealP/gzMnzjrOqc2RtPULwWlo4P0XEmzXhaUWat6+Xe3FXTzayVX5uZK74xM7Gct+oXNGtDBJuWYkbjpWGv4jERKZMv1+lg9fRv56CtP0ekTBUiAolNIAWzjfdMuCbSkZwOk//+9aSAPqXNl+B8GR6K1eX2MXs1XW+emXGOWbjm91CKUJ4OTmB09an48kdOCwUOhJoIJ/RP5z9m3pt7r1m3YXUHJxTf7mdiFs3hVcfEtxFB1M7jh45J6826kGaAdKqqMKMKg22ucF1TV6xb5JTES1iK6QtHqSHlB5vMtFwvaUIPpjNYXnqZWbZ2XuiFRCFnU5wGK3Qu2CoheUQBsYpMQAK8H+Ss0rbh3Zvr61LP/HZuAra24zapW8EIV+f51SDun9dn/TNqMLrkb222KNs/auWHWzAHMb0x60azcv2ScItS/jgT6F64rclJp9vHRutXuruZtuBJs2r90rBbxq8GDK043PUFF3dnb17jOFlDdfYm5oU/LjYLVv8adl3eCuBAPt5P9SzaPu7P6ry1MdBwchtZdaL5bflnZvX65YFm85uuIqsrZr+n60doVpHdBffNGfjdtSa0ApRLBEQg4QikxqFFdJRNhzizcCvnc7YFYXRWs40OsrSLEU6nN5dhfS0WdGyjo+9hVrDn4iSkW2UHYp3OynwLUQKd4e5QiHU6Br8D9cEFOM2zIGy/jTQtzudW/D3Wkr/8L/PIc6+13dcOR7n8BUCHY/4lvMI9PcqmI/FDUCm0m3uctf4w0rzqEX67te3owOpQhh0Udl+sgm7BMguynTft8o/ACt+o3WYHoO+bY304RCfsh+1wLq39cD5Ws6Hd3eMc1schjA6qj1j70z3J1diYDm2H+ug87MsOtiIvQTnz7YRYX4n1s3xl9BFHR1oeVzwmXYZ1Hm/XQxnQAVaw+2Ia0rRwcovgcb8B+hvieay7DOuzsbjI2nQ/7u19wP4stOKZnk9JvfXnRMSx7EOQrsX53MrLOvj0aD+7LLcl70rYrhbHYax/hTCePz3BvsAhTzBB4ezjK9AWMg/XaqwC5qE8HhPRMPvNSIWXwk9FOM+Lk6Ch0EvQ9mjPHPf04M1rOK9FvJs/wT0uzPXXUNdzHmXciO0foW1RbxPjQriu8bjm9YHX5pZzxa4HdfK4i4bdZ9VnnyuuOtD+TCx4Z8xziwM+bLNZHo42LXQL53k1BdvTIKfzwz2pvW5fj/50igwgzP7b5++6FkBRISXhOb8TVIu+V2N5l79SwHUrpOHfaB4vmyHfydCxWB8A8Zp7FdI0+ytH8SIgAiIgAiIgAiIgAiIgAiIQLwJ0Gt8EL9TCtea8wa7PX9NRh47t3hRv53O7n3zMUIHZi3oX7+yaZWr/TneYo7s9DQeoqzBT+aGGL7T5MjBU40zvY2rPipjzud0OzjS6Z+N1hrNfykSgvRMoz+qUsC/B8zPKzQQ4TOb6+SoBnUv5EhzPh0LeXXS42qv5elOb0yfkMpSxYxLgjMUNeX3bdJ6fjh8IJ4r9Ot1u6nI2axOvgNgR4D0H752SyXg94zEUSSvJanAN7OE1MxArzWzC4Jx/wnn/2Kg4n7MNvFfk/WAiWSqcl+ox83M8jPuc980yEYgHATpnelpz3hamPKuzZ7C240SA98QcOGkbB2EXpFdGpTWVWd3MDrVntnLYjUpFYRRKJ/weDsdtGEUqa5wIbF5Cl56/LCst33UfHa71w0zqB3WeYjYt3sHrscwZt/n1oh6FowOujs+i6nP7mcEYoLFF+YE+tWv9JWjD3aZv6YSE/d0dcMethPydvjWc+iNlqXD33K7m9LDufel8HsuvMEaq7ypHBETAO4HI/hr2Xo97zJbWhruzn7ec/a2I1zwTwJntO/yg/w3hnbAs9nASXIhtOr552h9MD33sGYFtOoSmQXSws51D7WQfOaRnWTQ6Ea/ziLfz0+nctn7WyltI7zSMh33kkESmu9ctH1ed6p9hpSnxSOtv06msYPvCOtjGy6AjoKsZgP2QgcUkaAH0KMMsG2Iti5Bmslu4vWo74fZ0iHMP8nU8rEXZbyJxM0SGv/ooyy7nDYc0byGsxenbId5b0K/Yr07Oz1ORgY7E9v53z/+BZ2HoQwHCukK/o7xvPOOxbZ8L7uXZ6287pH8PYa36gzr4tIpPY+ZCJ2HbIZvLQdhpf3yPdi12yGAfj8WIW+IQH2hQOPu4Dc9AK/VIZwOhs3S0zGcdYMzrEM+pKizozMtz7VNsj0XcJ26NOhnrw6GdEc7zzqch/wgkoNxtOvJO8Qh7w2ObjtfrkJ/HWBeIx9wv1pJJA7qu8dhBGc8i/S7QNKw/zrzQ+4ijo3NUDGX/hrpeReEcoLIptr+yKmI7SqFrEeZ+nvCaxev0nki/p0OjMhFWgbgy5JvnEO8eVGZt+N0/Xsqhwzat3Et8VIPRv9WoIJC/1+7tOMra4KCYln5jfTqY3YS4v0MHQ7w2ykRABERABERABERABERABEQg4Qjgt4vr876c0fezBU+F1D46Re5Ud15YL2RCqjjCmTh7Jh31bGe9tetXuWaH/WT+vzF7wvqAa+MLqh3rzsGMx9sEnCeYhHTi37X+YjMVM6r9byEfPchEoH0SqMnpldAdK8bXFCY0Xm4e++VkrzPg8eU3X4KHa/zc+4SGy80rM6823y7moz+ZJwEOGirNbDS/r/jcM6pDbhdmVJutK307X3Bww+6NV5kvFr2AWTpv1mzoMT5SqjCrZZ/isTGuNXGr4+zqdIR6csYZXo9F3mMNKtsHjl/7uGbYjbbxXu7zhc9Eu5qAy6/L6d3qiwYBZ4xQwt5FO5kP5t4f1H1xhKr2WwwHL0RytlO/FSpBzAjwvO9WyNfDrY2/Y3k9ePGPSzyjtB0HAt1wvUx1m/uSDpeDyvc1r828LqKt4aCtXeovMhluX3eJaAURLIyDdqYteCKCJaqoWBOoydnU8Et0nkan9E/nP26WrwveJSIjJcfw6y/Dqo72LNZxm18y4jO7OgxAmzrrBsd7pFQMEmvMHYBnUENN5/ytWs3Y7lhoOw/sg3Pv28WvmT9W/C/sng4u3981eUW4xi95/Lniy3CLUX4REIEEIRAPB/Riq++2k7YvFEVWpLeZYxneCDHdQreCFnkp1OVgCCc4p3jb+ZBO1J7mK32bODoy8gYf5l5WIH1hnmLPyrG90DPMrY40zzg/223ai/R239vEeekLGS5BH+9H3qOwHInt17E+DqLj7HXYXunWDtsBczuEUd4s31uEFR4OQ/ei7XJmedaHdtPJ1p9DqWc2brcpy0o006Pt7nntOKe2+Trmmb7YLVOw/SlBXh6gFdD57pUHsL7QSxr7GAr2ePQsLpx97MTTs/xAtu0BGeU4FrI9juVA8geSptZK1GpGc8+MqJvH1ZNoB53Ov4PuhXozHcK6YXEJdDfSvcCwAGwE0nju8zcQNsUjb6DHcyj7ayLqOgPaF7rAqncl+vMY1k+1+uzRnIhsTkEpvP4cBLF+Gtdp91hLe8FrFv8+erLySGZ4zfJ3vVhhZcr2zBzgdo6Vzi4nwGxxTbatVftLDq14EWF0QGcaf3x5nH/sUAaDengJV7AIiIAIiIAIiIAIiIAIiIAIRIQAXxSPqDrOFOPTu2/OvhlOJRsCLrciqwuchfgCONSfggFXFfOE6alZZiic97oXjjCv/nmNmb3qe79t4Iv2nevON53yt/SbNpwE3Gcjq04wxZl15q3Zt6GowPdZOPUqrwjEkkAtHO0S3ThL+7iGS80Tv55u1np8YJKDc/jyO1JGZ8sdas7EtbrWvD/vvkgVm/Tl0IljQNlepl/pHmbOyu/NY7/+Len7FIkOjK4+JSBHVc7yTCdo3gM889u5OI5XRaL6kMsoy2rGcX6WWbp2rvlhyVvmp6XvmpXrFodcXqJm5OyU21af1MpZL1HbGst2cfZc3kc999t5GPq3rlXV1dk98NWaUw2PkVhZXe5mJju1wKxcvyRWVfqsJ95fwOGAqi5wbvthyZs+2xnLSN57b4+ZSWev/MF8PP+RWFatumJEgL+rOBDPyboWDMPfrylm4ZrfnaIVFkMC3fCb2dM2LdrBfDj3IbNkrbdX8Z45fG+np2S5BmJHYnCn75oiE1uW1WQ4oFZOp5HhGY9SNise51gtB0AMKtvXvDH7Jsd4b4H8ckNJ5gL6BHhL4hjO9L2LdzTVGLz4wh8XmQWrZxieD835W7iczvlFQs7MLttIgL9vRlf/zTww/Qg47DvNlxsYqYqsrhGZ7Z611eT0NJ8uCKzeSKbiIK71Ic1JG8lWqCwRaH8EUuPQpYVWnXUB1G07RFd7SVtjhbdxnPaSPp7B7akvNsdbrJUjrSVnQ6fdbiewlnbfT4RzaYoPHeyRz3MzUgztcqo8K8CNCh2obYd5z2hf223KshLbx67TMer0FiyUPtpPGtu0wUt/7Do+9bEvXPvJV4ejFBdK/+2mOPEMupno9wxk+hWiA/KwoAsILMNIK9n7gSRHm35BOs7a3Qv71J4eiNMdZUEHI2yDuxA23Cr3eyt8PLdRzmSHfT7CSuu+aHMsWZGex3PQ+wv1r7DawaGxHEC0P8SZ1bmkE3q07EkUzHNlf54XEAdg7Ah9hvZ85lEp+7XAgZXn9Yv7xZ/NthKEcl1hVjufXY6/+uIaD655aAD/vi8Fvz8dGmN7JrQdGu2QWEEiIAIiIAIiIAIiIAIiIAIiEG8C/OzuWDiTp6cE5kxejJlTxzdc1u5fdHFGzonNN7o+45uW4n22zXTE8YV4tJ3P7eMEv0tNfzhcjq2b7Hr5KBOB9kagNsFnQLd5c2a8XeovgCPpX3Mg0SGOL7358juSxvN+y4qDzJjaszFznrdXSZGsMXHL4sv0viUTzKQu9+LT8ge4nK2r8WKfDukd3foU74KvefQLCgPT8296PGcU7VW0o5nYdCNmGeyCv6Vb4HP3p5rDu/7b7NZwlaEDEGc9jaWRBZ3Eo1Evj13eX8jaEuC+3772DESkuCJ5Xzq88lizZ9M/Y+p8zro54K9zAT/cmxjWnD847g3ZrHjXuLfBbkA2nJJ5feheOBKO8VsnTLvUkMgS6FnE+bacjefowLK9nSMVGjMC+enlxum+nffDg8v3i1A7Ulz3v8n2tzOSg1EjBFLFBEggN60EX1/w7j7DGe7z0+mCEZjxqypj6ycH7XzuXno5ZuLep/kWfMHoanNEt8cxaO88s0nhtu3+mVxghFunKslqMIPLDgglqytPqknDb5HTI/alR/5OjbXxOeFujVfiN/Nusa5a9YlAuycQ2Sd9geF6z0pGxz9/9qmVYIRnQjxU7IqweuhnOLkt9IxPwG27L0PRdqeZ522HVM50nBQG7p+joe9AE9CnLbAcDb2J8K89OmDv83C/sevreCDToVa9/hja8baTrntz2Uan/ePRpTabjWDQ3CbUmBFWmN12hyR/BYEdpwz4EapDeU5P2pyOk5Zjy6HwLRHWqj+oYynCvoTozFzqkCfaQa4pGlA3nf09LVL72LPcYLftQRTnop0+r5OID+ptJtL3QGP2hDZADwbRMHvWdHuKi+nIe6cXzbTK/bcVz7TBWJtzw9pf9jlm76eWYw/xTueN0/Ha0g4cizOgBxCwA0THZF4f/T0xt/vvdPx47SPqWYHIRyFy5LWKv+7Z5nscMvGaVYK29HKICzaI10ka93soZuebFkrmOOQpsupc5KVuO7zYS3yrYOy3AU5Com8Cya80IiACIiACIiACIiACIiACIhAJAp0xw9yeTdf6dbbii7YJDZeb3PSSSFSb8GVsdG6YaPbrdLupx6ePPY0Oj+PBozFvgGdU1LfpcMMXkHSAl4lAeyFAh88CfJo8WYzn/o6158BdMsXVZL7s5kvvaBkdDSZ1vs/lgDC4bD9TmtkUraoSstxNCkeZAzvfZYZXHdvq7xAdnerz+iZkm2PVqIL0KjMUA6ZCMc4+vVvDlSYrNbYzKNI5gjMYj645pc0XVfj3twH7dGT18ebQLg+ZvZqux+CrPTEbbnEoXQw4Dwecjau/xDU7/L7Nt5mmvIEB5/WXkPdQW1ZM8pesQ8fzGsdram/wP6DTHYaDJHksxMPoMJYIxuMmEa71vA8uyeR8S/G1kswGDFi5wdTmbny1xRny6Swoa18E+AUCfwMvehSNDsoJtH0RSozecCZ6by4GPYu2j8igyaEVhyXlQBOyyUzNS4wdpVYERYD3IPxt4c34daotyjnnoH/beF9zrs/y/JeyMQW/Psi/xe3xK4SBMgg0Hb+SVZ7VOdDkrdINxr6tgMN/pIz3cdEY1OqtfXw+yN8y/H03rPIozJTvfTCFtzKCDafTPr/yJBOBjkAgHkf6LQC7Fvo7nPs29YSMMDqV23aXtUIn0JahUpYj5FWIY/vpgJnwBge639DIl6Fm6CT3BqM/dN7eF1oAPekelwTr3J98k/M4xCfJt3q2GX3/CGFvQbuhr4d4xnMb4X2gSqc4t7CnsD4f2gdp6Vjtbidhg3/tXkF9v3rEeW5OsQLOQTmldiTWs7H+D8/EAW7zKc/lKKPlnMJ6J4SdAPF4vz/AcpiMxz1ZXokyWp4eYb0cYX+3yrHPDW7ea4WxP7bzJ5lyv1xqxXkurkEA4+9CumLPSISVQP09wyO0Pc8qx+lpzFOIi8Q+btVU9GUqxFnCRwTYh2uR7jOIT9Hu9cIoH+HnIf7UAMvkPhmOtC9BZH8LjlXW4TLE9YCq7W238FSEX4Jtnh/vIg+vE5zNfBp0mJMQ/a2V/2wrfppdXoDLbVHnWI+0x2G7C/Q6yvyFcVgGdV1DmRUQr3eexl+ZBRDPldWekR7bvo4fP1nNFCvBgVhSrI8O8J7G/U/7F9prO/63pEFYHuR5DfIsw97mtW8d5Jge5RRB3Pf2Fz08y7Hzve4ZEeg2yp4OtZol3892MNerQJvhmW6DZ4C2RUAEREAEREAEREAEREAERCCRCbhm/MYMpOVZ/Gnc1jjbIJ3PC5PIObRtL0ILKcGs75xpcVT1yS0vkvlCeULj5a4XO/Gy6pwerllaZSLQXghwFkU800mq7tBJcVz9pWarikMNX3ZH28iH1+shFQebAzrfCSfNu8yQ8kNMZVa3aFcdt/L5Qpt/n8bUnmWKMts8ynS1K5KOwnHraBgVc9bwzLTckEvg3xPOlJedVhhyGcFkLMtsxkCKmw0d1PwZX0nxiwPbVB5pDunyoMtpPRrHO50nOKNkXe5mriblphdb5/YhEXGqGFF1nGvGfplvAt0LR+B+6yRTmNnmVZLvjBGObcjtDyev0M+pSDWH17ZE+LvINmyGryzE0+h4x8EoxW5/B3h96Jw/JJ7NUt1RIMDZ7X05gLJKxg8ojf59VxS6126K5PXam6VhTjcOlgzH+IWU/km6j+kkvHnJuHC6r7xxIMB7wUBmr+9ZtIMpynD+TWI3e9OiMbhnPSNuA+nigC9hquT1ZxS/ShakU3QFnoUOLNsnov3g/VN1dmxmQedA2l3wdUt7YDbvkXaoORNfqojuM0t+lYwDM2Qi0BEIcPbXmBocFr/CheQYVHor9CnWn8aSM9+WQRwuvwQayUYh7buIvwKrp0NfYP0xLJdBnD29N/Q2dCWULHYUGvoO24y+8MkRHbMboD2h9dDB6DP7n0z2bzSWzpp10FzoCS+N3xfhr0F3ou8nYPk+tBCqh/jEivuTv4JnQ44GNkuRlw7srPMNrHNJZ/MBEHnOhI50zOwWiHLeQd4bEHQ8ZB9Xa7DOO90F0J/+ynCI/xxhdKz9GGX/H5ZF0ESoGDoddf7okMdb0FWI4DHO9nyG8l7Akk9yeJxUQlegPB77LsM6WdyO1SOgL7H+OJbsD592LIL+gHh8tRjy0PGc3Hgu/oj1/2BJlnTI7wQNg+6GeMxG2l5FgezLE1bfVmD9F7TpvkjtY4cG2wMD6HDs19CO5WjbGCTkNWc/aBdsv4zlDxDL6gqNggqh4yBP64v0k63ALCyrIB4fm0LcF9dAvK65G+vjteFNLHm8zIOYbzjUGeLxfTgUC3sWlTyJtjyJJfu8ObQTNB/iMeNuwVzXeJ14D+V+jeUn0AyIDMdC1dD1YO/vGvg60pHhP1AOrxs8Z3keXMylL0ManvvsD4+/DOhZhLW55iDsVaQ7E/H/gL7HOs/Bn6F8qAniPuE5yH3m01DWIuTnMT8CyxJsu9rrZhOwfjd0DzTJPQLpeazxOPsW+b5wjwtyncfTyiDy8JoRqvGaQyvyUoAdbqfzkkzBIiACIiACIiACIiACIiACIpB4BAoyKsyejdeaF/+42Exf9kFLAzPgMDWh4TJTmtWYeI2OUYvwGxYzcu5smvO2MF8uehEzCW1jyrKaY1S792rovPfniq/M/xY+5z2RYkQgSQjU5PBRWPJZU/4gQ8XDeF0enLWvGVy+r1m8eqZ5e87t5vslfPzafqxbwXBDB2lfFo8vUfhqT7hx9bl9zdDKI8xn858y3y55zazfsNZrkZytuyGvn9f4QCM4sGGPxmvME7+eZpav83zEG2gp/tPRIYfO2KHM4EjnBv7d61G4netv32cLnjQ/LHkLD9PX+a/YZ4oUsx0c2zvhizDuxsfXg8r2heNGb9wbXWKWreUrjeCtc/5WSTmDa/A9bT85eKzRufW9uVPi2il/s0DHsnE9i7Yz78y5w6zdsCqW1brq6lm4vRlVc7KjU3JnfBXoi0V8xSVrLwQ4u3kg1qt4R/PBvAfMinULA0muNBEkwC+v+HOq7IFrxofzHjSL1vwZdM28DxpZfUJCDMAJuvFWBn655bMFT5vV65eFWoTyxZgAn/HkZ5T7rZUOzluWH2j+8+dljmn7YMDWyKrjcfza7juOyRQYRQL87di3ZDfz6YLH/NbCLxvyN8DWGFDOfRtpq8npaX5cSreb6Fkq2r1L3QVtvs7I+9ld6i8w//7lJDN/9a8RbQC/nsVBFp0L6AIpE4GOQSDyV4gAuMGRjrPK0pnuVGgENB6aC30O3QG1GNKegbSfIuA46ECIToN0pjsXuhrxq/9KndhraOtP6MtAq+105hwBLYZegi5B/IeJ3YO2rSN/9OkBxJwETcG24y9rhP+GdHR4Ph7aHaJTbxo0E/oKugH6H+TTUM7TKGdrJDob2gGiMyPLuBW6CPGBOk6eiPTfQcdCR0J8MvYkxHI/g4I1PnGk0/gV0MEQnWrZr6vQpgeDKcxiuh3y/A3aFyIzPkFlu05C/EMO5R2NsG8g9uUoyL0/v2Gb50wrQznHguWLVvrRWBZDdDDmX9croftbZYjcBs/xJmhviE7YvA69Ad3HKiK4j1kcf3ylYNELmg695woMwNCOmcg6DEl5vO4DbQmNhej8TEb/hu5Cunex9LTNEUDRlkM8Prh/eBdHR/sfNka1+v8VbN0O8fhm3mKIv7p4nJINnbO5f2JhT6AStuUcaGdoDcSws9AGtqfFsB3MdW06Mp4PjYBGQuUQ+/QtdCb0MOTTUN/X2C8HIRH/fhwDZVsZLvaZ8a/Ie7B6kbXJdUdDPfyiwTuIPAEaCo2DFkG/Q2QTzHl9M9JvD/GYvwUK1Hhe1kEnB5rBKR36MsopPBphqGsZuJFRHZY12PZ8ctLNqrfVcRSNtqhMERABERABERABERABERABEYgGAc6gyllz3ph1s/l84dN4AbPxE650TJNh5DZeSHKGn0SyYZXHmNkrvzezVvLxg0wEkpcAZ0CXhU6AMwYPrzrW/LT0PbMueV4r+e1wv1I+vvZtxRl1+EJHtVm8ZqbvhEkSS6fyquzuZvva083QtYebLxY+j7/Jz7ZxgKYD2NCKIyLWKw6s2qPpWvP4r6eirrkRK5cFpadk4asdJwY067m/ivlKpDa3l0tL18zFIKxnoedDdkKkk1CPIu+PmDkr+r7Nt7ocjX5d9rG/5rWKT8eHielwL0s+AgPL9jY/LnnbzFnl9Lor+v3hzJ0Nuf2iX1GANWSl5eM8Ge26HsXShpRPwkCQ/bw6opJRBs6zNRtWxrJZqitKBPj33J9js101BzL1K93NvDvnrii1RsV6I9C9cLjXc9LOQ0dOnruvzLzKWzGO4cX4+tjOdec7DjhxzJCggdlpBRjItKf5b5wHMiUonoRs1uYl4wNuF7/U8OG8h+DU+0urPP1K9nB9tWej+07AxSlhFAgMqTjI5fjt9PswO7UAjtNbuSaW4FdvojmDdzW+5BRNS4Ur2ti6yV4HxPMLV+Ma/mEe/eWENr8lQ21XdXYPs2Pd3zvkFzJDZaZ87YNACpzD2kdP1Iu4EcANwlRUTkfdTXA8fR+3hsSpYvSfJ9Eb6PuIODXBa7VoG99+0tHzYbSPTtQdzsBgM3T6M+hYMKAjsMwLAbCahKi7IX6NYYqXZAoOkgC4piHL/6DVUD+wDegPL/I9jvTDoS7IQuf3uBvaNBmN4CCCw9GmO5wahDT3IpzeBocgDY+nFkPchdj4O3Qh4lhOSIZyPu4P+/jj4F5ohFSZMomACIiACIiACIiACIiACIiAAwH+tPtl2YcmL73cVGR3dkihoEQisHjNLPPQ9KPNynWcC0QmAslHgE6aR3V/KiqzjiUfjfBa/Nbs28wn8zm3SPJbDQYl7NX0z4A68urMa2PuGBlQw4JMlJNWbA7t+lAbx6t1mAWds31z1m/O/k2b0HAFZrrrH2QN/pMvWv2HeWLG6Y4OG/5zO6VIMbs3XmXqczd3ioxI2Nr1q813i18308AnGIfhIeUH4wsC+wXUhg0b1sPZ6GHXrNgbWn+U12v+bSqPMv1L9/Aar4jEJjBn5U/mYdxfhT/LfvD9rM3pY/bEgJBEsjkrfzQPTuc8YdGzVMztVp7dxeWE3AXOYYF84eL53y/E9fHN6DVKJceMwJYYcLBF+f4B17dq3VJz14/7aZbpgIlFJuHeTTeZqpxN/Ba2fsM6c+9PB2MW9D/8pmUCOoVObL7RFGdy7rLkt9Xrlpu7fzoAv9ET4hV48gONYg8qsrqYfTDYEO/nA67l+8Vvmhf+oFvARhuMARe8hjmW8dLAjYnGfBRw+UoYPgEOHH1yxhmugvLSy0yX/K1xbzHU9Zsk1eXeEn1bs36lufW7cVG5l+Q900515wX0pSXew/3715PMmvUrwuo0B1lsXcnZ4jO8lRP4SeStBIWLQIIS4MzDMhEImQBuEAYjMx0kX8KLtw7nfB4yuAhnxH6oRpGzsQ/W20UjLBfr11nbT0a4ymQqjsfnLEhDvJNpr7WjtuK8XIfzkTO2c+qL3SA6lvs0pO+LBBOgExLF+dxng1tH3opNOqCfg348hfYvYDTWm7E4FloFtXJMZ7xMBERABERABERABERABERABJKJAF+aNefzsZgsGQgUZlSZMbVnm6dmnIXmbkiGJquNItCKAD+THY1PXndEzANL9zb/W/AcZoMN7+VyIrDrV+J/9nO7nY25A9qFA3qvojGOL/R5fmyC2RYpfvVi3YY1piZKM+oVZdaaPRqvwUzop8FpjB+DDM/6lkyIqvM5W8eZCzct3sE1w/qfK750OaLTYd+Xo3h/zEw6qGzfgDuXkpIKZ/V9TSfcH/22/DOzFLPEcwb2pWvnuNaXrZ3n2i+2VWR1Ney7LHkJcBDmYDjDvjf3nph3ojl/UMzr9FdhBRzDd2u4Ctegb13H/JI1c1xfS9h4/M/3eb55K5tfr6CzOe8DqnN6Gp43wc5E2iV/KzmgewOcZOE9Cr1/jcKpK5yZv1/Jbub9efc5RXfYsNqc3q6v4jz56+lm5folEeVQlFFjKvGVlkCMDp68hr785xVek3PgHWfT5TWge+G2cD6v9Zo22SL4dblBuC9/a85tydb0Dtdezn7OZ2DBWFc4MvPYnbnyGxPMgMZg6lDa8AhwENuejddh36a69hWXsTZ+rYMD62av5JyqkTN+KWdM7TkBOZ+zVt7Dja27wDw942w4w68NuiFZqflmu5rTAq4v6AqUQQSSgIAc0JNgJyViE3GDcTTaxeGVB0N0ej4/EdvZgdp0Evq6D/bLVCz/hOiQzl+h9dCLUPuY0gUdCdbg/HoD8lAyEYgbARyHL+D8PBENyA6wETVIx5nC6cwdV0O7z0QDeliN6GstD0b4UGv9bfSvZTZ0rL+LuGsQ9zfoc6w/hmUmNBEqhY5HmulWXi1EQAREQAREQAREQAREQAREQAREICYEmvIG4sXnQUF/5rsqexNDZ8MfMINXPGb4jAkcVZLwBKLlSJvwHY9CA3PSi0y/0t3NB/Puj0LpsSuSToldCrYOuELOBM4X8b4cjgMuLG4JU0zv4p381l6ZzQ/DRtcKMioxw/o/8HWNY8yq9UtDrqwoo9ZsVXFIyPmDzYhntaY2t7dLdI7938JnoefbzD66KRz9h1YcEbSzEdtTkd3VJU/j/EkrMMspnXGXr13gmh02VrMrerZF25EjMLBsH/Pjkrcxs/6PkSs0gJKa8hLPAZ3Nbsjr65KncaZjHvf2YAx/1+L0lCw4sXYzueklnkUFvd2cv0U7uP4H3e12l6EuZzP8JuGrw+CMDs4ZqTn4QsWDYf29Cq7W8FI35Q02dfg7tXEQ08ZBHFxfvo7zXYU+mDgjJQcz0x5mNivexeVo2at4R/Px/EfDa6xH7m6FI4L628lBBZ/Me9TMWz0dA+wyXec9HUGrOOgES97v8W93e7XNSnZ1fZlo2br5Cd1FfnWIA/g6onHm/U0w+CFY4zk2ofFK1xeDyrM6BZtd6WNEgL8J4m0caBdZB/QUOJ+fZboVDguqa/y9TCfy//z5j6DyVeFavVPtuaYwky56MhHouATkgN5x9324Pee3OOjc/BN0AJwJPwi3QOUPi8DLyL05tD1EB08Oy+Iwseuh67B/Qv81FlazlFkERMAmgNOQ52NAhrQcOEIlgo1BI4Z7NGQrbFO2tTigMwDtPwUPRD7H6nHQERAHKn0CXYm45+xMWoqACIiACIiACIiACIiACIiACIhALAlwJtc/V3xtpi9732+1dPwZUnGwa3ZWOsgtrTjS5aRHZ70V6xb6za8EIhBJApypURY5Av1L9zCfLXgqaRyxnHreFzOaBuO8y1lQOXvmnyu+ciouKcI4kIgDghLF2Jbta880z/52bohNSnE5OXDmv3hYQUaFy/l9cNn+5rvFr2NW9CdcTsRdC4aZUdUnR9zhjY5IdKaNhENtPHipTmcC/PrAdjWnm4cxGCNWA/Vy00owE3gX5wYlaCiv1/kZ5S7F2rLTCuDMuzm+TPBprKtWfREk0KNodEil8dgbULaXawDXR/MfMdPmP2HWbuCHihPTUk262bbqBEdHvnUb1rq+prFkzSz8nvvQ9SWPhat/C6gjzXlbmG2rTzQcQGbbZsW7wgGdc/hFzo2ie8GIgNpjJ+L+2aPpWtegsNKsxg73xaP01CwzqHw/M3VWYs8luHXFYfia2pkJfe4EdeAFkZgDNbifQrFMDH6R83ko5DpWnhp84eXzhU9HrNMD8BWn7vgqVijWo2iU6+s1b8/5V0DZ+UUyDmxKS8kIKL0SiUB7JiAH9Pa8d6PYNzgQNkex+KQqGiziPuwUbXgV0CiZCIRMAMfRFGSmZCLQQgDHxYhQcCDfPchHyURABERABERABERABERABERABEQgIQjQ+W0HOAs+NP1o10xc3qwxdwAcFE5qNcsgHYaGVBxkBmGmz++XvAFHvScjPEuTt9YoXARSjGZAj+xRQGdsOmO9O+euyBYco9IyU3NNL8xQHazx2pbMDuh9iscG2+Wop++cvyUcuPfDjPoPBF1X35LxcArtE3S+SGdIT800mxbvYHoWbW+WrJ1tCtIrI+58Huk2q7zEIlCR3cXlQPj+3Htj0jAORuE9nSxwAl3yt5IDeuC4Ei4lHdu6YXBQOMZ7n60rDjWbF48z78+7z3y58MWE/CoKHeW9zSLLAS+FGVUu1eVuZrYqP8TMX/2LyxH9B3yJYa7Dlxhy0orM8KrjDB3DPWcSZz38O/7T0v+Gg7Ylb3FmPZxtOwddFgeJUB3VuM8/xizwS9bOSkgE+ekVhoOBqV+Xf5yQbYxeo1LwxYBdo1e8ShYBEKiGA3qkrCSzwWyJLx+GYwPKJmL29BH4QtRin8VkpxW6/h7JREAENhLQrzMdCSIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAh2CAF/u71x3vusT556WlZrvmsVzfMNlXj9xT0e9nkXbmb2bbjJ7NV3vmlkp1aR5FqVtEYgYgbKsJkOnIVlkCfDrBjlpxZEtNEal9S7e2WSm5QZdWyOcNpPV8tLLTCc4iSWibVF+oGnMGxBU04oyal2zjyeS0TGPThSeDnqJ1Ea1JXEJ8CszsZqVvCl/cOKCSNCWdS5w/6BtgjZSzfJKoDMGEETqXpCDavmViwM63YkvXmzjtc54RPD3Ga8lgRr/XpVlNZstyg8w+3W6zRzU+V4ztOJwU53d0zUbbY/C0eaAzneZTfB7zdvfts0xGCxS5uTkHqmy23M53Ffch4lqPE94/NTn9U3UJkatXT0KR3kdEBK1SlVwhyPA30V05g7XUvBkjl+XCnXGfvf6+ZuoMrubT8n5PNw9pvztjYAc0NvbHlV/REAEREAEREAEREAEREAEREAEREAEREAEREAEREAEvBLgi6SR+LS7u9Fh4IDOd5tNMQOsNwcF9/RMw1mpd6w9xxzc5QHMgLt/yM6spZmNrvbsVHueqcvZzGu7FdExCdRgtj1Z5Alk4JPw/KJBshlfrIfqLFWd08Nw9vRktN5FO5nUlMQc7MN2jak523CGzEBtu5pTDY9BmQi0FwKcmZhOP9EflJeCAR/92wu2mPWDTlIVWV1jVp8qiiwBDn6NtJVkNbgG5U5sutHU5/aNdPEhlbd5yThDB/lQrTizFl+4mWgmNt9gju3+guvLV5wB3Zc15PY3JfgtFgnrjhlzZaER4DHOGeQT0boWDHU1qyG3XyI2L2ptGli6txmN+1WZCESbAJ+tceBQuNavdHd9OS5ciMovAmEQkAN6GPCUtWMRwB++ydAGaETH6nlke2sxnBrZUlWaCIiACIiACIiACIiACIiACIiACIiACIiACAROoFfxGNcMe/xM+i51F5od6841eeklgRfglpKOEkMqJplDujxotscM6pXZ3QMoJwWz+Q4xExouN/tjBsLNSnbFZ36HmT2arjH7Nt9mehXt6DhLewAFK0k7I1Cb06ud9ShxutOneBc4DYfu6BROT3oWbg9n8Lygi+AsjKHOtkZHaTpaJZvR6b5X8U4J3eyc9CKXI18qnHD9GQcQ1OVqsJE/TopPPgIV2V2Dmr04lB5WZ2/i16E0lHI7Qh7Ngp6cezkrtQCDLqL3BRMOTtu98SrX1516Fm4X0N+xaJDMSMkxA0onRqzoQAYUszKmo+N7uFaW2eyajV0WGgHeo25ZflBomaOYi19LqrF+i3EQeyj37lFsXlSK5kzUu9ZfYrauPAzPI/zf10alESq0wxGoyQnPAb0ks8EMKZ/U4bipwyKQSAQ6tAM6bih7QDdAX0CLoNXQH9Dz0KFQtvvOwjadjzdEYgeinE2hR6HZ0EroW+gCyOuUB4gbC02FFkFLofchr3diiEuDToI+h1ZA86EXIMfvbCF8O+hq6FUrLfv7tr/+Ik3AfUHaEZCLoxdd5lQf0m4OPQLNhLifZkD/guqc0nsLQ3rbiXyyjzSTkI7tm+ItTSjhLM8qtzmU/ImaB31qgC6DPoYWQGsgHtevQCdCvocW++kY8jdD3B/T3ZO6hbsfS2sRPgviObyjn6KTLhp9Cvn4Rd4+0B3Qp9AcaBXE84j7aTcoxRMIguxjdpJDHK8vd0Lk/wlUZafBemcrjuXzfOV5+xDUw7OcRNhGuw6BnoJ+gBZDy6CvIV5jNkmENqoNIiACIiACIiACIiACIiACIiACIiACkSewVcUh+Fz77SZSDjnpqZmmJ2ZQpwPHno3/NN0LRraZCZQvrfuV7I5PxN+DF7sXwaFkgMHzh1adq8jugtnGTjGHdn3IbF1xWFCz6kaekkqMNwE5oEdvD/Cc5dcLYmmc9XrH2nPN9rWnw2H5PEPn6mCsf+kewSRvk7Ypik5sbSqLUECn/C1MQUbgs4tHqNqgi6ET3/DKY33mK8qowXX9UJ9pFCkCyUxgUDkH93WJWhea8gdHrez2XnCX/K3bexfbZf/4NzAWTqBVOZvg3uQMDKh9CI7Ak0xuWmlMefYr3c3kphfHtE67MjreZ4T5hZhumv087H3XvWA4nPg7hV1OJAvokr9Vyxd46CRf384HENLZft/mWzFQfotIYlRZIuCXQENYX7dJcX2FJz01y289SiACIhA9Ah12yBIe6p8HrOdDfLr3HnQPtBSiE+UI6A7oaCjiQ0pRN/9ivwZlQI9BM6BtIbZpFOJHbdiwYRXWWwxhx2HjBmgedD+0GuKTRjqI9kH6U/9K7RqtybcWD1tpvsXyRoi/FDh09E1E7448T7vnwTqfjHGI50roB6jEI77NZih9sQp5A8upbQo05m3PMNTBqS2ehMjrWeg7iE6hfEq3K+K3Rl/Y3mgbGZLpr9GuKFnKB/vD0FZy4V/zz6CHoAVQGcTvEV0H/R0qh6Jli6x6WD4HjWwO8ZjZCe07EcfG9dGqOMnKHYD2jod4vXsXIrdqaBfoceh+6ADIr4ErB8o8AjHvq9AEcF7CjIjrj8XrUCHE6xzPmQZod2gXxI9GWrYhkYxvmWqg96GZ0HqIU0sdDB2INo9Hm19MpAarLSIgAiIgAiIgAiIgAiIgAiIgAiIgAolLAM8STG1uL5eWrjnSfL7wGTN75femM2Y874HPi2fCATUQ4yfjB5btbfqX7ml+XPKOeXvO7WbxGj66kLUmwEfhG+IOhTPk5WIW/VST5rUtazesNgtWB/54mc4/hXBYlUWPwKb4GsLH8x8xi9b8Gb1KrJIr4JS5E5zOizM3zqvDASgjq04wr826LqC66RRSHebscKwz2Ywz1SeL9Skea2au+Mp8vfhlxyaPrjkVTm6B/Q1wLECBIpDgBNJSMvA1mNPMw9OPxYuWdRFvbXPeoIiX2VEK5Fd/CtKrzJK1szpKl9tFP7sUxHbgAL8ItUX5/vgNMtH8sOQtM23+k2bmyq+jyjIrNd/1eydelpmWazbFIOLPFjwVchPoPC0Lj0BKSqprBuPnfqcLV2IYvzzkbvW5/cxPS/+bGI2LcCv4BYIhFQfHZMBLhJuu4toBgZqcTc2wyqPNW7Nvw5MduuoEbhygzfwyERCB+BJIj2/18akdLwDORs0XQDOgPeFYSKfDVoY0YxFwimd4uNsol0+f74ZyoXGo+xmWiXA6wj8K0UnzZOgyhtMQ14zFVdB8aCDyTLfCL8TyQ+gUpHkc4e53O3sjfA+IjqZ0aF9p5bkVSzp5c2bf1xDuchq17HIsz4G+gegw+vNfUW3XQumLWylTUffktqW2DkEd2Qi5E8qE6DT/hJ0CcfwlQmZ3QCPs8GgtUfdclE3JQAD898XiXxAdzrlvnvcEgzT8VXyTZ3iEtxd6Hkuol47Dd0GXYv0OxC+PcJ3JWNzD4DDFs+HgQ0fx96D9sX4D0nzgmcZ9G2k4kOVZaCvoEehA5FntlobnK8v8G8KvtcORbwjW34TuxXovxK1xyxPv1Z3QHtc10t3Qzu2w/X/Q1ZAc0D0BaVsEREAEREAEREAEREAEREAEREAERMAvgfyMcsOZ1sMxzrbWrXCYKclqMI9MP86sbT13SThFJ11eOuVXZfdwOeFytuGq7E3Mp/OfMB/Muy/qfUnFJ8g5iKAgvRKz0pcb7ts8LAvSK+B4XooZt/gI27dt2LDevDzzavP1ov/4TmjFchADnlEFlFaJQiPAWUW3KD/A/N+fV4RWQIC56ETNl+qex0mfkrEYlDDDfLrgcb8l8esJ4VpRZo0pyqiDw/3v4RYVk/yFGdWuL0Uki/F8HVl9opmz6kczd9VPrZq9efE4zJy5ebJ0Re0UgZAJVGR3NYPK9jXvR/hvc3ZqganM7h5yuzp6Rl6f6Mw8bUHLq/aoI+H+6lsy3pRmNpmnfzvHrFi3MOp1tqcKOKCjMU5fLmHdmxRu69LMFd/guHnSfL/4DbgFro044v6le5mstPyIlxtMgZsV7xqyAzq/+sDfabLwCXTGjOP8rTdrJd2V4mv8cll9Xt9WjWjI6xffRkWhdv5t5dcPOuVvGYXSVaQIBE6gX+nurq8gvPjHxWblusUBZSzOrHcNXJGJgAjEnwCdnjuU4cdVMzo8GaLzI50O2zifEwjCn8NiDNcjbBz+2BN6E3U8Y5eNdQ7jOd3aPgrtdH+qzDcUnGH6RqSb7paHjr+X2nnscGt5tLU8192xEut0WKfTaAVEB/UWQ9x/oS+hde7hPtZD6YuP4hyj6ORaDX2EdrX6RYztfyP8Y2g4cPVxzB3BQNQxGdoAjXAvFtvbQM9Cv0GroJnQe9D5djqsb8D6Qdb2z9y2NN2jrG4Ip4Pu79Bq6A9ru5t7Oq4jvKU9WN8D+gBaDs2HHobqPPNY+UoR9w/oa2gFtAh6FdreKb1TGNIWIPwGK25v7Is2zueMQ/g7WGzhpYxylHM79CdEbl9CdByPhE1BIcugPKiXvwLZH+jv0BfQYmgJ9CP0CNTm6TbCtoAeg7ivuZ9mQLdBtU51sQzoJYjlsvxXoCGQ4zHlVEa4YdgXbRysWSbCefdmv/Fqc5y514v21mP7LYjn5fXQPsjf4nyO+M4I6wvNhv4JtRjScYDM0xDraHVtRb7plvKxvBYiTx6b06DxLATLdOhs6HtoJcT9c9xfNbReQ9wO0AvQXIjHF9NfCRV75vHB5mWkXQh19cyjbREQAREQAREQAREQAREQAREQAREQARGINYFyvIwbVf23WFcb5/pSTG/MKDym9mwzqfO95vCuj5lxDZe4ZmZsgkNOdlqBGVy+r+GsntG0ooxaM7HpBrNz3flmWNXRpn/ZnqZ74UhTl9vHFGZWt3Eq9tYWzkMzGvuwS/5Qb0lahXPGa1n0CWxSOMqUZDZGpSLOdr1j7TlmWzglezqf2xUOrTwCTh+cv8O70RE7UrOgNiWRQ3fv4p0MB+Ekk2WkZuNaMRlfu+DriY1WhC8ZbF15WDJ1Q20VgbAIDMLfZjoRRtLoiJts14NI9j8SZdG5M9rGL8F0Lxhp9mz8p9m76SbTEzNLV+VsYvZovAYD98qiXX27Kr8xd0DAX06KZsc56HNM7VnmkK4P4h78QJObVhKx6vgFob6lEyJWXqgFlWY1GvIO1lLw7aOtMGu0LDIE8B7fNQt3IhgHHnMghruVZTYbHrPtxThr9D6dbpXzeXvZoe2gH415/V33DhzY499SzHbVp+E3Nl0pZSIgAvEmkBrvBsShft6x8E6BM4Z/4at+xK/yFe8rDjdHtmPpZI9021rbL3nmR32cDuE7qAlyf2LuNQ/S2bPy2mnoqMkrLH9BctZnOot6Wps8ngkC3PbaLh99sYvuinYeB9Gh9BDIm+Mrnc9praeK+KuBdvioANsc0WRoNx1pp0J8Y/AqxJmSn4J47BwD2XYBVj6zNv6JJbep6+wEKGsQ1j+C9oc4UOAq6D1oP4YjfqCd1mPJeu6HpkM3QTyuJ0J0cm711xbbPLbotH8mNAe6FeKAhJ4QHaQPxzIQ4+CFUug97Ov/85XBy3lUjDzvQHyq/hh0L1QL3YU22I76vooNJo7O/14N9aUgkufjhdBi6A7oFugDaBjU6sk/kvMawrbvCL0OXQdxv/HpMfdTqzcV2Oa5yPNwNMRz70ZoNTQV2gKKq6F9uWiAfS7/z1tjkG5TxP0X4vJs7NcTIU+29vk6HVEcVONpvs5XXpfp8L0T9DR0H8Q7y8dRN89vHqfHQFMh7qN86AbE8VhvZQg7DwHcp+T7PHQ99AN0KvQO4gtbZfCygXRDEVUMeeXiJauCRUAEREAEREAEREAEREAEREAEREAERCAqBHoUjTKbYRbdZLD63L6mGDMth2rpeLQ5tm4ynO5Pcs2+WJRZy2ffbYqjY8B2NacZOoBEw7oVDDP7NN+MWVe9PcIOrlY6z9GhvgGfcPdntXJA94coIvHcJ0PKI/1Y2rhenO/TfItrsIIvs48JXy/a+5ZMiJjjZTRmFOfsc5zhNpJGJ8ZNi/gKJvmsGNerHWr4Gmajja4+xXAwgkwEOgoB/m3eqe7vGCgW0OuYgLA05/M1qiwcAhw4l4XZbqNhdMocXLafObjLA2bHunOM51dc6OC7R+O1ri/JyAIj0LmAr5gTx/LwxZ8t4YBOR/Qdas6KyCCTgWV7J4STPSlvjtn6g7XRNafKeTdYaH7SN+b2N3U5m/lJFf3orgXbtKmEv0Ub8Bu3PViX/K3N7hgYVJhR1R66oz60IwL8YtdeTddhMNsIn73i18F4ryETARFIDALpidGMmLaCDoU0OgvHwzaxKv3OS+XfI5zfD6N+tNJ4zQMnT84evQzp6rHMxTadzrtCadBP2F5rleG+YB001hGOeW2XVahTX+z66FRNtRja/zg2DkebF7gFz7XWO7mndVvvbK338BLvLXgE6pvsJbKvl3CnYDps883GCLT7M/cEKL/c3kYcByQ0Y3tz6DpsT/dIyzcndMLmk5j9Ef+AHY98dLB9GLof65siztOxl09gByG8xUkW6R5E2D4Q30Y9apeF5T0QnwJz5mqW6TKkL8ZiKnQ91p9B3KyNMV7/D/c8Ioc7oSNR1zrWgnqvxeJz6AyI7QzHDkbmPIjnxpd+CuqNeP6CfwptmeCeFm3ivi2yw7DNc+Y2aDo0HOl/d4ujE/fL0D8hVzlIz/16F8Qny+OR/mmG0xB1IhbX2dtBLkM+flEvrw/7Q7xG8BfFzlAt9A+0j/ydbAgCr4H4VOpgpJvilAhh9vnaxL4j3QaPdL7OV7bhE4jn0irmQxH3YfEm9G+I18PeiFtoxbE930B8iv8Iw6zwkVheAP0X4lcuFlpRLG8S1u+24k+2w93i98A6jwfuL+5rOsPPh47zTKttERABERABERABERABERABERABERABEYgXgWFVR5nZK78zM1d+Ha8mBFTv0IrDXbODP/fb+eaPFV8ElMdOlJtWanZtuBiOLYE9wqZz+KCyfc0H8+4Pqh5fiVNT0s2wyqPh8L8rnyv5Shp0HGfCHlt/oXny19OwH/mIq62lpWSaimw+ypPFggCdTCrgMD5nlf1aJvRas9OKXMfNoLJ9vM567ll6JpyTd62/yDwy/TizbB0fSf5lGam5EXXE5uAQOnevN65H82FbfnqFmdBwuVm0+g/zxIzTwi7PLoAzvtPZLVmtc8EQM77+H+C83tTn9U3WbqjdIhAyATq1jcEXIJ6awdc4nq+Lgi+WM6DLwiPAAU+d8rcw3yx+JbyCPHLzyy4cXOftSx92cg7O2aPpWvME7n8Wrfkjom1of4Wl+P06Srz6zAEmHBRLzVzxtZm24Cnz/eI38NdubVBNyk8vd90vJYo15w+GM261WbxmZkBNGlZ5DO7Ptg8orRIFToC/uzgL+mO/tnmVH3ghYabkQGhvAzYb8vqZ75ZMDbOG+GbnvTsHT6Th965MBBKRAAfu8h6yYn43886cO9DE1veRHPysr08k4p5TmzoyATpXdjSrsTr8W5Q7fiPK7wlx6W62M+siL/Xb4cVu8YHmsdMFmt69Di/N8RkcSj1zUCKfNPSB6MxaAe0IfQrtDj2Lm0r34/IdhC2ABiF8HJYthu3dsDHACihxjwtgfTjSnO9FreoJoCwmWeGZDo6vtjOuZ5TTNh2ge0D/Rb4W53MmxDada9+G6PA/1CHz9UjT4nxuxf/LWg6204PX5lhnvzn7f4vzOeMtJ13yyIa4H/xZuOfRclTwN9Tb8oQb618hjPu7J9rKYyNQK0b6yZYuw/IFZLzTysyZutvsGy8Ft0mHvOshHn+2HY2VDOhEhLc4nzMS269h8Qy0i1v7uV+5395E/NNM52a8NoT6NiOc45dvrbivz4UOh8ohvhU4x61tnqtHIIDn2AXoxxTPSHsbcRxYQ9Gx/Xj3dGCyBbbtc8vb+XoSyljlVt5bWP8ZYvozELfQLe4nrPN46YOy6Uxv2wnWCgezLHQL5z6agu1pUKvBL25p9sA62ZwOjYd+gXZAvv+6pdGqCIiACIiACIiACIiACIiACIiACIiACMSVwMZZRc/D57+L4toOX5XXY3bvqpxNXG2c0HAFZjAf5St5q7iyrE5mYvMNATuf25kHl+9nmDcSVpRRYyY23YCZEMdF3Pncbh8djsc1XGr4KXcnq87u0eaz707pFBYZAnwtQWeXcKwiqytm4z/dHNrlIZR1kF9HPM+6CjIqzS5wQk/H4AN36120k8lK43wrkTGWVZ2zaUQK4zm+G85xOprSIYdOiJGyPsW7RKqouJXThBmb6ewpE4GOSqApbwC+MDEp7O7z+prMA1LCBhDBAji4J9LWq3jHgP/m8e/FHph1tySzMdLNaFfl1eDvdF66t9epidPV6pyecBI8C7OiP2AGlE7EvWvrexhfLR2EGfP9DVrwlT/ScRygEahD/OCyA0y/UrqqyKJBgF9r4Azd8TIORshIpdtMW+Pv3OS2FLND7Zn4Qkkw7jjJ3WO1PjkJwAfIDCybaMbVX4Kvt+S7dSLFbFd9Kv5+ZCVnx9RqEWinBDqiA3qKtS83RHOfwmFxLvQNl0HWE0r7gs0TbPogu9CSvE094PEldDn0BbTU4vQScoyA6GjKO8mWp4qIX4ZtOrKuh57AH5mnoCu4xPZj0OcQbZ21DHRBR1rO0NxGKODgQAtBOttZ/H206VZoIlQfRH47aX9rhU7MTmaHO93RfuSQYYYVVuIWN8RaL0IbbYftliXitrfiOXDCn7XZt/4yeMR/D/aLHfLY7S52iPMWxDdtdBqmToUGQi9CO6OO65kJ/R3v0OfxjIPR8X0atA/SvAOdDm0FOf1CthkOdyhvMsqohOgM3R2i2fv1DWu7ZYG28ZjlwIJQLOTjF/W+xOMelbJ/dEa/BLoUesZLn9m+lyGeg2chjb+3hUciHZ3I/4m0L0NXQg9h+02IrGlO5+tCtOtHK9598Ye18bFDHAcBkHe1Wxz30RpoTy/7iP2uQFyZZ3mof2+LDY8pXot4TeIxMckzrbZFQAREQAREQAREQAREQAREQAREQAREIJ4ECjIqXDNCpeBfItrAsr1bmkXHkh1qzjRbwFHDnzVhdtM9G68L6VPgdMzfHs6/Ka4PVoZuXQuGmX2abzGcVT3alp1WaMY3XOaabdHTavQ5aU8kUd/ulL8lZiG/xPQuHuuaDT2QY4lpuhUMdx23PG44E2Y4zlQcuLE9zhfbWH7fkvER7zudQsO1TMzMzuO3JKuhpahhlUcF5XzmrQ3FGXWGM7XLREAEkp8AvwbRKd9+vRZaf+gMKIsMAc4kz3umSBmdwhrz7NehgZWan1Fudm+8OmIDBwOrNblSdcnnHGfJY3npZWZo5eHmoM73mF5FO/q9h+K9b6/iMQnXQbaJs1/7ss1xX7Zl+YG+kiguAgS2qTwyoteqYJrka0AlByoXpNMlJDmNf5PrczdPzsar1R2SAO8B926+qWXgfr+S3U1tbu8OyUKdFoFEJpCeyI2LUtvozNgDCsVJOBJNWmQVQgdHJyu0Au103OQ6ZylmnnkOmew8tkNvKHU4FOs3KGL10BkZTp4PosZzoGHQ03btiHsAcXRMPsOK2xHLH6DjITrF3gzNttPHcom20Sl+LOo8BToEovMtHZ7pLHsW4um4G4jZx8OfXhLb4cUO8QsdwtZaYXTOtc12uN0OAZQ3y/cW4RZuOwWHeh4t9FKHU7u9JG0J/gWcm/0kGo/4gzzS3IPtp5B3HfbXtlg/D+IM2Jdb6ZYgnGm4H5daYTbD0zzK8ty0Gdr7dZZnAmt7ppfwqAejT3TSpsP3hejnaiz/AZ0AXeVQOc9NsqCeQ/rdkf8Fh3ScZXwq4vkk8FxouCWevxdDn0E8t53OV/t64lms65hAuU7x9vHi/qSM+4h/2873LMhjm/vI6XrKungtfRf92AXLj6BbsP4Kwn/zU6aiRUAEREAEREAEREAEREAEREAEREAERCBmBOjswxmb351zV8zqDKQizlLamNvaEQnPVsyWmBG6KLPWvPLn1Xioaz/W+atEOv2OrDrecPbBUI1O43yp/sG8B4IuIhWfIB9WebRr5kO2N1ZGJyzOEv/vX04yy9fNb6m2NqdXrJqgetwIcLZqe8bqNetXmNkrfzAzV3xtZq78xrVcunaOKzUHD/TBMctZujkgJJLWrXCY2WrNoTi37zTdC0eYwsy2AxTCrY8OiP+dOyXkYjjDKZ31PQdqsK0DSvfCOXh/yGUzI9nG8jwMq7HKLAIi4JMAvzCxQ80Z5qHpR5tFa7y9BvVZRFxnwvXdsuSL5RdYGnMHmJ+XvReRxnfGjOqhOLRzdu/dG68yT/16ppm96vuItKU9FUKuyWi8Jxpdc4rpX7qn6zfKj0vfduzGFnDgDuW4cSwsgoG8v9ukcFvz5aIXHUvtUTjaDK88RvcojnQiG8jfjTyOPpxHN4XYGX8TclCqN+P9aT2++vP1ov94S5Kw4fzCFs89mQgkG4HizDrXV/JmrfzO1OX0Sbbmq70i0CEIhDcVSHIisu9yR8Wp+d9a9Xb3Ur89rcp3bvFe8+AGpwbp8qDf4By53MrzA5broM6IpyOmpznV4ZkmkG2v7bIyB1vPxie3G/vTqn707U2IM1qXQllQL+gmJLLv/j4MpMHRSIN2PA/RgbkE4nF1LcS3A3TUDfQ7lrZzrbcnydzPNCcnXCvK78LOeyLa22bmd7ewg/2W9NfM3fE6jwJo4l9J0LdJDn2eZKdA3ALoZKgBYTxuD4O+gY6DbvmrpBb+RQ7luTN9w8pjM69yK8N91dv+9pI8asH2r+gR3mpAf/nWbiLEN4BP4tjezUfaz5F+L6gKyoS6QBch/QArTzTPVzJf4Gf/cF/94q39djjS0DH/VSgb8v5L019BihcBERABERABERABERABERABERABERCBKBEYWLq36RzmrKKRbho/k+zNcbRn0XZmQuPlbT6hvE3FkWbbqhPDcj63+zG4fP+gZ9SszO5u9m662WxeMs5r2yPNyb28YjhYeHKpyQn00XI0W9axy86Ak15dbh8zoGwvs3PdeebQrg+Zw7o+YvZovNYc2uUhs1XFIRF3PreJcyDFvs23mlHVf4vKTqDjeHZqQUhlp+IR8c5157vYOBmvAfnpoTvl0yGtJ2aSl4mACLQfAllp+a7rBgevBGO5aaXIN9nw6xCyyBHoXBC52bW74csxoVpOWpHZrfFKU53dM9Qi2mW+0sxGU5JZn9R9K81qNGPrJ5u9mq6Hs+BmrfpSgv71KExcFwPOcO5k/M01uuZU/FboiC5WTkSiHzYQ98OcXT+WxoHUWWl0v/Jujbn9vEcmaExGSo7ZofZs/B12ch9L0EarWSLgRoC/zTl7v67BOixEIDEJdMS7o7uxK9ZAu/tzDkZ8VhR222tWmWM8y0Z9nRFGx3Q6Rv7kFu81D9LsaKWz03D23lUIexfKhbZxK8debZPHIU0gQV7b5aMvvsq1HTzd++41Perg3eYEaAn0rNeEMYoA92XQaxCfCF8K8SmKzZqt4KAAGh14Pe1TK2CEZ4RH+Cde4gMJfs9K5HRMBJLfPc1j2JgPDcF+GO0rc5TOI19VhhWH/fcDdCcK4ezdS6FxbgUGy9DeXyyrlYELj4OhnuFx2q6z6m079ZRbg8DlcWzynNsAPYI+7Btoe63j4ECkXw89HGi+ENJxH5WgPg4CiYQFxCYSFakMERABERABERABERABERABERABERABEQiWAF++bY9ZRYsyaoPNGpX0bEdXP45IfGk4sekGtLnO9Yl7OqT1L9szYo7fdF7druY0k2L8v/5g/XR+Z3sqsvl4Pn5WntXJjGu41GSm5rocdTgDoyzxCNAJho7X6anReH3Uur8V2V1NRirnxoi88UsDDfiKQvCWYravPaNllnin/HQOGFp5hFNUQGGcXTQnvSigtEokAiKQPAR4Tdu2+qSAG9yraEdzQOc7cV+RKK/SAm56wifs5Bq8mBJ2O7NS8zSegNoAAFl6SURBVA2/yBOOcXDCTnV/xz1hdP7ehdO2UPOSy2bF7q+XgyspWWc/d+olB1Tu3ni1GVd/qSnP6uy6Px9acXhEBp061ReJsIrsLqbWY4bd+ty+Zsfav8t5NxKAgyiDX2wYWhH6PWUQVbUk7VLg35WGx0Oy2cjqEwwHPctEQAREQAREIBoE/D+BjUatcSwTDpTTUf1kiM7Bz8NJcaBTcxBOB3Hnb+s4ZfAIQ/5yqAeXHlFvYPtraBjidrXjsM59cbm1fSvaSQdP2+7GCp3Kj0O6ZjsQ6yVYP9vavrUl9caVW6zti5Gu5Rcb1gchnLMYz4HoTBqOBd0X1L+11ddW9SJsf6tdnHH4UfdIxBV4NhJh+Qi7H2LchcC12DNNLLbRjlFQjkNdVVbYcre4edZ6o0P6dxD2LTQU5e3hHm9tD0PYd9DbDnkDCgKjj5DwLWg3lHmIUyaE94EqneLcw1AWnf5PsMLoiLyDUx6Eb4nw/zrFJUoY2tgJcnJW5vnFtwgr3Np6I9Y5gOVa5Onu2QeEZULuv0reRRruV57vnk8ajkN4F88yorWN+nlsZXiWjzBORXOZFf68Z7znNvY904yFeK7eh/wHu6fBdh7UapCFVS+vSc3QLSjjR89yI7h9rVXWv1Bvm19RVvt4XLoM22VQH6f6Ec5+0uGeAxF4vZOJgAiIgAiIgAiIgAiIgAiIgAiIgAiIQMIR2Dir6GQ47vCRd3yNn0mnY6s/K8lqcDmVHdb10ag4llVhRnPOIO3L6jFz3X6d/uVyfg+kzb7KilQcnXQOwczaExqviFSRKkcEvBJozHN8PeY1PSNGVp1gNinc1mcaRnYvGNHGectvJiRg2SOqjg8kqdKIgAgkIYFN8XWD3sU7+2x5UUaN2a3hSsw0fAoGY7V5RewzryIDI5CXXmIi8aWVzvlbwSG3zavHwBrhlqogo9IMqZgUdL5EzJBq0s3Yuslm64pDPb74E3hru4BrezK8bzXN+YNdX3Y5otvjpnMBB0AktvGrSLbxS0m71F2IwYfx/62V2NSi0zreG9bkOLlyRL4+DpAI5Oti+Rnl+EpBQ+QbEKUSuxeOxFcHRkepdBUrAiIgAiIgAgZ3wB3Q4Px4KW502ffzoQ+xTidROufSyZCOw3T27WaFYdHakH6KZ5jb9jEon07HdC5l+RdAk+14xK1DfjpsvgY9hvXHsPwVGgXxaR8dkW0HSlc25PkZ6U7D6vXQR1h/BEs6f+4B1UNXI42nk+/DCN/NSvMp8jyL9TJoIsQ3AIcjTyunbaQZivDDIFq+tezm3l/kmWSFs11B9wV5H4BSUSaZ/wbROX4QNBhaCx2Jcqdj6W4HIf0pCJgK/QlVQLtA3Ff/gq6B4mVXo+JmtG8qltMh7pcBEJ/A/gJxP9j2Kla4H+kYy/3O420h+nsjtAFhB2H7ZYgO3U9j+Q20CTQeWgIdiGTrsQzH9kVmHnt3og46kL8PLYR4HG0G9Yb4q2825NPQlgdQRg4S3Qi9hPVpWHK/LoB4rLGczaG5UCIb2/gk2v8xll9Af0A8xvjLMgO63G48+vwN0h2C7bugL7H+EpbfWekasdwG4uCOHsxj7ddDscr9+jjSP4HlDxDrHA0x/xgoFsb9VI028BrDa846qBnaCeJ+fApiv/wa+vUKymG7n4d4LOUg7GYr40gs70DYK1jOgAoh1tEMMf2pUNQM7XgVdZ+JCv4BfY/1F7D8GeI1rQkaDr0N2dz565DXyE+w/BL6HSqG+kJbQhxwcBjK5XEtEwEREAEREAEREAEREAEREAEREAEREIGEJMDZu0dj1u//+/Nys34DH7PG3nLSig0dywI1fv47LS090ORBpxtUtp/5cek7Zt6q6a3yclbKbSqPRFvHRGzW9aAb5yODv0+++8iqKBEIikCws9ZujRlLNyvhqxn/huetZnjVMeah6ccg8Qb/GZBicNkBZsvyAxPyvAyoA0okAiIQEIHhlcea2Su/h/h67S+j41+/0t1xHTgoal9/CKiBHSQRBwr9uYKvxUK3boV85RYZ61sywXyz6GUzZ9WPkSkwTqVwlv/6vL6u2unE/MG8B4JqCb+0UpVN94D2Z5yPMVkGlXTBlxfoeE4bX/8Pk5mW2/52SJL0aOM95bHm4enHosWB3VOG2rW63M1MbnpxQNkbMJh5wWq6QiS2FWZUm22rTtT9dWLvJrVOBERABJKeQPSe7iY4GjgTXoiblX+jmXz6RYdJOoVnQ/OgaRCdTu+HnOwgp0Ar7CQsl/uIp1Pq+6h7ENLQOZ1P5AsgOitfCF2G+FWe+RF2A/JMRzidNw+EUqGvoHMRd49Dejo074PwdyE6zB4PrYTehC5GHoZ7WlcEePat0iNsknumEPpyC/KPhraGyqEUiA6fU6DrUN5nWHraRwj4GqLDKB2b6Yz9IcSZlJ/2TBzj7UtR3wRoIMR+rYfo3Mtw9qfFaRXr/8E+OQXhh0MnQ5kQ9/uNkPtxcS42WRaf5NJ5+yHoIuT/lunCMZTxG9owAGXweNgd2g9Kg2ZCPJ5ugP4HBWQoj47G/0Hi46DtIJaXBy2EvoDYz4CcmpEuXsbji87KfErCY6wEmgN9DF2PPr7o3jBs348+8zjlvhwJbQ8tg/6AHoMe8Uj/DtJvg7BLoB2tuPexHAHtANmO0O7ZorF+NQodD/Wz6uXxx+PrNeg+6FH0LeBfbUj6FvrF4/Ql6Cas0wmddfCJ4TsQefL6sQIirwuge5GG50hUDXVcjvawDSdAQ6Fx0CKI15rboQfdGvAL1nm+DoN4DJdBayCex7dB/0R5vP7IREAEREAEREAEREAEREAEREAEREAERCChCWyCmc2a8waZWXDomrniGyy/di2Xr2t5RBnV9vcr3Q0zA2ZFtY5gCucshdvVnG4emX4cXBU2PpLqWjAMsysfZ/LSS4MpSmlFoF0SKMyowuyNjXCe4aNQZ8tIzTW9inaA4/k4pK13TuQltDK7G2Y63sl8sfB5Lyk2BnO22NE1fzM9gxjA4rNARYqACCQ0Af593rnufPPQz0eZleuXuNpanoWBdNWnmKqcTRK67e2pcZsWjzHvzb3HrFq/NKRucUBfsAOZfFXEr9FsW32yeeQXvsIO+HWlryId47apPMr11aAflrxlflv+Wcs9omPiIAMHlE40vcDVts1LxpuP5//brNuwOuCSOuUPgaMo3UBk8STAgbL7NN9M/w057sZzR1h18+tWvTB4+MtFrdw2It6yrhh4EKjV5/Y1ny98JtDkcUnHgV071Jxl+MU0mQiIgAiIgAhEk0BKEP6G0WyHyhYBERCBDkcATtKT0enzoZG4Fk/tcADUYREIkQDOnY/7wz7+mONEZCIgAiIgAiIgAiIgAiIgAiIgAiIgAt4I8Pn/krWz4Yi+0Rl9JpzSw53t0qkuOqke2uXBhHy5/dmCp81XC18yg8r3M8E4FTj1U2Ei0N4IvDHrJjNtwZNtulWcUYeZW8e7nMLDmZV/+dqF5p6fDjKr13MOl7ZGB8axdZNbZottm0IhIiAC7ZXAjGXTzFuzb8Hf5uFmQNlehg6fstgS+O+cu4OeodtuYc/C7c32tadHvMGvz7w+Kk6dKZgTb3T1qWbTYs4NttFWrFtkflryX/PjkrfNL8s/CusrQl3yh2JgxXltnMeD7c+4+ktNc/7giHNVgSKQ7ASWr11g3VMuj1pXDu3ysMnPKA+ofF4/bv9+94DSxivRFviyEL8ulPD20sCNTRzDeSNlItCuCaS0696pcx2agH7Jdejdr86LgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAi0VwIYxG04yzHVvXCEq5t0+Hp15jVm0Rp+0C8y1qd454R0PmfvNsfMzZRMBESgLYHGvIGtHNCb8gabvnA8b0J4JGZgzU0vhuPLQebN2Te3qbwoo8bQ0a4kq6FNnAJEQATaP4GGvL5m3078EK0sXgQ2L5lgPpn/mFnb9uPsfpvUrZAfFo68bVVxCGYnfxtf8JkfscJdswDXnmX4tSB3y0krcs1YTq1at8xMX/aBa2b0n5a+G5QzeiVmZ96h9kzHv5v9S/c0/1v4XEAzrXNAJ2dVlomACLQlkJteYuhQ/dbsW9tGRiCkOrtnwM7nrI7Xj4qsLmbOqh8jUHvki6jJ6WUGl+0X+YJVogiIgAiIgAg4END3exygKEgEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAE2iMBOnzt1+l2M6B0IuaCDP8VQapJN/1KEnv2t/a4H9UnEYgEgca8AaZP8Vg4ne9mDuw8xYxv2DjzaiScz+32bVayqynNbGrVXDrF7NV0g5zPI7ETVYYIiIAIhEiAg4Q2LRoTdO7M1DzDvx/RsKy0fDO86uiIFc371J0wM7mn87lnBfzaB9NwFvNDujyEwVOTTF56mWeyNtv56RVml7oLTUZqdps4BhRl1phuhcMd4zwDm/MGmfTUTM9gbYuACFgE+HWekszGqPAI5UtZDXn9o9KWcAvNhnP8mNqzTWpKWrhFKb8IiIAIiIAIBEQg/KfLAVWjRCIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAolAgE4yQysPN3s33+SauS0c61m0XVCzxYVTl/KKgAhElkBaSrrZtvokOPsdA4ee+sgWbpXGOoahfNu6F4wwuzVcaej4KBMBERABEYgvgf6lewQ9ILFL/tYmLSUjag3vhr8T/BJHuMY2jq2/wATrWJrnmml5f3Nwl/vhxHmOqcnZ1LEp6SnZZtf6i/zeBw/EoM9ArHP+VoEkUxoR6LAEeE85vDJyA1TcQXYpGBo010T8YkEqGO1Sd4HrC2gyERABERABEYgVATmgx4q06hEBERABDwIbNmyYDKVAUwVHBERABERABERABERABERABERABERABERABGJNoDK7m5kIJ/StKg4N0ZEoBTOp7xXrZqs+ERCBJCPQhJlyR1WfbLatOsk1I6NmeE2yHajmioAItFsCwczQbUPoVjgsqjxSUlLMyKoTcG8a+mzg6SlZcA6/xHTK3yLkttKBnbOi79V0vZnYdKPpUTga3w5Kt8pLMTvi71lFdle/5TNNE2Y392WpJi2stvoqW3Ei0J4INOUPwrkyJKJd4uCP4sy6oMusy+mDa0JizTK+XfVppja3d9B9UQYREAEREAERCIeAHNDDoae8IiACIiACItCOCeAhXzfoDOg1aAa0GpoFPQ2N9NV1xB8EfQAthRZBU6GxvvIoTgREQAREQAREQAREQAREQAREQAREIPYEOJPcoLJ9zH6d/mXqcjYLqgGcAbMkqyGoPEosAiLQMQn0Lt7Z9CkZa1JS9GqyYx4B6rUIiECiEhhYunfATctMzTONGFQUbSvKrDWDy/YPqZqM1BwzvuEytLN/SPmdMlXn9DA71J5pDun6oBlacYQZWzfZdC4IfMZyf7Og1+VubrLS8p2qVpgIiIAHgWGVRxnO9B0J45cMRlQdF1JRmWm5pgrXhkQxXjN7FI1KlOaoHSIgAiIgAh2IgJ7ydKCdra6KgAiIgAiIQJAELkL6yyB+p+sF6GroHWhniE7pJziVh/CrED4FqoH+Bd0P9YGeRVxov+KdKlKYCIiACIiACIiACIiACIiACIiACIhAxAiUZNab3RuvMjvUnGm6F4zEZ7ur/ZY9sGyi3zRKIAIiIAIiIAIiIAIikLgEKrK7+J2h2259F8wUzJnBY2EDyvY0pZlNQVWVlZpvdmu4wtTl8pVU5C0vvdQMKNvLdCnYOqjC6WBele3dUTXY8oKqXIlFoJ0R4Gzl/Up2j0ivhlRMMgUZlSGX1ZDbN+S8kczYvWCE2bL8oEgWqbJEQAREQAREIGACkRkWFnB1SigCIiACIiACIpBEBF5CWy/fsGHDp+5thhP5cGy/DF2J9X8j/k87Htuc8uEU6EdoEOIWMA7hV2LxMXQV1p9D+HSGy0RABERABERABERABERABERABERABBKHAGcm7lE02iXa8rULzMyV35pZK742M1d8g/VvzOr1y1xx9bn9THVOz8RpvFoiAiIgAiIgAiIgAiIQEgHO0P3Lsg/95u1WyNdDsTE6um9bfZJ57NeT/VaYm1bqcgrvWzLBlGY1+k0f6wR4L2Y4cPP53y9wrLpz/hDHcAWKgAg4E9ii/ADXNWvuqp+cEwQQWpHVxXXNCMf4m/iDeQ+EU0TYeauze5rtak7ju/iwy1IBIiACIiACIhAKATmgh0JNeURABERABESgAxCAk/gUp24i/A38iJ2KuO0gOpw/7pbuKGv9Etv5nNt0OEeem7D6d+hg6Hy3PFoVAREQAREQAREQAREQAREQAREQARFIQAK56SWmc/6WLtE2bFhvFqz+zcxbNR0zS26WgC1Wk0RABERABERABERABIIlYM/QPQuDDb1ZZmqeacwb4C06KuGcyXzTojHmq0WcL6m1FaRXma4F20BDTU3OpnC+TPVMklDbXfK3NiWZDbiXntGqXZXZ3cOagTmhOqnGiECMCGSkZpud6843D00/pmWAdHBVp2CAy8kmNSUtuGweqXntSUvJNOs2rA6rnFAz8zo4tv5Ck56aFWoRyicCIiACIiACYRNI7LvwsLunAkRABERABERABKJEYI1V7lqP8re1tts+DTTmRSvOThOlpqlYERABERABERABERABERABERABERCBaBCgYw9nlexWOMzkphdHowqVKQIiIAIiIAIiIAIiEGMCG2fo3ttnrV3yt4KjZYbPNNGIHFp5uMlNK3EVXZLZaAaV7Wv2ab7FHNzlfjOs6ihTm9s74Z3P2XbeR/cv3asNos7gKhMBEQieQHFmndmh5ozgMyLHZsW74GtePULK654pPTXT1Ob0CrucUArISM0xuzZcbPIwaFwmAiIgAiIgAvEkoBnQ40lfdYuACIiACIhAEhLAg8gmNHsUtBx60+4CwvOwXgctxYznfzp07XsrrLtDXJsglPdxm8CNAeE/EfBSsIJFQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREoKMRoIO50wzdNoeuhcPjgiQnrcjs1+l2s2r9MrSvPi5tiFSlPQpHmffmTjHL1s5rKZLcZSIgAqER6FywlRlYto/5aN5DAReQm1Zqtqo4JOD0/hI25w02M5Z/6i9ZRONTTIrZqfbvpjyrU0TLVWEiIAIiIAIiEAoBzYAeCjXlEQEREAEREIEOSgBO4fyG1wMQl5PhaL7ADUWRtb7ICx47vNhLvIJFQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQARiTMDbDN1sRmZqrmnM7R/jFv1VXS5m+E1253P2hrMl9yvZraVjhRnVpkwOpHE7rlRx+yAwpHySacjtF3BnhlcdbbLS8gNO7y9h75KxLV9p8Jc2UvHDKo8xzfmDI1WcyhEBERABERCBsAjIAT0sfMosAiIgAiIgAolNAA7j06ENQeh+bz1CGWmIuw/aGnoEuspbWj/hG/zEu6Lh3D7ASYj6JpD8SiMCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIhAYAc7QnZde1iZxl/ytXc7TsvAJ9C4eC4d+flDYGHLFu7fwC1UJItCBCaTi9fWY2rNx7Sr3S6Epb6DpVjDCb7pgEmSm5pgtyw8KJktYafsU72L6lk4IqwxlFgEREAEREIFIEpADeiRpqiwREAEREAERSDwCP6JJ3wahP5y6YDmf0zl9T+hRaH84h3s6ktsznNszoXsW5W+GdM/02hYBERABERABERABERABERABERABERABERABERABERABERABEYgBgY0zdO/epqauhcPbhCkgNAJ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EIH4ETkDVnBXnYIgz4oRt+EHYG4XQ+fx36Hn3AvEDcgrUyvmc8Qh7A4upUCa0lUee3xD/FrTYPdzbuvVD+ygr/nTkczmf07D+NBZvQZtCdGKXiYAIiECkCLS762mkwKgcERABEQiBQDJcUy/HvWUr53Orn5djuQLqjfvSMrvvukcN4ShQFhEQgUgQaHfX00hAURkiIAIiECKBZLimtnE+Z19x38rntPxyD9+Rd7b7r3vUEI8EZRMBEQiXQLu7noYLRPlFQAREIAwCCX9NtfuGe89CrE+BXsX96a3e+qx7VG9kFC4CIpBIBOSAnkh7Q20RAREQAREQARGICwH8eOuJii+D/okfeW9GsBFHWmXdiXLXBVHuGivt2iDyOCXtgsBG6DvU/7NDghetMDrey0RABEQgbALt+HrqzmZH9PMM6FRovPWgMGx2KkAEREAEPAm0g2vqBvTJvp91vxfWParnzta2CIhAVAm04+upOzfdo0b1KFLhIiACNoFkv6ai/ZXoyxbQKuhbtz2re1Qd5iIgAjEl0I6vp+4cdY8a06NKlYlAxyWQhNfU67G3SqBD/ew13aP6AaRoERCB+BNIj38T1AIREAEREAEREAERiB8B/CDl/dB90K/Q2ZFqCcrNQVn7Q+uhOwItF/makHYUtBwK1xl+E6ve77zU/70V3t1LvIJFQAREIGAC7fx66s7hZg8oS9D3szDQ56aAYSmhCIiACPgh0E6uqXuimwXQe7hGLnTrsu5R/ex/RYuACESOQDu/nrqD0j1q5A4blSQCIuCFQDJeU9HmgejOWIjPgOuhXSHOOHk87lHnunVV96he9ruCRUAEIk+gnV9P3YHpHjXyh49KFAER8CCQbNdUtHcCunAQdBjuR+mf4Mt0j+qLjuJEQAQSgoAc0BNiN6gRIiACIiACIiACcSRwHuruBw3Fj7wVEWzHXiirGHoe5c4IpFz84MxCugcgLk9HvgWB5PORpsiKW+QljR1e7CVewSIgAiIQDIH2fD0lBw4KegF6D5oN1UJ8UHg+dCOu4Wtw3b4d6zIREAERiASBpL6m4prYCRBugDgD+ikeQHSPGokjRGWIgAgESqA9X0/JQPeogR4JSicCIhAJAsl4TaUDOn+327YEKwfj9zsnJHE33aN6ANGmCIhAVAm05+spwekeNaqHjwoXARHwIJA011Q8M61C22+DXsT96J0B7EndowYASUlEQATiSyA1vtWrdhEQAREQAREQARGIHwH8yBuM2jnr+dX4kfffCLfkCKs8/oj0a2hLGhLxxcfW0CPQVX4zhZ8gxSpiQ/hFqQQREIGOTKAjXE/xd+Iu6FHoV2gl9BN0Nfb7vta+v8S6lnfkQ0F9FwERiACBZL+mov2VwPAiVAGdiGvlu0Fi0T1qkMCUXAREwJlAR7ie6h7Ved8rVAREIPIEkvWaiuvkrRDvL/m1yk2hu6F70Z9bg6Ske9QggSm5CIiAM4GOcD3VParzvleoCIhA5Akk4TX1X6CQAR0eIRq6R40QSBUjAiIQOgE5oIfOTjlFQAREQAREQASSmAB+kPJLMHT4/g76eyS7grL5MmMr6DeIs+X6NKSn8/n90J7Qo9D+eEAXCadwe4Zze3S0Zzv4uVmanc4zXtsiIAIi4JdAB7meeuWAy/VziPwdKod4/ZeJgAiIQMgEkv2aivbT+fw1iJ+HPRHXyJsdYOge1QGKgkRABCJLoINcT71C0z2qVzSKEAERCIFAsl9T2WVcFzmQ/GvoRGxywpAj0a893HDoHjWEY0NZREAEgiPQQa6nXqHoHtUrGkWIgAiEQCDZrqlo74Ho5i4Qn5nynVIgpnvUQCgpjQiIQFwJyAE9rvhVuQiIgAiIgAiIQBwJ5KPu7lBPaCV+9G2whW37s6z/ssKuC7KdR1rp78QPyHW+8lo/jh9Cmr2hB6F9kWetrzxBxH1rpWU/naybFUgnfJkIiIAIhEqgI1xP/bGZYyXI85dQ8SIgAiLgh0DSXlNxX1uDvk2FOBjnWNzTXu+lr7pH9QJGwSIgAhEl0BGup/6A6R7VHyHFi4AIBEogaa+pXjrIr/XQRrjF6x7VCywFi4AIRJRAR7ie+gOme1R/hBQvAiIQKIFku6b2tzp2j7tfAtet8Dq38GIrTPeogR4NSicCIhA3Apz5UyYCIiACIiACIiACHZHAKnT6Ti8d5w/AftDbEH/Y/ddLujbB+GGYjcADoPWQt/Jd+ZA2EwvOeD4Ouhc6GI46zBcp+xEF/Qp1R12dUPbPHgXvaG1zlkqZCIiACIRKoCNcT72ywfWVX5noAfEh4XSvCRUhAiIgAoERSMprKq6F9ege7ym7QkfhvvN2H93VPaoPOIoSARGIGIGOcD31Ckv3qF7RKEIERCA0Akl5TfXR1Torzn0SEN2j+gCmKBEQgYgR6AjXU6+wdI/qFY0iREAEQiOQbNdU+hvQad7JDkXgcoiT1tHYN5ruUS0QWoiACCQuATmgJ+6+UctEQAREQAREQASiSABOMStQ/GFOVeAh2GSE0wH9HqS7w06D8Aysd4HWIJw/+JxsTwSWQM8hzQynBAxDWVlYPAHtBNFR/YgIO5/z07Kc1f1WlH0pdAXWJ9p1YJ1O79tAX0FvQDIREAERCIlAR7ie4ppZDTj56OsP7pAQzoeFUyAOPnoZ8TNDgqhMIiACImARSMZrKq6FjWj+61AzdCj6cLevHap7VF90FCcCIhApAh3heqp71EgdLSpHBETAH4EkvaYOR7/eQttbTfaBayef7Z5j9fl5u++6R/V3FCheBEQgEgQ6wvVU96iROFJUhgiIQCAEku2aivY+gn5RbQzXTjqgL0CaVr4Lukdtg0oBIiACCUhADugJuFPUJBEQAREQAREQgYQlwNlxvoZ+gZq9tPIIK9zXrI9MQsdwOp/PhX6HzsOPS88ip+KH5VT3QKSZ4rbNGXdplyN8ibV+B/Jw5nbbrsHKWGgP6H2kexVLOgnRUZ4jqQ9B+kjOum7Xq6UIiIAI+CKQbNdTXm9fxzWUM1Tw78BsiH3YDqJz+k+Q46AmhMtEQAREINoE4n1N5WDGZuhjqAnXyskOHZ6Ce87pbuG6R3WApCAREIG4E0i266nuUeN+yKgBIiACPgjE+5r6NNq2EPem72PJSUL4TpzO52Os9Rtwf/qyR/t1j+pjhypKBEQgbgSS7Xqqe9S4HSqqWAREIAAC8b6mBtDENkl0j9oGiQJEQAQSiYAc0BNpb6gtIiACIiACIiACSU0ALzR6ogNDod+gF/x0ppMVX47leT7STvWIO8gh7W5uYUzf4oCOFymr0K7RCDsT2hc6GVoMPQWdj3jOgC4TAREQgYQikIDXU371ggOLBkG7QsUQB/F8C90IXY/rqT0QCJsyERABEUgcAjG4pjZbvR2AJeVkUxE43Y7QPaoTIoWJgAgkOoEEvJ7qHjXRDxq1TwREwCuBGFxTz0fl20NbQrtAadAs6CmIE3j8B8tWpntUTyLaFgERSAYCCXg91T1qMhw4aqMIiIAjgRhcUx3r9RWoe1RfdBQnAiKQCARScKFKhHaoDSIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAglOIDXB26fmiYAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIJAgBOaAnyI5QM0RABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABEQg0QnIAT3R95DaJwIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIJQkAO6AmyI9QMERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABERABEUh0AnJAT/Q9pPaJgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQIIQkAN6guwINUMEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEEp2AHNATfQ+pfSIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiKQIATkgJ4gO0LNEAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREAEREIFEJyAH9ETfQ2qfCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACCQIATmgJ8iOUDNEQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREQAREINEJyAE90feQ2icCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIuCTQEpKyh7QDdBb0GJoA3S/z0wBRqKcEVZ5LNOXGgIsUslEIKkJpCd169V4ERABERABERABERABERABERABERABERABERABEWiHBPACoxnd+hm6Z8OGDZPaYRfVpSQgwBdpaOYbOAZHBNpc5JmCtAdBnZBveqD54pUulD7Gq62xrhdsXPsP+7E51nXb9SXb8RQvTqpXBERABERABERABERABERABERABESghcC5WNscWgr9BvWIIJvpKOsCL+X1Qfhu0Jd4ljLDSxoFi0C7IqAZ0NvV7lRnREAEREAEREAEREAEREAEREAEREAEREAEREAEEpUAHCl7QJx95wtoEbQa+gN6HjoUyk7UtqtdImATwHE6GeIMTyNE5S8C4DGVXMQk8QlgPxVDp0EPQF9Ba7nvoNGRaD3KGQats8q82LNMhNvnkK+Z0n50z4c8fa1872D5J8S/H79DD0H9PeuwtxGXBp0EfQ6tgOZDL0Bb+cgzAfGvQQuhldDX0HlQm79RCKuDjodehKZDq6B50MsQX7q3MYSPgHz1/TLPTEjvj9kYzzzBbKP8cmim1a63PfMG0Ga7Py0z3CFP0Gwc6kUxLpZ2+a0mFmMkbAzEe4tp0AKI++xb6DqoyrNMextxpVYae7/xfuQuqN4zD8KC7gvy9IHugD6F5kA8NmZAr0C7QSme9WhbBERABERABERABERABERABCJE4GSU0x0qhI6OUJmuYjjZAjTZSYhebdV1eyTrVFkikMgENAN6Iu8dtU0EREAEREAEREAEREAEREAEREAEREAEREAERKBdEICf1XnoyPkQJwV5D7oH4iw8dA4bAd0B8YXIQEgmAolCoCcasjzIxpyF9HQg/T3IfPFKHkof49VW1RsZAs0o5gqrKM6ENhfy6qgbTJW41hcgPa/vPG/yveSd6iWcwbtAdCh/0SPNrdjeAvoYegLi34++0N4QPy2+F15+P4n1FrMcfB9mPPQtdCNUCk2E3kT87sjz9F85jEHYRdjmTHEs/3FoHjQUugDaHvHbIc8KtzzHY/0MiF/seB2aCTVBdD4fjfTXIv3f3NK7r76BjakOcW87hNlBZDvdIf4Hh7Bggm5D4jwfGVgnGThZHwSyv54z3IXDxq7nOKyMhFZC2Q6VZyGMxwqdHN6EXoHSoG2hE6G9sQ+2wT74HusthrAybLwL0SHjNYjHSQ/oYGhnxA9Bnp9aMhgTSl8GIP946D2IdS2CqiEe4zy27ocOgGQiIAIiIAIiIAIiIAIiIAIiEFEC+D3D36cuw++bgMpGun2Q8AioL5QD8XfuA9CVKG+Vv0Ks31kTkI6/me/zl17xItBeCKS3l46oHyIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiKQiATwAuJstIuOa/z06p54afG+ZzuRZizCTvEM17YIxJMAjtVvgq0fef5EHiopLJQ+JkXH1EhfBH5B5GjoU+x/zgg+BesH+coQRNw/kbYI+gd0iVM+1DkV4VQrQzvoOHyoFeg5Wxpfeu+PvK0crZFnP4TTkfdfWH8e8fZsayxmb4jO53T+HYU4OjHz5futWNDJm3leQ/gSK7wfludAC6EBCHc5ICMN39ZfD9EZms7mkxlu2QdYjkDaN9zCmIcDO+h4fDLWH0D8x+7x1vpUhE92CPcVNAV5pvpKEGwc2ncg8tCB/BjoZqf8qHM6wic7xSH/Q1a45z4Lhw0ZboJyL4eugrgv6djvaesQcC50M9q4wI5E3lSGQUdC10B0+na3S7FB5/Nrke9vdgTynYB1HsPMO8YtQyh9eRhlT3Erw7WKOgqx4LGxP9ZvQBqWLRMBERABERABERABERABERCBuBHAb5M7Ufkh0G8QB30vhLaELoJGIZ6Dsddi3ZdNQmQWdK/77zNfGRQnAu2BAB9AyERABERABERABERABERABERABERABERABERABERABKJAAC8omlHsZGgNtBNeQLRxPme1CH8OC3dnLwa7jGVAD0NzoZXQRxAd1lsZwiZBG6zlGCynQosYZifEehH0D+hbiGUtgP4D0RnTs7wRzAtNhgZCL0GLIOZ5HGpgBiw7Q2zfHGgF9Dq0uWd5VtoaxN0ETYdWW3mewHKAZ3qEZUInQJ9ArHM5xHxPQ07t5QshtpEOpezbd9BlEJ1BWxnCyIZ9y4DOg36EmOcb6HA7MdaPgv4HsV+/QRdAjs/VEb4F9Bg0E2LfZkC3QbWe9XvbRlr3fchZaN+FlkHsP8vu5pQX4VHhinLJaKobj+lYP9/a5n5mvEtuaaZYYc2ebUX4XhBnXl4EkSnZngXxBV0rQxj3NZULXQn9Cq2CfoDOgOgU65mHHHpAbfa5Z1p7G2lb9ZHhCJtshY/AkrNLfwDx+OOx9TBU55a/mWmxPdzK28IE4VPd68V2PXQj9BPEvsyDnoEGuadzaMO+SPM+tBQikyEQ6+FLUUdD3NcQ6yi1yuP5dBz0AvSLFcf+vALt6FhIFANRJ/cr9yOvZ0sg9o1tvh6qcqoa4UdCPGZ4rs6Cbofa7GuE2cdOIdavsbbXYDmZ5fJFMPQqNN+pnlDDUP445D0YohPvHyGUsxPy1EPvoW2fu+fHNh11WzmfMx5hdEzn7NZlUB/3PFg/2to+F+lczuc0rH+IxSNQBUQHddsmYIXn1R1I85MdiHUe32dDXB6NftJR3mWIegJq5XxuhX+NJeugjbCWCbdAXxrRqOshOhu8GGwDkZ/cyW0FdJ97/nDYoNx0q7yfsTzfW7tQxxroEmiBR93rsX2hFTbCPQ5lc6b3A6BlkGfZNyJsOrQD0nW284XSF+RpOebc60f4Ymz/xwpz/Jvmnl7rIiACIiACIiACIiACIiACIhBNAvjtMwnlHwI9CXXHb5ZDoVOgrbF9ATQCOhbyZ4dZCW7zl1DxItCeCKS2p86oLyIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiKQYATojJgBPY4XF1/4ahviVznENyGMs4M2Q3Ruo0Nfb+hpvCAZ6ZCeQXQofA5aAt0KPcpApC/GgjPhngktgq6DHoeGQP+HeM6U6mR0jn3LivgXlmzPbtCryNPD2qbT5L3Q89Bw6GXE5WPZYtjuhI2PoGOgH6GrITqh7QzR0drTqX4Kwv8JkR/Lvh56E6KTZStnfavtLyOcL4eegq6D6Fx6BsSyi7F0socReAT0KkQHxBKITq2ToGuwfgn0CcSXR6uh86DToFaGtNzP70A7Qq9D10HsK18+0cGWTo7BGPk+Bf0GkcF/od2h91DWJu4FYTtqXN3rsdavw/INa/0eLC9wk0Pyv4LQTs64y+O3J/QgREfHFIjh/0E897OnMez/IPadzqF3QDnQZRD3haf9AwFfQxM8I0Lc5rF6PzQdugniOTwRotN2llXmQizJ4Rdr253JFCuM519/rE+DWOa30A3Qs9Aw6G3E74Slk52CwLugXyEyexHXCh4PLGMs8pV5ZkLYYITx3HwWaXke0Eqhf0IFEM8VHt/PQP0gOqXzWI2Joa4SVMRrEfcjrxPs3y0Q990hEI8RT7sCAdRnEPfF79DhEF8QO1kmAl+DxkM8htj3n50SBhKGNnMwQpvBCnZexFVindfHp8Ccx0woxmsR7fYgM6+x0q+186E9PD63gpZD9vXbvdgXrY1t3QKrrfWf3BNyHX1agsVciP3kNTgQa9Muj0xd0U4OijgbOgTqFkChQ5HuFIiDFyZC5QHkcUyCvCmImAItgv7mmMh/4CQkIet/g1ErJ3A/Wf2xORf5eW4e5OXewE/xrujVVqKW48LaHoIlr6PvWPu1pSxsr8cGzxfayJYI3yv++tIqN7DnIsA+7v7nHok4n+eZ72YoVgREQAREQAREQAREQAREQARCInAicvF30yH4TbTCo4SLsD0P2s9Xyfgtw2ehfA7zJcrg8w6ZCHQYAukdpqfqqAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAjEnsBQq0o6OIdiI5BpMl5eXGBnxkuNB7H+EnQaRGdnT9sJAZxtnWnc7XJsbArRufEoxG9gJMpj+EcQZx7+D4KnM9zNWN7+CH/ADkM6OmvTUZQvVa5G3CVucX/H+oXQoRCdPm27FSu1EGfjdU9/M8LehO5BuU2I40zIRdjeG/oY2gJh6+xCuER8i9Mt8yDoemgpNBhpv7HTIo5lHw1dAdnOnXY0l41Qb+RZyA2kvxoL5r8WYthmiKOjK+MmY/EDdCrTIdzl1If17ljcBk2HhtvprTg62dHZlxwmMCxA2wXpdkFZz9npUQ9fiF0HsU+j3MqJClendqI916EdxYjji7Up2J7qlM4zDHno8HgWNAPiPprJNAhn2JPQWIjH86UMdzMeL59B/NSx6yUg8vBc+A46GeuXItx2fnTPF6n1MShoEOpocZJEnTz/9oHGQY8ibiGWkxE+AssmbE/GspUhju9iHoXyoZFI84adAHHs44fQnVhvRtyq1rldjppDEP6pR/g92CYvtoWO6e52kLXBNLYtwArb95t7QtRZhO13oCuw/gDiPV+2uicn/2IEnNQq0P8GnbKnuSW7CeubQzx2j0UcnV5dhvILsEi1t92WW2K9D9L+aqUjUzqYj0QeHlMfeOSpwfZX0HDELfOIi8bm7SiU7T4qlMLRhzrk2xFaBD0SaBnItwXSbgrxOvWFW76uWE+DfkL/XdcqD/ve2ub1y7a51konz8TWfim3wntgOc0zjfs20hdie3doA/R/XtLyBX6rl/jI9zjCDkebebw62UUegZzl/0qEnYc8rCsYOwmJR0DbI+tilFMaTGYr7WHW8rZA8/pjg/hBKOsc6DK0i3+bQ7VDrYwveRSwibXN66iTOR0bTul4vvrdz0jDY3F/iMdjFbQzxOveP9C/zx0LVqAIiIAIiIAIiIAIiIAIiIAIxIAAfq/koho+n+Dv4ZOw7VQrn9P0dIpwC7OfOQb829BPeYoWgaQh4PQQL2kar4aKgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIgAiIQIIToBMkrZXTZxBt/gVpL3ZPD4et/2CbTpiDvZTzNNK0cjjDC5QMpKUD2FLoLHdHPazT2ex6KBM60KHMt5HmAY9w27GVzpKXecTda233tcNRfz3Wt4fY7ivc06Psd7H9EETnv92sODoS8q0PX/K0OKfa+ZBnnr2OJfvFtt+I8G/cwrlKJ74l0AFoA2ep9bQzkWehHYj1n7D+NlQMXYRtOnW6zEr3LFbphFlnh2N5NES+J7qnt/LQQfYZaBfUT8faQO01lPWcR+Ibsf0jtC3KamJclLkG2tZA0h1iJboY/ZppZ8A6HWNPgbiPD/NS0AlI1+IUjfXZSPc0RMdp25HSlRVxk6AUaIqXsoINvh5l/c8j07+sbW/nn1MddLjsAt2A8t5wT4DtP7DNc6IaGuWQ+Xak+dQh/D6EkdtB7nE4Jngu7A2R04t2HMpYBbW5DiGM5/BdUAlEx1d/VowE5wepvnahaF8l1idCf0Knov5W5ze2l1htsrPYywsRzuuHy7DOY+dua9PbvuAnsyPlfP4B6uIL5zbXSPSJx/c46BjUN8tqU7ALHv900L0fZSwPJDPq5T7jcUD7G/Ktc8vH84PG/etkdnixW6R9zTkMZTd7ZLoY27wm01ivV0NeprsDorPxLWjX1x6J52D7TKgPxOtiBbQjxON8d+hZFJGKpbt9hg1y7gzlQE3Q4dBC6FzoEihgQ/mbIvGl0K1o3ysBZ3RLiDKGY7MHFPAMd/7YIJ594z79CrowlHYxD8rhuczzdAlEPu4WyrHhUYSrDn/72c7TFStsC9vBfVYOnQbx77OneT3PPBNqWwREQAREQAREQAREQAREQAQiQIC/b/nbhr9L+bvFSbUIz/dWF34alSKOv2X57Mr+je4tucJFoN0RSG93PVKHREAEREAEREAEREAEREAEREAEREAEREAEREAERCBxCNgOextCbNI0D6dCu5gZWBnipUw6cHkaneRyoXdQ3nzPSGzTUZrOYf0c4j5yCKPTLM2pfb9bcfVu+exy30L9axzKY/37Q0x3L9JwNtpnsb4L68A6Z8V9C3rfwTmzv1Uey2hlSLsAeT9F4DCIDOjE6G6++vaxR1puuvftFyve3g/DLac/z2x0uE2DukNOZXqm5/YbnoE8DlD+2wjvApET648mV88mhLPtax99h37RMboTlsXo50K3ihZh+weHinn800oc4iIZ5HR8hFK3fYw0oY+THRrYzQqjg/MLHvFO5zMdsH9DWa8i7XZYboptOqzSeM7w5ee1CFtrhbkWSNcLCzp+8nyogbLd47HuPrDCI2rjJsqcjrUUx8jAAukYS+fiN1HWssCyuFIFuy9WIs/nQZTvMynaSqfwbzwTgWkzwq6D/o00j3rGB7KNMsjjECvt7QHmyUO6ZyAeO1eEULe9D1v+NqGMd9GW21DekdDnWOd1l38vtoa4376EeAytg3zZ1YjcE+I1+2+eCVEPy6FsW4qVl1Dfu1hOg1gfj+On7QTI86S9bi1/xfIO5PkEy/cgfpniGqSby3isn4RFMdfd7CnE8+9JBsLugzgI4nSPNMFsHmElJrNAzScbFHIF1BnirP5Ofyv91oP+8W/NsxD7uTfK+dFvptYJ2hwbXvL764srG+p/CQs0y8W9EdoPuhTi38zdEb/alRCGdW/n2UmILraS2QvX/vQI06YIiIAIiIAIiIAIiIAIiIAIBENgkZX4U/y+6B9MRre0B2E9C7oHZSwMsQxlE4GkJSAH9KTddWq4CIiACIiACIiACIiACIiACIiACIiACIiACIhAEhD4A22k47O7M3YwzV7oJTEdS+m06GQzHQKLrDA63DmZHV7sEGm/jHGPsh1b28ThZctaOJUxLZ3NbAul/onIfAa0L3SBVdBKlP0Y1jlz8iwrLJSyXVlRRpv2I9hr39zi3PtWZrXjNGvpbZHvLcIh3O6bZ5S9b+0+h9L3QLl61h3OdiDtpGMi0y10q8h93b1+ex+lhdOoAPI61R9K3fYxsqefOp2OEafz2S5mCla2g/iyk+cKjeu0e6yla4HzZkssOEiD74Vehei8vBhaD/WFxkF8YRptK7Yq+D3IihY6pPe1L2bj/N7gkCfSQXehwBXQMWEUvCPy8vh/D03+3F852Jd0Pn8eGgrR6dre9+5Z7Wubfe55FltoBbS6BqKso1A+Bz3QuXovK83HWO4AHQrRAX22Fd5mgbxXIvBk6E1oZ5S3qk0iLwFIy4FHDyL6HGgY9LSXpC3ByPOJ1V46rQ+B6HhNOwlqstbtxXSsTIPOgvpBI5Gfzu9BG+osRabdIe57OrP7NX9sED8chRwLTUa7pvkt0CEByuiG4Nchto/O5zzPPS2kY8O9EH998ayQ22gLHerpDH8h8tPp/B/QCdBVTuk9wk7Ctrf9GUB2JREBERABERABERABERABERCBtgT4mxC/T75ETC/+zsP2/Lap/IYcbqUIaEC539KUQASSjIAc0JNsh6m5IiACIiACIiACIiACIiACIiACIiACIiACIiACSUWAM1ZvC42C7oxRy52cLm2Hs2ovbaixwls5I3pJG0pw0PXjpQ8d+yZTeAnUgCUdEidB+0PN0DYQzb1svjTytFj1rQhtpkNvJKzKSyH2/rP7HE2uXpoQUrB7O51m4432Pgqp0RHMZPd/nBeHUF9VOZ3PdvonscJjbn+cI2djSadTOjN/hno+8yj0XGznQHS6neoeh7x0yB3nkd5xE2mLEXGSY6T3QPeZihdayeq8J49IjC9uEanAKqQ/lkXQHLBxKvcchNOh+mlwH++UAGEBz6SNsgqQns7nvP5x5nMn53NW8wO0DuqMPOlIZzvrM45GR2Xad9ayZYG0dKqnWhnKucMK+NAzjtuIvxaLk6DXobEoh7NZB2tzrAx5QWRskwd1N/vIz33GnTXVyz7bGuE8fvgFhmIv5RyE8CwooBnuAmTTz2rXBUh/gZd611ht7oe2TXNPg/Ce2H4V4oCXPRH/tJcyvrXCu3uJ93psMH2AffFSdEvwi1ijA/oIyK8Dup/96a8uxYuACIiACIiACIiACIiACIiALwLXIJLPbO/C751J+P2x0D0xwkqw3Qnhn3gWgjj+NudvsS8Q/65nvLZFoCMQkAN6R9jL6qMIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiIAIiEC8CNyNiuncuTteSmyKlxFfeWsI4rMQv8pbfJjhdDijM2BfvjhBPQs8yhtpbbd5mRJmvXb2T62VoajfyRnSZ/1o7wzkfwB5H8LyG4jllCF8HtZZ9m7QCIjOdy2GNMXY6AuthL52i4rk6nsobADEl050DI2EDfcsBH1JQ9hQK9zmGU2unk2wt+nUSmN7AjW2k06fI6Af3TOhX12xXQ/9jP250D0uydZdXLif0A+bkd0FHiM0HiPP2IHhLlHPCtT3KMo5DBoN8aUn3/vc41A2Oc9HnqkOcW2ON4c0dlAxVs73Ee8UNR2B06wIzq69HhqGtuehPcucMiRR2L1oa65De+nEy0Ez06CPIftcbZUUDGoRsDO0COK+9GpIW4TIlyDOZn8J2J3rLTHiViE9X37zmKNe90i7o7X9mrcy3MNR1vbYboLeQNm/e8SlYPtG6BjoZWgcj033NEGss2+0nwLJg3ZlIB2vLQHnQUK2ca5D+fkImwjNgp6DfDnQH27lv92hnJYgtC8YNl8g451eymO72D4ODNgAzXNPh2r6YPsViMfI7uDP9nszXo+4f+hoX4C0S+yE2E7FOvc1rdUxE2Rf7CK9LeusiLXeEihcBERABERABERABERABERABEIlgN8v45GXolVbyyEIn2Ktz8VvoVO5jiUdz/lcj79pf8T6f7D8FSqFOkH8bX83dBTkaUdYAT5/G3pm0rYItCcCfJAgEwEREAEREAEREAEREAEREAEREAEREAEREAEREAERiAIBvMSYjmInQ5nQ83iJMdCpGoSPQThnBI2KoR2rUfADEB3YLnSvBHV3wfYJ0Brovmg0APX/hnLp9NcMneReB+rfAtv7QgugJxmHsAor3D0p1zkrbgFEpzX2iXY/xLYfjzxdrTB7cRFWCpkGbYiWc/+NVv3Xov7uHvWzL5kQHUCDsW2RZ6xHhuOwzX31OvryC+OizNVbe23Hx0ZvCRzC6TRJO5f71o7HOp3Yr4L4rsKb46Wd3O8S5U2BNkCT/CaOfAJfXJ5GdT9Cx6JtOzlVjXC+CM11ivMTNsWKPxBLiucGz3VPm46AUtSxmXsEtg/F9g6eib1t85oGpQSpKXZ5yDcH6w9DNdBVqL/Veyps50N0ok0o476BekCtjnv05wToME+h8Xw5TXveirvJS4fIn+fBfUjn1eEZ9ZYgDR2M6aB9PtKe66U89+BbrI2LkT/bjsD6IKzToZn74nH3DIjj9bKVIYzXHb5MXwed6ZGeDtaMOwbi37Bd0TY6N3s1lEfH5zbvJxG2PzKxXby2P2oXgPACqK9ngQjj39XrIO6Tb6CPPNM4baN9N3nuL24jrd23H6z4E5zyo15ezznYw+cMd0gXFBvU+YpTu6y22deXI600M+y2WWzoLF4AjUO8L+dz/t1YinT3Qfx7Otkux1ry70wz9B+k+8mOC7YvzIc8HCjGAQKtDGH8G3CZFfi8eyTiHM8zzzK0LQIiIAIiIAIiIAIiIAIiIAJ+CPRF/EGW7Gcend3C9nDPj98/x2J7F+i/0Gjob9CuEJ9PXAldB7Uy/H7h73SWw9/A/I0lE4EOSSC9Q/ZanRYBERABERABERABERABERABERABERABERABERCBGBHAS4xL8VKCz2LPhz7E+rtY0lGOTmBV0DComxWGRdSMznV0nDsObaADIh3WyqG9IDquHYe2/hy12jfOFPQOyr8S9XOGVTJogPaE1kMHo/4lVv11WL6HdF9j+QlEZzs6Ro6FqqHr7bRYTke6kxB2E9NinY6LdKwcDg2B6Jh4BhQVQ/3foM5DUPhd0JdYfwnL7yA63jVCZM729IACtWeR8EmU9SSWP0CbQztB8yE6errbUdiIOFePOtw3edxwf/0D7euN5QJGgsPF3vIg7l2kvQLxp0NfYP0xLJdBnIWZZbwN8YVeuGY7ta4Nt6AQ8r+KPDyWn0D/XsCSLyB/Qd/pWLwGYbthm7NocSAKrwHTIDoc8xzg+cgXoXTKZljAhrLfQXk8Rlg3j7lnETbboYDrEMaXrm8jPc+RRdBAaCjE/cGXprGy41AR9zuP3RFoD7mshjizGNu4KzQVipqhzqtQOK9/NDKgnYbw/a31p8DxKWudi8EQj/03oBFu4SGvoi4er4daBdzup6AnEM/99SOUiryTHdKzzdPcwh/GOo877ttPkYfXlTJoIpQGHY70i93Sc/VOpGvC8mOI53ZXaBeIxxYd7d9jIjc7j+EQj/dp0JnI3yoBwz1YPoAw9oHnwW9QNsRzgIzXQnSyno6lbWwz2z8Ny8+hP6EKaCTEY2YutA/y8LoUCzvCqsTfPguFTVDtBxM6PPDaU2otOZCFf/c87TrwWegWeDbWR0B/Q/q+WH4A0al+HMTrB50v3C2UvtyIAqpR/jtY/gqtg5qhnaAc6CnoLsjdIn6eeZSvTREQAREQAREQAREQAREQgQ5AAL9/JqObVMCGPM8hMRWQIT1/M/O3jUwEOjSB9A7de3VeBERABERABERABERABERABERABERABERABERABGJAAC8lLoQT1r9R1TEQneYOhuh0Nw+aBl0O3Q9FzdCG+ZZj2lmohE6Jf4PoNEjHsysR/39RqxwFo/yfUP9ArJ4L0QFtBETnRzpsX4L4D7G0bTpWzodGQORVDs2HvoXOhOhY2WLIezPK/gEBp0K7Q7nQDIhOzZcifmFL4iisoPz7Uf9nKPoUiO3dHloG/QE9Bj0SZLV0Nr0dOgfaGVoDMews1PWde1nR5OrUZtT3Nfp6EOLImsczj2PaxdbScYF8ZyDfp4g8DjoQokMrnWl5PFyN+NWOGYML7IPkS6Dng8sWkdR3oBQ67u4NnQ7x/csb0H0sHf37HP3fHKs878ZCvAbQYZbOtOTC452OtKHYPch0kZWR620M9b+E+ulITN50QKYzKM99Hq+doT3aZIpSANqyAG3ZCsWfBLEtdOhle3jO0iH1Kyjaxv5yf7kbz1vbpmPlKffIKKzT2Z5teA9M/uen/E5WfBcseaw42XQETrMjUCa/BrAPtt+FDoGOh1ZCb0IXI5rhnvYcArg/9oIKoNnQ4//f3v2jNBAFcQBGC7HzD17ERhCx0gN4BE9h6QEEbSwsTW0l2HsH76BlwCP4G0lAJFgI4TnxWxjWZNd9875ZN4XDS+Kq7uHvJ+f1PK/6p3t9tiza6p58/HLgLj+fJo4Se4m1xFtikqhG6Zfsv2717L9NHCTKbDdRz4t6ftRn501+p/Jc+hbPnQxS9059dn7+bf+w/cbmh8stPLSVd8ujtpNZLDpxkjff5wfiNc1cDvO67qWzxHFimrhPXOb46/zc2f43c7meXXs/+6rbRqKecc+Jsnuoe3R2fTsCBAgQIECAAAECBAgQIECgoUB9TWLDtKVMgAABAgQIECBAgAABAgQIECBAgAABAgRWSyANgeeZUTUA1mrwk9Wa3XJnE7vtjFANlNXMfrHc0VydAAECBAgQIECAAAECBAgQIECAAAEC/1tg/X9P3+wJECBAgAABAgQIECBAgAABAgQIECBAgACBFRCoFXxrpfibFZiLKRAgQIAAAQIECBAgQIAAAQIECBAgQOBPC9RXQNoIECBAgAABAgQIECBAgAABAgQIECBAgAABAm0Fsur5U5LfbDsBiRMgQIAAAQIECBAgQIAAAQIECBAgQKCRgBXQGxVLqgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEBgpsJZVQUaOb2wCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQaCJgBfQmhZImAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIERgtoQB9dAeMTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgiYAG9CaFkiYBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRGC2hAH10B4xMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKCJgAb0JoWSJgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBEYLaEAfXQHjEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoImABvQmhZImAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIERgtoQB9dAeMTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECgiYAG9CaFkiYBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRGC2hAH10B4xMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKCJgAb0JoWSJgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBEYLaEAfXQHjEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoImABvQmhZImAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIERgt8AJKpNsMGTrxLAAAAAElFTkSuQmCC\n", + "text/plain": [ + "\u003cFigure size 1440x720 with 10 Axes\u003e" + ] + }, + "metadata": { + "image/png": { + "height": 617, + "width": 1488 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Build plot.\n", + "longest_transcripts = longest_transcript_extractor.extract(tal1_interval)\n", + "_ = plot_components.plot(\n", + " [\n", + " plot_components.TranscriptAnnotation(longest_transcripts),\n", + " # RNA-seq tracks.\n", + " plot_components.Tracks(\n", + " tdata=output.alternate.rna_seq.filter_to_nonpositive_strand()\n", + " - output.reference.rna_seq.filter_to_nonpositive_strand(),\n", + " ylabel_template='{biosample_name} ({strand})\\n{name}',\n", + " filled=True,\n", + " ),\n", + " # DNAse tracks.\n", + " plot_components.Tracks(\n", + " tdata=output.alternate.dnase.filter_to_nonpositive_strand()\n", + " - output.reference.dnase.filter_to_nonpositive_strand(),\n", + " ylabel_template='{biosample_name} ({strand})\\n{name}',\n", + " filled=True,\n", + " ),\n", + " # Chip histone.\n", + " plot_components.Tracks(\n", + " tdata=output.alternate.chip_histone.filter_to_nonpositive_strand()\n", + " - output.reference.chip_histone.filter_to_nonpositive_strand(),\n", + " ylabel_template='{biosample_name} ({strand})\\n{name}',\n", + " filled=True,\n", + " ),\n", + " ],\n", + " annotations=[plot_components.VariantAnnotation([variant])],\n", + " interval=tal1_interval,\n", + " title=(\n", + " 'Effect of variant on predicted RNA Expression, DNAse, and ChIP-Histone'\n", + " f' in CD34 positive HSC.\\n{variant=}'\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "D-uwrpfoNOGp" + }, + "source": [ + "This plot shows the difference between model predictions for the alternate\n", + "sequence (in which in `alternate_bases`, \"CCGTTTCCTAACC\", have been inserted\n", + "into the genome at chromosome 1, position 47239296) and the reference sequence.\n", + "\n", + "Interpreting this plot, we can see that:\n", + "\n", + "- RNA-seq coverage is above the horizontal, meaning that *TAL1* is increased\n", + " in the presence of the alternate sequence relative to the reference\n", + " sequence.\n", + "- DNAse-seq indicate changing accessibility near the site of the variant, and\n", + " increased accessibility near the transcription start site of *TAL1*.\n", + "- An increase in H3K27ac and H3K4me1 centered directly on the variant's\n", + " location, which mark active enhancers.\n", + "- An increase in H3K4me3 near the transcription start site of *TAL1*, which\n", + " mark active promoters.\n", + "- An increase in H3K36me3 over the gene body, which mark actively transcribed\n", + " genes.\n", + "- A decrease in H3K27me3 and H3K9me3, which are associated with silencing.\n", + "\n", + "Taken together, the model predicts the variant creates a de novo active enhancer\n", + "element that activates the TAL1 promoter, leading to robust gene transcription." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YlMS6y1Stx4x" + }, + "source": [ + "# Comparing predicted TAL1 expression change for cancer associated variants, compared to randomly shuffled variants\n", + "\n", + "Next, we will compare *TAL1* expression changes for these cancer associated\n", + "mutations, with synthetic, shuffled mutations at those same positions.\n", + "\n", + "To do this, we will:\n", + "\n", + "1. Generate a set of shuffled background variants.\n", + "\n", + "1. Use `score_variant` to return a scalar value for each variant corresponding\n", + " to the magnitude of predicted *TAL1* gene expression change in the presence of\n", + " the alternate allele (relevative to the referenece allele).\n", + "\n", + "1. Plot the predicted *TAL1* expression change, comparing background variants to\n", + " cancer associated ones.\n", + "\n", + "The shuffled background variants will be matched to cancer associated variants,\n", + "grouped by position and the length of the `alternate_bases`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7OoINJM_kYVt" + }, + "outputs": [], + "source": [ + "# Preparing variant groups\n", + "eval_df = oncogenic_and_background_variants(\n", + " input_sequence_length=2**20, number_of_background_variants=3\n", + ")\n", + "\n", + "# Additional annotations and variant groups.\n", + "eval_df['ALT_len'] = eval_df['ALT'].str.len()\n", + "eval_df['variant_group'] = (\n", + " eval_df['POS'].astype(str) + '_' + eval_df['ALT_len'].astype(str)\n", + ")\n", + "eval_df['output'] = eval_df['output'].fillna(0) != 0\n", + "eval_df['coarse_grained_variant_group'] = coarse_grained_mute_groups(eval_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6SPQ6zhukYVt" + }, + "outputs": [], + "source": [ + "# Score the variants in eval. Takes a minute or so.\n", + "\n", + "scores = dna_model.score_variants(\n", + " intervals=eval_df['interval'].to_list(),\n", + " variants=eval_df['variant'].to_list(),\n", + " variant_scorers=[variant_scorers.RECOMMENDED_VARIANT_SCORERS['RNA_SEQ']],\n", + " max_workers=2,\n", + ")\n", + "clear_output()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wqxRfVr0UvW0" + }, + "source": [ + "The scores contain values for all genes in the interval, and all RNA-seq tracks\n", + "in the model output. We'll filter down to *TAL1* and CD34-positive cells." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "KdLN9HpjVToz" + }, + "outputs": [], + "source": [ + "# Find the index corresponding to the TAL1 gene.\n", + "gene_index = scores[0][0].obs.query('gene_name == \"TAL1\"').index[0]\n", + "# Find the index for our cell type of interest.\n", + "cell_type_index = (\n", + " scores[0][0].var.query('ontology_curie == \"CL:0001059\"').index[0]\n", + ")\n", + "\n", + "\n", + "def get_tal1_score_for_cd34_cells(score_data):\n", + " \"\"\"Extracts the TAL1 expression score in CD34+ cells from the model output.\"\"\"\n", + " # Return the specific score.\n", + " return score_data[gene_index, cell_type_index].X[0, 0]\n", + "\n", + "\n", + "eval_df['tal1_diff_in_cd34'] = [\n", + " get_tal1_score_for_cd34_cells(x[0]) for x in scores\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5GjULd0vY6BH" + }, + "source": [ + "Finally, we'll loop through each group and make a plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "an-Ct99IwdlF" + }, + "outputs": [], + "source": [ + "plot_df = eval_df.loc[eval_df.REF != eval_df.ALT]\n", + "\n", + "# Turn variant into a string for easier processing\n", + "plot_df['variant'] = plot_df['variant'].astype(str)\n", + "\n", + "plot_df = plot_df.loc[\n", + " :,\n", + " [\n", + " 'variant',\n", + " 'output',\n", + " 'tal1_diff_in_cd34',\n", + " 'coarse_grained_variant_group',\n", + " ],\n", + "].drop_duplicates()\n", + "\n", + "facet_title_by_group = {\n", + " '47212072_22': 'chr1:47212072\\n21 bp ins.',\n", + " '47212074_7': 'chr1:47212072\\n21 bp ins.',\n", + " '47230639_1': 'chr1:47230639\\nSNV',\n", + " 'MUTE_2': 'chr1:47239296\\n1 bp ins.',\n", + " 'MUTE_3': 'chr1:47239296\\n2 bp ins.',\n", + " 'MUTE_4': 'chr1:47239296\\n3 bp ins.',\n", + " 'MUTE_other': 'chr1:47239296\\n7-18 bp ins.',\n", + "}\n", + "\n", + "plt_dict = {}\n", + "plt_list = []\n", + "for group in plot_df.coarse_grained_variant_group.unique():\n", + " subplot_df = pd.concat(\n", + " [plot_df.assign(plot_group='density'), plot_df.assign(plot_group='rain')]\n", + " )\n", + " subplot_df = subplot_df[subplot_df.coarse_grained_variant_group == group]\n", + " subplot_df = subplot_df[\n", + " ~((subplot_df.plot_group == 'density') \u0026 (subplot_df.output))\n", + " ]\n", + "\n", + " col_width = np.ptp(subplot_df.tal1_diff_in_cd34) / 200\n", + " subplot_df['col_width'] = subplot_df['output'].map(\n", + " {True: 1.5 * col_width, False: 1.25 * col_width}\n", + " )\n", + "\n", + " plt_ = (\n", + " gg.ggplot(subplot_df)\n", + " + gg.aes(x='tal1_diff_in_cd34')\n", + " + gg.geom_col(\n", + " gg.aes(\n", + " y=1,\n", + " width='col_width',\n", + " fill='output',\n", + " x='tal1_diff_in_cd34',\n", + " alpha='output',\n", + " ),\n", + " data=subplot_df[subplot_df['plot_group'] == 'rain'],\n", + " )\n", + " + gg.geom_density(\n", + " gg.aes(\n", + " x='tal1_diff_in_cd34',\n", + " fill='output',\n", + " ),\n", + " data=subplot_df[subplot_df['plot_group'] == 'density'],\n", + " color='white',\n", + " )\n", + " + gg.facet_wrap('~output + plot_group', nrow=1, scales='free_x')\n", + " + gg.scale_alpha_manual({True: 1, False: 0.3})\n", + " + gg.scale_fill_manual({True: '#FAA41A', False: 'gray'})\n", + " + gg.labs(title=facet_title_by_group[group])\n", + " + gg.theme_minimal()\n", + " + gg.geom_vline(xintercept=0, linetype='dotted')\n", + " + gg.theme(\n", + " figure_size=(1.2, 3),\n", + " legend_position='none',\n", + " axis_text_x=gg.element_blank(),\n", + " panel_grid_major_x=gg.element_blank(),\n", + " panel_grid_minor_x=gg.element_blank(),\n", + " strip_text=gg.element_blank(),\n", + " axis_title_y=gg.element_blank(),\n", + " axis_title_x=gg.element_blank(),\n", + " plot_title=gg.element_text(size=9),\n", + " )\n", + " + gg.scale_y_reverse()\n", + " + gg.coord_flip()\n", + " )\n", + "\n", + " plt_dict[group] = plt_\n", + "clear_output()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "C701Ry1udgbm" + }, + "source": [ + "Now we can pick a plot and visualize. Each plot indicates the predicted\n", + "expression level of a group of variants. In these plots, the y-axis is the\n", + "change in *TAL1* expression in the presence of the alternative allele relative\n", + "to the reference allele.\n", + "\n", + "Across the x-axis, there are three visualizations:\n", + "\n", + "1. A density plot of *TAL1* expression of shuffled variants (gray)\n", + "1. The individual *TAL1* expression data points for the shuffled variants\n", + " underlying the density plot (gray).\n", + "1. The individual *TAL1* expression data points for the cancer associated\n", + " variants in the group (orange).\n", + "\n", + "Here are a few examples:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "height": 317 + }, + "executionInfo": { + "elapsed": 1310, + "status": "ok", + "timestamp": 1753113478986, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "t95SJWiGdVeD", + "outputId": "3594f643-fbc7-48ae-b37e-608f6dc7d78d" + }, + "outputs": [ + { + "data": { + "image/png": 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\n" + }, + "metadata": { + "image/png": { + "height": 300, + "width": 120 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "plt_dict['MUTE_2'].show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "height": 317 + }, + "executionInfo": { + "elapsed": 811, + "status": "ok", + "timestamp": 1753113479984, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "QQBXH4zbkPsy", + "outputId": "caeadc14-d82b-4db3-b359-25df94002ea7" + }, + "outputs": [ + { + "data": { + "image/png": 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\n" + }, + "metadata": { + "image/png": { + "height": 300, + "width": 120 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "plt_dict['MUTE_3'].show()" + ] + } + ], + "metadata": { + "colab": { + "last_runtime": {}, + "provenance": [ + { + "file_id": "1J99_hrY2DKzefpyiN_vG2cGkqT-2AvhK", + "timestamp": 1727706471665 + } + ], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/alphagenome/source/colabs/quick_start.ipynb b/alphagenome/source/colabs/quick_start.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..2212ac1914ef08165a23e008f4afe607d7ccba40 --- /dev/null +++ b/alphagenome/source/colabs/quick_start.ipynb @@ -0,0 +1,4067 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "_" + }, + "source": [ + "# Quick start" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "on5cvb0VoK_D" + }, + "source": [ + "Welcome to the quick start guide for AlphaGenome! The goal of this tutorial notebook is to quickly get you started with using the model and making predictions." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XNBquB49Nk6W" + }, + "source": [ + "```{tip}\n", + "Open this tutorial in Google colab for interactive viewing.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "cellView": "form", + "executionInfo": { + "elapsed": 2, + "status": "ok", + "timestamp": 1753118702069, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "W28j15ApoK_D", + "language": "python" + }, + "outputs": [], + "source": [ + "# @title Install AlphaGenome\n", + "\n", + "# @markdown Run this cell to install AlphaGenome.\n", + "from IPython.display import clear_output\n", + "! pip install alphagenome\n", + "clear_output()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-lcTVQLvqB6F" + }, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "executionInfo": { + "elapsed": 53, + "status": "ok", + "timestamp": 1753118702279, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "KuLPy_awXaP_", + "language": "python" + }, + "outputs": [], + "source": [ + "from alphagenome import colab_utils\n", + "from alphagenome.data import gene_annotation\n", + "from alphagenome.data import genome\n", + "from alphagenome.data import transcript as transcript_utils\n", + "from alphagenome.interpretation import ism\n", + "from alphagenome.models import dna_client\n", + "from alphagenome.models import variant_scorers\n", + "from alphagenome.visualization import plot_components\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jEGP__L4oK_E" + }, + "source": [ + "## Predict outputs for a DNA sequence" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Bzov6O0goK_F" + }, + "source": [ + "AlphaGenome is a model that makes predictions from DNA sequences. Let's load it up:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ThpPPG1A8L-D" + }, + "source": [ + "\n", + "```{tip}\n", + "If using Google Colab, store your key in \"Secrets\" for persistent access across sessions (see [installation](https://www.alphagenomedocs.com/installation.html#google-colab)). Otherwise, `dna_client.create` can take the API key directly.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "executionInfo": { + "elapsed": 52, + "status": "ok", + "timestamp": 1753118702461, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "toBpbthy93gr", + "language": "python" + }, + "outputs": [], + "source": [ + "dna_model = dna_client.create(colab_utils.get_api_key())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "H_358E_nkYVt" + }, + "source": [ + "The model can make predictions for the following [output types](https://www.alphagenomedocs.com/exploring_model_metadata.html):" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "executionInfo": { + "elapsed": 55, + "status": "ok", + "timestamp": 1753118702682, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "U2Q-amDjkYVt", + "outputId": "30556f2f-c916-4a07-fc50-85d1319aa023" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['ATAC',\n", + " 'CAGE',\n", + " 'DNASE',\n", + " 'RNA_SEQ',\n", + " 'CHIP_HISTONE',\n", + " 'CHIP_TF',\n", + " 'SPLICE_SITES',\n", + " 'SPLICE_SITE_USAGE',\n", + " 'SPLICE_JUNCTIONS',\n", + " 'CONTACT_MAPS',\n", + " 'PROCAP']" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[output.name for output in dna_client.OutputType]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dpFi65J3kYVt" + }, + "source": [ + "AlphaGenome predicts multiple 'tracks' per output type, covering a wide variety of tissues and cell-types. However, predictions can be made efficiently for subsets of interest.\n", + "\n", + "Here is how to make DNase-seq predictions (as specified by `OutputType`) in a subset of tracks corresponding to lung tissue (as specified by `ontology_terms`) for a short DNA sequence of length 2048:\n", + "\n", + "*Note: We use ontology terms from standardized biological sources like UBERON (for anatomy) and the Cell Ontology (CL) to provide consistent and widely recognized classifications for tissue and cell types.*" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "executionInfo": { + "elapsed": 298, + "status": "ok", + "timestamp": 1753118703113, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "pRv5MT9hkYVt" + }, + "outputs": [], + "source": [ + "output = dna_model.predict_sequence(\n", + " sequence='GATTACA'.center(2048, 'N'), # Pad to valid sequence length.\n", + " requested_outputs=[dna_client.OutputType.DNASE],\n", + " ontology_terms=['UBERON:0002048'], # Lung.\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bHz9nXh9kYVt" + }, + "source": [ + "The `output` object contains predictions for all the different requested output types (in this case, only output type `DNASE`). Predictions for genomic tracks are stored inside a `TrackData` object: " + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "executionInfo": { + "elapsed": 53, + "status": "ok", + "timestamp": 1753118703310, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "KmQMwe1MkYVt", + "outputId": "cd4d0f58-5ac7-4369-c302-f19bae8a09b0" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "alphagenome.data.track_data.TrackData" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dnase = output.dnase\n", + "type(dnase)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HflwfcSxkYVt" + }, + "source": [ + "`TrackData` objects have the following components:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pudb_vcWkYVt" + }, + "source": [ + "\u003ca href=\"https://services.google.com/fh/files/misc/trackdata.png\"\u003e\u003cimg src=\"https://services.google.com/fh/files/misc/trackdata.png\" alt=\"trackdata\" border=\"0\" height=500\u003e\u003c/a\u003e" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0R24IDvokYVt" + }, + "source": [ + "The predictions of shape `(sequence_length, num_tracks)` are stored in `.values`:" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "executionInfo": { + "elapsed": 53, + "status": "ok", + "timestamp": 1753118703493, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "7OoINJM_kYVt", + "outputId": "19dd4243-7fb9-4c11-f2d1-60624907c97e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2048, 1)\n" + ] + }, + { + "data": { + "text/plain": [ + "array([[0.001297 ],\n", + " [0.00121307],\n", + " [0.00121307],\n", + " ...,\n", + " [0.00138092],\n", + " [0.00213623],\n", + " [0.00292969]], shape=(2048, 1), dtype=float32)" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(dnase.values.shape)\n", + "\n", + "dnase.values" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6kAs7jA_kYVt" + }, + "source": [ + "And the corresponding metadata describing each of the tracks is stored in `.metadata`:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "colab": { + "height": 135 + }, + "executionInfo": { + "elapsed": 56, + "status": "ok", + "timestamp": 1753118703710, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "6SPQ6zhukYVt", + "outputId": "8b5c384a-e115-4e90-de46-9a36697236dc" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \"dnase\",\n \"rows\": 1,\n \"fields\": [\n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"UBERON:0002048 DNase-seq\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"strand\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \".\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Assay title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"DNase-seq\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"ontology_curie\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"UBERON:0002048\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"biosample_name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"lung\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"biosample_type\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"tissue\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"biosample_life_stage\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"embryonic\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"data_source\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"encode\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"endedness\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"paired\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"genetically_modified\",\n \"properties\": {\n \"dtype\": \"boolean\",\n \"num_unique_values\": 1,\n \"samples\": [\n false\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nonzero_mean\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": null,\n \"min\": 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Visit the ' +\n", + " '\u003ca target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb\u003edata table notebook\u003c/a\u003e'\n", + " + ' to learn more about interactive tables.';\n", + " element.innerHTML = '';\n", + " dataTable['output_type'] = 'display_data';\n", + " await google.colab.output.renderOutput(dataTable, element);\n", + " const docLink = document.createElement('div');\n", + " docLink.innerHTML = docLinkHtml;\n", + " element.appendChild(docLink);\n", + " }\n", + " \u003c/script\u003e\n", + " \u003c/div\u003e\n", + "\n", + " \u003c/div\u003e\n", + " \u003c/div\u003e\n" + ], + "text/plain": [ + " name strand ... genetically_modified nonzero_mean\n", + "0 UBERON:0002048 DNase-seq . ... False 0.427505\n", + "\n", + "[1 rows x 11 columns]" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dnase.metadata" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KLVSBMxVkYVt" + }, + "source": [ + "In this case, there is only one output track, so the track metadata returns only 1 row.\n", + "\n", + "The track metadata is especially useful when requesting predictions for multiple tissues or cell-types, and when dealing with stranded assays (which are assays with separate readouts for the two DNA strands, such as CAGE and RNA-seq):\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "executionInfo": { + "elapsed": 220, + "status": "ok", + "timestamp": 1753118704112, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "LGYCvBbmkYVt", + "outputId": "7f1d7c61-6835-4aae-da7c-2da652c3bc14" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DNASE predictions shape: (2048, 2)\n", + "CAGE predictions shape: (2048, 4)\n" + ] + } + ], + "source": [ + "output = dna_model.predict_sequence(\n", + " sequence='GATTACA'.center(2048, 'N'), # Pad to valid sequence length.\n", + " requested_outputs=[\n", + " dna_client.OutputType.CAGE,\n", + " dna_client.OutputType.DNASE,\n", + " ],\n", + " ontology_terms=[\n", + " 'UBERON:0002048', # Lung.\n", + " 'UBERON:0000955', # Brain.\n", + " ],\n", + ")\n", + "\n", + "print(f'DNASE predictions shape: {output.dnase.values.shape}')\n", + "print(f'CAGE predictions shape: {output.cage.values.shape}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sA4plQHMkYVt" + }, + "source": [ + "Notice that in this example, we requested predictions for 2 assays and 2 ontology terms simultaneously.\n", + "\n", + "The CAGE track metadata describes the strand and tissue of each of the 4 predicted tracks (2 per DNA strand):" + ] + }, + { + "cell_type": "code", + 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'block' : 'none';\n", + "\n", + " async function convertToInteractive(key) {\n", + " const element = document.querySelector('#df-b3640533-d376-48b0-9990-ec8eec2dff00');\n", + " const dataTable =\n", + " await google.colab.kernel.invokeFunction('convertToInteractive',\n", + " [key], {});\n", + " if (!dataTable) return;\n", + "\n", + " const docLinkHtml = 'Like what you see? Visit the ' +\n", + " '\u003ca target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb\u003edata table notebook\u003c/a\u003e'\n", + " + ' to learn more about interactive tables.';\n", + " element.innerHTML = '';\n", + " dataTable['output_type'] = 'display_data';\n", + " await google.colab.output.renderOutput(dataTable, element);\n", + " const docLink = document.createElement('div');\n", + " docLink.innerHTML = docLinkHtml;\n", + " element.appendChild(docLink);\n", + " }\n", + " \u003c/script\u003e\n", + " \u003c/div\u003e\n", + "\n", + "\n", + " \u003cdiv id=\"df-78248d6d-b79f-42d7-8b68-ad7f0a613550\"\u003e\n", + " \u003cbutton class=\"colab-df-quickchart\" onclick=\"quickchart('df-78248d6d-b79f-42d7-8b68-ad7f0a613550')\"\n", + " title=\"Suggest charts\"\n", + " style=\"display:none;\"\u003e\n", + "\n", + "\u003csvg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", + " width=\"24px\"\u003e\n", + " \u003cg\u003e\n", + " \u003cpath d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/\u003e\n", + " \u003c/g\u003e\n", + "\u003c/svg\u003e\n", + " \u003c/button\u003e\n", + "\n", + "\u003cstyle\u003e\n", + " .colab-df-quickchart {\n", + " --bg-color: #E8F0FE;\n", + " --fill-color: #1967D2;\n", + " --hover-bg-color: #E2EBFA;\n", + " --hover-fill-color: #174EA6;\n", + " --disabled-fill-color: #AAA;\n", + " --disabled-bg-color: #DDD;\n", + " }\n", + "\n", + " [theme=dark] .colab-df-quickchart {\n", + " --bg-color: #3B4455;\n", + " --fill-color: #D2E3FC;\n", + " --hover-bg-color: #434B5C;\n", + " --hover-fill-color: #FFFFFF;\n", + " --disabled-bg-color: #3B4455;\n", + " --disabled-fill-color: #666;\n", + " }\n", + "\n", + " .colab-df-quickchart {\n", + " background-color: var(--bg-color);\n", + " border: none;\n", + " border-radius: 50%;\n", + " cursor: pointer;\n", + " display: none;\n", + " fill: var(--fill-color);\n", + " height: 32px;\n", + " padding: 0;\n", + " width: 32px;\n", + " }\n", + "\n", + " .colab-df-quickchart:hover {\n", + " background-color: var(--hover-bg-color);\n", + " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n", + " fill: var(--button-hover-fill-color);\n", + " }\n", + "\n", + " .colab-df-quickchart-complete:disabled,\n", + " .colab-df-quickchart-complete:disabled:hover {\n", + " background-color: var(--disabled-bg-color);\n", + " fill: var(--disabled-fill-color);\n", + " box-shadow: none;\n", + " }\n", + "\n", + " .colab-df-spinner {\n", + " border: 2px solid var(--fill-color);\n", + " border-color: transparent;\n", + " border-bottom-color: var(--fill-color);\n", + " animation:\n", + " spin 1s steps(1) infinite;\n", + " }\n", + "\n", + " @keyframes spin {\n", + " 0% {\n", + " border-color: transparent;\n", + " border-bottom-color: var(--fill-color);\n", + " border-left-color: var(--fill-color);\n", + " }\n", + " 20% {\n", + " border-color: transparent;\n", + " border-left-color: var(--fill-color);\n", + " border-top-color: var(--fill-color);\n", + " }\n", + " 30% {\n", + " border-color: transparent;\n", + " border-left-color: var(--fill-color);\n", + " border-top-color: var(--fill-color);\n", + " border-right-color: var(--fill-color);\n", + " }\n", + " 40% {\n", + " border-color: transparent;\n", + " border-right-color: var(--fill-color);\n", + " border-top-color: var(--fill-color);\n", + " }\n", + " 60% {\n", + " border-color: transparent;\n", + " border-right-color: var(--fill-color);\n", + " }\n", + " 80% {\n", + " border-color: transparent;\n", + " border-right-color: var(--fill-color);\n", + " border-bottom-color: var(--fill-color);\n", + " }\n", + " 90% {\n", + " border-color: transparent;\n", + " border-bottom-color: var(--fill-color);\n", + " }\n", + " }\n", + "\u003c/style\u003e\n", + "\n", + " \u003cscript\u003e\n", + " async function quickchart(key) {\n", + " const quickchartButtonEl =\n", + " document.querySelector('#' + key + ' button');\n", + " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n", + " quickchartButtonEl.classList.add('colab-df-spinner');\n", + " try {\n", + " const charts = await google.colab.kernel.invokeFunction(\n", + " 'suggestCharts', [key], {});\n", + " } catch (error) {\n", + " console.error('Error during call to suggestCharts:', error);\n", + " }\n", + " quickchartButtonEl.classList.remove('colab-df-spinner');\n", + " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n", + " }\n", + " (() =\u003e {\n", + " let quickchartButtonEl =\n", + " document.querySelector('#df-78248d6d-b79f-42d7-8b68-ad7f0a613550 button');\n", + " quickchartButtonEl.style.display =\n", + " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", + " })();\n", + " \u003c/script\u003e\n", + " \u003c/div\u003e\n", + " \u003c/div\u003e\n", + " \u003c/div\u003e\n" + ], + "text/plain": [ + " name strand ... data_source nonzero_mean\n", + "0 hCAGE UBERON:0000955 + ... fantom 28.432245\n", + "1 hCAGE UBERON:0002048 + ... fantom 30.655853\n", + "2 hCAGE UBERON:0000955 - ... fantom 28.432245\n", + "3 hCAGE UBERON:0002048 - ... fantom 30.655853\n", + "\n", + "[4 rows x 8 columns]" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output.cage.metadata" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HemMKknzkYVt" + }, + "source": [ + "See the [output metadata documentation](https://www.alphagenomedocs.com/exploring_model_metadata.html) for more information on the output types and output shapes. For the mapping between tissue names (e.g. 'brain' -\u003e 'UBERON:0000955') and ontology terms, see this [tutorial](tissue_ontology_mapping.ipynb).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rL6BQQfqkYVt" + }, + "source": [ + "## Predict outputs for a genome interval (reference genome)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cZuGzeStkYVt" + }, + "source": [ + "For convenience, you can also directly make predictions for a human reference genome sequence specified by a **genomic interval**. For example, let's predict RNA-seq for tissue 'Right liver lobe' in a 1MB region of Chromosome 19 around the gene *CYP2B6*, which encodes an enzyme involved in drug metabolism, and is primarily expressed in the liver.\n", + "\n", + "We first load up a GTF file containing gene and transcript locations as annotated by GENCODE (more information on GTF format [here](https://www.gencodegenes.org/pages/data_format.html)):" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "executionInfo": { + "elapsed": 13366, + "status": "ok", + "timestamp": 1753118717859, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "1WE7BBh6klIA" + }, + "outputs": [], + "source": [ + "# The GTF file contains information on the location of all trancripts.\n", + "# Note that we use genome assembly hg38 for human.\n", + "gtf = pd.read_feather(\n", + " 'https://storage.googleapis.com/alphagenome/reference/gencode/'\n", + " 'hg38/gencode.v46.annotation.gtf.gz.feather'\n", + ")\n", + "\n", + "# Set up transcript extractors using the information in the GTF file.\n", + "gtf_transcripts = gene_annotation.filter_protein_coding(gtf)\n", + "gtf_transcripts = gene_annotation.filter_to_longest_transcript(gtf_transcripts)\n", + "transcript_extractor = transcript_utils.TranscriptExtractor(gtf_transcripts)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Q5PjqsMCkYVt" + }, + "source": [ + "And then fetch the gene's location as a `genome.Interval` object by passing either its `gene_symbol` (HGNC naming convention) or ENSEMBL `gene_id`:" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "executionInfo": { + "elapsed": 1957, + "status": "ok", + "timestamp": 1753118719958, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "pngLCscokYVt", + "outputId": "8a84f41f-43a0-4f1a-8b91-437b500e00fa" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Interval(chromosome='chr19', start=40991281, end=41018398, strand='+', name='CYP2B6')" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "interval = gene_annotation.get_gene_interval(gtf, gene_symbol='CYP2B6')\n", + "interval" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6fLrfgyvkYVt" + }, + "source": [ + "We can resize it to a length compatible with the model:" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "executionInfo": { + "elapsed": 58, + "status": "ok", + "timestamp": 1753118720145, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "VV5k05MykYVt" + }, + "outputs": [], + "source": [ + "interval = interval.resize(dna_client.SEQUENCE_LENGTH_1MB)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pu1A32KJkYVt" + }, + "source": [ + "The `.resize()` method adjusts the interval to the specified width by expanding (or contracting) around its original center. Note that `dna_model.predict_interval()` interprets this resizing as an expansion of the actual genomic sequence rather than padding tokens.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "executionInfo": { + "elapsed": 21, + "status": "ok", + "timestamp": 1753118720325, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "3TdgcNRskYVt", + "outputId": "ceda0f78-f70e-46a6-b264-e733242bf7e2" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1048576" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "interval.width" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hI5hi6DckYVu" + }, + "source": [ + " See the [essential commands documentation](https://www.alphagenomedocs.com/colabs/essential_commands.html) for more handy commands like `resize`.\n", + "\n", + "Note that AlphaGenome supports the following input sequence lengths:" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "executionInfo": { + "elapsed": 58, + "status": "ok", + "timestamp": 1753118720553, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "22gZecZTkYVu", + "outputId": "cd8664a4-43b9-4902-fe22-8a4906eec969" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['SEQUENCE_LENGTH_2KB', 'SEQUENCE_LENGTH_16KB', 'SEQUENCE_LENGTH_100KB', 'SEQUENCE_LENGTH_500KB', 'SEQUENCE_LENGTH_1MB'])" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dna_client.SUPPORTED_SEQUENCE_LENGTHS.keys()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oRk3W7yjkYVu" + }, + "source": [ + "\n", + " We can now make predictions using our interval:" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "executionInfo": { + "elapsed": 692, + "status": "ok", + "timestamp": 1753118721420, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "ME7NH0B-kYVu", + "outputId": "5f63e820-9638-44f9-c169-a0ef018f7313" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1048576, 3)" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output = dna_model.predict_interval(\n", + " interval=interval,\n", + " requested_outputs=[dna_client.OutputType.RNA_SEQ],\n", + " ontology_terms=['UBERON:0001114'],\n", + ") # Right liver lobe.\n", + "\n", + "output.rna_seq.values.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HZD6iGfrkYVu" + }, + "source": [ + "In general, you can have multiple tracks for a given ontology term. In this case, we have 3 RNA-seq tracks for the tissue \"Right liver lobe\".\n", + "\n", + "Let's visualise these predictions. It's helpful visualise gene transcripts alongside the predicted tracks, so we extract them here:" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "executionInfo": { + "elapsed": 224, + "status": "ok", + "timestamp": 1753118721778, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "zU61GXrekYVu", + "outputId": "f0fb883b-4f1f-4069-bdd8-17671b6a727d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracted 29 transcripts in this interval.\n" + ] + } + ], + "source": [ + "longest_transcripts = transcript_extractor.extract(interval)\n", + "print(f'Extracted {len(longest_transcripts)} transcripts in this interval.')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9hUwvkl_kYVu" + }, + "source": [ + "We also provide a [visualization basics guide](https://www.alphagenomedocs.com/visualization_library_basics.html) that integrates nicely with `TrackData` and other objects returned by the model API." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "colab": { + "height": 0 + }, + "executionInfo": { + "elapsed": 2678, + "status": "ok", + "timestamp": 1753118724599, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "MjJsLGnGkYVu", + "outputId": "00e418dc-4782-4531-a84b-25b63922c210" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "\u003cFigure size 1440x320.4 with 4 Axes\u003e" + ] + }, + "metadata": { + "image/png": { + "height": 286, + "width": 1406 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_components.plot(\n", + " components=[\n", + " plot_components.TranscriptAnnotation(longest_transcripts),\n", + " plot_components.Tracks(output.rna_seq),\n", + " ],\n", + " interval=output.rna_seq.interval,\n", + ")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uBtGbT0skYVu" + }, + "source": [ + "This plot visualises the 3 predicted RNA-seq tracks and also marks the location of the longest transcript per gene in the 1MB region.\n", + "\n", + "We can zoom in to the middle of the plot by resizing the interval:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "colab": { + "height": 0 + }, + "executionInfo": { + "elapsed": 861, + "status": "ok", + "timestamp": 1753118725679, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "J8_0AvHbkYVu", + "outputId": "6205ebcd-a9d2-4285-e907-5684d8059f37" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "\u003cFigure size 1440x288 with 4 Axes\u003e" + ] + }, + "metadata": { + "image/png": { + "height": 261, + "width": 1406 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_components.plot(\n", + " components=[\n", + " plot_components.TranscriptAnnotation(\n", + " longest_transcripts, fig_height=0.1\n", + " ),\n", + " plot_components.Tracks(output.rna_seq),\n", + " ],\n", + " interval=output.rna_seq.interval.resize(2**15),\n", + ")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1o5l2yUGkYVu" + }, + "source": [ + "You can see here that predicted RNA-seq values are nicely aligned with the location of exons, and that the predictions are stranded – the predicted values are much higher for the positive strand, where the gene is located. We see that the *CYP2B6* gene is on the positive strand since the arrows in the transcript go from left to right.\n", + "\n", + "For more detail on the visualization library, please refer to the [visualization basics guide](https://www.alphagenomedocs.com/visualization_library_basics.html) and [library documentation](https://www.alphagenomedocs.com/api/visualization.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rPQJQirLkYVu" + }, + "source": [ + "## Predict variant effects\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ktZ0PqhQkYVu" + }, + "source": [ + "We can predict the effect of a variant on a specific output type and tissue by making predictions for the reference (REF) and alternative (ALT) allele sequences.\n", + "\n", + "We specify the variant by defining a `genome.Variant` object. The specific variant below is a known variant affecting gene expression in colon tissue:" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "executionInfo": { + "elapsed": 3, + "status": "ok", + "timestamp": 1753118725896, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "YaxavVV_kYVu" + }, + "outputs": [], + "source": [ + "variant = genome.Variant(\n", + " chromosome='chr22',\n", + " position=36201698,\n", + " reference_bases='A', # Can differ from the true reference genome base.\n", + " alternate_bases='C',\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HE2_iU6akYVu" + }, + "source": [ + "Next, we define the interval over which to make the REF and ALT predictions. A quick way to get a `genome.Interval` from a `genome.Variant` is by calling `.reference_interval`, which we can resize to a model-compatible sequence length:" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "executionInfo": { + "elapsed": 138, + "status": "ok", + "timestamp": 1753118726194, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "0jxThai2kYVu" + }, + "outputs": [], + "source": [ + "interval = variant.reference_interval.resize(dna_client.SEQUENCE_LENGTH_1MB)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Oupoz0bGkYVu" + }, + "source": [ + "We then use `predict_variant` to get the REF and ALT RNA-seq predictions in the interval for \"Colon - Transverse\" tissue (`UBERON:0001157`):" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "executionInfo": { + "elapsed": 1703, + "status": "ok", + "timestamp": 1753118728057, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "TZbfcADGkYVu" + }, + "outputs": [], + "source": [ + "variant_output = dna_model.predict_variant(\n", + " interval=interval,\n", + " variant=variant,\n", + " requested_outputs=[dna_client.OutputType.RNA_SEQ],\n", + " ontology_terms=['UBERON:0001157'],\n", + ") # Colon - Transverse." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YzYgHwSAkYVu" + }, + "source": [ + "We can plot the predicted REF and ALT values as a single plot and zoom in on the affected gene to better visualise the variant's effect on gene expression:" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "colab": { + "height": 0 + }, + "executionInfo": { + "elapsed": 3329, + "status": "ok", + "timestamp": 1753118731523, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "_OTUiWNjkYVu", + "outputId": "81c27134-b8e4-4a98-9601-c8b52b433a6a" + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "\u003cFigure size 1440x360 with 5 Axes\u003e" + ] + }, + "metadata": { + "image/png": { + "height": 316, + "width": 1472 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "longest_transcripts = transcript_extractor.extract(interval)\n", + "\n", + "plot_components.plot(\n", + " [\n", + " plot_components.TranscriptAnnotation(longest_transcripts),\n", + " plot_components.OverlaidTracks(\n", + " tdata={\n", + " 'REF': variant_output.reference.rna_seq,\n", + " 'ALT': variant_output.alternate.rna_seq,\n", + " },\n", + " colors={'REF': 'dimgrey', 'ALT': 'red'},\n", + " ),\n", + " ],\n", + " interval=variant_output.reference.rna_seq.interval.resize(2**15),\n", + " # Annotate the location of the variant as a vertical line.\n", + " annotations=[plot_components.VariantAnnotation([variant], alpha=0.8)],\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dSz201LRkYVu" + }, + "source": [ + "We see that the ALT allele (base 'C' at position 36201698) is associated with both lower expression and an exon skipping event in the *APOL4* gene on the negative strand. Note that we can ignore the uppermost line plot which shows a very minimal predicted amount of expression on the positive DNA strand (check the y axis scales). It is possible to adjust the y axes limits, see [visualization basics](https://www.alphagenomedocs.com/visualization_library_basics.html#visualization-library-basics) and [library documentation](https://www.alphagenomedocs.com/api/visualization.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7s4bDxczkYVu" + }, + "source": [ + "## Scoring the effect of a genetic variant" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8fTtdkpM-Ycm" + }, + "source": [ + "Scoring the effect of a genetic variant involves making predictions for the REF and ALT sequences and aggregating the track signal. This is implemented in `score_variant`, which uses specific `variant_scorer` configs for aggregation.\n", + "\n", + "We provide a set of recommended variant scoring configurations as a dictionary (`variant_scorers.RECOMMENDED_VARIANT_SCORERS`), covering all output types, which we have assessed for their performance at domain-specific tasks. See the [variant scoring documentation](https://www.alphagenomedocs.com/variant_scoring.html) for more information. Here is a quick demo:" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "executionInfo": { + "elapsed": 1524, + "status": "ok", + "timestamp": 1753118733237, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "Aj_T3sE8kYVu" + }, + "outputs": [], + "source": [ + "variant_scorer = variant_scorers.RECOMMENDED_VARIANT_SCORERS['RNA_SEQ']\n", + "\n", + "variant_scores = dna_model.score_variant(\n", + " interval=interval, variant=variant, variant_scorers=[variant_scorer]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ncUCGp0JkYVu" + }, + "source": [ + "The returned `variant_scores` is a list of length 1 because we only specified 1 scorer:" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "executionInfo": { + "elapsed": 55, + "status": "ok", + "timestamp": 1753118733696, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "xI2aR0OtkYVu", + "outputId": "7f4e7e96-be45-4699-fbc0-a2ab63fc3956" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(variant_scores)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Oow5vOLVkYVu" + }, + "source": [ + "The actual scores per variant are in `AnnData` format, which is a way of annotating data (the numerical scores) with additional information about the rows and columns." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "executionInfo": { + "elapsed": 61, + "status": "ok", + "timestamp": 1753118733889, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "WCy_9aQ2kYVu", + "outputId": "30f63808-7595-43ae-dae9-4ea1d5111076" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "AnnData object with n_obs × n_vars = 37 × 667\n", + " obs: 'gene_id', 'strand', 'gene_name', 'gene_type'\n", + " var: 'name', 'strand', 'Assay title', 'ontology_curie', 'biosample_name', 'biosample_type', 'biosample_life_stage', 'gtex_tissue', 'data_source', 'endedness', 'genetically_modified', 'nonzero_mean'\n", + " uns: 'interval', 'variant', 'variant_scorer'\n", + " layers: 'quantiles'" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "variant_scores = variant_scores[0]\n", + "variant_scores" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2URPdQGtkYVu" + }, + "source": [ + "`AnnData` objects have the following components:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ssPveqJvkYVu" + }, + "source": [ + "\u003ca href=\"https://services.google.com/fh/files/misc/anndata.png\"\u003e\u003cimg src=\"https://services.google.com/fh/files/misc/anndata.png\" alt=\"anndata\" border=\"0\" height=500\u003e\u003c/a\u003e" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "X1u94SdOkYVu" + }, + "source": [ + "We have a variant effect score for each of the 37 genes in the interval and each of the 667 `RNA_SEQ` tracks:" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "executionInfo": { + "elapsed": 59, + "status": "ok", + "timestamp": 1753118734113, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "eyv_SHT_kYVu", + "outputId": "5e2153b0-4d59-45bd-c09e-57ac4f09c752" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(37, 667)" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "variant_scores.X.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-3cT3a6FkYVu" + }, + "source": [ + "We can access information on the 37 genes using `.obs`. Here are just first 5 genes:" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "colab": { + "height": 0 + }, + "executionInfo": { + "elapsed": 57, + "status": "ok", + "timestamp": 1753118734306, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "AQBAsm2skYVu", + "outputId": "1d7d2c7a-c8f0-498a-fda0-23ab42cee4b9" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \"variant_scores\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"gene_id\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"ENSG00000100336.18\",\n \"ENSG00000100348.10\",\n \"ENSG00000100342.22\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"strand\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"+\",\n \"-\"\n ],\n \"semantic_type\": \"\",\n 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track is accessed using `.var` (this is the same dataframe as the output metadata, but is included alongside the variant scores for convenience):" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "colab": { + "height": 0 + }, + "executionInfo": { + "elapsed": 56, + "status": "ok", + "timestamp": 1753118734541, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "J_VRdJMakYVu", + "outputId": "af54c5ab-d955-467f-c0a7-004c60cd8d62" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \"variant_scores\",\n \"rows\": 667,\n \"fields\": [\n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 371,\n \"samples\": [\n \"UBERON:0001873 gtex Brain_Caudate_basal_ganglia polyA plus RNA-seq\",\n \"CL:0000792 total RNA-seq\",\n \"CL:0000223 polyA plus RNA-seq\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n 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\u003ctd\u003eCL:0000084 total RNA-seq\u003c/td\u003e\n", + " \u003ctd\u003e+\u003c/td\u003e\n", + " \u003ctd\u003etotal RNA-seq\u003c/td\u003e\n", + " \u003ctd\u003eCL:0000084\u003c/td\u003e\n", + " \u003ctd\u003eT-cell\u003c/td\u003e\n", + " \u003ctd\u003eprimary_cell\u003c/td\u003e\n", + " \u003ctd\u003eadult\u003c/td\u003e\n", + " \u003ctd\u003e\u003c/td\u003e\n", + " \u003ctd\u003eencode\u003c/td\u003e\n", + " \u003ctd\u003esingle\u003c/td\u003e\n", + " \u003ctd\u003eFalse\u003c/td\u003e\n", + " \u003ctd\u003e0.100934\u003c/td\u003e\n", + " \u003c/tr\u003e\n", + " \u003ctr\u003e\n", + " \u003cth\u003e4\u003c/th\u003e\n", + " \u003ctd\u003eCL:0000115 total RNA-seq\u003c/td\u003e\n", + " \u003ctd\u003e+\u003c/td\u003e\n", + " \u003ctd\u003etotal RNA-seq\u003c/td\u003e\n", + " \u003ctd\u003eCL:0000115\u003c/td\u003e\n", + " \u003ctd\u003eendothelial cell\u003c/td\u003e\n", + " \u003ctd\u003ein_vitro_differentiated_cells\u003c/td\u003e\n", + " \u003ctd\u003eadult\u003c/td\u003e\n", + " \u003ctd\u003e\u003c/td\u003e\n", + " \u003ctd\u003eencode\u003c/td\u003e\n", + " \u003ctd\u003esingle\u003c/td\u003e\n", + " \u003ctd\u003eFalse\u003c/td\u003e\n", + " \u003ctd\u003e0.135553\u003c/td\u003e\n", + " \u003c/tr\u003e\n", + " \u003ctr\u003e\n", + " \u003cth\u003e...\u003c/th\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " \u003c/tr\u003e\n", + " \u003ctr\u003e\n", + " \u003cth\u003e662\u003c/th\u003e\n", + " \u003ctd\u003eUBERON:0018115 polyA plus RNA-seq\u003c/td\u003e\n", + " \u003ctd\u003e.\u003c/td\u003e\n", + " \u003ctd\u003epolyA plus RNA-seq\u003c/td\u003e\n", + " \u003ctd\u003eUBERON:0018115\u003c/td\u003e\n", + " \u003ctd\u003eleft renal pelvis\u003c/td\u003e\n", + " \u003ctd\u003etissue\u003c/td\u003e\n", + " \u003ctd\u003eembryonic\u003c/td\u003e\n", + " \u003ctd\u003e\u003c/td\u003e\n", + " \u003ctd\u003eencode\u003c/td\u003e\n", + " \u003ctd\u003esingle\u003c/td\u003e\n", + " \u003ctd\u003eFalse\u003c/td\u003e\n", + " \u003ctd\u003e0.268222\u003c/td\u003e\n", + " \u003c/tr\u003e\n", + " \u003ctr\u003e\n", + " \u003cth\u003e663\u003c/th\u003e\n", + " \u003ctd\u003eUBERON:0018116 polyA plus RNA-seq\u003c/td\u003e\n", + " \u003ctd\u003e.\u003c/td\u003e\n", + " \u003ctd\u003epolyA plus RNA-seq\u003c/td\u003e\n", + " \u003ctd\u003eUBERON:0018116\u003c/td\u003e\n", + " \u003ctd\u003eright renal pelvis\u003c/td\u003e\n", + " \u003ctd\u003etissue\u003c/td\u003e\n", + " \u003ctd\u003eembryonic\u003c/td\u003e\n", + " \u003ctd\u003e\u003c/td\u003e\n", + " \u003ctd\u003eencode\u003c/td\u003e\n", + " \u003ctd\u003esingle\u003c/td\u003e\n", + " \u003ctd\u003eFalse\u003c/td\u003e\n", + " \u003ctd\u003e0.258522\u003c/td\u003e\n", + " \u003c/tr\u003e\n", + " \u003ctr\u003e\n", + " \u003cth\u003e664\u003c/th\u003e\n", + " \u003ctd\u003eUBERON:0018117 polyA plus RNA-seq\u003c/td\u003e\n", + " \u003ctd\u003e.\u003c/td\u003e\n", + " \u003ctd\u003epolyA plus RNA-seq\u003c/td\u003e\n", + " \u003ctd\u003eUBERON:0018117\u003c/td\u003e\n", + " \u003ctd\u003eleft renal cortex interstitium\u003c/td\u003e\n", + " \u003ctd\u003etissue\u003c/td\u003e\n", + " \u003ctd\u003eembryonic\u003c/td\u003e\n", + " \u003ctd\u003e\u003c/td\u003e\n", + " \u003ctd\u003eencode\u003c/td\u003e\n", + " \u003ctd\u003esingle\u003c/td\u003e\n", + " \u003ctd\u003eFalse\u003c/td\u003e\n", + " \u003ctd\u003e0.215190\u003c/td\u003e\n", + " \u003c/tr\u003e\n", + " \u003ctr\u003e\n", + " \u003cth\u003e665\u003c/th\u003e\n", + " \u003ctd\u003eUBERON:0018118 polyA plus RNA-seq\u003c/td\u003e\n", + " \u003ctd\u003e.\u003c/td\u003e\n", + " \u003ctd\u003epolyA plus RNA-seq\u003c/td\u003e\n", + " \u003ctd\u003eUBERON:0018118\u003c/td\u003e\n", + " \u003ctd\u003eright renal cortex interstitium\u003c/td\u003e\n", + " \u003ctd\u003etissue\u003c/td\u003e\n", + " \u003ctd\u003eembryonic\u003c/td\u003e\n", + " \u003ctd\u003e\u003c/td\u003e\n", + " \u003ctd\u003eencode\u003c/td\u003e\n", + " \u003ctd\u003esingle\u003c/td\u003e\n", + " \u003ctd\u003eFalse\u003c/td\u003e\n", + " \u003ctd\u003e0.365676\u003c/td\u003e\n", + " \u003c/tr\u003e\n", + " \u003ctr\u003e\n", + " \u003cth\u003e666\u003c/th\u003e\n", + " \u003ctd\u003eUBERON:0036149 gtex Skin_Not_Sun_Exposed_Supra...\u003c/td\u003e\n", + " \u003ctd\u003e.\u003c/td\u003e\n", + " \u003ctd\u003epolyA plus RNA-seq\u003c/td\u003e\n", + " \u003ctd\u003eUBERON:0036149\u003c/td\u003e\n", + " \u003ctd\u003esuprapubic skin\u003c/td\u003e\n", + " \u003ctd\u003etissue\u003c/td\u003e\n", + " \u003ctd\u003eadult\u003c/td\u003e\n", + " \u003ctd\u003eSkin_Not_Sun_Exposed_Suprapubic\u003c/td\u003e\n", + " \u003ctd\u003egtex\u003c/td\u003e\n", + " \u003ctd\u003epaired\u003c/td\u003e\n", + " \u003ctd\u003eFalse\u003c/td\u003e\n", + " \u003ctd\u003e0.045404\u003c/td\u003e\n", + " \u003c/tr\u003e\n", + " \u003c/tbody\u003e\n", + "\u003c/table\u003e\n", + "\u003cp\u003e667 rows × 12 columns\u003c/p\u003e\n", + "\u003c/div\u003e\n", + " \u003cdiv class=\"colab-df-buttons\"\u003e\n", + "\n", + " \u003cdiv class=\"colab-df-container\"\u003e\n", + " \u003cbutton class=\"colab-df-convert\" onclick=\"convertToInteractive('df-609d8311-f935-4409-967f-a71df23ec7f8')\"\n", + " title=\"Convert this dataframe to an interactive table.\"\n", + " style=\"display:none;\"\u003e\n", + "\n", + " \u003csvg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\"\u003e\n", + " \u003cpath d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/\u003e\n", + " \u003c/svg\u003e\n", + " \u003c/button\u003e\n", + "\n", + " \u003cstyle\u003e\n", + " .colab-df-container {\n", + " display:flex;\n", + " gap: 12px;\n", + " }\n", + "\n", + " .colab-df-convert {\n", + " background-color: #E8F0FE;\n", + " border: none;\n", + " border-radius: 50%;\n", + " cursor: pointer;\n", + " display: none;\n", + " fill: #1967D2;\n", + " height: 32px;\n", + " padding: 0 0 0 0;\n", + " width: 32px;\n", + " }\n", + "\n", + " .colab-df-convert:hover {\n", + " background-color: #E2EBFA;\n", + " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", + " fill: #174EA6;\n", + " }\n", + "\n", + " .colab-df-buttons div {\n", + " margin-bottom: 4px;\n", + " }\n", + "\n", + " [theme=dark] .colab-df-convert {\n", + " background-color: #3B4455;\n", + " fill: #D2E3FC;\n", + " }\n", + "\n", + " [theme=dark] .colab-df-convert:hover {\n", + " background-color: #434B5C;\n", + " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", + " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", + " fill: #FFFFFF;\n", + " }\n", + " \u003c/style\u003e\n", + "\n", + " \u003cscript\u003e\n", + " const buttonEl =\n", + " document.querySelector('#df-609d8311-f935-4409-967f-a71df23ec7f8 button.colab-df-convert');\n", + " buttonEl.style.display =\n", + " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", + "\n", + " async function convertToInteractive(key) {\n", + " const element = document.querySelector('#df-609d8311-f935-4409-967f-a71df23ec7f8');\n", + " const dataTable =\n", + " await google.colab.kernel.invokeFunction('convertToInteractive',\n", + " [key], {});\n", + " if (!dataTable) return;\n", + "\n", + " const docLinkHtml = 'Like what you see? Visit the ' +\n", + " '\u003ca target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb\u003edata table notebook\u003c/a\u003e'\n", + " + ' to learn more about interactive tables.';\n", + " element.innerHTML = '';\n", + " dataTable['output_type'] = 'display_data';\n", + " await google.colab.output.renderOutput(dataTable, element);\n", + " const docLink = document.createElement('div');\n", + " docLink.innerHTML = docLinkHtml;\n", + " element.appendChild(docLink);\n", + " }\n", + " \u003c/script\u003e\n", + " \u003c/div\u003e\n", + "\n", + "\n", + " \u003cdiv id=\"df-3c6ff036-b522-4f44-86e9-f720f12add0b\"\u003e\n", + " \u003cbutton class=\"colab-df-quickchart\" onclick=\"quickchart('df-3c6ff036-b522-4f44-86e9-f720f12add0b')\"\n", + " title=\"Suggest charts\"\n", + " style=\"display:none;\"\u003e\n", + "\n", + "\u003csvg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", + " width=\"24px\"\u003e\n", + " \u003cg\u003e\n", + " \u003cpath d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/\u003e\n", + " \u003c/g\u003e\n", + "\u003c/svg\u003e\n", + " \u003c/button\u003e\n", + "\n", + "\u003cstyle\u003e\n", + " .colab-df-quickchart {\n", + " --bg-color: #E8F0FE;\n", + " --fill-color: #1967D2;\n", + " --hover-bg-color: #E2EBFA;\n", + " --hover-fill-color: #174EA6;\n", + " --disabled-fill-color: #AAA;\n", + " --disabled-bg-color: #DDD;\n", + " }\n", + "\n", + " [theme=dark] .colab-df-quickchart {\n", + " --bg-color: #3B4455;\n", + " --fill-color: #D2E3FC;\n", + " --hover-bg-color: #434B5C;\n", + " --hover-fill-color: #FFFFFF;\n", + " --disabled-bg-color: #3B4455;\n", + " --disabled-fill-color: #666;\n", + " }\n", + "\n", + " .colab-df-quickchart {\n", + " background-color: var(--bg-color);\n", + " border: none;\n", + " border-radius: 50%;\n", + " cursor: pointer;\n", + " display: none;\n", + " fill: var(--fill-color);\n", + " height: 32px;\n", + " padding: 0;\n", + " width: 32px;\n", + " }\n", + "\n", + " .colab-df-quickchart:hover {\n", + " background-color: var(--hover-bg-color);\n", + " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n", + " fill: var(--button-hover-fill-color);\n", + " }\n", + "\n", + " .colab-df-quickchart-complete:disabled,\n", + " .colab-df-quickchart-complete:disabled:hover {\n", + " background-color: var(--disabled-bg-color);\n", + " fill: var(--disabled-fill-color);\n", + " box-shadow: none;\n", + " }\n", + "\n", + " .colab-df-spinner {\n", + " border: 2px solid var(--fill-color);\n", + " border-color: transparent;\n", + " border-bottom-color: var(--fill-color);\n", + " animation:\n", + " spin 1s steps(1) infinite;\n", + " }\n", + "\n", + " @keyframes spin {\n", + " 0% {\n", + " border-color: transparent;\n", + " border-bottom-color: var(--fill-color);\n", + " border-left-color: var(--fill-color);\n", + " }\n", + " 20% {\n", + " border-color: transparent;\n", + " border-left-color: var(--fill-color);\n", + " border-top-color: var(--fill-color);\n", + " }\n", + " 30% {\n", + " border-color: transparent;\n", + " border-left-color: var(--fill-color);\n", + " border-top-color: var(--fill-color);\n", + " border-right-color: var(--fill-color);\n", + " }\n", + " 40% {\n", + " border-color: transparent;\n", + " border-right-color: var(--fill-color);\n", + " border-top-color: var(--fill-color);\n", + " }\n", + " 60% {\n", + " border-color: transparent;\n", + " border-right-color: var(--fill-color);\n", + " }\n", + " 80% {\n", + " border-color: transparent;\n", + " border-right-color: var(--fill-color);\n", + " border-bottom-color: var(--fill-color);\n", + " }\n", + " 90% {\n", + " border-color: transparent;\n", + " border-bottom-color: var(--fill-color);\n", + " }\n", + " }\n", + "\u003c/style\u003e\n", + "\n", + " \u003cscript\u003e\n", + " async function quickchart(key) {\n", + " const quickchartButtonEl =\n", + " document.querySelector('#' + key + ' button');\n", + " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n", + " quickchartButtonEl.classList.add('colab-df-spinner');\n", + " try {\n", + " const charts = await google.colab.kernel.invokeFunction(\n", + " 'suggestCharts', [key], {});\n", + " } catch (error) {\n", + " console.error('Error during call to suggestCharts:', error);\n", + " }\n", + " quickchartButtonEl.classList.remove('colab-df-spinner');\n", + " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n", + " }\n", + " (() =\u003e {\n", + " let quickchartButtonEl =\n", + " document.querySelector('#df-3c6ff036-b522-4f44-86e9-f720f12add0b button');\n", + " quickchartButtonEl.style.display =\n", + " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", + " })();\n", + " \u003c/script\u003e\n", + " \u003c/div\u003e\n", + " \u003c/div\u003e\n", + " \u003c/div\u003e\n" + ], + "text/plain": [ + " name ... nonzero_mean\n", + "0 CL:0000047 polyA plus RNA-seq ... 0.143617\n", + "1 CL:0000062 total RNA-seq ... 0.094144\n", + "2 CL:0000084 polyA plus RNA-seq ... 0.124296\n", + "3 CL:0000084 total RNA-seq ... 0.100934\n", + "4 CL:0000115 total RNA-seq ... 0.135553\n", + ".. ... ... ...\n", + "662 UBERON:0018115 polyA plus RNA-seq ... 0.268222\n", + "663 UBERON:0018116 polyA plus RNA-seq ... 0.258522\n", + "664 UBERON:0018117 polyA plus RNA-seq ... 0.215190\n", + "665 UBERON:0018118 polyA plus RNA-seq ... 0.365676\n", + "666 UBERON:0036149 gtex Skin_Not_Sun_Exposed_Supra... ... 0.045404\n", + "\n", + "[667 rows x 12 columns]" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "variant_scores.var" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rrbvEe4xkYVu" + }, + "source": [ + "Some handy additional metadata can be found in `.uns`:" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "executionInfo": { + "elapsed": 64, + "status": "ok", + "timestamp": 1753118734789, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "n3GGvosdkYVu", + "outputId": "70e77e62-99c8-40b5-da4b-d4fe8cfaab67" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Interval: chr22:35677410-36725986:.\n", + "Variant: chr22:36201698:A\u003eC\n", + "Variant scorer: GeneMaskLFCScorer(requested_output=RNA_SEQ)\n" + ] + } + ], + "source": [ + "print(f'Interval: {variant_scores.uns[\"interval\"]}')\n", + "print(f'Variant: {variant_scores.uns[\"variant\"]}')\n", + "print(f'Variant scorer: {variant_scores.uns[\"variant_scorer\"]}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "B0WK-6IpkYVu" + }, + "source": [ + "We recommend interacting with variant scores by flattening `AnnData` objects using `tidy_scores`, which produces a dataframe with each row being a single score for each combination of (variant, gene, scorer, ontology). It optionally excludes stranded tracks which do not match the gene’s strand for gene-specific scorer.\n", + "\n", + "The `raw_score` column contains the same values as stored in `variant_scores.X`. The `quantile_score` column is the rank of the `raw_score` in the distribution of scores for a background set of common variants, represented as a quantile probability. This allows for direct comparison across variant scoring strategies that yield scores on different scales. See [FAQs](https://www.alphagenomedocs.com/faqs.html#what-is-the-difference-between-a-quantile-score-and-raw-score) for further details." + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "colab": { + "height": 0 + }, + "executionInfo": { + "elapsed": 636, + "status": "ok", + "timestamp": 1753118735589, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "cVG5ApfkkYVu", + "outputId": "907a9862-0c77-4a7c-974d-091f8b71f284" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "repr_error": "Out of range float values are not JSON compliant: nan", + "type": "dataframe" + }, + "text/html": [ + "\n", + " \u003cdiv id=\"df-f7d731b6-15c1-4217-9e7f-1eb2bb107e35\" class=\"colab-df-container\"\u003e\n", + " \u003cdiv\u003e\n", + "\u003cstyle scoped\u003e\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " 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'block' : 'none';\n", + " })();\n", + " \u003c/script\u003e\n", + " \u003c/div\u003e\n", + " \u003c/div\u003e\n", + " \u003c/div\u003e\n" + ], + "text/plain": [ + " variant_id scored_interval ... raw_score quantile_score\n", + "0 chr22:36201698:A\u003eC chr22:35677410-36725986:. ... 0.000903 0.606708\n", + "1 chr22:36201698:A\u003eC chr22:35677410-36725986:. ... -0.000363 -0.477020\n", + "2 chr22:36201698:A\u003eC chr22:35677410-36725986:. ... -0.007063 -0.989667\n", + "3 chr22:36201698:A\u003eC chr22:35677410-36725986:. ... -0.007229 -0.990997\n", + "4 chr22:36201698:A\u003eC chr22:35677410-36725986:. ... 0.000461 0.449849\n", + "... ... ... ... ... ...\n", + "14647 chr22:36201698:A\u003eC chr22:35677410-36725986:. ... 0.006467 0.989427\n", + "14648 chr22:36201698:A\u003eC chr22:35677410-36725986:. ... 0.007303 0.992679\n", + "14649 chr22:36201698:A\u003eC chr22:35677410-36725986:. ... 0.004375 0.964537\n", + "14650 chr22:36201698:A\u003eC chr22:35677410-36725986:. ... 0.003006 0.905090\n", + "14651 chr22:36201698:A\u003eC chr22:35677410-36725986:. ... -0.002312 -0.822706\n", + "\n", + "[14652 rows x 19 columns]" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "variant_scorers.tidy_scores([variant_scores], match_gene_strand=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "055sHwjykYVu" + }, + "source": [ + "## Highlighting important regions with *in silico* mutagenesis\n", + "\n", + "To highlight which regions in a DNA sequence are functionally important for a final variant prediction, we can perform an **in silico mutagenesis** (ISM) analysis by scoring all possible single nucleotide variants in a specific interval.\n", + "\n", + "Here is a visual overview of this process:\n", + "\n", + "\u003ca href=\"https://services.google.com/fh/files/misc/ism_green_v2.png\"\u003e\u003cimg src=\"https://services.google.com/fh/files/misc/ism_green_v2.png\" alt=\"ISM\" border=\"0\" height=500\u003e\u003c/a\u003e\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "O93kx8lckYVu" + }, + "source": [ + "\n", + "\n", + "We define an `ism_interval`, which is a relatively small region of DNA that we want to systematically mutate. We also define the `sequence_interval`, which is the contextual interval the model will use when making predictions for each variant.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "executionInfo": { + "elapsed": 57, + "status": "ok", + "timestamp": 1753118735829, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "9Xhk_TsHkYVu" + }, + "outputs": [], + "source": [ + "# 2KB DNA sequence to use as context when making predictions.\n", + "sequence_interval = genome.Interval('chr20', 3_753_000, 3_753_400)\n", + "sequence_interval = sequence_interval.resize(dna_client.SEQUENCE_LENGTH_2KB)\n", + "\n", + "# Mutate all bases in the central 256-base region of the sequence_interval.\n", + "ism_interval = sequence_interval.resize(256)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "d9LSDA0QkYVu" + }, + "source": [ + "Next, we define the scorer we want to use to score each of the ISM variants. Here, we use a center mask scorer on predicted `DNASE` values, which will score each variant's effect on DNA accessibility in the 500bp vicinity. See the [variant scoring documentation](https://www.alphagenomedocs.com/variant_scoring.html) for more information on variant scoring." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "executionInfo": { + "elapsed": 102, + "status": "ok", + "timestamp": 1753118736084, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "twYn3L9qkYVu" + }, + "outputs": [], + "source": [ + "dnase_variant_scorer = variant_scorers.CenterMaskScorer(\n", + " requested_output=dna_client.OutputType.DNASE,\n", + " width=501,\n", + " aggregation_type=variant_scorers.AggregationType.DIFF_MEAN,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UbQxCvutkYVu" + }, + "source": [ + "Finally, we can use `score_variants` (notice the plural s) to score all variants.\n", + "\n", + "Note that this operation is quite expensive. For speed reasons, we recommend using shorter input sequences for the contextual `sequence_interval` and narrower `ism_interval` regions to mutate if possible.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "colab": { + "height": 49, + "referenced_widgets": [ + "f498e553954c4f3abcb765b7d6d139a3", + "2c5b1e877d234eb9a91fb7bc66ecdaaa", + "1fb9b0087c7448d4a7e1d2f1848dcfa8", + "1904dd6a03324a908a3607aaa0f1e90c", + "b4a6bc24513548c9a1d25afa113fe779", + "16901cf43ce54c94a63c3bdaceb103fe", + "8e5bd3a0a8f4483c818586a8b42b3543", + "dde2bae36b724b85b067e619459f454e", + "589d793508b34bb1b6430b9f6e5ff989", + "8a0a6684b29c4fe3992320e7de6bf8d3", + "bd84c93fcf7f4d7aa56dc92bf076965d" + ] + }, + "executionInfo": { + "elapsed": 14099, + "status": "ok", + "timestamp": 1753118750342, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "WMuA2NQXkYVu", + "outputId": "caef670f-6618-48b8-93bf-16d243e9a959" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f498e553954c4f3abcb765b7d6d139a3", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/26 [00:00\u003c?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "variant_scores = dna_model.score_ism_variants(\n", + " interval=sequence_interval,\n", + " ism_interval=ism_interval,\n", + " variant_scorers=[dnase_variant_scorer],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r3iJ9E-ckYVu" + }, + "source": [ + "The length of the returned `variant_scores` is 768, since we scored 768 variants (256 positions * 3 alternative bases per position):" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "executionInfo": { + "elapsed": 63, + "status": "ok", + "timestamp": 1753118750565, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "NrRgRYF1kYVu", + "outputId": "7babc03c-87f1-4d4f-a062-c6e715706cc5" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "768" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(variant_scores)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_e2WoG6pkYVu" + }, + "source": [ + "Each variant has scores of shape `(1, 305)`, reflecting the fact that we are not using a gene-centric scorer and that there are 305 `DNASE` tracks:" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "executionInfo": { + "elapsed": 61, + "status": "ok", + "timestamp": 1753118750798, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "f-raKWmbkYVu", + "outputId": "229de7c0-9d10-403b-9b71-09a769e616a8" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 305)" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Index into first variant and first scorer.\n", + "variant_scores[0][0].X.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nyDoTBdAkYVu" + }, + "source": [ + "To understand which positions are most influential in the predictions, we can visualize these scores using a sequence logo. This requires summarizing the scores into a single scalar value per variant.\n", + "\n", + "As an example, let's extract the DNASE score for just the K562 cell line, a widely used experimental model. Alternatively, you could average across multiple tissues to obtain a single scalar value." + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": { + "executionInfo": { + "elapsed": 216, + "status": "ok", + "timestamp": 1753118751142, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "jK9UF4PZkYVu" + }, + "outputs": [], + "source": [ + "def extract_k562(adata):\n", + " values = adata.X[:, adata.var['ontology_curie'] == 'EFO:0002067']\n", + " assert values.size == 1\n", + " return values.flatten()[0]\n", + "\n", + "\n", + "ism_result = ism.ism_matrix(\n", + " [extract_k562(x[0]) for x in variant_scores],\n", + " variants=[v[0].uns['variant'] for v in variant_scores],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UYtOmG4xkYVu" + }, + "source": [ + "The shape of `ism_result` is `(256, 4`) since we have 1 score per position per each of the 4 DNA bases.\n", + "\n", + "Note that in this case, our call to `ism.ism_matrix()` had the argument `multiply_by_sequence` set to 'True', so the output array contains non-zero values only for the bases corresponding to the reference sequence.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "executionInfo": { + "elapsed": 57, + "status": "ok", + "timestamp": 1753118751330, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "ONduc-kwkYVv", + "outputId": "041e64bb-7281-4ede-d81e-fbd610a81f44" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(256, 4)" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ism_result.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Wj0VMFwCkYVv" + }, + "source": [ + "Finally, we plot the contribution scores as a sequence logo:" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "colab": { + "height": 0 + }, + "executionInfo": { + "elapsed": 4998, + "status": "ok", + "timestamp": 1753118756464, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "0X9fwKw6kYVv", + "outputId": "dd42944f-0ae2-4ec6-9b6b-bbcbefb61ee0" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "\u003cFigure size 2520x72 with 1 Axes\u003e" + ] + }, + "metadata": { + "image/png": { + "height": 98, + "width": 2084 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_components.plot(\n", + " [\n", + " plot_components.SeqLogo(\n", + " scores=ism_result,\n", + " scores_interval=ism_interval,\n", + " ylabel='ISM K562 DNase',\n", + " )\n", + " ],\n", + " interval=ism_interval,\n", + " fig_width=35,\n", + ")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Qkuuj1p7kYVv" + }, + "source": [ + "This plot shows that the sequence between positions ~225 to ~240 has the strongest effect on predicted nearby DNAse in K562 cells.\n", + "\n", + "These contribution scores can be used to systematically discover motifs important for different modalities and cell types, find the transcription factors binding those motifs and map motif instances across the genome. Here are a few tools you can use to do this:\n", + "- [tfmodisco-lite](https://github.com/jmschrei/tfmodisco-lite/)\n", + "- [tangermeme](https://github.com/jmschrei/tangermeme)\n", + "- [tomtom](https://meme-suite.org/meme/tools/tomtom)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j6Vum-X6ILaz" + }, + "source": [ + "## Making mouse predictions" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r-E-kEitg6ru" + }, + "source": [ + "So far, this notebook has focused on predictions for human (`Organism.HOMO_SAPIENS`). To generate predictions for mouse, specify the organism as `Organism.MUS_MUSCULUS` instead. Please note that the supported ontology terms differ between species.\n", + "\n", + "The following example demonstrates how to call `predict_sequence` for mouse predictions:" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "executionInfo": { + "elapsed": 108, + "status": "ok", + "timestamp": 1753118756745, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "QjFVRG1QLVba", + "language": "python" + }, + "outputs": [], + "source": [ + "output = dna_model.predict_sequence(\n", + " sequence='GATTACA'.center(2048, 'N'), # Pad to valid sequence length.\n", + " organism=dna_client.Organism.MUS_MUSCULUS,\n", + " requested_outputs=[dna_client.OutputType.DNASE],\n", + " ontology_terms=['UBERON:0002048'], # Lung.\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WwpupBP1hPgh" + }, + "source": [ + "And here is an example of calling `predict_interval` for a mouse genomic interval:" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "executionInfo": { + "elapsed": 476, + "status": "ok", + "timestamp": 1753118757347, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "lKOIZAtMLrEu", + "outputId": "4b1fa86c-1099-4752-91fc-272c8c9e8c51" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1048576, 3)" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "interval = genome.Interval('chr1', 3_000_000, 3_000_001).resize(\n", + " dna_client.SEQUENCE_LENGTH_1MB\n", + ")\n", + "\n", + "output = dna_model.predict_interval(\n", + " interval=interval,\n", + " organism=dna_client.Organism.MUS_MUSCULUS,\n", + " requested_outputs=[dna_client.OutputType.RNA_SEQ],\n", + " ontology_terms=['UBERON:0002048'], # Lung.\n", + ")\n", + "\n", + "output.rna_seq.values.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-1isDYNjkYVv" + }, + "source": [ + "## Conclusion\n", + "\n", + "That's it for the quick start guide. To dive in further, check out our [other tutorials](https://www.alphagenomedocs.com/tutorials/index.html).\n" + ] + } + ], + "metadata": { + "colab": { + "last_runtime": {}, + "provenance": [ + { + "file_id": "1J99_hrY2DKzefpyiN_vG2cGkqT-2AvhK", + "timestamp": 1727706471665 + } + ], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "16901cf43ce54c94a63c3bdaceb103fe": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": 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/dev/null +++ b/alphagenome/source/colabs/tissue_ontology_mapping.ipynb @@ -0,0 +1,1054 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "Vjnah732ZKsd" + }, + "source": [ + "# Navigating data ontologies" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3x-2dOnDNaP7" + }, + "source": [ + "```{tip}\n", + "Open this tutorial in Google colab for interactive viewing.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "cellView": "form", + "executionInfo": { + "elapsed": 53, + "status": "ok", + "timestamp": 1753113509961, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "C0A344lPJLJs" + }, + "outputs": [], + "source": [ + "# @title Install AlphaGenome\n", + "\n", + "# @markdown Run this cell to install AlphaGenome.\n", + "from IPython.display import clear_output\n", + "! pip install alphagenome\n", + "clear_output()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lE9Xg8TqGlMr" + }, + "source": [ + "## Setup and imports" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "executionInfo": { + "elapsed": 2049, + "status": "ok", + "timestamp": 1753113512140, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "Zny4b7-RGrsD" + }, + "outputs": [], + "source": [ + "from alphagenome import colab_utils\n", + "from alphagenome.models import dna_client\n", + "from google.colab import data_table\n", + "import pandas as pd\n", + "\n", + "data_table.enable_dataframe_formatter()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aK_L6CqKGtj0" + }, + "source": [ + "## Interactively view output metadata" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "D41TRBITJsg_" + }, + "source": [ + "First, we load the model." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "executionInfo": { + "elapsed": 701, + "status": "ok", + "timestamp": 1753113512981, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "8tYSSMFFGp4X" + }, + "outputs": [], + "source": [ + "dna_model = dna_client.create(colab_utils.get_api_key())\n", + "\n", + "output_metadata = dna_model.output_metadata(\n", + " dna_client.Organism.HOMO_SAPIENS\n", + ").concatenate()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BWD4q1A1G5md" + }, + "source": [ + "Click `Filter` on the upper right hand side of the interactive dataframe and type a cell or tissue name like \"brain\" into the `Search by all fields box` to find the `ontology_curie` term corresponding to a tissue and output type of interest:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "height": 1227 + }, + "executionInfo": { + "elapsed": 2017, + "status": "ok", + "timestamp": 1753113515165, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "xbkxmkGvpcx3", + "outputId": 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145\",\n\"cell_line\",\nNaN,\n\"fantom\",\nNaN,\nNaN,\n{\n 'v': 57.9163932800293,\n 'f': \"57.9163932800293\",\n },\n\"OutputType.CAGE\",\nNaN,\nNaN,\nNaN],\n [{\n 'v': 163,\n 'f': \"163\",\n },\n\"hCAGE UBERON:0000002\",\n\"+\",\n\"hCAGE\",\n\"UBERON:0000002\",\n\"uterine cervix\",\n\"tissue\",\nNaN,\n\"fantom\",\nNaN,\nNaN,\n{\n 'v': 35.90520477294922,\n 'f': \"35.90520477294922\",\n },\n\"OutputType.CAGE\",\nNaN,\nNaN,\nNaN],\n [{\n 'v': 164,\n 'f': \"164\",\n },\n\"hCAGE UBERON:0000007\",\n\"+\",\n\"hCAGE\",\n\"UBERON:0000007\",\n\"pituitary gland\",\n\"tissue\",\nNaN,\n\"fantom\",\nNaN,\nNaN,\n{\n 'v': 38.91667938232422,\n 'f': \"38.91667938232422\",\n },\n\"OutputType.CAGE\",\nNaN,\nNaN,\nNaN],\n [{\n 'v': 165,\n 'f': \"165\",\n },\n\"hCAGE UBERON:0000014\",\n\"+\",\n\"hCAGE\",\n\"UBERON:0000014\",\n\"zone of skin\",\n\"tissue\",\nNaN,\n\"fantom\",\nNaN,\nNaN,\n{\n 'v': 90.9419937133789,\n 'f': \"90.9419937133789\",\n },\n\"OutputType.CAGE\",\nNaN,\nNaN,\nNaN],\n [{\n 'v': 166,\n 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'v': 272,\n 'f': \"272\",\n },\n\"hCAGE UBERON:0013777\",\n\"+\",\n\"hCAGE\",\n\"UBERON:0013777\",\n\"skin of palm of manus\",\n\"tissue\",\nNaN,\n\"fantom\",\nNaN,\nNaN,\n{\n 'v': 43.870784759521484,\n 'f': \"43.870784759521484\",\n },\n\"OutputType.CAGE\",\nNaN,\nNaN,\nNaN],\n [{\n 'v': 273,\n 'f': \"273\",\n },\n\"LQhCAGE CL:0000077\",\n\"-\",\n\"LQhCAGE\",\n\"CL:0000077\",\n\"mesothelial cell\",\n\"primary_cell\",\nNaN,\n\"fantom\",\nNaN,\nNaN,\n{\n 'v': 272.4184265136719,\n 'f': \"272.4184265136719\",\n },\n\"OutputType.CAGE\",\nNaN,\nNaN,\nNaN],\n [{\n 'v': 274,\n 'f': \"274\",\n },\n\"LQhCAGE CL:0000136\",\n\"-\",\n\"LQhCAGE\",\n\"CL:0000136\",\n\"fat cell\",\n\"primary_cell\",\nNaN,\n\"fantom\",\nNaN,\nNaN,\n{\n 'v': 41.71626663208008,\n 'f': \"41.71626663208008\",\n },\n\"OutputType.CAGE\",\nNaN,\nNaN,\nNaN],\n [{\n 'v': 275,\n 'f': \"275\",\n },\n\"LQhCAGE CL:0000767\",\n\"-\",\n\"LQhCAGE\",\n\"CL:0000767\",\n\"basophil\",\n\"primary_cell\",\nNaN,\n\"fantom\",\nNaN,\nNaN,\n{\n 'v': 59.45576095581055,\n 'f': \"59.45576095581055\",\n },\n\"OutputType.CAGE\",\nNaN,\nNaN,\nNaN],\n [{\n 'v': 276,\n 'f': \"276\",\n },\n\"LQhCAGE CL:0000771\",\n\"-\",\n\"LQhCAGE\",\n\"CL:0000771\",\n\"eosinophil\",\n\"primary_cell\",\nNaN,\n\"fantom\",\nNaN,\nNaN,\n{\n 'v': 119.22451782226562,\n 'f': \"119.22451782226562\",\n },\n\"OutputType.CAGE\",\nNaN,\nNaN,\nNaN],\n [{\n 'v': 277,\n 'f': \"277\",\n },\n\"LQhCAGE CL:0000775\",\n\"-\",\n\"LQhCAGE\",\n\"CL:0000775\",\n\"neutrophil\",\n\"primary_cell\",\nNaN,\n\"fantom\",\nNaN,\nNaN,\n{\n 'v': 339.38720703125,\n 'f': \"339.38720703125\",\n },\n\"OutputType.CAGE\",\nNaN,\nNaN,\nNaN],\n [{\n 'v': 278,\n 'f': \"278\",\n },\n\"LQhCAGE CL:0000784\",\n\"-\",\n\"LQhCAGE\",\n\"CL:0000784\",\n\"plasmacytoid dendritic cell\",\n\"primary_cell\",\nNaN,\n\"fantom\",\nNaN,\nNaN,\n{\n 'v': 103.1607666015625,\n 'f': 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59.10931396484375,\n 'f': \"59.10931396484375\",\n },\n\"OutputType.CAGE\",\nNaN,\nNaN,\nNaN],\n [{\n 'v': 386,\n 'f': \"386\",\n },\n\"hCAGE CL:0002588\",\n\"-\",\n\"hCAGE\",\n\"CL:0002588\",\n\"smooth muscle cell of the umbilical vein\",\n\"primary_cell\",\nNaN,\n\"fantom\",\nNaN,\nNaN,\n{\n 'v': 48.645050048828125,\n 'f': \"48.645050048828125\",\n },\n\"OutputType.CAGE\",\nNaN,\nNaN,\nNaN],\n [{\n 'v': 387,\n 'f': \"387\",\n },\n\"hCAGE CL:0002589\",\n\"-\",\n\"hCAGE\",\n\"CL:0002589\",\n\"smooth muscle cell of the brachiocephalic vasculature\",\n\"primary_cell\",\nNaN,\n\"fantom\",\nNaN,\nNaN,\n{\n 'v': 26.21996307373047,\n 'f': \"26.21996307373047\",\n },\n\"OutputType.CAGE\",\nNaN,\nNaN,\nNaN],\n [{\n 'v': 388,\n 'f': \"388\",\n },\n\"hCAGE CL:0002590\",\n\"-\",\n\"hCAGE\",\n\"CL:0002590\",\n\"smooth muscle cell of the brain vasculature\",\n\"primary_cell\",\nNaN,\n\"fantom\",\nNaN,\nNaN,\n{\n 'v': 54.19729995727539,\n 'f': \"54.19729995727539\",\n 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'f': \"441\",\n },\n\"hCAGE UBERON:0000057\",\n\"-\",\n\"hCAGE\",\n\"UBERON:0000057\",\n\"urethra\",\n\"tissue\",\nNaN,\n\"fantom\",\nNaN,\nNaN,\n{\n 'v': 21.93443489074707,\n 'f': \"21.93443489074707\",\n },\n\"OutputType.CAGE\",\nNaN,\nNaN,\nNaN],\n [{\n 'v': 442,\n 'f': \"442\",\n },\n\"hCAGE UBERON:0000178\",\n\"-\",\n\"hCAGE\",\n\"UBERON:0000178\",\n\"blood\",\n\"primary_cell\",\nNaN,\n\"fantom\",\nNaN,\nNaN,\n{\n 'v': 13.531624794006348,\n 'f': \"13.531624794006348\",\n },\n\"OutputType.CAGE\",\nNaN,\nNaN,\nNaN],\n [{\n 'v': 443,\n 'f': \"443\",\n },\n\"hCAGE UBERON:0000305\",\n\"-\",\n\"hCAGE\",\n\"UBERON:0000305\",\n\"amnion\",\n\"primary_cell\",\nNaN,\n\"fantom\",\nNaN,\nNaN,\n{\n 'v': 44.147254943847656,\n 'f': \"44.147254943847656\",\n },\n\"OutputType.CAGE\",\nNaN,\nNaN,\nNaN],\n [{\n 'v': 444,\n 'f': \"444\",\n },\n\"hCAGE UBERON:0000310\",\n\"-\",\n\"hCAGE\",\n\"UBERON:0000310\",\n\"breast\",\n\"tissue\",\nNaN,\n\"fantom\",\nNaN,\nNaN,\n{\n 'v': 203.0039520263672,\n 'f': 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11.435539245605469,\n 'f': \"11.435539245605469\",\n },\n\"OutputType.CAGE\",\nNaN,\nNaN,\nNaN],\n [{\n 'v': 494,\n 'f': \"494\",\n },\n\"hCAGE UBERON:0001905\",\n\"-\",\n\"hCAGE\",\n\"UBERON:0001905\",\n\"pineal body\",\n\"tissue\",\nNaN,\n\"fantom\",\nNaN,\nNaN,\n{\n 'v': 27.537044525146484,\n 'f': \"27.537044525146484\",\n },\n\"OutputType.CAGE\",\nNaN,\nNaN,\nNaN],\n [{\n 'v': 495,\n 'f': \"495\",\n },\n\"hCAGE UBERON:0001954\",\n\"-\",\n\"hCAGE\",\n\"UBERON:0001954\",\n\"Ammon's horn\",\n\"tissue\",\nNaN,\n\"fantom\",\nNaN,\nNaN,\n{\n 'v': 15.256468772888184,\n 'f': \"15.256468772888184\",\n },\n\"OutputType.CAGE\",\nNaN,\nNaN,\nNaN],\n [{\n 'v': 496,\n 'f': \"496\",\n },\n\"hCAGE UBERON:0001987\",\n\"-\",\n\"hCAGE\",\n\"UBERON:0001987\",\n\"placenta\",\n\"tissue\",\nNaN,\n\"fantom\",\nNaN,\nNaN,\n{\n 'v': 48.42684555053711,\n 'f': \"48.42684555053711\",\n },\n\"OutputType.CAGE\",\nNaN,\nNaN,\nNaN],\n [{\n 'v': 497,\n 'f': \"497\",\n },\n\"hCAGE 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RNA-seq\",\n\"UBERON:0002037\",\n\"cerebellum\",\n\"tissue\",\n\"adult\",\n\"gtex\",\n\"paired\",\nfalse,\n{\n 'v': 0.05265718325972557,\n 'f': \"0.05265718325972557\",\n },\n\"OutputType.RNA_SEQ\",\n\"Brain_Cerebellum\",\nNaN,\nNaN],\n [{\n 'v': 623,\n 'f': \"623\",\n },\n\"UBERON:0002038 gtex Brain_Substantia_nigra polyA plus RNA-seq\",\n\".\",\n\"polyA plus RNA-seq\",\n\"UBERON:0002038\",\n\"substantia nigra\",\n\"tissue\",\n\"adult\",\n\"gtex\",\n\"paired\",\nfalse,\n{\n 'v': 0.062162790447473526,\n 'f': \"0.062162790447473526\",\n },\n\"OutputType.RNA_SEQ\",\n\"Brain_Substantia_nigra\",\nNaN,\nNaN],\n [{\n 'v': 624,\n 'f': \"624\",\n },\n\"UBERON:0002046 gtex Thyroid polyA plus RNA-seq\",\n\".\",\n\"polyA plus RNA-seq\",\n\"UBERON:0002046\",\n\"thyroid gland\",\n\"tissue\",\n\"adult\",\n\"gtex\",\n\"paired\",\nfalse,\n{\n 'v': 0.04541570320725441,\n 'f': \"0.04541570320725441\",\n },\n\"OutputType.RNA_SEQ\",\n\"Thyroid\",\nNaN,\nNaN],\n [{\n 'v': 625,\n 'f': \"625\",\n 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polyA plus RNA-seq\",\n\".\",\n\"polyA plus RNA-seq\",\n\"UBERON:0002108\",\n\"small intestine\",\n\"tissue\",\n\"embryonic\",\n\"encode\",\n\"paired\",\nfalse,\n{\n 'v': 0.14074227213859558,\n 'f': \"0.14074227213859558\",\n },\n\"OutputType.RNA_SEQ\",\n\"\",\nNaN,\nNaN],\n [{\n 'v': 629,\n 'f': \"629\",\n },\n\"UBERON:0002113 polyA plus RNA-seq\",\n\".\",\n\"polyA plus RNA-seq\",\n\"UBERON:0002113\",\n\"kidney\",\n\"tissue\",\n\"embryonic\",\n\"encode\",\n\"single\",\nfalse,\n{\n 'v': 0.22629043459892273,\n 'f': \"0.22629043459892273\",\n },\n\"OutputType.RNA_SEQ\",\n\"\",\nNaN,\nNaN],\n [{\n 'v': 630,\n 'f': \"630\",\n },\n\"UBERON:0002167 polyA plus RNA-seq\",\n\".\",\n\"polyA plus RNA-seq\",\n\"UBERON:0002167\",\n\"right lung\",\n\"tissue\",\n\"embryonic\",\n\"encode\",\n\"single\",\nfalse,\n{\n 'v': 0.1865345537662506,\n 'f': \"0.1865345537662506\",\n },\n\"OutputType.RNA_SEQ\",\n\"\",\nNaN,\nNaN],\n [{\n 'v': 631,\n 'f': \"631\",\n },\n\"UBERON:0002168 polyA plus 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},\n\"OutputType.RNA_SEQ\",\n\"Prostate\",\nNaN,\nNaN],\n [{\n 'v': 637,\n 'f': \"637\",\n },\n\"UBERON:0002369 gtex Adrenal_Gland polyA plus RNA-seq\",\n\".\",\n\"polyA plus RNA-seq\",\n\"UBERON:0002369\",\n\"adrenal gland\",\n\"tissue\",\n\"adult\",\n\"gtex\",\n\"paired\",\nfalse,\n{\n 'v': 0.05743984505534172,\n 'f': \"0.05743984505534172\",\n },\n\"OutputType.RNA_SEQ\",\n\"Adrenal_Gland\",\nNaN,\nNaN],\n [{\n 'v': 638,\n 'f': \"638\",\n },\n\"UBERON:0002369 polyA plus RNA-seq\",\n\".\",\n\"polyA plus RNA-seq\",\n\"UBERON:0002369\",\n\"adrenal gland\",\n\"tissue\",\n\"embryonic\",\n\"encode\",\n\"single\",\nfalse,\n{\n 'v': 0.32479262351989746,\n 'f': \"0.32479262351989746\",\n },\n\"OutputType.RNA_SEQ\",\n\"\",\nNaN,\nNaN],\n [{\n 'v': 639,\n 'f': \"639\",\n },\n\"UBERON:0002370 polyA plus RNA-seq\",\n\".\",\n\"polyA plus RNA-seq\",\n\"UBERON:0002370\",\n\"thymus\",\n\"tissue\",\n\"embryonic\",\n\"encode\",\n\"single\",\nfalse,\n{\n 'v': 0.17336340248584747,\n 'f': 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0.05494289472699165,\n 'f': \"0.05494289472699165\",\n },\n\"OutputType.RNA_SEQ\",\n\"Heart_Left_Ventricle\",\nNaN,\nNaN],\n [{\n 'v': 651,\n 'f': \"651\",\n },\n\"UBERON:0006631 gtex Heart_Atrial_Appendage polyA plus RNA-seq\",\n\".\",\n\"polyA plus RNA-seq\",\n\"UBERON:0006631\",\n\"right atrium auricular region\",\n\"tissue\",\n\"adult\",\n\"gtex\",\n\"paired\",\nfalse,\n{\n 'v': 0.04985639825463295,\n 'f': \"0.04985639825463295\",\n },\n\"OutputType.RNA_SEQ\",\n\"Heart_Atrial_Appendage\",\nNaN,\nNaN],\n [{\n 'v': 652,\n 'f': \"652\",\n },\n\"UBERON:0006920 gtex Esophagus_Mucosa polyA plus RNA-seq\",\n\".\",\n\"polyA plus RNA-seq\",\n\"UBERON:0006920\",\n\"esophagus squamous epithelium\",\n\"tissue\",\n\"adult\",\n\"gtex\",\n\"paired\",\nfalse,\n{\n 'v': 0.05086713656783104,\n 'f': \"0.05086713656783104\",\n },\n\"OutputType.RNA_SEQ\",\n\"Esophagus_Mucosa\",\nNaN,\nNaN],\n [{\n 'v': 653,\n 'f': \"653\",\n },\n\"UBERON:0007610 gtex Artery_Tibial polyA plus RNA-seq\",\n\".\",\n\"polyA 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RNA-seq\",\nNaN,\nNaN,\n\"UBERON:0002097\",\n\"skin of body\",\n\"tissue\",\nNaN,\nNaN,\nNaN,\nNaN,\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n\"OutputType.SPLICE_JUNCTIONS\",\n\"\",\nNaN,\nNaN],\n [{\n 'v': 292,\n 'f': \"292\",\n },\n\"junction_UBERON:0002099 total RNA-seq\",\nNaN,\nNaN,\n\"UBERON:0002099\",\n\"cardiac septum\",\n\"tissue\",\nNaN,\nNaN,\nNaN,\nNaN,\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n\"OutputType.SPLICE_JUNCTIONS\",\n\"\",\nNaN,\nNaN],\n [{\n 'v': 293,\n 'f': \"293\",\n },\n\"junction_UBERON:0002106 gtex Spleen polyA plus RNA-seq\",\nNaN,\nNaN,\n\"UBERON:0002106\",\n\"spleen\",\n\"tissue\",\nNaN,\nNaN,\nNaN,\nNaN,\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n\"OutputType.SPLICE_JUNCTIONS\",\n\"Spleen\",\nNaN,\nNaN],\n [{\n 'v': 294,\n 'f': \"294\",\n },\n\"junction_UBERON:0002106 polyA plus RNA-seq\",\nNaN,\nNaN,\n\"UBERON:0002106\",\n\"spleen\",\n\"tissue\",\nNaN,\nNaN,\nNaN,\nNaN,\n{\n 'v': NaN,\n 'f': \"NaN\",\n },\n\"OutputType.SPLICE_JUNCTIONS\",\n\"\",\nNaN,\nNaN],\n [{\n 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\u003c/tr\u003e\n", + " \u003ctr\u003e\n", + " \u003cth\u003e3\u003c/th\u003e\n", + " \u003ctd\u003eCL:0000623 ATAC-seq\u003c/td\u003e\n", + " \u003ctd\u003e.\u003c/td\u003e\n", + " \u003ctd\u003eATAC-seq\u003c/td\u003e\n", + " \u003ctd\u003eCL:0000623\u003c/td\u003e\n", + " \u003ctd\u003enatural killer cell\u003c/td\u003e\n", + " \u003ctd\u003eprimary_cell\u003c/td\u003e\n", + " \u003ctd\u003eadult\u003c/td\u003e\n", + " \u003ctd\u003eencode\u003c/td\u003e\n", + " \u003ctd\u003epaired\u003c/td\u003e\n", + " \u003ctd\u003eFalse\u003c/td\u003e\n", + " \u003ctd\u003e0.938715\u003c/td\u003e\n", + " \u003ctd\u003eOutputType.ATAC\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003c/tr\u003e\n", + " \u003ctr\u003e\n", + " \u003cth\u003e4\u003c/th\u003e\n", + " \u003ctd\u003eCL:0000624 ATAC-seq\u003c/td\u003e\n", + " \u003ctd\u003e.\u003c/td\u003e\n", + " \u003ctd\u003eATAC-seq\u003c/td\u003e\n", + " \u003ctd\u003eCL:0000624\u003c/td\u003e\n", + " \u003ctd\u003eCD4-positive, alpha-beta T cell\u003c/td\u003e\n", + " \u003ctd\u003eprimary_cell\u003c/td\u003e\n", + " \u003ctd\u003eadult\u003c/td\u003e\n", + " \u003ctd\u003eencode\u003c/td\u003e\n", + " \u003ctd\u003epaired\u003c/td\u003e\n", + " \u003ctd\u003eFalse\u003c/td\u003e\n", + " \u003ctd\u003e4.365206\u003c/td\u003e\n", + " \u003ctd\u003eOutputType.ATAC\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003c/tr\u003e\n", + " \u003ctr\u003e\n", + " \u003cth\u003e...\u003c/th\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " \u003ctd\u003e...\u003c/td\u003e\n", + " 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\u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003c/tr\u003e\n", + " \u003ctr\u003e\n", + " \u003cth\u003e8\u003c/th\u003e\n", + " \u003ctd\u003eENCSR740IPL\u003c/td\u003e\n", + " \u003ctd\u003e-\u003c/td\u003e\n", + " \u003ctd\u003ePRO-cap\u003c/td\u003e\n", + " \u003ctd\u003eEFO:0002067\u003c/td\u003e\n", + " \u003ctd\u003eK562\u003c/td\u003e\n", + " \u003ctd\u003ecell_line\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eencode\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eFalse\u003c/td\u003e\n", + " \u003ctd\u003e15.765458\u003c/td\u003e\n", + " \u003ctd\u003eOutputType.PROCAP\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003c/tr\u003e\n", + " \u003ctr\u003e\n", + " \u003cth\u003e9\u003c/th\u003e\n", + " \u003ctd\u003eENCSR797DEF\u003c/td\u003e\n", + " \u003ctd\u003e-\u003c/td\u003e\n", + " \u003ctd\u003ePRO-cap\u003c/td\u003e\n", + " \u003ctd\u003eEFO:0002819\u003c/td\u003e\n", + " \u003ctd\u003eCalu3\u003c/td\u003e\n", + " \u003ctd\u003ecell_line\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eencode\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eFalse\u003c/td\u003e\n", + " \u003ctd\u003e12.281321\u003c/td\u003e\n", + " \u003ctd\u003eOutputType.PROCAP\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003c/tr\u003e\n", + " \u003ctr\u003e\n", + " \u003cth\u003e10\u003c/th\u003e\n", + " \u003ctd\u003eENCSR801ECP\u003c/td\u003e\n", + " \u003ctd\u003e-\u003c/td\u003e\n", + " \u003ctd\u003ePRO-cap\u003c/td\u003e\n", + " \u003ctd\u003eCL:0002618\u003c/td\u003e\n", + " \u003ctd\u003eendothelial cell of umbilical vein\u003c/td\u003e\n", + " \u003ctd\u003eprimary_cell\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eencode\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eFalse\u003c/td\u003e\n", + " \u003ctd\u003e13.973692\u003c/td\u003e\n", + " \u003ctd\u003eOutputType.PROCAP\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003c/tr\u003e\n", + " \u003ctr\u003e\n", + " \u003cth\u003e11\u003c/th\u003e\n", + " \u003ctd\u003eENCSR860TYZ\u003c/td\u003e\n", + " \u003ctd\u003e-\u003c/td\u003e\n", + " \u003ctd\u003ePRO-cap\u003c/td\u003e\n", + " \u003ctd\u003eEFO:0001200\u003c/td\u003e\n", + " \u003ctd\u003eMCF 10A\u003c/td\u003e\n", + " \u003ctd\u003ecell_line\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eencode\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eFalse\u003c/td\u003e\n", + " \u003ctd\u003e8.229502\u003c/td\u003e\n", + " \u003ctd\u003eOutputType.PROCAP\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003ctd\u003eNaN\u003c/td\u003e\n", + " \u003c/tr\u003e\n", + " \u003c/tbody\u003e\n", + "\u003c/table\u003e\n", + "\u003cp\u003e5563 rows × 15 columns\u003c/p\u003e\n", + "\u003c/div\u003e\n", + " \u003cdiv class=\"colab-df-buttons\"\u003e\n", + "\n", + " \u003cdiv class=\"colab-df-container\"\u003e\n", + " \u003cbutton class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9861ec9a-bcc9-46bf-bc3e-b9133cf576cb')\"\n", + " title=\"Convert this dataframe to an interactive table.\"\n", + " style=\"display:none;\"\u003e\n", + "\n", + " \u003csvg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\"\u003e\n", + " \u003cpath d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/\u003e\n", + " \u003c/svg\u003e\n", + " \u003c/button\u003e\n", + "\n", + " \u003cstyle\u003e\n", + " .colab-df-container {\n", + " display:flex;\n", + " gap: 12px;\n", + " }\n", + "\n", + " .colab-df-convert {\n", + " background-color: #E8F0FE;\n", + " border: none;\n", + " border-radius: 50%;\n", + " cursor: pointer;\n", + " display: none;\n", + " fill: #1967D2;\n", + " height: 32px;\n", + " padding: 0 0 0 0;\n", + " width: 32px;\n", + " }\n", + "\n", + " .colab-df-convert:hover {\n", + " background-color: #E2EBFA;\n", + " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", + " fill: #174EA6;\n", + " }\n", + "\n", + " .colab-df-buttons div {\n", + " margin-bottom: 4px;\n", + " }\n", + "\n", + " [theme=dark] .colab-df-convert {\n", + " background-color: #3B4455;\n", + " fill: #D2E3FC;\n", + " }\n", + "\n", + " [theme=dark] .colab-df-convert:hover {\n", + " background-color: #434B5C;\n", + " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", + " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", + " fill: #FFFFFF;\n", + " }\n", + " \u003c/style\u003e\n", + "\n", + " \u003cscript\u003e\n", + " const buttonEl =\n", + " document.querySelector('#df-9861ec9a-bcc9-46bf-bc3e-b9133cf576cb button.colab-df-convert');\n", + " buttonEl.style.display =\n", + " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", + "\n", + " async function convertToInteractive(key) {\n", + " const element = document.querySelector('#df-9861ec9a-bcc9-46bf-bc3e-b9133cf576cb');\n", + " const dataTable =\n", + " await google.colab.kernel.invokeFunction('convertToInteractive',\n", + " [key], {});\n", + " if (!dataTable) return;\n", + "\n", + " const docLinkHtml = 'Like what you see? Visit the ' +\n", + " '\u003ca target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb\u003edata table notebook\u003c/a\u003e'\n", + " + ' to learn more about interactive tables.';\n", + " element.innerHTML = '';\n", + " dataTable['output_type'] = 'display_data';\n", + " await google.colab.output.renderOutput(dataTable, element);\n", + " const docLink = document.createElement('div');\n", + " docLink.innerHTML = docLinkHtml;\n", + " element.appendChild(docLink);\n", + " }\n", + " \u003c/script\u003e\n", + " \u003c/div\u003e\n", + "\n", + "\n", + " \u003cdiv id=\"df-681b5ae8-b296-4a47-8995-615342637234\"\u003e\n", + " \u003cbutton class=\"colab-df-quickchart\" onclick=\"quickchart('df-681b5ae8-b296-4a47-8995-615342637234')\"\n", + " title=\"Suggest charts\"\n", + " style=\"display:none;\"\u003e\n", + "\n", + "\u003csvg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", + " width=\"24px\"\u003e\n", + " \u003cg\u003e\n", + " \u003cpath d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/\u003e\n", + " \u003c/g\u003e\n", + "\u003c/svg\u003e\n", + " \u003c/button\u003e\n", + "\n", + "\u003cstyle\u003e\n", + " .colab-df-quickchart {\n", + " --bg-color: #E8F0FE;\n", + " --fill-color: #1967D2;\n", + " --hover-bg-color: #E2EBFA;\n", + " --hover-fill-color: #174EA6;\n", + " --disabled-fill-color: #AAA;\n", + " --disabled-bg-color: #DDD;\n", + " }\n", + "\n", + " [theme=dark] .colab-df-quickchart {\n", + " --bg-color: #3B4455;\n", + " --fill-color: #D2E3FC;\n", + " --hover-bg-color: #434B5C;\n", + " --hover-fill-color: #FFFFFF;\n", + " --disabled-bg-color: #3B4455;\n", + " --disabled-fill-color: #666;\n", + " }\n", + "\n", + " .colab-df-quickchart {\n", + " background-color: var(--bg-color);\n", + " border: none;\n", + " border-radius: 50%;\n", + " cursor: pointer;\n", + " display: none;\n", + " fill: var(--fill-color);\n", + " height: 32px;\n", + " padding: 0;\n", + " width: 32px;\n", + " }\n", + "\n", + " .colab-df-quickchart:hover {\n", + " background-color: var(--hover-bg-color);\n", + " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n", + " fill: var(--button-hover-fill-color);\n", + " }\n", + "\n", + " .colab-df-quickchart-complete:disabled,\n", + " .colab-df-quickchart-complete:disabled:hover {\n", + " background-color: var(--disabled-bg-color);\n", + " fill: var(--disabled-fill-color);\n", + " box-shadow: none;\n", + " }\n", + "\n", + " .colab-df-spinner {\n", + " border: 2px solid var(--fill-color);\n", + " border-color: transparent;\n", + " border-bottom-color: var(--fill-color);\n", + " animation:\n", + " spin 1s steps(1) infinite;\n", + " }\n", + "\n", + " @keyframes spin {\n", + " 0% {\n", + " border-color: transparent;\n", + " border-bottom-color: var(--fill-color);\n", + " border-left-color: var(--fill-color);\n", + " }\n", + " 20% {\n", + " border-color: transparent;\n", + " border-left-color: var(--fill-color);\n", + " border-top-color: var(--fill-color);\n", + " }\n", + " 30% {\n", + " border-color: transparent;\n", + " border-left-color: var(--fill-color);\n", + " border-top-color: var(--fill-color);\n", + " border-right-color: var(--fill-color);\n", + " }\n", + " 40% {\n", + " border-color: transparent;\n", + " border-right-color: var(--fill-color);\n", + " border-top-color: var(--fill-color);\n", + " }\n", + " 60% {\n", + " border-color: transparent;\n", + " border-right-color: var(--fill-color);\n", + " }\n", + " 80% {\n", + " border-color: transparent;\n", + " border-right-color: var(--fill-color);\n", + " border-bottom-color: var(--fill-color);\n", + " }\n", + " 90% {\n", + " border-color: transparent;\n", + " border-bottom-color: var(--fill-color);\n", + " }\n", + " }\n", + "\u003c/style\u003e\n", + "\n", + " \u003cscript\u003e\n", + " async function quickchart(key) {\n", + " const quickchartButtonEl =\n", + " document.querySelector('#' + key + ' button');\n", + " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n", + " quickchartButtonEl.classList.add('colab-df-spinner');\n", + " try {\n", + " const charts = await google.colab.kernel.invokeFunction(\n", + " 'suggestCharts', [key], {});\n", + " } catch (error) {\n", + " console.error('Error during call to suggestCharts:', error);\n", + " }\n", + " quickchartButtonEl.classList.remove('colab-df-spinner');\n", + " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n", + " }\n", + " (() =\u003e {\n", + " let quickchartButtonEl =\n", + " document.querySelector('#df-681b5ae8-b296-4a47-8995-615342637234 button');\n", + " quickchartButtonEl.style.display =\n", + " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", + " })();\n", + " \u003c/script\u003e\n", + " \u003c/div\u003e\n", + " \u003c/div\u003e\n", + " \u003c/div\u003e\n" + ], + "text/plain": [ + " name strand ... histone_mark transcription_factor\n", + "0 CL:0000084 ATAC-seq . ... NaN NaN\n", + "1 CL:0000100 ATAC-seq . ... NaN NaN\n", + "2 CL:0000236 ATAC-seq . ... NaN NaN\n", + "3 CL:0000623 ATAC-seq . ... NaN NaN\n", + "4 CL:0000624 ATAC-seq . ... NaN NaN\n", + ".. ... ... ... ... ...\n", + "7 ENCSR182QNJ - ... NaN NaN\n", + "8 ENCSR740IPL - ... NaN NaN\n", + "9 ENCSR797DEF - ... NaN NaN\n", + "10 ENCSR801ECP - ... NaN NaN\n", + "11 ENCSR860TYZ - ... NaN NaN\n", + "\n", + "[5563 rows x 15 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output_metadata" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qPqA56MVKPmT" + }, + "source": [ + "### How many tracks are there per output type?" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "height": 359 + }, + "executionInfo": { + "elapsed": 640, + "status": "ok", + "timestamp": 1753113516048, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "iKiat1vXjMKp", + "outputId": "f585168a-70d5-4569-a336-6990b6d10abd" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \"pd\",\n \"rows\": 11,\n \"fields\": [\n {\n \"column\": \"output_type\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 11,\n \"samples\": [\n \"OutputType.CHIP_TF\",\n \"OutputType.ATAC\",\n \"OutputType.CONTACT_MAPS\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"# Human tracks\",\n \"properties\": {\n \"dtype\": \"Int64\",\n \"num_unique_values\": 11,\n \"samples\": [\n 1617,\n 167,\n 28\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"# Mouse tracks\",\n \"properties\": {\n \"dtype\": \"Int64\",\n \"num_unique_values\": 10,\n \"samples\": [\n 90,\n 188,\n 127\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", + "type": "dataframe" + }, + "application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/9e65f7085e7ffcb7/data_table.js\";\n\n const table = window.createDataTable({\n data: [[\"OutputType.ATAC\",\n{\n 'v': 167,\n 'f': \"167\",\n },\n18],\n [\"OutputType.CAGE\",\n{\n 'v': 546,\n 'f': \"546\",\n },\n188],\n [\"OutputType.DNASE\",\n{\n 'v': 305,\n 'f': \"305\",\n },\n67],\n [\"OutputType.RNA_SEQ\",\n{\n 'v': 667,\n 'f': \"667\",\n },\n173],\n [\"OutputType.CHIP_HISTONE\",\n{\n 'v': 1116,\n 'f': \"1116\",\n },\n183],\n [\"OutputType.CHIP_TF\",\n{\n 'v': 1617,\n 'f': \"1617\",\n },\n127],\n [\"OutputType.SPLICE_SITES\",\n{\n 'v': 4,\n 'f': \"4\",\n },\n4],\n [\"OutputType.SPLICE_SITE_USAGE\",\n{\n 'v': 734,\n 'f': \"734\",\n },\n180],\n [\"OutputType.SPLICE_JUNCTIONS\",\n{\n 'v': 367,\n 'f': \"367\",\n },\n90],\n [\"OutputType.CONTACT_MAPS\",\n{\n 'v': 28,\n 'f': \"28\",\n },\n8],\n [\"OutputType.PROCAP\",\n{\n 'v': 12,\n 'f': \"12\",\n },\n\"\u003cNA\u003e\"]],\n columns: [[\"string\", \"output_type\"], [\"number\", \"# Human tracks\"], [\"string\", \"# Mouse tracks\"]],\n columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n rowsPerPage: 25,\n helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n suppressOutputScrolling: true,\n minimumWidth: undefined,\n });\n\n function appendQuickchartButton(parentElement) {\n 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rgba(0, 0, 0, 0.15);\n", + " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", + " fill: #FFFFFF;\n", + " }\n", + " \u003c/style\u003e\n", + "\n", + " \u003cscript\u003e\n", + " const buttonEl =\n", + " document.querySelector('#df-f658cda3-fa77-4f46-990d-245cc6d83f66 button.colab-df-convert');\n", + " buttonEl.style.display =\n", + " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", + "\n", + " async function convertToInteractive(key) {\n", + " const element = document.querySelector('#df-f658cda3-fa77-4f46-990d-245cc6d83f66');\n", + " const dataTable =\n", + " await google.colab.kernel.invokeFunction('convertToInteractive',\n", + " [key], {});\n", + " if (!dataTable) return;\n", + "\n", + " const docLinkHtml = 'Like what you see? Visit the ' +\n", + " '\u003ca target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb\u003edata table notebook\u003c/a\u003e'\n", + " + ' to learn more about interactive tables.';\n", + " element.innerHTML = '';\n", + " dataTable['output_type'] = 'display_data';\n", + " await google.colab.output.renderOutput(dataTable, element);\n", + " const docLink = document.createElement('div');\n", + " docLink.innerHTML = docLinkHtml;\n", + " element.appendChild(docLink);\n", + " }\n", + " \u003c/script\u003e\n", + " \u003c/div\u003e\n", + "\n", + "\n", + " \u003cdiv id=\"df-26020fd0-f84f-459f-8114-fbd0d297f104\"\u003e\n", + " \u003cbutton class=\"colab-df-quickchart\" onclick=\"quickchart('df-26020fd0-f84f-459f-8114-fbd0d297f104')\"\n", + " title=\"Suggest charts\"\n", + " style=\"display:none;\"\u003e\n", + "\n", + "\u003csvg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", + " width=\"24px\"\u003e\n", + " \u003cg\u003e\n", + " \u003cpath d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/\u003e\n", + " \u003c/g\u003e\n", + "\u003c/svg\u003e\n", + " \u003c/button\u003e\n", + "\n", + "\u003cstyle\u003e\n", + " .colab-df-quickchart {\n", + " --bg-color: #E8F0FE;\n", + " --fill-color: #1967D2;\n", + " --hover-bg-color: #E2EBFA;\n", + " --hover-fill-color: #174EA6;\n", + " --disabled-fill-color: #AAA;\n", + " --disabled-bg-color: #DDD;\n", + " }\n", + "\n", + " [theme=dark] .colab-df-quickchart {\n", + " --bg-color: #3B4455;\n", + " --fill-color: #D2E3FC;\n", + " --hover-bg-color: #434B5C;\n", + " --hover-fill-color: #FFFFFF;\n", + " --disabled-bg-color: #3B4455;\n", + " --disabled-fill-color: #666;\n", + " }\n", + "\n", + " .colab-df-quickchart {\n", + " background-color: var(--bg-color);\n", + " border: none;\n", + " border-radius: 50%;\n", + " cursor: pointer;\n", + " display: none;\n", + " fill: var(--fill-color);\n", + " height: 32px;\n", + " padding: 0;\n", + " width: 32px;\n", + " }\n", + "\n", + " .colab-df-quickchart:hover {\n", + " background-color: var(--hover-bg-color);\n", + " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n", + " fill: var(--button-hover-fill-color);\n", + " }\n", + "\n", + " .colab-df-quickchart-complete:disabled,\n", + " .colab-df-quickchart-complete:disabled:hover {\n", + " background-color: var(--disabled-bg-color);\n", + " fill: var(--disabled-fill-color);\n", + " box-shadow: none;\n", + " }\n", + "\n", + " .colab-df-spinner {\n", + " border: 2px solid var(--fill-color);\n", + " border-color: transparent;\n", + " border-bottom-color: var(--fill-color);\n", + " animation:\n", + " spin 1s steps(1) infinite;\n", + " }\n", + "\n", + " @keyframes spin {\n", + " 0% {\n", + " border-color: transparent;\n", + " border-bottom-color: var(--fill-color);\n", + " border-left-color: var(--fill-color);\n", + " }\n", + " 20% {\n", + " border-color: transparent;\n", + " border-left-color: var(--fill-color);\n", + " border-top-color: var(--fill-color);\n", + " }\n", + " 30% {\n", + " border-color: transparent;\n", + " border-left-color: var(--fill-color);\n", + " border-top-color: var(--fill-color);\n", + " border-right-color: var(--fill-color);\n", + " }\n", + " 40% {\n", + " border-color: transparent;\n", + " border-right-color: var(--fill-color);\n", + " border-top-color: var(--fill-color);\n", + " }\n", + " 60% {\n", + " border-color: transparent;\n", + " border-right-color: var(--fill-color);\n", + " }\n", + " 80% {\n", + " border-color: transparent;\n", + " border-right-color: var(--fill-color);\n", + " border-bottom-color: var(--fill-color);\n", + " }\n", + " 90% {\n", + " border-color: transparent;\n", + " border-bottom-color: var(--fill-color);\n", + " }\n", + " }\n", + "\u003c/style\u003e\n", + "\n", + " \u003cscript\u003e\n", + " async function quickchart(key) {\n", + " const quickchartButtonEl =\n", + " document.querySelector('#' + key + ' button');\n", + " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n", + " quickchartButtonEl.classList.add('colab-df-spinner');\n", + " try {\n", + " const charts = await google.colab.kernel.invokeFunction(\n", + " 'suggestCharts', [key], {});\n", + " } catch (error) {\n", + " console.error('Error during call to suggestCharts:', error);\n", + " }\n", + " quickchartButtonEl.classList.remove('colab-df-spinner');\n", + " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n", + " }\n", + " (() =\u003e {\n", + " let quickchartButtonEl =\n", + " document.querySelector('#df-26020fd0-f84f-459f-8114-fbd0d297f104 button');\n", + " quickchartButtonEl.style.display =\n", + " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", + " })();\n", + " \u003c/script\u003e\n", + " \u003c/div\u003e\n", + " \u003c/div\u003e\n", + " \u003c/div\u003e\n" + ], + "text/plain": [ + " # Human tracks # Mouse tracks\n", + "output_type \n", + "OutputType.ATAC 167 18\n", + "OutputType.CAGE 546 188\n", + "OutputType.DNASE 305 67\n", + "OutputType.RNA_SEQ 667 173\n", + "OutputType.CHIP_HISTONE 1116 183\n", + "OutputType.CHIP_TF 1617 127\n", + "OutputType.SPLICE_SITES 4 4\n", + "OutputType.SPLICE_SITE_USAGE 734 180\n", + "OutputType.SPLICE_JUNCTIONS 367 90\n", + "OutputType.CONTACT_MAPS 28 8\n", + "OutputType.PROCAP 12 \u003cNA\u003e" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Count human tracks\n", + "human_tracks = (\n", + " dna_model.output_metadata(dna_client.Organism.HOMO_SAPIENS)\n", + " .concatenate()\n", + " .groupby('output_type')\n", + " .size()\n", + " .rename('# Human tracks')\n", + ")\n", + "\n", + "# Count mouse tracks\n", + "mouse_tracks = (\n", + " dna_model.output_metadata(dna_client.Organism.MUS_MUSCULUS)\n", + " .concatenate()\n", + " .groupby('output_type')\n", + " .size()\n", + " .rename('# Mouse tracks')\n", + ")\n", + "\n", + "pd.concat([human_tracks, mouse_tracks], axis=1).astype(pd.Int64Dtype())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dEO3Gho3i9Ur" + }, + "source": [ + "Note that `PROCAP` outputs are not available for mouse." + ] + } + ], + "metadata": { + "colab": { + "last_runtime": {}, + "provenance": [ + { + "file_id": "10gappDzgyC_Y2CGJQXyZNccFKV-o-oIv", + "timestamp": 1729172092420 + }, + { + "file_id": "1UcU771Brz691HgrtQBrA41Qrq55_GgJS", + "timestamp": 1728927969951 + } + ] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/alphagenome/source/colabs/visualization_modality_tour.ipynb b/alphagenome/source/colabs/visualization_modality_tour.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..8a3b9f240a9d5e810289f52932a19590879f7971 --- /dev/null +++ b/alphagenome/source/colabs/visualization_modality_tour.ipynb @@ -0,0 +1,3187 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "y4k5PhBwckLM" + }, + "source": [ + "# Visualizing predictions\n", + "\n", + "AlphaGenome can make predictions for various different modalities. We built a visualization library to aid interpretation of the outputs from the model. In this tutorial notebook you will:\n", + "* Learn how to visualize different data modalities for a specific genomic interval (and any variants) from model output.\n", + "* Understand how to use the primary functionality of the visualization library `alphagenome.visualization`.\n", + "\n", + "We also recommend visiting [\"Visualization Basics\"](https://www.alphagenomedocs.com/visualization_library_basics.html) for an overview of the library and its primary functionality." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HWAeLvV6Nio8" + }, + "source": [ + "```{tip}\n", + "Open this tutorial in Google colab for interactive viewing.\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "id": "e1dQWw1HJap7" + }, + "outputs": [], + "source": [ + "# @title Install AlphaGenome\n", + "\n", + "# @markdown Run this cell to install AlphaGenome.\n", + "from IPython.display import clear_output\n", + "! pip install alphagenome\n", + "clear_output()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PhxWI_U1KbdK" + }, + "source": [ + "## Setup and imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "QX39iOcbKbdK" + }, + "outputs": [], + "source": [ + "from alphagenome import colab_utils\n", + "from alphagenome.data import gene_annotation, genome, track_data, transcript\n", + "from alphagenome.models import dna_client\n", + "from alphagenome.visualization import plot_components\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WuAGnIXs5-ER" + }, + "source": [ + "### Load model and auxiliary objects" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "KUa_l1ceDmmz" + }, + "outputs": [], + "source": [ + "dna_model = dna_client.create(colab_utils.get_api_key())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Rg_upvGia3-j" + }, + "outputs": [], + "source": [ + "# Load metadata objects for human.\n", + "output_metadata = dna_model.output_metadata(\n", + " organism=dna_client.Organism.HOMO_SAPIENS\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "XxkkkStMuJn5" + }, + "outputs": [], + "source": [ + "# Load gene annotations (from GENCODE).\n", + "gtf = pd.read_feather(\n", + " 'https://storage.googleapis.com/alphagenome/reference/gencode/'\n", + " 'hg38/gencode.v46.annotation.gtf.gz.feather'\n", + ")\n", + "\n", + "# Filter to protein-coding genes and highly supported transcripts.\n", + "gtf_transcript = gene_annotation.filter_transcript_support_level(\n", + " gene_annotation.filter_protein_coding(gtf), ['1']\n", + ")\n", + "\n", + "# Extractor for identifying transcripts in a region.\n", + "transcript_extractor = transcript.TranscriptExtractor(gtf_transcript)\n", + "\n", + "# Also define an extractor that fetches only the longest transcript per gene.\n", + "gtf_longest_transcript = gene_annotation.filter_to_longest_transcript(\n", + " gtf_transcript\n", + ")\n", + "longest_transcript_extractor = transcript.TranscriptExtractor(\n", + " gtf_longest_transcript\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "O5jsAYA8ak3T" + }, + "source": [ + "## Gene expression" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E41aNDzmd02o" + }, + "source": [ + "Gene expression is primarily captured by the model outputs `RNA_SEQ` and `CAGE`.\n", + "Here is an example of expression predictions for colon tissue in a genomic\n", + "interval containing the gene *APOL4* . Positions of the longest transcript per\n", + "gene for genes in this interval are shown at the top:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "8DF1fR_DtLQ2" + }, + "outputs": [], + "source": [ + "# Define interval to make predictions for (used throughout this tutorial).\n", + "# Note that the interval width must be one of the supported sequence lengths.\n", + "interval = genome.Interval('chr22', 36_150_498, 36_252_898).resize(\n", + " dna_client.SEQUENCE_LENGTH_1MB\n", + ")\n", + "\n", + "# Define the tissues/cell-types to predict expression for.\n", + "ontology_terms = [\n", + " 'UBERON:0001159', # Colon - Sigmoid.\n", + " 'UBERON:0001155', # Colon - Transverse.\n", + "]\n", + "\n", + "# Make predictions.\n", + "output = dna_model.predict_interval(\n", + " interval=interval,\n", + " requested_outputs={\n", + " dna_client.OutputType.RNA_SEQ,\n", + " dna_client.OutputType.CAGE,\n", + " },\n", + " ontology_terms=ontology_terms,\n", + ")\n", + "\n", + "# Extract the longest transcripts per gene for this interval.\n", + "longest_transcripts = longest_transcript_extractor.extract(interval)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7Ns0fN4eZ2Zb" + }, + "source": [ + "Build the plot by constructing multiple plotting panels or 'components' and\n", + "feeding these into the `plot_components.plot()` function. Refer to\n", + "[visualization basics guide](https://www.alphagenomedocs.com/visualization_library_basics.html#visualization-basics)\n", + "for a description of available plotting components." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "height": 511 + }, + "executionInfo": { + "elapsed": 3176, + "status": "ok", + "timestamp": 1753109990410, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "Gqe0PA-2DLn2", + "outputId": "0a313e0b-b3e6-4851-a418-c433fa12b591" + }, + "outputs": [ + { + "data": { + "image/png": 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LvGuJWbNmaf369dpxxx2LTNrq3r27JOnDDz9UTk5Ovi89PfDAA5oyZUre+ahly5Z68MEHU473sccekxRc48S7Fkh0jurQoUPKnz3UqlVLBxxwgKZPn66PP/64yC8DfvTRR/rmm2/UtGnTIpOBkV/ks6pu3bql3DeV6+DIerp06RL3y3nJGDJkiMaPH68BAwbotNNOU40aNeK2nTFjhiSpc+fOJVpnQQsWLNC7776rGjVqqFOnTqU27pZywQUXaNSoUerfv3+hLwEiNQsWLFDHjh31559/avLkySl/Hottw7p169S1a1fNnDlTDzzwgC6//PJ0h4QthE/WAAAAUCFF/om3ceNG/fDDD3rllVfk7rrnnnu0ww47xOyzcuVKDR48uND8Dh066LTTToubuPb555/r888/LzS/ZcuWeYnUv/zyiySpWbNmRcYeafPzzz8XWnbMMceoc+fOmjp1qpo3b57wQ7pYZs6cqcGDB6tWrVq644478i1L5dvwkcpZ2PasWbNGnTp10uzZs/XII49QhTqNli5dqo4dO+rnn3/WhAkTqEKNQtasWaPOnTvrs88+43hF2n377bc6/PDDtXHjRn344YdUod5KzIzrLmyT3F0jR45URkaGzjnnnLz5WVlZ+vTTTzVy5EjdddddhfpFJ2SsXLlS2dnZmj17tqpXr64hQ4ZI2vzeqkGDBqpevXrCOKpXr64GDRpo2bJl+vnnn0ucSB39frFWrVoaM2ZMoS/cpvK+qrSO8VmzZqlbt26qXLmyvvjii1KtQh29Pbwelc99O9q8efN0+eWXKzMzU/fee2++ZemoClzW9rlIUsro0aN16623asGCBXrnnXd00UUXJUzqkYLq3k899ZT23XdfjRw5UrVq1dKTTz6ZMMkn3v+bEjn22GM1YsQIDRo0SM8++2ze+H/88Yfuu+8+SdL69eu1du3aImPGtuO///2v+vTpo+23314//fRTqX65ojzIzc3VOeeco2eeeUb/+Mc/ylwV6rL4eofSFeszh86dO+cloxbns4MNGzbojz/+UKNGjfKW1apVS7feemtecZZHHnlEtWrVSjje/Pnz865R1qxZo1mzZmnKlCmqXbt2XkJ1LInOUVdeeWWxirg0bdpUkvTbb78lbPfnn3/mXYPfd999+c6l8a5XOMY2i+xvqZ4rUr0OLu56CmrWrJmuuuoq3XXXXbrvvvt0yy23xG0b+WWOyL5UUKzk/6uuuirhlwfXr1+vs846S+vXr9fdd9+tevXq5VteFqqMR/z666869NBDtXDhQr3yyitUoS6hJ554Quedd5723HNPffvtt1ShLqdmzpypI488UpUrV9aXX35Z7qtQV/T/8cT+3S8AAACgnBs8eLAGDx6soUOH6oUXXtCmTZs0atSohN8ibdGihdw9b1qxYoWmTJmitWvXqlevXnErxfbr1y9fv8gU+UeoFPsne+NJ1Pa9997TtGnTJEmLFy/Whx9+WOR4Ed9//7169+6tjRs36qmnntIuu+ySdF9s+3799VcNHz5cjRs31qJFi/Ttt9+SlJkmP/74o+6++261aNFClStX1uLFi0miRj7Rx+vChQs5XpFWX3/9tW688Ua1bdtWu+22m5YtW0YSNYAivffee/rxxx/VvXv3fB9in3nmmapSpYrGjh2rjRs3Fur3+eef572Xe/jhh7V8+XL17dtXs2bNUocOHSSl9t6qOO3jufjii+XuWrt2rb755hude+65Ouecc3TxxReXaNyS+OKLL3TllVfq4IMP1sEHH6ylS5eWahI1CiuP+3bEr7/+qp49e+q3337TsGHD1LFjx1IZtzw5+OCD1bZtW40ePVq5ubkaOXKkcnNzdcEFFyTVv1WrVrrkkkskSf/4xz/UqlWrhO3j/b/J3TVlypSYfW677Ta1aNFC48ePV7t27XTVVVfpwgsv1F577aWMjIy85OnSrNKI9Jk1a5b69eunnj176oQTTtD8+fNJoo6yadMmPfXUU9p555310ksv6Y033ihzSdSoGGK9jmdnZ+dbLqV+Ti/Yfu3atfrXv/6Vd3/8+PFFjrFgwYK8a5R///vfmjJliurVq6epU6cmvBZIdI564IEHUtqOiGQeh9WrV+u4447T3Llzdf311+vUU08t1roqsuLub6leB5fmteqNN96o7bffXnfffbeWLVsWt11R64zs69HTihUr4o6Xk5Ojvn37asaMGTrttNN03XXXlWg7tpR58+bp3nvvVfPmzZWbm6tFixaRRF0C77//vo455hhlZWXpiiuu0BdffEESdTn0xRdf6IorrlCHDh10yCGHaOnSpeU+iRpUpAYAAEAFFfmHyerVq/XBBx/ovPPO08UXX6wWLVok/dNLderUUZcuXfTiiy9q11131Q033KC+ffsWWRkqlkgV7IULFxbZdvHixfn6RKxYsUJZWVnKzMzUgw8+qMsuu0xZWVn64osviqwsMXfuXHXt2lXLly/Xc889p+OOOy7lbYhWlr5lH1GSmMri9mxJq1at0m677ZbuMKDgCw7bb799usNAGcbxirLkgw8+KNZ1EICKJ/Il1Ogvl0pBpd3evXvrpZde0muvvaaTTz453/J+/fpp7NixCceOvE/6/ffftXbt2oSvS2vXrtXy5cvz9SupatWqac8999SwYcO0fv16PfbYYzryyCMLbUtEUe81Suu9yDvvvKPKlSuXyliIr7zu27/++quOOOIIfffddxo2bFhesm9RKtp7aSmoSn3FFVdo0qRJGjNmjA444ADtt99+SfePPK9b6pqqSZMmmjlzpu644w5NnDhRw4cPV7169XTsscdqwIABatWqlerUqaMqVaoU6hv9fCb73CZqF29ZcfabdFRETzWGdB8Pzz77rJ599tmU+pQ05nRvc6p69eqV7hCAmFL57GDRokWSpCpVqqh+/fr5ll1//fWaM2eOrrzySk2dOlWjR49Wnz591Lt377jjde7cOS+pe/ny5XrppZd02WWXqXfv3po5c6aaNGlSzK1KXeQXOuP9r3T16tU65phjNH36dF1zzTX5ksaLUpzX8C31GhcZN12VQHfccUfNmTMn73OoZKV6HbzjjjtKUsrriaV27doaOHCgLrvsMg0aNEiPPPJIzHaRY2nJkiUxl0c/5ocddphmzJgRd505OTk6++yzNX78eJ166ql66qmnSuUaZkufO3/66Sc1btx4i66jIrnvvvvyftUF5dd///tf/p9SQVCRGgAAABXadtttpyOPPFITJ05UTk6O+vXrpzVr1qQ0RuvWrVW/fn2tWLFC33//fbHiOOywwyRJkydPLrJtpM2hhx6ab/4ll1yiRYsWafDgwbr44ot1ww03aP78+br66qsTjvftt9+qc+fO+v333zV+/HiddNJJxdqGaPGqTaRrKklMZXF7ttS0evVqHXzwwapUqZIeeuihtMdTkadly5apdevWqlKlil5++eW0x8NU9qbVq1erQ4cOysjI0IMPPpj2eJgq9jRnzhxtv/32qlmzpv73v/+lPZ6KNAHbot9++02vvvqqJOmMM86QmeWbXnrpJUmK+4s/RWnevLmaNWumTZs25avkF0t2drY2bdqk5s2bb5EKnT179sxbTzxFHeMlfZ345JNPVLduXdWrV0+ff/45r0NbUHndt3/55Rd16dJF33zzjR5++GFdccUVSfetiOfAyBfsL7roIi1ZskQXXnhhukMqZPvtt9ewYcP0008/acOGDVq2bJlGjRqlefPmyd3j/rpIKq9NBfsk+/qWzNhb4rWypOsrKoatHWP0NHnyZFWvXl077rij5s+fv9Ue13RucypTTk6OsrKyZGa64IIL0h5PvMcSFduBBx6oqlWr6ueff9a3336bsG3ks4MOHTrk+3WBt99+Ww8//LDatm2rf/3rX3ryySdVtWpVXXDBBfr999+TiqN+/fq64IILdN9992nx4sVJf7GqNPz999/65JNPJAW/ABFrec+ePTV16lRdf/31uvfee1MaP9XXs6L6lMZrZ7pEPqt69913k+5TnOvgyHqys7OVk5NT4rgvuugi7bbbbhoxYkTc4yTymVp2drZyc3OLva5NmzbpjDPO0HPPPaczzzxTzzzzjDIzi1fHdGudO3/77Tftvvvuqly5ssaPH5/2c9u2Pj311FPKzMzUnnvuqT/++CPt8TBtmemTTz5RnTp1VK9ePX355Zdpj2drTBUdidQAAACApH322UcXXHCBFi9erPvvvz+lvps2bdLff/8tScX+58vJJ5+sunXr6uOPP9Y777wTt90777yjjz/+WPXq1ctXwer555/Xs88+q0MPPVQ33HCDJGnQoEFq166dRo0apddffz3meF9++aW6dOmSV02Cn/Oq2GrUqKEPP/xQAwYM0JVXXqmjjjpKmzZtSndYFVKjRo00d+5c9e3bVyeddJL69+9fon/uovypUaOGPvjgAw0cOFBXXXUVxyvSavfdd9fPP/+sTp066dBDD9Wtt96a7pAAlGHjxo3Thg0bdMABB+i8886LOW2//faaPHmy5s2bV6x1nH/++ZKkoUOHxv0gKDc3V0OHDpWkLZboGKl0VtwP1UvD/vvvr2XLlqlt27bab7/9Uk4qQfLK4769ePFide7cWXPmzNGjjz66VROmtlV169bVySefrMWLF2u77bbTGWecke6QkjZixAhJ0llnnZXmSFCaunXrpqVLl6pBgwZq3bq1nnjiiXSHVKZkZGRozJgxGj9+vMaNG6fddtst6aRSYGupXr26zjzzTEnSHXfcEbfd2rVr8yqjRl8DLF++XOeee64qV66sp556SlWrVlWbNm10++23a9myZbr44otTiufiiy/W3nvvrVdeeSVhxd7S9O9//1tr167Vfvvtpz333DPfspUrV6pHjx56//33dfPNN6dUiRqFRfaVl156Sd98803CtuvXr5dUvOvgzp07a4899tDixYs1ZsyYhOvJzc3Vxo0bE7bJzMzUv/71L+Xk5Oj666+P2aZLly5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+ "text/plain": [ + "\u003cFigure size 1440x576 with 8 Axes\u003e" + ] + }, + "metadata": { + "image/png": { + "height": 494, + "width": 1449 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Build plot.\n", + "plot = plot_components.plot(\n", + " [\n", + " plot_components.TranscriptAnnotation(longest_transcripts),\n", + " plot_components.Tracks(\n", + " tdata=output.rna_seq,\n", + " ylabel_template='RNA_SEQ: {biosample_name} ({strand})\\n{name}',\n", + " ),\n", + " plot_components.Tracks(\n", + " tdata=output.cage,\n", + " ylabel_template='CAGE: {biosample_name} ({strand})\\n{name}',\n", + " ),\n", + " ],\n", + " interval=interval,\n", + " title='Predicted RNA Expression (RNA_SEQ, CAGE) for colon tissue',\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AVF7aiwI8eh7" + }, + "source": [ + "Note that the positive and negative stranded tracks have clearly different\n", + "signals. Let's check which genes are transcribed from the positive strand:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "executionInfo": { + "elapsed": 62, + "status": "ok", + "timestamp": 1753109990817, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "M-nGhwYw7J1h", + "outputId": "c6ab4f29-89da-4bf5-ec72-6bc545a7313d" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['APOL5', 'APOL1']" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[t.info['gene_name'] for t in longest_transcripts if t.strand == '+']" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-0rVlCL-8lSF" + }, + "source": [ + "For both of the positive stranded `RNA_SEQ` tracks, we can see that RNA-seq\n", + "levels are highest around *APOL1*. Similarly for `CAGE`, the peaks on the\n", + "positive strand fall around the start of this gene. We do not see peaks around\n", + "*APOL5* as this gene is not expressed in colon tissue\n", + "[(GTEx)](https://www.gtexportal.org/home/gene/APOL5#geneExpression)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lxOBhD5vw4eT" + }, + "source": [ + "### Visualize the effect of a variant\n", + "\n", + "A previously identified variant in this region (`chr22_36201698_A_C`) affects\n", + "both the expression and splicing of the *APOL4* gene. Specifically, the\n", + "alternative allele (C) is linked to reduced *APOL4* expression.\n", + "\n", + "To visualize what AlphaGenome predicts for this variant, we can:\n", + "\n", + "* Compute predictions for the REF and ALT sequences using\n", + " `dna_model.predict_variant`\n", + "* Remove positive-stranded tracks (as *APOL4* is transcribed from the negative\n", + " DNA strand)\n", + "* Zoom in to the region around the gene *APOL4*\n", + "* Highlight the location of the variant using\n", + " `plot_components.VariantAnnotation`\n", + "* Increase the relative height of the transcripts section to better view the\n", + " gene structure as annotated by GENCODE" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "executionInfo": { + "elapsed": 4, + "status": "ok", + "timestamp": 1753109991101, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "ORkUrqj3xAGH", + "outputId": "9fa4484f-df02-49d5-8a3d-5ae68b72daa5" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Variant(chromosome='chr22', position=36201698, reference_bases='A', alternate_bases='C', name='')" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Define the variant of interest.\n", + "# TODO(b/369592335): Upate to chr22_36201698_A_C once we have a better .from_str() method.\n", + "variant_string = 'chr22:36201698:A\u003eC'\n", + "variant = genome.Variant.from_str(variant_string)\n", + "variant" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "id": "gDb89LLPKGoh" + }, + "outputs": [], + "source": [ + "# Make predictions for sequences containing the REF and ALT alleles.\n", + "output = dna_model.predict_variant(\n", + " interval=interval,\n", + " variant=variant,\n", + " requested_outputs={\n", + " dna_client.OutputType.RNA_SEQ,\n", + " dna_client.OutputType.CAGE,\n", + " },\n", + " ontology_terms=ontology_terms,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "x8Vp0rT55NhT" + }, + "outputs": [], + "source": [ + "# Zoom in on the region around APOL4.\n", + "apol4_interval = gene_annotation.get_gene_interval(gtf, gene_symbol='APOL4')\n", + "\n", + "# Add 1KB on either side of the gene body.\n", + "apol4_interval.resize_inplace(apol4_interval.width + 1000)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "height": 403 + }, + "executionInfo": { + "elapsed": 1301, + "status": "ok", + "timestamp": 1753109996307, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "5s7qC5Nvw2yb", + "outputId": "637b7df8-a557-4744-fbd0-e68a954efd2d" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "\u003cFigure size 1440x432 with 6 Axes\u003e" + ] + }, + "metadata": { + "image/png": { + "height": 386, + "width": 1452 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Define the colors for REF and ALT predictions.\n", + "ref_alt_colors = {'REF': 'dimgrey', 'ALT': 'red'}\n", + "\n", + "# Build plot.\n", + "plot = plot_components.plot(\n", + " [\n", + " plot_components.TranscriptAnnotation(longest_transcripts),\n", + " # RNA-seq tracks.\n", + " plot_components.OverlaidTracks(\n", + " tdata={\n", + " 'REF': output.reference.rna_seq.filter_to_nonpositive_strand(),\n", + " 'ALT': output.alternate.rna_seq.filter_to_nonpositive_strand(),\n", + " },\n", + " colors=ref_alt_colors,\n", + " ylabel_template='{biosample_name} ({strand})\\n{name}',\n", + " ),\n", + " # CAGE track.\n", + " plot_components.OverlaidTracks(\n", + " tdata={\n", + " 'REF': output.reference.cage.filter_to_nonpositive_strand(),\n", + " 'ALT': output.alternate.cage.filter_to_nonpositive_strand(),\n", + " },\n", + " colors=ref_alt_colors,\n", + " ylabel_template='{biosample_name} ({strand})\\n{name}',\n", + " ),\n", + " ],\n", + " annotations=[plot_components.VariantAnnotation([variant])],\n", + " interval=apol4_interval,\n", + " title='Effect of variant on predicted RNA Expression in colon tissue',\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6hvjr9-LeKNg" + }, + "source": [ + "You can see here that the predicted RNA-seq values for the alternative allele\n", + "are much lower across the gene (ALT lines are lower than REF lines), which is\n", + "also reflected in the bottom CAGE track. Additionally, you can see where the\n", + "variant might be inducing an exon skipping event (in the exon where the ALT line\n", + "is zero but the REF has a peak)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GEasjDBTbvwB" + }, + "source": [ + "### Plot custom annotation (e.g., polyadenylation sites)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Oa_fgTCJbtFJ" + }, + "outputs": [], + "source": [ + "# Run predictions with an additional ontology to highlight differences across\n", + "# tissues.\n", + "ontology_terms = [\n", + " 'UBERON:0001159', # Colon - Sigmoid.\n", + " 'UBERON:0002048', # lung.\n", + "]\n", + "\n", + "# Make predictions.\n", + "output = dna_model.predict_interval(\n", + " interval=interval,\n", + " requested_outputs={\n", + " dna_client.OutputType.RNA_SEQ,\n", + " },\n", + " ontology_terms=ontology_terms,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "K2fDMPmnLJNb" + }, + "source": [ + "There are 3 annotated pA sites overlapping APOL4 transcripts, as defined by\n", + "[PolyADBv3](https://exon.apps.wistar.org/polya_db/v3/misc/download.php) [(Wang et al. 2018)](https://pubmed.ncbi.nlm.nih.gov/29069441/).\n", + "We can define these locations as 1bp intervals in hg38 coordinates." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Qxv7hhJrcwy6" + }, + "outputs": [], + "source": [ + "# Define pA sites in hg38 coordinates.\n", + "apol4_pAs = [genome.Interval('chr22', 36_189_128, 36_189_129, '-'),\n", + " genome.Interval('chr22', 36_190_089, 36_190_090, '-'),\n", + " genome.Interval('chr22', 36_190_144, 36_190_145, '-')]\n", + "\n", + "# Define plotting interval as first/last pA plus some offset distance.\n", + "\n", + "offset = 200\n", + "pA_interval = genome.Interval('chr22',\n", + " 36_189_128 - offset,\n", + " 36_190_145 + offset,\n", + " '-')\n", + "\n", + "# Define intervals annotation.\n", + "pA_labels = ['pA_3', 'pA_2', 'pA_1']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "height": 403 + }, + "executionInfo": { + "elapsed": 1102, + "status": "ok", + "timestamp": 1753109999160, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "JX1wMyFrdZ4x", + "outputId": "639d66ec-6828-42f5-a0d1-56f5f48fb649" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "\u003cFigure size 1440x432 with 6 Axes\u003e" + ] + }, + "metadata": { + "image/png": { + "height": 386, + "width": 1349 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Build plot.\n", + "# NOTE: Depending on annotation distance and interval zoom some annotations may # appear as overlapping.\n", + "\n", + "plot = plot_components.plot(\n", + " [plot_components.TranscriptAnnotation(longest_transcripts),\n", + " # RNA-seq tracks.\n", + " plot_components.Tracks(\n", + " tdata = output.rna_seq.filter_to_negative_strand(),\n", + " ylabel_template = 'RNA_SEQ: {biosample_name} ({strand})\\n{name}',\n", + " shared_y_scale = True,\n", + " )\n", + " ],\n", + " annotations=[\n", + " plot_components.IntervalAnnotation(apol4_pAs,\n", + " alpha = 1,\n", + " labels = pA_labels,\n", + " label_angle = 90)],\n", + " interval = pA_interval,\n", + " title='APOL4 polyadenylation sites annotation',\n", + "\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "q0lfa_JphWux" + }, + "source": [ + "## Chromatin accessibility" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iMBTQtrvmUQ7" + }, + "source": [ + "Chromatin accessibility is primarily captured by the model outputs `DNASE` and\n", + "`ATAC`. Here is an example of predictions for the same genomic interval\n", + "containing the gene *APOL4*. There are a large variety of tissues and cell-types\n", + "available, but let's focus on plotting predictions for intestinal tract tissues:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "MfwL6sNJCtgZ" + }, + "outputs": [], + "source": [ + "# List of IDs corresponding to various intestinal tissues.\n", + "ontology_terms = [\n", + " 'UBERON:0000317',\n", + " 'UBERON:0001155',\n", + " 'UBERON:0001157',\n", + " 'UBERON:0001159',\n", + " 'UBERON:0004992',\n", + " 'UBERON:0008971',\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "1E196OgliWf1" + }, + "outputs": [], + "source": [ + "# Make predictions.\n", + "output = dna_model.predict_interval(\n", + " interval,\n", + " requested_outputs={\n", + " dna_client.OutputType.DNASE,\n", + " dna_client.OutputType.ATAC,\n", + " },\n", + " ontology_terms=ontology_terms,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nWv31y-IzNro" + }, + "source": [ + "Chromatin accessibility can indicate regions of regulatory activity. Let's use\n", + "`plot_components.IntervalAnnotation()` to plot additional annotation on putative\n", + "promoter regions from GENCODE\n", + "[human](https://www.gencodegenes.org/human/release_46.html) or [mouse](https://www.gencodegenes.org/mouse/release_M23.html)\n", + "to see if they overlap regions predicted to be accessible:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bnIspPV8wkQ9" + }, + "outputs": [], + "source": [ + "# Two putative promoter intervals from Ensembl.\n", + "promoter_intervals = [\n", + " genome.Interval(\n", + " 'chr22', 36_201_799, 36_202_681, name='Ensembl_promoter:ENSR00001367790'\n", + " ),\n", + " genome.Interval(\n", + " 'chr22', 36_204_705, 36_205_330, name='Ensembl_promoter:ENSR00001367792'\n", + " ),\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "height": 729 + }, + "executionInfo": { + "elapsed": 2447, + "status": "ok", + "timestamp": 1753110003682, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "S6mXwvbAimQ-", + "outputId": "7e9b64a1-bd17-4fd8-a0fc-7dd29063aae8" + }, + "outputs": [ + { + "data": { + "image/png": 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Bu7l5BC+AdSFeltmueI/NC8B4M6sUVL4XXibOUsC3eEHQY4FTCMhaC2BmV+Nl670XL7i7I/An8D9gkpnFMjV7ofVrZscA/+IFUf/jl18NDPLr5JuZvQJ8CszACzANlYk2r87zl8silhIREREREREREcmH1JS0bOsuo/Azm7qM8IGsGUXQv4iIiIiIiIgcOEoW9wBkn3SambX278cBNYHr8bJYPuOcWxKm3s3+FO2h9HXOLcitYzO7GC+YMQWYFLT7Qn/5R6Q2nHPbzGwqXvDnBYSYvt05N8rMhgLXmdmNgdlBY1QfKAEsc86lhdi/2F8eG7AtMyPkojBt5rXOsf5taW51nHPrzGw3UMfMyjvnsl267me4vBAoDRwJ3ODvauYHbRYoM6sNXAPswMv+WtD+BtKAQ4B6hHhNAO3wAl1fNLOuuQQyA3wAPBmcwdXMHga6A0/gBTxnBmnfBUwFzglRp0bA/WrAD3iByxc75+YF7DsJmOi3n2ugaRH02wWoATznnOsYUP4mvEDTfDGzN4G3/fFf5ZzbGqFsKeBoINU5tzRg+0nAuuC6ZlYXLzsqwHf5HauIiIiIiIiIiEg4aSnZJ4HKiBAAWlAiZUvNSC/8/kVEohH8XuWcI3SuEBERERERKU4KMpVQGvi3YD8AoyLUu8m/hTIDyBFkGhDMWgovYPMWvAyjL4UI8jvUX66KMIZMmWUOi1CmBV4mxw/N7LcwQaKBTgixrYq/3BGmTub2qvtonQp+ueD5ka4AXglYXw80cc79Hqat/GqGF6z7XXDAa0FwziWb2Ra8gOmDCR14nGBmrfAykr7jjylSmyvC7OoJfAI0xg8yxcuea3gZdnME6TrntgSsPoD3PD4VGOjpl5trZt2A58zsxOD9oYZZWP2aWR3gSrzHsnNQ+cFmNga4JJfxhWVmD+IFmKbjZQl+JsSJpXjnXC//fm1gPrACL5A40+1ASzP7yx/rLrxg1OuAssBvxJDRWEREREREREREJFapKdkn7YqUZbTARAgyTU9LD7tPRKQoBQeZZmRkUKJEiWIajYiIiIiIhKMgUwnlG+dck8wVM6uJF3TYEbjezBo556aFqPdQQMBXtFoFrTvgYefc1xHqRHMGLjMaLWxZP1CuB172yubA55EajCYTa17GsS/Wcc61xAvMq4CXHfUlYJiZvemcey+GPnIfhFkc0NRf/aog2w7uyl9Geoy6A88AD5lZR+fc7LCNeZkzH8XLFHoiXrBuXECR2pl3nHM7zewXvIywM8zsJ7zAyYkhgmozp3BvEBCEHSgzw+0JwDwzawQ0CioT75zrVZj9Aqf7638HZ0j1jSYfQaZ4GXTBCz5+LkyZMUCvXNr5Cy+r7+l4x1gB2I6X3bY30NtFSusgIiIiIiIiIiKST2nJ2fMbFEWQaaQzXkUS5CoiEoUc70d6exIRERER2ScpyPTAk5mtMC5Cmcx9UU177pzbAHxvZuWAbnjThDfO8wizt20AfjDjeUAPoKuZrXDOBWdNXY8XeHYEsDCXpuv4y9ymPH8LuAdoZWa9Yxm7LzNLaJUw+ysHlSvqOgf5dbbkqLG3zs4wbeKc2w1MB+41s+rAO2Y2wjk3OVydPLgG7zn91zk3qwDbzWJmZYHq/uqmcOWcc+lm1gL4FWjnjy2cfniZd5cBg/Fen8n+vueAMkHl78TLDnsP0MbflmRmP+Jl7t3gb8ucwv6RyEdFRX/ZiJzB2oHBl4XVb+ZrcUOYcutzaSci51xroHUM5ePZG0gcuH0M3uMhIiIiIiIiIiJSLNJSs1+jnZEe1an5fIk2kDSuRKSfEkRECldGRohMpiiTqYiIiIjIvkZnDw48mUGGNSKUOchfbo+x7Yn+8uwY6+XKObfbOfcHXsbFEsA3ZlY+qNjf/vKKSG2ZWTXgDH91fC79bgA+Ag4BWsY6bmAJ3nTeR5lZqKDtY/zlooBtmQGyxxJaodcxs0PxMjqujmF6+uF4QXz5yU4ZSnN/+WUBtxvoQryg+g1+MGJYzrmhwCjgajO7MlQZMzsTL8D0D+B459xDzrlX/cDIt4HSIdpNdM61ds4dixdUex/ea/o+4MeAopl/ww2ccxbh9o3fbusQ+xoVdr8B5WuGeShrhdkuIiIiIiIiIiLynxIcVBocVBVs9449JO9JyVef0U7eoyBTESlOwe9VyrQsIiIiIrJv0tmDA89CvGyKx5pZuEDTzGmxZ8bYdjV/WWivGz+TZTe8TKTPB+3u7i+bmVm4wDbwpnYvA/zhnFseRbftgbV+f3VyKRs83mRgAlAeuChEkcxMmIFZWf8FEoELzKxSYGF/6vir/NW/AnZl1r86uAMzOwovkHQFXlbNXOuEGVduMqd/T4tYKgZmdhhwHV7AYv+CajeojzjgdX+1T5TVXsKblOUjQr/e6/vLIc651KB9ZwPlIjXunFvlnPseLyPwYuDCgL/Xf/1lqNdTvhRwv9P95YVmFuqy4kZ5HqiIiIiIiIiIiMgBZEjnEdnWIwWALpy0lMdObsHjp7ySv06jDjLNMTmQiEih27ZhO+/f24FOT3TLtj23IHwRERERESkeCjI9wDjnkoC+eFkbPzKzbGeIzKwO8LK/2ivadv0gsmf91dH5Hmhk7wJJwEt+VlIAnHNjgd54057/6h9L8Dgfw5saPCFgvBH5mTzfxAsMDJ52PLDt483s+BC7vsgctz8te2b5s/CmKt8E/BTQX4J/HBXIOR34U0A94HfnXGDA6BhgPnCxmd0Y0Ecc8KG/2tVlPzv5NV7A8VNmVi+gTjXgtcw6AdvLmNn5YY79LOAxIAMvo2lBeRgvc23vGDKqRs3MDsH7e2gErATej6aec2468B3QALg7RJF4f9koRH9dQozjYDM7J0Q7FYBKeIG7makJvsbLMtzKzHJkDTazODNrFLw9lMLs1zm3GhgJHIn3ug0sexMFn/FWRERERERERERkvzT+p0nZ1l1GRpiS8MGdnUhJTGX3jvydLo0yxpQSJTQttYgUvc+f+5q/fhjPrDHzsm2PNguziIiIiIgUrVDTe8v+70XgLOAh4DwzGwnsBOoCN+EFl33onBsTpv5pZtY6YP0Q4DLgOGAz0CJMvZsDgxmDxDvnekUzeOfcGjP7Ei9ItAXwasDu5niv27uBhWY2DC8jYwXgUuBkYAtwq3Mu+zfTyHoBzwGnRCgz318GX9rdF/gfcBsw3cx+AWrgBZiWAB5xzu0MqvMaXoDiC2Z2GjAJOAHv+dkIPBlY2DmXbmYP4WUe/dHMfsQLmrwcOBMYD3waVGe5mb0MdAKmmFk/vIDC2/Aytn7snPsnoEo5YLyZLQCmAavxMrSegPf8A7zsnFsQ2I+ZXQg081cr+stjzKxXwFiaBB1/ZoDsw/7qV8H7YxXwmo0DqgInARfiTV0/CbjXObc5hiZfB25nb9bSQJPxHvP/mdkEvOnna+JliF2Ilxk3UG3gXzObj/fYrgIqA9fjTSvfyTm3C8A5t8XMbgMG+nX+BObiBfgegZeJuAZQltwVdr9PAv8AHczsKrzsyPWBW4BfgBuiGKOIiIiIiIiIiMh/SqRMfYm7kgqkj2gDteJKKheJiBS90f0mhNzulMlURERERGSfpCDTA5AfLHYO8AxesFcTvADCLXgZMb9wzv0WoYkG/i1TEl7mxo5AO+dccABdppv8WyhjiCFzKvAB8AjwjJl1cM5tgKxMrff4AYyP4AW+3eCPcQnQBi9wbmsMfeGcy/ADMmPO0umcc2Z2NzABaAo87Y9nLPCucy7HN2X/OToPL3PqzXhTlG/Byyb5lp8lMrjORD+jaBvgKrxg4RXA20Bb51xyiDqfmVk83vTvD+AFYM4D3nDOfRNUfDfwFl4GykuAg/CmjF+Dl9Wzi3NuYoiHoD7wYNC2Q4K2NQlRrzFe4PO/zrnZIfbHKjMLbQqwC++x+RYvi+wI51z49AAhOOdWmVkHoGWIfel+Rtl3gWvx/tbWAN39bcEBzvH++BrhBUMfBGzFC0htiReoHNj+n2Z2Kt7z1hjv9ZGCF7w6ioDMuLko1H6dc4vN7FygLXCF388svNf0wSjIVEREREREREREJIeiCKKKNsi0REllMhWRfUekTM8iIiIiIlJ8FGR6gPKnZH+fKKcH9+v0IrZA0Mx6TQgdRBipTnA20OD9G/Cyk4bbPwIYEWOfvYhwfM6538mZpTRwf6R9aXiZRD8NVyZEna142VqfjaHOPLzsmlFzzv2Cl1Uyt3KpwDv+LZb2e5G3180wIjzeMbST5zZye+06514leybdwH1bgSfCVK0XVHY7XjDw2zGMLZ6gaehjVRT9OueW4GXHzcbMmkTbhoiIiIiIiIiIyH9JRnrhB1FFnck0Lt+naEVECkyUb10iIiIiIlLENA+KiIiIiIiIiIiIiIhIIcgIkZWvSKaDjrILi9PPRCKy7wj1nikiIiIiIsVPZw9EREREREREREREREQKQUZazoCpjCJI1ZcRZSDrtvXb+fPbcSTuSirkEYmI5C7aIPzVi9eRkpxayKMREREREZFMCjIVEREREREREREREREpBOkhgkyLJpNp9H189cJ3/PDewEIcjIhIdKIJkO/XbjAPHfcMj5z8POlp6UUwKhERERERUZCpiIgUOOdcL+ecOed6FfdYREREREREREREisrOzbuyrWekhwoyLfzpoF2M2VJ/7z66cAYiIhKLKN67urf8DoC1Szcwpv+Ewh6RiIiIiIigIFMREREREREREREREZEC8fWr/bKtJ2zfnaNMUWQyLZJsqSIiBSy3TKYjvhmdbX3XtpzvsSIiIiIiUvAUZCoiIiL/eQsmLWbsj/+QlppW3EMRERERERERkf2ImWVbnzBwcrb1bi9+l6NONNNBR2vnloSQ22NMZCoisk/ILdPzRw91KaKRiIiIiIhIIAWZioiI/AdtXruV1rd+xMfNviA1JbW4h1OsVi9ex9PnvsY7d3zCkC6/F/dwREREREREROQAsHXdNt64ui0zR83LsW/z6i3Ez1lVIP30/2BwyO2pyf/t8z0isu9ITEhk59ZdUZV96+Z2pKelR922U0S9iIiIiEiRUJCpiIhIEcnIyCB+7ioycrkauyCFO8nW8fGvGD9wEsN7jmJgx9+KbDz7om4temfd/+KFXsU3EBEREREREYnZto072LJuW3EPQySHbi98z+Ipy0Pu6/lKX15p9G6OrKd5sWVt6Nf/wA7D8t22iEh+rY/fyF11HuWu2o8yd8LCXMsvnrqMX7qOCLlv9rj5OTcqxlRERGSf5Jzj189H0uv1fuzcHN3FJiKyb1OQqYiISBFpdUs7HjnlBT64r1OR9De633juOPQROjz6ZY59//4yNev+H9+NLZLx7KvSUtOKewgiIiIiIiKSB8tmreCewx/l3rqPM3/i4uIejvwHpaelM/+f0K+9aSNn51q/4yPd8z0GMwu5fcPyTfluW0Qkvz5t3pU9OxNJTU7l9evej6rOnL9DBJMCL1zyVo5tymQqIiKyb5o+cg693/qRYV+O4ts3BxT3cESkACjIVEREpAgkJyZnBXaO7ju+SPp87+4ObN+4g6Hd/mDR1KVF0qeIiIiIiIhItFKSU3nr5g956pyWrFq4Jub6be/vRFpqOulp6bS6+cNCGKH8l6SlpjP+58nMGbsg6jqTf5tReAOK0tTfZ+0T4xARCWX1onVZ93fv2ANA0p7kiHViiRtVkKns61KSU/nz+3GhM/EWkOTE5CKdQU9EDixLpsXz1QvfMW/8ogJtd0TP0Vn3xw2YWKBti0jxUJCpiIhIEchIz/4FP5qTX6sXraXvh4NYu3R9vvtft2xj2H06ESciIiIiIiLFYcBHQ/hnyBQWTl7K+/d0jLn+umUbsu5v27CjIIcm/0Gjev9Np+bdeed/n9L+wS9ISUqNWH73jj182vSrAh3Dv0Om5l4ohPYPfJHj3JOIyL6o7f2duKnKAxHLxHS+Wqe2ZR83uPNw2t7fiRcueYuVC2K/qCo300fN5o5aj/Dwic+RmJBY4O1L7JxzDOz0Gz1e/Z5d2xKKezgiuXrr2g/589txtLnpY9JS03Mtv2nVFras3ZZruRKlShTE8ERkH6IgUxERkSIQ/KE8PS30h/TNa7bwbev+TB81mzdvbEuPV7+nzW3t891/+j4wJfzMMXMZ9/PEsMdeXBRjKyIiIiIiUjwm/rY3oG7J9OUx1w+eJnzjqs35HpP8d/Vo0Sfr/uShMxjZa0zE8v0+GFzgY8hP0OqenfkPLNm2XsHaIlK4/vx+XO5B8TGcsP3ihV7cWbs5A9oPyefIRArHVy9/m3W/2yu9C7z9Fle8zZ5diaxetI4+7w8s8PYldv/8MoXPn/uavh8O4uvXfyju4YjkKj1t7//lPX7W8XAWTFzC0w1f58nTXmXZzJURyyrIVOTAoyBTERGRIhBtkOnHzb6g99sDaHHF21lTCS2buYKUpJR89R/4BaE4LJyylJcubc3bt7Vn5LeRf6QpcooyFRERERERKRbBQSY7Nu+Mqb7FZQ8y/fnTX/M9Jil+zjnWrS9Z7F/Xv31jQNb9reu2MXX4TNJS9l7E+3v30WHrxhKsmTnVfX6nuQ0cW159/WrffLchIpJfsb7/b123ja/f+EEzdsm+r5Bfoivnry7cDiQqP37yS9b9X7qOKMaRiOQu1v+dnzTpinMOl+Ho2KxbxLIlSuQMMl2/bCMr5xd8VmcRKRoKMhURESkCwZlEw003MOX3mSG3zxo7P1/9p0XKZFoE5946PPpl1v2Pm31R+B1GoVuL3tx/9JNMHj6juIciIiIiIiLynxQcZHpX7ebMHhf999/gTKY/dRhaIOOS4vXe3Z/yQNPDaf/pQcU6jhKlSrBy3ho6Ne/O46e0pN19n/PtWwNyrwg8dnKLqPtp/4B3niQ1KX9BoilJqfmqDzDxl2kR92f+CJ24K4lx/f9l/fJNCuoSkQIX6n1l1th5EeukpqSRmpz/90ERkfwK/o4isi9LD/q9OvD366XT4/nxo1/5pctIuj77LVOHz2THpl1Z+zev2Rqx7aTdSdnW42ev4tmz3+Tli95mxqi5BTB6ESlqJYt7ACIiIv8FOTKZhgkyDefVq99lZEZ0P2RE039BSUxI5K8fxlPvlCM48dxjI/Sf/YcS51yxftFeuWAN/TWFkoiIiIiISL5lZGSQtDuZ8pXKxVw3eNaNtNR0Xrv2Pfqu+Yoer/ahRMk4mrW9lzLlyoSsH+575aRh05n51xxueKIxteodEvO4pPgkJyYzpv8/AIz4oxIvB+1PT09n5fw11Dvp8EI/r1CqdEnev6MT29Zvz9r2e/fRNG17d6H0l99ZbPJbP9PADsO4tvnllClfOtv25bNW8nGTrlSrWYXqh1Xj38FTATioTnVe//E5Dqtfs0D6F5EDS2JCUu6FggUFmc6fuJgXG7XKtdoNle7nsY8f5JZnro29zzzYsm4bgzr9xrFn1eei/51TJH2KRKLYxn1DXJyeCNl/BM+GkLB9N5vXbOXvARP5vcfobPv++n58jroZGRnExYXObThtxOxs669c+m7W/Q/u6ES/zV8GVxGRfZwymYqIiBSBHEGmaYUT9AmwYt4qvni+V/b+CinItHebAXz66Je8cPFbbF4b/oq1xF3ZTyYO7jy8UMYTrQ3xG8PuGz9oEkl7kotwNCIiIiLZzfhrDp880pUFkxYX91BERCJKSU7l0QYvcXvNh/l74MSo6mxZty1rWvDgTKYASbuTubnqg/zyxe8M+mwYP378a7b9zjm6PNuT5y56g4Ttu0O2//p179O//RA+fbR4f7RaOHkJfdsOjPh9WbIL/pEz+PzJ69d9QPNTX6Rdk86FPpak3cnZAkwLW3Ji/oJEk/d49Z1z9Hq9X57b6fvuIH778g92bU3gty//ZOn0eAA+uOszNq3cwqLJy7ICTAE2r97KR/d/zpJp8cpqKlKIEhMSad/0c9re34ld2xKKezhR27U1/2Nt37RLVOUy0jP4/Lmv891ftDo90Y2+Hw7i7dvaM37QpCLrV0T2cYr2lf1I8O/X/dsO4a1r2+UIMA1n3IDozgOIyIFBQaYiIiIFLNQJ9fSgTJ55ySz6+BktmPhb5GnL0tPSefXq9/i549Ac2wvDgI9/yWo/XODokunL2bBiU7ZtXZ7tWSjjcc4x75+FbN+0I3LBCF/yW//vIz5t3rWARyYiIiL7in09+CE1JZWXL2/DsB5/8sLFb0Vdb+fWXcwZvyArcEtEpCj82nUE8XNXkZKUSptb2+da/ocPBnJX7eY8f9GbZGRkkB4iyDRY3w8HZt2f9scsnrvwDQZ9Noy54xeGLD8p4HvztJGzojiKwpGYkMhLl7Wmx2t96FAIwa6JCYmsXLCmwNstbinJaUHr3tTH4wdN4omzXmHqiJkA/NF7bMR2nHNRXUCanpbOlBEzWb1obdRj7Prstwzt+kfU5aMx/ufJJGzNGTQdi3+HeIGfk36dzrAvR+Wrrb7vDeab1/vzzev9efvmT9izM5EdG3eGLb928Xpev+oDBrT9JV/9ikh4P3wwkN97/cWf34+j52t9ins4UcnrOengr2xb123PUzsJ23czftAk9uxKzFP93EwYPDnrfuv/fZSvtrau38bT573GM+e/xrYN2/M5sv+2lORUJv8+g9078vd/NVBeziMM7jKc1659r8DGIHvNn7iY1657n0GdhxV427E+10umL2d4z1EkJux9n8lPJtOuL/Ti/qOeYNzPCtyTohF8kd/koTNwGdH/HXz+ZC8Stnnvt2mp6YwbMJFpI2ZH9bc08usxbFyxObYBi0ixUpCpiIhIAUlP96b1u6t2c75/9yeePLslHzf7gi3rtuW4Yjtz+viMjIyov7Qumb6cN67/gKkjZzJlxEyWTF+etW/H5p18/PDntL71Izat3pKjbqSg1vi5q9i9c09UYwDYvmlHyDG7MAEN7971adRtB0tJSqHLsz3p8OiXITPUBOv34SCeveANHjru2YjHlNuX/FF9/s61LwVwyMzRc2ne4EU6P92juIfyn9P1hV48cuoLTPtzdu6FC1BKUgq93x7A9+/+lPVj9/5o6/ptDGg/hIVTlhZ42/P+XcQvXUfE9H9FpCj93GEojUveWaTZbWIV+LkxNehEbzjJick8fOLzPH/Rm/R4df/4wVlEDgxrl6yPqXzP1733qHn/LKLdg51ZFU2QpP/1c+t6L0PpvH8WhS1qZmxZuy3XJhdMWsz4QZMKdZaR2eMWkLTbC3KcODTyBaOxStydxAP1n+bhE59j0GfZf1x3zvH+vR24Mu52bqx8f8hzBJHk57tuRkZGzP0FSw36nJ2anIpzjtb/+4jFU5dl2/dX3/F0a9GbHz/5hXvqPkab29qTkZFBakoqj5/RgtsObpojs9yCSYuZOnJm1nmNnzv+xqtXv8tDxz8b9Rj/+n48374xII9HGFqn5t2zTR+ZF0M+GwHAoI4FM3tMZlaipN3JDPz0t6jq/PTx0NwLSbHZsnYbHZp149s3B+i81n4o8P3+1y9HFuNIopeSlLcMzcGvzz0xnmNI8f93tGz8Dq3/91G+A0CLwhfP92LBxMXM/3cxnZ8pnAQN/xUfPvAZr13zHs9d+Ga+LjL95YvfeeOGD7gy7nauKnEHQ7+K/u9ueM9RdH66B5OHz4ipz7VL1zOw029sWr2FXdsSGN5zFBOGTCYtNY3Na/L3GWt/F/i+0OLyNkweNp0uz/RkzZJ1IcuP+GY0bR/oRPzcVVH30e2V77j1oIcY3CW6z1I7Nu/k2Qte5+NmX9Dz9R+ytluYqcNzEz93FT91GMr6+E28fVvuF/BJaCnJqbRr0pnXrnufjSs35V5hP7V59VY2rcr/+0K05x4jefiYFxj/82TuPfQJOj/ekw/v6cyMP+bkWq/7y31497YO+fpc+u+QqQz94g+SEpJyLywi+VayuAcgIiKyv9u+cSdjBvzDd28PyPrC2uutvgAsmrKU4T1zZo9IT01n5YI1vHbNe2xYsYlTLzmR65pfGVV/LRvv/dHhq1kfc+TJR9C95fcM//qvsHW6t/yOP78fS4NLTuLJTk1z7O/z7k880u7+XPvu8er39P1wEAB3vXJzVONdszj0l/xoDO48POsEatkKZXjskyYsnraMUX3+5pzrGvLTp7+yeNoyPhj2OkeeUpce/lX8Cdt382vXkdzZ4qaQ7Vo+pyvp8mxPhvccRZO37+LW56/PV1uh7N6xmzLly1CylD6qFZRVC9ewZe02vmnVj0rVK/L6D89RplyZqOsnbN/NX33Hc8K5x1D/tCMBeOmy1gAsn72Si28/j1MvPrEQRr5/mj1uPnPHL6DxQ5dSrWbVAm173j8L+amD98PlK1e+zciMgv1xN5JfvhjBt637A1ChanlufuqaqOrt2ZXI9+/+RPlK5bjzlZty/dtOT0tn7I//Ur5SWc6+tmG+37OCffZUD/7+eSIVqpTnh1VdKVexXIG0u2XdNl5q1IrUlDRWzl/Nkx1z/r8paulp6fR8rQ+7tu2mWdt7qVyjUnEPSYrRmiXr+OKFXgAM7PQbtz5/PTXrHly8gwoh+OKg9LR0SpQsEbHOXz+MZ/tGL5N7/48GU/Xgylz/2JUF9vct+7b18Rs5uE6NXF8nIsVpx+adfPjAZ9m2/fn9uKjqOudYOGUpk4ZOy3VWEOcc37TKPk34hhWbsr3fr5i3iqfPfQ2AJzs25eanvc90O7fsol2TzpgZLb55ikrVKkY1vrBjCfqhzDlHakoapcuUylN7GRkZLJ0RT92TDufXriOz3ve7PNsz6xgA/h44ib9+GA9AYkISHR//ivNvPIvxgydx4rnHccgRB9HwylOpcWi1HH18+dK3DP1qJPe3uoPbX7wB8DJslyhZgrgwP5YvnLKUtvd15NCjarJ94w4WT1tOk3fu4t7Xb831eEK1GRxk+lGTLmEDi9+/p0O29U2rttC45J00fe8els6IB7zMcpnfGRZMWpz13D/Q6g4a3XU+X738bcRx7o9iyTwUrcwA1uLmnCM1OY3SZfP2d1QUFk1ZRpfHv6b2sbV45qtmlK0Q/bmHaKxeuJZVC9ZxRuNTY34cur/0PdNGeBdr1j2pDpfcdV6Bjk0Kl4W5aH73zj0M7Pgb1WpW4dpHrijwcwj5kZyYtyBTgt7GMmJ8X7uu3D0ce+bRLPIvrp3+p5dRLS+PzZzxC1g5fw1VD6nMP0OmcP2jV3LcWfVDlt26fhvVa+X8/xpo6FcjmT5qNve+cRtHnnwE4AXDje43IavM2AH/QL9wLUhuxg74B/AC9tYsXkedYw+LuY0Zf82h05Pds23r8NhXuf6Gk56ezrDuo+j4+Fch92cmH1k+ewWH1D2YCpXLZ+3LyMjg1WveY+2S9YzuNx7MmDche+b+5h89wM1PX01qchrlK8X2fX/pzHgGdx7O+TedxbnXnxFT3eKUlppGiyvfZtX8Nbz2w3Ocftkp2bLVL566jNr1D81WZ93yDXz0UBcApv4+k3d+aclxZ9WP+B6wbeMO+n80GIDOT/fgpievznVsw7r/SUqS99l10GfDeOyTBylRokSeMpkunraMz57qnmN7RkYGCdt2/yfPaaanp+MyHCVKlmDpjHhq1K5OtUOq5Ci3etFa2tzWnkrVKvLur6/ya9cRjPx2DAAdHu/G+0NfK+qhF7ql0+N5vXFbcNBm6Mscd/bRUddNSkii/YNd2b1jD892e4RV8wtmdoxOzbO/fj+6//Oo6m1YvontG3ZQPcT309wsmbacT5t677cpSSnc8vy1UdcN931URCLTX42IiEg+bVq9mXfv/CSmKyLT09Jpe3+nrGnkZ42Zxwf3doy577b3dWLVwjUhA1mDLZ+9kkGdh4X8Ia9/+yFhr+pNT0vnm1b9+Py5r7MCTIFs9yH2wM309HS2bYw8rf0PH/ycdf+nDkMZ1uNPnjjzFX785BdevrwN//46lS1rt9G8wUu8eGmrbHUjZT7N7YdJCJ+9ZcfmnQz6bBhJu5Pp+uI3BZ75YcqImdx68MNcU+Zufu4wVJklCsDUkTNpesJzvHx5G+b8vYB/hkyhf7shgPc6/OC+jjxyygvMn7g4bBtdX/iGTk9044WL3yJh+262rMueHSnzB8yCtm7ZBlr/rx1fvfxtoU7vvGn1FmaNnRfx9bZ26Xo6PdHNO8kZwY7NO3n16nfp8Vofur74DeD9Pa2Yvzpk+4FTCUVjyfT4bOtv396eiUOnxtRGOJOGTefpc19lQPshOfatmLcq63jAyzARSdKeZAZ1HsbfAyfSv91g+n80mF5v9eWaMnfz6Gkv8dq177F42rKQdUd8M5r37+nAGze0ZdaYeSHL7Ni8k63rc8/SFSglOZWFU5bytz/d0u4de5jx19yY2oC9r8svX8r+uhzeY1TWlc/BGbXCcc6xec0WVi5YQ1pqGoO7DOfJs1sy7qd/Yx5XKL91+4P+7YcwrMefOQJO9iXr4zeSuDv71dYJ23czdeRMUlP236y50XLO8Vu3P+jz/s8xvyfEosNj2X/kyQzOSU5Mjipj+u6de+j64jd8/cYPWT8MhZORkcG0P2ezOoqLbVYuWJP1mSg1JZXdQWMJ9cPsnl2J/NxxaNb736KgzMRftejNN2/l/TXvnCvU/zv5kZaaRpvb2vNYw5dZPntF1PWSE5P599epdHz8K/4emLdp55ZMX86gz4axbvmGPNUvDN+/9xP3H/UkT5z5CunphZeRUf7b5vw9n26vfMfqRWtZMn05Ywb8Q0pyKsmJyUwdOTNb2W6vfEe3Fr1JTsw+TfmLjVrFnMkpU3JiCk+d3ZJv2/TPU/37jnyCnzvuza4YmGWoy7M9efS0l7gy7nZuPbgpE4dO499fp/J1QJlohPr/tXnN1mzrzU5+njtqNWPm6Oyfv9LTvO8kb9zwQdY5AvAurhr74z9Z2VY/e7I7T5z5Cg/Wf4pfu/4ediwz/8qeMWbi0Gl8+uiXTPptOr3e6ku7Jp15+txXc/wvS9i+mx8/+YXEhKSs7x8LJy/hrtqPcs8RjzHv35yBnrt37uGps1uyetE6Jg+fweJp3owrvd7sS1pqGs45erzWhybHPZM15ee6ZRt45NQXaFzyTtre3ylHm6nJ2cf1769T2bllV9jjDSUzY26gtNQ02jXpkrX+bZv+ND3huZja3dfVOKwaCyYuYfmslcU9lKitnLeGNjd+zLdv9Of9OzrS9q7P2LFpZ45yzjkmDJzMXQc/xgNHPM2kodNj6icjPYNhX43il84jcgQyF7S2d33G+uUbmfr7LB6s+wx93hkYU/20lDR+/XwkQ7/4I0e25Z2bd9Hy8vfp8PBX3F/nKd685kN++/JPls9aSYtG7/BJ0y8jZmjODDAFGNvvn9gOTApVcmIyz174BtdXuJeZY3J+T09LTWPPzuz/a75t3Z9RfcbR78NBfNOqHx0e+yrru3ioz/Ipyan80nUEo/qMwznHumUb+P69n1g+ewWThk1ndL/xpKelZ507yOs5yYyMjKzPpZnBV7HK7ftWNIK/I3V+ugcr5q/GOZfj+3coGRkZrFmyjpcubc2nzbvS6uZ2DO85ilev9hJATB6e833ozsOaR5yifeWCNXR47CvG9P+H5qe+yMShU5kzfkFWMJzkX/BrPz09utfxoM+G8dQ5LZkweDILpyzl5cvb5Knvnq/2CRtgCjDl95n0/XAQzRu8RJNjns76DOmcY94/i7JmCZj3z6IcAaYAX738LdeWvYebqjzAkM/Dfx4M5c0b2zKsx5+8eWPbqM6B7CuGdf+T2WPns33TTlpc8XaO/aGC0GeO3ns+dfumnTx97mt8/+5PEftJ2JZ9NsJ+7QaHzcY8adh0fu4wNMcMhncd1pxpf8yCGH8rS0lK4bVr3mP+v9l/n/j316ncV+8Jbq/VLGKSmWAFeT4nPT2dCUMmM2f8ggJrM5IJgyfT7OTnufXgplxd6i7+V+Mhur7wDY+f0YIHjnoy5G+Kbe/vRPycVcweN5/v3h7AqB/2zlQ4eVhsnxnzK7fH3jnH/ImLWbcs+vNKK+at4uUr2tChWTcy0jOY/sccXrvyA1yGd+6uY7Nu2cpvWbuNsf3/JWHbbn79fCSdH+/JhvhNWWP7sf1QZo+Zz7IZK3j2rDdo/8AXsR9oFNLTov8csWvrbsb1/5cN8ZtYOj0+5Of19LR0er7yA5889CVb/d/nfgj4nN33vcFR9ZWRkUHbuz6j+QkvM31k0c6Ul1fOOZZMW8765QduZt4DweY1W+jbdmChzCCYtCc5pveNwmT76o8GIiIi+wMzm1qJqg3PsSuKeyj5dmbjBrzR93kqVKlAWmoaE4dOY9xP/5KSnMq4H6ML9jn1khMxM2aOnktcnHHc2fVzfDEOZGY83aUZNzx2FRtXbuKzp3uwfvlGXv3uWT599EsWRAj6y82J5x/Htc0uZ96EhWzbuINmbe/jiONrAzD2x394545PItavc+yhfDD8DWrVO4T09HTMjLi4OFYuWMPDJz6XrezVD13KfW/dnpUdJzkxmRIlS2RlK1w8bRmVa1QKmS1t8bRlbNuwgzOuOpW4uDiuKnFHtv1v/fgSFauWZ8aoOVz/2FUcXKdGjja2bdjOVy16U6JECZ7q/DBly+fMkuGcY9fWhP/cFa9zJyzkuQvfCLnvuuZXkrQ7KSvw+qhT6/LljL3T0MwaO489OxM5+9rTaVzyzqzthx9fO8e0njc/dQ2ly5Vmxl9zWDRlKWXKlabD+Hepf9qR3snrhKSsq8uTE5NJ2p1MlYMqZ2sjOTGZMf3/ofYxh1L1kMpM+2M2P3f4ldWLvACltwa8yEW3nhvxeH/r9gdz/1nIPa/9j6qHVGHVgjUsn72Svm0HcnXTy7n71VuYPHw6qxau5YRzj6Vm3YNI3pPCo6e9RKI/nchbA17kvBvPJGl3MhWrVshq++XLW2cFJPZe1oVa9Q7J1vfGlZv4qkVvxvTP/kPVyIwBtLqlHRMGT/ba//El6p9ej0GdhrF0Zjyzx83n3OvPoPXPL4cMVk9OTGb98o0celRNSpctzaDPhtHl2ZzThlWqVoF3f32VE887juTEZH78+FfKlC/NLc9eS4kS2TO7bduwnW/e6sfhx9fmpqeuzvpbvTLu9qwyD7a5k7tfu4USJUowf+Jinjkv5xXPjZtcytGn1eOWZ3JeIdu37cCs7MrhHHFCbXrM7ZBt2/ZNO7i9ZrNs23rO78Dhx9UmPT2dhG27GdFrNF+16A3AVU0acVqjk6lRuzonnX9stgy92zbuYOqImdQ76XCOPPUInj3/dRZOzv4F950hLTn3+jNI3J1Ex8e+Yva4+Vz7yBXc/uINrI/fxOHHHZbjeXml8TtMGzkLgDf6vcApFx3P9D/nsGDiYgZ13htcOjSxDxviN3L4cbXDPgafPNKVYT3+BOCg2tWzBWPUO/lwDju6Fg9/cC/Va1Vl8vAZnHLxCRx0WPWsMrt37smW9SE44+P9Rz3B+vi9J1+CM99mZGQwd/xCKh9Uibon1Ak7zsI08tsxtGvSGYD737qdW1+4nrIVytDspOdZvWgdl997ES17P5Ontjev2UKNw6rvU5lsQpkyYmbWD3UPvXs397z2v6jqrY/fyPfv/Ej9hkeFzTDx/bs/MeKbv7i/1R188XyvbEEqn/37PpVrVOLBY56mdNlSfPRnK04877is/cGvp29a9eO7d34E4MlOTSNmMx7Y6Tc+f+5rSpYqwTeLP+OQI/Z+Bpg/cTEfNenMESfW4eLbzuODeztSqkwpXv3+Wdo9+FnW1MqZPhnzNqdcdEK2bV+/8QN93vcuxjnmjKNyTB+caUR6f2aPm8/qReu47J4LQ35GCPTjJ78wvOcoVsxbzREn1KbdH61CZrorToO7DKfz0z0A739yz3kdcq2TkpTCQ8c/y8aVm3Psa/9XaxpcchLgBYlt27CDw46uBXgn8OLnrOTo0+qxbcMO7q37eFa9e1+/lSbv3EV6Wjpb129n5ui5nH3t6VSuHvqz1vLZK5g1dj5b1m7l8nsvou6Jh4csl7B9N7t37AmbZXf57BWsWriWc284k9JlSmX73/Xe0Nc4+5rTc308RGKRnJjM7TWbkZiQRLWaVdi2wftRr9Gd57NnVyKTfgv9o93D79/DXS1vAbwfxZqd/EKRjTmczM8BL1/Rhhmjcp+6r/7pR3LOdQ25/63bI2YKHtxlOF8834sGl55E2+FvsHX9dob3GJU1w0mwUqVL8luSd8HCnl2J3HrQ3uzvZ119Go+2f4CPHuqS9bnt4Q/uZenMeEb3DX+x1yMf3kdaajq3vXA93Vp8l+0zWTi3Pn89teodwhX3X8yY/hNYv3xjtotJG911QY4+AzOUZmRk8MSZr4S94K5yjUo5gkNHpPfnybNeyQpGBTj3hjN4s98LJGzfTe82A1i5YE3YC63y6oJbzuafIVPIiDLQZF90yd3nkZGWkTWF/b7swtvO5qgGdbnu8eznyjat2kKl6hXBOXq27MuOTTuZ8WfOYLrL77+QB9+7k5SkFMpWKMO7t3Zgwb9LcpS78qFLOOva05gybAYnnn8sR5xUh69b9mXPzkSe7tqUKcNmkpyYwnWPXcHU32fx2WPe54dHO9zPZfddmK9j3LJ2G6sWrOWkC45l3bKN1Dn2UOJKeDld7jzo0Rzlv135GVvWbuOw+jXp/daPTBk2k/vfvo0zr2mQo+xffcbT9Zm9GXZr1juINr+2oMohlXj/9k7MHjM/4tga3XM+j3d6MOS+4LFd0/wyrnroEg6tX5Nt67fnKYPUvq569eq5Fyomu3fu4deuI6l/ej3+/nkiv365dyru4O+u7Zp0zsrKlpvDjzuMLeu2cdvzN3B/q72fFYd8/ntWlry2v79Bj9f65Pge8WL3x/n1yxEsnLyUKx+8hBZfPxXTMW1eu5XnL3qT9cs3csEtZ3PS+cfnKWP0qZecyMd/7Q3yC/zMm18Vq1YgJSmFl79+knOua0i5iuVYvWgtDx3/LAD3vPY/1i5dny2zaLAnOzWlS5gp7SvXqMRXsz4O+T0q8DnIzYj0/swaM49K1Sty1Kl1o6oj3ne/68rfm7XedfpHHN2gXtb61vXbqHJw5WznCnds3slthzyctV6iZImwAfs/berJ6sXrGNJlOJfccT7n3XAm4H2Pu6V6k5jH+/gnTah6SGU+bvZFnoKyS5YqkS2hxgU3n0Xrn1tkK5OWmkZcibhs57c7/P0uJ51/HPkxc/RcNq3ewsW3nxdVpv5pf8xi5ui5HHvm0VQ5uDInnX9cVOergv/+n+nSLFuW2RbfPEV6WgaJuxJZt2wDpcqU4tCjaoYM9h2yqzezRs/luLPrU/Xg7Bkxl86M57HTX8627dH2D3DbCzdkre/alsD65Rt58qyWEYMJz7muIROHTsu27dwbzqDVjy+FnOFq2h+zeOWqd8K2l2l4al8WTVlG+crlKO0fZ6CMjAzeuulDFkxczIs9nuCc6xpmy9SYnp7OzNHzOOzomvzW7Q+m/zmb5h89kHXOKSUphVJlSmFm7Ni8kwWTlrBu6Yasc/FdJrfl2DPCZ8xcMn05nz3dg6Mb1OPh9+9m9aJ1HHPGUcTFxbFzyy4qVa8Y8Tl3znFNmbsjXjBTtnwZhuzqjZnhnCMjPYOrS9+VrUy9kw7PkRTolItOoOn793DyBceHbTu/PnuqO6P6/M2Dbe7k0KNrUvXgyiQnpjDtj1mcctEJnHFlA0Z8M5qPHupCXJzRc0HHHFl4wZuJqVuL3hx+fB2avnc3Dx7zdK7BZf02fwl4F1a9cF4r1i3bSLlKZUnclf2iijOvacCUYTNDNbHP+fSfNhx2TK2s9T++GUu3F78H4LybzuC5Hs1pc+PHzJuw92LINkNfZuXcNZxx9alUP7Qqqxeuo/YxtbI+pwP8M2gKHQICczMfu0zOOTat2sLSafGcdvnJAJSrVJbkPSnMGTufhB17OP3yk6l8UNH91jpuwEQ6P94TizM+Gd862+MSSUZGBjiIKxFH4q4kylUqm2udpIQkpv4+i6MbHkmtI/M/A1i4z+LOOXq92ZdVi9Zy7nVncOL5x1HnmENzlIn0npGYkEiJUiV58ZK3vAsKOjfLOi8aamawpD3JpCSlsGzmClJT0jjjylOjyma7e8duVsxfw/Fn1w9ZfvumHZSvXJ42t37EpN+mU6laBb5f8UWuM4w555gyYiZ/fjeW65pfmeP8f+BxNjnuWbau28Yznz/CDY9dFbHd+RMX8907Azjr6tOz/X6xZ1ci5SqWDXxM8/SjkYJMRURE8uFACjLN1KDRSSyYuDjv0xnlQeeJH9Dl2Z4RA1Lzq1rNKrzY4wlq1j2YR07J2w+cL3Z/nOTElKzAhkBnNm5Am4EtWLVwbdbJiP89ex1HnlqXjx/2poU4/LjDqHVUTR5pey9HnlKX5XNW0vzUFwF49ovmTPtzVq4BvcFf5Pu8/zNfv7E3y85dLW9hy7qt7Ny8i8OOrsXk4dO5uunlTBw6ldnj5tOs7X3c2eKmrPLjB00ifu4qbnyicb6ng8yPWKascs6xfdNOylYoQ7kKe78Ubdu4g+E9RrFzyy7ue/NWKlSpkKeT0CVLleCMqxrkOBGUF4cccVBWQMvDH9zL0hnLs05S3/v6rVz98GXUrHswZsatBzeNKkNPq59e4sJbzgG8E0MDO/7Gb93/5OxrTuenT3+NWPfUS06M6gfbUqVLghnXP3olc/6ezyV3XED3lt/lKHfOdQ054vjaDOo8PGw2mMOPO4xVC9fm2mejuy5g4q9TSUxIou6Jdbjk9vO5qkkj7jvyiawyZcqVzvW96fTLTwG8qdAAXv3uGVbOX8OWddt4uvPDjBnwD+0e7JxV/pkuzbjh8cbs3Lor2w/8ma5q0ogRvUZH7PPdX1/l9MtPYUD7IZQuW5r01LRcA0wzXXTbubzY/XEqVC7PxlWbswUwBYoURJbp8OMO49Nx7zBrzDx2bU3g00e/jFgevODq6x+7ik5PdAu5/4HWd3BXy5vZEL+Jv3+eyD+/TAk7XWkkz3/1GFc3vTTbl/BIxxvJuTecwY2PN2bqiJn83PE3Lvzf2bzZ/0W+fuMHfvzkV85s3ICjG9RjWI8/2bI2e7bXkRkDSE5M5q++Ezjs6JqsW7aB9k0/x8y4+9VbWDFvFbe9cAMnX+h9oXfOkbB9N6XLlmLGX3M5puGR2aa+c84x6LNhTB4+nUvvupDylcsxZsAEbn7qGqoeUoV1yzZw2qUnZzuhkbQnmdTk1Kz33OD3qdteuIG4EnFZU3SB9yNGbidiM2VkZJCcmEKvN/ryc8ehnHNdQ9795dWYH+dQErbvpkSpEtnee/Pbxp5diTx7/uvZTv6+0fd5zr6uYbZ+nHNsWr2F9NR0+n80mJTk1Gx/m4H/H/u1G0z3lt9x0gXHMXd8zswf4Rx8eA36rOgKeFMYfvF8L8694Qxe6/McI78dQ/um2ad5uvzeiyhTrjQPt72XqSNmcVj9Whx3pjeG4Of1rGtOz3f2hOq1qvJo+wcoWbpkrhfLZKp38uHEz/Ee26Ma1OXL6d7FFBtXbaZ8pXIk7U6iQpXyzPtnESO/HZMj431ciTiGp/TNeu0tnRlPr7f6UrFqBR5odUeOHzQKUmJCIiVLl2R03wlsWr2Fm5++hvKVyvHmTW3595e9Gayf7tyMax+5nPS0dL58qTcZaek8+vEDAEwZMYsTzqlPvw8HRwy4evW7Z+j0ZHd279iTta3WkYewfvnGiGOsc+yhWReDgBcs/13852xbv52mJzyXdQFHKG0GtmD+v4s44bxjmTFqDkefVo/TLz+Fh098jqTdybQZ2IJzb/CmMNy9Yw+VqlVk0+otPFj/KVJT0rj71Vu47cUbsv3vevW7Z7jsnosijln2Cnw/Tk1JJWl3crF+Ht5XLZq6lCfPapmnukP3fM/cCQtDZhwqDp3+eZ95Exbybev+7NkVW+bs3su6UKFKeZqd/AJb123LClDfun4bdx7WPOaxvPLt03z4wGcx18tNjcOq5fj8U9Cq16rK1vXbC7UPyeny+y/kkU/uY+OKzRxS9yAARvX+m69eyPk9bV9x+QMXcf4tZ1G/YT2GdRtF33cHxdyGxRkuxmmyo/HiN4+xdHo88bNXcWWTS0IGe25atYWM9Axq1tv742paShqrFqyl5WXvZSt7+hUnc+PTjfnmjf7Ez45+pqPMH7Un/zaDhG27OenC43j6jNfzeFR79Vz6KRWqlM+2bc+uRB468rkcZY889QgOqlOdyb/N4MqHLuHet/7HwslLqXFYNQ49uiYlS4UPst8fFHaQqXOOxdOWUefYw7JNXT3y2zHM/3cRd75yc46Lh9LT09mzM5FPm3dl3E+hA8d/3f0dy2at5KhTj+DVa95j9tjIwcXhDN3zPUl7ktmxaWe27M3RvpcHB7sC7Ny6i18+97IC3/BEY+b9s4iTLziOajWr8t7dn0YMzoxp7Il96PPeTyybtYJ/hkwpkDZDiRRQmFfnXn8GV9x3MSddeDwHHVY9a0reHz/5hS9fij3o9qLbzuXYhkdxxf0XU65SOf78bhyHHl2TsQP+ITU5lZueuoYXG7UiNTmVbrM/od5Jh7Nx1WbKVSxL+Urlsp2T2Ll1F12e6Um5CmV5olNTSpSMo/NTPdi8ditPf/Zwtgsk9wUJ23cza+w8Tr34xGwXw2fKyMjgrx/Gk5KUQqXqFfni+V7ZLi4MDKbMvGCx3kmH88W0dpQsVRLnHNP+mEXLxu/maXw/be5J8p4U7jnisTzVr33MoayJYgaUaJUoWYIy5Upz+uUn81TnZgzpMpwfPsiZUTvzcUlPT88KuI1l6ujAgMzm7e7n9pduzDq/v3PrLvq89zPbN+0gPS2Df3+ZQqkypXJk/Xyp5xOUr1yev34Yx01PXcOJ5x1LiZIlWLVwLRviN1Hn2EN566YPWTFvdT4flZxq1TuYVj+/TL92g6lQuTxDvxoZtmybgS0465rTuPOw5jmOIS9a/fQS65ZuYN2yDdRveBSfNu+a57ae7NSUDfGbaPzQpezYtJOXLmudo0zlGpU49KhDaP7RA7zYqFWO/eUqlmXIzt6MGfAP794Z+VxT1UOq0GthR3798g8StiVw92v/o3ylciQmJPLY6S+zdmnkQMiDalenz8quWUGsnzzSlaUz4jn98lOof/qRlK9cLtt5+3DeGdKSUy46nucufJPNa7ZGnZm3VJlS3PrcdayP38iF/zuX5bNWcOk9F4ZNPJC4O4mxA/6hQpXyjOk/gdWL1vFi98epf/qRgPf/fPWidRx+3GFsiN/EA/UjX5jxWp/neP+eDlnrB9epwXFn1+esxqdx6d0XsGvbbg6uU4MbK91P0p7k8A2F0HdTV5ISkomfs4rWN7TPvcJ+4oM/XqPeqYcTFxfHq1e8z7IZe2c0Ou6co1k4MfeMkWXKl+aUi09g4eSlpKWk5Qi8PeLE2mSkZxBXIo6V89ZQsVoFErZlf03FlYjLdsHisWcdxTvDXslaz8jIwMyyzp9mvR9u3sXW9dupe1KdbOf1nXMsnR5PxaoVqHVU9mQuqxeuZVTvvzn9qlO8BCBxRvMT9gbAn3rpidzR8kaOanBExIthMwNTa9SuxhEn1Gb6H96Ftk90acIld56Xrey29Tv4Z/AUTr74eIZ+8Qej+3if5S6//0KatruHAR8OYdPKLdz/9u1Uq1UlR1+RVKtWjZUL1nBwnRqUr1SO5bNXMP3POWxZty3b7x/gXdS6ccUmzr/pLPq3H8KsMfM4/6azeLT9A/z7y1TOu/FMDj2qZlbipT96j83R37k3nMHS6fFsWr0l27YTzz0ux4wnT3Zqyk1PXo2ZZQuyD5SSlEKTY5/Jau/pzs1odNf5DOo0jKMa1MXMePv2jzGzbJ8nn//qMSYPn07JUiV45MP76PRkd5J2J/PKt0+zYcUmur3yXciM5ZVrVKJ+wyOZNnIWl9xxHhWrVmTC4ElZF3wDvP7Dc5x/89nExRkJ23dT9eAqrJi/moWTlnDqJSdy/1FPZpV9sfvjXHbvRfz+9V90froHJ51/HO8NfZXEhCSq16qmIFMREZGidiAGmUrROPva08Nm/clN1YMrc8olJ7Jz864cUy1GY9C2Xsz4ay7Je5L54D5vesJrHr6cF7o9FlWwZ0ZGBhtXbqZ6raqULls6T8cA3pTA37YewNgBE9ixeRflK5fjjb7Pc8K5x7J26Xq6t/yeytUrclWTS9m0ajMHH34QHR79MlsgUqXqFSlbvgyH1D0oRyBRhSrlswWLiMi+47TLTo4qg1hBC87wEM4jH97H5fddzAuXvJU1VVmmxz9tQqVqFbOyj0br7GtPZ+74hVnvS83a3sdpl53MU2dHF7hTqkwpjjihNne2uJnTLjuZClXK8/vXf1H3xDpsWLGJUX3GsWLeajat2pKj7rdLO1OqTCm+eL4XtevX4r63bqd0mVJZV/wHnpBKTEjk74GT+KP3GGrWPYSbn76Gw48/jIWTltDiircpVaYUXad/RNKeZNYuWc9ZV5/mBVenpWNxlu2EWibnHKP7Tch2IvWdIS1JSUqJGCz5YJs7Sd6TzJIZy5nye+5X2R9Uuzp7dibGHDgU7MYnGsc87Vyms645ndManUS3V/bNgI+7Wt5C37axTdl63o1ncuTJR2RlTg3cfsnt5zNj1Gwm/z6Dxk0u5eqHL6NazaqUKbf388H2TTtZMXcVxzQ8kvKVyzN3wkJWzF3FSRccT683fyCuRBwtez/DpGHTKVOuNAfVrk7zBi+FHEtRBE/tzy69+wLWLdvIlfdfQqO7zmfLmq0cVKdG1IGTgVf5p6Wm8cMHAzEz7mp5c8hsKyHbSE8nJSk1W5B4SnIqGekZuWbSLUrr4zfy2Okvs3vHHh758D5+6jCUHZt2clfLm6l74uFsXLmZy+65MOQsAuEsnLyECYMnc8X9F0fM4r2v271zD4umLGXl/DUc0/BI9uxKyso2LTnFxVnIKTol/7q87WW7f/Kty4usz4OOrFpkfcXq7Osbcsvz2bOop6dl8EbjD4ppRAeW8pXLcfebt2AWx6zR85j0a/aLTq9+5DKGdxtVTKOL3f1v386JFxybtb59406+eaM/63MJ/gAgziDgfe2IE+tw/Ln1qVS9IiVKlWDx1GWcceWpHN2wnjdrzpYEJv82gxKlSnBG4wYkJSSRnJjM0C/+oGL1itz0dGMqVssZGBYoI927UK5U6ZJYXBybVm6mR4s+HH/uMdzy/DVs37CDaSNnc8yZRzFn3EL+HvAvD753J7WOOoQqB3tZpH79fCTLZ63ixqeuou7Je3/Ir1evXsS++7YdyNKZ8Tz07t1Uq1WVsuXLhD0vlp6WDgZ7dibS87U+/NV3fLbzTpffexG1jjyEky44ntevfT8ry92VD17CyG/GUKl6RR7/pElU3yUPrlMj24/jxaVxk0tJ2LGbu165mSoHV2bruu30azcoZNBnQQfLScG565WbOeKEOsTPWUn/9kOy7TvihNqsnO/N2HRm4wbZvnufdulJvPPLq5QtX4bEhESmjpzF8ecck22GmcKQlprG0pkrqHNMLV66rA1LpnsZ0J/o8BCbVm1m3M8TWb98Y1R/J2dc1YBXvnmKGX/NzXZOQrK77pErGNrtD8A7h7R2yXrMjA0rNlGmXGkOP742R55yBOlp6Xz3zk85zitEe75N9k03PHYVv3QdUdzDKDb3vPY/TjzvWCYNmx7V+cBTLjqBW569jrdv2xvMGRyEWBj25e8qha3hVacybcSs4h5GNjWPPJgah1WnQpVyzB2/iIy0DA6uW4NV89ZErFf/zKMoV7Ess0d7yWCafnQPaclpHNngCEqVLkXHR7qxKcRMTMHqnXI4p156EhWqlmfFnFVM+Hlyno6j3qlHED9rZdTlb3rmGhpeeTIJ23eTsH0Ph5/gzYSXkZFBRloGv3QZyY6NOznxwmNJ2L6HH94clFX3oz9b8fLlbcI3HoWDD68R8vePwnJ108sY3nPf/h6Ymdk5ViMzBijIVEREpKgpyFQOVNFexV/1kCocecoRHFS7OnMnLMwWjHX8OcdQqXpFkhKSqHxQpawTU6sXro2Y2UtERIpG5gmIajWrkLwnJd/BmYHKVypXoO2JSMFoeMUpVKpekbTUdMpVLEvynmQ2rNjMxhWb2L5pZ6716xx7KGaWI0t5/dOPZOX81dmmeDz8+NpUrlEx60KgGodVo8Zh1Vk0ZSllK5QhabeXmaNcxbLZPhtWP7QahxxxEKXKlOSQww/yMng4R7WaVUlMSGLPrkTSUtJI2pNM9VpVSUtJY/wg72R6XIk4ylUsm+NCo4pVK3DuDWcwe+x8EhOSosoeD3DJHedhZlSqVjFbQH5GRgYJ23eTvCeZkqVLsnDy0hyZbw854iDqHHsoyYkp1D7mUEqVLoXL8AL7d2zZRfmKZSlTvgzOOQJPz5p5789b128DMypXr8S65RvISEvnsKNrUbpsadav2MjGlZupXb8W5SqVI3FXIqXKlKJMuTIsnLyEMuVLc+TJR7B26XoqH1SZchXKkrg7ia3rtnHIEQdRrkJZls9Zyfr4TdQ4rBpVDqpE+crlGT9wUoFn8hLJqwMxyPTMa04jaXcSc8YuiLnuyZecwL1v/S/H9rWL12dNA5+bWkcdwvplkbN0ixSWsv70nEl+9qgTzj+W7Rt3si7oor6CVOWQyiyd6P1oXqlaBXb5Waky/z+H+4G6dNlSWZ9pDj++NnVPrENiQhJTR+wf08uKFLSDaldn85qtYfdXql6RxF2J1D7mUMpXLkeZcqWpcnBl0tMy2LBiE/VOOjwrUcGubQlUqFyejPQMzLwL0maOnsuWtdsoVbokFatVyJatS0REPP/lIFPZt5UoVZL01LSQ+zYv3160g5GoKchURESkGCjIVERERERERCTvCmOaWtn/HYhBpo92eIDKNSoydsBEJg6ZGrFscDakC287h+sez3nuaev67Xx0b5eIbd352s00uOxEb8rGLQl8cEfHrH03P3sNgzoOi/FIRPYf+mF731bvpMOzzVgkIiIioSnIVPZH+iy+71KQqYiISDEws+8BnHP3FvdYREREREREREQOCH3Mm4v5HndmUXXZv3//M4qqLxEpGnfccUfkiG4RERGR/YC+q8j+SJ/FDzwli3sAIiIi+zMFl4qIiIiIiIiIiIiIiIiIiIjIgSquuAcgIiIiIiIiIiIiIiIiIiIiIiIiIiL7HgWZioiIiIiIiIiIiIiIiIiIiIiIiIhIDgoyFRERERERERERERERERERERERERGRHBRkKiIiIiIiIiIiIiIiIiIiIiIiIiIiOSjIVEREREREREREREREREREREREREREclCQqYiIiIiIiIiIiIiIiIiIiIiIiIiI5KAgUxERERERERERERERERERERERERERyUFBpiIiIiIiIiIiIiIiIiIiIiIiIiIikoOCTEVEREREREREREREREREREREREREJAcFmYqIiIiIiIiIiIiIiIiIiIiIiIiISA4KMhURERERERERERERERERERERERERkRwUZCoiIiIiIiIiIiIiIiIiIiIiIiIiIjkoyFRERERERERERERERERERERERERERHJQkKmIiIiIiIiIiIiIiIiIiIiIiIiIiOSgIFMREREREREREREREREREREREREREclBQaYiIiIiIiIiIiIiIiIiIiIiIiIiIpKDgkxFRERERERERERERERERERERERERCQHBZmKiIiIiIiIiIiIiIiIiIiIiIiIiEgOCjIVEREREREREREREREREREREREREZEcFGQqIiIiIiIiIiIiIiIiIiIiIiIiIiI5KMhURERERERERERERERERERERERERERyUJCpiIiIiIiIiIiIiIiIiIiIiIiIiIjkoCBTERERERERERERERERERERERERERHJQUGmIiIi+WBm35vZ98U9DhERERERERERERERERERERGRglayuAcgIiKynzu+YcOGDYF7insgIiIiIiIiIiIHhOpnZN5zRdXlFVdcUVRd5cmkyfG0az8ia/3Hfs2LcTQiIiIiIlJU9vXvKiKy37G8VFImUxERERERERERERERERERERERERERyUFBpiIiIiIiIiIiIiIiIiIiIiIiIiIikoOCTEVEREREREREREREREREREREREREJAcFmYqIiIiIiIiIiIiIiIiIiIiIiIiISA4KMhURERERERERERERERERERERERERkRwUZCoiIiIiIiIiIiIiIiIiIiIiIiIiIjkoyFRERPY5ZtbEzFwut/Qo24qP0Mb6wj4WEREREREREREREREREREREZH9VcniHoCIiEgIM4A2YfZdBFwGDIuhvR1AhxDbE2IalYiIiIiIiIiIiIiIiIiIiIjIf4iCTEVEZJ/jnJuBF2iag5n949/9KoYmtzvnWudvVCIiIiIiIiIiIiIiIiIiIiIi/y1xxT0AERGRaJnZycC5wBpgaDEPR0RERERERERERERERERERETkgKZMpiIisj951F/2cM6lx1CvjJndBxwB7AZmAWNjbENERERERERERERERERERERE5D9FQaYiIrJfMLNywH1ABtA9xuq1gN5B25ab2UPOuTFR9j81zK7jYxyLiIiIiIiIiIiIiIiIiIiIiMh+Ia64ByAiIhKlO4CqwDDn3KoY6n0NXI4XaFoBOAX4EqgHDDOzBgU7TBERERERERERERERERERERGRA4MymYqIyP6iub/8MpZKzrk2QZvmAI+ZWQLwItAauCWKds4Itd3PcNowljGJiIiIiIiIiIiIiIiIiIiIiOwPlMlURET2eWZ2InA+sBr4rYCa7eovLy6g9kREREREREREREREREREREREDigKMhURkf3Bo/6yh3MuvYDa3OgvKxRQeyIiIiIiIiIiIiIiIiIiIiIiBxQFmYqIyD7NzMoC9wMZQI8CbPo8f7msANsUERERERERERERERERERERETlgKMhURET2dbcD1YDfnHOrQhUws1JmdryZHR20/SQzqx6ifF2gs7/6XUEPWERERERERERERERERERERETkQFCyuAcgIiKSi+b+8qsIZWoD84EVQL2A7bcDLc3sL2A5sAs4GrgOKAv8BrQv4PGKiIiIiIiIiIiIiIiIiIiIiBwQFGQqIiL7LDM7AbgQWI0XEBqrv4DjgNOB84AKwHbgb6A30Ns55wpksCIiIiIiIiIiIiIiIiIiIiIiBxgFmYqIyD7LOTcfsCjKxYcq55wbA4wp+JGJiIiIiIiIiIiIiIiIiIiIiBz44op7ACIiIiIiIiIiIiIiIiIiIiIiIiIisu9RkKmISCEys3gziy/ucYRiZm+bWZKZHR5F2fJmtt7MehfF2EREREREREREREREREREREREpPgpyPQ/ysxc0C3ZzDaZ2TQz625m15hZiTB1ewXUuzZMmdb+/mYRxnC4maX75d7PZbwlzOwRMxtjZlvNLNXMNprZLH+8NwaVbxTiGHPconmschlX5mNRL79tiRQlP7D0JeAr59yq3Mo75/YAHwD3mtnZhT0+ERERERERERERga1bd9P1q7EMGjKzuIciIiIiIiIiIv9RJYt7AFLs2vjLEkBV4CTgfuBhYIqZ3eucWxShfjsz+905l56HvpvhBTo74CEze8s5lxZcyA92/RW4GtgODAVWA9WBo4F7gOOBISH6WAH0ysPYRA50bwJlgI9iqPMl0Ap4F7iqMAYlIiIiIiIiIiIie33+5VhmzMj1GnERERERERERkUKjINP/OOdc6+BtZlYT+Ay4HfjDzM50zm0MUX0JXlBqU6BbLP36gaNNgZ3A98DjwI3AzyGK340XYDoTuMQ5tyOorfLAOWG6ig91jCL/ZWZWBbgX+DOaLKaZnHNJZtYPeNTMjnHOLS60QYqIiIiIiIiIiIgCTEVERERERESk2MUV9wBk3+Oc2wDcBYwGDgdeC1P0HWAP8LaZVYixm2uAOkA/4HN/2yNhyp7vL3sFB5j6493jnPsrxv7zzcwc8KC/utzMnH+LDygz2t9W2szeMrOFZpZsZr38/VXM7GUzG2Vmq80sxcw2mdkQMzs3XL9+uweZ2Vdmts5vc66ZPRSivJnZg2Y2wW87ycxWmdnvZnanX6asmW03s41mFjL43My6+n1fF7T9eDPr5beZbGYbzKyPmR0Xoo1efhtHmdnTZjbLzBLNbHS0Yw1qr46ZdTazZX7fW/zH7qyQT1oEZna2mfUzszV+W+vMbISZ3RGi7B1mNtbMdvjjn21mr5pZmRj6K2NmLf3HYI+Z7TSzcWH6q+c/br38+33NbLP/+Ewxs+tjPNy7gfJ4f3+x6gsYXpC4iIiIiIiIiIiIiIiIiIiIiIgcwBRkKiE55zLwpsQGuNvMLESxtcDHQC2gRYxdNPeXvZxzc4BpwFVmVjdE2S3+8tgY+4iJmTXKDOCMskobvOyqAB399TZAhxBlfwKeACb4+2f7208A3gMygKHAJ8BI4DJgnJldHabvqsB44DzgR+Bb4DCgp5k9GFT2PaAX3vPU3+/jD6A2XrZanHNJeAGHB+MFAGfjB0/eAWwAfg/YfjXec3cvMNl/HP4E/gdMMrOGYcbfES9IebZ/f3y0Yw3ouyEwA+9xXYiXffcX4GLgbzO7NkzfOZjZI3jPzc3+8mO85+MQv/3Asu/jPVYnAH2AznhBl+8Dv5tZqSj6K433OH4AlAK6AL3xXuP9/D5CqQtMAur55fsBJwODzezSaI8XuMJf/h1DnUyTgFTgyjzUFRERERERERERERERERERERGR/UjIjIUivr+BNLxAu3rA8hBl2uEFjL5oZl2dc+tya9TMagPXAouccxP8zb2ATkAz4M2gKj8DrwCPmVklYCAw1Tm3IopjqGdmrcPsW+Cc6xtFGyE551qbWT2gAdDBORcfoXhd4GTn3Oag7fOBw4K3m1kdvGC+T4HhIdprAPQAHnXOpft1PgVm4T1W3wSUfRRY4/e/J6ifgwJWe+E9lw/iBWsGuhGoBnzinEvz61YDfsDLZnuxc25eQLsnAROB7kCoQNOGwOnOueDXVFRj9bOt9gcqApc658YE7DsML+C1h5nVc84lh+g/sN0T8bLp7gQucs7NDdpfJ+D+ecCrwCrgbOfcen/7q3ivy+uBl/ECTiN5EbgEGAbcGPCYtsF73l81s18D/j4yNQJaO+faBIypD95r5GUg2oy+FwK7gEVRls/inEs0s7nA6WZWyTm3K9Y2RERERERERERERERERERERERk/6BMphKWH5yXmUX04DBlEoBWQAW8zJTReBgogRfUmKkPkAI0NbMSQX1MB+7Dy6J5H15W0Hh/avSBZnZDhL7q+uMLdbsrqOwkvOyUD0R5HLF4M0SAKc65HWG2r8bLUHq8mR0Ror09wAuZAaZ+nXl4GUFP8INxA6UC6UHbCOzbOfcPXtDhDWZWPahoZnbUwODVB/AyqrYKDDD125oLdMMLRDwxxPjbhQgwjXqswHXA0cBngQGmfrm1eMHPtYDLw/QR6HG8gPt3ggNM/fZWB6xmThH/bmaAqV8mDS9wNAMvUDo3TQGH9xymBbSzkb1/R6HaWcHeDMOZdX4HVgJnR9FvZhbVmsB655yLpk4I6/H+f9TOY30REREREREREREREREREREREdkPKMhUcmP+MlIwWndgHvCQmZ0SsTGzOLwAuwy8Kd69xp3bAvyKN+X7dcH1nHP9gSOAxnhBeL/ivX5vBoaY2TdmZsH1gDHOOQtzuzmojz3OuQXOuZWRjiGPJoXbYWYXmFl/M1tlZslm5szMAU/7RUIF8i12zu0MsX2Vv6wasO17vEy0c83sAzO72syqhBnON0BpAgJwzawm3uM+3Tk3K6Dsef6ygZm1Dr7hTf0OXuBusHCPR7Rjzey7bpi+MwMuQ/Ud7Fx/OSyKsplZWUcF73DOLQJWA0eaWdVwDfgBwPWBtc65BSGKZLZ9eoh9MwIDiwOswss0G40a/nJblOVD2eovD4pYKp/MLD7z7yHEbX3uLWRrq46Z9TSztf7fWbyZdfAz8oqIiIiIiIiIiBxwUlLSiI/fQt6vNRcRERERERER8bL3iYRkZmWBzIyWm8KVc86lm1kLvMDPdsA1EZptjJdd9Hfn3JqgfV8D/8Obsn1IiH5SgRH+DT/j6a1AT7ysmgOBQbkdVzEJGRBnZrfgZSxNAkYCS4HdeEG4jfCmVC8Tour2MP1kZsUMzAb7vN9uU6Clf0szs9+AF51zSwLKfosXxPsg3hTyAPfivVcEZjGFvcGKj4QZS6aKIbaFCxCMdqyZfd+eh76DVfWXwa/HUDIDXteF2b8OLxi6CuGfo2jaCBxXoHBtphH9RQOJ/rJslOVDKRfUVmHaAXQIsT0h2gbM7GhgAnAIMBhYgBeI/CxwtZld4Ae6i4iIiIiIiIiIHBAyMhwvv/Iza9Zu56YbTuX++87NvZKIiIiIiIiISAgKMpVILsR7jWxwzsVHKuicG2pmo/ACtq6MULS5v2zsZ+sM5WozO9w5tyrM/sw+04H+fvbUN4DL2EeDTCNMS/4OkAKc6ZybH7jDzL7ECzLNb9/pQEego5kdgve83oUXoHmSmZ3knEv2y672n8crzOx4P9Pmg3hT2PcJanqHv2wQlOE0qmHlc6yZfd/knMsRkByj7f6yNl7wYSSZ/dbCC4YNdmhQudzaCCWaNvLMObfdzFLYG6ibF5l1NxbAkHKz3TnXOp9tfI4XYPqMc+6zzI1m9gleYPN7wGP57ENERERERERERGSfMX/BOtas3Q7A4F9mKchURERERERERPIs2sx38h/jT2v/ur8aHFwYzkt4wYMfEeK1ZWa1gOuBnUCPMLfxeFk4m8Yw3F2ZXcRQp6BkTl1eImKp8OoD80IEmMbhBVgWKOfcRufcz865O/CmZT8aODmoWC9/+aCZnQacCgxzzgVns/3XX15U0OOMYqwF2XdmW5Ey8Gaa7i8bBe8ws/pAHWC5c257uAacc7vwAlRrm9kxIYpc6i+nRTGevJoNHGpmlfNY/zhgC7C64IZUOMzsKOAqIB7oErS7FV7m4PvNrEIRD01ERERERERERKTQuIziHoGIiIiIiIiIHCgUZCo5+Bkk++IF0q0E3o+mnnNuOvAd0AC4O0SRpniZUb93zjULdQOa4AWqPuwHWmJmd5vZlZnrQWOtxd7p2sfGcJg5mFl5MzvezI6IoVrmFNux1AkUDxxjZocFjMPwgt9OzGObWcysjJld7rcZuL0UUN1f3RNU7We8QOD78J4P2Bt4GuhrvCygrczs7BB9x5lZo0Ia62C8QM0nzezaMO2dZ2blo+j6C7zp5t80sxyPuZnVCVjt6S/fMLODA8qUANrjvaf2iKLPnnhB0R/5dTPbOQh4M6ivwjAab6w5njd/HEf7fwulQuw7EqgJjI6QobcglTGz+8zsNTN71swuDXzMonCZvxzhXPZT637A73igPKBUDiIiIiIiIiIicsAoWy7HqT0RERERERERkTwpWdwDkOJlZq39u3FAVeAkvAyapYFJwL3Ouc0xNPk63tTm9YP6MeBhf7V7uMrOuSVmNgYvwPUaYChwDvAssN7M/gaW+8WPBK4DyuEFHf4Yosl6AccYSoeArJNnA38Bmf1H40/gZaCbmf0IJOBN7905yvqfAl2B6Wb2E9609BfgBZj+AtwQZTvhlAP+AOLNbCKwAigLXAmcAAwJzqLqnEs0swF4z9cTeIG0Q4Mbds5tMbPbgIHAv2b2JzAXyMALuj0Pb1r1sgU9Vudcqpn9D/gdGGpmE4AZeEGohwNnAUfhTT0fHEQbfBzzzOwJ9j4Pg4HF/tjPxMuUe6lfdoKZtQNaAHP853w33mv1ZOBvvEy+uWnv17kJmGlmv+EFOt6ON617O+fc31G0k1c/AS8CjfEe82B/AnXx/sbig/ZdFdBGUagF9A7attzMHnLOjYmi/nH+clGY/YvxjulYvOMOy8ymhtl1fBTjEBERERERERERKTIlS2bP2ZCamk6pUnmdkEtERERERERE/ssUZCqt/GUKXjDdCuBbvACyHJn/cuOcW2VmHYCWQbuuwAv6m+6cy20a8G54QZ7N8YIbP8YLBLsCb+r2xnjBh1vwMjL2AfqEyapYl73HGEovvGyceeKc+93MXsTLpvo8XnDuCiCqIFPn3Jdmlgw8BzwIJALjgIeAW8l/kOlu4BW8IMnzgZvxnuelwOOEz5bZCy/ItBTwg3MuJcz4/zSzU4GX8J6Xi/BeS2vxpriPJRAxprE652aZWQPgBeB6vMcsA1iHN619KyCqAGnnXDczm+MfRyO/783ALIKCop1zr5jZdOAp4AG8x2gp8AbwcbjHKqiNFDO70h/7PcDTeNlUZwLPOed+iGbceeWc+8c/hnvNrKVzLj2G6g8CmyiaINOv8f4e5uK9Fo7Ce9ybA8PM7Dzn3Mxc2qjiL3eE2Z+5vWr+hioiIiIiIiIiIrIPCTpbvicxhSqlyhXPWERERERERERkv2ZFM9uxiIjsS8zsbrwA7f855wZGWedUvEDYN51z7xbm+HIZR3u8TKyDnHO35FL2K7wg8EecczmyKJvZ+8CrwKvOubZ5HM/Uhg0bNpw6NVyiUxERERERERERicnwM73l1VOKrMutW7cWWV/RSk1N5+77eoTc92O/5hHrLo/fzMuv/Jy1/vlnd3PIIZUKdHwi+7rq1asX9xBERERE8m1f/K4ikht9Ft+nWV4qxeVeREREDkB9gYlAazOL9h/I28BqvOzCxamrv7w4irKZmUqrhNlfOaiciIiIiIiIiIjIPqHPD5PyXDd4jjIlHBERERERERGRvFKQqYjIf5Dzzio3BwYCh+VW3szKA9OB+51ziYU8vNxs9JcVoii70F8eG2b/Mf5yUb5GJCIiIiIiIiIiUsB+GTo7z3Ud2YNKMxRkKiIiIiIiIiJ5pCBTEZH/KOfcLOdca+fcmijK7nHOtXHOjS6CoeXmPH+5LIqyf/nLq8ws2/88M6sEXAAkAv8W3PBERERERERERESKl8vIHlT6zHP92LhxVzGNRkRERERERET2ZwoyFRGRfY6ZnWRm1UNsrwt09le/C9heysyON7OjA8s755YCI4B6wJNBzbXBy4b6rXNudwEOX0REREREREREpFgF5y11Djp1HlUsYxERERERERGR/VvJ4h6AiIhICLcDLc3sL2A5sAs4GrgOKAv8BrQPKF8bmA+swAsoDfQEMAHoZGaX++XOAS4FFgGvF9pRiIiIiIiIiIiIFAMXHGUKLFi4oegHIiIiIiIiIiL7PQWZiojIvugv4DjgdOA8vIyj24G/gd5Ab+dCnSrPyTm31MzOBN4GrgauBdYBnYA2zrmtBT56ERERERERERGRQuScw8wi7hcRERERERERKQhxxT0AKVhm1sjMnJmNjlCmnl8mPmh7E3978G2XmU0zs9fMrHyI9nqFqRd46xVUJ3h/upltNbPR/jjCnx3z6l9hZv3MbKWZJZnZNjObbGatzKxamDqBx9cul8fvu1D7I4ynhJk9Z2azzCzRP5bfzOz8CHWqm1kHM4s3s2QzW2tmPc2sToQ6J5pZfzPb6B/3QjNrY2blQpStamYvm9n3ZjbPzNL8Y7siQvsn+O0N9h/bzMcrbEC6/5xFeu7LBpVvHcXrZWm4/kL0H1w32cw2+a/Z7mZ2jZmVCFM38LV7bZgymeNtFu2YJP+cc2Occ3c75453zlV1zpVyzh3snLvSOfdtcICpcy7eOWfOuXph2lvlnHvIOXeoc660c66uc+5ZBZiKiIiIiIiIiMj+KLcYUgWZioiIiIiIiEhBUSZTCWUmMMi/HwfUAm4A3gOuNrNLnXPpIeoNBmaEaTPc9jb+shRQH7gFuAQ4E3gquLCZlQG6A/cBicAwvOmuKwKXAa2Bp8zsVufc2DB9AjxjZl2ccysilImKHxDbF7gNWAh0BqoDdwJj/bEMDqpTA2/67mOBUX7944GHgOvM7Dzn3LKgOuf4ZUsBPwKr8I75LeByM7vcOZccUKUekBlMuxrYDNTM5XAa++2lA4uBJLypyaPRJsz2tKD10RHauAFoiPe8xiqz/xJAVeAk4H7gYWCKmd3rnFsUoX47M/s9zGtbREREREREREREJM9mzV7Dz4Omc/65R3HVlSfmuz2HAyJlMs13FyIiIiIiIiIigIJMJbQZzrnWgRvMrCowC7jIv40OUW+Qc65XLB2F6OcCYCzwhJl97JxbHlTlC7wA02nAzc65VQF1DXgS6AgMNbOznXPzQ3S7BC+g9X3g3ljGG8ZdeAGmE4DLnXNJ/ni64k3t3c3MRjnndgXUeR8vwPRT59wLAcfwjD/+z/Gm9c7cXgL4GigP3OScG+JvjwP6A7cCzwNtA/pYAVwBTHfObfWzyT6Yy7EMA/4BZjnnEs3Ldls3mgch+LmMUG40IV4//jE+7K9+FU1bufVvZjWBz4DbgT/M7Ezn3MYQ1ZfgBaU2BbrF2reIiIiIiIiIiIhIJG+/OxSAOXPWcsEF9alQvnT+GlQmUxEREREREREpInHFPQDZPzjntgOT/dWDC7Gf8cACvEuwzwjcZ2YX4mX63AZcHxhg6td1zrnOwEd4mU07hemmPzAduNvMziyAYT/uL9/IDDD1xzMZ6If3eN0WcBwV8DJs7gZaBbXVGYgHGpvZUQHbLwFOAMZmBpj6fWQALfzVx/xA28x925xzf8YyHbhzbqFzbqJzLjHaOgXoWqAO8K9zblZBNOic24AXBDwaOBx4LUzRd4A9wNv+85MrMzvDzDqa2Uwz22pmSWa22Mw+NrNqIcqXNrNnzGyamW0zsz1mFm9mg83sihDljzezXma2ysySzWyDmfUxs+OiPf6i6NfM6pvZAL/t3WY2wcyuM7MmZubMrEks4xURERERERERETnQbNu2J9t6QkJSmJIFSDGmIiIiIiIiIlJAFGQqUTGzKsBZQAZegGahducvU4O2P+Ivuznn1kWo/yGQDFxhZkeG2O+Al/x+2kc9KC9gzgVtKwOcjxegOC5Etcxp3y8L2HYeUA4YH5TdNDNodIS/emnArsz6w4M7cM4tAxbhZRw9Knh/UTGzO82spZm9YGbX+I9NLJr7y5izmEbiP6bv+qt3BwbiBlgLfAzUYm/Qbm4ewQtgXYiXZbYrsA54ARhvZpWCyvfCy1JbCvgWLwh6LHAKAVlrAczsarxsvffiBXd3BP4E/gdMMrOGUY6xUPs1s2OAf/GCqP/xy68GBvl1RERERERERERE/vN27Mx+Tb9FmOY+WrklKs1QJlMRERERERERKSAli3sAsk86zcxa+/fjgJrA9UAV4Bnn3JIw9W42s3ph9vV1zi3IrWMzuxg4DkgBJgXtvtBf/hGpDefcNjObihf8eQGwPESZUWY2FLjOzG4MzA4ao/pACWCZcy4txP7F/vLYgG2ZGSEXhWkzr3WO9W9LIw24EPUNWt9oZk86537MraKZ1QauAXbgZX8taH8DacAhQD1CvCaAdniBri+aWddcApkBPgCedM6lB240s4eB7sATeAHPmUHadwFTgXNC1KkRcL8a8ANe4PLFzrl5AftOAib67ecaaFoE/XYBagDPOec6BpS/CS/QVERERERERERE5D8veOr6jIz8B4BmZGQQMY+IYkxFZD8xe84qxo5byLVXN+DIIwttMkUREREREckHBZlKKA38W7AfgFER6t3k30KZAeQIMg0IZi2FF7B5C16G0ZdCBPkd6i9XRRhDpswyh0Uo0wIvk+OHZvZbmCDRQCeE2FbFX+4IUydze9ViqFNUBuNlhJ0ObMHLqPog8CLQz8yud84Ni1AfoBlesO53zrk9uZSNmXMu2cy24AVMH0zowOMEM2uFl5H0HX9MkdpcEWZXT+AToDF+kCneKV3Dy7CbEaKtLQGrD+A9j08FBnr65eaaWTfgOTM7MXh/qGEWVr9mVge4Eu+x7BxUfrCZjQEuyWV8IiIiIiIiIiIiB7zgoNLgoNO8ePq5frzd6gZq1qwcuk9lMhWR/UBKShrPPv89ACNGzmHwz88V74BERERERCQkBZlKKN8455pkrphZTeAKvKmwrzezRs65aSHqPeSc6xVjX62C1h3wsHPu6wh1ojk7ljnfUNiyfqBcD7zslc2BzyM1GE0m1ryMoxjrFAjn3KdBmxYCr5nZWuAz4H0gbJCpmcUBTf3VrwplkH5X/jLSY9QdeAZ4yMw6Oudmh23MrBTwKF6m0BPxAoEDUwfUzrzjnNtpZr8ANwAzzOwnYBwwMURQ7Xn+skFAEHagzAy3JwDzzKwR0CioTLxzrldh9guc7q//HZwh1TcaBZmKiIiIiIiIiIjggoJM/8/eXcfHUed/HH99o01Td3elhQIFStHi7nbo4cfB/YDD4e7ww10Op0UKLbSlpUbdaEvd3dKmEmmkcdnd7++P2YRks5vsRivv5+ORx+zMfG12N8nuzGc+3+rIZJqSks2HH8/i5RcD5H1QkKmIHAKS92cWP87MzKvDkYiIiIiISHkUZHr4KcpWWM48OcX7ymQ29MdamwgMN8bEAF/gTBN+QaVHWLptA2CMicUJcvsK+NQYs9Na65s1NQHoCnTCCWQsTwfvsqIpz58FbgKeM8Z8F8rYvYoyiDYOsL+RT7narFPXvgTeBY41xjS01mYGKHcRzmv6h7V2dU0MxBhTD2jmXU0OVM5a6zbGPAFMAN7wji2QkTiZd7fjZHNNwMkYCvAwEO1T/gbgSZz32wvebXnGmFE4mXsTvduKprC/p/yjooF3OYSywdpzgGE13G/RezExQLmECtoRERERERERERE5IvgGlVZXltGNmwKdmquDbAQiIiIiIiIictgqLxBRDk1FQYbNyynTwrtMD7HtRd7lSSHWq5C1NttaOx0n42I48I0xpr5Psd+9y3PLa8sY0xQY6F2dX0G/icCbQCvgqVDHDWwF3EA3Y4y/oO2e3uXmEtuKAmR74V911alT1to8oCiwNLacovd6l5/V4HBOwwmqT7TWxpVX0Fo7EZgJXGiMOc9fGWPMCTgBptOBPtbaO6y1T1trnwdeBKL8tJtrrX3eWtsLJ6j2Fpz39C3AqBJFi36HB1hrTTk/33jbfd7PviE13W+J8q0DPJVtAmwXERERERERERE5ovgGlVZHJtOK+GZPFRERERERERGpLAWZHn424WRT7GWMCRRoWjQt9qoQ227qXdbY+8abyfILnEyk//TZ/aV3ebcxJlBgG8BjOFkkp1trdwTR7VvAXm9/HSoo6zvefGABUB843U+RokyYJbOy/gHkAqcaYxqWLOydOv587+qsEruK6l/o24ExphtO8OlOnKyaBwVjTG+c90wmsD9AmXbAJTgBiz/V0DjCgH95V38IstpjODf7v4n/93sP7/JXa22hz76TgJjyGrfWxltrh+NkBN4CnFbi9/UP79Lf+6lKqrnfFd7lacaYcD/7h1R6oIAxprkx5m5jzC/GmK3GmFxjzAFjzO/GmLu8r2uwbcUZY2yAH2VcFRERERERERGRGuWbuLQ2AkAVYioiIiIiIiIi1UVBpocZb/bIEThZG980xpiS+40xHYDHvavDgm3XG0T2kHd1dpUHWr6XgTzgMW9WUgCstXOB73CmPZ/gPRbfcd6HMzV4VonxlstamwP8Bycw0Hfa8ZJt9zHG9PGz65OicXunZS8qfyLOVOXJwOgS/WV5jyMWeN6nrX8AXYAp1tqSAaNzgA3AGcaYy0v0EQa87l391NpqmmcpSMaYbsaY9n62twCGeldHWGtdAZq4Cydz7Xfe16G6x9cK5/dhCLALeCWYetbaFcD3wADgRj9F4rzLIX76+9jPOFoaYwb5aScWaAi4gALvtqE4WYafM8aUyRpsjAkzxgzx3e5PTfZrrd0NTAO64rxvS5a9AjgzmDGW4zqcgPNBOFmU38P5PeqPE3D+k+/ftwocAF7w8/NWFccpIiIiIiIiIiJSLo/H47NeC0Gm5ZwqzszM45l/j+WxJ0eTlJQZsJyIiIiIiIiICDiBiHL4eRQ4EbgDGGyMmQZkAJ2BK3CCy1631s4JUP9YY8zzJdZbAWcDvXEyUj4RoN6VxpguAfbFWWuHBTN4a+0eY8xnOEGiTwBPl9h9L8779kZgkzFmMk5GxljgLJwAtBTgGmvt+mD68xoGPAwcXU6ZDd6lb2DbCOBq4FpghTFmPNAcJ8A0HLjHWpvhU+cZnADFR4wxxwKLgb44r08S8EDJwtZatzHmDpyMpqOMMaNwgibPAU4A5gPv+g7YGPMW0MK7epp3+bgx5hbv47HW2rElyregdNBdUd2vjDFFZyVfs9Zu9D4+A/jSGDMH2Aak4kzJfjHQGFhKgPeLN0D2Lu/q5/7KhKLEezYMaAL0wznmKJzn92Zrrd+MqgH8CyfQsYeffUtwnvOrjTELcKafb42TuXYTTmbcktoDfxhjNgDLgXigEXApzrTyH1hrMwGstSnGmGuBX7x1ZgDrAA/OczsY5/1Vj4rVdL8PAAuB94wx5+NkR+4BXAWMBy4LYoyBbAYuByZaa4vPwhtjnsF5Pa/B+b0b7b96GenW2uerMB4REREREREREZEy3B4Pw39YTFpaDrfePIhmzWLLlLGlY0zx1EKugPK6GPrNAjZvSQLg08/n8uy/L6nx8YiIiIiIiIjIoUtBpochb7DYIOBBnGCv23GydKbgZMT8xFo7qZwmBnh/iuThZG58H3jDWusbQFfkCu+PP3MIIXMq8CpwD/CgMeY9a20iFGdqvckYM8y7fzBOIFsesBUnM+EH1trUEPrCWusxxjwO/BZKPW9da4y5EVgA3An8n3c8c4GXrbUL/NRJMcYMxsmceiXOFOUpONkkn/VmifSts8ibHfUF4HycYOGdwIs4gZ/5foZ3LU5wcUnnl3gcB4wtsd4A+Kufdm4r8XgYUBRkugwn4+dA4FicAMZMYA3wE/CZtbYA/y7wju0Pa+2aAGVCUZSFtsA7hp3AtzhBiFNLBioGw1obb4x5D3jKzz63N6PsyzgBtQ8Ce3AybL4M+AY4x3nHNwQnGLoFTkDuJm/7I3zan2GMOQZ4DOd5Ot17XHtxAo2DDays0X6ttVuMMScDrwHnevtZjfOebkkVgkyttTMDbE8wxnwK/NfbX7DPhYiIiIiIiIiISLWbNWszv45fDUBOTgFPP3lhmTIHWybTJUt3Fj9evWZPjY9FRCSQ0CYsExERERGRuqIg08OUd0r2VwhyenBvnWGEFghaVO92nEDWUOqU+63RG1Ra9pbvP/dPBaaG2Ocwyjk+a+0UymYpLbm/vH0unEyiZbKJllMnFSdb60Mh1FmPk10z2PJdgi3rLR9HOc+Bn/JrCPG1L1F3cih9ldNOpduo6L1rrX2a0pl0S+5LBe4PULWLT9l0nGDgF0MYWxw+09CHqjb6tdZuxQlmLsUYc3uwbVRCoXfpCqFOtDeDbycgGycYdq611l3dgxMRERERERERkSPH7Lmbix8vW77LbxnfmNLyAkCrS3l9uF0h3Y8vIlJjfP9WWWsVeCoiIiIichBSkKmIiBwyjDER/JlZN5TMw22A73y27TDG3GGtnRNk38sC7OoTwjhEREREREREROQIUzeZTP1vX71mDy63gkxF5OBgff4eejyW8HAFmYqIiIiIHGwUZCoiIoeS14D+wCRv9uFgDAXmAeuATKAbTrbWe4HJxpjB1tpVNTFYERERERERERER34BP36CqkjIz8xj+42JiYqKq2Kf/Pl58eWKV2hURqS7Z2fnkF5SesKw2Mj2LiIiIiEjoFGQqIiKHBGPMg8CjwEbg1mDrWWtf8Nm0FrjPGJPlbe954Kog2hkYYFzLgOODHY+IiIiIiIiIiBxZfIOmystk+v0Pi5kxc2M19BlcOWUMFJG6sHbdbp54aiR5eYWlttdGpmcREREREQldWF0PQEREDj/W2mHWWmOtHVYd7RljHgDeB9YDZ1lrU6uh2U+9yzOqoS0RERERERERERG/fIOmPOVEgFZHgCkEnw0wPFyXiUSk9v3rP6PKBJiCgkxFRERERA5WOnsgIiIHNWPMw8BHOBlIz7LWJlRT00neZWw1tSciIiIiIiIiIlLGhElrSq3XRhBVsJlMIxRkKiJ1IDMzz+/2YAPkRURERESkdunsgYiIHLSMMU8C7wIrcQJMk8qvEZLB3uX2amxTRERERERERESOIBVNNu/xWNav31dqW+0EUQWZyTRCl4lE5ODh9njqeggiIiIiIuKHzh6IiMhByRjzH+A1YBlwjrV2fzllI40xfYwx3X229zPGNPNTvjNOdlSA76tx2CIiIiIiIiIiIsXc7rIBU7WRyTTYPsLDdJlIRA4etoIYU7fbw5q18eTlFdbOgEREREREBICIuh6AiIiIL2PMX4EXATcwD3jQmDJ5IeKstcO8j9sDG4CdQJcSZa4DnjLGzAJ2AJlAd+ASoB4wCXirRg7iIJGdnU/9+lH4ef5ERERERERERKQG7d+fRUGBq8z2g2k66LAwnTMSkYOHp4K/j2++PYmp09bSvVsrPv/0Dp33FhERERGpJQoyFRGRg1FX7zIceDhAmTnAsAramQX0Bo4DBgOxQDrwO/Ad8J09mM7qV7MxY5fyv09mMPD4Lrz2yvU64SYiIiIiIiIiUsPcHg/hYWFs3JTAs8+N9xswdTBlMkXni0TkIOLxk/25pKnT1gKwbXsScTv307VLy9oYloiIiIjIEU/zoIiIyEHHWvu8tdZU8DOkRPk477YuPu3MsdbeaK3tY61tYq2NtNa2tNaeZ6399nAOMAX46OPpeDyWJUt3sHbd7roejoiIiIiIiIjIYcf35NKYMSsAeP3NKQEz8tVGkGmwCgtd7N6dVtfDEBEBQsv07HK5a3AkIiIiIiJSkoJMRUREjgBpadl1PQQRERERERERkcPeyJ+XAZCZmR+wTG0EmQYbqJWZmc/Dj/7MuF9X1fCIREQqFsrfx8M7hYSIiIiIyMFFQaYiIiJHgIMpQ4aIiIiIiIiIyOGiMpPNBwoAnTV7U8htBTrnE2rw1XfDF4Xct4hIdcvODhygX4ZOeYuIiIiI1BoFmYqIiBwB3O7SZ9xSU7N46b/jeP+DqZpWSERERERERESOeKmp2bzz3nS+Gjoft9tTo32tXBnPnLmbKfQ5J/PxJ3NCbuuPRTv8bl+yNK4yQxMRqVN33P0lGRm5fvcVFLhKre/ek1obQxIRERERERRkKiIickTweEpfHPngo2nMmr2BceOXM/bX5XU0qoOH2+0hN7egrochIiIiIiIiInXki69+Z8HC7Uz+bR3TZ2wMqo7L5WHDxoSQ+5o1ZzMffjybadM2FG+r7Cw077w33e/2RYvjKtWeiEhd+/b7+X63D/1mXqn1l1/5lZycEDKfioiIiF8HAtzg4Y/HY1m9Zg/7Eg6UW87l8vDOe9N55t9j2bMnvYojFJGDgYJMRUREDlJ5eYXMX7CFPXvSqtyW7zRsc+f9Of3ab7+trnL7h7Ls7Hxuu/0zrrn+Q5Yvj6vr4YiIiIiIiIhIHViydGfx4+9/CG7q+IV/bK9Sn18PW1D82DdDXyji4lKqNA4RkYNJamqW3+0jfyr7t/nZ58fwz0eHM3tOcDcHiIiISGlDv1nAXfd8x3sfzKywbEpKFtff+AUvvjyR/3toJKmp2QHLTpuxgQULt7N5SxJvvj21OocsInVEQaYiIiK1JDU1i6nT1pCenhNU+af/9RP/eW40d//tq5DuIPPH7Q6cDcNWLlHGYeO74fPZl3CAvLxCHntyRF0PR0RERERE5IhSUODiwIHgvif7s21bIuvW7ylzc6VIVeTmFrJu/d4Ky33ymf/p7TeGkN20KFC1oMAddB1f/352XKXriojUpIICFx98NJXX35hQfI47N7cAYwLXCeVf+vIVO1m1Op4XXx6rzwJy0CosdNfY+9Pt9rBo8TZ27EiukfYldNZapk1fy4iRf2gGPTkkTJy0FoDf528lO7v8DOFffPV7qfWfRy0LWHbK1PXFj3crk6nIYSGirgcgIiJyJLDW8tQzP7F1WxL9+3Xgg/duCVhu8+YE2rRpzKrV8QDk57uYM2cjl192XKX7r+yUa0eCuLj9dT0EERERERGRI1JaWjZ33P0lOTn5/Pel6zjxhK4h1V+zNp6H/jm8eH3CuH9Sv350dQ9TjlDvvj+DLz+7tdwyMTFRFBSUvTH438/9GnQ/b787nVEj7yW/CplM8/Jd5Be4iI6KwO3x8P3wxZVuS0SkOo0Zu4yx45YDEBYeRkGBi1mzN5QbSFrZYLyH/jmcm248mZMH9ahUfZGasHpNPM8+N5oWLRry4fu3EhMTVa3tjxu/nI8+nk5YmOG7YX+jbdsm1dq+hG71mnhefX0C4ATV33H7GXU8IpHAfK8fu1we9u07wLLluzh5UFdatGiAx2M5cCCXpk3rs3TZrlLl8/IDf4fZvbv0TJ1ut4dfx6+moMDFFZcPoF69yOo7EBGpFcpkKiIiUgtycwvYui0JgLXrdgc8UfbV13P5+z++4ebbPi21/b0PpoTUn9vtKbXu8XgClARL7QSg5uTkk5YWeNqEuqI73EVEREREROrGZ1/MIiMjF5fLw5NPj2Ta9LW4XMFnc3zNe/G2yNfD5lX3EOUIlp6ey/gJq3nsydFMmbbe4zPRpgABAABJREFUb5no6OrL41FQhSBTgKwsJ+vQrFmbGT9hdaXaWLt2L+vW7/V7rsRay/IVu9i+XTfrikjwxo79M8PZ5N9WM2Pm+goTIlT2fO3adbt55t+jKlVXpKY8/uQIMjLz2L4jmR9G/FHt7X/08XTACRT7aujcam9fQje0xHeS74YvqMORiFSssLD09+/UtGz+++pkhn27kKf/PZZpMzbwyGM/c8993/POe9PL1Pf9l52dnU9+gMDTl16ZxPAfF/Pz6OWMn7im2o5BRGqPgkxFRERqge+UZ74f2ov8MGIhADk5ZafQSE3NYvfuVAoKXIyfsILf52/228b8BVs478I3Sm1LL2fqweTkzHLHXh2SkjK4/saPuf7Gj1m+Iq7G+wvGr+NX8NQzP7Fm7e66HoqIiIiIiMgRafv2pFLrr74+gSlT1wZdPyMjr9T6okXbqmVcUrfS0rKZsaEjB3KDz/SVn19YIzeRfvPdH8TFpfDFl79z733fs2793uJ9W7clk5RUPedUrr3hc377bV2V2sjMdH4fps/YUOk2nn9pAs+9MIE/Fu0os2/suFW88tpvPPnMGHbtSgVg48YEVq6M1ww6IhKYCb1KVf+cK6mAHExKXgvati2xRvvKzy+s0fYlOGFhlfjD55WamsX8BVuqfPORSLB8A0I//t8cEhIzAEhLy+Gzz+cVT3W/YOH2MvV/n7+V9ev3AbB8xS7+euc33P2379iXcKBM2bVr//wuNfKnpdV1CCJSi6rvNlsRERHB5XKTkZlHs6ax7NiRjMdamjdrQPL+jFLl8vIKiYoK7d/wtTd8BMAxR3dk9Zp4AD58/1b6HdWehIR0Xvrvr2zYuNdv3aHD5nHVlQNpEFuP+N2ppfZlZ+ezdu1u+vfvENJ4AFas3Mnrb06kd682PPefq/x+efZ4LHfe82Vx4OxjT4xg7OiHaNQoJuT+qktSUkbA7LC3/PVT7r7zTIac2Tdg/dzcAjZu2ke/o9qH/DqKiBzOrLUUFrr1t1GkinbEJbNgwRaGDOlL+3ZN63o4IiLVZld8Cr/8sowTTujKqaf0LHNDJsDb707mkosH+K0/+bfVvPn2JABiY6PJzskvtT8r2wmyW74ijtWr47nk4gG0bNmomo/i4Hcofyaz1vLUv35iy5ZBHN1+P+9f9ef2vXvTiY6OYPmKOAad1J3GjesDMGfuRl57YwJdu7bkg3dvISIiPOj+0tNz+HHEQsLCK87HkZqWw3MvTGDUyHsBeOPN0GadqcjkKVULMt2zJ50unZvjqYbgqrffnV58nEWG/7gYcIK/fhixmGXLdxUHgl1w/lG0bNGAs87qTeM6PN8jcjjzeCzTZ6zF5fJwwflHEx7E361Dlb+Zt0IJZi/6H5iXV8iGjXtr7Bxubm4Bs+dspHv3VvTq2aba25dD1/wFW5i/YDM9ureu3Y4rH9sowPgJK5gybS033nAyp57SM6g6Y8ctY/2Gvdx6y6l07NAMAGMq90IUFLj42/3DSEnJ4sILjuaJxy6pVDsiofANaI7bmRJyG8++MJ5337qWV177DYDc3ELee39mhfV+HLGEE0/sQo/uLUPuU0TqxqF3lklEROQg4/FY8vIKefvdycyY6X/6NF/5+YW43dH8sWgbMfUiadeuCQT5xbMowBTgf5/M4OMPb+O9D6YGDDAtcvmV73Hyyd3Ztze9zL6XXhnHyB8eqLDvggIXq9fE07VLS/LyCnnsiR+x1gnanDtvo9/AzN/nby6TmfXb7+fzj/vPrbA/ay2LFm8nMzOXYwd0Kr44mJ6eQ5MmzsWkHXHJbN2aSPL+TC6+cAA7d+6nffumtGjRMGC7cTsDT+22d286L748LmCQqcdjefDh79m2PYkzTu/N889eVeFxSN0qLHQz6bdVREdFcMH5R1fqJI/HY1m3fjedO7UoDpDOyclnztyN9OrZhu61fcLwIJeQeICtWxM56cRuNXISf+Wqnaxbt4eLLhpAs6ax1d5+TUhLyyYyMpwGDeoBzt+38t6LbreHsDBT6ZOSFUlPz6Fx45hqbd/t9vDQI8PZti2Rp564lDPP6BNyG6lp2axdG88JA7tSv350tYxr7750cnPy9XsqAMz7fRPf/7CAq64YyIUXHFPXw/HL7fbwzL9/JjExg9/nb+aTj2+vsI61lrffmcxvU9fwn39dUanfPxGRynC7Pbz033Fs357EE49dEvDmRWstfyzaxr/+40xhO278ck47tRe74v1fwLrnb1+zKz6Fnj1a89+XrqVx4/ps3pJQHGAKzg2TvnJyCnj/g6mMG78cgO07knnx+auDPp60tGwAmlbTZ0xrLQcO5BZ/f61O3w9fwMZNe7n7riF06dyieHtBgYu/P/ANO+KSiY2N5vtv7yMqMpycnAKaN2+A2+2p0cCkvLxC6tWLLLfMsuVxfPHlbAad1I07bj+j1L6cnAK2bHGyfK3Z04Kx45YxafIqtm5L8tcU9epFkpfnZO3auHEfo0Yv4YbrB/HeB1PZujWRhx48v0zgT35+IV98OZtCl4eJk1aGnIVz8m9riYgIJzUt8KwxdWHL1iROPaU7rgCz54QqJSWLKdPWc1TfdvTtW/o5XLpsV6n1KVOd82G74tN48B9nVUv/lZGf7yIqKrzGvkcFq6Lve3UlJSWLb777g2bNYrntlpOrlGlNat+ChVt47Y2JAEREhHH+eUfX8YiCk5SUUXEhXz5/lvfvz+T6Gz8Ouvr9//iG6OjI4vPlJ53YjddeuT70cVTgq6FzGfOLk4nt/PP689gjF4V0o0NJ+/al8+rrE7DW8uLzV1fbZxGpfbt3p/Kf50Z710KbDnr4DwsYP2ElN904GICvhs4hMzOPc84+iuuuOYlevRTMDH9+ljx5UHdu/+vpfsvsiEtmw4a9nHFGbxrE1quwzaysPN5937mB6D/PjWbmtKcqrLNtWyIffDQNgN17Unn/nVuIjAyv1P/XRYu38f6HU0lJyQLgtylryM93ccbpvTl2QKfim6vEv+zsfCIjw0tdizhYP48dDBYviSMtPYdOHZuxZs2eamnzn4+NKrW+bXtyhXVG/7KCmbM38clHNxEREfp3VI/H8u33f7B/fxa33zaYFi0ahNyGiITGaMoAERGRyjPGLGvQsN3xJw76v5DqvfPWjezek8Y77/5W5TE8/eSlvPr6hCq3c/NNp3D5pccWB3J6PLb4y3BKShaZWXkM+2Yec+dt8lv/L9cP4p67h7B0WRxYS1Z2PuMnrGDlql1+y/828bEyX/hWrNjJ9rhkTjulJ4UuD8O+mces2X9O89auXRP2eoNkL7zgaJKTM1m2PM5v+59/cgdr1u7mpJO60a5tk1JfJpcu28ETT40s9/mYMfVJ8vIK2bsvnW5dWxbX37cvnZtv+7S43Lnn9OOBv5+jL/kHsQmTVpb6XTtrSF+efPwSjDFERoaTlZVHalo2nTo2D9jGp5/P5KefF9O8WQPeeuMvtG7dmHfe+43pM9ZRr14kP3z3d8LCDNu2J9Glc4tSwYSh2L8/k0aNYoiMDGfPnjQaN67PxMmraNe2CWec3rtSx+/L47Hk5zsXfkv+XrjdHtxuT8CgULfbw9q1u+nUqbnfE91ut4fUtGxycwq4854v8Xgs55/Xnwf/cR7Z2fksXrKD447rTJvWjQkLMyTvz6Sp92J7YaGbmJiogCd+Sl4IT03N4qZbPy2+w7ZXzzYcf1xnrr7qhHKDy30VfQ8q2V9GRi7zF2zmmKM70b79n5kDc3Lyee2NiaSn5xAZGU6TJvV56P/O59vv55OTk8/f/3YODRuWfr09HsuaNfE0aeIERPj+na5fP4onHrukzOtaUOBi46Z9PPbEj7hcHj58/1Z69WxDWJjB47FERjoXKzZu2seBAzkcO6AT4eFhQV3EWLVqF/987Ifi9aOP7sC7b91MWJghOzufbduT6NmjNYWFbho2rBfwJFxGRi4//byYdu2acPFFTpaxLVsTmD59HT+PXlJcbua0pyo8mZeSksXiJds54YSuNG4Uw4WXvFW878vP76RhwxhatmjInj1pTJ+5jsGDeoR0Qn3b9iT+9veheDyW55+9yu/vUWGhm7Awc1hngTkUJCQeIDsrj27dWoV0Ajg1LZtGDesF/B1wuz3s3ZdOh/ZN2bUrhTvu/rJ43y+jHqRx4/q43R6MMRVeBEhMOsALL42lXr1IXnz+6govUuyKT6FJ4/p+s7eX/N3Iyyss/t0eNWYJs2ZtYPOWhOKyUyY9Xry/iMvl5o9F22jcOIaj+rbn9TcnMn3Gn9nPBp3UnX8+dAGtWh2e2fsOZORy4EBOuf+7faWmZRO/K4X+/TuE/Pt+4EAOHo8l/UAOW7YkcPppvYmJCX4K55yc/OLA+cJCd5nXMys7j/ox0YSFGWbMXE9qahaXXXpchUFaIrWpoMBFVFQEHo/F5XKygx3IyOWttycxf8EWABo3juGrL+4mPMyU+X40ddpaXnujct9bO7RvSuvWjQN+96vIjX85mauuGFjqs+KChVtISDxAj+6tmTFjHeMnrixV59/PXM7ZZx1Vqf6KFBa6eeSxH1i3fg933XEGV105kKysfL9/mxcs3EJqajbnn9e/+PP4rl0ppKRmMeCYTmRl5TFrzgbmzNlI+/ZNGXRSd559fgwARx3Vno/ev7W4rXHjl/P+B1PLHdu55/TjmacuK7N96LC5rFi5ixuuH8Spp/TEWsuatbtp0CCabl1blSnv8Vh270ll48a9tGvXlOE/LGTpsh3cduupFOS7aN26MYWFbtasjee6a06iT5+2Tga+i98sNZbHHrmI1LRsGniz09548yd8cpkzm8vfx/8jiGe7fC+9cDVbtibSsUMzfhzxB9t3+L/g2aJF8H/bjwRH9W3L+g37Qq53019OpHnzBnTsUDobu8fjISzM+R+cnV2Ax+Ph1/GrWbVmN7H1o7jt1sHUj4mkdWv/n182b0liyZI4WrVqyKmndKd+/Sg8Hsuu+FRatWzItu3JjBi5lGbNYnnowbOJ8Pl/79zwbalfv2Zf5+3bkxn+42LCwsIYfHI3jh3QgWbNYkMKdCi6Wds5xj+fN2stS5bsJH5PGmcP6Y3b7SEnp4AOHZoCtrhcIN8PX8zqtbsB+Mv1J9C3b1tiDvPPG126dKnrIQQtJSWLvLxC2rUrfR6zKDv1dX/5iMzMvOLt0357grAww/oNe/nhx4UUFLh48fmriYqK8Ht+x1rLipU7ia0fTe/ebZkzdyMjflrERRccw+WXHVcjx+RyuTn/ojcrLujDNyj05Vd+Zeas4JI7BPLFZ3eSlZVHTEwUkRHhdOnSoszvZEZGLg0a1GPV6l00aVKfrl1aMnXaWrbvSCI3p4DfF2zhtltO5YrLjyd+dyp/vePzUvV79mzNh+/dWu4N1zt2JLPwj60ADDy+C61aNeKZf//Mps1/fge84Lz+PPnEpVU6XqkeJW9e+mX0Q+Vm6968JYEvvpxd4WfWQSd1J/1ADv36tuOB+8/FGENC4gHGjlvGTz8vLrfu6af1okf31kRGRTDw+M706N6ac85/vXj/qaf25KXnr/Fb90BGLgsWbOaYYzodcjOmbNy0j4SEdE4e1IN69SI5+7zXivd99cVdYJ3PGa1aNcbj8RAfn8rT//qZ7Jx8YmKiGHBMR+6680y6dyv7ebbIrl0p3H7XF8Xr1193Evfde7bfstu2JbJnbzrbtiXy3fAFpfY98PdzWLR4O0uX7Sje9t03fyMmJorGjWL8no/Iys7j8ivfK/c5eOyRi7j4ogHl3jCWlZVHdk4+rVs1LretUB3IyCU6KqJWzlGkp+fwy9ilbNy0j2XL42jXtgnvvXMz0dGRGAMREeFl/sauWRvP40+OpF69SL787E7qxUTyyGM/kpGRy39furbU9b2aZK0lOzu/1HUhay2JiQdo3rxh8bmgggIXERHlByP7fm5ctHgbmzfvoH//dkRHRbB27V5S0rJp3jSWE07oFPAzYEZmHmlpObRt05jMzDwaNqrHunV7+XHkEr/l68pZQ3qzZUsSp5/Wg969WhMTE+n3NcvLKyS/wEUj77WYlat2lzqWfz99MQ0aRBEWFobH42Hfvgxat2lU5ntBUlIGCYmZ9O3Tpsw5uvLUVeCytZZ9+zKIqR9ZfE0tGHl5hVhriYmJKnXNvyL+zl1WVnmfxbOy8ziQnkvbtk2qdPNbenoOO+KSObp/h3Kvk1lr8XhsjV0HKrq+2L9/x+LM1sEI5rVJSEhn9540jju2c3WOv1JPuoJMRUREqqCyQaaHkvDwMNxuT420HRkZzvXXDWL9+j2sWLmzRvoocuYZfejTpy1ZmXkM/3Fhpdo4dkAnYhtEM3/+Fr/7e/duy523n8GPIxeycuUuXn7xGnr2aM1XQ+ficnno3q0V9WOjGHBMJ5o0rs++hHSWLNlOWnoOf731NDKz8pg4aWXxyaRuXVvyyMMXsmp1PMn7MzjpxO507NCMNm0aEx4eRl5eIes37GHK1DUkJWUw+OQenHlmH4Z98zuNG8fQulVjUlKyOPPM3rRu1Zj43an07dOuzIfVQ+mOzvz8QjIycomJiaJevUjCw8NIT8+hUaMYVq+JZ/36PWRl53PDdYMwYYY5czby3gehT2HYs2drcnML2b8/k+joCA4cyA25jYYN6zH45B6sXLmLpOQMjurbjgMHctmzN42+fdpx4oldCQ8L47jjOrN8eRzDvv29wjYbNazH3+49my6dW7B9h5OZKCEhnZTULDq0b8bceZtYsXInTZvGEhUVTmLin9ki+vZpx3nn9iu+u7pIbP1owiPCyMj48xijoyPIzy89TYo/TZvGUj8mij1700J4ZoLTpEl9evdqy6LF20Kqd+qpPcnIyGXNGufC2XXXnkT7dk354svZNGsey5ln9GHixJWkpTtZh66+ciD9+nXAGMOLL48tM4b09IqzEzVtGsvVVw5k67Yk6kVHEFM/irHjloc27lN6smx5XHEWpsq67poTCQsPIz4+hezsfNZv2EthOZmMYmKiOLp/BxYv2V5mX1iY4awhfalXL5ItWxIxBurXj67U3+tLLzmWc84+ik4dm+PxWCIiw0lNzeKue76qsG7v3m3ZtKn0xe3evdqQ7b2Yekz/jjRsGMOoMUvYuzeNE0/oSl5eIR07NC++EFDk26H3kpNTwM+jFxMVFUFsbDSjRi+hVatG3Hn7GcTFJXPuuf2oVy+Kdm2bkJWVx4aN+2jWLJZJk1cx8PgunDLYmS6rKDNZ/O4UjDF06dKC8LAwsrPzad68AcaY4v+f5X3xLyhwMXvORlwuN82axXLSid0xBhYt3s7GTXs584w+dO3SslJZx0I5eVTTrLVs357Eho37yM0r4MrLBxIREcbceZvIysrn7XcnA9CjR2uOO7YzZw3pS/PmDcjKzGP7jiQaNoxh5879dOjQjP99MoPcvAJSU73Z5prU54XnrmbM2KXs35/F2Wf15YOPpnHlFcczafLqMtM+BfLh+7fSs0fr4v8tEyet5PjjutCiRQO++GoOs+dsoOQplL//7Wx69mxN715t2bhpH00a16dNm8bk5haw8I9txcdUr14kzZs1KP5b2bJlQ5KTMwHns1B5v6NFevVsw8mDunPM0R35dcKKgDf9+KpXL5KwMENOTgFPPXEpJw/qjsvtIT+vEI+1pKRk0ad3W5L3ZzJz5nrWrd/D3r1p7N6TRufOLXji0Yvp06dt8eeEokCGggIXzZvXbHaA3NwCwsPDyM0rJDe3AFehm4aNYkhOyuD+//sGl8tDs2ax/PXW0zj3nH7sT8liw4Y9pKblcM1VJ2CtZfmKOLp0bkFaWg4PPPit3366dmnJTTeeTH6+i6+GziUtLZvTT+vFuvV7it9jgXTq2Jxd8SnExEQRERFG3z7tuP66k8jJKeDd934r/l/jz2mn9iJ5fyaFhS7i4vbj8dhS2fiOObojnTs5QbTHHdeFJk1imDFzPXl5hcTERHHmGX1o3rwB+/al88mnMzjppO5BzRQgEiprLf99dTyz52zgmqtOKHVDSUU+ePcWPNayatUuhn4zrwZHGZyhX96Nx2P5ff7mkMZz9NEdOPaYTlx26XHsiNtPWloWA4/vSv36UaxYuZNVq3ax8I+tpKRm06VzCxIS0sv9/e/duy2dOjajZYtG/DCi7HfTYP83+OrfrwMDB3bhmyC+V5TUtm0T9u1LD7r8lVccz7LlcRQUuEp936hu1RlkGqyDOcj0+GM7ctFF/cnMzKNBbDQL/9hBx45NmTJ1PYmVyRJYS47p34Fzz+nDhIlr2Lw1Mag63bu3JCkxg9jYaI4/rhOTfltb5XGU/G53dP/2XHD+UWRm5uF2Wbp0aU5BoYt60ZGEhxsKCz1k5+Szbt1eJk5ey5DTe3H66T3IyMgjeX8mERHhJCRmkLI/i9y8QuLjUznhhM706tma1av38PuCrQHH0b5dEyLCw4iJjeKqK44ltn4UGMMfi7aTl1tIl87NWbp8JytX7S5Tt0vn5kFNXxoZEc5LL1zmN9jgiafHlNl23LEd8bgtmzYncOvNJ9OuXRNWroonLNzQt3cb8gtcNGsaW3yhuWTQaxGX21Pm4n1eXiG5eYU0qcQMGh6PJSExg3rRkTRrVt8b0J5Gq5YNcbk9JCVm0qFDk1JBJmlpOezfn0X37i1LfQeqKMg0N7eArOx8WoZw02qwSn6H83ej69atifw8ejEbN+0jPj61ePu55/TjqisHMu/3zRx3bGc+/Hgau3enUhnPPHUZy5btoGGjGDZt2scab5DxFZcdX+r7cr+j2uNye7jxhpN58eWxeDyWN1//CwOP7+K3XZfLjdvtITo6stR5xYICF2+8NanKAaEAr/73OrZuTcSEGb78ak6V26tNZ5zem9NP68UxR3ekWbMGxTPVJCVl8Jeb/xdUG0U3ZjRr6pzLat++Kf2Oak94eBiJSQeIjY0mN7cQt8tNy5aN2LBxL/WiI+na1fkdKJoJLD/fVSowzOVyM+aXpWRl53PjDScTExPFgQM55OYV0qZ1Y9xuT6mbnA8FJc995OcXsntPGhMnreS0U3tx/HFdgqq/es0uFizcysUXDWDv3jRGjV7iN3nGR+/fSmxsNJOnrKZ7t1Z06ticQpebiZNWMWVqaFlLwfle+Pv8zSHXK0+3ri3p2aM1jRrX5+dRznWGjh2bFf+dad68AcO+uqfU+auwMFN8U1lFPB6LMbBu/R6SkjI47dRexcHtRX8L1qyJJzw8jKbNYklMOEBBoYvPvpiN9VjOPKM3qWnZXHbpcSxfHsfGTfvo1asNxx7Tieh6kSQmHqBly4Y0blSfUWOWMCqE7x8VOe3UXnTv3oq2bZrQsGE9vh46l23bk4pv7vfnoguP4bxz+zFp8upSN/dWxVlD+tK/XwciI8N5572qJ6K55uoTSEg4UHzzX5ELLzia5s0akJB4gCsuO56163azbv0emjZ1bn6Z9/tmHn7wfFwuD0lJGUz6bRUd2jejR/fWpKRkErcrhczM3OLEL7ffdhqdO7cgeX8mDWKj6dqlJQcycunRvRUzZ29g1KglTiILj4ej+3dg/Ya9uFzu4msr/ft1oF+/9rRv58xEmJaWxZ496UyfsY6k5IxyX4eSPv7wNowxzJi5jqVLd7BzV8Wfj8LCDH//29kMOKYTbreHxo1j2LI1kWMHdC6VPMLt9lBQ4CIyMpyk5ExmzVrP0Ud3pGnTWPbvz6RP77aEh4cRFRWBtZasrHwaNHBuKn7m36OKr2W0adOYhIQDpcZw8qDupKZms3lLAk2bxnLdtSexadM+rr/2JJo3b8CWrYlkZ+fx3fcL/F5vCfRd5eIL+3PiCV2IiDCkp+fy9nvTASd67FCPBOvZoxUDj+/EgGM6kJKSzVvvTqu4kh+DTuzC8hXxFLrcREaEU+j68/v2oJO6ErdjPy1aNmT//iz69m3Dhg0JpKVl07BRPfJyXTSIjSLRey4X4LZbTqZN60ZERoXTqGE9EhIz8Xg8tGzRgOzsAqyFyMgwMjPziYoOp1nT+oBzraDo/2tyciZR0RFlbl7Yl5DB4kU7MOGG6OgIGjeqx4qV8eyIc97nd/z1FKKjImjfoQlRkc4MDh6Ph5SUbGJjo9kVn8rmzUnk5RWydLlzHSfMGDzWEhkRzq23DKJpk/okJmXStEkMrVs3ZsvWRFat3kPfPm3YvDmRZSuc/38XXdiffke1JSUlm5ycAvr2aVPqhj1/15Z9P/N27tyZ7TuSad6sAU2a1Gf5ijgWLNzKgQM5ZWaHbdiwHpmZeRzdvwO5uQVs3ZbEsQM6cXT/DqSkZnPN1SdQLzqSTZsTmPzbKpav2FkmfqFFCyegOyMjl6ZNY0lLy+akE7vh8VjmzN1Yquyj/7yI3LwCzh7Sl5iYKNxuD1lZeTRr1oCUlCzyC1x07NCMcb8uZ8KklRzVtz033ziYtet2k5dXSHR0JC1bNuSl/44rPl9e5O67zmT//kzcLg/xu1NJTc0mMyuPQSd1o0vnFsTt3M/v8zeTlVV2hp6GDesVJ8E57ZReGGP44qvZxfuvvnIgV1wxkJzsfDKz8ujTpy0FBW62bU1ky9ZENmzay4YNe2nXtil33nE6LVs0Yv/+TDZvSaB/vw50796K/HwXDRvWU5CpiIhIbTsSgkzl8NOlSwtycgpo0aIhKSmZHDiQS9s2TWjcJAbsn18CihhjwIDBUPR9oegLgjHOtsoEqRb1U1DgIiU1m9j6UcV3eWZl5bEv4QCFhW5at25Ebq7zxaxSU2yJiFRCbGy032l4KyMyMtwJWs3OL572CqB160YhBWc0aBBNm9aNMWGGsLAwsjLziI6OICo6goJ8Fzm5BaSn5xAeHlZq7N26tSQyIpw9e9No3Kg+TZvGEhZmii90FS2ttbg9Hjxui8vtITzMFJ809Vhb/D/CWufkrNvjwXogIjKM3NxC6sdElbqg63K5KSx0k51TwM6d+6vluZS6Eej3oU2bxuTnu8jJyadd26Y0bhxT6jODMYbsnHw2b06gY4dmxZmXMzKdLBhJyZnk5hbQtEl93B5LeJihQ4dmxRe/pXIaNqxHi+YNiYmJ9AYaO8ENoX5cc7k8WCxRkRVfaDwUFWVxDy1rRcnHgc+pltxXqg6lVipdvryx2ACFArVb6jB8jslaJytzVW+CEakMBZmW1r9fO2675eQy23ftSuWjT2bX/oDkoNeqRUOi60VgMKSkZVfbdxuAMAxNm9cHCyklbsqJjo7AVejG7RMg0rJFA+rXj8YYsB6LCXM+K4aHh+PxeEjYl0FOnpO9NdTvYc2axoKF1PQ/x9GqRUMio8OpFxVBXn44ubmF5OUVEhXt/UxjLZlZeU72QJ8AEHCCsax1Pis0aVy/+PsSUHzx+ECGE7Rc8vtcUXBMh/ZNSUvLISc3n+joyFL/R5s2jSU/v5DCQnelbiioC7Gx0bRt0xiX24O14Cp018jNxnJwKsoKVjJguuh8cHWo6JxyQYGLrGxvEJeF7Jx8XIUeXG43WVn5RESEkZycSWRkOI0b12f//sxS9Xv2bE3DBvWKv5smJ2USFu7MblUUOCdOEoLsHOdvb+fOLUhLyyYjI5cWLRoWn+eJiYkiIjzMmYmmGv+niBwMoqMjMMYc1N99D+bvKlJ5ERHhWI+lQYNo6tePYp+fz6YHm6jIcAq8n2PDw8Jo0iQGl8tDRERYqe8GAPv3F9TFECUIM6c9pSBTERGR2qYgUxERERERERGR6nU4BpmedWZvunVrwdhfV5KSUn627IiIcKIiwosD76664lgGn9ytTLmMjFxefnVyhX337NGKhg2iadQ4htlz/syYduIJXViyNC60AxE5hOjCtoiIiBwOFGQqhyJ9Fj94KchURESkDhhjhgNYa2+u67GIiIiIiIiIiBwWfjBLAbjJnlBbXf70008Da6svEakd119//bK6HoOIiIhIVem7ihyK9Fn88HN4znclIiJSSxRcKiIiIiIiIiIiIiIiIiIiIiKHq7C6HoCIiIiIiIiIiIiIiIiIiIiIiIiIiBx8FGQqIiIiIiIiIiIiIiIiIiIiIiIiIiJlKMhURERERERERERERERERERERERERETKUJCpiIiIiIiIiIiIiIiIiIiIiIiIiIiUoSBTEREREREREREREREREREREREREREpQ0GmIiIiIiIiIiIiIiIiIiIiIiIiIiJShoJMRURERERERERERERERERERERERESkDAWZioiIiIiIiIiIiIiIiIiIiIiIiIhIGQoyFRERERERERERERERERERERERERGRMhRkKiIiIiIiIiIiIiIiIiIiIiIiIiIiZSjIVEREREREREREREREREREREREREREylCQqYiIiIiIiIiIiIiIiIiIiIiIiIiIlKEgUxERERERERERERERERERERERERERKUNBpiIiIiIiIiIiIiIiIiIiIiIiIiIiUoaCTEVEREREREREREREREREREREREREpAwFmYqIiIiIiIiIiIiIiIiIiIiIiIiISBkKMhURERERERERERERERERERERERERkTIUZCoiIiIiIiIiIiIiIiIiIiIiIiIiImUoyFRERERERERERERERERERERERERERMpQkKmIiIiIiIiIiIiIiIiIiIiIiIiIiJShIFMRERERERERERERERERERERERERESlDQaYiIiIiIiIiIiIiIiIiIiIiIiIiIlKGgkxFROSQYoy51hjzoTFmnjEmwxhjjTHfV1DnFGPMJGNMqjEmxxiz2hjzsDEmvBrGM9wYM7yq7YiIiIiIiIiIiIiIiIiIiIiIHGwi6noAIiIiIfo3MADIAnYDfcorbIy5AhgN5AEjgVTgMuBd4FTguiqOp8/xxx9/PHBTFdsRERERERERERGAZgOLHtna6vLcc8+tra4qZe2eJP7+w+Ti9XmP/7UORyMiIiIiIrXlYP+uIiKHHFOZSspkKiIih5p/Ar2ARsDfyytojGkEfAG4gSHW2rustY8DxwILgWuNMX+p2eGKiIiIiIiIiIhUTVpOXl0PQURERERERESOUAoyFRGRQ4q1dpa1dou1NphMFtcCLYER1tqlJdrIw8mIChUEqoqIiIiIiIiIiIiIiIiIiIiIHKkUZCoiIoezs73L3/zsmwvkAKcYY6Jrb0giIiIiIiIiIiIiIiIiIiIiIoeGiLoegIiISA3q7V1u9t1hrXUZY3YA/YBuwIbyGjLGLAuwq0+VRigiIiIiIiIiIiIiIiIiIiIicpBSJlMRETmcNfYuDwTYX7S9Sc0PRURERERERERERERERERERETk0KJMpiIiciQz3qWtqKC1dqDfBpwMp8dX56BERERERERERERERERERERERA4GymQqIiKHs6JMpY0D7G/kU05ERERERERERERERERERERERLwUZCoiIoezTd5lL98dxpgIoCvgArbX5qBERERERERERERCUeE0PCIiIiIiIiIiNURBpiIicjib6V1e6GffGUB9YIG1Nr/2hiQiIiIiIiIiIiIiIiIiIiIicmhQkKmIiBzORgH7gb8YY04o2miMqQe87F39pC4GJiIiIiIiIiIiEixT1wMQERERERERkSNWRF0PQEREJBTGmCuBK72rbbzLwcaYYd7H+621jwFYazOMMffgBJvONsaMAFKBy4He3u0ja2fkIiIiIiIiIiIiIiIiIiIiIiKHFgWZiojIoeZY4K8+27p5fwB2Ao8V7bDWjjXGnAn8C7gGqAdsBR4BPrDW2poesIiIiIiIiIiIiIiIiIiIiIjIoUhBpiIickix1j4PPB9infnAxTUxHhERERERERERERERERERERGRw1VYXQ9AREREREREREREREREREREREREREQOPgoyFRERERERERERERERERERERERERGRMhRkKiIiIiIiIiIiIiIiIiIiIiIiIiIiZSjIVEREREREREREREREREREREREREREylCQqYiIiIiIiIiIiIiIiIiIiIiIiIiIlKEgUxERERERERERERERERERERERERERKSOirgcgIiIiIiIiIiIiIiIiZbk8Hhbv2MuO/el1PRQREREREREROUIpyFREREREREREREREROQgNGb5Rj6ctaSuhyEiIiIiIiIiR7Cwuh6AiIiIiIiIiIiIiIiIlKUAUxERERERERGpawoyFTkEGWOGGGOsMeb5uh5LVRhjuniPY1gIdW731rm95kYW9FgO2tfBGBNljNlijJkYZPlrvMdyTk2PTUREREREREREREREREREREREDg0KMpVq4Q1OK/mTb4xJNsYsN8Z8aYy5yBgTHqDusBL1Lg5Q5nnv/rvLGUNHY4zbW+6VCsYbboy5xxgzxxiTaowpNMYkGWNWe8d7uU/5IX6OscxPMM+VSC15EOgBPBtk+THAcuAdY4z+N4iIiIiIiIiIiIiIiIiIiIiICBF1PQA57LzgXYYDTYB+wK3AXcBSY8zN1trN5dR/wxgzxVrrrkTfd+METlvgDmPMs9Zal28hb7DrBOBCIB2YCOwGmgHdgZuAPsCvfvrYCQyrxNiq22KgL7C/rgdSRXtwjuNAXQ/kcGKMiQX+BUyz1i4Lpo611hpjXgdGAn8BfqjBIYqIiIiIiIiIiIiIiIiIiIiIyCFAQaZSray1z/tuM8a0Bj4ErgOmG2NOsNYm+am+FSco9U7gi1D69QaO3glkAMOBvwOX42Rn9HUjToDpKuBMa22pAEdjTH1gUICu4vwdY22z1uYAG+t6HFVlrS3kMDiOg9BNOEHew0KsNw4n8Pp+FGQqIiIiIiIiIiIiIiIiIiIiInLE05TIUuOstYk4mRFnAx2BZwIUfQnIAV70ZmIMxUVAB5wsjP/zbrsnQNlTvMthvgGm3vHmWGtnhdh/tTDGtDbGvGWM2WSMyTbGpHsfDzPGdCtRbogxxhpjnvfTxonGmKnGmExjTIYxZroxZrAx5nlvnSE+5a0xZra376+NMYnevhcYY073lok1xrxpjNlpjMk3xqwzxlwX4BiijTFPGWNWG2NyvGOYZ4y53k/ZLt7+h/nZ18MY87MxJq3EeC4J9Tn1thVujLnPGDPfGHPAGJNrjNlqjPnSGNPTp2xjY8yr3uc9z9v/FGPMuSH22dMY860xZo8xpsAYs9e73tNP2eLXxhhzrTFmsfe5SzXGjDDGtA/xkO8CCoCxoVSy1uZ765xqjOkTYp8iIiIiIiIiIiIiIiIiIiIiInKYUZCp1AprrQd42bt6ozHG+Cm2F3gbaAM8EWIX93qXw6y1a4HlwPnGmM5+yqZ4l71C7CMkJQJBZwdZvj4wH3gU2Al8AnwFrAGuAI4Koo3TgbnA2cAk4CMgF5gFnFRO1Sbevo8DfgRGAycAU4wxA4AZ3jFMAL4BOgEjjTEn+/QfBUwBXgUigY+B73Ce65HGmFcqOgZvOz2BP4BrgYXA+8BunADIq4Npw2dMv+E8nx1xMnR+ACwDrgJOLVG2CbAAeAo4ALyH81wMBqYaY/4WZJ8nAkuBW4AlwFve47kZWGqMOSFA1fuB74E4nOduLXADTgbg6CD7bozz2i33ZrwN1XzvMqSgWhEREREREREREREREREREREROfxE1PUA5IjyO+ACWgFdgB1+yryBEzD6qDHmU2vtvooa9WZ5vBjYbK1d4N08DCeQ8G7gPz5VxgBPAvcZYxoCvwDLrLU7gziGLv6yh3pttNaOCKKNQM4BugPvWWv/WXKHN1Cy3CBDY0wY8DVQD7jYWju5xL77cIIsAxkAfAbc7w0IxhgzDfgWJ0B1PjDEWpvn3fcdTjDrkziBmkUeBc4EJgOXW2td3vIvAIuBp40xE0q8ToF8DDQHHrbWvl/iOK4gxOycwPM4AZPjgeu82TqL2osGGpUo+zpOMO/nwH3WWust9zpO0OgHxpgp1tq4QJ15A6i/9bZ7i7V2eIl9NwAjgO+NMUcVPdclXAicaK1dU6LOD8CNOEG+PwVxvIOBcO94K2OJd3kGTpCyiIiIiIiIiIiI1IEN+/bX9RBERERERERERJTJVGqPN7ivKItoywBlsoDngFjgpSCbvgsnqG5YiW0/4EwXfqcxJtynjxU4GSYTvcvRQJwxJsUY84sx5rJy+ursHZ+/n7/4lF0M9AVuC/I4iuT6brDWFlhrMyuodwrQA5hVMsDU63Ngczl1c4DHfYIef8AJCm4KPFQUYOodzzycbJvH+rRzJ2CBR4oCTL3lk/jz9by7vIMwxnQAzsMJQi4V5GitHQfMKa++T1vhONlBc3GCRvNL7rfW5ltrk71lI3HeD1nA00UBpt5yW3CClqOo+PU8BegDLCwZYOptZyROsHVv4DQ/dT8oGWDq9YV3WV4m2pI6eZcVBmgHkODTjoiIiIiIiIiIiNSBB370Pc0rIiIiIiIiIlL7FGQqtc14l7acMl8C64E7jDFHl9uYk73zTsCDkz3SadzaFJyp3dsBl/jWs9b+hBNEdwFO8OMEnN+HK4FfjTHfeDNS+ppjrTUBfq706SPHWrvRWrurvGMo2TawB3jKGPObMeZBY8xA3yDZchznXf7uu8MbPFpe9tDNvkGs1lo3TiBuurV2u586e4AORSverLA9gL3W2o1+ys/0GWcgxcfhHYOv2RXUL6kP0BhYba3dG0TZ+sAqa22qn/3Bjv94n/KhtOMv+2i8d9m0gn6LNPcu04Is76vo2FtUsr6IiIiIiIiIiIhUg0K370RIIiIiIiIiIiK1T0GmUmuMMfWAZt7V5EDlvIGFT+C8P9+ooNkLcLKLTrPW7vHZN9S7vDdAP4XW2qnW2mettZfhBNXdAGTjZKu8ooK+q5W1NgM4GWfcA4H3cYIOE4wxL3gzbZansXeZGGB/oO0ABwJsd1WwL8JP/4EyaBZtb1LOOEq2E2i8CQG2+1PUl+97o7x+q2v8lWkn3c+2ooywwQYbF2XCrRdkeV8xPu2IiIiIiIiIiIhINXB5ajdoNDkzm6HzV7Jub8DT8SIiIiIiIiIiFVKQqdSm03CCEhOttXHlFbTWTsTJ+HihMea8cooWBZBeYIyxJX+A8d59FxpjOlY0OGut25vh9F3vprMrqlPdrLW7rbV3Aa2A/sCDQArwrPenPBneZesA+wNtry5FwahtAuxv61OuonYCjTdQ+/6ke5ftgyhb3eOvajuVleRdNi+3VGBF9ZLKLSUiIiIiIiIiIiJBsdby73GzufiDH/lt7bZa6dNjLf/8aRpfL1jFP3+aSn6hq+JKIiIiIiIiIiJ+KMhUaoV3Wvt/eVd/CLLaY4AF3sTPe9UY0wa4FCe48qsAP/NxMkDeGcJwi6aNNyHUqVbWsc5a+yFQFGR7ZQXVVniXp/nu8D7/p1TfCMuy1mYC24D2xpiefoqc5V0ur6Cp4uMwxvjL3jkkhGFtxAk0PcYY066CspuAHOBYY4y/qelDHf+QAPuLtlfUTmWt9i77VLJ+Ub2VVR+KiIiIiIiIiIiIbExIYc7mneQWuvjv5N9rpc996ZnsTHXuc88tdJGSrYmLRERERERERKRyFGQqNc4Y0woYgRNctwt4JZh61toVwPfAAOBGP0XuxMmMOtxae7e/H+B2nEDVu7yBlhhjbjTGnFe07jPWNsA93tW5IRxmGcaY+saYPsaYTkGW72+M6eJnV1FGz5wKmpiPE+R5ljHmIp999wK9ghlHFX2NE5z7ZskAUWNMC+A/JcoEZK3dDUwDugL/KLnPGHMFcGawg7HWuoH/4UwB/6kxJtqnvShjTEtv2QJgONAAeNGnXHecrLKFwHcVdDsfJ2D1NGPMtT7tXAucAWwGaups8jogGTjZ384g3pdF9WbVxOBERERERERERESONFn5BbXeZ4HbU2p96vrtuD2eAKVFRERERERERAKLqOsByOHFGPO892EY0AToh5NZMwpYDNxsrd0fQpP/Aq4Devj0Y4C7vKtfBqpsrd1qjJmDE+B6ETARGAQ8BCQYY34HdniLdwUuwQlIHAeM8tNklxLH6M971tp07+OTcAL1ivqvyLnAO8aYBTgZOJOADsAVgAcno2tA1lqPMeZu4DfgV2PMaJyg02NwsqFOxnkOavJM4lvePq4AVhljJgH1cV7DVsAb1tpggisfABYC7xljzgdW4bwHrgLGA5eFMKYXcF7zy4DNxpgJONlqOwLnA48Dw7xlnwJOB/5hjDkR5/VrAVwPNAT+Ya3dQTmstdYY81ecQNmRxphxOK9nb5xstJnAbdbaGnkdvP3/AtxrjOlnrV3nU6Si9+X5ONlfZ9bE+ERERERERERERI40DetFlVp3eTxEhNVsDhBrban1r+avpF2Thpx/VLca7VdEREREREREDj8KMpXq9px3WYATTLcT+BYYDUwNNbDOWhtvjHkPJ/ivpHOBbsAKa21F045/gRNMdy9OkOnbwBZvG8cAFwD1gBRgNvAD8IP1PQvn6Myfx+jPMJwAvcqYAryHk+nyCqARsA8nWPEda+2Cihqw1s42xpwJvIwTMAuwCGeq95u96xmVHF+FrLUFxpjzgEeAm4D/A1w4QaIPW2t/DLKdLcaYk4HXcF6nITjTwF8JtCSEIFPvmC4E7gNuA/6Kk211L/ALJTKKWmtTjTGDgaeBq73HkYsTIP2mtXZqkH0u8gap/ts7/suA/cCPwEvW2k3Bjr+S/ofzfr8NeDLYSsaYXjiZTN+31laUOVdERERERERERESC4ORM+FNGbj7NYmNqtE9/J7dfmjhPQaYiIiIiIiIiEjLjP45ORA43xpj5OBk9G1trs+t6PFKzjDFTgAFAV2ttbpB13gb+AfS11m6vyfEdTowxy44//vjjly1bVtdDERERERERERE5PPx2grO8cGmtdZmamlpjbW9KSOHu7yYUr4/627W0bhRbYb3T3/wm4L55j/+13Lpbk1K545vxIdcTOZw0a9asrocgIiIiUmU1+V1FpKbos/hBzVRcpKyanY9FRGqVMaa+MaaJn+23A6fgZJNVgOmR4TGgBXB/MIWNMW2BvwMfHo4BpsaYOGOMDfCTUNfjExERERERERGRw5fHJ9lHbST/UH4REREREREREakuEXU9ABGpVp2AFcaYacBWnN/x44DTgHTg0bobmtQma+0aY8ydQMMgq3QBXgfer7FB1b0DwHt+tmfV8jhEREREREREROQIYn0mr/cNOq2NPkVEREREREREKktBpiKHl0RgOHAmcBYQDSQAQ4H/Wmu31eHYpJZZa78NoexCYGENDudgkG6tfb6uByEiIiIiIiIiIkcW35jSWgkyVYypiIiIiIiIiFQTBZmKHEastWnA3XU9DhERERERERERERFx+AaVKpOpiIiIiIiIiBxKFGQqIiJHimhjzC1AJyAbWA3Mtda663ZYIiIiIiIiIiJyOPONKa2OGFNrLcaYgPtrI5BVRERERERERI4MCjIVEZEjRRvgO59tO4wxd1hr51RU2RizLMCuPlUemYiIiIiIiIiIHLZ8s4p6rKfKbf5t+CT+e8UQWjaM9d+nYkxFREREREREpJqE1fUAREREasFQ4BycQNNY4GjgM6ALMNkYM6DuhiYiIiIiIiIiIocza32DTKve5oZ9+3lj6sKg+ywpIzefB0dM4b7vJ5GYkVX1wYiIiIiIiIjIYU1BpkcIY8wQY4w1xswup0wXb5k4n+23e7f7/mQaY5YbY54xxtT3096wAPVK/gzzqeO7322MSTXGzPaOI/D8P079c40xI40xu4wxecaYNGPMEmPMc8aYpgHqlDy+Nyp4/r4vr38/9cKNMQ8bY1YbY3K9xzLJGHNKOXWaGWPeM8bEGWPyjTF7jTFfG2M6lFPnKGPMT8aYJO9xbzLGvGCMifFTtokx5nFjzHBjzHpjjMt7bOeW035fb3vjvM9t0fMVMBuy9zUr77Wv56dOpDHmUWPMSmNMjvc9tsA7xXlI/PSXb4xJ9r5nvzTGXGSMCQ9Qt+R79+IAZZ737r871LFJ7bPWvmCtnWmtTbTW5lhr11pr7wPeAWKA54NoY6C/H2BjDQ9fREREREREREQOYpsSUliwbTduj/8Mpb7xnuUFgIbij+17Au4rr4ePZi9hRXwC6/Yl8+bUP6plLCIiIiIiIiJy+AoYICbixypgrPdxGE5GwMuA/wIXGmPOsta6/dQbB6wM0Gag7S94l5FAD+Aq4EzgBOAfvoWNMdHAl8AtQC4wGdgMNADOxgkg+4cx5hpr7dwAfQI8aIz52Fq7s5wyQfEGxI4ArgU2AR8BzYAbgLnesYzzqdMcWAD0AmZ66/cB7gAuMcYMttZu96kzyFs2EhgFxOMc87PAOcaYc6y1+SWqdAGKgml3A/uB1hUczgXe9tzAFiAPKBMkGsALAba7fI4jCud1OxuIA4Z5d10MfGeMOd5a+0iQffrrPxxoAvQDbgXuApYaY2621m4up/4bxpgpAd7bcuj7FHgUOKOuByIiIiIiIiIiIoeeuJR07v5uAgCPnz+Yywf0KlPG4xNU6q6FuezL62Lu5l3FjxftCByoKiIiIiIiIiICCjKV0Ky01j5fcoMxpgmwGjjd+zPbT72x1tphoXTkp59TgbnA/caYt621O3yqfIITYLocuNJaG1+irgEeAN4HJhpjTrLWbvDT7VacgNZXgJtDGW8Af8EJMF0AnGOtzfOO51Pgd+ALY8xMa21miTqv4ASYvlsyoNIY86B3/P8DLiyxPRxnGvD6wBXW2l+928OAn4BrgH8Cr5XoYydwLrDCWpvqzSb71wqOZTKwEFhtrc01TrbbzsE8Cb6vZTnuxwkwXQicZ63N9h5LLE4Q7T+NMb9aa2cH2V7A/o0xrYEPgeuA6caYE6y1SX6qb8UJSr0T+CKUfuWQUfS6x9bpKERERERERERE5JD07vRFxY/fnLrQb5Cpb7xndWUyLY8tJ5dpbQS5ioiIiIiIiMjhI6yuByCHNmttOrDEu9qyBvuZjzMltQEGltxnjDkNJ9NnGnBpyQBTb11rrf0IeBMns+kHAbr5CVgB3GiMOaEahv137/LfRQGm3vEsAUbiPF/XljiOWJwMm9nAcz5tfYST3fMCY0y3EtvPBPoCc4sCTL19eIAnvKv3eQNti/alWWtnWGtTgz0Qa+0ma+0ia21usHUq4Wrv8r9FAabevrOBl7yr/1cdHVlrE3GCgGcDHYFnAhR9CcgBXvS+PhUyxgw0xrxvjFlljEk1xuQZY7YYY942xjT1Uz7KGPOgMWa5MSbNGJNjjIkzxowzxpzrp3wfY8wwY0y8MSbfGJNojPnBGNM72OOvjX6NMT2MMT972842xiwwxlxijLndGGONMbeHMt4aNNi73F5uKRERERERERERET9cbk+FZXyDSn0zm9aEQIGsb01dSF6hy+8+ERERERERERF/FGQqVWKMaQycCHhwAjRrtDvvstBn+z3e5RfW2n3l1H8dyAfONcZ09bPfAo95+3kr6EE5AXPWZ1s0cApOgOI8P9Ume5dnl9g2GIgB5vtkNy0KGp3qXT2rxK6i+r/5dmCt3Q5sxsk42s13f20xxtxgjHnKGPOIMeYi73PjTxvv0l+wX9G2c6prXN7n9GXv6o0lA3FL2Au87R3bE372+3MPTgDrJpwss58C+4BHgPnGmIY+5YfhZKmNBL7FCYKeCxxNiay1AMaYC3Gy9d6ME9z9PjADJ0B3sTHm+CDHWKP9GmN6An/gBFEv9JbfDYzlz2DiWmOM6WeMaeZne2ecAG6A72t3VCIiIiIiIiIicqQoE2TqKT/INKegkEK3u4p9+t8+btXmKrUrIiIiIiIiIkeeiLoegBxSjjXGPO99HAa0Bi4FGgMPWmu3Bqh3pTGmS4B9I6y1Gyvq2BhzBtAbKAAW++w+zbucXl4b1to0Y8wynODPU4EdfsrMNMZMBC4xxlxeMjtoiHoA4cB2a62/28K3eJcl504qyggZ6CxfZev08v5sK2/ANWiEz3qSMeYBa+0on+37gZ5AV2CDz76iINnGxpg21tqEahrb74ALaAV0wc97AngDuBd41BjzaQWBzACvAg9Ya0udBTbG3AV8CdyPE/BcFKT9F2AZMMhPneYlHjcFfsQJXD7DWru+xL5+wCJv+xUGmtZCvx8DzYGHrbXvlyh/BU6gaW27DnjKGDML5zXOBLoDlwD1gEmEEFguIiIiIiIiIiISCt94z/JiTNfvS+afP00jOiK8in3WfLZUERERERERETkyKMhUQjHA++PrR2BmOfWu8P74sxIoE2RaIpg1Eidg8yqcDKOP+Qnya+tdxpczhiJFZdqVU+YJnEyOrxtjJgUIEi2pr59tjb3LAwHqFG1vUgd1ass4nMC9FUAKTkbVvwKPAiONMZdaayeXKD8BJ5vrM8aYWdbaXABjTH3gXyXKNQWqJcjUWptvjEnBCZhuif/A4yxjzHM4GUlfAu6uoM2dAXZ9DbwDXIA3yBTn/LLBybBbZl4ta21KidXbcF7Hf5QM9PSWW2eM+QJ42BhzlO9+f8OsqX6NMR2A83Cey498yo8zxswBzqxgfNVtFk5A9nE477FYIB0nyPg74DsbaP4wERERERERERGRcvidH8mHx+fUU3kBoE+NmUlOQSE5Bb4TeoWmgmSpxcLDgjgAERERERERETmiKchUQvGNtfb2ohVjTGvgXJypsC81xgyx1i73U+8Oa+2wEPt6zmfdAndZa4eWUyeY02ZFZ8wClvUGyn2Fk73yXuB/5TUYTCbWyoyjDutUC2vtuz6bNuEEkO4FPgReAUoGmb4PXIOTZXadMWYSzvgvBhriTF3fDqjaPFFlBfMcfQk8CNxhjHnfWrsmYGPGRAJ/w8kUehROIHBYiSLtix5YazOMMeOBy4CVxpjRwDxgkbU2x6fpwd7lgBJB2CUVZbjtC6w3xgwBhviUibPWDqvJfnECOQF+982Q6jWbWg4ytdbOAebUZp8iIiIiIiIiIiJFfG9v9pQTAZqWk1cznQYQHhZWcSEREREREREROaIpyPTIUZStsLwzRkX7ymQ29MdamwgMN8bEAF/gTBN+QaVHWLptA2CMicUJcvsK+NQYs9Na65s1NQFnivVOOIGM5engXVY05fmzwE3Ac8aY70IZu1dRBtHGAfY38ilXm3Xq2pfAu8CxxpiG1tpMAGtttjHmDOApnOnN7wGygRnA0zhBkADJ1TUQY0w9oFlF7Vpr3caYJ3Cyrb4BXFROsyNxMu9ux8nmmoCTMRTgYSDap/wNwJM477cXvNvyjDGjcDL3Jnq3FU1hf0/5R0UD73IIZYO15wDDarjfovdiYoBy1ZKFVkRERERERERE5FDhO4mOb2bTGukzyHLhwaRiFREREREREZEjmm5RPXIUBRk2L6dMC+8yPcS2F3mXJ4VYr0LW2mxr7XScjIvhwDfe6dNL+t27PLe8towxTYGB3tX5FfSbCLwJtMIJegzVVpyMm92MMf6CuXt6l5tLbCsKkO2Ff9VVp05Za/OATO9qrM++bGvtf6y1fay10dbaZtba63CeyzbAVmttWjUO5zScYPtEa21cBeOeCMwELjTGnOevjDHmBJwA0+lAH2vtHdbap621zwMvAlF+2s211j5vre2FEyh9C857+hZgVImiRb/DA6y1ppyfb7ztPu9n35Ca7rdE+dYBnso2AbaLiIiIiIiIiIgclhbH7S217ht0WhOCDWRVJlMRERERERERqYjOHhw5NuFkU+xljAkUaFo0LfaqENtu6l3W2PvJWrsaJ1tqB+CfPru/9C7vNsYECmwDeAwni+R0a+2OILp9C2eK9n/yZwbUYMebDywA6gOn+ylSlAmzZFbWP4Bc4FRjTMOShY0xYcD53tVZJXYV1b/QtwNjTDec4NOdOFk1DwrGmN4475lMYH+Q1YqyaA6vxnGEAf/yrv4QZLXHcJIAvIn/93sP7/JXa22hz76TgJjyGrfWxltrh+NkBN4CnFbi9/UP79Lf+6lKqrnfFd7lacaYcD/7h1R6oCIiIiIiIiIiIocYay1jVmwstc1dC0GmwQoPUyZTERERERERESmfgkyPEN7skSNwsja+aUzpOXCMMR2Ax72rw4Jt1xtE9pB3dXaVB1q+l4E84DFvVlIArLVzge9wpj2f4D0W33HehzM1eFaJ8ZbLWpsD/AcnMNB32vGSbfcxxvTxs+uTonF7p2UvKn8izlTlycDoEv1leY8jFnjep61/AF2AKdbakgGjc4ANwBnGmMtL9BEGvO5d/dTWxq3xJRhjuhlj2vvZ3gIY6l0dYa11+exv5KfOxcCjwB7g/WoaXyuc34chwC7glWDqWWtXAN8DA4Ab/RSJ8y6H+OnvYz/jaGmMGeSnnVigIeACCrzbhuJkGX7OGFMma7AxJswYM8R3uz812a+1djcwDeiK874tWfYK4MxgxigiIiIiIiIiInI4cHvKnppVJlMREREREREROZT4m8ZbDl+PAicCdwCDjTHTgAygM3AFTnDZ69baOQHqH2uMeb7EeivgbKA3TkbKJwLUu9IY0yXAvjhr7bBgBm+t3WOM+QwnSPQJ4OkSu+/FeT/fCGwyxkzGycgYC5wF9AdSgGusteuD6c9rGPAwcHQ5ZTZ4l763fI8ArgauBVYYY8YDzXECTMOBe6y1GT51nsEJUHzEGHMssBjoi/P6JAEPlCxsrXUbY+7AyWg6yhgzCido8hzgBGA+8K7vgI0xbwEtvKuneZePG2Nu8T4ea60dW6J8C5zMrkWK6n5ljCk6W/matbbolvwzgC+NMXOAbUAqzpTsFwONgaX4f79sNMasBjbiZN49Aec9lgxcZq1N81OnXCXes2FAE6AfzjFH4Ty/N1trg82oCk720+v4M2tpSUtwnvOrjTELcKafb42TuXYTTmbcktoDfxhjNgDLgXigEXApzrTyH1hrMwGstSnGmGuBX7x1ZgDrAA/OczsY5/1Vj4rVdL8PAAuB94wx5+NkR+4BXAWMBy4LYowiIiIiIiIiIiKHPH9ZS/3EnVa7YONYw40ymYpI3Zq3KY7f1mzihkEDOKZjm7oejoiIiIiI+KEg0yOIN1hsEPAgTrDX7ThZOlNwMmJ+Yq2dVE4TA7w/RfJwMje+D7xhrfUNoCtyhffHnzmEkDkVeBVn6vQHjTHvWWsToThT603GmGHe/YNxAtnygK3ACziBc6kh9IW11mOMeRz4LZR63rrWGHMjsAC4E/g/73jmAi9baxf4qZNijBmMkzn1SpwpylNwskk+680S6VtnkTc76gvA+TjBwjuBF3ECP/P9DO9anODiks4v8TgOGFtivQHwVz/t3Fbi8TCc4FCAZTgZPwcCx+IEMGYCa4CfgM+stQWUNRy4EDgFiMQJmH0bJ/g52U/5YBRloS3wjmEn8C1OFtmp1lpPKI1Za+ONMe8BT/nZ5/ZmlH0ZJ6D2QZwMrF96t/kGOMd5xzcEJxi6BU5A7iZv+yN82p9hjDkGeAxnavvTvce1FyfQeDTBqdF+rbVbjDEnA68B53r7WY3znm6JgkxFREREREREROQI4fGUPf3oCXBKcneab06Cygs2W6piTEWkLuUVurhv2C8AjF22nnWv/rOORyQiIiIiIv4oyPQI452S/RWCnB7cW2cYoQWCFtW7HSeQNZQ65Z7S8gaVxpazfyowNcQ+h1HO8Vlrp1A2S2nJ/eXtc+FkEi2TTbScOqk42VofCqHOepzsmsGW7xJsWW/5OMp5DvyUX0OIr7233uPA46HWC9BWpU+PVvTetdY+TelMuiX3pQL3B6jaxadsOk4w8IshjC0On2noQ1Ub/Vprt+IEM5dijLk92DZEREREREREREQOdf6mrQ+UyfTRn6dVW7+WYINMFWUqInUnKSOrrocgIiIiIiJBCKvrAYiIiIiIiIiIiIiIiBwOMvNKT+D07K9zypQJlGV074HqC7YKMpEpiRnZjFiyjoxcfxNiiYiIiIiIiIgoyFRERERERERERERERKRafDZ3WfHj5MxsFsftLVPGX3bTysotKPS7PVAgqz8fz17KJyXGLSIiIiIiIiJSkoJMRUREREREREREREREqsG4VZuLH2cHCAD1F2Ra2cDTHxav87s91NYmrN5Sqf5FRERERERE5PCnIFMREak11tph1lpjrR1W12MRERERERERERGpOuN36970TG79epzfff6yjLo9nkr1PmzhKr/bqzNbqohITTH+/4SKiIiIiMhBRkGmIiIiIoQ2jZyIiIiIiIiIHF72pmfy4IgpPPfrHApc7hBq+j+f8Mrk3wPWcHv8BZlW/rzE9uS0StcVEalLvqdkdY5WREREROTgpCBTEREROaLlF7r46+c/cf4bX7MmPqGuhyMiIiIiIiIideClifNYEZ/AzE1xjFy6Pqg6yZnZrNqd5HdfoO1QNqgKKp/JFGDpzn1B9SEicjDJLSik0F06qF9ZmEVEREREDk4KMhUREZEj2tB5y1i6Yw970zO4d+iYuh6OiIiIiIiIiNSBtXuTix/P3xofVJ13pi+qVF8eP9lPXVUIMs0uKCzbhwK1ROQgtiY+gbNe/YLL3/221PaqBNyLiIiIiEjNUZCpiIiIHNFW7/oz20dGbn4djkREREREREREDgbWTxCoP4t27PG7PTu/oNx6mxL2s2jHnlLBVG5P5YNC/fW3UrO1iMhB7IFvx5GZV/ZcbFX+FoqIiIiISM1RkKmIiIgc0XTaUkRERERERERKWr9vP/sOZFVYLiLM/yWWoQtWlVtvzIpNPDZqOpPXbiveVpXsfXmFrjLbxq3aXOn2RERqWkpWjt/tysIsIiIiInJwUpCpiIjIEcjl9jB93VaWbN9d10Opc1YnLkVERERERETEx+u/LaiwTHiAINPlu4LLIvr6lD/7qEr2PgWUisjhoioB9yIiIiIiUnMUZCoiIlJLMnLzmL5ua4VTppUsP23tFg7k5lX7WH5dsZ6Hvh/P7V/8zNrdR/b0aQoxFRERERERERFfy3btq7BMZLj/SyxbklKD7qfoPFFVA6v8ZTMN1fszFvPab/Nr5FyUiEgwPBUE3G9NTOHV8bNZvD2+dgYkIiIiIiKAgkxFRERqzb1f/8JD34/n/777tcKy1lru/mo0Dw+fwEPfja/2sfxn9LTixy/8MqPa2z+UKJOpiIiIiIiIiPgyQZQJFGQaiqELVgHgruL5iXxX1YNMRy3fwMQ1W/lkzvIy+wpcbiau2cKiHXuq3I+ISCAV/S28/5uxfL9gBXd8MYqcgsJaGpWIiIiIiCjIVEREpBbkFhSyxpsxdNG2iu+yzne5WbcnCYAlO3azOr7mso26amEKouz8Au75egzXfzScnfvTary/UCjEVEREREREROTQlpyZjctdvec3LHD/D5N5aswMPAGCnsLDqn6JZeTS9UDVM5kWuNxVHkuRiWu2lNk2evkGXvttAY+Nms6mxJRq60tEpCRPOX8LrbXsScsoXr/ry1G1MSQRkZCtiU9g1oZtVf58JyIicjBRkKmIiEgt8P0iWdEXy0J36QsDN/7vx5D7zCkoZEvCfjLz8sstVxuZPD+btYgFW3aybk8ST478rcb7C0ah283WxBQWbNlZ10MRERERERERkUoas3QtZ736OZe+M4yCasjmWdKaPUnM37abM9/6ll2pB8rsr45MpkXcFUwRXZH8agwy9ed/c5YVPx65ZF2N9iUiR67yMpl+NXdpqfXV8Qks2b67pockIhKSrYn7+cv/fuQf3/7K6CVr63o4IiIi1UZBpiIiIrWg0CebRqDsGmviE/j7sLF87XPCLFTzNu3gxOc+4sr3v+OsV79gV0p6wLK1kclz7sYdxY+LMrrWJWstN/5vBFe8921dD0VEREREREREquA/o6dhLcSnHuDnxWsq1caqXfsqLHPzV2P59o/Vxesut4ddqRnl1AhNVYNM8wqrN8C2PPWjIgHIzMsnJSu31voVkcOfp5y/he/+9nuZbXM2bmf4ghVsT0rl981x5Nfi30IRObx4PJa45LQqJ2Z5feLc4scvjJ1R1WGJiIgcNBRkKiIiUkP2pmew25vlwjczqe96kbu+Gs3cTTv4fNbigO0WuNz8unw98zbFBSxz37CxxY9zCwq56K2hwQ/8CLB851427E3yu+/yd79h+rqtFbYR6DWUg5O1tlay9oqIiFSHMUvXcvMnI5i2tuxUteXR5xMRORJ5PFbTUB5BcgoKWbBlZ8CMpfvSM0Nqb+O+ZK7/aDg3fTIiqPJfzFtR/Hj86s0h9VURVxXfx0WZTF0eDx/MDHxeqTo0iolmd1oGV37yM9d8+jNzNu9k+a59+l08BOjcyKErv9DF25Pn8dqE2WTnF9T1cGrMF7MXh/Q+HTpvGa+Mn81l737D34b+wiM/Tqy5wYnIYe2er0dzyTvDeGnczCq1U92Z9UVERA4WCjIVERGpJhm5eYxasoahc5eyYMtOznv9Ky56ayjLduwm3+dLpW9m0yLlnSBMOJDJ1sT9/Lx4DU//PIX7hv3C2hCygi7aFu93e0EVplPbl57Jy+NmljvlR2ZePlsSU0ptm7Kmei/ExKemM2vDtqADK3LyCwPu25aUykPfjy+3/otjZ3DS8x9XOeOs1I741HQuf+9brvvoB1Kzcup6OCIiUoeSMrIYNm8ZWxL21/VQAsotKOQ/o6exctc+Hh4+Iag61lruG/oLp7z4CZNXb6rhEYqIHDz2pWdy4Vtfc/4bX7Fzf3pdD6fWBZolJS07l3V7Eg/qYLLyxjZj3VbuXzOEuSntcLk9LI/bw9Idu3l53ExOfO4j7vl6TMD/kcGeF0g8kMXetAzu/XoM6/b4vwm1Iu9MX1Spev48MXoG8VXMilp0fmfSmq38vGxDdQwroEU79nDjl79Q4HLjtpZ/j5vNQyOnlgrClYPP5sQUbvhiDA/8MLlWM99K9Ri+cCVfz13Kd/NX8MXsmg0kr0ljlq7l7q9GB9z/0+I1zN+ys9Ltz96wvdR6TQfkHsz/a0UkeHvTM/jDew1t5KLVFZQun8FUx5BEREQOOhF1PQAREZHDQcKBTM557csy2z3WctvnP5fZ7qpElil/7b86fjbD//6XUtvmbNxephzAvE07GNS9Y5ntu1LS2Zq4nx6tW4Q8pufGTCs+6XdU+1b0bdeqTJmPpy8ss+2RHyay7tVeQfXhcnt45ucp7Ek7wIvXnEf3Vs1L7U/PyeOq978nt6CQe886iYfOP7XCNk0VvuOnZOUUn2R4e/I8Lji6J+2bNq58g1Ljnvl5CtuTUgF4c9JcXr3+wpDbKHC5mb8ljr7tWtGmccPqHuJhJ6/Qxa6UdHq2bo6pyi+cHJKstXrd5aD171FTmb9lJ0Mb1mfaE3cTFRFe10MqIysvP+Q6czbuYN7mOAAe+3ESFx3Tu5pHJSJSMzwey0fTF7I/M5sHzz+FFg1jQ6r/8q8z2ZPmBOZd/PZQ1r7y8BHxOcTt8XDXV6PZuDeZ166/kCF9uxXvO5Cbx2kvf1q8PvmxO+jUvEkdjDKwKWs288r4WZzZpxsvXn1eqX0ut4cHvx8PtGdOanvOHzGJqX4ye8/ZuIPMvHy+mrOk1PZgbqTdsDeJGz7+ocrT01enhdt3s3D77iq18cvKjRzXqQ0T1oSWCT0YHp8gqs2JqX7LDV+8lvvOHBhS24fj94f9WTk0rBdN9EH2WfPpX2aRlJnNvgNZ/LB4LXeeemxdD0lC8MmMP4offzF7CQ9fcFrx+tbEFBrXr0fLEP+P1rb07Fz+M3paheXGLlvHab26VLqfor8rL/wynZ+XrOH20wfy2EVnVLq9QOZv3sl/Rk+lf4fWvH/LZYfd3zKpHnHJabw4bgbtmjTihavPJTysenOApWbl8OWcJbRv2pibBg/Q+7CSCgqrcWaYKrwEyRlZrNmdyCk9O1MvUqE8IhVxeTzkF7qIjY6q66GIHBGUyVRERKSKXG4P//p5Skh1fDOZFrrdpOfkhdx30QURj8fyxezF9Hv6Xe7/ZpzfskUnFyau3Fhm3xMjfwu5b6DUXeWBsmZ9N79qWSxe/nUmE1dtZOWufTz6gzPdUdFFo5370zj1pU/ILXAyk34+azGTVm2qMDtZVe4kLeqryJ1fjMJzEF2YkrKWx+0tflzZTAhvTZrLP779lWs++J4c73tg1a593PrZSN6fOr9axnm4KHC5ueLdb7jq/e9KXQCpTqOXrOWpkZPZ6pMluSbt3J/OtqTK9ZeRm8fL42by9uR5JGVksSY+gR8WriQjN/S/+1W1Ny2DeZt21MiU2tZaHvlhAue+/hULt1Y+60h127QvmWU7diu7iJCenVv8f2B/Zg6b9iXX8Yj8K/SU/ZxYkd2pB2pqOCIiNWrCyg18NmsRo5eu5aK3hjJ8wQryQ8iu5ztjx9jl6ys9Fmst8zfvZMGWnQf954YJKzeyZPtuMvPyeeDb0ucAvv19ean1i94ayuwN25m3KQ63x3NQfH995IeJ7M/MYfSStazbk1hqX25h6e/c/gJMi1z81lC+mF06yPSnxWtIycph2Y7djFy0ym8Gu9cnzKlSgOm3f6xm8Y49la5fU4oDEarp/ZuRm8+Y5RvZlJjCqvjEiiuEyGMtj4+ezhlvfcvHs5aUCWQNVaHbzbwtu9hVx5+Lpm/YzjWfjuL6z0fXWAbFyv6NSsrMLn68anf1v6ZSswIFjk1Zs5kr3vuWc1//kj1pwb//07JzGbtsHYkHsqpriH5Za5m5fhvjV2wgPsjfz6r+Pch3udmblsFPi9dgLQydu6xG/rffO3QMiRlZzFi/jcd+nFTt7cvh4cmfJrNoWzy/LFvH6KWBZ4SrrHen/M43vy/nlfGz+GPbrmpv/2BU4HIxe8N2kjMC//1avyeR4QtWBn3dzffvTrCfmfemlz3XGlbJQN8Cl5vrP/6B//vuV27834/8a9QU5m7cUam2RGrSzpQDTN+wnZSs3DodR2ZePjd8PprLPh55UH4/Ezkc6fYHERGRKtqcsB9XgKnoAyl0uyl0u5mwYiOFHjcv/DKjSmMYv3ID700pP9AtKy8fj8fyxMjJZfYFG2SxPG4PwxesZE9aBi0bVe3O+EK3m8jw0hkdNuxNYuSi1Zzfvyen9OzMip17+XnxmuL9WxJTeG/K7wybt5zbTjuuVPBgkcdHTCI6IpypT9wVMAtOMCcqC1xuv9nN8nwueO1OyyAtJ5fmDepX2KbUvZSsHO75ajTv33o59aMig643fOFKwMmcO23tFq44/iju+XoM2fkFLI/by1l9unFMp7ZVHl+h201EWFjxhQOPx7J85x7aNG5Ih2aHRsbcmeu3stubTerjGX9w/7mDq7X9HcmpPDvGyXqxLG4vT156Jr3atKjR7Exrdydww8c/AjCkbzdO7dmZ6086hojw8u/ZK8qc8enMRfz4xyoAvp67tHj/xn3JZTI3gTOV2xezF2OM4YFzBhMRHkah202By12lO3ITD2Rx3htfFa8/etHp3HnGCcXr/v4uh2LG+m1M8WZNuvurMax79Z8ht7E9KZV5m+O44Oie1ZI1eMPeJK79cDgAb/7lYi4eoOyOh5tQ3rfvTPm9Sn3lFhQydN4y6kdFcuupx1V79pEiyRnZpdbzCl0VHmN2QekAhrTsXJrGxlT72CR0hW43cclp9Agyu/fyuD2kZOVwVt/uFf6fETkclPxslFNQyCvjZ2OM4abBx1ZYt9DtJtzn9+rd337nqoH9mLp2C39s3cVtpx5Pl5ZNy20nv9DFtLVbiE89wEfemTg+vf1KTu/dNeTj8ZWdX8CmfckM6NS23P8bHo+lwO0uN1vRsh27+WrOUs47uic7kv1nkQQ44OdCelEgav8OrdmRnMb5/Xvy8rXnlynn9niI259Gt5bNKpUFa8n23SzcuotrT+xPswb1iQoPZ1X8PhZs2ck1J/b3+/kuIT2Tfu1bF6/73thZntRs/xc1z/jvZ8WPp6/bxotXn0fbJg2x1vLt/BUs2VG1jKF1NSX81cf14fZTBnD5xyP97s/33pBbXXHE789czNT1/mfLqQ6zN+3kj+3OxeARS9fTrWVTzunTtdxM83mFLlKycmjftFGZfd8sXM03C1cTFRHOqHuvqbPPQi9MmAc478/7f/iNj2+6kAbVlFnJYy3P/DKTDQkpPHPRqQzq2h6X21Opzwz51ZmxTWpFoKClR7w35bvcHt6YOJf3b7ksYBs5BYVEhBmiIiJ49MeJLNoWT9eWTfn14b8SFlYz2Q8Xb9/N/333KwC3nHJsUHWqGg868NkPy2wrcLmJDjIr4N60DBrFRNOgXjTgfObYnXqAuZviOL9/T9o2Kfv/7Lc1m7l774l+Z/oKVl6hCwNBj1MOXtuSUtiRnMYZvbuytkRQ/6z127n+pGMC1lsdn+A999yXTs2bsCY+gaPat8bl8dDQ+370NWbpuuLHwxesZHCPztV3IAepV8bP5ufFa2jRsD5TH7+rzO9MZl4+t38xiuz8AtbuTghqZrMCnxt8l8Xt4cRuHcqtk5Gbx1XvfUdWfgF3n3ki/7zQyTBdmb+mm/YlexMUOOeENifsZ3PCfsYuW8+Pf/9LtVx7EClPUmY2LRvUr/B74IpdCTw48s/ES+Puv55mAT53p+fk8c+fptK+aSMeP/9k6kVG8uHMxeQVunjw7JMwhuK/bZWZ3eCr+StJyswB4NFR0xn1t2tpXcVr1werpMxs6kdFVtv3isPJ6l37WL5zL5cf15dmdXyt/kBuHo3qRR/WWcX1KVVERKQOFLrd/Lp8Q3GgVGXlu1ykZ+fyTBCZVH9avIaFWwPfyfrb6k1cWM7UqtZaHh4+gZSsHL/7DYasvHzen7qA6MhwsvIKSgWI+pqwciNXDexXvL41cX9xINDPi9dwzYn9Gb2k7J29RVlKvpqztMy+IvkuN+9Pnc+1Jx5Nn7YtcXk8pQKzgskItjVxP73btiQ1O7fUVFM5BWWz6oSSaScYWxNTaBobE3TgalJGFi0bxh7WH1qr04Ktu3h1/CxO6dmZ03p1ITI8nLTs3OKLjhU9j8/8PKXM79yq+H1EhIexZMceBnZpR9PYGNo3/TMoNJh2F2zZyaM/TKRzi6Z8cOtlxKeks2Z3Im9OmktkeDiTH7vD70nsUG1N3M+ulAOc3rtLqaClvEIXi7fFc3THNn4vxi3Zvps3J83huM7teerSM4HSWTTGLF3rd8qzF36ZziMXnc6ulHSiIyJoGhtDUkYWw+YtY3CPTnRv1ZwerZsTER7Gip176dm6Ran+C1xu4vankZqdQ5+2rVhQIhvt3vQMHvp+PAADOrZl2L3XBbwguXLnXpIyszmrbzciw8PJzi/AY22pE7TbklIY+cdqzujTldN6dWFrYgpXvPdtqXZmb9jO7A3b2ZuWwSMXnh7wIsy6PYk8+N14GsVEszlAduXRS9aWCTJdHZ/AZ7MWMXuDczH381mL+fyOq/nP6KkcyM3jvrMH0bN1C+Zu2sGoJWv421mDCDOGc/r1oFebFn77Aec9ePZrX5Ta9vbkeZzbrwedmjfh6Z9+Y8qazVw5sB8ndevAgE7tAr7fUrJy+HzWIjo2a8Itpx4HQHxqOtPKyTJVHrfHQ3iYE0h7z9djSDiQyYx1W/n2b9eXKleZi6cvjv3zJo7HR0xSkOlBKqegkP/7dhxpOXm8c+MlFQYDFZm9YTtPjpxMr7YtGHr3dX7fH8vj9jBz/TauOqEfM9ZtLbXPYy1T1mxm7qYdXHNCf47v0r7c/kYuWs3H3uCjlg1jueTYPuUe06/L19OxWRNO7RXcBZ55m3bw9uR5bPHJ0rxuTyInd+9UatuWhP08+uNEtiWlctmxfRjvk6n+tJc/5cNbL+fso7oH1fehJj0nj7TsHLq2bBZ0neELVrJi517uP+dkurUKvp7L7eHj6QvJzi+ge+tmbNq3n9tPH1h8c0PR/3h//+vTc/I49aVPADi+SzuuO/FoMvMKOKdf9+JAqwO5eYxZspZebVrSvGF9bv3sJwD+dflZQQXZiRwMfl68hk37krlnyEm0btwgYLm96RmsiU9gzNJ1nN6rCyf36Fjmbx7Af3+dxV8GDfD7OevruUt5e/K8gH2kZOXw4tgZjFy0GoD4lHS+uOuagOULXC5OffnTMoGN9w0by/x/30eTKgSpudwern7/O3anZXDDoGN49spzypTxeCzJmdnc9tlI0nLy+N9fr+CErs7F7OSMLA7k5tOjdXO2JqZw2+c/AzBn0w56tm4esN/yvncUBTn8smwdt59+PD1a//n5MS45jUveGQbA5cf15dXrLyQtO5d3f/udxvXr8fAFp5YJlPV4LEPnLeXj6Qu5cfCxjFy0mtyCQr79fRnGGGKiIovPIUxft5Uf778Rl8/38cXbd3P2Ud3JLXQREWZCCjINxoItOzn39S+rtc26cHH/Hvzz3EEA/OOsE/hoVtlzInnecxO+QRKVVdkA0/u+n0SXFk144oLBfoPirLX8vjWebxauKrX9lcnz+Xj2Uj78ywV0bVH682BOQSH3fj+RnSlOFsRHzh3EVcf1ITEjm19WbuSY9q35ZqHze1/gcjNmxSbuOu3YUm3s2J+O2+OhRwifA6pq+/40nv5lJnecMoDNialc3L8HjWL8BwkV2Z+VwzNjZxGG4fELBrM5MYUTu7SjcUw0L4yfy/xtTpD0Y6Oml6p3Vu/OPHj2SbQI8lzSun3JfPn7Ck7u2p7+7SsfFCdV9/bkefyybB1n9e3GQ+ef6vem+dW79pGZl19q27B5y3hz0txS26av28rSHbuZvWE7Fw3oXSqIf3V8And+OYqYyAhG/uOm4mzgO5LTSM7MLvd/eFXc+eWo4sffL1gZVB1/CQKqmol73Z5E9qVn0rFZY47u2Kb4Mzw4/zuz8wsodLlZvGM3j/wwgfpRUYz6v5t5auRvbE7cX/z/aerazXz/txv4cs6SMn3849tfmfHU3SGP7ceFq3hvyu9k5RfQsF40P/z9LyF9Z5GDS3JmNtd+OLx4NraSyjs7nJWXz43/c25yn7tpB2HGlDmn+MYNF3HB0b0wBr83MB0p1weKrjvtz8xh3uY46kVG4PFYTu/dBWMMM9dvK84m/uuKDazctY/nrjyHk3t0Ctim7zWee4eOYcnz/yj3XOQvy9aT5e3nyzlLGNyjEyf36BTy65Bf6OKer8cEvPZ24ycjyiQLONxl5eWTlpOL220rPE+YU1DI9/NX0CgmmhsGHYMxhjXxCRzIzeOUHp1r7CaKw8lbUxcybtVmzuzZiZevPAtrLfuzcsgtdLFs5z7yXW42JaZwdu8uPDN2Vqm63y9aw4Nnn4S1lvGrt7B2bxI3n3Q0nZs35trPRpHvcrM1OY05m0vPfDbF+13jnD5dOLFLOz6evZSze3fhsfMDJy2x1uK2tvhm061JpW++/HDmYl6+8iwAtienMXndNob06ky/di1LlZu7eScbE1O45ri+NG9w8N+kv2Dbbp4aM4N6kRF8f9eVtAqQYKmkou9cFmjdKJZp67dzbt+u9CnnGhLA5sQUvl24mpO6tufyAb2q6Qj8j++9KfPZlpTC4B6dOL5Le/q0bcnGfcl0bdmMAzl5NKgXVWHSld9Wb+JRbzb5NyfNZcT9N9K/Q+syNxcVuFw8N2Y6B3LzeP6qc2nVqPo/934xezHvT53P4B6d+fyOq4L6XzBz/TZ+WbaOG08ewCk9A19DmL95J5sTkrn6xP40jqlXncMOmTnYp/4RERE5mBljltVr3eH4bnc8UtdDqRaXHtuHhy84jRYN67MrJZ3Y6ChioiK556sxxKemk5GbX3EjIbj2xP5cObAf09Zu4RufKf2q2zs3XcL5/XtyIDef//tunN8sqIE0jqnHHWcM5M4zTuD3zXHc/03p6QgjwsMYft8N9O/Qpkxday3Ld+6lWWwMbRo3JMYne+avy9eTlJnNTYOPpX5UJBNWbODJn34DoGOzxox44Caa1K9HXqGLepERZQIY3pg4h29+X87pvbrwye1XFu+z1lLgcrMtKYUerVuUmwnkcJJX6CLf5Sr+kF3yQqk/x3Rsw+r4hOL18DCD22M5q283TurWkdaNGzCkTzeO95MBoSKXH9eXF685j2s++J5t3i+7x3Zqy8pd++jdtiX/vfZ8+rRtye60A0xcuYkPpy2osM2/nTWIB849udRJxLTsXJbu2M2g7h0JDwvjrUlz2ZaUylUn9GPhll2s2Z1AodvNracez3n9e3DxW8NKBVpfemwfzj6qe3Hmi/pRkQzq3pEl23dz95ATuWpgP7LzC7n47aGlxtK+aSPyCl1cemwfNu1L5o8QMzr7+svJAxjxx58XGYf07UaPVs39nrQvz8MXnEq/9q255+sxAPzy0K14rOXaD7/HWnj84jOYuHIj6/cmATDi/hs5umMbsvLyGfTC/4rbuWRAHyau2ui3j5LO6N2V92+5lKiICDLz8gkzhjkbd/D4iOCmSXv2yrPJzCvgtlOP54+tu/j7N2NDOt6Sbhp8LE9deiZhxrBhbxJREeFMXr2ZrYkp9G7Tgo9n/FGmzn+uOJv1e5P8BvUP6NiWJy89k26tmrF4Wzz9OrSmYb1ozn71i+ITqL3atGBgl/aMXLS6zEWgy4/rS9eWzbjupKMDZhF6edxMfvxjFc1iY8pkourasiln9O7KHacPZPzKjXw0bQFHtW/NC1efS/dWTmBFySD7ApebjXuT6N66OfmFLpo1qE+/p98t1WZRdlWPx5KVn8+ulHT+NWoqzWLr88Gtl5GckU2XFk3LnHyMS07jh4UrOaVnZ4b07VZqX1UzwBa1UTKLcZECl4uoiMPn3tC96RnsSc2gaWy9UkEtH09fyP9KvD/vOGMgN548gPZNG2OtJd/1Z2a3eZt2MGLRaprWj+GXZX9m6/jvtedz5cB+xZngdu5Po3OLppzz2hcBp0a79sT+jCrx3v/kr1dyRp8/s9YlZ2TRvEEsYWGGvEJXmWw4a/77cMAT1R9NW8AnMxcBThZdl9vN0z9PoV5kBM9deQ6r4hNYsj2eFg1j2bQvmQbRUURGhLMjOc1vey9fez5hxnBW327sTj3AdR/94Lecr1tOOZa96Zms2rWPz++8mj5tWwYsm5yRxar4BA7k5DF743bqR0Xy7JXnVCmDck1IzsjioreHkVtQyKvXXcBlx/Ut9fkHyl5Y83fTQNeWTakXGclTl57JcZ3bER4WRmZePou2xTO4Rye2J6Xy+IhJAaf0HNCxLSd178DwBSvJKSikflQkD5w7mNtPH0h2fgGfzPyDoXOXBTyOd266hLZNGvHr8vX8+McqwsMMbRs3LM4GDjDmwVvo0bp5qf/7O/6fvfsOj6M62zj8vOpWcZN77xhTDDZgTO89lBBICBBKCCEhEJKQhJQvQDokEAgBklBCQu/VdIwNLrj33rstS7J63z3fHzMSqy3Sqlmy/buvS9d6Z87MObtarXdnnnnP7nxtySvUxJGDlJyYqBXbc/T4lDk6buRgXXr0oU17MoE4Ldy0XZ+t2qiLx4+Rc04fLl2j0X17KsES9Ie3JmtTXoEk6ZD+vfS7S8/SiN7Zck71TsjO27C1LiTZFFcdd4R+cu6J2rB7j/709pRmVaB89JqLdeyIQXXfiYJBp4c+nqGPlq6J+b7r9X2kfvGVU+Scqwt9RLM1v1AvzFqkCcMG6bABvdU1o5OqagI68v/+Xq/ddSeOV15JmX58zgnKzsxQeXW1vvbQs9rsP3+17rn8HI3p31uXPPi0aoJBZWemxzzhXOvN265W7y5ZykpL1a9f+bDe/5Gx3HHByTpq6ACN7ttTgaDTBfc/1eA0xpcdc5h+feFp9X6vZ9zzuHYUFDfaV3O8OPx+SdLX1+294y19O8U/28XedvTgfvruyePq7u8oLFH39DT96OWPooZYOopfnnu8hvXsplnrt+mFucuUmZqiHY1Mzd23S6bOGjNMSYkJOmJAb23ILdDL85Zry57mv9YO699LS7Z53wFvOfVojR3Yu5EtGuacU3UgKJlpU16BBnfvopSkRJVUVum2Fz+Mud2g7M7KSElR57RUXT5+jLqkp6qyukaPTJ2n0soq3XjiOL21eHVdhddavTpn6MQRg/Tq/BWNjm14z26645zjop7UvOF/70QsS05M0I/PPFard+XpmCH91TNr35yppyoQVEFpuXqFVa8aMmRI+wwoDqWVVXp97jL96Z0p9Zbf/dUzdOGRB2tdTr5G9emhP709pW52lKZITUrUp7+8UTPWbNL4If116p8ei9n22Zu+rn7dOqtX58xWvZh9fU6+vvK3/zZ5u9PGDNdDV19Ydz/8mE1r6JGVrtziMvXr2lmdUpLqjt211A0nH62bz5gY81jsi7MW6bdvTJYknXv4QTpm2ADd/Ub9Wc6OHNxPz9z09VYZD/auYNDp5v+9qc9WxZ7i/DcXn6Z1u/J1yIDeumjcGBWUVWjSwhX649tTmtTX0cMG6N6vn1vvbzv8byfUF2s3699TZuucw0bp8gmxq6m2lZyiEiUnJtY7RlhZXaPNeQUa3is76vGVYNDpn5/Oqrvgt1fnDB3cr5emhkwhf/qY4fpk+bq6+2cdOlJd0tOiFkCZc/cP6s1u5pzTpIUrVVheITPTH96qH5676rgj9I1jx9Zd4FpYXqGkhAR9sGS1lm7dpc15BRGFXYb17K6enTPqgvyh4zz/iNE6efSwiNkLXpq9OK7ZDj+54wa9v3i1jhs5uK7YQOhxycnL12nD7nydPHqockvKlJGSosMGRp6zkrww9LqcPI0f0j/qcU3nnArLK9U5LVWz1m9W1/ROTa7U7JxTfqk3C2CsY5zOOS3btkudO6VpUHZXrdmZq6/+/Zl6x5l/cu6JGti9i56duVAH9+2lH597gpITE+WcU00wqKenL6i7EPGRay6SpLpzeL+/9CxdctQhEf22hfU5+Zq9fovOPHSksjPTtWDTdpVVVqusqkoH9+ulAd27qCYQ1OQV69QjM73Bi9037M5Xdma6OndKUyAY1J/fmar5q9ZqZK/uunDsSBWWVynBv6hvUPfICv+Ncc4p6JyKKirVJS1VNz7z5bmMMw4eqo9XxH4Pi+b6449Q1/Q03f/Rl8d3rz72cD39xeImj63WuYcO16XjDq67X1ZVrT+/P0M7i0oUDDqlJiWqOhiMuAhl3OC+OrRfT/3PvwCtc1qK7v3aGUpKSNCmvELN2bRd7y9dV9f2huOPqKvimldWrpqAU0pSgtbt3qMjB/ZRWnKStu0p1paCIi3blqMRvbrr+BGDlNKGsw/VHsvOKS7VsJ7ddFPI72fi8AG6/rixMT+nrcvJ1xcbtqkm6PT5mvrvT90yOumer54WcSHgwi079dbi1ZowpL8mLV2rskrvwprfX3yK+vhhzOZUmpUiP4s751RYVqFZ67fUnZNsSJ8umfr5Bado+54inTZmuAZld9We0nK9vWCFpq5cH/W85Oi+PbUuJ1+/vuhUpSUnq1Nyku5/f5o25nrHf7Iz0zX5ju8oKTFBwaBrNIj+4AfTNWXlen19wuH6+oTD5ZwitimvqtZRd/6j7v6LN1+hoT27KzUpSU9+NlcPfjhdxw4fqN9/7Wz17ZqlxVt26o4X36s7niZ5v59u6Z10+KA+2lFQrNSkJH331GPULSO97vzsZcccpp+ed1LM4/VLt+7UpyvWq7SySqcePFwThg+sWxfld9isD/uETAEAaIH9LWSKlvvuqRP0r0+9UEl2ZrpG9s6O+JA7one2bjnzOE1bvbHuYMf4If01pn8vPT294Wn3UpMS9cOzT9Apo4fp2RkL6qZxl7xpJV6+5Zt6cupcvRR2EOWm0yaoOhDQNyaMVd+uWXUfJEsqKlVUUam+XbLa9Urn7QVFys5Ir7uyzDmndTn56tk5Q53TUlVYViFLML2zYKVW79yttOQknXjQUO0uKtXs9Vv0zYlH6MnP5upDv5Jiv66dtb2gqKEu92lpyUk6efTQuqnJgX3BlROP0Amjhqg64F1FOmf91iaHiGMZP6S/5m3c1njDOCQnJursw0bq2hPH67+fz6tXITIlKVEf/uzbSk5I0Kz1W/SrVz5UeVW1UpISdc5ho9QjK0MDunfWkYP7KyM1WXkl5Vq1Y7e6ZXTSqQcPU1lVtXYVegfVe2SlKy05SdNWb9Rtz75TFw44buRgfe3oQ+sOsmSmpujVW6/SrqISjenXq+5iBeecKqprlJqUpLKqKu0qKtHg7G4qLK9QcXml8kpKtX73Hk0YPlADu3dp8nt87YHa0PBrZXWNEhKsWaHax6bM1gMfTK+7/7crL9Cg7K569JMv9HFYhdFayYmJcVUfr9U1PS1moDRej15zsXp2ztALXyzSK3OWqmdWhg4f2KfeCYtQw3p11/ljR+vTFetkMl11/JHKTE2pm5a4o5l8x3e0fPsu5ZWU6b/T5qtrepouO/ow/aKBqvh/+cZ5mjB8oHKKSvyD/znqkZWukw8aprKqKqUlJ6usygtfRXud1QaFU5MSFQi6euGkYNCpdpPa13PtQbr5G7fJScotLlVucZmWbdulUX166NkZCyP+j//dpWfqpVlLtGSrd+HIX684T855V7P37JxZ7yKGfdX4If3VJT1Nk0Nei7eceZzenL+8LqD25A1fU/9undW/W2c5JwVcsMUh+P1d6AHW5h4wD9XcKYs7GuecAkGnBDOVV1frjHseb9YFhz8863iN6J2t4orKuGbfaGuPf/ur2llQonsmTY2oBhePS486VDsKilQTdPrKkaOVV1KmlKQk3TtpahuMFoRM6zusfy/98PRjIpav371Hf3xvepQtOo6u6akqKGvdi5Zb02VHjdHh/XupU3KSFm3NkZm0Ka9AU1d7J2WPGNhbxw0foM/XbKkLqraWK445RM/PbjwU3lQ3njRO4wf1qbtQpaomoLcWr647od6Q0X2ytXKnV2V6aI+u6topVROHD1DPzAx16ZSqwvJK9e1ae7LZO6G6OidPfTpnql+XTJmZdheX6d2la1VeXaMLDx+pPl0ylWAmM1PQuYgT22VV1Vq8NUd9u2YqEAhq3e49enHucqUmJeryow6R5LQht1CDundWflm55m3aoasmHKpu6Wnq2yVL5dU1+u07nym3pFxfHTdaZ4weqsQEU2JCggYPHiwzb/altORkJSUmyDnnTxufoOdmetXuv3f6scotLlN2Zif169ZFJRWV6pqeVjdderjiikolJyaqJhDQ9DWbNLRndyUmmKoDQfXrmqVVO3K1cPN2jR/SX4s279CesnJdMv4QZWema1NugVbvzG3xDFdtYWD3LnUXHPz58nN0wRGjVROM/Ey5PidfBWXlykpLVc/OmcpKS/Eu/K6u0e/fmtwqx6t+feFpWrR5u4rKKzW1gcBeR3Xe2IN0yuhhOnRAb3Xp5L2W3l+8qq64QDxSkhJ1cN9emjhykIb17K7TDxmhxATT2l15ykpLVcAP1lQHApq7YavGDuqrbhmdlJiQoE25ezSidw/VBALKzkyvC2w5Jz07c6GSEhL0jWMPV2JCggrKKlReVV03y1N5dY1SEhOVmGD7bGXM8qrqiGIPsTjnVFxRqY25BTrEr+xcHQgqNSlRU1du0O3PT9L5R4zWry48VSlJXsXMovIKJSUmKD0lRVWBgHKLS3X1v16sm+o8XkcPG6A565t+EVVDRvbO1ojePbR4yw5t2xP9+Pibt12tsspqBZ1Tv26dlZ2Zri15hRrQvUvd95maQFBOLuKi6K35hUpKTNAjn3yhV+cs1Z2XnK6jhw7Q1vxCZaalKi05SfdMmqrdRaU6dsQgrdyRo4WbdkSMIfT9RvLCmcePGqx3F63SuYeP0smjh9UVEWgLWWmpcX8nSEpMUE0g2GZjaW//u/Ey/frVjyIufguXlZaqcw8fpTfnL9dBfXvqe6cdq8LyCg3u0U23PP2mcosbvjBOkn52/skqq6zSq3OXanB212YVrrj+pKM0vFd3PfDBdO0uju9vLiUpUTeecoyOGNxXNYGgqgNBFVdU6qSDhtYFn2srWq/csVs9sjI0c+0mLd6yU2P69dLofj21Oa9AYwf1U/eMTurVOVOBYFAbdudrUHZXrxjH/f9t9OLAUBePH6OstFSN6tNDfbtmaebazcpMTdWDH0b/bB/ru8p1x4/VEQN6K8O/mOvxaQsUdE5HDOyjtxetrtd2eM9uOrR/T725cHXUfXVEp40eohNHDNJDn86JKFLRng7t30sje3bTmH49taOwRIlm6tU5Q/M371BFTUD9umRqc36RDu3XU327ZKqgvFI7Coo1snd3pSUlad7mHcrOSNdRQ/rWOx718twV+mhF4zNKHDO0n8YN6qOyqho5Jy3bnqPc0jJtzmv8vGhqUqIq/XMR5x02Qu8uiX5sPpofnHqURvXOVkV1tcqqatTP/+yQU1ym3p0ztKOgWBXVNcpMS1WnlGS59Cw9P3OhOndK01HDBuhHz0Ze9NYUo/r0iDlzYHNdNG6MTh49VCt37Nb2PUXKLynT2pw8HT6wr846dKR+9uJ7rdpfa8rOTFen5CRtKyhSrOhnl05pKiz3zpmMHdhXXTPSNKxXd91+7kmETAEA2NsImWJflpacVDednuRN/Rt+FW1riva5s6Syql4gKC05SQlmKmv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\n", + "text/plain": [ + "\u003cFigure size 1440x864 with 12 Axes\u003e" + ] + }, + "metadata": { + "image/png": { + "height": 712, + "width": 1356 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Build plot.\n", + "plot = plot_components.plot(\n", + " [\n", + " plot_components.TranscriptAnnotation(longest_transcripts),\n", + " plot_components.Tracks(\n", + " tdata=output.dnase,\n", + " ylabel_template='DNASE: {biosample_name} ({strand})\\n{name}',\n", + " ),\n", + " plot_components.Tracks(\n", + " tdata=output.atac,\n", + " ylabel_template='ATAC: {biosample_name} ({strand})\\n{name}',\n", + " ),\n", + " ],\n", + " # Plot an 8kb window around the variant.\n", + " interval=variant.reference_interval.resize(8000),\n", + " annotations=[\n", + " plot_components.VariantAnnotation([variant]),\n", + " plot_components.IntervalAnnotation(promoter_intervals),\n", + " ],\n", + " title='Predicted chromatin accessibility (DNASE, ATAC) for colon tissue',\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MXxYBvMlvGjG" + }, + "source": [ + "Some observations from this plot:\n", + "* Elevated `DNASE` and `ATAC` signals overlap\n", + "both putative promoter regions highlighted in grey, but additional chromatin\n", + "accessibility peaks suggest there may be other regulatory elements in this\n", + "region.\n", + "* `DNASE` and `ATAC` signals are similar but not identical, reflecting\n", + "differences in assay protocol.\n", + "* Accessibility is not especially high around the variant (orange line)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ibR_Luiqzwve" + }, + "source": [ + "## Splicing" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cASBC9c0zwve" + }, + "source": [ + "Splicing effects are primarily captured by the model outputs `SPLICE_SITES`,\n", + "`SPLICE_SITE_USAGE`, and `SPLICE_JUNCTIONS`. Here is an example of splicing\n", + "predictions in a genomic interval containing the gene *APOL4*.\n", + "\n", + "Since `RNA_SEQ` data also captures splicing patterns, we can plot it together\n", + "with the splicing outputs, allowing us to see how predictions from different\n", + "modalities are related:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bzgBbXQqzwve" + }, + "outputs": [], + "source": [ + "# List of IDs corresponding to various intestinal tissues.\n", + "ontology_terms = [\n", + " 'UBERON:0001157',\n", + " 'UBERON:0001159',\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "PDDdUrCgzwve" + }, + "outputs": [], + "source": [ + "# Make predictions for splicing outputs and RNA_SEQ.\n", + "output = dna_model.predict_interval(\n", + " interval=interval,\n", + " requested_outputs={\n", + " dna_client.OutputType.RNA_SEQ,\n", + " dna_client.OutputType.SPLICE_SITES,\n", + " dna_client.OutputType.SPLICE_SITE_USAGE,\n", + " dna_client.OutputType.SPLICE_JUNCTIONS,\n", + " },\n", + " ontology_terms=ontology_terms,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JAcrIt_-_ENZ" + }, + "source": [ + "Note that `SPLICE_SITES` are tissue-agnostic predictions, so the\n", + "`ontology_terms` filter is not applied to it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "height": 175 + }, + "executionInfo": { + "elapsed": 62, + "status": "ok", + "timestamp": 1753110008315, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "4JUQL4jWPw66", + "outputId": "ab360915-44c8-487b-aced-631544d94875" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \"output\",\n \"rows\": 4,\n \"fields\": [\n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": 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Visit the ' +\n", + " '\u003ca target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb\u003edata table notebook\u003c/a\u003e'\n", + " + ' to learn more about interactive tables.';\n", + " element.innerHTML = '';\n", + " dataTable['output_type'] = 'display_data';\n", + " await google.colab.output.renderOutput(dataTable, element);\n", + " const docLink = document.createElement('div');\n", + " docLink.innerHTML = docLinkHtml;\n", + " element.appendChild(docLink);\n", + " }\n", + " \u003c/script\u003e\n", + " \u003c/div\u003e\n", + "\n", + "\n", + " \u003cdiv id=\"df-ddbe2953-3c1f-4eb5-9826-710bdcb63d54\"\u003e\n", + " \u003cbutton class=\"colab-df-quickchart\" onclick=\"quickchart('df-ddbe2953-3c1f-4eb5-9826-710bdcb63d54')\"\n", + " title=\"Suggest charts\"\n", + " style=\"display:none;\"\u003e\n", + "\n", + "\u003csvg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", + " width=\"24px\"\u003e\n", + " \u003cg\u003e\n", + " \u003cpath d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/\u003e\n", + " \u003c/g\u003e\n", + "\u003c/svg\u003e\n", + " \u003c/button\u003e\n", + "\n", + "\u003cstyle\u003e\n", + " .colab-df-quickchart {\n", + " --bg-color: #E8F0FE;\n", + " --fill-color: #1967D2;\n", + " --hover-bg-color: #E2EBFA;\n", + " --hover-fill-color: #174EA6;\n", + " --disabled-fill-color: #AAA;\n", + " --disabled-bg-color: #DDD;\n", + " }\n", + "\n", + " [theme=dark] .colab-df-quickchart {\n", + " --bg-color: #3B4455;\n", + " --fill-color: #D2E3FC;\n", + " --hover-bg-color: #434B5C;\n", + " --hover-fill-color: #FFFFFF;\n", + " --disabled-bg-color: #3B4455;\n", + " --disabled-fill-color: #666;\n", + " }\n", + "\n", + " .colab-df-quickchart {\n", + " background-color: var(--bg-color);\n", + " border: none;\n", + " border-radius: 50%;\n", + " cursor: pointer;\n", + " display: none;\n", + " fill: var(--fill-color);\n", + " height: 32px;\n", + " padding: 0;\n", + " width: 32px;\n", + " }\n", + "\n", + " .colab-df-quickchart:hover {\n", + " background-color: var(--hover-bg-color);\n", + " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n", + " fill: var(--button-hover-fill-color);\n", + " }\n", + "\n", + " .colab-df-quickchart-complete:disabled,\n", + " .colab-df-quickchart-complete:disabled:hover {\n", + " background-color: var(--disabled-bg-color);\n", + " fill: var(--disabled-fill-color);\n", + " box-shadow: none;\n", + " }\n", + "\n", + " .colab-df-spinner {\n", + " border: 2px solid var(--fill-color);\n", + " border-color: transparent;\n", + " border-bottom-color: var(--fill-color);\n", + " animation:\n", + " spin 1s steps(1) infinite;\n", + " }\n", + "\n", + " @keyframes spin {\n", + " 0% {\n", + " border-color: transparent;\n", + " border-bottom-color: var(--fill-color);\n", + " border-left-color: var(--fill-color);\n", + " }\n", + " 20% {\n", + " border-color: transparent;\n", + " border-left-color: var(--fill-color);\n", + " border-top-color: var(--fill-color);\n", + " }\n", + " 30% {\n", + " border-color: transparent;\n", + " border-left-color: var(--fill-color);\n", + " border-top-color: var(--fill-color);\n", + " border-right-color: var(--fill-color);\n", + " }\n", + " 40% {\n", + " border-color: transparent;\n", + " border-right-color: var(--fill-color);\n", + " border-top-color: var(--fill-color);\n", + " }\n", + " 60% {\n", + " border-color: transparent;\n", + " border-right-color: var(--fill-color);\n", + " }\n", + " 80% {\n", + " border-color: transparent;\n", + " border-right-color: var(--fill-color);\n", + " border-bottom-color: var(--fill-color);\n", + " }\n", + " 90% {\n", + " border-color: transparent;\n", + " border-bottom-color: var(--fill-color);\n", + " }\n", + " }\n", + "\u003c/style\u003e\n", + "\n", + " \u003cscript\u003e\n", + " async function quickchart(key) {\n", + " const quickchartButtonEl =\n", + " document.querySelector('#' + key + ' button');\n", + " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n", + " quickchartButtonEl.classList.add('colab-df-spinner');\n", + " try {\n", + " const charts = await google.colab.kernel.invokeFunction(\n", + " 'suggestCharts', [key], {});\n", + " } catch (error) {\n", + " console.error('Error during call to suggestCharts:', error);\n", + " }\n", + " quickchartButtonEl.classList.remove('colab-df-spinner');\n", + " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n", + " }\n", + " (() =\u003e {\n", + " let quickchartButtonEl =\n", + " document.querySelector('#df-ddbe2953-3c1f-4eb5-9826-710bdcb63d54 button');\n", + " quickchartButtonEl.style.display =\n", + " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", + " })();\n", + " \u003c/script\u003e\n", + " \u003c/div\u003e\n", + " \u003c/div\u003e\n", + " \u003c/div\u003e\n" + ], + "text/plain": [ + " name strand\n", + "0 donor +\n", + "1 acceptor +\n", + "2 donor -\n", + "3 acceptor -" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output.splice_sites.metadata" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "height": 239 + }, + "executionInfo": { + "elapsed": 658, + "status": "ok", + "timestamp": 1753110009295, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "3p4XZwL76F6X", + "outputId": "beec4400-f98b-4dd4-d562-7c68bef4bca9" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "\u003cFigure size 1440x216 with 3 Axes\u003e" + ] + }, + "metadata": { + "image/png": { + "height": 222, + "width": 1295 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Build plot.\n", + "# Since APOL4 is on the negative DNA strand, we use `filter_negative_strand` to\n", + "# consider only negative stranded splice predictions.\n", + "plot = plot_components.plot(\n", + " [\n", + " plot_components.TranscriptAnnotation(longest_transcripts),\n", + " plot_components.Tracks(\n", + " tdata=output.splice_sites.filter_to_negative_strand(),\n", + " ylabel_template='SPLICE SITES: {name} ({strand})',\n", + " ),\n", + " ],\n", + " interval=apol4_interval,\n", + " title='Predicted splicing effects for colon tissue',\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pUkCJQF68le0" + }, + "source": [ + "Note how the model predicts acceptor splice sites near the beginnings of exons\n", + "and donor splice sites near the ends of exons, whereas splice site usage\n", + "predictions have peaks on both sides of exons.\n", + "\n", + "We can visualize the junction split read predictions as arcs using\n", + "`plot_components.Sashimi()`. We can also visualize the effect of the variant by\n", + "showing both `RNA_SEQ` and splicing predictions on the same plot:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ZsW6gqZ3zwve" + }, + "outputs": [], + "source": [ + "# Make predictions for REF and ALT alleles.\n", + "output = dna_model.predict_variant(\n", + " interval=interval,\n", + " variant=variant,\n", + " requested_outputs={\n", + " dna_client.OutputType.RNA_SEQ,\n", + " dna_client.OutputType.SPLICE_SITES,\n", + " dna_client.OutputType.SPLICE_SITE_USAGE,\n", + " dna_client.OutputType.SPLICE_JUNCTIONS,\n", + " },\n", + " ontology_terms=ontology_terms,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "height": 971 + }, + "executionInfo": { + "elapsed": 4252, + "status": "ok", + "timestamp": 1753110016182, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "ou8Yju8s-I0R", + "outputId": "60c40696-3b63-4fa1-f7fb-ff49d3c88ade" + }, + "outputs": [ + { + "data": { + "image/png": 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amqZPn64777xTO3fu1Mcff6xjjz22xO2MGjVKw4YN05FHHqlp06YpNTW1wPzHHntMt912m8aPH68zzjgjf3qfPn00depULVu2TK1bty71/gAAcCjZvXu3brzxxqjz5s6dq3nz5umEE05Qu3bt1L9/f/3ud78rtK0VK1aodevWatGihVasWKGkpF/HQtu5c6caNWqkvLw8/fTTT6pevbok6YMPPtB5552nAQMG6NNPPy3QXlZWlho0aKCMjAxt3LhRDRs2LIM9BgAkqtGjR+u6665To0aN1LdvXzVr1kxpaWl69913lZGRoQsuuEBvvfWWzKxE7a5du1YdO3ZUbm6udu3apeeee05XX13wX4kXX3yx3njjDR111FE64YQTlJqaqiVLlujDDz9Ubm6unnrqKd10001lubs4mEzo6u8HzIlvHQAAIFzJvjSKoDsAACgBM3tT/soGLZ1zv8S4zpPyVx5o65zbWI7lAYcyvtQDAACgUKtXr1bbtm3Vpk0bLVy4sETr/uMf/9ANN9ygo446SpMmTdoryLZs2TJ17NhRl156qcaMGVNgHkF3AADKx7333qv77rtvr0Dgnj17tGLFClWqVEmtWrUqsM5pp52mzz77TKNGjSoQoL/lllv0xBNP6JprrtHo0aPzp2dnZ6t9+/b68ccf9d///lf9+/fPn/fnP/9ZDz74oHr37q0pU6aU344CABLC5MmT9fPPP+vMM88s0Flq06ZN6t69u9auXau3335bF1xwQcxtOufUv39//fjjj/rtb3+rRx99NGrQfezYsercubO6dOlSYPrUqVPVv39/mZlWrVqlRo0alW4ncXAi6A4AQCIi6A4AAMqPmTWTNETSW865RTEsb5Jul7TEOfd+OZcHHMr4Ug8AAIAidenSRfPnz9dPP/2kevXqxbTOk08+qeHDh6tDhw6aNGmS6tevv9cy77//vs4///yY2nvvvfd03nnnlaRsAADKhXNOOTk5ysrKUnZ2trKysvJvJfm5NOtGayvW/9vm5OQoNzdXFStWVIUKFQrsV3Z2tiSpcuXKe+1zaF5SUpLMTHl5eXLOycxUqVKlvUbizcvL0549e6KuI0mVKlUqEHiUpOTkZCUnJ6ty5cqqXLlygcel/Xlf1k1OTi7xCMMAgNiNGDFCd999t2644QY9/fTTMa/31FNPafjw4ZoyZYomT54ctQNXcU499VRNnDixxCF7HEIIugMAkIhK/Et6xfKoAgAAHJycc2sk3VuC5Z2kh8utIAAAAABATDZs2CBJBcJwRXn44Yd155136phjjtHEiRMLDce3aNFCV111VdR548eP16ZNm3ThhReqVq1aatGixT7VDgA49OzZs0c7d+7Uzp07tWPHjgL30aaF7jMzM2MOox8Mg4Hl5OQoJycn6rysrKxC18vLyyvwc3gIPtZ1JOWH4GPdbryEAvDFhearVKmiGjVqqGbNmqpVq1aB+2jTQveVK1cmTA/gkFWpUiVJUsWKscePFi9erDvvvFPDhg3TSSedpMmTJ++3bQMAAODAw7c9AAAAAAAAAChnzjnt2bOnQNAuFMbbl5/DH+fk5CgjI0PJycmqWrXqXtudP3++Nm/erMMOO0zDhg2T5MNqO3fuVFJSkmrWrFlgnQULFui7775Tamqq2rZtq1tuuWWf91mSsrOzlZ2drccff1wVK1bMD5RVqVKlQMCsqJ+LWzbaKLQAgP0rFE6PJZBeXHg9MzMz3ruDg0jou0h5qVSpUonD8YUtS2gewIEkJydHL730kiRpwIABMa8zaNAgNWvWTCNGjNjnba9evVqTJk1StWrVdNJJJ+1zOwAAAEh8BN0BAAAAAAAAHJKcc9q1a5cyMjIK3NLT05WRkaFdu3btc/g82s/xHjn2p59+0ssvvxzz8tu2bdObb75Z6u1+8MEHpW6jOGYWc2g+llB9jRo1lJKSopSUFNWuXTv/cUpKimrUqEEADcBByzmnjIwMbdu2Tdu2bdPWrVuLfbx9+/b80dSBQ9GePXvy3w+lVbFiRdWqVUspKSlKTU1V3bp1lZqaWuzj2rVrM6IxgP3uzjvv1MKFC3XGGWfotNNOi2mdv/3tb5o3b56+/vrrvTpqxyorK0uXXXaZsrKy9Mgjj6hOnTr71A4AAAAODAfMb7tm1kLSj5JedM5dGd9q9o2Z9ZH0haT7nHP3xrUYANgHZnavpL9K6uucmxLfaoDSM7NVkuScaxHfSkrHzKZI6u2cI2kBAACAQ4ZzTrt3744aUC9qWvjPO3bsUG5ubrx3BWXAOafMzMz9ErJMSkoqEHyPFoYv7ueqVasSlgdQ7vLy8rRlyxalpaUpLS1NW7ZsKTSwHrrfvn07n41AHOXk5OS/P3/88ccSrZuSklJsML5u3bpq0KCBGjZsqPr166tSpUrltCcADnajRo3SY489piOPPDLmDtWzZs3SiBEjdOutt6pHjx77tN3c3FwNGjRI06ZN0+9+9zvddttt+9QOAAAADhwHTND9QHCQhPGnSOqtIkKsZjZW0hWSBjvnxoZNXyWpecTiOZI2S/pG0qPOuRkRbbWQf86Kc4RzblWwzr3yQdtwWZLWSZosaURo2ULqryPpRklnSWojqVpYjc855yYWst4q+f3bJam1cy4tyjJT5J+/Ns655THsV2i9oyTdK6mPpFqSVkt6XdJI59zuQtY5S9JtkrpIqiBpkaRnnXMvFrJ8Bfn9HiK/37slzZD0gHNuepTl+0saIOmYYBt1JE1zzp1QxH5cJal7sE5HSVUlPeic+3Mhy/eR7/xRmIedc3dGrBPL0GeXO+diH56sBBI9FGtm1SUNlXSOpA6Sakv6RdJSSRMl/cc5tzJuBZajsPdfrA7YczUSm5ktlT/PfuOc6xnveva3sO8J4XZLWiXpU/nPtp+irDdF/j2cJ+kY59z3RbTd3zn3eSHb7yXp6+DHa5xz/96X/QAAAED5C4V4Yg2lR/t5z5498d4NHILy8vK0fft2bd++fZ/bqFSpUqnC8qmpqYzaChyicnNz88PrmzZtyg+xR7v99NNPhNaBQ0joO/LKlbH/Gyg1NVUNGjQo9NawYUM1aNBA9evXV+XKlcuxegAHkn/84x8aNmyYjjrqKE2aNEmpqanFrpOTk6NBgwapbdu2uv/++/dpu7m5uRo4cKDeeustXXTRRXrllVfoQAwAAHAIOJD+Er5eUntJGfEupBRmye/DlngXUs6ekpQePK4hqZOk30o618zOcc59GmWdDElPFtFmepRpUyVNCR7XlXSyfMD3/8zseOfcssgVzOwkSe9IqidpsaRxknbKBxLPlHShmb0i6WrnXFYhtdSQdJ+ka4uoN2Zmdrx8QL+SpLclrQ325R5J/cysX2QtZnaDpKclbZX0iqRsSf8naayZdXTO3RaxvMkH5/9P0hJJz0hKlfQ7SV+a2QXOuchrSP9B0rmSMiUtlw+6F+cxSSmStkvaIKlVLM+BCr6W4b6OMu2+QtqoIelW+c4VUTsrHOzM7Dfyx9Dh8h0/PpF/HarLd1a4Q9Ifzew3zrm5cSu0/IzV3sfReZI6S/pA0vyIeZE/A6VmZn3lP1OcpB5m1sE5tzDOZcVL+PuugaQzJN0i6QIzO845t7WQ9ZIk/V2+s9W++H1w74LHBN0BAAD2I+ec0tPTtWnTpmJvP/30k5yLpT87cPDZs2ePtmzZoi1b9u1PxWamww47TA0bNiz2Vrt2bcIfwAEgKytL69ev19q1a7V27Vpt3LgxapB9y5YtysvLi3e5AA4SodHjFy9eXOyytWvXjhqGb9SokZo0aaKmTZuqadOmqlmz5n6oHEC8PPnkkxo+fLg6dOigSZMmqX79+jGtt2vXLi1dulSSVKVKlajLDB06VEOHDtWwYcP05JNPFpiXk5OjSy+9VG+99ZYuvfRSvfTSS6pQoUKp9gUAAAAHhgMm6O6c2yPph3jXURrOuV90gO9DjJ6MHFHdzG6V9Kh80DZa0D3dOXdvCbczJXwdM0uS9JF8kO5PkgZH1HCUfPC3qqSbJD3jwv6bamZNJb0vaaB8cPyqQra7XNLVZvaUc674v/oUIRhl/QX5UeXPdc59GLYvb0q6QNJwSSPD1mkh/1xuk9Q1bKT7v0maLelWM3vHOfdN2KYulg+5T5fUzzmXGawzWj5M/pyZTXbO7Qxb52FJd8sfs00V28j7F0ta7JxbbWZXBvsWiymxvv6FLWdm1wQPP3LObYpxuwcNMztS0n/lA/93SnrMOZcTscwR8q9rrf1fYfkLv8JESPB+6Szp/WjzgXIQClk/Iv+Z93v5z5xDUYH3nZlVkb+SSGdJN6jwjkvLJZ1mZv0Lu8pKYcystqQLJS2T9L2k35rZsQdp5x4AAID9avfu3UpLS8sP3RV1y87Ojne5wEHPOafNmzdr8+bNWrBgQZHLJicnxxSIb9iwoapWrbqf9gA4tGRnZ2vDhg35IfZ169bt9Xjz5s3xLhMAipSenq709HQtWbKkyOVSUlLyg+/hAfjwx9WrV99PVQMoSw8//LDuvPNOHXPMMZo4caLq1asX87qVK1fWVVdFj2DMnTtX8+bN0wknnKB27dqpR48eBeZnZ2froosu0gcffKDLL79cL7zwgpKSkkq1LwAAADhwHDBB9yCs+KOkF51zVwbTpkjq7ZzbaziasJDt4IiQ1arg4VHyAavfyY8yulbSc5IecVGGkjKz7vKjRZ8gPxr4NvkA1fPOuTfN7F5Jfw0Wv8LMrghbfbBzbqyZ9ZH0haT7IsO6ZtZG0l8k9ZN0mPyo759Luj9yZPKwbfUNarldUgf5kbc/k3Src2595D7E2WfB/WHltQHnXJ6ZjZUPuneLssgo+ZGtRzrnno6y/lozO0vSIklDzGyMc25alHbukvSWfIjx7FKW3Vt+lP8vQyH3sH25XT7ofq2ZPRx2XA6RVFnSw+EdCpxz281shKT/yI82Hx50vy64/3Mo5B6sM9vM3pA0SD4I/0LYvPz1Yx3xyTk3IaYFy0coXPqvkqxkZm0lPSQ/in6ypO8kPSh/rOafQ8Lev6H1ws8T+eelYN6R8mHzfpLqy1+RYJL8e39J2HKjJN0o6XHn3K0RdV0l6Xn588Bpzrnihuh5Wj7A/pBz7uFoCzjnfpR0kZkVuLZkSc4/RTGzfpL+KKm7fOeNNZLeDWrKiFh2ivzxX0n+HDZYUjNJmyW9KukvzrlyS0ZEnEcbSxom6WhJW5xzLYJlrpR/j3eR1EjSHvnz/j+dc69EabPE+2RmJwbLdpF/7rdLWiXpU+fcfcEy/5V0qqRjnHPfRdnuxZJek/Soc+6PYdNT5V+P8yS1kO/AM0f+3PFZRBtXKjjeJW2SP367SKoV+oyNpdaw9qoFz+nv9Ovo5t9LGuWcey1yHwoT9pndWf59eb78FTxWShot6elCPrMvkg9Sd5Z/Xy+Xfw0eL+JqHaF1r5X0T0l/dc79Lcr8hvLfGX5wznWMmFc3qHGZpD9LulLSQDO7PfzcW5yI47O5pJslHSl/BZKPJf0plg49hX0XCpvvJE11zvUJm1Yz2N7v5I9fkz+G58h/R/o21v2I5JzLNLNx8q9LtM/pkD9JekPS34OQekmGKBso36FtrIKgu/zVXq4rYp29lPR5CK7O8kf574mpktLkO9fd55zbEKX94+SP6V7y749Z8ufh0xS89s65KSWpGQAAYF/l5uZqw4YNWr16tVavXq21a9dGDa9nZBzIF1kEDm3Z2dlas2aN1qxZU+yytWrVihqAb9q0qZo3b67mzZurcePGqljxgPnXAlDunHPauHGjfvzxx0JD7GlpaVzFBMAhIyMjQxkZGVq0aFGhy9SuXTtqAL5JkyZq0aKFmjVrpkqVKu3HqoFDQ25urnbv3r1Pt0mTJmnatGmqV6+eWrdurWuuuUaZmZlRrzSTl5enX375RUlJSapWrVqxde3YsUOStH37dq1fv15jx47V2LFj89uaN2+efvrpJzVp0kRpaWk699xzVbVq1RLfqlSpUuh0roIFAACQuA7Vv0ZXkg9eN5YfXTxHPgg4UlIVRYwwamZD5UNvuZI+lA+w1ZfUVdL18iNvT5FUWz7Y9538yOAh84sqxsy6yYdKawbt/08+0HaZpHPNrJ9zbk6UVa+XdE6wzlRJx8sHsjqb2THFhfn2s1OC+2j7UZZCv33sKTDRj2TdT1KWfEA9KufcRjN7Xj6s9ntJ0YLu70v6UtJZZtbXOfdFlGUKFvVraDGyk8PJwf1eAXHn3EozWyqpraSWklYUt45+HS0/tIyCUHNPSb9I+qqQdQYF68Q6AntZa21mN8gHtTdJ+qqEAetjJR0rH7qNefTfIJA+TT6UOF7SAvnn+j35gGK4VfLnhpuDn58Mmzc/rM0B8uHuSvJXGFguqYl82PLM4JgJjSx8m/xrMzwYUX980MZR8h0z0iQNLC7kGRzfp8h3din0+A4JPzeU4vwTWcM18ufJn+U7gmyW1Ed+ROuzzayXcy49yqqvSjpR/jjcId9R5Xb5c+zgKMuXtVsl9Zd/rb6QlBI275/yz8eXkjbKB6zPkPSymbVzzv2lkDZj2qfgWBkfLPOhpPXyx2J7+fN76LNorHzQ/fKg3kiXB/cvhrXdXP5zqYX8+36CfEefsyRNMLNrnHPPRWnr/yQNCGofHaxfklpDI2pPlg/Ez5U0RlKSfID3VTM72jn35yjbLkyy/DFaW9Lrwc8XSHpKUjtJfwhfOOjwc5d8h41XJe2SdLqkEfp1lPACnxERXpG/8sHVZvagcy43Yv4Q+e9P0TrVXCHfEWmscy4nCHXfIj/C+Mux7nCY4fKv/Rvyr+EJ8sdQHzM73jn30z60WSjzf8GbIH9e+ka+s02O/FU9+sgfS/scdA9tJrgv6jWYJ/86DJJ/Tkvy2TRUUp6kl+Q/T9IkXWpmtznnfo6pwBI+D2Y2WL6zZJb8+2OtfAePq+XPf79xzq0JW76n/DGdLP95sVzSMfLv2ckl2FcAAICYZGZmas2aNflB9sjbunXrlJsb+bUXwKFqx44d2rFjh5YuXVroMhUqVFCTJk3yg++Rt2bNmqlKlSr7sWqg/OXk5Gj16tVasWKFli9frhUrVuQ/XrlypXbv3h3vEgHggBIaHf7777+POr9ChQpq3ry5WrVqpdatW6tVq1b5j1u2bBlTcBY4mGRnZ2vbtm3aunVrgVvktJ07d2r37t3KzMyMGlbfs6eof8/EZsuWLXr77bfLYK/2tmjRoiI7yaxbt07r1q0rl20XFoIP3WrXrq26desWuKWmphb4OSUlhZHmAQAAysGhGnRvLB9G7++c2y1JZnafpKXygdMRoRBcEDh9Vj7cd6JzrsC3ajNrIknOuSnByLPDJM2PHLG9MEGY6iX5gO9A59y4sHm/kw/1vWJmR0UJuw6Q1M05933YOq9KukTSufIB/Hi42czSg8fVJXWUD5JOlw8fRlM7CINHs8k5N7q4jZpZBfkAoiR9HTH7hOD+W+fc9mKamigfdO9VxDK3SZop6VEz6xptROEYtQvuC/vP0TL5oHtb/Rp0L3SdIKj/s6QmZlbNOfeLpNaSKkha6ZzLKWQbCrYRL5cFt3xm9o6koTG8XpJ0TXD/XAlH/v2HfFj3eufcP8O2fboigu7B6Pn3BiMkK9p73MzqyI+s/Yukk5xz/wubd7T8MfO8fChfzrnsYDTuuZLGmtkx8leLeFO+0825zrm0GPYj/PhOj2H5UE2lOf+Et9NcPpi/S1J359wPYfOelR9J+RH9Oup+uFaSjnbObQuWv1v+/Hy5md0Vy6jVpXSypB7OuXlR5nVwzq0In2BmyfIh8DvNbHQhV8+IdZ+GygfA+0SO1G5m4df5e09ShqTLzOyO8PdxMLr4qZLmOucWhq3zovxI4Jc4514PW762fJh2lJl9GOX4OkPSGVGuzhBrrZLvBNJF0h3OuUfClqsi31HoT2b2tnNuvmLTSH4E9w6hThpm9ldJsyVdb2ZvOOe+DKb3kP+cWSt/LG4Kpt8l/zyeJX9+H1HYxpxzu8zsZfkA/enyI6iH9sHkw8u/KHpw/Wr9GrKWfED7Fvljf1+C7qdLOj78+DSzJ+Q73IyUFP36jvuug3y4+33n3PnhM8wsSQU7gpSYmVWVH3Fd2vtzOtLd8h0E7g9e419iaP83kjpJ+sw5ty6YFupscLH8FU9iEfPzYP6qIP+S7wzVO/ycYGYny3+neEp+pP/QMTRGftT585xzH4QtP0wFO1EBAADEJCcnR6tWrdLSpUu1cuXKvYLsaWmx/FoJALHLzc3NP8cUpkGDBmrWrFmBAHyrVq3Utm1btWjRghHhkZB++eUXrVy5skCIPfR41apVdAw7CCUlJaly5cr5t+Tk5Jh/Lsmysa6bnJysChUqxPtpKRXnnLKzs5WVlaWsrKxCH5f059Ksm5MT7V9jSHS5ublauXKlVq5cqYkT9x7jqlGjRnsF4EOPU1NT41AxEJu8vDxlZGREDakXFl7funWrdu3aFe/SD3qZmZnKzMzU9u2xxBOiS0pKUmpq6l4B+Gih+PDpdN4BAAAo2qH81+SbQiF3SXLObTazD+RHxm0nKRQYvE7+ebo/MuQerFfa7qI95UdP/iY8ZBq0/UYwyvUJwe3LiHVHhYfcA8/JB927K35B92FRpq2VH1m3sNBqivyI59F8Jz+ycKQ+YeH4VPkw/ZHyIzDfH7Fso7A6ihNapnFhCzjnZpvZG/LBucvkR54tyjPyoeEtEdNDYbnCrv8dml67hOtUD5b7ZR+3sb/8JOlO+ZGiV8mHu7vKh1AvkNTQzE4qJmRdQ/6Yz5EPD8bEzJrKh5yXK2JkZufcp2b2uX69EkGsLpd/Hm8ID7kHbS4ys+fkO4IcFZrvnFtuZr+XD8i/Kt+h4WhJDzrnPo9xu6Hju6Tno9Kcf8INlB+Z+LHwkHvg7mD+IDO7McqVJu4IBcKD7f4cBFPvkT8WPlb5+nchIXdFhtyDadlm9g/5Y6effg00hyvpPu013JNzbkvY40wze1M+bH6a/PslZKB8R5bw0dw7S+ot6e3wkHvQVnoQEn9f/j32bMSmP4gSco+5VjOrG9Q0JzzkHrYfdwT7cKmKudpJhLvCjx3n3DYzu18+SD5Yvx6foc5OD4R3kghGV79VPsh/tYoIugf+KR90v0YFX69TJR0h6QXnXIFzqpmdJD/CfX7I2jm30MzmSjrBzNo75xaXYJ8l6eUox+e98vt8qZldX05Xb4n2OudJKulf9s4zsxbB4/ryHQ2ayr9e/yxspWB7a83sSfnPiFskPRDD9kKdacaGTQt1Nhiq2IPuIbE8D9fJX8FjWGTHF+fcZDP7UH5U95rOuZ3y5912kr4MD7kHnpF0o3xnGQAAgAKcc9q0aZOWLl26123FihVlMiIbAJSltLQ0paWlafbs2XvNq1ixYn7ovW3btmrXrl3+44YNG8r3EQbKh3NOGzdu1KJFi7Rw4UItWrQo//N0w4YN8S4PEWrWrKmaNWuqVq1aUe+jTatWrVrMgfMDPVSeqKpWrRrvEgrIy8uLOTS/e/du7dy5Uzt37tSOHTv2elzYPR1h9r+NGzdq48aN+uqrvS9mXbt2bbVu3VqtW7fW0UcfraOPPlodOnRQy5Yted+j3OTl5WnTpk1avXp1gauqrV27dq8Qe15eScZtw4EkLy9PW7Zs0ZYtkbGQolWpUqVA+L1+/fr5V8sK7zxcq1atcqocAAAgsR2qQfcM59zyKNNDAec6YdN+E9x/Wk61HBvcTy5k/mT5kGkX7R00nRNl+Wj7sL8dEYx+HRrBt7V8wPMZ+YDXZVHWWe2ca1HC7fQObuHmy484HBnqDv13JJaR12Nd9i75EVofDEYnzixswSAIWrLfZkpWS2nW2ZdtlImg80h4B5JdkiaY2XT517KXpLMlRYYBw10iqaakd0s4+vcxwf03hQTpv1bJg+49gvvOhVyhIDRqfnv5DhmSJOfc62bWTz6Ae1Kw7cI6fkSzr69hac4/MbXjnNtuZvPk9+tI+Y4r4eJ9HptV2AwzaybpDvlAezP5EZjDHV7IqrHu0zhJv5U0M+g484WkaYV0oBorH9S9QgWD7ldI2iPfSSIkdBymFHIcHhbct48yr7DnI9Zau8kH710h265UxLYLkyN/RZBIU4L7LmHTijoWl5rZOklHmFntoq5+EHRM+VLS6WbW1DkXev1CQepona+GBvcvREwfG9Q1VD5wXRJTo9SWYWbz5T//2qtkHQaK87+gvUuCKzV8IH8+muOcy96H9s4NbuEmSjrTBVfOKcZD8qPW325mzxV1hQszqyXpIvnOW++Fpod1NjjezDo55xYEy5+nXz8HQuY7595XyZ6H0Putt5l1i1Jaffn3RFtJ3+rXYzTaa5trZl+LoDsAAIe0HTt2RA2zL126VDt37ox3eSgDFSpUUOXKlVWlSpUCobvS/lypUqUDIhzsnNOePXsKhMsyMzNL9HNhyxAwO3Dk5ORoyZIlWrJkyV7zatSokR96Dw/Ct2nTRikppbrQGA5BW7ZsyQ+zh9+XZpROFM3MVKNGjZgC6cWF16tXr66kpKR47xIOAklJSapSpYqqVKlSLu0756IG5Iu7L2we32lKLz09XXPmzNGcOQX/XVOlShW1b99eHTp0yA+/d+jQQc2aNTsgvksjvrKysrR27doCIfbQbc2aNVq7dq2ys/flXymAH01+/fr1Wr8+2sXEf1W7du29wu/hgfgGDRpwPgMAAAelQzXonl7I9NC168K7ctcO7ov+RrnvQn+d31jI/ND02lHmpUeZFm0fSiIU+C3qr4ehecV2NQ7C3wvN7DL5AOSlZvaMc+6bfawv3H3OuXvNLEk+cHqbpJskvWlmp0eEl0PPY7MY2m0SsU5UzrlVZvZ0sN1hkh4uUfVeKJBf2H9pakUsF3pcL1hnaxHr7CjFNuLKObfDzF6VHw38JBUddA8FT/9dws2Eno/CgpP7cm33usH90CKXkmpEmfa2fNBdkp52zpXkL5mhoYaaFLnU3kpz/imTdgoJG5f2PFYSUTtHmFlL+dB3HUlfSfpM/j2SK6mFfMC8crR1Y90n59y7ZnaWpFvlRyK/Jtj2t/IjmE8MW3a6mS2VdI6Z1Qk6EBwrqYOk98NHVdevx2H/4FaYaMdh1OejBLWGtt0tuJVk24XZUsj7IVRr+LktlmOxWbBcejHbfVb+/HO1pL+aWUNJ58iHoQt0CDCzOpL+L2jz/Yh2XpX0qKTLzazAyPQxKOw8FG3fSy0IWp8s3znt//Tr59pOM3tR/rUuybUxBzvnxppZBUkt5a+28jv50dyvLnJN5X8W3CffUe5e+dHTC3OZ/NVM/hWl49kL8gHz30u6IZh2nvz7ONyL8u+nkjwPoWP+j8XsTuiYL+6zpyQdtgAAwAEsNzdXS5cu1XfffVfgxkiy8VG5cmWlpKQoJSVFtWvXzn+ckpKiWrVq7TUybGlC6YwgWX5yc3PLLDy/e/du7dixQ+np6crIyMi/hX7OyiqPi2tBknbt2qW5c+dq7ty5e81r3LixOnfuXODWpk0bVax4qP6bAyEZGRl7hdkXLVqktLR9+RMvJP/ZWLduXaWmpubfF/a4bt26qlOnjlJSUgin45BkZqpWrZqqVaumBg0alKot55wyMzOVkZGh7du3a9u2bdq2bVv+KNDhjyOn7dpVkj/dHpoyMzM1b948zZtX8EKqNWrUKDDyeygI36hRIwKjh5CMjIxCQ+yrV6/Wpk2b5Nx+HzcOKCA9PV3p6elasGBB1PmVK1dWs2bNCg3DN2nSRMnJyfu5agAAgNI70P8CnCdJZlbROZcTMa92GW0jPbg/XNIPZdRmuFC4uGEh8xtFLFfeQtupW8Qy9YL79Fgbdc7tCUZUbSGpu6SyCLqH2s6THy15mJk1lg+l3SBpVNhiXwf3xxU3kq9+HcV7Wgybf1A+9HmXmf2nRIV7oWGL2hYyv01wvzRinXrBOgWeRzNrJB/0W+ec+yWYvFw+nNuykPdKtG0kgp+C++qFLWBmx0jqKulH+SBySYQ6AhT2V8d9+Wtk6P3TOTRqcCzMrJ6k/0gKvWZPmtkXzrmfilgtXOj47mpmKVGuaFCYsjr/hLezKMr8/X0eK4nC/iJ1i/x5cLBzbmz4DDO7RHsHZPdt486NlzTezKpLOl7SWfJh3o/NrItz7n9hi78k6QH5oPDosBpejGg29DwPc86NUskU+he6GGsNbfsJ51xJRy8vTD0zqxAl7B46biM7AoXmrYjSVkmOxXflw8hXmdnf5M/1FSX9K8qyl0uqEtx2F/KH77qSLlDB0feLU9h5KNq+RxPq8LXX9z0zqx1tBefcdknDJQ03s9byI8dfI/+5WlvSoGK2Ga3NXEnLzOxS+e8BV5nZh865D2NY/V/yndiGmllRx3Oog9E1ZnZNIcsMNLM/Oud2O+eulHRlETXH+jzkdyZzzu2IbCeK0PLFvbYAAOAgEvoHZHigfeHChcrMLPTCcCiBihUrFgimRwbVi/s5JSWl3Eb3xP5VoUKF/JBZecvKyioQfI8Mwkf7OXJeTk7kn+hQnA0bNmjDhg369NNfL75apUoVdejQoUD4vVOnTqpdu3b8CkW52r59u2bPnq1Zs2Zp5syZmj9/vtati3aBRIRUrVpVDRo0yL/Vq1ev2PD6/jiXAtibmalq1aqqWrWqGjYs2Z8Ks7OztX379kKD8Nu2bdOWLVu0efNmpaWlKS0tTRkZifhvm/1v165dmjlzpmbOnFlgep06ddSpUyd169ZNxx9/vLp3766mTZsSfj+AZWVl6YcfftD333+vBQsW6IcffsgPtPN+wMEgKytLy5Yt07Jly6LONzM1btxYzZs31xFHHKEOHTqoY8eO6tixI+c3AACQ0A70oHvo+pJN5YOu4bqW0TZmBG2druKD7qEQXkmGZgp1Ge9TyPzQ9L2HsCkf38mPctpD0luRM82son59br8rYdt1gvvyHM7jVklnS7rHzMaGQmfOuZVm9oWkvvKjrt4dbWUza6BfR5ktdoRw51y6md0v6QlJf92HeicHtQyQ9FBELS3lw+yrJa2MWKdXsE5kh4HTw5YJ1ZhlZtMlnRjcvihunQTxm+B+ZRHLhMKMz7uSd6EPvfd6mFlSxBUAJOmEQtbLlVRYN+cZ8kHWEyXFFHQ3/9viWPnONKGg5nOSXjKzM2LZL+fcj2b2uXwnjT9K+nMx26wcjCpdVuefeZJ+Gyw/KWJbtSUdIylT0uJi2kkkrYP7d6LM613WG3PO/Sz/HpxsZtsl/U3+vRkZdP+bpCuCjjWXSNoiaXxEczOC+xNVsMPP/qh1lny4+sQy3GRFST3lR9YP1ye4Dx96ZZ78yN19FBF0D8LKTST9WExnJ0n5HbSelz9Hny3/2bBL0rgoi4feu6/p1w4r4VLkO2ENVcmC7r3lX/d8Zpai2N9T4d+TIhX7Pck5t1zS8uAKG5slnVvcOsW0l2dmw+SP0UfMbHxxV69wzuWY2R2S3pP0iKJcycTMukrqIn91i08j5we6Seok6SLt3TmkuLqLeh5mSDpO/piPfC9GEzqf7nUeCUa+L+yzBwAAHCDWrVunmTNnFgi1r169Ot5lJbTq1aurXr16+xxWr1atGv+IxX5XuXJl1a9fX/Xr19+n9Z1z+uWXX2IOyUf+zKitv8rMzNScOXM0Z86cAtObNWuWH3w/5phj8oNpOLBkZWVp/vz5mjVrVv5t6dJEG68lPqpXr14gvB66NWzYcK9pNWrU4LMSOAQkJyfnv+9jlZmZWSD4vmnTpvzHkbft27cX3+BBZvv27Zo6daqmTp2aP61Bgwb5offu3burW7dudLBLQM45rVmzJj/Q/v333+v777/XkiVL6HCKQ5pzTuvXr9f69es1ffr0AvNSUlLyQ++dOnXKf1yrVq04VQsAAPCrAz3oPks+XDlU0p9CE82sn3wAsCz8U9K1kv5iZv+NGF1XZtbEORcaLmS7/Gi4zUrQ/jT5EbpPMLP/c869Hdb2/0k6SX6k7a8LWb+svSIfkh1qZi84576PmP9nSYdJmuKci/k/tWbWTb8GH6cWtWxpOOfWmNlz8iOu3qqC4fNQuO4OM1vnnPtnRI2HS3pfUqqkF5xzsYzoLknPBtu7Rj5kt5dgxO56krY457aEzZoqH1Q8yczOCY1ua2ZJkh4OlhkdEXZ+QdLtkm4IXqNVwTp19Ov7YHRECf+Uf/4fMLN+zrnMYJ1u8qND/6Togd5yZWa9JH0TGTI3s4FBXdmS3ixk3eqSLpWUI2lMSbftnFtrZlPkA7HXyD9HobYH6NeR/SNtldTJzKo653ZHzHtBPhT7VzOb7ZybFVFzkqSTnHNTwibfIulMSW86554Plusn6WL50PojMe7SjZJmyl9dYLukpyJH7zezZkF7oyVNUdmdf16RdI+kG83sxSAUGnK/pFrynREOpOt6rwru+0j6KDTRzE7Tr51hSiV4nadHOY5Cf4UuEJgOjtnJ8sfmMPlz8Sjn3J6I5eaY2VeSfmtmQ5xze70/zKyjpDTn3OayrNU5t9nMxkkaZGZ/kfRQlOOwlaQ851xkB7WiPBScu7KCNlL1a4eOF8KWGyPpKkl/DkYM/ylYvoKkR+U7WpXk6hv/lnSnpGfkO6P82zm3M2J/eko6WtJi59yl0RoJ3vsrJfUxs7bOuVj/IzvIzJ5xzoWH+e+VD86/EMN7ao58x4NLzezh0JU+gudvr3OLmR0hqZpzLvLKDHUkVdavwfl95pybaWYfy18R4HIVfP0KW+f94Jg+S/4qJZF+H9w/5ZyLes4MjuHPg2WLDLqX8Hl4JmjzCTNbFvnamlmypOOdc6GOGtPlz7snmdm5zrkPwha/QVKromoDAACJJScnRwsWLNC0adM0ffp0TZs2TWvXro13WQmhYsWKatiwYbG3UPgOONSYmapXr67q1aurcePG+9TGzz//nB9IK+y2ceNGpaWlac+ePcU3eJBZs2aN1qxZo48+yv+zjpo2baqePXuqV69e6tmzpzp37qyKFQ/0f5EcPPLy8rRs2bL8kdpnzZql+fPnH3LHb506ddS0aVM1adJETZo0UePGjaMG2KtXL/RCqAAQsypVqqhZs2Zq1qz4f21nZ2cXCMWHbhs3btS6deu0du1arV27VmlpaSr52FQHjrS0NH344Yf68MNfL1jarl07de/ePT8A37lzZyUnFzZmFspaRkZGfpA9PNS+Y0csF2EFEJKRkaGvv/5aX39dMBrQvHnzAuH3Tp06qW3btvwuBQAA9qsD/ZvHC/JB0LvMrLP8aLJt5UeWfU9+ZOVScc79z8yulw+GzjOzDyQtk1RXfjTUnfKjhMs5t8vMZko6MQj6LZUf/flD51zU0Z2dc87MrpA0UdIbQfs/SGonP7L6TkmXRxltulw455YHo62OkjTbzD6S348q8qOPHicf5i4q6HmzmaUHj6vIj4x8jvzx9oxzLtro0LXN7N4i2hwbCnTHYIR80HG4mT0dCpY75743s7MkvS3pWTP7g/zo5juDGs+UVE1+tN7rYtyWnHPZZnaXfCC7eSGL3SAfur9PPqQYWjfXzAbLj478tpm9LWmNpH7yx9c0+dHiw7f3o5n9Uf41mmNmb8gHwv9PfrTix5xzkSO9vy7fKeT/5I/jj+SP4d/JX4FgaGj0+xAzO0G/vs6h/zq3MbOxYbVcGbHO1fp1NNrQiNhnm1mT4PEPzrmRYauMk5QUjDi/Tv546Sapu3yA/ZoiXveL5QPU7zrnNhWyTHH+IP8cP2tmZ8iPwt5S/tzxgfyovZHvvUlBjRPM7EtJWZK+c8595JzbGgTE35M0w8wmSVoUtNFM/koJdYP9DHU0eEj+ihS/D9vGNcE2HjSzL51zM1QM59wPQQj7HflA77Bg+xskVZfUWf5KAE5BJ4qyOv8451aZ2c2S/iFprpm9Kd95onewzz9IuqO4fUgwz0oaLOktM3tH0npJHeSvpPCm/HuntB6T1CLocLFK/n18nKST5a/k8HqUdV6UD7qPCPs5mkvlzyv/MbOb5DtBpMufIzrJ70sP+dGpy7rWGyS1kR/pfZCZfS0pTVJjSe3lj+1LtPeVWAqzUT5cvNDMPpRUSf5c1kjSs865L0MLOuemm9kj8p2BFgbn1J/lvxd0kO+08fcYtxvqPDVe/jNMkv4VZbHQe/f5ItrJM7MX5M//Q+W/u8TiU0nTgvfURvnz6wnyr8GdMdS/MdTxQNL8YF9qSTpD0pfyo6CH6yzpPTP7VtJC+fPHYfLnwkr6tQNWad0j/5n7VzMb55zLjmGd2+Q7q7UOn2hmNeSPpxwVHWCfLN/ZoKeZHR0lxB4u5uchOPcOke9kscjMJsh/b6okf94/Uf58eGSwvDOzq+TPu++Y2bvy4f3O8u/tCfLnGQAAkIAyMjL0zTff5IfaZ86cqZ9//jneZe1X9erViynAXqdOHSUllecF/QBUr15dLVu2VMuWLYtcLi8vT9u3by8yEB+6bdmypci2DnRr167VG2+8oTfeeEOSfw6PP/74/PD7b37zG0Zk3Y9ycnI0a9YsTZw4UdOmTdPs2bOVnp4e77LKVUpKSn6IvWnTplEfE2AHkKiSk5PzO+EUJTs7Wxs2bCgQfo98nJaWtp+q3j+WLFmiJUuW6OWXX5bkn6suXbroN7/5jU455RT17t1bNWvWjHOVB4ddu3Zp1qxZ+uabbzRjxgwtWLBAa9asiXdZwEFt9erVWr16tT7++OP8acnJyWrfvr26dOminj17qmfPnmrfvj1/CwIAAOXmgA66ByPH9pYPrZ0kH6qcI6m/pCNUBkH3YDvPmdlC+ZBVH/kA6Bb5UGxksG2QfDB5gHzwyuQDvFGD7kH7M4PA65/lQ05nB+2/Jul+59ySstiPWDnn/mFm8yXdJB+MPVc+1LhS0kj5IHVR//UYFvY4T9I2+dGjn3fORR2dW3502r8WMk/B+quKrz4/2PdP+ZGy75If2T007wszays/+nVoNNkq8iG0TyQ955z7LJbtRGzzLTP7Rj44WtJ1Q6//fZJOlVRTPjj6N0kjo43Y65x72sxWyR+Tl8uPUvw/SX92zu0V9AuCdZfIjyQ7RH7/M+WDjg8456ZHriMfJrwiYlr9iGlXRsw/Ico6nYKb5EewDw+6/1P+mO8lP+K9yQeKx0p60jn3XZS6QkLh0n8XsUyRgo4sPeRDwycHtwWSzpcP5J4rKbK7/wOSasu/T3vJdxR4UcGo3865SWbWSf61OU0+5JgtH5ScrGDkfDNLkfRG0ObFzrmMsLp2mNnF8iH8183sGOdcegz7M8PM2skHac+RD5LWkR9te7l8WPnf4SNpl9X5xzn3rJktD/b7AvlOI2vlz88jYqk/kTjnFphZX/nX+wz5z8vv5DuMpKtsgu4j5I+1rvLPfZ58R5cR8sd/tNGz35XvUFBL0sJCOg7JObfOzI6Tf69fIOky+WN1k/y54mlJkVfsKJNag+O3t/x79NJg+1Xkw+7LJA2XD/nGKlu/hvsvlj9XhD6Pno5c2Dl3h5nNkw/cXy4fOF4hf4w/FmOoOtwY+ffTnMjnO3gfXxjU+FIM7dwj6QozuzvGOp6Q7zhzs/wxt0v+/PinWEfjlz8fpMl/J/mD/Os2Sv69eVHEsnPkO9/0lv8eU0f+8/Fb+asHfBrjNovknJtnZu/Jv5+uUZTXMco6s4KOXRdHzLpEvjPWe865Qv87EnwO/kfSg/LH5rDCllUJnwfn3Ctm9p38942+8p/lP8uf99/Wr+f60PLTzOzEoJbTg8kz5b9jniaC7gAAJIx169bpiy++0LRp0zRt2jQtWrTooB2ZsGrVqmrevLmaNWuWf9+oUaMC4fX69eurUqVK8S4VQAklJSWpbt26qlu3ro4++ugil92zZ482b96816jwa9euzQ83rF69Wrt3R17w7cD0888/a/LkyZo8ebIkP9L+0UcfnT/ie9++fdW0adM4V3nwcM5p2bJlmjhxoiZOnKgvvvjioBpptWLFimrRooVatmyZH1wPD7I3adKEkCOAQ0JycrJatGihFi1aFLpMVlaW1q9fHzUM/+OPP2rFihX65ZdfCl0/0WVnZ2vmzJmaOXOmnnrqKVWsWFE9evRQ//791b9/f3Xt2pWRkGPgnNOaNWs0ffr0/A7n3333nfLy9sv4gAe1KlWqqGrVqmVyq1Klyn4/np1zys7O1u7du/NvmZmZBX4u6e1Qu4pQaWVnZ+u7777Td999p7Fjx0rynTp79OiRH3zv3r07338BAECZsQPlH3RmdqSkxfIhzWviXQ+Ag1swCvKlko7c351NAHhBhx4551rEsYZ75TtiXe2c+89+3mZf59yU/bFNJIZSvvYHxpd6AAASWE5OjmbMmKHx48frk08+0YIFhY5ZcMBJTU0tEGSPvNWrV09mFu8yARwAnHPasmVLgeD7mjVrCvy8bdu2eJdZZjp27KgzzzxTZ5xxhnr06EEorYS2bNmiSZMm5YfbD/QRV6tWrapWrVqpdevWBe5btWqlZs2acXwAQBlxzmnTpk1asWKFVqxYoeXLlxd4fKB/10hJSdHJJ5+cH3xv1aoVv4/Jv+7ff/+9Jk2alB9u37BhQ7zLSigpKSn5nVhDt9TU1AI/16lTR9WqVSs0mF65cmWOtyhyc3OLDMLv2rVL27Zt09atWwvcIqdlZmbGe1cSRlJSkjp16qSePXvqhBNO0CmnnKLDDjts/xcyoau/HzBn/28bAAAUpsRfSA+koPs5kj6QdI9z7v541wPgwGdmSZLqO+c2RUzvJ+m/kpY454oe6gpAuYl30N3MasqPQl9JUlPn3H4ZQoag+6GLoDsAAPvfTz/9pAkTJuiTTz7Rf//7X23fHu3CSomvSpUqatOmjdq2batWrVqpRYsW+SH2Zs2aMYIWgP1q586de4Xfly9frqVLl2rZsmUHbPijdu3aOu2003TmmWdqwIAB8QlpJLisrCxNmzZNn332mSZOnKh58+YdcFdDSU1NzQ+vhwfZW7durYYNGxIMA4AEkJ6eXiAAHx6EX79+fbzLK7EWLVrkh9779eun1NTUeJe036Snp2vixImaMGGCJkyYcMgE26tUqRI1pF5YeD0UYKdTXeL75ZdfogbgC5u2detWbd++/ZC4UoGZqWvXrhowYIBOP/10de/eXRUqVCj/DRN0BwAgER18QXcz6yTpsuDWSFJn59zC+FYF4GBgZlUk7ZT0haQfJOVIOlpSf0nZkgYQMgXiJ15BdzM7U9Kxks6W1E3Sbc65x/bj9u8VQfdDEkF3AADKX15enubNm6dPPvlE48eP16xZsw6YAF5SUpJatGihtm3b7nVr2rSpkpKS4l0iABQrLy9P69at09KlS/NvS5Ys0dKlS7Vq1aoDJuBhZurevbvOOOMMnXnmmerSpcshex7Ozc3VF198oXHjxundd9/Vjh074l1STJo3b66jjz5aHTp00NFHH6327durdevWqlOnTrxLAwCUwu7du7Vy5Ur98MMPWrRokRYtWqSFCxdq6dKlysnJiXd5xapQoYL69++vyy67TOedd55q1KgR75LKVOh38gkTJujTTz/VjBkzlJubG++yykxycvJeV1Jr1qyZmjRponr16uWH1qtVqxbvUpFA8vLylJGRkR9837x5814dh1evXq1NmzYV39gBpE6dOjr11FM1YMAADRgwQA0bNiyfDRF0BwAgER2UQfcrJT0rH0K9xzn3cXwrOrAEz1+LGBad75x7v1yLARKMmVWQ9KSkkyU1kVRN0hZJX0oa6ZybF7/qEBIEP2PxvnNufjmWgv0sjkH3sZKukJQmaYykPzvn9tt/2gm6H7oIugMAUH5++OEHvfDCC3rllVcSfoS4ww47TEceeaTatm2rdu3a5YfZW7ZsqcqVK8e7PAAoN1lZWVq5cmWBEPzSpUv1ww8/aPPmzfEur0iNGjXSwIEDNWTIEB155JHxLqfcOef07bffaty4cXr99dcTOnTTqFGj/DB76P6oo45SrVq14l0aAGA/ys7O1tKlS7Vw4cL88PvChQu1YsWKhO38XK1aNZ1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+ "text/plain": [ + "\u003cFigure size 1440x1184.4 with 16 Axes\u003e" + ] + }, + "metadata": { + "image/png": { + "height": 954, + "width": 1501 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Get all transcripts, not just the longest one per gene.\n", + "transcripts = transcript_extractor.extract(interval)\n", + "\n", + "ref_output = output.reference\n", + "alt_output = output.alternate\n", + "\n", + "# Build plot.\n", + "plot = plot_components.plot(\n", + " [\n", + " plot_components.TranscriptAnnotation(transcripts),\n", + " plot_components.Sashimi(\n", + " ref_output.splice_junctions\n", + " .filter_to_strand('-')\n", + " .filter_by_tissue('Colon_Transverse'),\n", + " ylabel_template='Reference {biosample_name} ({strand})\\n{name}',\n", + " ),\n", + " plot_components.Sashimi(\n", + " alt_output.splice_junctions\n", + " .filter_to_strand('-')\n", + " .filter_by_tissue('Colon_Transverse'),\n", + " ylabel_template='Alternate {biosample_name} ({strand})\\n{name}',\n", + " ),\n", + " plot_components.OverlaidTracks(\n", + " tdata={\n", + " 'REF': ref_output.rna_seq.filter_to_nonpositive_strand(),\n", + " 'ALT': alt_output.rna_seq.filter_to_nonpositive_strand(),\n", + " },\n", + " colors=ref_alt_colors,\n", + " ylabel_template='RNA_SEQ: {biosample_name} ({strand})\\n{name}',\n", + " ),\n", + " plot_components.OverlaidTracks(\n", + " tdata={\n", + " 'REF': ref_output.splice_sites.filter_to_nonpositive_strand(),\n", + " 'ALT': alt_output.splice_sites.filter_to_nonpositive_strand(),\n", + " },\n", + " colors=ref_alt_colors,\n", + " ylabel_template='SPLICE SITES: {name} ({strand})',\n", + " ),\n", + " plot_components.OverlaidTracks(\n", + " tdata={\n", + " 'REF': (\n", + " ref_output.splice_site_usage.filter_to_nonpositive_strand()\n", + " ),\n", + " 'ALT': (\n", + " alt_output.splice_site_usage.filter_to_nonpositive_strand()\n", + " ),\n", + " },\n", + " colors=ref_alt_colors,\n", + " ylabel_template=(\n", + " 'SPLICE SITE USAGE: {biosample_name} ({strand})\\n{name}'\n", + " ),\n", + " ),\n", + " ],\n", + " interval=apol4_interval,\n", + " annotations=[plot_components.VariantAnnotation([variant])],\n", + " title='Predicted REF vs. ALT effects of variant in colon tissue',\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wYXc0CmVqyVD" + }, + "source": [ + "## ChIP-Histone" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Oq5nwdhCqyVE" + }, + "source": [ + "Histone modification marks are captured by the `CHIP_HISTONE` output. Here is an\n", + "example of predictions in the same interval and tissues for histone marks\n", + "thought to indicate key regulatory effects (e.g., H3K4me3).\n", + "\n", + "Some additional considerations for visualizing `CHIP_HISTONE` predictions:\n", + "\n", + "* ChIP-Histone (and ChIP-TF) predictions are returned at a coarser resolution\n", + " (128bp), so we need to adjust the interval size plotted to be compatible\n", + " (i.e., its width must be a multiple of 128). This is done automatically by\n", + " the plotting library.\n", + "* Not all biosamples have predictions for all histone marks, but four of the\n", + " major ones (H3K4me3, H3K4me1, H3K27ac, H3K27me3 and H3K36me3) are available\n", + " for 40% of biosamples. Let's see which histone marks are covered by\n", + " biosamples corresponding to colon tissue:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "height": 1000 + }, + "executionInfo": { + "elapsed": 55, + "status": "ok", + "timestamp": 1753110016570, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "oplX3UfpucoO", + "outputId": "6e9a8974-ef9d-49e4-e60f-e0820db15041" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \"]\",\n \"rows\": 24,\n \"fields\": [\n {\n \"column\": \"name\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 24,\n \"samples\": [\n \"UBERON:0001157 Histone ChIP-seq H3K36me3\",\n \"UBERON:0004992 Histone ChIP-seq H3K4me1\",\n \"UBERON:0000317 Histone ChIP-seq H3K27ac\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"strand\",\n \"properties\": {\n \"dtype\": 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\u003cth\u003e1040\u003c/th\u003e\n", + " \u003ctd\u003eUBERON:0004992 Histone ChIP-seq H3K9me3\u003c/td\u003e\n", + " \u003ctd\u003e.\u003c/td\u003e\n", + " \u003ctd\u003eHistone ChIP-seq\u003c/td\u003e\n", + " \u003ctd\u003eUBERON:0004992\u003c/td\u003e\n", + " \u003ctd\u003emucosa of descending colon\u003c/td\u003e\n", + " \u003ctd\u003etissue\u003c/td\u003e\n", + " \u003ctd\u003eadult\u003c/td\u003e\n", + " \u003ctd\u003eH3K9me3\u003c/td\u003e\n", + " \u003ctd\u003eencode\u003c/td\u003e\n", + " \u003ctd\u003esingle\u003c/td\u003e\n", + " \u003ctd\u003eFalse\u003c/td\u003e\n", + " \u003ctd\u003e1.079408\u003c/td\u003e\n", + " \u003c/tr\u003e\n", + " \u003ctr\u003e\n", + " \u003cth\u003e1095\u003c/th\u003e\n", + " \u003ctd\u003eUBERON:0012489 Histone ChIP-seq H3K27ac\u003c/td\u003e\n", + " \u003ctd\u003e.\u003c/td\u003e\n", + " \u003ctd\u003eHistone ChIP-seq\u003c/td\u003e\n", + " \u003ctd\u003eUBERON:0012489\u003c/td\u003e\n", + " \u003ctd\u003emuscle layer of colon\u003c/td\u003e\n", + " \u003ctd\u003etissue\u003c/td\u003e\n", + " \u003ctd\u003eadult\u003c/td\u003e\n", + " \u003ctd\u003eH3K27ac\u003c/td\u003e\n", + " \u003ctd\u003eencode\u003c/td\u003e\n", + " \u003ctd\u003esingle\u003c/td\u003e\n", + " \u003ctd\u003eFalse\u003c/td\u003e\n", + " \u003ctd\u003e1.686818\u003c/td\u003e\n", + " \u003c/tr\u003e\n", + " \u003ctr\u003e\n", + " \u003cth\u003e1096\u003c/th\u003e\n", + " \u003ctd\u003eUBERON:0012489 Histone ChIP-seq H3K36me3\u003c/td\u003e\n", + " \u003ctd\u003e.\u003c/td\u003e\n", + " \u003ctd\u003eHistone ChIP-seq\u003c/td\u003e\n", + " \u003ctd\u003eUBERON:0012489\u003c/td\u003e\n", + " \u003ctd\u003emuscle layer of colon\u003c/td\u003e\n", + " \u003ctd\u003etissue\u003c/td\u003e\n", + " \u003ctd\u003eadult\u003c/td\u003e\n", + " \u003ctd\u003eH3K36me3\u003c/td\u003e\n", + " \u003ctd\u003eencode\u003c/td\u003e\n", + " \u003ctd\u003esingle\u003c/td\u003e\n", + " \u003ctd\u003eFalse\u003c/td\u003e\n", + " 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\u003ctd\u003eUBERON:0012489\u003c/td\u003e\n", + " \u003ctd\u003emuscle layer of colon\u003c/td\u003e\n", + " \u003ctd\u003etissue\u003c/td\u003e\n", + " \u003ctd\u003eadult\u003c/td\u003e\n", + " \u003ctd\u003eH3K4me3\u003c/td\u003e\n", + " \u003ctd\u003eencode\u003c/td\u003e\n", + " \u003ctd\u003esingle\u003c/td\u003e\n", + " \u003ctd\u003eFalse\u003c/td\u003e\n", + " \u003ctd\u003e0.842116\u003c/td\u003e\n", + " \u003c/tr\u003e\n", + " \u003ctr\u003e\n", + " \u003cth\u003e1099\u003c/th\u003e\n", + " \u003ctd\u003eUBERON:0012489 Histone ChIP-seq H3K9ac\u003c/td\u003e\n", + " \u003ctd\u003e.\u003c/td\u003e\n", + " \u003ctd\u003eHistone ChIP-seq\u003c/td\u003e\n", + " \u003ctd\u003eUBERON:0012489\u003c/td\u003e\n", + " \u003ctd\u003emuscle layer of colon\u003c/td\u003e\n", + " \u003ctd\u003etissue\u003c/td\u003e\n", + " \u003ctd\u003eadult\u003c/td\u003e\n", + " \u003ctd\u003eH3K9ac\u003c/td\u003e\n", + " \u003ctd\u003eencode\u003c/td\u003e\n", + " \u003ctd\u003esingle\u003c/td\u003e\n", + " \u003ctd\u003eFalse\u003c/td\u003e\n", + " \u003ctd\u003e0.892749\u003c/td\u003e\n", + " \u003c/tr\u003e\n", + " \u003c/tbody\u003e\n", + "\u003c/table\u003e\n", + "\u003c/div\u003e\n", + " \u003cdiv class=\"colab-df-buttons\"\u003e\n", + "\n", + " \u003cdiv class=\"colab-df-container\"\u003e\n", + " \u003cbutton class=\"colab-df-convert\" onclick=\"convertToInteractive('df-66943585-2814-4196-b5f1-10d02dc4458f')\"\n", + " title=\"Convert this dataframe to an interactive table.\"\n", + " style=\"display:none;\"\u003e\n", + "\n", + " \u003csvg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\"\u003e\n", + " \u003cpath d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/\u003e\n", + " \u003c/svg\u003e\n", + " \u003c/button\u003e\n", + "\n", + " \u003cstyle\u003e\n", + " .colab-df-container {\n", + " display:flex;\n", + " gap: 12px;\n", + " }\n", + "\n", + " .colab-df-convert {\n", + " background-color: #E8F0FE;\n", + " border: none;\n", + " border-radius: 50%;\n", + " cursor: pointer;\n", + " display: none;\n", + " fill: #1967D2;\n", + " height: 32px;\n", + " padding: 0 0 0 0;\n", + " width: 32px;\n", + " }\n", + "\n", + " .colab-df-convert:hover {\n", + " background-color: #E2EBFA;\n", + " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n", + " fill: #174EA6;\n", + " }\n", + "\n", + " .colab-df-buttons div {\n", + " margin-bottom: 4px;\n", + " }\n", + "\n", + " [theme=dark] .colab-df-convert {\n", + " background-color: #3B4455;\n", + " fill: #D2E3FC;\n", + " }\n", + "\n", + " [theme=dark] .colab-df-convert:hover {\n", + " background-color: #434B5C;\n", + " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n", + " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n", + " fill: #FFFFFF;\n", + " }\n", + " \u003c/style\u003e\n", + "\n", + " \u003cscript\u003e\n", + " const buttonEl =\n", + " document.querySelector('#df-66943585-2814-4196-b5f1-10d02dc4458f button.colab-df-convert');\n", + " buttonEl.style.display =\n", + " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", + "\n", + " async function convertToInteractive(key) {\n", + " const element = document.querySelector('#df-66943585-2814-4196-b5f1-10d02dc4458f');\n", + " const dataTable =\n", + " await google.colab.kernel.invokeFunction('convertToInteractive',\n", + " [key], {});\n", + " if (!dataTable) return;\n", + "\n", + " const docLinkHtml = 'Like what you see? Visit the ' +\n", + " '\u003ca target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb\u003edata table notebook\u003c/a\u003e'\n", + " + ' to learn more about interactive tables.';\n", + " element.innerHTML = '';\n", + " dataTable['output_type'] = 'display_data';\n", + " await google.colab.output.renderOutput(dataTable, element);\n", + " const docLink = document.createElement('div');\n", + " docLink.innerHTML = docLinkHtml;\n", + " element.appendChild(docLink);\n", + " }\n", + " \u003c/script\u003e\n", + " \u003c/div\u003e\n", + "\n", + "\n", + " \u003cdiv id=\"df-746abbeb-66b3-4dcf-8a53-a88a4dcbb1aa\"\u003e\n", + " \u003cbutton class=\"colab-df-quickchart\" onclick=\"quickchart('df-746abbeb-66b3-4dcf-8a53-a88a4dcbb1aa')\"\n", + " title=\"Suggest charts\"\n", + " style=\"display:none;\"\u003e\n", + "\n", + "\u003csvg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n", + " width=\"24px\"\u003e\n", + " \u003cg\u003e\n", + " \u003cpath d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/\u003e\n", + " \u003c/g\u003e\n", + "\u003c/svg\u003e\n", + " \u003c/button\u003e\n", + "\n", + "\u003cstyle\u003e\n", + " .colab-df-quickchart {\n", + " --bg-color: #E8F0FE;\n", + " --fill-color: #1967D2;\n", + " --hover-bg-color: #E2EBFA;\n", + " --hover-fill-color: #174EA6;\n", + " --disabled-fill-color: #AAA;\n", + " --disabled-bg-color: #DDD;\n", + " }\n", + "\n", + " [theme=dark] .colab-df-quickchart {\n", + " --bg-color: #3B4455;\n", + " --fill-color: #D2E3FC;\n", + " --hover-bg-color: #434B5C;\n", + " --hover-fill-color: #FFFFFF;\n", + " --disabled-bg-color: #3B4455;\n", + " --disabled-fill-color: #666;\n", + " }\n", + "\n", + " .colab-df-quickchart {\n", + " background-color: var(--bg-color);\n", + " border: none;\n", + " border-radius: 50%;\n", + " cursor: pointer;\n", + " display: none;\n", + " fill: var(--fill-color);\n", + " height: 32px;\n", + " padding: 0;\n", + " width: 32px;\n", + " }\n", + "\n", + " .colab-df-quickchart:hover {\n", + " background-color: var(--hover-bg-color);\n", + " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n", + " fill: var(--button-hover-fill-color);\n", + " }\n", + "\n", + " .colab-df-quickchart-complete:disabled,\n", + " .colab-df-quickchart-complete:disabled:hover {\n", + " background-color: var(--disabled-bg-color);\n", + " fill: var(--disabled-fill-color);\n", + " box-shadow: none;\n", + " }\n", + "\n", + " .colab-df-spinner {\n", + " border: 2px solid var(--fill-color);\n", + " border-color: transparent;\n", + " border-bottom-color: var(--fill-color);\n", + " animation:\n", + " spin 1s steps(1) infinite;\n", + " }\n", + "\n", + " @keyframes spin {\n", + " 0% {\n", + " border-color: transparent;\n", + " border-bottom-color: var(--fill-color);\n", + " border-left-color: var(--fill-color);\n", + " }\n", + " 20% {\n", + " border-color: transparent;\n", + " border-left-color: var(--fill-color);\n", + " border-top-color: var(--fill-color);\n", + " }\n", + " 30% {\n", + " border-color: transparent;\n", + " border-left-color: var(--fill-color);\n", + " border-top-color: var(--fill-color);\n", + " border-right-color: var(--fill-color);\n", + " }\n", + " 40% {\n", + " border-color: transparent;\n", + " border-right-color: var(--fill-color);\n", + " border-top-color: var(--fill-color);\n", + " }\n", + " 60% {\n", + " border-color: transparent;\n", + " border-right-color: var(--fill-color);\n", + " }\n", + " 80% {\n", + " border-color: transparent;\n", + " border-right-color: var(--fill-color);\n", + " border-bottom-color: var(--fill-color);\n", + " }\n", + " 90% {\n", + " border-color: transparent;\n", + " border-bottom-color: var(--fill-color);\n", + " }\n", + " }\n", + "\u003c/style\u003e\n", + "\n", + " \u003cscript\u003e\n", + " async function quickchart(key) {\n", + " const quickchartButtonEl =\n", + " document.querySelector('#' + key + ' button');\n", + " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n", + " quickchartButtonEl.classList.add('colab-df-spinner');\n", + " try {\n", + " const charts = await google.colab.kernel.invokeFunction(\n", + " 'suggestCharts', [key], {});\n", + " } catch (error) {\n", + " console.error('Error during call to suggestCharts:', error);\n", + " }\n", + " quickchartButtonEl.classList.remove('colab-df-spinner');\n", + " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n", + " }\n", + " (() =\u003e {\n", + " let quickchartButtonEl =\n", + " document.querySelector('#df-746abbeb-66b3-4dcf-8a53-a88a4dcbb1aa button');\n", + " quickchartButtonEl.style.display =\n", + " google.colab.kernel.accessAllowed ? 'block' : 'none';\n", + " })();\n", + " \u003c/script\u003e\n", + " \u003c/div\u003e\n", + " \u003c/div\u003e\n", + " \u003c/div\u003e\n" + ], + "text/plain": [ + " name ... nonzero_mean\n", + "769 UBERON:0000317 Histone ChIP-seq H3K27ac ... 0.749292\n", + "770 UBERON:0000317 Histone ChIP-seq H3K27me3 ... 1.773918\n", + "771 UBERON:0000317 Histone ChIP-seq H3K36me3 ... 1.865919\n", + "772 UBERON:0000317 Histone ChIP-seq H3K4me1 ... 0.941760\n", + "773 UBERON:0000317 Histone ChIP-seq H3K4me3 ... 0.739352\n", + "774 UBERON:0000317 Histone ChIP-seq H3K9ac ... 0.950228\n", + "835 UBERON:0001157 Histone ChIP-seq H3K27ac ... 0.785410\n", + "836 UBERON:0001157 Histone ChIP-seq H3K27me3 ... 0.825184\n", + "837 UBERON:0001157 Histone ChIP-seq H3K36me3 ... 0.823532\n", + "838 UBERON:0001157 Histone ChIP-seq H3K4me1 ... 0.812938\n", + "839 UBERON:0001157 Histone ChIP-seq H3K4me3 ... 0.805694\n", + "840 UBERON:0001159 Histone ChIP-seq H3K27ac ... 0.737607\n", + "841 UBERON:0001159 Histone ChIP-seq H3K27me3 ... 0.862530\n", + "842 UBERON:0001159 Histone ChIP-seq H3K36me3 ... 0.825066\n", + "843 UBERON:0001159 Histone ChIP-seq H3K4me1 ... 0.814169\n", + "844 UBERON:0001159 Histone ChIP-seq H3K4me3 ... 0.908285\n", + "1038 UBERON:0004992 Histone ChIP-seq H3K4me1 ... 0.940036\n", + "1039 UBERON:0004992 Histone ChIP-seq H3K4me3 ... 0.984380\n", + "1040 UBERON:0004992 Histone ChIP-seq H3K9me3 ... 1.079408\n", + "1095 UBERON:0012489 Histone ChIP-seq H3K27ac ... 1.686818\n", + "1096 UBERON:0012489 Histone ChIP-seq H3K36me3 ... 0.959263\n", + "1097 UBERON:0012489 Histone ChIP-seq H3K4me1 ... 0.938512\n", + "1098 UBERON:0012489 Histone ChIP-seq H3K4me3 ... 0.842116\n", + "1099 UBERON:0012489 Histone ChIP-seq H3K9ac ... 0.892749\n", + "\n", + "[24 rows x 12 columns]" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "output_metadata.chip_histone[\n", + " output_metadata.chip_histone['biosample_name'].str.contains('colon')\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "T8U7yiGMqyVE" + }, + "outputs": [], + "source": [ + "# List of IDs corresponding to various colon tissues in `CHIP_HISTONE` output.\n", + "ontology_terms = [\n", + " 'UBERON:0000317',\n", + " 'UBERON:0001155',\n", + " 'UBERON:0001157',\n", + " 'UBERON:0001159',\n", + "]\n", + "\n", + "# Make predictions.\n", + "output = dna_model.predict_interval(\n", + " interval=interval,\n", + " requested_outputs={dna_client.OutputType.CHIP_HISTONE},\n", + " ontology_terms=ontology_terms,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uY9FpDS4jcWN" + }, + "source": [ + "We can extract the locations of transcription start sites as annotated by\n", + "GENCODE using `gene_annotation.extract_tss()`, and plot these using\n", + "`plot_components.IntervalAnnotation()`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "SIxvrfPSd3W5" + }, + "outputs": [], + "source": [ + "gtf_tss = gene_annotation.extract_tss(gtf_longest_transcript)\n", + "\n", + "tss_as_intervals = [\n", + " genome.Interval(\n", + " chromosome=row.Chromosome,\n", + " start=row.Start,\n", + " end=row.End + 1000, # Add extra 1Kb so the TSSs are visible.\n", + " name=row.gene_name,\n", + " )\n", + " for _, row in gtf_tss.iterrows()\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qdastNqEg8Vc" + }, + "source": [ + "We can also reorder tracks so they are grouped by histone mark, and also specify\n", + "colors based on their histone mark." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DKasK1YEg7DB" + }, + "outputs": [], + "source": [ + "reordered_chip_histone = output.chip_histone.select_tracks_by_index(\n", + " output.chip_histone.metadata.sort_values('histone_mark').index\n", + ")\n", + "\n", + "histone_to_color = {\n", + " 'H3K27AC': '#e41a1c',\n", + " 'H3K36ME3': '#ff7f00',\n", + " 'H3K4ME1': '#377eb8',\n", + " 'H3K4ME3': '#984ea3',\n", + " 'H3K9AC': '#4daf4a',\n", + " 'H3K27ME3': '#ffc0cb',\n", + "}\n", + "\n", + "track_colors = (\n", + " reordered_chip_histone.metadata['histone_mark']\n", + " .map(lambda x: histone_to_color.get(x.upper(), '#000000'))\n", + " .values\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "height": 1000 + }, + "executionInfo": { + "elapsed": 9110, + "status": "ok", + "timestamp": 1753110028524, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "O_YZ5pwWqyVE", + "outputId": "93657aab-2bd1-4627-8a05-94bfcb685be6" + }, + "outputs": [ + { + "data": { + "image/png": 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Osi+sqpNE5CEA1wGYISJvA6gEcDqsEPZVqrrUsc2HABwL4BQA34vIlwA2A3AqrPfwBarKX71EREREREREFBddvabjhrMyFFFPx+pxRABSB6GLNWR5qdNwYvDS/+776HPAAYXbvmpCpczkBgXrSt5o1Oi4nqfPbtPvh6dfv7ysqzfI9PHQ3ha+jSbeH2X4k4iIiHLEX1BUklS1WUQmAzgRwPYAkuqvx9ocBuBjWNVCx4jIcarqS7VeEbkGwMMAZsEKftZm2KWLYpdPp1jvBgCujq13X1X1Ouaf6bJYM4B1RaQvA6CUoy1jl2WwAshuxqOjau0rAE4CsAes4dcrANQAeBPA46o6wW0Fqnq9iMwAcCWAiwGYAH4CcL+qfuTSPiQihwK4EcBZsKqKtgJ4H8AdqjonmztJRERERERERL2f+my71XpbGIB6PVb+JLKo1+s+3e+HDBpU4N6UvqSqm8FgYbfv9ydWykxuUbC+5Etk9uz49XxUngx89DEaL7kUQ+75J/qfc3bO6yt1ocmTUX/KaRh4/XUYdN21jrmO4iVpX1ulJ2nY9xJ8fxAREVHPwlOjqZS1D5Oeco9ZLOh5JICxAA4EMDY2RHsSEbkBVvBzOoCDMg1+isheAHYGMF9Vx6VothWs99tYl+DnprH5Tt/B+oWTamh7ooyo6ihVlU7+DrS1f15Vj1XVLVR1gKpWqepmqnp6quCnbdmXVHUPVe2vqgNV9QC34KetfUBV71DVbWLbWV9VT2Xwk4iIiIiIiIjcmK0du9aclc6IejoNsfInEQCYTc2u09XXVtiO9BJJny2VFQXdvtnUlL5BCQ773nT5FR038vDZ3XzzLdbljTflvK7eoPWe+wAA3gcfcpmb+HrpbcO+J72ezNJ7fxAREVHPwvAn9VgisrmIuIUiISKXwKpKuALAzHTriVXNPB7AaAD7APhSRIY61ncbgHsATIVV8TPV0O1uLo5dPpOmzdLY5f4iUmbb7gAAz8K9Cu+/YpcPisgw50y3aURERERERERERL2ZvVpcvoZhJSoUDfg7rpdgGIooFxoMwmyzwp1GY6NrG9PP8GeXOCsJFvjzpTeGP+3y8X2jbMMN89CT3Bi1tag//Uy0vfZ6sbuS/gQew0i83csqvSdV6uWw70RERJQjDvtOPdlvAbwrIpMAzIc17PS6APYGsCMAH4BzVNVIvQpLbHjpPwD4L4BTAYwTkUNVtVZEzgNwFwADwAQAV4uIcxVLVfVF50QRGQTgdABhAC+l2X61iLwO4AwA00VkLIDBAA4DEIRVbXQXxzJjReRvAG4DMFdE3ocVdt0QwP6wKoOO7Oy+ExERERERERER9RamtzV+XXtZGIB6P/X6Om6YJlBWlroxUS9Tf9oZiCxYgI2+m5QyLNj2wn8w+G93QfjeyIpzGGltK2yIttPwZ4nRaOKAg/kIf8pg10EJC6r51tsRmjgRoYkT0f/MM4raF3sAMjB2LPoefnjHTEcYMiksWeKSgq8MfxIREVGOGP6knuwnWMOw/w7AMQCGwgpKLgbwIIBHVXVFpitT1YiInAkgAOBcAONF5BAAW8aalAG4JsXi4wG86DL9jwD6A3g9g2qhF8b6fjqAKwDUAfgQwO0A3knR59tF5DsAVwM4NratWgBTALzcyfaIiIiIiIiIiIh6FW3rqJwIVv6kEqKRCMJTpnRMYPiT1iJGdTXCU6cCAKKLF0MDAdd2bS+9jLJNN8XAyy8rZPdKnjOcmPC/sgDMFJVc40qs8qczPJuX8GFy0ZmCUlUEv/iiqH2wsz+mjedfiE0WL4RUVVnzDEf4s5d933O+npThTyIiIsoRw5/U7VS10180qrqFy7TlAK7PcltLAaTcXqxK6Hmxv3ajYn9ZU9UnATyZYVs/gFtif04HplluDIAxXekfERERERERERFRb6J+27DZ6YYMJephQpMmJU5g2IPWIkZNTfy6tvmBNGE6/2uvM/yZLUelSrPNl6Jh9zCbmtPOj65cBTMwBJ6+fQvToRwlhWfzUfnT48l5Hblovfsfad93BecIeIZnzkLV7rtZN8zEAR97XeVP5/0psXB0b8QALhERlbriftMkIiIiIiIiIqK1itncjJa7/gajoaHYXSGiEpQY/uSw71RCHOEOBg1obWI2dFSGNFtbocFg6rYtLYXoUq+hkUjSsOvqd6+s2l06G/Y99M038D2RUR2VHsH0Oyp/5qPyZJErf/qefKqo20/ieDwCH47uuOGs/BlK/XlRijTIYd97nF4WMCYiorUPw59ERERERERERFQwzTffAt/Tz8D/9jvF7goRlSDT3/mw74GxYxFdtqxAPSLKTFIluQKGPUyvF6HJkxmqo6IxbSf9mN7WtJX8Cl21Ml8i8+fD98yzBQ92N111dVKwTwOFDX86h0l30+PCh2moL/E1mJ/wZ+6ryKeE71M9QGTmjPh1NRyVP51hyVLn/Pxz3F8qvN5WXZaIiNY+DH8SEREREREREVHBhL61hr1Vnw/RpUuL2xkiKjn2amZuB2rDs2aj8fwLUbPv/lAOo0k9iOkIExUy/Nl8080ITZqMwMcfF2ybRHZGY0flT/X60lb+RDBUkp/ftQcdgpY770Jg9OjOG+eJRqMIjP4oeXoBg30ajcL31NOdtvMMGdL9nckT9Tkqf4ZKv/Jnxa67JtyuO+KoIvXEIlVVCbcjv8zveN87hn3vbVUZnWFiVgIvvrwEvImIiIqI4U8iIiIiIiIiIioYe+Un/zvvFrEnRFSKNNARaEkK0wEwli/vaMsqh9SDJFXGK2DYI/DBhwAAY011wbZJvZ+qwv/ue4iuXInQpMmIzJuXsm1o4rcdywUCnVZZM2tr89bPQrCHtwr5PjOWr3CdXsjKn77nns+soad0DkmbPm/ihHAewodFDn9qa2vC7ejixUUNWUufPgm3taUFZntI3HQO+97Lwp+s/NnjMPxJRESlrrzYHSAiIiIiIiIiorVICVZyIqKewz4Uq3NYVgAJw1qbbf6SqjRGvZt6HWGiQlb6Kisr3LZoreF//Q00//kvCdM2nj3T9XM39NVX8etGXV1S1T8n33PPo2zYMAwYeV5e+trd7O/vTKtuajgMNQx4+vbt8nZTVdHXQACqCilA4LC9qn+nSij8qd7uGPa9uOFP0xH+BACzqQllQ4cWoTdwrf6rra3AuusCRi8PfzrvezRanI5Qh3Ck2D0gIiLKSel80yYiIiIiIiIiIiKitZo9vGB6XcKfturC9iqhRMVmOirRFnKYV+nXr2DborVH4P0Pkqat2X5H+N96O+1ybc+/AN8TT6Zt43viSbTcciuMEqkAajY3x68b1Z1X/lRVrNlpF6z51QhEFizo+nbbKyUmbwCIBcwis+fAWL2my9vojPrbOm8EAJ7ihh+z4aws3hvCn0nVpwFEFy8pQk8sboHO9sddHcO+F7KSbSE4w5/Kyp9Fpy7VfYtZGZeIiChbDH8SERERERERERERUUmwB+jUOSwrEsOhmVZfIyoEezgMQEGHeZUcKgsSpaKGe7W6tlf/m9gu0vWKauGfZ8CwhfqLTU0T3sf+hfD06QnTE8KfGQz7rn5/vFpoy11/73J/kj5X7PNaW2HU1KD28CNQvceeXd5GZzKuyugpnQrESWH9PFeeLHSoTE3TPfy5aGFB+2HnWvmzPXTr+P9o1NUVoksFw8qfPY9rwJuhXCIiKiEMfxIRERERERERERFRSdAWW+VPRzgD6NrQu0SFkPR6LWCowNO/f8G2RWsPqahwne4MyuVSta9x5Pmo+f0BXV4+3wKjR6P13vtQd8xx8WkaiaDlrr/FbxvLl3e6Hvv/Mk8XK/MGxnyCljtGpZwfXbEStYce3qV1ZyPV6yCpXQkN++4M1WogOaiYNXvAL4dAdFekeg8Wq/KnmibMpqak6dElS9F8082IzJiZMN1YuSqv2w/9OAX+d97N6zqzkVT5M8qQYbGp27DvBX6fEhER5aJ0vmkTERERERERERER0VotYdj3+uRqcPahWrWN4U/qOZxBF/trudtVVsav5mX4YiIAqKjsvA1yH7JZm1tg9pBhn83ajgqE7e8l7yOPIvzd9/Hp0YULYdTXp19PS3PHjUr38GR0+XJE5sx1HUY+Mm8eGi+6OO02Gi/8v4Rh4bur2qRk+DpAKYU/m5oTbmc8tH0a0eUr4tcj8+fnvL5sqGMY+3bGsmUF7Uc7s67ONVgXGv8N2l5+Jbl9J++nbNWfeBKarv4TQj/+mLJNcPx4+F74T7e8bzToqCQb7T0hw8jsOQhOmJhTxeeicPluVHL3gYiI1mql802biIiIiIh6DFVFeNZs+D/4AMphcKjAVBXRpUsLPlQaEXWP8LRpxe4CEZUINYyEAIPbMKCs/Ek9lTNMFPjo48Jt3BZgMFblt4IarcUiKYLEkngz1/AnkP/wV1fZK0KaseHoQ5MnJ7erqU2/Hlv42204bqOxETX77Ifaww5H9W57JP32jcyd23lfHY+Z/5VXO10moQ81NQiM/qjz3919+mS2Qo903qaHUGflT39ur+HwtGkwli6N36474ihEV63Ork857P9IdTJMYPRHMIvwXSlVJc/wrJmu080U4dWuCHz+Rfx6ZNr0lO0azjobLbfdjuDHY/K27Xa9ufJn7eFHIPzDDwh//0Oxu5IVdfl/xvAnERGVEoY/iYiIiIgoa8GPx6DuiCPRdPmVCH71dbG7Q2uZtpdeRs1+v0PrP+8pdleIKEtuB1Dqjj2+CD0pbUZNDYLffFPsblABeJ94EoFuOOhciqLLlmH11tskTDPr66GmmTjNFv7sKZXiiAAkVOBrp/ZhgLtJZOEiRBcujN8OfTup27dJa4eU1ZUlMeSXj/Bn8w035ryOXIRnzUbrI48iagvwtVf3lP79k9obDcmVqRPmr1gZvx78bCyq99gLbW+8iVXDhmPVsOHwPvJYQnttaUm4HV2afcXG5ptuTjkvMnsOois7+qTRKKp33R2Nl17WaWhUyjI81Cz5D3+GJk9G8+2jYKxZk9f1Jg37nmNA0vfc80nTIj/9lPHyrfc/gDW/GpH1/rfIggVYNWw4an73+5Rtgp9/ntU68yG63P31a6R6XYfD0FBHtczIvHkwXP6ndkajUTSOPL9je7XpQ9oAEJ7pHkjNhTP8CaP7vwsUWuj775O+o/dkGnIJfzortBIREfVgDH8SEREREVESjUbT7gQNfvVV/Hp08eJCdIkorj0E4/v3E0XuCRFlyx7KsuMQtJnTQADVu+6OhjP/iNCk5EpT1HtEly1D693/QOPFl3TpAHdv433iqeQhQqNR+P/7v4RJZktHNTVjxQoQFZuqovX+B1wrF9rDLN2l6ZprE25nEnYh6kzbK68iPHWq+0zDEcrPQ9W+0PhvihokqjviSHjvfwCB9z+ITzPrrPe025DaZmP68GfEFsgGAGP1ajRfd338dtvziWHB8JTEx9pYvjyzjjv75fWi+eZbEkLgRmMTao86GjV77ROvSBqa1DE/MHZs2nVmXB3PzO/IHRoMov6U09D2/PNovv2OvK7bXpkVyD38aX/dxLeR4neRG+8jj0KDQfheeCGr7To//92YzS2dtslE8JsJiC5ZklFbY3X2Yd32z5HwjBmoPeQwNJx9Tnx6pt+TI/PmJdy2h7lT6obQsjMQ35sqf9qFv/++2F3InMvnmLMCMBERUU/G8CcRERERESXx/utxVP92NwTGulcAENuwXtINO0KJ0lFf5gdJiKhncVYtauesrkOpNVz4f/HrkVmzitgT6m4JQ8u6DG++1klRFan5xptgNDbFb9s/Z7wPPczgLBVddMECeB951HVeUvWvbmBWVyfejoVQg99MQGhKivAeUSeab7wp5TyjpiZ+Pbp0KZouvzIv2zSLFFxOVaHXiP1vVl/ysO2dfbfVLIJ/AOB75tnEbWc5ZDgAlP/qV6jZ73doe+ll1J92evx+Nf3pT4Bhhc/aT+6NLugIp3YafIxkVrVQQ/n9vLMHCINjPok/H7lSVUTnz0+YZga6Hv5MFfI0VrkPfZ52XTXZvQfU23nwOtt+aCSSFOgOTfwWDWeehboTTspsHW3J75lOl4kFcttPBo78PAMaiaD2sCNQvePOMDNYZ90RRyXcbg+rRn75BW3/e801yKxtbWg4/wJ4n3gy6z6nkvT50AsrfwJpqkP3QG4n45hNTS4tiYiIeiaGP4mIiIiIKIn3gQcBAM1//ovr/IQwQhd22hLlxFYxpPWhh4vYESqk4DcTEF2W/fCGxeZ/510ExnxS7G70GGaq8Kejug6lFhrfMdy7DB5UxJ5Qd7OHSfIxZG7JS1PxrfHii2E2NSH4xZcwVicGYiIzZ0JVoZrfimNEmYrMmZNyXiGGFC3bfLOE22ZDI4zaWjSceRbqTziR7w3KO7OuLh5sbr33vqTP5a6qP+0MhH6ckpd1ZaPt5Vdcp7eHut0qm2pLK0LfToL/3fcAAP633kbzqDvR+uBD8D72r6z/r5t+P4w1a6DRKNQwuhR0jC5cCNM2HH1o0mTUHHQIQrahxCO//AJjzRqEf54Rn+YMmqppovmWW7Fqsy0QGP0RNJJZBX/15/e7jFGXGIRse/GlrJZP9dnnf+ON5InBUJf3fbVXiHWKrswsdGnvZ3Tlyuw+sysrOm1irMmuCmfdiSeheo+9YNpCwcGvrdeQ/fWVTle+17a/zwKjR8enNZx9brwKbvv/WjVNNF5xJZpvujlxeZffm9HFS6DhMJr+dC2a//JX+J63Kquq0VGJs+3lVxAc+zla7/5H1n1OeV+clYFTBMxLnZljxdxCMluT91Mw/ElERKWkvNgdICIiIiKinkf69YP6/Sl33Bq2ndcMI/QOqgoNBODp16/YXUkrPGtWQqU774MPod8fTkb55psXsVfU3ULfTkLDmWdBhgzBJrNnFrs7GYsuXoKmq/8EANjopyko23DDIveo+FINNautrOibiaTKU6HMDrZTabKHDPIdmChFZkPqCp7hyd9hzQ47uc7zv/kWGs46GwAw6MYbMPCq/FSgI8qUka5SW7gA4c/110+4bTY3Izx9esft2lrX7yhGbS1k4EB4+vbt7i6SC++TT0EDAQy6rvNhm3siY80alA0fjsCHoztvnKHookWoP7GjsuAGn49FxW9+nbf1p9Jy2+2u043ly2EGAq5VPlvvuz9+3WxtRcsttybMr/rd77LqQ2TaNFTvvifKt9sW0Xm/ZLVsKs3XXZ8U/Gu+7s+QgQMTPjeMujqoanzUl+DHY+JBy8ZLL8t4e27VHsOzZsGsrUOfgw/Kuv9mbWIANlWFVjeR2XNQd+qpGHTDDRhw3rmJffr+B9dlgmM+Qd9jj4Fk8JmowSB8zzwLs6kJfY4+2rVNdH5mz6O9orm2tkKbmyHrrJPRsplUZQ3/OCXh+U3bl1AIkek/AwCi8+ejcpddktsEAp0+Rl3Zj6itXoRnzYKxbHl8WmjixPh1Y/kKYI89EJk5E4H3PwAAVO27L/oedyyMxkY0XXZF8krDYXifeBKRmTPj9wlwVOW2va40HIZUVmbddyfnd9r2aqa9TaZh4J7AbHYJf6Y4cZWIiKgnYuVPIiIiIiJKUr711vHr9jPeAaDpL39FeNKkjvlZnskdXbIE/rffYYWZHqZx5AVYs822CE2aXOyupOUcpgvoGJqOeq/A558DALTEhgaPLFoUv+5//wNomEE9+/CMdm6VNiiZ6Ri+2q3SFPUe2tbx/K7tJ9uoKozams4bumgPAABA6z338jtoCmoYaPvv/xCePj1hyGbKnfOz207DyUPM5n37jvC42dKSUIkuunxF0jKhyZNRvefeaDjn3KR51P00HEbr3++G98GHemTlsUz+J7X87e9oe+E/3doP72OPQdNUhbYzAwE0XXd9vBJnpjobvr3ljlHx4ahTtnEEPwEgNGFCVv1ol6/gJ5C64qN6vYm/sUOhhABiV0dj0EAg4flSVdQdcRQazjkX0S4MgW40JFbU7Ox5aBddvAS1hx8BbWlFy823JPfTZehvAGi65tqkapKpNF3/Z7Teex98zzwL70MPubaJzJ2X0XcSoz7xfqYaRt7J9PsRXbq08/WvWAEzRSVZZ6A2urwjeGkPMNrfJ+meS6OxCUZ1ddL/pUyY3laEp/6Udj6QuH+oPZzceNHFCUFRO+/9D8Svtz/3CeFPG6MLr9OkfjY3Q53PoWki/PPPOa+72FQVKCuL3y6l75Nun/WmjyeoEhFR6WD4k4iIiIiIkpXbdtatSDwY6P/fawm3TW/mwRNVRc3+v0fTn65BeMrU3PpIeWPU1SH4xRcAAO+jjxW5N9krxaHAKTv2A3lGCVWPMGyvzda7/oamP/+1iL3pGVIN+2k29rxgQ0/kDOiwGknvljDsewkNm9gdvI88isiM/FR+bnvu+bysp7fxv/UWmv96A+qOOQ7Vu+7uOjwqAAS/mYDqvfdFaOK3Be5h6TKbmlPOy3S45FyoP7HaXmTmTAQ+/Sx+22xIHpK49Z77gEgE4cnfYfX2O/L/TTcyGhrQdM21iMQqzgGAUV0dv97TfuuoKqr33a/TdsHPxqLlzru6tS+B0R/B/9rr8H84Go1/uhZmmlCq/4034X/jTTRddXVWJ88YaapOA4D/v//LeF2lrL16v+n1pvz/0CnVhFCdaQuGRRdlf0KnWZ/4uzDTUGTzrclh3IT1pAlc+996O6NtBEZ/FL8eTvX9JRLJKLBqLE38DMj08a896JCMhxN3+5zxvfAfrB6xXcL/e9NWyTpqO9HRfpJDdPES121oOIzaAw5EzcGHuv7f6Yx6fQm/r5Pmt1iPi7Oqpv+ttxH+7vuMthF49z2YLS2ILlnqOj8yb15mnU0jsnCR+/Q8BruLRb1ewFZAwCyh8Ke6fM/RLPZ3ExERFRvDn0RERNRjmM3NCE2ZykosRD2AvZJH+KdpHdNd3p/ZnPluH/InusR9hzAVnj2MVbbpsCL2JL1UBzlSVRKk0qSBQMKBWzUM+N94M367Zq99itGtLoksWJhwO/DOO0XqSc8RXep+wK6UqoIUk/OAp/2gK/U+9vBBukDL2sD7+ON5W1fLqDvztq7eJPzDj4m3U5yo1XDmWTBWrEDrww8Xolu9QtrwQ6gA4c/W5EBU6Kuv4tedAarwrNkIT5nSsXxzc+rwEuWs5dbb4H/rbdQddUz897a9Wp9bZdZiMpuakobbLqbmv96ApssuR+Dtt9H8l9QnWgXHfBK/Hp42Hc033YzWRx7tdP09sfJqMdQedAgar74Ga7b7DXz/fqLL67EP/W6s6tgP0XjJpVmvy6hzVP7MMKhlrEzch+Ws9NlZtddMhpeXior4dU+/fqn7Ygt6A1bFydDkyQl9ajhvZGIbl8/0pPXW18NYubLTdvH2tqHUAcD79DNoue12IBRC0zXXdrSzhTbDM2cBAJpvvQ3BsZ/Hp6f6nxf+6SeYjY3QlhZEZs/JuG/tInPmpK2k3R7+dQ413nzb7Vltp+Hckag/4UTXefZga3Tlyi7t1zSWL3ednqr6ailxvnecr++ezO1k1EwD5URERD0Bw59ERETUY9SdcBLqTzgR4e8zOxuXiLqP2oZgCn3/Q8d0lx1f4cmTM64EY9/57Kwoam3re4S+/z7lMFvUPeyVEfyvv5HxUGaFlqrKRqowmRs1TYRnzeJrrIdSVdSd/Aes2X5HNJx3PhouuBD1p56W2CYQyOiAW08Q/aX0q3eko9Eomv7yVzRefgUazr8Aoe++S98+FELw449d56UadpISGXW1Cbcjs2YXqSdUCIbtILBbNZreRgMB+N99L+lkD7O5GQiG8rotVjFMplEj4Xbgs7Fp2/ek8FdPl27YXQ13f/izs9e7c0hht30ypVS9Kxtqmmi+8SZ4H/tXymF+u1t7hTkNBrF6+OZYNWw4Gs78Y3x+02WXI+ry27lY6k8+pdhdSCnw3vtouPhStL35Vnya6fXC+/i/Efq2o3phwxlnou3lV+C9/wE0/fVGGPX1iMydG69uqaaJ4BdfwmhsZPjTJh8nstnDcvYRHTIdsj1hXY4qjO3DfqejpomoIxTp/Iw2m9N/ZkaXuYf37Mo23yx+vf115abt5VcSbjdeeRXqTzkNbS+9HO+vUyavycisWZ22sbM/L2YggNa7/tZxu7UVLXf/A6u2+hWCX9hOHKipgaqi7T8vJvYv9j/HaGxE7bHHw/fCfxBdsgT1fzi1o00Xgo5tb74FI81oEWZLCzQYTBoa3m0/Zjr2kx+cvA89jMicuTAaGlB78KGoPeoYmG1tKdu7iaYIf6Z7nZQK52uzlE7wdHtfdeVziYiIqFgY/iQiIqIewQwEEF1oVceKphj+hIgKx75zNDq/I7zke+E/ru3tQ9QlzVuwwDqgsnpNwk5y70MPJ1QYDXw8BvUnn4L6k09B3Ukn59L9XkdNE8233ob6c0d2y0FJZ2WEtpdf6XHDCwKA//0PXKdHpk/PuGp02/MvoO6Io1B30smsNJ0nba+9joaLLkZ0lftw3tkwliy1hvUNhRD84gsEPxuLsC2A3i4yJ/tKIYUWnj4d4R9/TJqeLgBSaoJfj4P/f68h8MGHCI79POGAnpOGQliz/Y4p50cXZz/U49rIOYyhsWIFQwm9mGEbWrPUDgj7P/gQwQkTM24f+u47NF3/ZzRddTVqjzwaGgohsnAR6k45Le1nR1c1Xpx9hbFeL5QYsA199VXSdyW1DeUZXbwYzbekHzq3WEI/TkHg4zHF7gYAK9Tcvq8j1fzu1ln407SFP9vefAstt9+R1KaUAhzZCP80DW2vvIrWe+/Dmp12KU4AtLy843qK3yeBDz4sUGdi3bA9Dvb3fXjGDEQXLChoX7IV/PhjNF97HVYNG47gN99gzXa/Qes/70nZ3v/f/6J659+i9tDDUX/KadBQCN5HH0PDeSNRvePOaBx5fgF73/vZg3nO/RDq+D+kponGq65G4xVXIjJ7TsLvqPDUn5JCep0Nh66BALwPPJj0/67h3JGJ7Tr5zIzMSl8J2airQ/QX2z6yNCcuRubOBQB4//U4ao89DsFPPgUAhKdZI/CYLt//Gi++BP6330Hj1dcg7Ah5qmHAaGzMeghx7yOPwhcLnDpHGtC2NvieeBIIhRB49934dKO+LuH/R7v26o+BDz9EZNo0tNx2O2oPOyKr/rjR5maEvvwy5Xz//15DzSGHJgS9802DQdQedjhq9twb2tYG9XoR/iF5f0U60RT7T7uz8meh9n05K30a1aXz3cHtN3Vv/e5DRES9E8OfRERE1COYazp2DjTfcCPC06d3uoxRV4fA51+4ngWdKaOhAXWnno62V17t8jrcRObOhe+553PqW1eoaqc7O4nSUdNEw8WXJuz0Cv/wI4JfWgd/vfc/4Lqc78mnEm6bbW3xnYstd94F/3//i7o/nJJ0oCg8s2OneWBMxwHayLTpKc+GX9sYtbWoPfhQtP3nRYS+/BKhyZPztu7wjBlovOpPaHv1v0nzavbdv9PnQE0TkTlzEw4IdhcNhxGZMcN1nrFqVUaVLaJLlsSHeo1Mm476E07i6yxHkV9+QfOf/4LgmE/QcM45Oa/P9/LLGbVrve/+nLeVD2ZzM/wfjnatROp/9z3XZWr2+x3q/3h2p1UyS4HbezJVBbOWf/wzbcAlOr9nBwnSicybByPNEIT55DxIDqSuHtOdNBJB+Oefs650Q5lTw0g4mFxK1XEjv/yCpsuvQMMZZ6L22ONg1Nai7bXX4XvpZahhJB2AbnvlVdT/4dR4uMlYtgyrt/oV6k44AeE8fu+xC02ciNC3k1B34snwfzi6W7ZRSlQVkYWJn8PG6tXwPvxIwvPlPIjf9uJL8D3/AiJz5hakn5kwvV7Un3gSGi++JOG3RndJF6iIzJmL1b8akXb5dJXqIvPnw/vUU/C99DIic+dmtH8hunw56k45DU3XXGuFgOrqOq16Fvz0MzRechki8+ah+drrUqzXvfKk2dICo64OoR9+KKkTq1QVkfnzE4b21bY2BD77LKmdsdr98zeX/T0aDML3zLOoPe4ERGIhr3Ra/3kPqvfYC95/P5FQLTEfNBSyKuapwvf8C1g1bDhWb70N2t54E41XXoXVm22BVcOGw/fc86g76pi8bru72SuoZiK6aBH877xrBQSpW4QmTQIQCyk6vsMmjEjy4WisHr45Au++h8D7H6D28CNQe/SxUMNA8MuvUHf8CUnr7uy7Usu998H76GPJyy1fjuAXVqgw+NXXnZ5YFV2yFEZjE9peeRWm7fdNZOEihGfNigc4M2GsXIXIggVovedeRKZNj08PvP8BWv72dzTfcJPrck1/ugaBd95B8w03xqepKhovuhjVO+2C1r/9PeM+tGu5+RbUHn0Mag85LKP20V/mo+mKq5KmhyZMhBoGQt9O6uhbAU50AAAji1FhcmEPyPuefS7j4c3VNBNGV7Iz6uqhwSACn34K0+fLSz8BoPWBB1G9484I/Zi6omm+JISeYQWpnUPB90Sm3+/6m7q9MjcRUS40GERg7OcchYy6nZTSD3IiIqJMicjUXXfdddepU6cWuyu9zoEHWpfjxuV3vaGJ36L+9DMSpg1b5X5wwVizBo2XXIZw7PkdfMftGHDxRVlv0/T7Ub3r7vEDIev86zH0PfEEiCfz82OM2logaqBsk43j04LfTEDDmWcBAMo23hgbfjsBUlWVdf+ypapoOPschMaNx5D77kX/P57V7dvstE+hEIJffgWjpgb9TjoRKCuDUVuH8uGbQiorM1qH6fXC98yzqNj+N6jad194Bg3q3k6vxYzqatQcchg0xY65IQ89iObrrk+5/CZLFgEVFWh78SW03DEKA6+4HH1PPgn1J58CM0UoZtDNN2HApZcA0SjW7PzbhAOTfU88AUP//XhO96mdRqNAJALp2zfzZVQRGjcO5SNGoHzYsPh6wlOmoGK77eAZMiQvfUvHbGrCmh12SpjW/8ILMOSuO3Nar5ommi67AoGPPuq07cZzZye97zQYRHTpUrS9/AraXnoZg26+CQOvuDyxTSAA0+9H2brr5tbXSATh6dMReOfdtEH9gX/5MwZefhmM2lqUbbwxpKwMgBVw9QwdCqmqQs0BB7lW8dhk4fysXhtkUVXUHnoYoraqImVbbIENxn4KT//+Ga8DoRCkTx+EfvgB9Sf9IbONV1RgkwW/QCoqsu93MAioxp/zyPz5MFavRuCDD6FRA1JVCWPlKgz6619QscvO0EAg6f6oKtqefwEtd4wCAAy4+CIMuu1WIBRCdOVKlG+1FRpGXoDQV185Nx9Xtumm2PA764CYiCTMM6qrIf365fw/T4NBRObPR2TGTFQdeADKN900dVvDgP+ttxD8bCyM2lpU7b03+p12KoJffImKHXdA1Z57IvzTNFTusXv8cW+46BIExyRWVhtw5RWo2nNP9Dnk4I51myaqd909qZrJKXVvAgDeXv80oKwMmyxeCLFXvyoB0ZUrUbPv/pABA7DhhPE5f+Z1pu6UUxGenBgcXufJJ9Dv+OPyto3wrNlo/stfoJEo+v/xLAw4f2Ti/OnTUXdMx/bW+fe/0O/EE/O2fbKEfpyC+hNPit8uGzYMG06a2O3vEVVN+kzKlu/FF9Fyy23pG1VWYuDll0GqqtB67305bS8fKvfeCydO/gc8662PCTPWKXZ3kqhpIrpgAcq32CKr35aqCg0G4UnxPcf/4WhEFy1KG3Tqe8LxGPy3u9B8w40pAy3lI0ZgwMUXwTNgAI687yhIWZnrPgPT54OxejUqRnQEIsPTp8P7yKMIfv4F+o88D4P//jdEZs5E+fDh8KyT/XPhe+75+P/nfmeegXUe6L4TRvxvv4OWv9+NARdegKrf7Q+UlyM08Vv0+d3vAACtDz6I4GdjUy5/St2bkH59Mal5l6R5RmMTqnd0/A4YeR6G3O0e6FFV+J54Eq3/+Gd82qDbboXv2edgxkIpCf93u2DQTTfCM2gQ/O+8i74nHI/guPEJVdgGXHkFBt90Y5o1ZE9N0/qu6HgNR+bORf2Zf4RZV4c+Rx+NAReMhGf99VE+fDh8zzyL6LJlKP/V1jBWroJnyBCUDR+Oqn33Qfj7H9D6yKMwbEMc25Vvty08AwbCs+GG0IAfoa++jt/3gVdeYVWF/PcT8D3/PLSlFX1PPAH9zzkb5cM3AzyCyOw5kAH9UbHjjjCbmhH5+WdU7LgDAh9/jOCnYxGZOxfSr59rNb9MedZbD+v861FEFy5CaPJ3qNp/PwS//Ap9DjkYFdtvD9/TT8NsboGxYgWM2HDx6776MoxVq+F94gmo14eqA36P6C/zIYMGIrpkCcya0qouTaUl4bPH48HQZ59G44XJ+3HX/+RjVO60EzQaxerNt3RdV9kWm6cN+K337tuo3HVXhKdMQeUuuyR8dlTvtQ8Mx5Dvdv3OOhP+/73W6f3pe9yxCIy29qX0O/tsrHPvP2FUV6N6tz2Aqir0OeSQpN9I6VTtt19O1So3nj8Pnv790fba62j+81+6vB7KzYYTv0H5llsiPG0agp9/gar994f06YOK3+6CyOzZkKoqaFtbwu8o+3ujbPPNULXXXvC/+RYAoGL77bHem693ad+jUVcHs7YO5b/5NVZvull8+pB770H/s7MLxWdKTRNrtt8R2tqacL+GPHA/KnffDRXbbAM1TZi1tQj/NA3l224LT7++KNt4407WnJrR0ICmq/8EqaxE1b77ot9pp8IzeHDW6wmM+QSNF10MwPF5JWLtd+L+QiLKQfONN6HtlVdRtf/+GPriCyl/m1N+dVeWoUC6tFOO4U8iIuqVRGTqrjvttOs3N9+C8s2Go3KXXWAGAtYBdMOIHyyJLl0KDQZRtummgMcDT79+0GAQ4ZkzYdbVw2xqQmTWLISnTUfVfvuiav/9AAAVO+8Mz8CBHZV3VBFdsQKeddZB2YYbWtsYMACm14uyoUNT9lMjkfjBc1UFDAOIRqGGkTY0oaoIT5kKbW2F9O0Lo64WFduMQNkmGyMydy7KR4ywDgyNGIHIrNloe/ll9D3iCPQ5+ihIeTnMlhbrh3BlJUQE0RUrEJk1C1X77guprLQq+JgmPOus09E/wwBMEwcdVgFVxfjxArO1FdFly6B+P6SqChU77QSEQjBqa+FZd11EFy+G2diIih12QNl668GorrYqCni9CE/+Ll4ZyqirQ2TGjKRhXYc+/RTKR2wDsyVWCcOIIvzjFHgffSzxjOGyMiuIUFWFyt12g/Tti8rddrUqUKxeA0///lbwYdYsRObOQ/kWW6Bsk41dh80q23hj9P3DyajcbVdIn76ILl6M8NSp1jA2FZWo3P43kEGDEPj4Y5hNzTDsQ7d6PPBssEH8wEq7QTf8Ff3OPhvhKVNg1tXBbGiA6fWifMstUfGbX0NDIUj/AZC+faE+L6JLl6F8883gGTIEps8Hs77eum+77gqjpgae9de3VhwKASIwVq8GKqvQcMaZ1vWY8q23xuA7bodnww0h5WXwDB4Mz9ChCE2ahOiSpehz+GGQfv0QXbAAFdtvD+nTB4hGEV26FP433kR01Wr0O/lEmM3N8AwdivCPUxD46GNUbLctyrfaChBBaMJEmI2NkD59UL71VghPmYqqgw6EVFRYIZo0Q6YNuulGlG+1FUyvF5U77QizpQXl22yD8A8/wFi1Gp511oFRVwvvgw9D/f74cn2OPAKDrrsO0SVLYKxZg/CMGSjfckuUb7kFyoZvhvLNN0N46lSUbbwxyjbdFMbKldBQCJ6BAxFdtBgaCKBy331RNnQdtP33fwhPmYKqffdF1SGHQAN+RKb/jMo9dkfw668B8aDi19uhfLPNULbRRtBIBKHvvodUlMNsbEJkzhxU7LA9yoYNQ2TOHJRv/SuIx4O219+IvZ42gtnUjL7HHYPI9J/h2XBDlK23HqJLliDw6WfwDBpo7Rj8eQYqtv8N+h51JIzVqxFdshTlw4cDlRUIjPkUwTFjUPHrX6PqoAPhGToUFdtsg9B336F8661Ruftu8PTrB1RWWhXYKiqBaATR5Stg1tai36mnIPzzDJh1ddBgENK3Lyp22B7Srx/U74dZVw+jrhZmbR28Tz+TMvhZTP3PH4nKXXZB+XbbonKHHazqKytWoGz4cOt9PXs2yjbeGOVbb43oggXQSBTlwzeFZ731EF28GMEvvrSGqAJQsdOOqNh+e1TsuCOkvByedYciOn8BpG9fSFUVguPHA54ylG86DKbPF9/x3/e4Y1H+q18h+NnY+HDT0q8f+h53rNXJsjIrcNi3LzQYjPfPM2QIPEOHWs/FmWdYj3soBPV6EfhwNCp+/Wt4hq4DmIrg11aFicj8Beh7xOGACMJTpsR3/NrJoEHof9656LP//vBsuAF8zzwLz6BBkAEDrMDYkMGQvv1QvtWWkD59rM/8aBRmUzMCH31kVajIokrdgKuuhGfIYFTtvTfKNtkEtUcelXSAsHKffVC5804o32orhCZOROCjjwHTROVee2LgVVcCpsKz/npQXxuC48ZBAwH0PeZoSL9+KNtoI5gNDQh88in877wD9frQ98QTEfp2YkKwsLuUDRuGARdfhPItt7T+p69ciYoRI2D6vPAMHAgoEJk9G9EVK9Dv1FMQ/OJLVO62K8o33xy+Z5+DZ+BAlA0fjopfbwejvgEIhawDx7H/PzBNmF4fogsWWJ/FG2yAsnXXhbF6tfU/c+hQVOywPcqHD4dRW4vybbaBBgLWe7a83ArOr1oF6T8AbS+/DP+bb6Hyt7ugcs89UbXHHlDThLa0wLPuUCs02KcPyjbYENGVK1Dx618jNGEi4PGg3+mnwf+/1xAcNw59DjoI5VtuaVXpDQTgf+ttVO6wPWTgQKjPhwGXXgLvv5+AWV+Pyt12AzyC6OIl8AwYAKii7c23Ev8HOgx96knIkCEIjR8P6d8fUlmJ4LjxKFt/PZRtvDGMVasQXboMkVwqclVUoGr//VC5884w6hsQ/v77eHXhyt13B1ShRhR9jzoKKC+DWVuHyC+/IDRufNbbqdx9d5RtuAFgGFZwZc2atK/Nil12RmT6zxlvomzzzWAsWw5UVSUNQ1i22Wao+PV21vfZtjbr+6dpomyTTRBdvBiRmbNQtf9+GHDlFQh9OwmBd99DaMIE1+30P38kosuWo+9xx0JbWlC2xRao3HEHBD4cjZY778qor31POB5Dn/g3AKDmgINSDmVb8dvfos9BByL4xRcw1lS7DmPnFkIZ8uD9kH794RkyBNKnCp7Bg63vtNOno2zYMOu7zMSJCI3/BqEff0SfA63vHWWbbIKyLbdE5S47AyJQnw+R+QsQXbgQgdGjEV2+AlV77oHyLbe0vqtUVVnfZ1RhLF+Bss2GA2VliEyfDs/666Ns2DDre1urF+WbbwaUlyO6cCGkshLlm28e729g9EdovPSy+O2NfvwBno03gojAaGxC4P33YaxejT4HHYTKfazhAWXAgI73dzQKjUbhGTgwHoY2fT541lkHZnMzjKXLIAMHIDRpMrStLSHUk/DcXngB+h53LDyDBlnVdsJh9B95HqSiAkZNDaSqClJVFQ8UR5ctg+n1oXKH7aGGYX1vXrUKxtJlaLrhxoSg/OC7/wZP/wEo23hjVO6+G+rPPAvhH37s2Hh5Odb/6EMYK1aicpddrN8q33+PwIcfQqr6IDxrJvqddBKq9tkHlfvug+i8eWh7/Q1EZs9B1d57YcCll1ifLTHhmTMB00T51ltD+vWzJkajkMpKRBYuAgBInyr4nn4GVb//PSq2+RW0zY+yTTaOB8XUMBCeNh2evn1Rvs2vABFE5syBZ+AgqGlC+liPh4bDgGHAM3QotNULVMZ++wQCKFt/fUifPjDq6+FZZx2o3w/f8y8gumgR+hx8ECp32w3q9aF8a+v7rPp8KNtiCxir18CsrUHZsGEo22ADhH+aBs+6QxGaPNn6vrLFFpCB1vNgLF2K8u22RXT+fJheq8JPdNEiVO2zN1ruuDPpvVyxww4YeN010GAQVfvvDxhGPNgkA/pbn7d9+8JsaLACf7bHVQYMsN4bwSDU60Vo8ncwW1tQMWJb6/GaOhXhyd8hPG0a+h5zNPoedxykbx9Ely5DeOpU63vnbrvCbGiAZ8gQhL6ZgPKttoL074/Ap5+h4jfbwf/2uyhbd12EJmY+3HtP0v6Z9NHIl1C5y86oGDECng3WR/lmm0H69UPkl1/gGbouyjbeCAiFEPpxCqSiHBW77ALp0wfa2gqUl1sVw0zT+j/U5odnow1hrFgBz7rrwtO/v/V9MBhE8MuvrO95HkHVfvuhap99oNEIzJpayID+8Ky/gTXE6pgx8D31NABAhgzGui88j/JtRkB9XmgoBGP1auv3zoCB8cpTfY48AuHJ36HlzrsQXboUfQ471PqOs3gJKn7zayvs5fWlDMDl4lTfh/BsuCE+f/AnmM3NkMGDYVZXw2xpgf+992AsWw7PeuthwOWXwTNkMJqv+7PremTgQPQ78QSUb7MNKnffLT4ccPnmm8MzeDBC330Hs6UVUlYGz4YbIPjpZyjfait4H38chq1KZdnw4Sj/1dYYMHKk9du7qgoaCCD0/feo2muvWGXH1fF9KGVDhyK6ZAnKNt0UZmsrPOusg+iiRdbnUN++1net/v0RXbwYoa/H5fRYtb/mPr31C/Q/9xzAVERXLIf0H4DmG25w/a4x4NJLULXPPgAAjUas/VseD8JTpsL70MMZba+r4c9MDLj4ImgohMq99kTZuushumwZosuXw7POECAUhtnWhj6HHYryrX8FT/9+MJubEZk3DxqOQL1elA0fbg3lbJoo23RT+J54EtGFC9Hv1FPQ59BD0fbGm4jMmJE2wNVd1n3pRRg1NWj+6w0F3zZRKcv0s6fq97+DUVMDs7k5b4HkAVdeEf+O010G3XwTWu9/AChSRa9+fzwLoW8npf19ToUh/ftDsxgZobP3hvTpg/VHf4jguHEI//ADPOuvj77HHWt9Z/T7EZ4xExrww2xqRtlGG6Js0+HwPfEE/LH90m4GXnsNTJ8PlbvsDOnXD5W77wGzsQHReb/As9661n6H5mZoNAoNBCBVVTBbvSjbcAOo1wfp2weejTZC8KOPEV29GtraisicOQiO/Tzj+2VXvu0IVGy7LcpHjEDFDjvArK21jpn17Yuq/faFWV+P6MJFMFtboT4fqvbbD2WbDkPgk09cTzar3GMPVB3we+tYVF0dytZbD5711gPKy6C+NpSPGGEdu6mqQnTuXLT977WEUaqcfe939tkY9Jfrrf2qsVGPzJYW67hZZSUiv/yCss02s75fhkKILlgAo6YWniGDUfGb31i/Zw3DqoAajkDWGQJj5UpIv/4wli1DaOJE9DnkEHjWXw9lw4fDbGiAsWKFFTgtr4CUl0GjBow1a+AZ0N/a9718uXXfK8oxeNQdqNxhBxj19dZxv9ZW67kQsfb7BAIo22gjGGvWoGzzza39/iKQ/v2BsjJEFy8BohFoIGDtnxw0CJV77gkpK4sfDzRbWiDl5TBWr4EMHAD1+YCKCpRtuCHMVi/EI/BsuCEQicRfM/bArBoGYBiILlgIeATlW2wBlJVZv02WLYP06WM9TtGotV+7f39rXeEwNBy2juksXhw/CU5N0zr+WlFhVVI0Tes3flkZUF4eL+yiqkAwCHg8gMdjHcMcNMgqPGGaMOrrEXj3PZiNjdbJ0pttjvJfbxffl+MZPMha1jCs3xCx5xKxYgPq9Vr96NcPGo1a2+3TByJiHbusrISUlcGMHTs1Vq1C2fDh8RMtTZ/PepyiUaCiIqEgjapCW1ogsTBz+zJqmtbz135b1drfX1cPo76u4/i0aVrb93ji60JFhVWMpazMmt7+u8gwrP1UAwdar9W6OkRXrYanfz94NtgAUI0fO5c+fax9KKZpnSi/zjowYvsH2h+r9pNV2/uKSMSaF4l0PD6GES/aAHQU7VDTjB8XV8Owth2NWn0dNMi6HetPwuPYpw9QVgZtaUF0+XKYTU3W7yXThNnWZj1HgSDKhiaeWNj+Gkl4vaoC4XDSCZcaCABVVdbrqH//+PaN2lrrM0bVetyB+DF1o7EJ/jffTKjG3ffkkzH4zlHW68s0gfbHq/2YnIh1PHbwYKCsDJ5Bg6zXeTRqPXcpCupEly6FDBoMz5DB1jHZSMQ6HlBZYR3379sXZm0tZPBgaFsbPO25hdjjaaxejbJhw6yKzV4v4ClD2QbrW69/1aTAanueQX0+ax9nOGw9x2VlUJ/P6n95ufXchsPWa8cwrP57PNa+79j9MWPFYGTAAOs9FtunK5WVMANBIBKGDBxo5TfaH2OPp+O5DQatxzISgQwcGH9t2cOf8c+DPn2sY/2xz0GpqAAiESs/Mnhw/LWlgYD1eVJREX+fQdWaXlbW8VyoWq9zkcTPnhizqRlSUW7t841GE/Ip7a97APH3htHQACkvx5rf7FA2bNWKrIeZYPiTiIh6JRFp6CMydJueUDko9o8fCuuLAGJfBtB+u9MVWMsUU+zANAAsit4DKLB1RbbVHHrA/SAiIiKinHmGDLFCGv7chvxeFLkHQFe+V+aRpwwwjeyWKS+HlJVDQ6lPcslaZSUQOzGqW4kHUlkRD1H1CBUVKNtoI+uAjM/nOrR90Xg88R341Pv1iM+kXoCPY+YK/VjxuSGiYuBnD5G73vreKOX7lVXfu7IvgXIjkuEx1SIqK4sHhZP3J+TxGKntmG3BlFcA0QxOdMjkeWoPu3J/S9eUwnuhO4kH0PSvnY7P85uQ1fuuBzy2W5WXm+/7/WWdt0zUAxIxRERE3aK1CkhdcrOQ2s9Gck7LfAV57U6X2H5EDJC2LhYc7wH3g4iIiIhyZuapSvQAT27h0bzoysGaWLXOvCpE8BMA1OxZwU8AiETiw9L2ODwQsVbpEZ9JvQAfx8wV+rHic0NExcDPHiJ3vfW9Ucr3K6u+M/hZeKUQdjNsr4uk/Ql57H+hg59AZsFPILPniftaclMK74Xu1EnwE7B/nmf5WPWAx1YAT+etXJZj5U8iIiLKhghGAYCqdUlERERE1BX8XklEPQk/k/KDj2PmCv1Y8bkhomLgZw+Ru9763ijl+1XKfSciog5r4+d5lxKjRERERERERERERERERERERERERERUHAx/EhERERERERERERERERERERERERGVEIY/iYiIiIiIiIiIiIiIiIiIiIiIiIhKCMOfREREREREREREREREREREREREREQlhOFPIiIiIiIiIiIiIiIiIiIiIiIiIqISwvAnEREREREREREREREREREREREREVEJYfiTiIiIiIiIiIiIiIiIiIiIiIiIiKiEMPxJRERERERERERERERERERERERERFRCGP4kIiIiIiIiIiIiIiIiIiIiIiIiIiohDH8SEREREREREREREREREREREREREZUQhj+JiIiIiIiIiIiIiIiIiIiIiIiIiEoIw59ERERERERERERERERERERERERERCWE4U8iIiIiIiIiIiIiIiIiIiIiIiIiohLC8CcRERERERERERERERERERERERERUQlh+JOIiIiIiIiIiIiIiIiIiIiIiIiIqIQw/ElEREREREREREREREREREREREREVEIY/iQiIiIiIiIiIiIiIiIiIiIiIiIiKiEMfxIRERERERERERERERERERERERERlRCGP4mIiIiIiIiIiIiIiIiIiIiIiIiISgjDn0REREREREREREREREREREREREREJYThTyKitYCILBURTfFXnWKZfUVkjIg0iohfRGaIyDUiUpZmO+eJyA8i4hORFhEZJyLHpmnfV0TuFJFfRCQoIrUi8qaI/Dof95uIiIiIiIiIiIiIiIiIiIiIqDcqL3YHiIioYFoAPOIy3eecICInAHgHQBDAGwAaARwH4GEA+wE41WWZBwBcD2AlgGcBVAI4A8BoEblKVR93tK8C8HlsfVMAPApgeGzdx4jIwar6fVfuKBERERERERERERERERERERFRb8bwJxHR2qNZVUd11khEBsEKbxoADlTVKbHptwH4CsApInKGqr5uW2ZfWMHPRQD2UNWm2PT7AUwF8ICIfKSqS22bug5W8PNtAKerqhlb5g0A7wN4QUR2bJ9OREREREREREREREREREREREQWDvtOREROpwBYH8Dr7cFPAFDVIIBbYzcvcyxzaezy7vbgZ2yZpQD+DaAKwPnt00VEbMv81R7wVNUPAEwA8BsAB3T1TojIf0Xkv11dnoiIiIiIiIiIiIiIiIiIiIiop2LlTyKitUeViJwNYDMAbQBmAPhGVQ1Hu4Njl5+6rOMbAH4A+4pIlaqGMljmEwC3xdrcEZu2dawf81V1SYplfhdb5uvO7lgK2+266667Ajiri8tTCnfc0XG1iN0gIiIiohLH75VE1JPwMyk/+DhmrtCPFZ8bIioUjUaxevMtAQC3XT0DnnXWAfjZQ5Sgt/5fLuX7Vcp9JyKiDiX+eS5dWYjhTyKitcdGAF5xTFsiIuer6njbtG1jl/OdK1DVqIgsAbA9gK0AzBWR/gCGAfCp6hqX7S6IXY7IZBtplnElIlNTzNqus2WJiIiIiIiIiIiIiCh/jOUr4tc1HC5iT4iIiIiIej8O+05EtHb4D4BDYAVA+wPYEcDTALYA8ImI7GxrOzh22ZJiXe3Th3SxfVeXISIiIiIiIiIiIiKiHkxDQdt1hj+JiIiIiLoTK38SEa0FVPVOx6RZAC4VER+A6wGMAnBShqtrLzWt2XYji7YZb0NVd3NdgVURdNcstklERERERERERERERDnQSKTjejhUxJ4QEREREfV+rPxJRLR2eyp2+XvbtPaqm4PhbpCjXWft3ap8ZrsNIiIiIiIiIiIiIiLq4TRsC3+GGP4kIiIiIupODH8SEa3damOX/W3TfoldjnA2FpFyAFsCiAJYDACq2gZgFYABIrKxyza2iV3Oz2QbaZYhIiIiIiIiIiIiIqKeLNIx1LsGAkXsCBERERFR78fwJxHR2m2f2OVi27SvYpdHurT/PYB+ACapqv2U3XTLHOVoAwCLACwHMEJEtsxwGSIiIiIiIiIiIiIi6sEShn33+YrYEyIiIiKi3o/hTyKiXk5EtheRoS7TNwfweOzmq7ZZbwOoB3CGiOxua98HwN9jN590rK59+PhbRGQd2zJbALgCQAjAf9qnq6ralrlPRDy2ZU4A8DsAcwCMz+xeEhERERERERERERFR0dmHfW9rK2JHiIiIiIh6v/Jid4CIiLrdqQBuFJGvASwB4AWwNYBjAPQBMAbAA+2NVbVVRC6CFQIdJyKvA2gEcDyAbWPT37BvQFUnichDAK4DMENE3gZQCeB0AEMBXKWqSx39egjAsQBOAfC9iHwJYLNYf/0ALlBVM18PAhERERERERERERERdS+1D/seCqdpSUREREREuWL4k4io9/saVmjzt7CGee8PoBnARACvAHglVokzTlXfF5EDANwC4A+wQqILYYU7H3O2jy1zvYjMAHAlgIsBmAB+AnC/qn7k0j4kIocCuBHAWQCuBdAK4H0Ad6jqnJzvORERERERERERERERFYzaK39GI2laEhERERFRrhj+JCLq5VR1PLowfLqqfgvg6CyXeQnAS1m0DwC4I/ZHRERERERERERERESlLBJxv05ERERERHnnKXYHiIiIiIiIiIiIiIiIiIio9EXmdAzqpZFoEXtCRERERNT7MfxJREREREREREREREREREQ58z39TMcNVv4kIiIiIupWDH8SEREREREREREREREREVFeaThc7C4QEREREfVqDH8SEREREREREREREREREVFO1Fnp0zSL0xEiIiIiorUEw59ERERERERERERERERERJQTs7ExcYJqcTpCRERERLSWYPiTiIiIiIiIiIiIiIiIiIhyYtTWJk5g+JOIiIiIqFsx/ElERERERERERERERERERDkJTZqccJvRTyIiIiKi7sXwJxERERERERERERERERER5cSsr0+cwMqfRERERETdiuFPIiIiIiIiIiIiIiIiIiLKiYZCxe4CEREREdFaheFPIiIiIiIiIiIiIiIiIiLKiYbCjgms/ElERERE1J0Y/iQiIiIiIiIiIiIiIiIiotyEgom3Gf4kIiIiIupWDH8SUa8hIluIiIrIi8XuixsRGSciM0Wk089eEdktdl8uLETfiIiIiIiIiIiIiIiIcqFhVv4kIiIiIiqkvIU/RWQ7EfmXiMwSkRYRCYvIahH5WEQuFJE+jvYqImm/8YvI0li7LbKc3v5nikiziEwSkStEpDzD+zIqtvyoNG1GuoXMUk2PzdtERB4WkTki4heRgIgsF5HxInK3iGwda/ei43509jfOsR0RkT+IyAex5yAsIg0iMlFErhORfp3cbxWRyzu5339Ps2xG/eyK2HO8NNf1EBWaiJwC4AAAd6iq2Vl7VZ0K4H0AfxeRAd3cPSIiIiIiIiIiIiIiopxoKJQ8jQFQogRqmmh78y34XnwJZltbsbtDREREJS6jMGRnROR2AHfACpN+B+AlAD4AGwI4EMBzAC4DsHs+tpeBRwE0AygDsCWAkwHsA+CQ2PWCE5EdAIwHMBTATFiPUQuAzQDsAOBmAEsALIIV+FrqWMWBsIJj4wGMc8yLtxWRIQDeBHBYbP1jYvOHAjgCwIMArhKRY1V1dpoujxKRV1W1NfN7CaToX1I/idYmIiIA/g5gPoD3slj0nwC+B3A1gH90Q9eIiIiIiIiIiIiIiIjywi38SUSJzOZmGCtWAACiCxagcpdditshIiIiKmk5hz9F5GYAdwJYAeBUVf3epc2xAK7PdVtZeERVl9q2vz2AHwGcJCIHqOr4AvYl3idYAcxRqnqnc6aIbAWgEgBU9X1YAVD7/FGwwp/jVHWU2wZiQ0m/BeBQAJ8B+KOqNtjmlwO4C8BNAMaKyK6qWuOyqoUAfgXgBgC3ZH4XgXT9I1qLHQpgWwC3aBanuKrqDyIyD8AlInKvqhrd1kMiIiIiIiIiIiIiIqIcaCjsMlEBSMH7QtRjmbYBAk1WxiUiIqLc5DTse2zY9VEAIgCOdgt+AoCqfgTgyFy2lYtYhctxsZt7Fqkb+8YuH3WbqaqLVXVejts4C1bIbDGAk+3Bz9g2oqp6M4A3AGwCqxKhm38BWA3gWhHZNMc+5YWIHCgiCmBzAJs7hpN/0dZORWSciGwkIs+JyCoRMURkZGz+CBG5R0SmiEidiIREZJmIPON2X9u3GxvWfhcR+VhEmkXELyLjRWRfl2UGishtIjJLRFpFxCsii0TkDRHZLdZmn9h6301zn+fG+jfUMf0IERkjIvWx+YtE5P5Y1VfnOpbG/gaJyEOx65FYmDijvjrWt5eIvC0i1SISFpEVIvK0iGyS6n6kuX+ni8iXItIoIsFY314Tkd0d7apE5EYRmRF73FtFZIKInJbl9jYWkX/HthOOPf/vprifI2PPz0gROSj2mvLGtv2xiPw6y7t7YezyjSyXA4DXYVUIPrQLyxIRERERERERERERERWGW+VPDvtOlMjoqPWi9iAoERERURfkFP4EcD6ACgDvqOqsdA1Vtdh1/ttPKSvWL4z2IOaIbtzGRbHLB1TVn6bdXbHLc0Skj8t8P4DbAPQFcHce+5cgFqjU9iBiJ5bCqjDbEvu70/b3vqPtUADfAdgbwLsAHgfQXuH0ZACXwqpU+xqsoOscAP8H4EcRGZZi+7sDmASgD4DnAHwEYH8AX4rItrb7JAA+hfUYt8baPgngBwC/B7APAKjqZAC/ADhWRNZ1bkxE9gSwHYDRqtpom357bP17AfgYwGOwKrX+GcC3IjLIpe+VAL4CcCKAsbACyEsy7att2+cD+BbAUQC+hlXNdkrssZsiIpuleOyc901igd3XAewE6zl6GMAEAL8DcKytbSWsKrb/hPVZ828Ar8B6H70hIhkNhS4iW8b6ejmARQAejK33GACTxKpO7OZYWI9ZK4CnYn08GsB4EVkv0/sL4GAA1aq6KJNlHL6NXR7WhWWJiIiIiIiIiIiIiIgKwnXYd4Y/iRLZA5/K8CcRERHlJtdh3/ePXX7Z1RV0Evwb0tX1OraxPawh0wHAtTppCgem6d8uWXbjDQDXA/hQRJ6EFZ6brqqtWa7HlVhDuu8du/lFuraqOkdEVsOq/rk7gIkuzV4E8CcAZ4vIw6o6PcOupHvMPlXV7zJcTwJVXQpglMQqeHYytPyOsAKCF6hq1DHvFQAPO8PIInI4gE8A3ArgMpd1HgPgfFV90bbMJbACgX+CFSoEgB1gVXl9X1VPcmzDA2CwbdJLAP4B4ExYAVW782xt2pc/CFbYdTKsSrvNtnkjAfwnNv9ax7o2hhVwPUBV22zL7JhpX0VkBICnYYVwD1DVVbZ5BwP4HFaoNGE9KVwUu38/AjhMVVts6yoDsIGt7fWw3rufADi+/fkUkTthhVRvEpGPVHVSJ9t8Ctbr/VZVjQeaReQJAN8AeElENldVn2O5EwEcoapf2pb5J4AbAVwA4L4M7u+2ANaHFRjuih9jl7/v4vJERERERERERERERETdToNBl4kMfxLZqX2od1b+JCIiohzlGv7cOHa5Mod13JFjH9xcIyLNAMoAbAmr2mNfAO+p6oQs1nMAOkKjuboFwCBY1VJHxf5URObDqr74mKouzmH9Q2FVeASsqpadWQErDOc6XLeqmiLy11jfHkDmQ06ne8yaYVXkbPc4rOqP9RmuO1NhAH92CX7CHlp0TB8rIrMBHJFind/ag58xL8C6D3u6tA+4bMME0GSb9AqAv8MKQsbDn7Fql2cAqIUVemx3dezyInvwM7buF0XkTwD+iOTwJwBcbw9+dqGvl8GqvPkn52Ooql+JyIcAjhORgarqTbGddlfFLi+xBz9j6zIArLFNugBWtd7r7M+nqtaKyN9gVSv9P1hVWV2JyKYADgewHI6wpqpOEpHXAJwN63PiZcfir9uDnzHPwAp/uj3vbtoroq5J2yoFVW0RkaBtPURERERERERERERERD2Oa/iTKEsaCED69i12N7qPadiuM/xJREREuck1/JnzUOqqKqnmichSAJt3YbV/al89AB+AGQBehVX9Lxt3pqowaau0mJFYpcmLReQ2AEfCGrZ7V1iVN/8Um3eaqna1OmDKx7GT9imfO1X9TETGAjhcRI5W1TEZrDflY+ay/nrkP/gJAEtVtdZtRmwI7j8CGAlgZwDrwAoJtwunWOcU5wRVjYhITWwd7eYAmA7gTBHZHMAHsCqrTlHVsGP5lSLyJYDDROQ3qjonNus4WGHehx0B1n0ARACcKiKnuvSxEsD6IrKuqjbYpgdhvQecMu4rOoaAP0BE9nBZ1wawHscRAKa6zAcAiEh/WNVRa1R1Wqp2sbYDAfwKwCpVnefS5KvY5W/Trcc2f4KqRlKs5+xYO2f4M+l5R0e4eh2XeW7WjV02pW2VXiOADXNYnoiIiIiIiIiIiIiIqFux8iflKjJ/PmoPOgT9R56HIXf/vdjd6R72wKfJ9wcRERHlJtfw52oA2wHYNA99yactY8OE9ziqWgNrKO+XAEBEhgK4F1b1whdEZFOX4F0mGmAFFysBDAewoJP27c9ZZ9UI/wwrIHifiHzWhX4VQ3WaeQ8BuAbW/f4MwCp0VL4cidRh4+YU06OwhUdV1YgNg347gFNgPbcA4BWRlwDc5Bha/EUAh8Gq/nlDbFrSkO8x68J6z3ZWLXcArNdDu1rV5F/WWfa1PcD4lwy2nc6Q2KVrBVaH9mHnU71G26cPSTE/H+tpdk5Q1aiVIU4IDafT/vrqk2F7N33hUqGViIiIiIiIiIiIiIiop9CAy6EMhj8pC77nrdpLbS++hEE33wRP//5F7lE3sIU/1TDSNCQiIiLqnCfH5SfGLg/JtSNrK1VtBHAJrCGp14dVFbEr64kC+D52M+0Q7SLya1jDvYeQpkpjbL0zYYUQt4c1BHcpcP0VKSIbwBo6fRaAbVX1bFW9QVVHxaqVhvKycdUmVb1WVYcD2AZWsHcegCsBPOlo/h6AVgBni0iZiKwP4CgAP6vqz462LQCaVFU6+Vvm7FIe+to+PPvgTrY9vpOHpzl2OayTdvZtbpRi/saOdt29nq5qr0K7btpWKYiIB1Yw1bWaLRERERERERERERERUU/AYd8pZ5GOGkk1vz+giB3pPmrYKn8qh30nIiKi3OQa/vwPrGGo/yAiv0nXUESqctxWr6WqJoC22M1sh2+3ey52eZ2I9E3T7tbY5Suqmkk1wVsB+AHcBaDYp1cZyLziotNWsF7zY1XVa58hIpvG5ueVqi5U1ecBHADAB+AEx/wAgDdhhXEPhTUkfTmSq34CwHcA1hGR7fPdzwz6+l3s8nc5bqMNVvh2QxFJO1x77DlaBGCYiGzj0uSg2OVPnWy2fXj5/UXErdpxpuvpqtmwXrfbdXH5bWF9LkzPV4eIiIiIiIiIiIiIiIjySSMRwK2KISt/UhY03BH+NKtritiTbmQPfJoMfxIREVFucgp/xoZWHwVrqPGPRWR3t3YiciSAT3LZVqkTkTtEZIsU806BFQxrghWM66r/AvgawK8AvC0i6zi2UyYidwE4C9ZQ17dlslJVXQ3gQViVE6/JoX8JRGQ9EdlORNbLYrEGAOt3Em5NZWnscn8RiQdIRWQAgGdhhS5zIiJbpghnrgOgCu5Dd78Yuzw39heF9Vw6PRy7fFZENnHZdn8R2bub+vo4rKD3wyIywmVdlSKSaTD0sdjl0yIy2D5DRDwisrFt0guwgo/3O56z9dDx+n0h3cZUdSWAzwFsAcfrV0T2gvV+aIJVhTXvVLUFVnBzJ7fXrYhUxN4HW6dYRftz+nV39I+IiIiIiIiIiIiIiChXKat+MvxJWdBQuPNGpc4e+DT5/iAiIqLc5Bx2U9V/xKrp3QHgRxGZBGAKrMqBGwL4PazhpKfkuq0Sdy2AUSIyDdZjUQdgMIBdAewDK/B3qap2eehxVTVE5A8A3gZwNIDFIvIxgGUAhgI4AsCWsEKQx6lqdRarvw/AxbCCpekcKCKjUsxrVtVHbLevhPW6uRNWiDgTXwLYA8CnIvINrKHaf1bV0Z0tqKrVIvI6gDMATBeRsbCeg8MABGEF9HbJsB+p7AzgPRGZCivIuxrA+rCqaFYAuNelX9+KyEIAp8bajFbVpCG+VfVLEbkRwD8BLBCRMQCWABgAYHNYFTsnAjgy331V1XkicgGsoOVsEfkUwPxYu81gVQStQ2bVLZ8DsD+soOsCEfkgtuwmAA6ObWNUrO0DAI6K9enn2H3uB+ux2gDAfao6MYNtXgrgW1gh0sNhvQeHx9ZjAjjfWQ02z94BsBus+/exY94wAHNhvU+3cFn2cFiVQz/oxv4RERERERERERERERF1WarwJ6NtlJXwWhD+tA37rqz8SURERDnKOfwJAKp6l4i8BeByWEMonw+gD6wqjdNhhchezce2StixsEJsB8AK520IK/C5ElYY7jFVnZnrRlS1SUQOhRVqOwfAIQDWhRXGnQvg3wCeVFV/luv1icgdAJ7qpOkBsT83ywA8ks12XfwdwBAAxwHYD9YQ8C8B6DT8GXMhgMUATgdwBazQ4YcAbocV0MvVFFjhzPbneZ3YNqbCeo5TVcB9CcDfbNddqeq9IvItgKthBShPANACYBWAZwD8r7v6qqqvisjPAK6H9T4/HEAbrNDo2wDeyGSjqqoAzouFby8GcBqsSqNrAEyA9Xy0tw2LyGEAroNVofMqWO+bnwFco6qvZbjNxbHKxLfCCkYfCKAVwKcA7lbVHzNZTw6ehxVoPRfJ4c+UYpVRTwTwkaqu6JaeERERERERERERERER5UgDboPfgZU/KSsaTqyTpJEIpKKiSL3pHqEfbYclleFPIiIiyk1ewp8AoKpzYQWzMm0vGbTZIh/Ts6Wqo9BJJUpVfREdw3VnMn0irKqM3dYnW1sF8GbsL2/bUNWnATyda/9yXKYNwGWxP7f5aV9XsdDrLbE/pwNd2o+DNex4qvVt4bi9EsDN6fqQYj1/hxVszaRtxq+ldO+JrvQ1FlAemc0yadb1X7gPb+9sFwTwj9hfZ22XIsXzpaqrkOJ149L2Rbi8j23zO/38crSvFZEXYYVeN7JX3U3XZ1hh0T4AHsxme04isi6AkwAcA2BHWNVGwwBmAvgPgP+odvy6FJEtYFWVTeUNVT0jxbbOgxWs/g2siqXTADygqh+laN8XwI2wKvJuDiuUOw7AHbH/K0RERERERERERETUy6gqzPp6eNZbDyJZ7XKnHkhNE9Gly1LMZPiTMqeRaMJt0+tD2dB1itSb7tFy8y2wBg0FYBhF7QsRERGVvryFP4mIKK3bAZwJK3jcaVA+Foq8CcA7qjohx22fCuBJWNVVvwawHFb14ZNhVR4+SkROjQXH7X4G8L7L+mal6PMDsCrDrgTwLIBKWKHO0SJylao+7mhfBeBzWFV8pwB4FMDwWH+PEZGDVfX7rO8tEREREREREREREfVogdEfoemyy1G2ySYYfPtt6HvcscXuEuWg6eo/IfDe++4zGf6kXER6+TDwJt8fRERElBuGP4mICkBVa0TkbADbi4jHXmkzhS0APIM0FUizMB/A8QA+dlT4vBnADwD+ACsI+o5juemxCr2dEpF9YQU/FwHYQ1WbYtPvBzAVwAMi8lGs0mm762AFP98GcHp730TkDVih0xdEZMcMHisiIiIiIiIiIiIiKiFN11pV74zVq9F46WUYxvBnSUsZ/CTKkUajnTcqZSYPgREREVFuPMXuABHR2kJVP1TVf2YSZlTVuao6yhGW7Op2v1LV0c7txoaffyp288AcN3Np7PLu9uBnbBtLAfwbQBWA89unizWOT/syf7X3TVU/ADAB1tDxB+TYLyIiIiIiIiIiIiLqYaQssT6N6fcXqSfU7Vj5k3IR7t2VP5XhTyIiIsoRw59ERGu3SOzS7dTJTUTkEhG5OXa5U5r1HBy7/NRl3ieONgCwNYDNAMxX1SUZLkNEREREREREREREvYC2tSXcNlavKVJPqNsx+0nZEEm4qZFIioa9BAe/IyIiohxx2HciorWUiJQDODd20y20eVjsz77MOADnqepy27T+AIYB8Kmq2x66BbHLEbZp28Yu56fontsyRERERERERERERNQLqbe12F2gbsP0J2VOHOFPRHrXsO9qGIkTTL4/iIiIKDes/ElEtPa6B8AOAMao6me26X4AfwOwG4B1Yn8HAPga1vDwX8YCn+0Gxy5bUmynffqQHJdxJSJT3f4AbNfZskRERERERERERIUSXbwENYcehsAnn3TemKgXUcNAw/kXoOXvd6dsY/raUs6jns+z7rqpZ3LYd8pGUuXPXjbsu7OSKYd9JyIiohwx/ElEtBYSkasBXA9gHoBz7PNUtVZVb1fVn1S1Ofb3DYDDAXwP4FcA/q8Lm81mD0/7r3vuFSIiIiIi6mXM1la0PvAgzKamYneFiIiooFruugvRufPQ+H8XF7srRAVlLF+O4NjP4XvyKRgNDVCXMKD6vEXoWe/he/ElBL/6umjbTzs0N8OflI2k8Gcvq/zpeK8kVQIlIiIiyhLDn5Q3IqIikvYXnIgsjbXbwjH9LyIyJjbfJyKtIjJTRB4SkU1d1rNFbD1LU2xnTxGpExFDRC6NTVtXRP5PRN4TkYUiEhCRFhGZKCIXikjS+0FEXmy/X2n+vszmcSIqNhG5AsCjAOYAOEhVGzNZTlWjAJ6L3fy9bVZ7lc7BcOdW5bOzZQa5LJOqX7u5/cEKthIRERERUQ/TMupOeB9+BG1vvlnsrhARERWURntXgIUoUxoIxq+b1TVQvz+pjen1FbJLvUpk4SK03HIrGs45F8033gQtcCVBVYW2pancyvAn5SJdsLgEJQWllZU/iYiIKDflxe4AUcwlAHwAxgOoAVAB4LcArgVwoYgcqKrTMlmRiBwJ4G1Yr+9TVfXd2KxTATwJYA2s4auXA9gQwMmwAm1HicipmnjK6fsAlqbY1DkAtgLAMXqoZIjINQAeBjALwCGqWpvlKupil/Fh31W1TURWARgmIhur6hrHMtvELufbpv0SuxyRYjtuyxARERERUS8Q/nEKAEA5tCcREa1lpG/fYneBqChMW9jT9PuBNdVJbdTH8GdX2Svqt73yKqoO+D36HnVUwbavXi/A6oWUL4mFP3vfsO9hx/0xGY7uCQIfj4H//TDg8SB05Aao2nvvYneJiIgoYwx/Uk+xg6oGnRNF5CIAzwC4G8DRna1ERM4G8AIAP4CjY0NVt5sP4HgAH6t2nEYlIjcD+AHAH2AFQd9pn6eq78MKgDq3MwTAXwGEAbzYWb+IegIRuQHAPQCmAzhMVeu7sJr2XzuLHdO/ghWIPhLAfxzzjrK1abcIVgB7hIhsqapLMliGiIiIiIh6AQ0Eit0FIiKiopCqqmJ3gago7JU+NRCA6Qw/AWi57XYMuOD8Qnar14ouW17Q7ZnNzWnnKyt/Ui56+bDvKHClXnLXePEliLZeCwCo/8MVGLZqRZF7RERElDkO+049glvwM6Z9DLhtUsyPE5HrAbwMqzLh7xzBT6jqV6o62h78jE2vBvBU7OaBGXb5HAB9AbzrDNCJyAgRuUdEpsSGng+JyDIRecZtCHvbcoeLyGgRqY0ts0JEPhCRQzPsE1FKInIbrODnVFgVP1MGP0VkLxGpdJl+MKxqvADwqmN2+3voFhFZx7bMFgCuABCCLRQaq7Dbvsx9IuKxLXMCgN/BGpZ+fCb3j4iIiIiISofJ8CcREa2tWBmP1lIa8CdcN1ta3NtFe1fIq2BMx2dLgR/HzsKfHPadsiKJpT97W+VPDTP8SURERPnFyp/U0x0Xu5yRqoGICID7AVwPayjpI1R1WZbbaf+mnekv4otil8+4zDsZwKWwhpafBKs66PYA/g/AcSKyu6qusi8gIncCuB2AD1al0RUANgGwL4CzAXyR6R0hchKR8wDcBcAAMAHA1eL48Qxgqaq+GLt+L4DtRWQcgJWxaTsBODh2/TZVnWRfWFUnichDAK4DMENE3gZQCeB0AEMBXKWqSx3bfAjAsQBOAfC9iHwJYDMAp8Kq3nuBM6xNRERERES9QDDV+Z9ERES9W1K1L6K1hLPyZ6phjs3mZpStt16hutVrOCvrm42NBd2+fdh5opw5w5/OsGSpC4cSbirDn0RERJQjhj8p70RkVJrZQzpZ9v8AbApgAIAdARwKYBmAG1MsUg6r2ufZAL4HcIyqNmTZ33IA58ZufppB+31ifZuvql+7NHkFwMOqGnIsdziATwDcCuAyx/TbASyBVbHUGQxNWS2UKENbxi7LAFyTos14AC/Grr8C4CQAe8Aafr0CQA2sSryPq+oEtxWo6vUiMgPAlQAuBmAC+AnA/ar6kUv7UKyy7Y0AzoJVVbQVVgD6DlWdk82dJCIiIiKi0mCv6BRdsQLA8OJ1hoiIqIB6XYCFKMb/3nso22gjVO2zj+t89QdcrzuZjY0Mf3aBM/xpD9sWglnfyWE5Vv6kbDjDkOFeVvkzFHJM4PuDiIiIcsPwJ3WHO3JY9v8A7GW7/SOAs1R1YYr2w2AFPxthVfx0HyskvXsA7ABgjKp+lkH7i2OXz7rNdIY3bdPHishsAEc4Zl0Vu7zebVlVXemcRpQNVR0FYFQW7Z8H8HwXt/USgJeyaB+A9ZmRy+cGERERERGVEtvBvMCYMQAuKV5fiIiICqmXBViIACCycCGarrwaADBs1QrXNtrW1nHd7wfKylzbNZx9Ljb4/DN4Bg/Of0d7MQ0kVtY32wob/jTq69M3YLiNsmEkhj+1l/3vTLo/hlGcjhAREVGv4Sl2B6j3UVVJ9Qerime6ZfeOtVsPwOGxyVNF5MgUi9QBmAZrWOmXRaQqm76KyNWwhoufB+CcDNoPBnAarKHcX0zRRkTkbBH5QkTqRCQqIioiCqti6DDHInsDUGRQdZSIiIiIiIio5NkO/qqvLU1DIqJkappovOpqNF56GZQHy6nEaCjYeSOiEmG2tKD+7HPgfejhztvah32PRKBB9/eCsWoVWu+7P299XFskVf4MFLjyZyfDvvvffgehH34oUG+oVGiKULCaid/vkiplljgNOu4Ph30nIiKiHDH8ST2Sqjao6uewAqABWMHOvi5N/QAOBvAdgOMBfJiiXRIRuQLAowDmADhIVRszWOxsAP0AvKuqqU5lfAjWsNm/AfAZgAcB3Bn7Wwag0tF+CICmWAVEIiIiIiIiot6NlX+IKAfRJUsRePc9BEZ/hOjCVIMFEfVMvS3AQmu31gcfQujrcQh88GF8WtOf/4LoyuTBzOzDkGsolDL8CQCRWbPz29ECabzkMlTvsReMNWsKvm3nZ4u90mpBtt/JMPPq96P+jLMK1Jv88T72L9SdenqvqzzZE0TmzMWaHXZC2/9eS55p9PLwp/P9yvBn0aUKIhMREZUKhj+pR1PVZgCTAawPYPs0bQ4D8A2ssOgYERmQbr0icg2AxwHMghX8rM6wSxfFLp9Osd4NAFwdW++2qnq2qt6gqqNiQ2+7/UJpBrBOpqFVIiIiIiIiIiKitZW9upgWeFhbolypv+P8fwYNqNRF5sxJmuZ/7XW03v9g0vTw1Knx6xoIwPtAcpt42ylT8tPBAtJwGIGPPoKxejWC30wowvad4c/C/n/sLPwJACjBAF/rvfchPGkSQhMmFrsrvU7zTTdDm5vR/Je/IjRlauJM57DvJfjaSct5f/h9oPh622uMiIjWOgx/UiloHyY9mqqBqvoAHAlgLIADAYyNDdGeRERuAPAwgOmwgp+1mXRCRPYCsDOA+ao6LkWzrWC9r8aqqtex/Kax+U7fAZBY/4mIiIiIiIiIiCgFe/jTLHBlM6JcJVTjc1Q2Iyo5UffXcHThgqRp4e++d72eSjGqZ+bCbGmJX48uWVLw7TuHkTabmwu7/UzCn1VV3d+RPEoI6FeU576+YBCBz7+AmcljtRawf4erP+HEhOqXajgOB/eyYF5SJVlW/iw6VvclIqJSx/AnFZ2IbC4ibqFIiMglAPYAsALAzHTriQ2bfjyA0QD2AfCliAx1rO82APcAmArgkDRDt7u5OHb5TJo2S2OX+4tImW27AwA8C8DtF+K/YpcPisgw50y3aURERERERERERGujhMqffoY/qXRoNAqzsdE2gZW+qMSVpTjE6AgyOUM1mVT2NFaXbvjTWLGi4NsOT/0pYZpRn82hr9yoKoLjx3faTsrKOm3Tk5hNzbYbuYfz2l5/HY0jz0fDH8/OeV29gQYSQ7D273e9vfJn0v3hySBF5/YaU4ZyiYiohOR+qhJR7n4L4F0RmQRgPoAaAOsC2BvAjgB8AM5R1U6//apqSET+AOC/AE4FME5EDlXVWhE5D8BdAAwAEwBcLSLOVSxV1RedE0VkEIDTAYQBvJRm+9Ui8jqAMwBMF5GxAAbDGpY+CKva6C6OZcaKyN8A3AZgroi8DyvsuiGA/WFVBh3Z2X0nIiIiIiIiIiLq7dTbMdgOh32nUhJduBAaDHZMYKiASp0nRZjPkWtOCD1nqNCVK3Ph/3A0mi67PH7bWLmqoNuvPfJoGMuXJ0zTlhaYLS3wDHYdIC+vAqM/gja3dN6wxALv9hNM8vF9IzTOCsiGf/gx53X1BhoIJNw2a2tRtu661g1HGLL3hT+DibejKQe+pAJxrfwZjQKVlYXvDBERURcw/Ek9wU+whmH/HYBjAAyFFZRcDOBBAI+qasanSqpqRETOBBAAcC6A8SJyCIAtY03KAFyTYvHxAF50mf5HAP0BvJ5BtdALY30/HcAVAOoAfAjgdgDvpOjz7SLyHYCrARwb21YtgCkAXu5ke0RERERERERERGsF9fk6rnPYdyohzjCbmiaSShMQlZCUlRwdIT+zIfvwZ2Ds5wCAPoccnPWyhWYPfgKFHbLeqK9PCn62C30zAX2PO7bb+xAYPTqzhqUW/gx2BA5Nft/Iv2hiwNOoqUHFr38NAFCze8Ofwa++RuSXXzDg0kvgUiSo29lfWwCASKTgfSAHl/CnRqMQhj+JiKhEMPxJeaOqnX5DVtUtXKYtB3B9lttaCqTeNxarEnpe7K/dqNhf1lT1SQBPZtjWD+CW2J/TgWmWGwNgTFf6R0REREREREREtDawBzBMWxVQop4uqXIcK39SqUsV5nO8to2G7Icg97/6KvyvvgrPBhtg4J+uwoCRI7vQwfxTw4Dv2edQtf9+qNxhB9c2Rl0dVLXTUFlgzCeIzJ+PgZddCqmq6lJ/wj/9lHKeUV3dpXVmLcPgmjpLwvZwdcceF7+el5NNPJ7c15EjNU34nnkWVfvti8oddyxuZ5yfE6ttoWlHMNR+4k8+NJxzLgCgcvfdULXHHnldd0YcYVZW/iw+18qfDOUSEVEJKf43TSIiIiIiIiIiIiKiDNgDAGZTUxF7QpSdpMpxBQx/Go1NCI4bB6MLw28TpaLBoPt0R9U+s76hy9swa2vRcsttiMyb1+V15FPgo4/R+re/o+6Io+LTxDm0ejgMs6k57Xo0GkXjFVfCe/8DaHvl1S71RQ0DxvLUg+YZdXVdWm/W/cg0uFZC2U+ztRVqO8HEOUR5l/SA8Kf/7Xes1++RRxe7K1B1hD9XreqY5wjimc0t3dKH6KJF3bLezjgrmSpDhkXnFv5kKJeIiEpJ8b9pEhERERERERHRWoEHtogoV6bPVvnTMYw2UU+m/uKFP1vuGIXw1J/gf+vtgm2Ter+U4U9bUC6yYAGarro6523VHnJYjwjiGMuWxa9rrPJp2QYbJLUzG9MHXtXniw8zHFmwMGW74LhxaL7pZtdhx+uOOx4td4xK3dfqGqsvTU0pn6u8kAwPNZfQsO/RxYsTbufl8SvC8OJO4e+/L3YXOpiJrwf7Y67hxHBkPr/vqdERTjcbU59EFJk7F8EvvszbdhP64Hw99YDPtrWdhlwqf7pVAyUiIuqhGP4kIiIiIqIu0xLaeU9ERMXXrQeeiWitoL6OSlypKn9GlyyB6fe7ziMqlmIO+x6eOsXqQ56HzqW1V3TVKkRmzXKdZ6962XjhRXnbpllbmEqW6dhPZGqvDOk2JLjZkD78adqqSkZmzYThEkIz1qxBwx/PQdvLr8D72L8Sl29pQeTnGWm3YSxfDrOlBWt22Ak1hxyatm0uPEMGd94IKKnwp7NabX7Cn8U/JK/hHnQinuN/YGT+/I4bjiBePiu9a2tr/LqxInXl3NpDD0fDeSMRWbAA0ZUrYbbkr/pocuXP3hP+DP/8M4Jfj3MfRr0ni7hU/nQ8T0RERD1Z8b9pEhERERFRSWp9+BFU/3Y3RBamrlJBRERkx/AnEeXKXv1JvclBtujy5ag54CDUHXNcAXtF1DnTFjgBEquPdbtoAbdFa4XGkReknKetrfGQZD6HVTY6qabZnbz/fgLVe+6dEHhtD6S5VSU0GxvTrk9bbeHP6T+jZs+9Epf3elG9R8c05/qiCzt/XCNz52LNb3YAABhLl3XSOgeZBgpLKPxpNHRD+NO+vgKG/+2iizteN8XqQzvp3y/hdnTBwnjYLmnYd9uJP7lqffiRjm0uX95p+8AHH6Jmr33QcOH/5a0PSaHCaA8K5eao7uhjEf7pJ4S+/6HYXcmK67Dv3HdBREQlhOFPIiIiIiLKmqrC+8CDMOvqEC6xHXpU+sLTp6Pu+BPR9uZbxe4KEWXJPgxou7aXXylCT4h6PjVNNF5+BXzPPlfsrvQo9pCN2ZYc/ozMnAUYBqLz5+e1ShNRrsza2oTbxpo1Bdu29O1bsG3R2iEyZ07a+WZDAwzHaz5XnVXT7C5qGGj9xz9hrFqF4NjPO/rT2AgNhaAulabNhvThT2eYTQOBhGHto4sWJYQlndswamo677ej0m93hf2cQ3SnVOSwYTacYdu8hMDsAb8iVBQMfPIJItN/jt/2PvRwwfuQwFnt0jBgVFcDSA7iqdeXl5GH2l5/HW3Pv9CxyZWrOl3GGwuLhid/l7fRj3pz5c924R9+KOxJLjlyG/ad4U8iIiolDH8SEREREVHW7EMj2Q9QEBWC77nnEZ46Fc3XXpe3ne9EVBhuB1Cab7rZNRRKtLYL/zQNgQ8+RMuoO4vdlR4lofKnL3moXaOuI2yUNMw2URG1h1ra2UNk3c1ZYY2ou5kNjVYYP48is2bndX2ZCrz7nut07xNPwqhzH4q+s2GqjZrkYKw90On8vHBWDo4uz76SZ3TuvKyXyUR7lddOlVL403HyiAZyD4HZHyfTJTDc3Rr/7+KE215bBcxCU8NwrZjrf+NNNI+6E3C+pgwDyEMQr/n6vySuduVK131KqUKL6tLnrnD+Jg68806v3LcVmjS52F3InMvnGId9JyKiUsLwJxERERERZc1vO/jBwA4Vmv1AWWcH1YioZ0lVPcP05m8ov7WB999PoHr/38PoZEhRKm3i6dh1y8ozFlWF2dTccduXXPnTqO4Iz7hVBiUqFmeYCyjg0O9l5R3b5O83KgCjoR6RhQvzus7WBx+CRiJofeRR1J1yKqJLl+Z1/akEPvnEdXpwzCeo2Wsf13mdhj+XLEma1nzdn7Fq2HDUHHIogl98mTAv9NXXCfc39M2ETnqdrPHKq1LOa77xJtQee1zC54P/gw9Qf9YfO30e3SrmlTp1/DbJx/cwe7VnbWlN07JwivUbzGxudg0Dex99DG0pKt6bLt/5suH2v0+DQdeKwm7VfAEguqrzSqEZ9cXl9WT/fttrlFB40i3oyd9fRERUShj+JCIiIiKiJNFVq9Aw8nwEv/7adb595yh3hlGhmY0drz9j+fIi9oQKScPhXlkNY21jpjjQySBK5kyv1xp6dMkS+N98s9jdoW5k/46V76FzS5W2tQG2oUCNNWvQfPsdCf8fTFsVNm1LrgxKVCz2YHJcphXzcmV/3/DEAcpRJqN/GCtX5X9Y6VAIq7fYCt77H0B48ncIfPpZftefQvjnGVkvYzQ0wvfc81aF+2gUoe++Q8PFlyK6arU13+X/emjiRABAdN4v8L/2etJ833PPd6w/g2HfnaLz5ydNU8NA0/V/RtsrryIybTrCP1vDghtr1qDp8isRGv8NvI89nnKdwa+/RnjSpIz7UCq/58zW/IY/I7PnJFReNVuac1pfttQwAJGk6dE8B7QzZaaomJuOejvCn9FVq7MOgxqN7oFsY/kKGI2NCE+b1rGtFOs2lq9wnZ4tbU0O3Zo1ySeIUOE4qysD3N9NRESlheFPIiIiIiJK4r3/AQQ//wINIy9wnW+vvMjADhWafYjX8NSfitgTKhRj9Rqs+e1uaL7+z8XuCuXIHt624/+SzNWffkb8ulRWFbEn1N3sVYc47KDFLaTQ9vwLMO1D5dbaw58c9p16Bo1GXcMuGi5M+NP+GRJdnFxxkCgbmQQPm//y15QBqnwJfPRRt4cJzbY2mC5VezsTePtttNwxCm0vv4LAe++j/g+nIvjxx6jZcy+YLS1J4cJMtP3nRQQnTLSGzE4RZEunfOutYTQ2oun6PyP4zTcAgND4b+B//Y14m/CPU2A0NiE4YWJ8WnRe8nDxqgpVRcPZ52bXiRL5PqPexCCY+nM7mSQ44ZuE23XHnVDQ73ZmSwvg8l6J/PJLwfqQsN35C7Jepr2ae3TxEtTsuVfK/ZVuVBWBd95xnReaPBkN545E3bHHIzDGqvKbKlgaXb4sy167cwsadiXQ3dOV0m98Z7VfgOFPIiIqLQx/EhERERFREqP9wGQ06nowxVi1Mn6dO8OokMIzZsBY2fH6a7n9DgZi1gLep5+GNjfD/0bpVTk0vd6ch6jrTcwG92pjpXRgqNgitupT0r9fEXtC3c1k+DNB+OefUXfMca7zois6KjGZDfUd1znsO/UQ4ek/uwZvNFqg8GfYHv5cXJBtUu8VeNs9RFVokWnTEfx4TLduw7Vib5aarrk24faa3+yA4KefdmldDWecidWbbQGzCxXBjYYGVO+4M/yvv4GmK68GAIR//DGhTes996L2gAPR9tJL8WmRBQugtirFRnU1avbaB6s33SzrPpTK9xnncOj2qpNdEfrWUR1VNeVIO90hVVg4MmNm3raRTRC7KyO4tD8Hgc+sir/hyZPRcs+98D33fNK2jTVrYNR3fB9se/Y5tN57n+t6W//xT0RiVT9DkycDiA1L79bvlfkZ9t1sbUmaZj/JuVQ5n4dS2g/ifM8D3N9NRESlheFPIiIiIiJKFjXiV+1VpwAgunw5ItN/7pgfLI2d95Seqqbcwd2T1J9yWtK0iEslEuplSuQgoZPZ3Izq3fdE9V77wOhCxaDeyEwx1Kz6Gf7MRFJINhR2b0i9ggZs4U9+34L3X6mHnTVWdRyMt4fMnd9jiYrBbG5G/Qknus8swLDvxpo1MJZ1BG3MpuwrBlL3MRqb0PKPfyK6pDQqsqppwvvkU8XuRlzbG2+g8fIrUL3vfgh9913e1x9dkDxUej4U4/+T2n7vmw0NMBqbEHAJz5qNjQn7fBAOI7p0afxm4OMxCf93s+pDifyucw7LnevJJG4hy0L+PtQU/bcPdZ4Lo6EB1bvtgaY//yXD/mQfdDR9PpiBAPxvvx2f5vvX42i5YxRqDzgIappWu6Ym6zf4zr9FaMpUq93Lr2S0DWP16tg6ml3nR5dlH1p14zrsexcekx7HEZZ0q3DaU5ktLsO+cx8FERGVEIY/iYiIiIgoienr2BHpPDhYs89+CbdZra13aLzkMqzZfkcEx40rdldS0mjU9SBBdEHyELDUu7QfyAGAlrv/UcSeZCcyew7U54M2N6N6tz3gf//9Ynep6FIFTvi/JDPOg8RmjkNQUs+WMGR5iYQlulO6wIhpG+rdHjLnsO/UE0TSfFfVcPeH+Jv+ekPC7VI44Wtt4n3wQfj+/QTqTjw5aV53D2neFWZzi+vwuMUS+uprBD74EMay5Wi8/IqE3w12RkMDGkaeD+/j/85q/b7nXshHN3sk/2uvIbpoUUZtg198CQAwGhsR7ULVxnalUt0wn5U/NRyGaatC2S4yN7sh143Gxi59Jpg+H3xPPe06LzJjZkKwt6v8r70Os6YG/tdez6xPXQg/q88H3xNPIjov+XGLLlqE6ELrf21ockcIPH7iRTSa0TaCn36G4DffIPjJJ67zo4sze7+ko4GAa/i7N5yw5AywagmFP9XrFv4s/eeEiIjWHgx/EhERERFREvuObfvBdNe2DOyUPKO2FsGPPwYAtN7/QJF7k5pR4z60HSsq9n72yhu+J56EZnjwpticB9KarriqOB3pQVIFTnpFpZMCsA9tDbAaSW9nP+GhVCpldStJvSvbqLO+r5qOA+qt/7ynR4anaO2SdnjbSPd/p4n8PCPhtjL82aNEF1phImc4rO6Ek1B72BFQw3BbrGh68uvHrKlFaOK3rvOab7wZwc+/QOs/74GR4ZDpGgohMnt2PrvYo7T9978Zt239+90IT/0J1TvujLbnnu/yNo3ami4vW0imIwhmer1d/j5h1CUHPwEgMuNn1+lu2l79L6p33BmBLE8mVMPAmm1/jcCHo1O2STcvU/awrJFipIeEfnVhP6Lp9SI8ZUrK+ZG51ogwzv1Dgc+/gOH4DZVOw5l/hP/Nt1znGUuX5bwP1GhocJ3elWqoPY3zxICSqvzp0leGP4mIqJQw/ElEREREREnslT/tlSBUFaisTGhrH5KUSpM9oOYZNKh4HelEdL57ZQyGP3s3VUVkVuIwefmoTlIIzqAepan82YMqSPVkxqrVCbfNFAcPqXcwfR0n42gomKbl2sFsTP16D3//AwAkVbYym5oQ+TnzcAVRdzBcKr6100j3V/6s2P43CbeNxiaoYaD+jLPQeMWV3b59Sk/694tfrzvlNGggAKOxCeEpUxCdOxfRJUuL1zkXvueeK3YX0mo48yz4XnwxYZqGQgh+8UX8dvU++6Ltzbfge/ElGI3Wd9PwtGkIfPppvE1k0WK0PvBgSVWuy5aR5RDWDeeNzH2b3fDbXQOBvJ7ooaaZXOkzHIa2tHRtfS4VBQErrJiqUq1T8w03AgC8jzyW1bbbnu+8cm145qys1ukUXbwEPltF3Uy+d3Ul6GgsXw6UV6SeHxuy3fkaa7nt9qy3lU7LXX/LaXmzzv0E+94Q/nSe0Ok2lHpP5T7sO/d3ExFR6WD4k4iIiIiIEqhqQpW94JdfdsxrawMcQxOy8mfpa99JDgChbybA/+57RexNauHp7gcRjDVrCtwTKiRj9RoYyxNDlNkeqCwWYzVfm05GimrSzqEVyZ2zcqr985t6H/sBx7W98md45kxEpk1PPX/qVHgf+xe8DzyYNM9YsRLefz2OhpHnI/TDD93YSyJ36SolaiTS7duXfv0Sboe+/BLVO/8WoQkTEHj/g5TfpSNz5yK8FoSnQ999h8gv2Q2/nFe20Fp48mQEPv0U0YUL4tPs14stPG0a2l56udjd6FTLLbfBqLEqTBqr16Du+BMT92MEQ2i+9jq03HIrvA8/DDUM1B17PBovvAiRhQsR+Owz1P7+APieeLI4d6CHSnUSVzaMNfkLf6oq6k46Gat/NQKrN90M0cVL8rLeyKxZgEvF3WyDq6bfj+CXX6X+nROJwGzOLlDqrEjameCXX3XaJvzTT67hWVVFeObMpO+g4ak/IfDZZ/HbUEmbcgABAABJREFUNYceljA/unRZp9vsSqgu/PPPaV+D7b+LnFWUs6n6mYm2l1+B9/F/w6iv71KY2Wxwr4zaG0bCUEeAMtWoHz2R28moJkfZICKiEsLwJxERERERJVCvN+HASNQ2TGHDOecmt88y/Nly511ovuXWjCscUPdzVpJruurqIvUkvfBP01ynO4OB6agqWh94EF5bZQrqWVQVrffeh9aHHoZRU4PogvlJbaLphk/tQdyGeS2VIeszparQYBC+Z57N6OBXqrBib66qlE9J4c9VDH/2ZonDvnd/dcCezPuvzv9vt957n+v0xksvQ+s99yL4+ReoP+kPHAY+BTUMeJ94EpG5c4vdlV4nbfihAOFPs9Ul0GAL0LQPlWsX+v571B56OOqOPhahyZO7tX/FZNTUoP6U01B78KFo+L+LitIH01FhsOnKq9H0p2s7bl97fY8JBYUmTCx2FzLWMupOhKdNQ/Uee1phvhTaXvgPVm+2Rfx2eOpUNN90cwF6uHZy/hbQSKTLJ7gYq1Yh/MOP8dstd9+dU9/ahVOcbJLt6BPN1/8ZDeeeB+/9ySemtDPrajtdj9r2z2mWYTSzzdd5m+pqmI6h2lvu/gdWb7oZ6o48Gi1/T3xc644/AY0X/F9H2Nbx/KX6TaiGgdb77kdg7Nikz71MRGbNhlmb+vEyVq0CgKT70h1a/3kPqnf+LWoPPTzroc1TjoTRC6pMOr/vmA2pK5/3NG7f1dTfM/73EhERZYLhTyIiIiIiShD46OOE2/az0u071ttFfp4BdamK4MZsaoLvmWfR9uJLCP+YvC5KLTDmE/jf656KnMbKlcnT0uxUL5bInNmu06NLl2b8GgxP/g7ehx9B6z/v6TEHUUtddMUKNN8+Ku2Qplmtb+FCq3rbgw+hetfd0fDHc5Lb9LDhL92oKqKLFydN702VGtUwUHfcCVi99TZoufMu1J30h7TtW/55T8rhEln5MzPOkGw0dpCTeifTZwt/Bkr/gHBnVBXRVauSwpmqitC33+ZtO20v/Cdv6+pNfE89jda7/4HaQw9H7XEnpKxIqcEgIgsWMESbBSPFEK9AYSp/dnaChfOkGg2H0TDygvjt+lNOQ3RZ55XcSo2qonqf/eKVN4OffAqjAKEhO6OxEWGXcK39BCJtbYX/9TcK2a3EvjQ0wPfiS/A++RR8JfT5GfhwNOqOPT7r5cJTpsKs6Xm/hXuL0OTv4tfNtjbUHHQIqvfZD2YXgm9JQdJwZp+nahhoffgRNF55letv2OgC92q7jRdehMii5N93bkITv0Xgw9HW9TTfYaJLOq9Wav/8Va8347BhZM7ctFXTE7Zh+91q+nwJVW/tI8PYg4vhmTNd96mkGv0i+OWX8D76GBrPvzDlY5yOer3xgKeb6OIlML1eBD//Iut1d5XZ1ITQ+G+yXsZNrxj23XHfzLrSCH9qNJpUMRYorWHriYiIGP4kIiIiIqI4DYXQ/Je/JkwzGxqsym7h1BWnWu/+R0brj9qGW2p79X+J2w4G0XzjTWi+/Y4uDZ3UmzVd/2c0XnQxmq68GuFZ7gHIrtJg0LWyRWDMmLxuJ1emzwezusZ1nvr9iC5alNF67ENXuoVeKXv1Z5yJtuefz/hzIB2zqQm1Bx7cabueNPxlKoHRH8FsaEiaHvkluZJpqYrMmIHItI6KvG6VTuPzamvhS1Nx1+BB9oyYjvCsNjczyN6L2SsXqa93P8+mzwf/f/+Hmj33Tvp/YjY2ph02O1stt9+Rt3X1JvZwSuSnnxCePt21XdN116P2wIPh/+//XOdTsuj81N9bClHVt7MqaM4AVfjHKUmB0cjsOXnvV7FFZs9OqlgXmV64Ye41EkHDWWdn1LYQJ06qYcD7+L+xathwrBo2HKGJ36LlnntRvdMuaLnlVrT+/W6YaYLMvYX/f68Vuwu9WnTePGgwCMAaPtxYsgRmTQ28Dz6U9bqcn12ZDjHtf+steB94EIH33kf9yackrzfN/qjGSy7NaBsNF1+SUbtUo5vY+Z58KuG2kUGgTk0TtYcdnlEfAKD+xJPj4fekYKZt5Ap7+DU6f777icQuJwtEFi1G4/n/z959h7lRXW0Af4/KVu+62xjbNIPpzWB6DRB6h5AQegIhtNB7MZ0k1ND5gBB6xzbdFDc6NsbY4IKN+/a+6tLM+f4Y7a7KSKutWq3f3/Pso9XMnZmrNhrNnHvOX1rv2wXadVXk119R86dTu3297QkvWdKh9obNOQIAML25P9ArMfjTqK7OicFCZnV160CQuOkpAnWJiIj6IgZ/EhERERFRq8DnnydNU78fwc+npz2R7vnvcxmd0Isdpe9/+20Y5W0ZAXxT34X3hRfhfeZZVEzcPSdOEPaG4Ndfx2V6CX37bbetu/nJp1A2bguEFyxImtebFz4z0V5p9/D8n9pdR2TNGjTFlCyLdKBcPNnTYBDGSuviju+dyV3+3AZmzc6oXfiXvlGSVlWhfvvSe8Ev7MtiNlxxJUxPx8vc9UXBb79LmpYqENHzzLNp12Ws5ecxE7HZuFsY5b07YEJNE/5pnzDotBfElrbMpey4qoq6v1+Asi23ziiLXvCLL1G+9bZouOZaAFaQQ0ugWWD6dFTssFO397EvZjjPOpG4u83/eSSpiarCP2UqAKDhppsR6aMDadQ0U34/9zbT47HNBN46vxcCC9oN/kwYgNF4621Jbfpb5k9VRcOVVydND82Z0yvb902ejLJNNrP9HWbH/+57qDr0cARmzuzQdlQ1ZYUEVYVRUwOjrg7hxYvheexxNN19T+v8ugsvgufh5P0AUZeoIrLWOi8UG2ToeeLJDq/KWBufBTLT4GTfK23nVyLLlyf9hk0X/BlZtCij36Kpqh0kCv+cfnCvmib8774XNy0wbVq76+3MQNeWbJ+Jwf7q8aDx9jsQmD4dNcce1zo9smx50msAAOGlS6GhENQ04XnuOQQ+n47GW3pn4E2mmU67U2Tlyg61T/XbrT+UGI+sSXjfhUI9Eujb3SLr7KuzMPiTiIhyCYM/iYiIqM/wvfMOqmNGGvcGo6IClQf/Hs1pMlF1RuNdd6Nsm21TnjzoKRoOZzzSnchOy8XcRPX/uDR9ZsVQKOWJdt/kySjbdnsEZs2GkZC5MRhTRj44fXrbDNNkxg1YWTkbb78jblrohx+6Zd3hZcvRdNvtKef73ngTwW4MNO2q9kobhxe1fwGm7i/nxt33/u9/DDLuoprYkuzhMCKLFnd6XaoK7wsvZNTWrK7OuNxdTzHq6lF76p9RtsWWCMTuv6JSBXqYtbUo33Lrbj/26C5GeTk0JrtLOnYXK1uCgRMFv0ouaRq3XIrygBTPLlOM2cvZsj3/9zTqzj4H5eO3QtVRx3SqTCe1T1Xjgg80h4I/I8uXwz/1XajHg/rzL2j3u7b5yacA04ybVvX7Q1G+486oPe2MHulj0KbM8vpOEz7LwRkzkrINx10ED4VQe/qZvdG1Dqs55U+o2H3PPhGw6Hv9DdtsUi3aC8z0f/ghQgsXdnr7ps/XmmUvZZuqtt9ooXnz7L/f02T37qvCixej8a67EV68OO69G160CGVjNrINvEzcN6QLnsxE7P4vsm4dmu67Hw03T0L9hRd3eF3hhQtRe+ppCMyY0W5b0+dD82OPo2zMRq3ZRTUYRPOjj6HhhhtRd/E/UDZmI1TsuDMqtt8RVQcdgqZ7/hm/jhwI2KHcZKyx9ieJQXOJQftqGCl/l6hpwvfa63HTMnnPmh5PUpC377XX4u6n+j3TItUgv9a+hUKAy9VuXwDASAiW03A47rx4ZPnypO/npjvubHfwRbgTFWOCX3wJ76uvtg7GieV54smkY7LIurUIL1uW1Fabm+F79TX4330XjTfchNrTz0Bw+owO9ydX+N+ZjMCnn2Xc3lhn/9r1hyz/hk0gbC5U3LHLVgu0f4xGRJSJlnM7vAZBPY3Bn0RERNQnqGla5Yy//x4VO+7c7shKNQx4nnkWlQcf0ulsIxoMovaMsxBZtAhNd9+DmtPP6NJJ/RZGZSU8jz4GbWxC5W67QxPKiPWkuvP/jvIJuyL4XXImrp5g+v29+vioZ4XmzYP/Y/sMAmZ9PWpO+kPa5SNLf4WqIjD7CzRcfwOCX1ilI+svvBja0IDaP52KyIoVccuEoyW4TZ8P/g8/ipuXeCK/t6lhwPvqqx0u4dSdvC+8mJTRMjRnbpfXazY1oWr/A9ptV3PCSSk/42ZTEyIrVqQMujSbmxGa134Js/aoaaL6pJNRd9bZaduly76h4TACn09PupAdnD4D/qnvdrmP6yujuhqhhAvkVYcfkfH3QuLF9OD0GQh9/U3G2+9oho2U/QgEEPx+Dpofexx1F16ExjvvQt2FFyG8LHXAu4bDqDnxJARnzQZU0XDt9fHzVRFZnH7f0XT3PdCEYKfIqlUI/TAPjbffgWAvlPdM1HjPP1Gx624o23hTrBs9FnV/+zt877wDI8WFVLsyto233wHfO+/EndRU00SknX2pWVNjXSiltMy65ODPSIoLiJ2lpgnva6+jcv8D0RiTLbmF7403Wv8Pz5sXl52auo9ZXgGNya6amPWpL4ssWdr6f/DLL9H8r3+j5vQzsW70WPgmT45ra1RXIzjbPutzTwYe+SdPgYbDVslZXtiFhsOI/JoQwGGaCM2NH3SUGBATWboU3hdfyvqAjFjhZcsR+uormLW1SaVye1vzfx5G4003p21jNjSkPA8RmDULdX89D9WHHo7Gu+5u93yFUVOD8p0mYN3osag5/UzrdU2TdbRFcNZsVJ98CiJr16L5of/Ytgknvj8QPd5ZsQKN9/wTNWecBaOXS4I3P/EkKn93EELRIE6jrg7B775D3QUXovbcv6HqkEPhefQxVB10CMq32wHVJ/0B60aPRdXBqUshh777HuXb74jqk09B9XEnoGzMRijbaBN4/vc8gGjA2dR3UX38CajYdTc03HAjjEoreDayrgwNN9+CugsuRHjxYtRdfAnKt9sB4UWL0Pz4E6jcbQ803/8AvM8806XHXfvn07Fuo02wbvRYVOyxFxpunoTGW2+D9/U3EFq4EPVXXY2Kibuj6c67AFiBatXHn4CKPfZC0113w/vc/+B/++0u9YGoKyLLf7OON5/9b9z02KzcoQULULHzLijbeFOU7zIRzU88idCPP7bOb/rXv5P2b+r3pw36V9W47LYtGm+5tfW3S/jXX9s9Jx1Zk75iQfCLL+NKpadjVFRAAwHUXfwPVO53AKqPOgYVO+yEwGefW6XbD/id7XKVe+yFir33hf+jj5Lmqd+P4FdfZbT9WOEFC9BwxVWZt5/3Y8oBxU0PPIimf93b4T7kqtozz2p3sCMQ/d60+Q0NWINEc13iOV8gORuoRiJofvKppGPMbAr/av+aaGNjn8kkT0S5y/v8C6jYZSLqz78g212hfk4YYUxERP2RiMydMGHChLlzux4gQ/EOOMC6zSDRQIeEly2PC0QqOORgDH3uv7ZtvS++FDcKuei001B69ZVwDBoEcTpTbkMjEcAwIPn5MJubUfeXcxH88su4NqXXXYuSiy5MXtYwYJSXQwoKIMXFcBQWRk/YLLXuDxgAKSyE5Oej4aab405gDrr/XhSfckqmT0WnBaZPbxuF7XJh6PPPoWD//Tu1ruD3cwAjgrzddoM4HFBVBGd/gcCHH8IxfDgiS5fCbGpC8IsvIW43hr74vNXW6YQGgzC9PjiHDIbp88Eor4CjqBAaCqHu/L8j/PMvGPLM0yg85OCM+mKUlaPqiCORt9OOKPnHJXBvsw0kP79Tj6svUlUYZeVwjd4wu/0wTVQffUzXS307HK2Zm6S4GMPffzflyWoAQF4eHEOGWCc5w+Gk2YPu/TeK//THrvUphqpa+4EMsjB4nnsOjTfcBCkowAY//gC4XPC/+x6abrsdZn09Co8/DoVHHoGCww6DJJTJ7AqjuhqOoUNhVlejYsKutm2GvvQCClp2yB1df00NKnbeJSnDViqlN96Akr+fHzct+P0c1P75tNaglIIjj8SQhx+K+2zW/vVcBD78CMVnnYmSiy6Ec9Sodrelqgh+8SXcW2wO5wYbAADqL7vcypiUASktBVShzc0oPPFE5O0yAZ5HHoVRlj4L84hZM+Eet1lG28g2NU2EfpgH9/gt4CgtzVo/zIYG1P71vKTgTwAouexSlF55RcplVRW+F15E8xNPQD1euLffDurzIfRdx4Id3TvtiKEvvIDw/B+hHq/1fTx4EIzqGiASQf6BB8BRWJi8fdOEUV4BKchH6Id5KQOLnaNGYcB558JsbkbBob9H3nbbWctHImh++BE033tfW+M8NwY/9BBcm24Co6ISjpIBqDnx5HYfw/CpUyBFhXCMHInQV1+j7qKL4/aFA87/GwoOORh5EyfaHuOYzc1wlJS03o+sWoX6K65C6OuvIUVFcO+0E0LRC3+uzTbD8KmTIYMG2e6zWvZ5ts/FmDEoueJyuDbZGHm77modG4RCKNtyayBFwGbB4Ydh8L//BRk0CM0PPhT/fEWdVG0F+b853BpcMPKbr+AaOzbV09UlapoQR/ePgW4ZTGR6vRj2xmu277nuYjY1oXzrbZOmF59zNgbdnlyit7Mab7sdniefar2f+LpUH3cCQjHByYVHH4UhTzzebdsni3/aJ6g7+5y4acM/eA95O+6YpR5lrvnhR5IyyCVyjh0L1yabpAz87FUuFwb87Twc8+7lkPz8bv+tm22mxwMpKrLdBwZmzkR40WI0JWSajzX8ow+Qt/32CHw+HU33P4CwzeCe/H33xbBXXwbQuXMGZmMjYJqQ0tK0v+kzEbcPczgwctYMuDbdtEvrTMX0++GfMgV5O+6I0Jy51sCoJUvgGjsWRnkFAp9+mnb5lu/BWYtGJh2Lml4vqn53cFzGrAF/Ow8Db7b/rlbTRP3Fl8A/eUrrtNIbrgccgqbb74zbXsv3boeIoOCgg2A2NiK8cCGK//oXhH9ZhOBnbdnO3DvvbB1r9MD3baKmf/4Lzf95uPW+a6st2x1401XFZ5yOvF12Qf0/Lk2a5xg8mOVhiVLIdN8z7M3Xkb/nnjDKy1F15NEwKyuT2uTtuivCS5akzIhees3VKLnEPrNuw7XXwfvCi7bzSq68Ao6SEjTeMiltHwHAudFGGPrcswj9+CMKjzoKjuJiaDiM5gcehHP0aPjemWz7OzkV1+abI5KQQdMxYgTMmGDYlMuOH48Rn38KEYFvylTUX5B8Tpt6h3PTTTHk0YeBiIGGSbfCWLcWhYcdhoG3ToLvlVetawsVFfDEVABJ/GyM+mUhgt98A/8HH8FRWoLSq67s8nkf39R30Xzf/XDvsAMG3ng9nCNHdml9qZg+H8q32BJA/ONy77wTBv/7X3BvvTUAoPmJJ1qPSwZc8HcMvOF6+xVmKLx4MSIrVsA5cgPkTdi5w8sbNTWoOvj3rdWsEl+TXDpfSER9T2TFClTus1/r/SFPP4XCww/PYo/WHz0Vy9BLOnWxkcGfRETUL7UEf8755huI293p9RhVVQgv/BlmfT3cO+4I17jNbC+Wq2HAbGy0gg974SR3i8i6MkhhIRyDB1mBAg5HXECTqlplURsa4Bw1CmZjI5wbbGAb9JTpBXG7AyZV7VDgk+n1wli3Do5Bg+AYOhQwDHhffjkp4GDoq6+gYN994qb5P/4Ydef81Xa9ri3Ho+SCC+Aatxmco0fDWLPWygARCsH/8ccIffsdpLgYro02gllfD6PcvrxowZFHoviPp8A5ZjTC839CeNkyBD78qK3ktdsNcbuTyu4gL88a2W0TUDXwjtvh3mpLqD8As7kJZk0tzNpaODfeGO4tx0PceVA14SgqBlxOOAYPtkZ/ezxWINigQdYFK6cLiIRhVFVZJ39ME0Z5OYJffgXPk08l9ankyivg3nZbSEE+pLAIUBNmXR3MmlrA5YRz2HCoaWUPcRQVI7JmDfxTpsZdhJWBA6EJ5f5SkYICq6ScwwHnmDFQjydliZSCww5F/r77wjFkCNybbw6jugrO4SNg1NRYJ1xEYNbWonHSrXHLuXfYHsVnngHJy7cCzVQRXrQIZmMj8nbdBVJUDMeggdb7q6QERm0tzOpqOEoHwqishFFVhbydd4JZU4vI8uXWCaINN4RjxHA4Bg6EWVML9447WBfZIgbgcEAjEZgNDZDoa+wYPsy6gOp0WdnQIhErQ4HDAQSDCM2fD7OuDo7Bg+HadFO4t9oKUlQEs77eKtEmDri3HI/w4sUIRLNd5u02EXm77w7HwIFAOAzXZptBCgpgVFfDP3kKnKM2gHPjjeEcOhQysBSBTz+Da9w45O28E9zbbguEwggvXgzHiOGAYVjZ6FwuuLfdBmZtrfV8FRfBrK0DHAINheAoKUVk5UqEvvsO4V8WwWinrHa2OMeMQeHxx8G99VYoPOIImNU1VsbA5iZISQnU74dRXgH3Fpsjsmw5Al98AceggcjfYw+YzR4YZWUIz5uHwOwvrJPnbjdKr7ka7m23hXqaYaxZCw0EEFqwEIGPPoLk58O58UYIz/sxo/5JQQFc22yDouOPg3u7beHccEOYlVUwmxqhEQOOUiswyz1+PIzyCivwbM1qmPUNyNt9d4hDrGDqmbMQnDM37gJqOgPOOxeO4cOtYLPKKjiHDUNk1SpIYSHc48fDscEGQCQMiEAKC2FUViLw0cfwPPFkh55/x6hRGHz/vdZF7fk/ofHWW20vrObvvTcKTzwe4Z8WIPzLL/GBfG43Si+7FHl77gGjvNwKpnfnoeB3B1qB+arwvfIqPM8/DwSsrJHOjTeCY8iQjF+HrnBtOR6Fhx8Ox/DhcJSUQPLy4Bg2FEZFBcKLFiM070e4Rm8IGTAAzjFjEJ73I8yGBiA/H6E5c5C3806tF8PN6mo4R42Ca4vNAYcT6vdDCgsAAGZtXet8x/BhCC9aDGPdOrjGjYN7iy3g3mF7SFER4HRa3+vR72sNBGCsXg2jvBzeF15E4ONpkNJSDLrtVuTtsTtgmtCIARgRRFatBkwDrs02gwZD1n6/thYwTDg3Gov6yy5H5NdlcO+wA1xjx8AxfDjC835EcM4c5O+9FwoPOQTunXeCUVGJ0Ny5EIcDzg02gFFVhchvv8ExZAgkLw++yZNhVqa+GJW/zz5W3wAYa9bAMXw4xOmEUV6O0E8/9fjF+RZ5e+0F59Ah0FDIOg4aNhyhhQtty5G1u67dJsIxfASCM2dCPZ7u72warvHjUfSHk6E+H8ymJphVVQjMmg1taIB7++3hHL0h3NtsA9/rb6Qtq+YYPhzq88G5wQZwbT4Ozg03BJxOhH6Yh/APmWXeyNtzTwx94X8ITPsko4uLjqFDU2YxSbyw4hw1CsOnfQzHwPTBP6bXi8C0afBPmYq8iRNReOQRcAwcmBQ0pKoIzf0BTXfdhfBPC1B4zNEYdN+93RqwH/jsc9SeYZU9Lj79NJTecnNcAGhk9WoYlVXWMUoXtmv6fPA++1/bbEUAMOjuu1D0pz8CLld0v1PYqe0ZtbWo2GViXBBy3h67Y9Adt8O11VZAIIDyXXeDNjS0zpfiYgx/dwrcW24Z32ePB44BA+y3U1eHwEcfA4aBwhNPgKOoqEP9DC9dCu//nodzzBgU//GUlEHN3U0DAWuQwoSd4RwypHvXrQqowqyvh6OoCPVXXQ3/O5Pj2hQefxwG3nB9RgMqOrz9cBjhX36B7823UHDIIcjfZ29oMAhtagKcTuu3R/R7qeV3o0YiMFavgVlfD+cmG8NYtw6Bzz63DfbOBSdVvw7H4EH45JGf4dx4I+v7etAgyIABKd9fLefWM3n/mdHMQeJ2A04n1OtF4LPPgFAYhSee0BpYrz6ftc3o8+176200P/oozMZGDLzuOhSddGJ8H6K/5VUV6vNBG5vgGLUBwj/+iOZHH2v9vTHgogut49bNNoVzxAg0P/Y4gp9Pb/+JEUH+PvtkFKjr2nprnLTuRThGDMfMmQKjpgaRX3+FUVUN35tvIrL0VzjHjkHRCSdY+wynA80PPoTAJ1aQpGubbTDk4Ydg1tZBCgrg3HRTOAbHf75VFer1wqyqhlFTDfc22wDhMPwffQwpKkLDNdcmBQUVnnQSSq++qnXAXcvxJ1wua+BQMGjtO91uOEpKosfra6xj1vx8SEkpHIMGwli71vrtUVYODQbRcNXV7T9/abR8D75/3ssouehCOIYOhbF6DeB2wfvSK/C9GB+kJKUlGPnll3AOGRw3XQ0Dnv/7v9Zgiva216ngzwwNuPgilFx4AaSoCOr1QgoLgUjEuoUV6KuGCSksgBQURB+Atu1XAgEYtbXWb+y16+B7+22EF/4M12abwr3NNoisXYfAp5/CrKjoscdARN2rI/uevD337FDgpC2XCzJgAPIn7grnxhtDA0EEZ8zokfLT7h22R+GRRyIwc1broLve5hw71jqfl+EAX+o72vtsuLbeCqVXXQnfW+/AWLcWpVddibwJE1IGhGogAP+HHyIwfSbc22wFR1ExGq5rC67siUEaahgIz/8JgU8/bc0enupxOTYYCbMiPqh78H8egnu7beEYMgSOkhKYDQ1wDB5sHd8WFADhMDQchqO4OH67pgn/lCmov+iS1ml5u+6KARddCMfgwXAUF1m3gwfHDVTXSCR6vaIe4WXL0PzwI3HnQhL7XnjcsRj84ANdusaYilFejtDcH5C3x+5wDhvWsWWrqwGnK+mYELCuaWo4DNfo0d3V1X5JTRPBL76ENjdbr8HQodnuEnWRmqb1Xeh09sr5oUxUH39C3DWSvF12wfCpk7PXofUIgz+JiIj6CRGZu31R0YQPBw+FY+hQuDbeGKbPZ43EjV40dwweDDgEMK0T/WZNDZxjRkMGlMBYtcoK6EooWyqFhVbQQ2mpVfYhHG67IAbAMWwY8vfZ22qblweNROAaM8YKCgSsYLBBg6wAhPp6qD8A5/BhgNsNGIaVqQqAuF1wbrhh9AJEAI7iIpher3XxJy8PCIUQmjev7aAxujxME44RI+AcNgxqGq1BJnFcLrjGbQYooKEgHEXFCC9fDgSDcE+YAOfIEVbwnmkCqoisXgMpyIcGQ3BvsTmOnnIpYBiYvMf10EjYCk5bu876QbnZZtZFp3DICnSrqYVRWWllyxwwAI7CQpiNjdaP05YsUU6n1fcUHBuMhLjckOIiGJVVcReaiYiI+h2HwwpG4G91Wg/YXhTKzwNCYSA/D+LOA0TgHDbMugg0dCgkPx+RZcus49VE+flwDBoIcblhejxWm4TjeeeGG8IxZAgiy5fDNW4cpMg6PgUAY/UaOIYOtQaQjBgO9Xqh4UhbqbdQyBpIET1mR34ejIqK1qB1AJCSErg23xzq98FsaGi7uFWQD+eQoZDSEjgGlFgDZsIhRFathhQWIm/77ayA8MpK6zjZ7YZjyGAgYljTMiwd2ZKB2zFkCFybbWYNklm+HOK2jqfd224HbWhAePFimLW1cG05HlI8AOr3Q5uaOratBM7Ro+EYOgRmXT3gdMBYtRqOIUOAPDeco0ZBHE6oacKsro67+J635x4Y8sjDcAwcCO8rr8LzzDOAqXAMGQznyJEwysphNjdZg6Ty3FCPNyk7khQXwzlmNMyqaqhpWq+9AM4hQ+EaNw7Iy4OxahUkP996rGHrd4yjtNT6bWIYVvC9ywXJc1vvu+HDrdfJMIBQGEZtLSJLlliBecXFyNtpJ+s35pAhEKcDMNU6Pet0QlxuaDAAKSmFsWYNNByCo3gAHEMGW+9Nvz/aR4E4rMB4o6rKCpbKoHy2DBoI97jNreezogKu8VtYv7kKCuAYPBiRZcutvg0a2JaRvagYCIdaXx+zsRHq9Vm/JQErS3bC5yWO23peAMA5YkTbMv0o0CDlBXiHwxpgBUAKCqF+n/Ua+3wwPR4gHLYuLBcVWYGEfj8cw4a1vt8gAtPTbA2aULVeq8LC+CyBIlbmwJaBcw6HdRG8tDSprK1zk43hGFBirbOu3srE7nBY5yCiWdnTBd73tJOqX4fk5+HtHf6RVCa+w0SscyDBIJybbGI9Z6FQcgnOmOoDKVdVVGQNejBNq2RvS5B74rJOp/XZjz6XPalTwZhuN5yjN4RjyFCI2wVjXZm1H0v3+e3K9jrD7vVwOq2/2Izh0fNbcLms74+a2rTnh4goN/Xavocox3T6s+FywTliBKRkABxDh0FcTpj1DYj8+qv97+RY+fnI22EHK/mAywkpHgCYhjWwprYOZnMzzMYGIBS2EgcMHmwNhsrPhwaDgNM6Z2U2NQOmCbOhIen3S3d95qW42DrmM02gIB/iiB6jmWaHrhHJgAGQAQMAw0i+VpYgVd+dG21kDQgqKIDk5UFKSyBOF8I//wzACoKVggI4NxhpJTsRgQaCUL8PjtJSaDAIR2kpZMAAqNeL8M+/ILJqVevxaN6ee8A5akMYq1e3JpmBqnWNUgSOEmtQpfV7sRnhRYus3wtDh8K9/faQ/Dw4hw6FUVuLwOfTgUAAjhEj4N52GziKB1jX8aK/VRCOAG4XJD/fCrT1+RBZsxZmbY31mz4aaGtUVUHy8mB6fYARgXP0aIR/+QXq8UJ9Pmvb22xtZe8fMCCa+KIBZk014M6Do7AQUlRoXXttbGz9XSTuPCsxRSAAs74BztEbQps9cIwYAceAYuv91dgIx7BhMBubICIwqqus3/XhMKQlENgwrPW1/AaLHkOaDY1wFBdZv9uj75eWIGJt9sD0eq3BRmvXtr4fpLAQ+fvsHR285IMUFsAxYgTE7YZRXWOtLxCwAonz8oFIGBoIwLXFFjDr6mCsWWNV5okOmDfKy4GIYR3f1jdYg/bDoehvDJ/1OXW7rOuehYUwm6KJV1QhA0rgHDG87fmK/s43m5oBNa3nvqQU+XvvBdPnReS3lTBrqqE+v/W8en1W5cOmJuvc1siRQDgE9fpg1NVFB7y5IANKrHNcAwdCXE7rmnV1DdSwBnjC5YJj8CC4xm0OGBFIXh6cG20UPb8RgWNgKSAOmFVV1jk1ATQcgWujsVCP17ouXFQIx8BB1nkJp9N6r5SUWL+ja2utzyXEutZuGoA4ANOIJl1xWoObgyFIfl7rb2hj3TrreSophaOoEEZdPRwDimGsXYfwsl+hjdb+KG+3iYDDAWPN2rb9STAAKSqGuF1wDBkC5wgrEY11DjIfjgHFkAElMOvrreRBK1fCudFYOEeOhEas50A9XoQWLrAqlhmmlcinqBhSbCVz0GYPNByyPr9V1bbJB/ImToQMKIajeICVPCS6r1W/H2ZNLaS4KPo6WfsaR/EAmH6/9TxEB7xrIACzoTEaMD/Ieg0cDjhHbWi9p2prgIhh9UsVZrMH4nTCMWQwNBwGwhHrfGizB87Ro6ENDYisXQP3+PHI22UXhOb/hMjixdb1+eg5EBiG9ZqFrPNpyMuDFBZYCRJcbqv/9fXWIL9o0h6zsQnidgHuPEBNAGL9bnU4rIRF+QXW4E6/H+rzWvuHvDzr8Tsc1jYK8qMDNkOAEbEeVzTWQA3DivuIhKEer3XeKhxGeMECnOx7D+J2Y+rR91v7PhFr3xKtoNVyXhAOJ8QVTSTgzgNU4SixzpOadXXW/j0a4wERa/8etM5JaEMDNBiEDCiGuPOs8zn5eVYcSCBovV7RZa19iPW/OF3R568Qkh89j+N0WvvfvDz4Xnr56dHr1pyb9gvLBoM/iYioXxKR2gJgyBY9MCJvfbc8bGUbGue+tp2WRERERESp8biSiPoS7pO6B5/HzPX2c8XXhoiygfseInv99bORy48rl/tORERtcnl/Ps7lwjs+X4ezfybXfCUiIuofmvJFurcOHwEABjh6PvsFEREREfV/PK4kor6E+6Tuwecxc739XPG1IaJs4L6HyF5//Wzk8uPK5b4TEVGb9XF/zsyfRERE1CEimAQAqtYtEREREVFn8LiSiPoS7pO6B5/HzPX2c8XXhoiygfseInv99bORy48rl/tORERt1sf9uSPbHSAiIiIiIiIiIiIiIiIiIiIiIiIioswx+JOIiIiIiIiIiIiIiIiIiIiIiIiIKIcw+JOIiIiIiIiIiIiIiIiIiIiIiIiIKIcw+JOIiIiIiIiIiIiIiIiIiIiIiIiIKIcw+JOIiIiIiIiIiIiIiIiIiIiIiIiIKIcw+JOIiIiIiIiIiIiIiIiIiIiIiIiIKIcw+JOIiIiIiIiIiIiIiIiIiIiIiIiIKIcw+JOIiIiIiIiIiIiIiIiIiIiIiIiIKIcw+JOIiIiIiIiIiIiIiIiIiIiIiIiIKIcw+JOIiIiIiIiIiIiIiIiIiIiIiIiIKIcw+JOIiIiIiIiIiIiIiIiIiIiIiIiIKIcw+JOIiIiIiIiIiIiIiIiIiIiIiIiIKIcw+JOIiIiIiIiIiIiIiIiIiIiIiIiIKIcw+JOIiIiIiIiIiIiIiIiIiIiIiIiIKIcw+JOIiIiIiIiIiIiIiIiIiIiIiIiIKIcw+JOIiIiIiIiIiIiIiIiIiIiIiIiIKIcw+JOIiIiIiIiIiIiIiIiIiIiIiIiIKIcw+JOIiIiIiIiIiIiIiIiIiIiIiIiIKIcw+JOIiIiIiIiIiIiIiIiIiIiIiIiIKIcw+JOIiIiIiIiIiIiIiIiIiIiIiIiIKIcw+JOIiIiIiIiIiIiIiIiIiIiIiIiIKIcw+JOIiIiIiIiIiIiIiIiIiIiIiIiIKIcw+JOIiPolEXlJRF7Kdj+IiIiIiIiIiIiIiIiIiIiIiLqbK9sdICIi6iFbTZgwYQKAU7Pdkf7mllva/s1iN4iIiIgox/G4koj6Eu6Tugefx8z19nPF14aIekvktxWo3Hc/AMAtt6xpmcx9D1GM/vq9nMuPK5f7TkREbXJ8fy6dWYiZP4mIiIiIiIiIiIiIiIiIqMtMT3Pr/2oYWewJEREREVH/x+BPIiIiIiIiIiIiIiIiIiLqOmlLWKTNzWkaEhERERFRVzH4k4iIiIiIiIiIiIiIiIiIui4Ubv1X/f4sdoSIiIiIqP9j8CcREREREREREREREREREXWZhkNt/wcCWewJEREREVH/x+BPIiIiIiIiIiIiIiIiIiLqMg21BX9GVq7KYk+IiIiIiPo/Bn8SEREREREREREREREREVHXxZR9D/3wQxY7QkRERETU/zH4k4iIiIiIiIiIiIiIiIiIuiy27DsREREREfUsBn8SEREREREREREREREREVGXaUzmTzh4KZqIiIiIqCfxiJuIiIiIiIiIiIiIiIiIiLou3Bb8KQz+JCIiIiLqUTziJiIiIiIiIiIiIiIiIiKiLtNQW9l3zWI/iIiIiIjWBwz+JCIiIiIiIiIiIiIiIiKiLlO/P+YOwz+JiIiIiHoSgz+JiIiIiIiIiIiIiIiIiKjLQj8tyHYXiIiIiIjWGwz+JCIiIiIiIiIiIiIiIiKiLgt9/13bHWb+JCIiIiLqUQz+JCIiIiIiIiIiIiIiIiKiLlHThLF6TcwEBn8SEREREfUkBn8SEREREREREREREREREVGXaFMTAz6JiIiIiHoRgz+JiIiIiIiIiIiIiIiIiKhLIst/i5/AQFAiIiIioh7F4E8iIiIiIiIiIiIiIiIiIuqShhtvTJqmDAAlIiIiIuoxDP4kIiIiIiIiIiIiIiIiIqIucQwbnu0uEBERERGtVxj8SUREREREREREREREREREXeLaeKPkicz8SURERETUYxj8SUS0HhCRlSKiKf4qUiyzl4h8ICJ1IuITkZ9E5FIRcabZzpki8p2IeESkUURmiMhRadoXisitIrJERAIiUiUir4vI1t3xuImIiIiIiIiIiIgo+zQUYvnv9YCGQjYT+boTEREREfUUV7Y7QEREvaYRwIM20z2JE0TkWABvAQgAeA1AHYCjATwAYG8AJ9sscy+AKwCsBfB/APIA/BHAuyJysao+ktA+H8An0fXNAfAQgLHRdR8pIr9T1W8780CJiIiIiIiIiIiIqG/QQAAV++wH9/gtMOzll7LdHepBGgjaTGTwJxERERFRT2HmT6JuIiKTolkUD8h2X7pCRM6KPo6zOrDMc9FlNum5nmXclz77OojIeBEJichVGbZ/WETqRWRYN3WhQVUn2fzdm7DdUljBmwaAA1T1L6p6FYCdAHwN4CQR+WPCMnvBCvxcDmAHVb1MVS8EsAuswNF7bd4fl8MK/HwTwO6qeo2qngrgJABFAJ4VEX5PEREREREREREREeWwyJo1MMvLEZw5Cxq0CQ6k/iPE15coE6GFCxGcM4cZkYmIiKjLcjqoRkS2igZHLYyWFw6JSJmIvC8ifxGRgoT2KiJpj6BiSiNv0sHpLX+miDSIyFcicqGIZJRdNSZgbVKaNi1Bec9lMj06b0MReUBEfomWbfaLyGoRmSkid4rIuGi75yR1SWi7vxkJ2xEROVFEpkRfg5CI1IrIFyJyuYgUtfO4VUQuaOdx35Fm2Yz6SZRl9wOoBfBohu3vBJAPYFJPdSiFkwAMB/Cqqs5pmaiqAQA3Ru/+PWGZ86O3d6pqfcwyK2E93nwAZ7dMFxGJWeZqVTVjlpkCYDaAbQDs3w2Ph4iIiIiIiIiIiIiyJSa4KbJmbRY7Qj2NZd+pq8zGRtSc+mf4330v213pMabHg8DH0xCcOQuRJUuy3R0iIiLKcTlb9l1EbgZwC6wA1m8A/A9W6eKRAA4A8DSs4KRde6lLDwFoAOAEsCmAEwDsCeCg6P+9TkS2AzATwBAAC2A9R40ANgKwHYDrAayAlalvMoCVCas4AFbg1UwAMxLmtbYVkUEAXgdwSHT9H0TnDwFwKID7AFwsIkep6s9pujxJRF5U1abMHyWQon9J/ewFjwB4FcDqXtxmT3gH1meqPNsd6U+imTGPBHCDqvoyWUZVK6JB3X8TkX+palffW/kichqsfYAXwE8AZqmqkdDud9Hbj2zWMQuAD8BeIpKvqsEMlvkQwE3RNrdEp42L9mOpqq5Iscy+0WWmt/fAiIiIiIiIiIiIiKhvis32qYFAFntCPY2ZXamrmh97HMGZsxCcOQujjz4q293pEXH7RL8/iz0hIiKi/iAngz9F5HoAtwJYA+BkVf3Wps1RsEoQ95YHoxnuWra/LYDvARwvIvur6sxe7Etrn2AFYE5S1VsTZ4rIZgDyAEBVJ8MKAI2dPwlW8OcMVZ1kt4FoSeY3ABwM4GMAf1bV2pj5LgC3AbgOwDQRmaCqlTarWgZgcwDXALgh84cIpOtfb1LVGgA12e5HV6lqI6wgXupeFwIwAbzQweX+ByuQ/Ty0Zd3srA1str9CRM5O2EdtGb1dmrgCVY2IyAoA2wLYDMAiESkGMBqAR1XtgoZ/jd6Oz2QbaZaxJSJzU8zaqr1liYiIiIiIiIiIiKhnaSAmIDCaGVJDIZi1tXCOGpWlXlFP0CAzf1LXmPUNrf9rKATJy8teZ3qKEZOTxeTng4iIiLom58q+R8uuTwIQBnCEXeAnAKjqewAO672eJW3/Z7Rlo9wtS93YK3r7kN1MVf1NVRd3cRunwgr8/A3ACbGBn9FtRFT1egCvAdgQwB3JqwAAPAygDMBlIjKmi33qViKyg4i8IiIrRSQoItUi8oOIPCgi7ph2LWXoD7BZx5+jy/hFpEpEXhCRDUVkhohoQtsDouuZJCK7ishHItIoIvUi8paIjI2220xEXo32xy8i00VkxxSPYZSIPBp9DKHoMm+LyC42bc+Kbv8sm3kHi8hsEfGKSJ2ITBaRTgXYicgQEblTRBaKiC/6GOeLyD3RYMLYtluIyPMisi7a/7Lo/S06uM2Dos9nnYgERGRpdHsDbdrOiD4PLhG5XkR+jb7+a0TknyKS8a9NESmFVUr9K1Vd05E+R/dxKwGcEy2V3ln/hZWJeAMAxQC2B/AkgE0AfJjw3ml5PlIFAbdMH9TJ9p1dhoiIiIiIiIiIiIhyTWyWu7AVHFh/2eWo2HU3hBYsyFavqAfYZv5k8Cd1hBFp/bfq0MOz2JEeZJqt/2rM/0RERESdkXPBnwDOBuAG8JaqLkzXMKYccba0BGpl61dNSyBmu5nzuuDc6O297ZSyvi16e7qIFNjM98EqC10I4M5u7F+cmADNSRm23wHAtwCOhVUK/X5YJe6rAVwAID+DdVwF4EVYQXb/gxWEty2AL5E+sG0igNnR//8PwHcATgDwWTTg8jsAYwA8D+B9WFlaPxGRAQnb3xTAnGh/lwO4D1aW1iMBfBXNktsuETkputyusLK9PglgKICvAWyayToS+vQDgOsBBAA8DuBZAGsBXAZgeEzbidH+nwYrm+69sF6LPwOYIyK7ZrjNvwH4BMDesLLcPgigDla22a9EZFCKRV8GcDGs1+JxAH4AV8N6/JnaD1aW3S86sEysLwGMgvW+6RRVvVVVP1fVSlX1qepCVT0f1nu6EFZQfaY6u2/rSPuMt6Gqu9j9AehqcDsRERERERERERERdZGG2rJBtmSG9E+eAgDwPPZ4VvpEPSQQyHYPKMdppC0rZmRpquJxOS4226cy+JOIiIi6JhfLvu8Tvf2ssytoJ/BvUGfXm7CNbWEF4wFW8GCmDkjTv5062I3XAFwBYKqIPA5gOoAfVbWpg+uxFS3pvkf07qfp2qrqLyJSBiv7566wD4J7DsA/AJwmIg+o6o8ZdiXdc/aRqn6T4XrsnAmgAMBxqjoldoaIDIYVtJqSiGwG4C5Y5eAntGR9FJFrYQUV/jHN4kcAOE1VX4pZ3zMAzgHwFYD7VPXOmHk3wQqy/Qvis70+Aet5vzGh/WMAZgH4n4hsrKqeNI9jAKxgRxPAvqo6J2beAwAuTfM47LwIYGMA16vq3QnbGgbAE/1fYAW3liL5uTgFwKsAXhSRbVRT/zoSkY0B/Ce63t1iM95Gn4e/A/gXrNLqicYB2FZV66LtbwAwH8AZInKdqlZk8Hhb9ltz0rZK7XtYwa77AUgb9N4JT8DaT+wXM60l62ZSRtSo0oR27bW3y/LZ0W0QERERERERERERUQ7SYExAYCi+LLhRnskpdsoVpt+fPJGZP6kjYjJ/9ldxlzSZ+ZOIiIi6KBeDP0dFb9d2YR23dEdHElwqIg0AnLCyIJ4AK5veO6o6O92CCfZHW9BoV90AK4DqbFhZ/SYBUBFZCuAjAP9R1d+6sP4hsLIZAkAmpazXwApC3NBupqqaInJ1tG/3wionn4l0z1kDrCyRLR6BFTBYk+G6WyT9WlXV+gyWOxXW5+zh2HLfqqrRANCTYb1n7HwRG+wY9T9YwZ+NAO5JmPc8rODPnVomiMgYAL8HsBpWcGNs/78SkVdgZdQ8Ibp8KsfCer2fjw38jJoE6z2WKogvTrTU/F4AfgTwz8T5qhr72uwFYCsAXyc+F6r6mohcBCuwch9YgaypnAbrvXpfbOBn1A3R+aeLyMU2GYOvaQn8jG7XKyIvAbgZViDze2m222Kj6G15Bm3ttJz92ihtq86pit4Wx0xbAuuxjQcwN7ZxNOh7UwARAL8Brc/JOgCjRWSUqiY+zi2it7FDNJdEb1NlJrZbhoiIiIiIiIiIiIhyTGwpcE0I/jRraxObUy6zy/zJ4E/qiEh88KeqwsoV048YbdlN47KAEhEREXVCLpZ973IpdVWVVH8AVnVytf+AFVR6A4BjAPwEq1T0Hzq4nlvT9O3sjqxIVYOqeh6s0uBnwSpZ/R2AzaP9XZhpye8UOnqk3e5rp6ofA5gG4CAROSLD9aZ8zlT1wYT116jq4oQAw3ReA2AAmCwiz4vIGSIyLsNlAWDn6G1SplNVXYX0QbN2WSLLorc/qqqRMG9d9HaMzfZnq2rYZn2fJ7RLZUL0dmbiDFVthBXImamWbLEfp8vWmbDdz1PM72j/k9YTDeKdByvD61Y2y9q9Di2v2+B2tttiaPQ2k4BhOy3Bp8M6uXw6e0ZvYwPBW56nw2za7wegCMBXCYGy6ZY5PKENACyHFZQ8XkQ2zXAZIiIiIiIiIiIiIsox8cGf8fkXIr/9BqOqKnERylF2mT8Z2kYdoUb85U/1erPUkx5kMvMnERERdZ9cDP5sCX4bk7ZV79s0GmzoUNVSVd1DVR9R1aznplfVSlX9n6peoKp7ABgB4GlYmUmfFZG89GtIqRZAyxDNsRm0b3nN2st+eCWs8uL/EpFUWTF7hap+B2BfWAFoJ8HKvLlMRBaLyJ8yWEVLNszKFPNTTQfsy11HUs2Lea+5bbaf6jlvmT4oTT9i15Oqvx2py9KyrXXpGiVst7v63+H1qGqDTfuW5zrT92fL2Y6CDNsnKkxYT4eIyLYiMsRm+sawsuECwIsxs96ElR33jyKya0z7AgB3RO8+nrC6J6K3N4jI4JhlNgFwIYAggP+2TFdVjVnmXyLiiFnmWFifu19gE3BMRERERERERERERH2HUVdnX+47SgMxwZ/BUNL8ip13gTIAql/QQKcuYxC1Scz8aZdNNtfFZPvkvo+IiIi6KheDP1syKB6U1V7ksGgJ67/Byro3HMB2nVxPBMC30btpS7SLyNawyr0HkVBG2ma9C2AFWW4Lq8R5Vqnq16p6FKwsj3sDuB3ASAAvi0h7pemborcjU8xPNb27tASJbpBi/qiEdu2tJ1V/U63fTkP0dnQGbbu7/11dT2e1DFsemrZVai3LdXb488kAykTkQxF5TET+KSJvAlgMKxPwBwDubWmsqk0AzoUV3DpDRJ4WkX/ByvC6J6zg0NdiN6CqXwG4H8A4AD+JyAMi8iiszKlDAFypqisT+nU/gK9gBVZ/KyL3iMjL0fX7AJyTQXZYIiIiIiIiIiIiIsoSs6EBFdvviKqDD0nZJi54KxSC2pQBj6xY2QO9o96kpgkEgjYzmPuTMqeRhMKHYbvChrlNzdiy74mFHomIiIg6JheDP/8LIAzgRBHZJl1DEcnvnS7lnmhAVUue/I6Wb4/1dPT2chEpTNPuxujtC6qaybC/G2EFf90GoLgL/es2qhpU1a9U9WYAl0QnH9vOYvOit/skzohmXcwkY2pXtG5fRFw28w+M3v7Qznpa5u+fOENEBgLYqQN9+iZ6e2hstscUWvp/QIr5LdPb63/K9YjIIFj9DwBY1M56Ouun6K1dWflMtCz3YyeXnw7gHQCbAjgVwOWwXssvAJwJ4ChVjRturaqTo21mATgRwMWw9r2XA/ij2pydU9UrAJwFKxPseQDOAPAzgKNV9RGb9kFYgeO3wcq6ehmAQwBMBjBRVb9NXIaIiIiIiIiIiIiI+obQD/MQmDkLAGCsXJW6YUzwp4ZCtmWc1evp9v5R79KgTeAnwOBP6piEYEiNZL3IZveLK/vOzwcRERF1Tc4Ff0Yzx00CkAfg/diSxLFE5DAAH/Zez/oeEbklWnLZbt5JsALK6gEs7MJmXoIVWLY5gDdjyz1Ht+MUkdtgBZyVA7gpk5WqahmA+2Blary0C/2LIyLDRGQrERmWYft9o8GNiVoyYPraWcXLsEqEXywirYGeIiIA7kbmZcM7RVXXAvgEwCZIeB5FZHdYr0s9rMDAdKZE251q85mbhLay6pn0aS6sbI87Abgmcb6IDI2WFweALwEsgRW8elJCu5MA7AdgKdoyAqfyIqzAxYtFZPOEebcDKAXwYjQYsSfMiN7uYTczg/flHgAMWIGYHaaqM1X1T6q6laoOUlW3qg5X1UNU9Xm7QM7ocl+q6hGqOlhVC1V1e1V9QFVTDkNU1f+p6kRVLVbVElXdX1XfS9Per6q3qOoWqpof7dfJqvpLZx4rEREREREREREREfW8wGefo/roY1B/wYXtto3N/KmRCCJLlia38bV3uYX6MjVNGL+tSDGTwW3UBeH+HvzJAnhERETUNXaZAPs8Vb0rmsXwFgDfi8hXsEoLe2AF5e0HYIvotPXZZQAmicg8WM9FNawgvQmwSjdHAJzflYA3VTVE5ERYZZqPAPCbiLwPYBWsUs+Hwso2uBJW9r+KDqz+X7CyByYG6yU6QEQmpZjXoKoPxty/CNb75lZYQYvtuQLA70VkBoDfYL3HtgVwOKxgyKfSLayqy0XkZgB3AZgvIq/BKi1+CKznZz6AHTLoR1ecDyuI8t8i8ntY74WxsEqBmwDOVtXmdCtQVY+InAer1Pfs6OMoh5XRdDtYQYn7daBPp8EKiLwr+v6ZASsD7RYAfg8rMHmlqqqInAkrgPU1EZkCq1T5lgCOA9AM4Iz2SoOr6koRuRTAowB+EJHXYX0e9of1WVgMm0DU7qKqC0VkCYCDRMRpEzyZ8n0ZDT7eDcBnqtpTZemJiIiIiIiIiIiIiDLmf/fdpGmqCiv3Rbzg99+33YlEYFRVJi/ry6RoHPVV9RddDP+UqdnuBvUHCfsQDYdSNMxhRttlTWXwJxEREXVRTgZ/AoCq3iYibwC4AFbp6rMBFACohVUa+Z+wsv2tz46CFaS4P4DDYAXGRgCshVWu/T+quqCrG1HVehE5GFYw4ekADgIwFFag5CJYAXePq2qHhm1GAw5vAfBEO033h0058qhVAB7syHYTPAYryHN3AHvD+sysjU6/T1XT1DGxqOrdIrIWVrnss2EFLH4M4GoA0wA0daF/7VLV36LZOm+EFaB7QHSbHwG4U1W/T7N47HrejGbUvQXAHwAEYQV97gngWnQg+FNVV4jIBFjPwXGwgh8DsIKE7wNQFdP2WxGZGO3/wQCOBlAD4BUAt6vqkgy3+ZiILANwJawy5kUA1gD4N4C7VLUh0/530uOw3ou/R8eyEp8Ca9/2eA/0iYiIiIiIqNepaSI09wdoeEeI253t7hARERFRJ5ie5DLtoW++Qf6eeyZP//qbtjuRiG15cPUz+DOXpQ38ZOZP6oik4M/+l/mz5rTTAfzZupM+vw0RERFRu3I2+BMAVHURgIs70D55uGFym026Y3pHqeoktJOJUlWfA/BcB6Z/gfbLYXepTzFtFcDr0b9u24aqPgngya72r7PLqOo0WAGaXVq3qr4A4IXYaSJSCmAcrGDl2LYzYGXBtFvPylTzovNTLbcOwN9TLZfQ9jnYvJ+i8z6BlYUz0VnRv4ypai2sbJvtZtyMBnienuF6JyH169CR1/OANPOeQ4rnKI3/wgpgPR8JwZ/tvC/PA7AEQPIwaiIiIiIiohzkfeZZNE66Ff6Rj6HomKOz3R0iIiIi6gT1eJOm1Zz0Bwx96QUUHHBAW7uEwD9NFfzJsu/9F2M/qSsi4Wz3oNuZVVUxdxj8SURERF3jyHYHiPo7ERkuIu6EaS5YGS4LALyTlY5Rr1LVJlhZU4+JZmJtl4gcB2AXAFfalIonIiIiIiLKSd6XXgYARH79Ncs9ISIiIqLu5nn2ufgJiYGekQgQTC7jXH/pZT3XKcoyRn9SR/T/zJ9xTH4+iIiIqGtyOvMnUY44EcBtIvIprDLjQ2CVSB8PK+vnw9nrGvWyJwEMAjAyw/aFAC5T1fd6rEdERERERES9jCU9iYiIiPoBp31+GfXFZwQ1EzJ6psr8CVhZQkXaLeJHuYZl36kjEvcB/TDzZxxm/iQiIqIuYvAnUc/7FsAXsAI+h0anrQBwJ4B/qiqveq0notk77+pA+1d6sDtERERERERZwZKeRERERP2Aw2k/PbHMuzehPHwkAg0lZ/4EAIRCQH5+N3SOiHJVYgB4f8/8qQz+JCIioi5i8CdRD1PVeQBOyHY/iIiIiIiIiPoCZv4kIiIiyn2SIvNnIvV44u9HIvA884x920AAwuDPnOTcaCMYq1fbz2TmT+qIxOS/ke4N/gx89jnCS5ag5IK/d+t6O00Z/ElERERdw+BPIiIiIiIiIiLqNanKfBIRERFRDnFmlvnT9MZnfQ8vXAizssp+Ub8fGDiwW7pHvUuDgTQzGfxJHZCY+bOby77XnnEmACB/jz2QN2Hnbl13JpIyfZr8fBAREVHXZDYsj4iIiIiIiIiIqDuwrB0RERFR7osYGTVTX3zZ9/DSX1O3DaQJIOzjjJoaBGbNgq6ngY4c4EU9pofKvhvV9kHoPS6cEMzK38dERETURQz+JCIiIiIiIiIiIiIiIqKMaShFsF9C8KP64jN/akND6nX6/V3tVtZUH3Msav/0ZwSnz8h2V7IiXeDuehoPS52VmPkzHOqZ7WQYwN7dNKGMvRrZ6QcRERH1Hwz+pD5BRFRE0v78E5GV0XabJEy/SkQ+iM73iEiTiCwQkftFZIzNejaJrmdliu3sJiLVImKIyPnRaUNF5K8i8o6ILBMRv4g0isgXIvIXEUn6LInIcy2PK83fZx15noiIiIiIiIj6E9Prbb8RERFRP2HU1aPh2usQWrgw210h6jJNzF5nI7x0KeovuiTzdeZy5s9VqwEAwW+/zXJPep8aBhBIl/mT0Z/UBd2Y+TM2Q23Wgs0T953KzJ99henxwP/JpwgvW5btrhAREXWIK9sdIOoGfwPgATATQCUAN4CdAVwG4C8icoCqzstkRSJyGIA3YX02TlbVt6OzTgbwOIByANMBrAYwEsAJAJ4GcLiInKzx9TwmA1iZYlOnA9gMwIeZPUQiIiIiIiKi/ifw8TTg38dnuxtElGO8r7wKRCIoPv20bHeFqEOa7r4bvpdfgfeFF4Fz12S7O0RdE0oR/Gm0BTLVnnl2hwKsmu5/AEOf/j9Ifn5Xe9erNKZssxQUZLEn2ZGY3TW5AYM/qQOM+GDITALNMxU7+NBMk4W4JyVm/oTJz0dfEZj2CSIrVqD62Eux4c8cqENERLmDwZ/UH2ynqknDQUXkXABPAbgTwBHtrURETgPwLAAfgCNUdVbM7KUAjgHwvmrbECwRuR7AdwBOhBUI+lbLPFWdDCsANHE7gwBcDSAE4Ln2+kVERERERETUXxnl5dnuAhHlGKOiAg1XXgUAyD9gf7jGjs1yj4gyF1m5KttdIOo2qUoxx2bvNFav7tA6g59Ph+fJp1ByycUw6+tRd8GFyJswAaVXXdmlvva02ABXs74+a/3wTZ4M/+QpGPzYo3AUFfXadtXjaacBg9soc7HB1ACgoe4r+x77XjUbG7ttvR2SGDhvMvNnX9ESEKwNWXpvEBERdRLLvlPOswv8jHo9ertFe+sQkSsAPA+gGsC+CYGfUNXPVfXd2MDP6PQKAE9E7x6QYZdPB1AI4G1VrUnox3gRuUdE5kRLzwdFZJWIPGVXwj5mud+LyLsiUhVdZo2ITBGRgzPsExEREREREVGvy+XSnkSUHWZtXev/2tScxZ4QdZzkubPdBaJukyobX2xmPTg6fhky+NXXAAD/J58iOGs2mh98CM2PPd6pPnY3DYXQcNPNCMz+onVaePFiNFx9Tet9s7IqG10DANRfeDECn3xqZRfuRaY3febPwKefIbxkSS/1hnKeacTf78bMn97n/te2maxl/mTwZ1/k/+TTrAbvExERdQWDP6k/Ozp6+1OqBmK5F8C9sLJ77qWqCzq4nZaj9EjaVm3Ojd4+ZTPvBADnA1gD4BUADwP4BcBfAXwvIqMTFxCRWwF8DCv49GMA9wH4DMDWAFj7iiAiQ0XkryLyjogsExG/iDSKyBci8hcRcSS030RENM3fq2m2daaIfCcinug2ZojIUWnaF4rIrSKyREQC0QDm10Vk6+58DoiIiIiIiIiofzA9bQGfGgpmsSdEHSduBn9S/6EB+32wxgR/SmlJh9fbsp831q1rndZ0510wm7Mf8O97/Q14n/0vav/4p9ZptX89D/7JU1rvG1WV2eha3KAqs64uTcse2LbPm3a+sW4dak4+pZd6Qzkvsex7N2b+9DzZdmk2a8Gf4fjLyYmZTik76s46O9tdICIi6jSWfac+RUQmpZk9qJ1l/wpgDIABALYHcDCAVQCuTbGIC1a2z9MAfAvgSFWt7WB/XQDOiN79KIP2e0b7tlRVp9s0eQHAA6oaTFju9wA+BHAjgL8nTL8ZwApYGUvXJSyXMlsorVdOBvA4gHIA0wGsBjASVrDx0wAOF5GTVZNqr8wHMNlmfQvtNhINpL4CwFoA/wcgD8AfAbwrIher6iMJ7fMBfAJgbwBzADwEYGy0v0eKyO9U9dsOP1oiIiIiIiIi6re0ua1cZ3cGAxD1Cqcz2z0g6hbel16GsWKF7byW4E81TSBi2LZJx1hXBlWFUV4ev95mD1DS8WDS7mRUJWf1THwejKrq3upOKw2FUDYupgie0fHnvSvMDEokm7UduvxG67MeKvueGOyZ0fu2sRHNjz+BopNPhnvcZt3SD4QTHk/SpTkiIiKijmHwJ/U1t3Rh2b8C2D3m/vcATlXVZSnaj4YV+FkH4FBVbf8oP9k9ALYD8IGqfpxB+/Oit/9nNzMxeDNm+jQR+RnAoQmzLo7eXmG3rKquzaBP1P8tBXAMgPdVtfVXs4hcD+A7ACfCCgR9K2G5H1V1UiYbEJG9YAV+LgcwUVXro9P/DWAugHtF5D1VXRmz2OWwAj/fBHBKS99E5DVYQafPisj2sX0mIiIiIiIiovWb2dzU+r8GmfmTcouGuq90LVE2xZY5T6Q+H9Q0YaxaBfV4UrZLxayqgrFyJTQh06fp8yLb4dN22XsdI0bAjAkKNWtqerNLAIDQT/EF7TSSaaG67pGNx0z9l/ZQ2ffas8+J305j+5eFfZOnwPPwI/A8/AhGr1vTLf3QxMfDzJ9ERETURSz7Tn2KqkqqP1hZPNMtu0e03TAAv49Onisih6VYpBrAPABDADwfzUKYMRG5BFaw22IAp2fQfiCAPwAIAXguRRsRkdNE5FMRqRaRSEupbVgZQxPLvu8BQJFB1lFaf6nq56r6bmIQpapWAHgieveALm7m/OjtnS2Bn9FtrATwKIB8AK01E0REYpa5OrZvqjoFwGwA2wDYv4v9IiIiIiIiIqJ+RD1tpWU1yMyflGMYsEzrCfV6EfnNPjNoJprufwD+qe8mrTPrYoK0WgYgOIcNi2uiHg9Mv79Xu4WE/Am9/VwZ1b2f7ZT6sYTMtUnBkp0U+u77uPtmBsGfkd9+65Ztx0o6fmXwJxEREXURgz+p31HVWlX9BFYAqB9WYGehTVMfgN8B+AZWVsSpKdolEZELYZWo/gXAgapal8FipwEoAvC2qqYaBnk/rNLv2wD4GMB9AG6N/q2CVUY71iAA9aray2cSqB9p+dVsNxR4QxH5m4hcH73dIc16fhe9tQtE/jChDQCMA7ARgKWqancW0G4ZIiIiIiIiIlrPaWxADcu+U45htlpaX6jHA6OsrNPL+99+J3mdXl9XutQl4V8Wofk/D8Ooa7sUZLb0Jz/xsk3vlzhXfyD+fkLW1J5m1te336igQ/lXaH1mxpdB766y70mbSRP8Gfh8OpqffAqO0tK2fnRXefaEx6MM/iQiIqIuYtl36rdUtUFEvgZwHIBtAcxJ0eYQAO/DChb9QESOVtWUtUhE5FIADwBYCOAgVa1K1TbBudHbJ1OsdwSAS6Lr3UtVmxPm/8lmsQYAQ0WkkAGg1FEi4gJwRvSuXdDmIdG/2GVmADhTVVfHTCuGlZXWo6rlNuv5NXo7PmbaltHbpSm6Z7cMEREREREREa3nYoM/GUhHuUYDgfYbEfUDZnMzzE6UfE+7zm5eX0dUHfL7pGnq8wJDBscPSogyGxqBMWN6o2tWX5qa4rffi5k/jepqeB5+pN12AumF3lB/oEb3l323y8ZrNjRAVWEVqotXe7p16azg4IPbJgYCQGFGOYTS0nBCMKsy+JOIiIi6hpk/qb9rKZNul9UQABAN9DwMwDRYpa+nRUu0JxGRa2AFfv4IK+NnRoGfIrI7gB1hZTmckaLZZrA+k9NsAj/HROcn+gaARPtP1FH3ANgOwAeq+nHMdB+A2wHsAmBw9G9/ANNhfUY+iwZ8tmj5vKQaJtkyfVAXl7ElInPt/gBs1d6yRERERETUu7otWwoRrbdig+c0xOBPyi0M/qT1hTZ7oN0crBlZsqRb15epVFn5Wkqr2wV/akNDT3aplVFVhcqDD0HzY4/Fb7+59wJlm/59X69ti9YTZveXfVefTebgcLjd/VRkdWselIzKxGfUl6Sy7/yNTERERF3D4E/KaSKysYjYBUVCRP4GYCKANQAWpFtPNGvmMQDeBbAnrOC2IQnruwlWsNxcWBk/U5Vut3Ne9PapNG1WRm/3ERFnzHYHAPg/2GfqfTh6e5+IjE6caTeNCABE5BIAVwBYDOD02HmqWqWqN6vqD6raEP2bBSs77rcANgfw105stiO/YFuGWvJXLxERERFRf9INF+6IaP0Wm7lJAwz+pNwSG3zCMq/Un5meZpjdHIAY/nVZt64vU2ZMqfdYLWXo1WeTUbCbgsTSCS9Zgoqdd0Fk0WKE5//U69tv3Va9/fND1GmJZd+bmlM0zJxdkDYAmLW1aZeLLG0rXtdtn6vEMvaJmU6JiIiIOohl3ynX7QzgbRH5Clb56EoAQwHsAWB7AB4Ap6tqu0fOqhoUkRMBvATgZAAzRORgVa0SkTMB3AbAADAbwCU2ZQBWqupziRNFpBTAKQBCAP6XZvsVIvIqgD8C+FFEpsHKjngIgACsbKM7JSwzTURuB3ATgEUiMhlWsOtIAPvAygx6VnuPndYvInIhgIcA/AIrkDmjszOqGhGRpwHsDmC/6DqAtiydthlzYZ/ls71lSm2WSdWvXeymR7N/TmhveSIiIiIi6j0s0UxEXaX+mMyfzKJIOUQjERixQSamCTiYn4Nyj5omIAKkyehuVFTCP3lyt27X/9ZbCP7hZOTvs3e3rrc9kaW/2k5vKa1ulzkwkyAxz7P/BRyCAWed1eE+aSCAqt8dnHJ+ewFt3SqSsvAeUeckBEMaNdVdXqVt5k8ARk0tXJtsktE6ui3zZ0LZ96Qy99Tr+BoQEVGuY/An5bofYJVh3xfAkQCGwAqU/A3AfQAeUtU1ma5MVcMi8icAfgBnAJgpIgcB2DTaxAng0hSLzwTwnM30PwMoBvBqBtlC/xLt+ykALgRQDWAqgJsBvJWizzeLyDcALgFwVHRbVQDmAHi+ne3RekZELoX1mVkIK/CzqoOraPmV3Vr2XVW9IrIOwGgRGaWq5QnLbBG9XRozraVGz/gU27FbhoiIiIiIchyDP4moq2IzN6W6kE/UF0WWLwdivweZ+ZNylDY1pQ38BICGy6/okW3XnPJHDH9vKvJ23rlH1m/H89//2k5vvPkW5H/ysX3Z93ZKSWs4jMabbgYA5O+9N9xbbJHUJvTTT3AMHAjXxhsnzQv/sijt+s36eqhpQnohwFwZ/EndTBPKvpvVHSnEmGKdqTJ/lidezkqdmdtsaOhyPwBAmfmz72GFEiIiynEcVkp9gqqKqial0kxos0m03cqYaatV9QpV3U1VR6qqW1VLVHVHVb3SLvBTVVdG17NJiu0YqnpmtM3WqlqmqpNa+pjm74AU63s8Ov9PGTwPPlW9QVU3V9UCVR2rqheqaq2qHpDqOVLVD1T1MFUdoqr50eWOV9XP29smrT9E5BpYgZ8/AjiwE4GfgJVVF7CClGO1vNcOs1nm8IQ2ALAcwGoA40Vk0+RFbJchIiIiIqIcp8FQ+42IiNJQf0zZ7GjWNaJckBQ0wuBPylHdFQDVWdVHHYP6a67rte2Ff1pgOz2ydGnKDJtmO8GfsRkEPU8/mzTfqKtD9eFHonKvfeB/973k+e1lQjRNmBWVVtuKinb70yWS4aXm5Gp6RPaM+O/HrmbcNJua0DjpVtt5kXVrkyemGLBo1jd0qR9t60/I/MnAw6zja0BERLmOwZ9EROsBEbkJwD0A5sLK+JlyqKSI7C4ieTbTfwfgsujdFxNmPxG9vUFEBscsswmsLLZBAK1DpFVVY5b5l0jbGSIRORZWNt9fYGXUJSIiIopjVFai6cGHYFRUZLsrRNRRzPxJRF0Um02NwZ+USxIz1abKLEbU10VWrcp2F+B78UUYNhn7upuaJozKypTzG6673n659oI/6+tjGidnUTXKylr/rzv/74j8tiJ++QwyIYbmzUP1iSehYpeJqNh9z3bbd5ajqDCzhu1kiyVqkRiI19VM742334HQd9/bzossWQrPc8+h5s+nwWxutraX4jerdlfZ936c+VP9fhhVVdAc+7wnvSZgKXgiIsotLPtORNTPiciZAG4DYACYDeASSR5lu1JVn4v+/08A24rIDAAtwx53APC76P83qepXsQur6lcicj+AywH8JCJvAsgDcAqAIQAujs3aG3U/gKMAnATgWxH5DMBGAE4G4ANwjqryLDgREREl8T73PzT/52H4XnsdG3z9Zba7Q0QdoCEGfxJR15ietoBP08uy75Q71JdQcpZBBZSjGm+7PdtdAAAEv/gSRSef1KPbMBsa05YDDnw8zX45jwcNk25FeOFCDH36/+AYNChhvQ2t/9uVozZr4oM7va+/joHXXtN6P5PAV+9rryP0zbfWNnoyW6vT2XPrpvVTQiCe+v1QVdhc18pI8OtvUs4LzJwJ3+tvAAD8U99F8Z9PhQYCtm17qux7f8o6WXPGWfB+tDsKjz4awBbZ7k7m7CqUhMPcvxERUc5g8CcRUf/XUlbdCeDSFG1mAngu+v8LAI4HMBFW+XU3gEoArwN4RFVn261AVa8QkZ8AXATgPAAmgB8A/FtVk2rTqGpQRA4GcC2AU2FlFW0CMBnALar6S0ceJBEREa0/Wk7cG6tXZ7knRNRRdllUfG+9jaITT8hCb4j6PtPrhRQUQHjhsVV85s8eLGNL1M2SMpcx8yflIPX7EVm8JNvdAAAYKUqud6fOfs8EZ82GsWYNAMD78isw6+sRmPYJhr3+KpwjR8aVi1d/8kAGoyq+rLtZV4fAjBlwb7stnMOHI7xkabt9MBMylmokAnF1/2VhDfWfwDXqPRqJIPDxNOTtuSecQwbHz0sMhlQFAgGgMLMss6GFCxGcNRuFRx8F19ix1rIpmJVVrf+3ZDXu8eDPxN/E/WgwSOirrwDsDv+770L1sk4H7PY2Ddtk/gyHIQUFWegNERFRx7HsOxFRP6eqk1RV2vk7IKb9M6p6lKpuoqoDVDVfVTdS1VNSBX7GLPs/VZ2oqsWqWqKq+9sFfsa096vqLaq6RXQ7w1X1ZAZ+EhERUTqOmAsD/SlDAqWmqvA8/QyC38/Jdleoi9QmS1/9Jf/IQk9yV/Cbb1B/xZVofuIJlgzu50yPBxU77oya40/Mdlf6lLjgTw/LvlPuMBOCP42qqhQtu58GAgh+/31rSVuizkp8H2eTenv+O6C98u2ptAR+AkDTnXfB89jjiCxbhobrb7DmxwSc2ZW0NhMye/peehm1fz4dFTtNgG/KFBjlZUnLJAr/En+KPVXZ6y6LZPabXJFbZaC7W2jhQlQfezxCP8zLdlf6BM8TT6LuvL+hYvsd4JsyJW6eXQluMyZDbmjuDzAqKmzXa9TWovrQw9F0512o3GMva30pyrgn0pay7ykyy5v19Rmtp93tJPann57XCn33Xba7kDHbsu/hSBZ6QkRE1DkM/iQiIiIiIqKcEnsivrtOvlPfFvx8OhpvmYSa447Pdleoi8ymxmx3IefVnHgyfK++hqbb74T/3ZRj7agfCC9aDPX7EZo7l4G+McyYQByTmT8phyRm9/NPfbfXtt1w8yQEZ82Gf8rUXtsmZUYjEQRmze5TQZXp9EbAZaaa738AgenT0fTAg6jc/8AeyQRqdvMgg8BHH6P5kUdhxgR/B2fOgve11+B55lk0/evfAIDIutTBnfUXXITwvB/b31jCsUPTAw92uL9GXR1U0wdtZjwg01i/j2UarrsBoTlzUH30MdnuSp/QUmodsN7TLeouuRTamPybUaPBn8Hvv0f1Mcei+jj7yhGhuT8kL5sm82eslmPMVMeX3TZoI7Hsez/K/BmruzKl9ga1K/ueYWA7ERFRX8DgTyIiIiIiShJe/huaHnwo7QWo4DffoPbcv8G0OSlL1JNig8fMXij1R9kX/vnnbHeBuonZZJ9xrL9e8OppmWR9otwl7rbSrGZFZZqW6w9Vjc/82Zx8cV5V0fzEEwjOmdubXSNqlzY2JU/rpe+/wMcfAwCMSu5L+hrP00+j9k+nov4fl2a7KxnpaxmXfa++juZ770Nk2TJ4Hn+i29ffE79Dmu6+B5HVq+OmNVx+JRpvvgXND/0HRnk5zOoeyAysHQu+9L70Miq23zHt82p6vQjNyzCTZQ4e7xvV1Qgt7Kb3QAef//4uMVjb9HqhhgH/W2/Ztm/JkBv44ENr+ZjsunHrqa+Lux9ZudI2u67tNqKZPxOz5rb2OUW20Y5aXzJ/5lTAt13mT5tpREREfRWDP4mIiIiIKEnjjTei+d/3ov6CC1O2qTnxZAQ++ACep/6vF3tGBKgvptzXj/Oz2BPqLWYfyjBEXaNNyYEv6aZTPDOh9KgUFmWpJ9QbNKa8Za5khOtp6vfHZTKzy8wU+v57NN1+J2qOPY6B5dSnGLV1yRN7KeBDSkp6ZTvUcf63JwNoC2jq67wvvpjtLsTxv9eWBd3z+BMps1SaPh/q/n4BGu++p0Prb7zhxi71LxX/2++knFd98ikITPuk27epXi80FEJo4cL2s3n6/Wi4+hoAVhBoKrVnnQNtyHBAcA5mMa/cYy9UH3pYUrBuZ/C4PUFCAGRw9mwYaQY7qc8H0+eLOwdpNxjdrG+Iu9903/0Zdynwyafwv/8BGm+4yXa+UV7R7mcnE4lBhf32eDWHPvMaCiZPi/ktRkRE1Ncx+JOIiIiIiJIEv/kWgHXi03b+t9+2/i/5+b3SJ6IWsSdgG668itln1wcxF1gyLdlGfZPZnCLzJ1/XjBiV8VmgpIDfwf2Z+mM+F8w8A7OhATXHHh83zS7zp7FmbdwyRH2FWZecsT7jcsld5Bg0sG2b3RC4Qt1HBhRnuwsZU9OE93/PZ7sbaQVnf2E73f/OZPinvgvPI492X+nmHmKsWNEj61WvD2WbjkP1oYfD9+JLtm1CCxYg+O238E2d2jrNrK62X59pIvTVVx3rQw7tf9QwWn+jRH77rcvrEwcvycdKDHj0PPkUzDSZNdXnQ/ND/4mb5nvjzeR2CeeHAp9+1qF+1Z33t9Qzg0GY9fUdWp+dxBLjmqI6Rq7TcO78ftFQ8vEYgz+JiCiX8EiTiIiIiIiSuMZtlnZ+4+13tt3Jy+vh3hC1CS38Gca6dXHTIsu7fiGG+rbYEr8N112fxZ50jKqi6d774Jv6bra70mekCtZm8GdmkkqABnPnghp1XOwFR7tsNOub5v88nFSGU73epIy4Rnl523xPcnAoUbYkBvADACKRXu9HdwSu9CeR31ZkNRhQ8nJnIEdw1qxsd6FdtX86FY133Jk0PThrduv/4cVL0q4jsq4M1SeeBO9rr3V7/7Ipsnx56/8N114XN883ZQoarr8B1YcdgZoTTorLPKp+f9KxulFXj5qT/9DxTuRQeWszZp+t4W7YVzv77yV5DQRQe85f4Hn6mcwXSvj+C82fDyPxt07sNnx+GKtWxU+zqRBiJlSU6O4KE7Wnntb1lSRkPQ18+mm/zP4ZW7Wnz7Mr+87gTyIiyiH990iTiIiIiIg6zVHUln1EbS5Iujcf1zafJ8OoF1UfeljStNggD+qfzJjMbr7X38iZjDGRJUvQ/MCDqP/7BSkzXq5vNFXmTz+DPzORGDhk8ju4X4sNtEgsD7k+MsrKbKeXb7l13PNjVLVlKDNtMoMSZUNk9WqE589Pmt5bmT9j9yfhH5P7sb4y6upRue9+qJiwa/Y6EZvhvo8f47YXNNlXeB5/Iul7MzR3buv/kaVLk5YxamtbS3vXX3AhQt98i4bLr+zZjmZZ+Ndf0fSvf2Pd6LGov+CiuKyugY8+bmtomoisWdO6TGRdGSq23wGhb75NXGW7NJg7g1n8H37Y+n9iNslO6ceZP31TpiDw8TQ03jIp84USS4IHQwj//It9W1iZP5EQLG9UJpeJV58v8z50QnjBAphd3IbdoK50Je9zReIxTS6dA7H7rcXz3URElEv675EmERERERF1WuwJebOuLm5eeNnyuNJKPBmW+zQYxLrRY7Fu9FiEbC5K9xWaeHEgKl12COof1BN/0aC7s3f0lNjA5FRBS+sbM0VJO2b+zExSCWs+b/1a3DGWTSnC9U26ILnYbGZmbU3bMp7cuehM/ZdRV4/K/Q8AbLJ69VrwZ8wgi/Cvv/bKNnOBsTqaRU4VkYTqAtlQNm4LBGbMyHY3UjLSlGTua4y11uvZ/ORTqD33vLjj8sZbJqHuggtRf/kVMKqqoKaJmpP/gKrfHQyjvByhOXOy1e1eVX/xP5LKaKcSnDkLwW++QdUBv0Plbrt3epu5FPzZePMtrf+nql7QISKt/6Y6t5GrjPK2fUMmjy1VoHswTUCx6fMmHdcZ1dVJ7Xoj26Tvtde7tLxdUKSxdk2X1tkXJGbcTzXwsy+yC8jNqcylRES03mPwJxERERERJYkNNkgsgVd/6WUp21Juii2b7nn62Sz2JD3T5sQ+AJi1dbbTqX9Q1aSgZGNdbgRSxmbvqPrdwQh89nkWe9M3pM78ye+STCRmssmlbCrUcWZMKUtm/kRc0ESiyG8rWv83a2pb/1dPcjlQot4Wnv9j6gDuXgj+NJuaYKxc2Xa/tjZ14/VM0133tP5ftf+BWelD3ACYYBC+d6YAsAZ8GHV1rRkXsyW0YAFqz/kLmh9+BOEf5mW1Lx1Rue9+CC9diqbbbkfggw+T5vunTIXvtdfRePMtCM+fj8iSpVC/H4EcKG3fXcILFmTctvGWSV0OeANyZ8BXj2QwDLYdy+XK85Cp2N9yZlX7g3PNcvtA8tBXX6VcJrJsedJAQrMyeVu9URmh8cabUHvu31B/6WWdythsdw6rP5zXiv3tAuTWb1WzMXmAcU9nkSUiIupODP4kIiIiIqIksSe4IkvjM8NEFi9O2ZZyk1HVFqDWly9CBGbMtJ3OC9j9m1lVlXRRpy9kZspE4oWv2jPOzFJP+g6zscF2el/e9/QlidlUIqtWZ6kn1Bs0LvgzdzJl9RRNU8I9NvOTEZP502TmT+oD0u2reyPzZ2JmP7PG+owYZeW2WdPWF0ZdPYJfftl6X/1+GDU1aZboGYnBMoGPPkJo/nyUb7s9KrbfEZV77IXArNm90hejvNwKaLr6Ghjl5Qh+8SWqDzsCgY+noemef8aVTs8FVQce1G4b/7vvofqoY1rvR3KktH02+F5/o8vrSDWgs69JrICj3q4NJlFVRFa3fReY9Q1dWl9fY9bXt/4fWdv+b/XOZDgOTPsk6bdQaO5cVJ90MgLTp7dO661BhYEPPoDvjTcRWdLxfYZp812TS4GSqSRl/syRii0AoDbZfTlAlYiIcgmDP4mIiIiIKIkZc8Iu8Omnrf+rKjQSiWvb1ZPglH1GZVvwp1nfd7MNhL75xna6Ud37F2mp98ReJGthrFubhZ503PocUJFKqowmiYEPZC/xeTJWrcpST6g3xAV/Bpn500gz2CN2IEjsfoaZP6kvMBsaUs/sheDP0Pyf4u4bNbUwPR5UTNwNVQf/vlNZy/qD8A8/JE1LHPjY0/yffIrIokVx09TjQfURR8VN8732Wo/3JbxoESp23c0KaHrpZTTe/U/UnPLHHt9uX+Of9km2u9CvGRX2GR/7msTfLIlBbR0V+u47GDFZfCt3271fBPsBgP/DD+F76eXW+5mULzc6MZgzsmyZbbba0NffoPa0M1oDUNXfuwPUQz9lnkEXsAIKE4OLgf5xbtVMGKhl5lDwp92xGs9REBFRLmHwJxERERERxdFQKK4sbyQmsCSyaHHSBUqziyfBKTNGWXnca9GdzKq2ALXQ19/A99bbPbKdrgonZJ1tkSuBgP2VUVWF4FdfQ02z29YZmj8foYU/Q4NBBL/4Mmm+sSY3XnNzPQhMNioqEJg+HZUHHQy/TUnNRHYXugD7TBuULDHbtpFBWUXKXWZs4OJ6XvY9tGBBUoBUrOb7H0B40SKoacbtZ5offqQ3ukeUVmxGtESaqhx8N3JtNDbufvCzz1C+7fYArOxjxtrcOK7qbrFZP1tEli/vte0HPp+OurPOzqhtb7xP/B99HH//rbd6fJt9kbFyZba70K8Z68qy3YWMJAapmt6uBRQ2XHVN0jT/5CldWmdnRX5bkVFQmwaDCMya3W4AX91fz4u7n2qwnwYCqP3ruai/6uoeCXwNfv0N/B98iPC8H7t93ekYK1Z0qH0kxT5G+0EwcGIAZbrjn77GLvizPwTkEhHR+oPBn0REREREFCe88Oe4+7En66oO+X1Se23K/ROUfV14+W+omLgbKvc/EEZZebevPzFzZv0l/+j2bXSVqiKy/DfbeZEVKztUMjO8bBnCv/ZuVp/+rP4fl6Lm5D/A++x/u2V9Rm0tqo89HtWHHoayzTZH8733JbWJrG4/m0hfYFeeXg0jCz3pGRoIoOqQQ1F72hmILF6CunPPS9u++YknU5ZOy6ULQ9mUmHXIrKlJyshN/Yd6215vXc+DPz1PPtVum7qL/4HAhx8BMZ8JY+1ahNMEjVIbNU343nwLkfU0ELAnmQ2pBzj0Rtn3xExcAOI+J5Fly5Jmq2lag3uCwZ7sWtZoJALfO5OTpqcKyulunv89j9rTz8i4ffiXn1F/5VUd+g2jqggtWNDuYE3/tE/QePc9CM2Zk/G6iTrL/9FH2e5CRoyy+CDV2GOyjlJVGOXJ53GCc+Z2ep2dVXfJpajcdz+Uj98KjXfcmbJd0/0PoGyzzVH7p1PR9K9/p2xn99s21e+64FdfI/DhR/C9/ArCCxd2vPPtiKxeDc9/n+v29ba73Q4GNKcKgO4PA+sTX3uzJnXW/r6GwZ9ERJTrGPxJREREREStzPp6VB99TMK0BgBImdXPSJHFjbqH+v2oPjJa9i8cRmjBT+kX6Oj6DcO2LJeZIkArW9TrTXniVZuaEJo3L6P1GHX1qNr/QFQfdgQDprqBqiI4azYAwP/e+11fXyCAiom7t1sC1bApBd/XaCSCsM370uxHpeBDP/6YlMkzVelYDYfRdPsdKdeVtiQutTITS1irxpW7pv7FbGzLtJSY9XV9E/q+/aCkyKJFqDvvb0nTjX603+1JvjffQv0/LkXl7numLXusponQ/Pm9ErTYX6Qtb9sLgd3qST9Yz25QjfeFF1Fz8h9QttnmqD7xJJg5uA8ym5vRdO99CP2UUPa+thblO+xke0zWkyWpw4sXo/acv6DuwovQeP0NHVrWWLkKvldeRdUBv0Pw22/bba+qCHzyKaoPOwI1J57cNj0cbj1WC371NZof+g/qzj4HnkceRXDGzI49IKJOCH39TcrfC31JUubPLgx6NquqbI/jAp98kvFzocGgbQBpJozKSmg4DN8778Rl9PU8/gRC8+cn99fnQ/N997fe977ySsp1mzZVCOyCPzUSQdMDD7bet9tuVzXdfgdCX33V7ettj9nB7w2jxv64tF9k/kx47Y0c+p1qX/Y99459iIho/cXgTyIiIiIiauWfNi1pmjY1waiqSpmxxlixolsvqht1dRmVoFpf+N97P+4kcKrsl53hffEllG20CQIfJ7/ugQzKN/em9gLmQhmW9grOnAHACjLsyYu764vYTGzhpUu7XPo9NPcHIIMMU5FVq7q0nd7gefoZ2+mRNbmRtTQT4cWLk6ZpirKAvldeTbuuxAzEZM8ugCcxALfH+6CK0E8/rfeZKHuDWVnZ9n+OZQMy6+s71Gf1+6F+P3xvvJkUZKahUPrguXaEF/6Mun9cBv+0TxiwmEZw1qzW/+vOPgeRFGVMG2++BdVHHAXv8y/0VtdynpHmuMXsQja5TLVXXjcxwx0AeJ9/vvX/0Dff5mRWyKb77kfzAw+i+vAjUXXEkTDq6mCUlaNih52gjSl+23YyuMpOZNUqBD79DIC1T6w66BAEPp7W5VLPNSecBDUMmM3N0EDA+l6e+0Pcb+jGG25E3dnnAADCCxdCAwGEl/+G8m23R+Uee6H+0stQc/If0mb0I+opqT5/fYmRENTYlT6nOlbXxsbWwdbtqbvoElRM3B2Bz6d3aNuBWbNRMWFX1F90CTxP/l/SfP/Ud5OmJX5nSX5ByvUb5cnnVIyY49cWvjfeRPiHH2I61n+ySofmzu3Q4Gkzxe/e/nAe1Ex47bWxMWcyiNtm/mxn8AwRUSYCn36Gsm23z0p2alq/MPiTiIiI1muqCu8LLyK8dGm2u0LUJ4S+/sZ2etXhR6QtyxT46ONu2b5/2ieo2H5H1J52eresL9epYaD5Pw/HTYus7J6gt/DSpWi45tqU8yPLl3fLdrqLUZM+MMyuXGWi0IIFqL/okrZ15kjp8L4sNpOjNjZ2+X2T6fexNjf3iQxUpseDwKef2Qa9hhcssF2m+eFHcyLbTSYivyUHBtld7APaL/FolHesXN76Sm1K95p19qUVe4r/zbdQffiRKNt0HAIzZvTqttc3sUFIuZQNyPR6Ub7dDijfcuukjHt2Ir+tQMXue6Js8/Gov/QyNFwXnxHPqKgAurDfbLrzLvjffBN1Z5+Dxltv6/R6+jsNxQfGNj/xlG07b/SiVePNt/TZAQ196XvWbGiwDa6Mnd/jfWhnkICZcJwdWb0akcVL4qYZFfbf732NhsPwPP0MvC+/Av+7bUFN4fk/wf/W26i74MK0y4e+/Q5VRxyJsq23RfkuE7Fu9FgEv/8+qZ339TfQeOddCH7/fWtQuen1wvviS/C+8CLUMFB71jmoPfMs+N97H9UnnNStj7P+4ktQuc++KJ+wC2pPPwPVxxyLxhtvQmTtWjRcex28/3s+rn3j3feg+cEHoV4vjLVr4XvjzW7tD1FHRNZ2fkBHb0nc53VmXx2aPx+1Z5+D8JIlKdsYq1a2ux6NRBD44ANANemznU74519Q+6dTAQD+996z/X3qeeLJpO/MpAF+MRmDE9kNqI38ap2bMZubUXvu3+D5v6fRcOVVGfc712gggIpdJ0IDgYzapxqErDk20MuO3SDd9s7l9RV2Qbm5VLaeiPou31tvQRsa0HjjTaxCRj2KwZ9ERES0Xgu89z4arr0OVQce1K0XiPzvvoe6v1/Q64EpRmUlgl9/3avbpP7DqKtPeRHIrKhE8733pVzW+/obXd5+YPYXrdlJQt99j8i67AcCBefMzWqZouCXXyHyW3ymz1RZmDqq+ZHH0s9/6D99qvR7e2WNWy4wpKLhMKoPOyJuWleyiJEVnJzI8/SznV5fZF0Zmu6+J+P2iYEKXWVUVMA3ZSpMny8p04tt+5oaVB9+JGrPPAuex59Imp/qsxr87DOUjdkIzY88mnLdXc2g2lnB7+eg6uhjUb7TBHhfebXdftgFsxir1yRl91DDQGjO3A6vi5LZZVLsqQtq/vfet30fx2a1rf3z6R3OQESZMZub4wK2jNrezfDaFZElbYH81Ycf2e7vk4ZbJsV9z/vffBNmNIuw6fej8ZZJ3dY373+fg/e117ttff1KKH7fHXj//aTvgcSMv7VnntXTveowNU3UnnEWKn93UNaPZdXvR/m226dv02ifMRuw9u8V++yHij33zujYJBWznf1HbPZt0++PKxPe2ibNcbPvrbdRf811vZ7dy/vCi6g546y4IGTvf59D4y2T0HDV1TATgrcaJ92KkE0gZ6Lw/J+gTU2tpXxrjjsBoR/mAbDeX1W/PwwNl10Oz2OPo+a4E1B16GGIrFuHur+ei4ZrrkXDtdeh4ZprEYkOaqr72/mt/3cX/5SpMGtqoY1NCE6fAQDwvf4Gqo89Dt4XXkxq7336Gfjffqdb+0DUWbHZAUPz59tWoOlpRnU1as86G8Ev7cuEJ2Yw7GjwZ+DTz1B9xFEITPsE9VdcmbZdu31du7b1/8TzQ6loMIiq3x+aUdvAJ5/G3Q8viB/4rT4f6v9xGXxvvQ2NRFD713NRddTRUL/f9rdCZNkyBGbNhu/tdxD44AM0Tro1o37kMm1oRGB6Zr+JUn2fmp7cz/xpF/xpVnb++KW3mI2NtgNljJruq3JFROuv2CzZoW+/y2JPqL9j8CcRERHlrOYnnkTFxN0RXta5LGORNWtQd/7fW+/XnXtetwSAajCIuvP/Dv/Ud1F/0cVdXl9H1J37N9Sc9IeMTzh1lqpCTRP+Dz5EOINse7HLUd/VdNddaeeH5qYO3AnPn986yl1VEYq5nyn/W2/F3586tUPLd7fgd9+h5tjjki5qB2bObL3419PsLgRkkuGyPf5PPk16vu1UHXhQ2vmm34/Q3B9sA8TM5uZue57Cixej7i/npm0T+v57eF95FQ033Ih1o8ciMGt2W1+8XtuShvWXXmZbtpoyY3ehx/fiiwgvWpTR8mqacd8Lgfffh3ag1FmqcmkdoaoI/TAPvqnvovqkP6D+ggtRvsWWqNh5F/gmT069nGmi+rgTWi++NT/2eFIbY83apGmx7AJdjfJyqxzQuC1Q86dTO12eWMNhhOb+gOD3c1B58CEo23JrBL9KH3xlVFej5rjjEf7hB5jV1Wi48iqUjd047ah0u6wltWeehcp9948LSAz/9FO7r61RUcnjhAyYTcklJxMvTneVUV2Nyv0OQN3fzkflQYckvQ8dQ4fE3Q/MmNmt2ydL4j7W/+abSYF3fVUkIYtV88OPwPvqq/C+8mpSW6OiAsGZye+h8q23ReW++6N88/EITPukW/vXcPkVHT5OXR8kZTmrr08KWEsMWIgsWdormSs7IrxwIYKff47IkqUIzv6i17cf+13W/KR99tRYpsdjO6BGVdFw9TUwVqyAsXo1qg45FKF5HT+2Vr+/3Wxiwc8+Q9XhRyCyYgWabr8jxeCO1a39Cn73nXWssWAByneZiPpL/gHfiy/C839Pd7h/naWGgYZrr0Pws89QucdeMBsaUH/lVT2W3bf2jDNhVFQg8OmnCP/8c9y8yJKlqNxtDwRjfn/4bPZ3vSEx4JWoL2oJZjf9flQfcRTqzv4LfFPsz/+YTU1WYJbXaxtgHlm1Cp6nn0FkbfrfXrECs79AxU4TEPjkU9T84ZSk+eFly5Mq36jXi3Wjx8KXYRB1450x59fSlDgPfpcckJ74OGODRyO//Qazvv2s/8Fvv82glxb/O/GPKfxL8u95/1tvof6Sf6DhyqsQ+PAjhOf9iObHHkfTXXfbrrPprrsR+ODDjPvQH2R6zBFZsdJ2eq6XGFdVGKtWJ003quODP02v16rC1snrOT0hnGIwea5kPSeiviv8669xg89SHe8QdQcGfxIREVGHzJhh/WWTmibqL7u89aJEewFrLcymJhjRspiqippT/hg3P/DhR/A+97+U27Q7uWbU1SVlBfR/2HZyK/DxNNuTZj0hsm5da3Be7WlndOkkSuiHeQh8+hnMmECNwGefo/qEE1Gx9z4o32Y71J56GurOPQ9VBx2CYEw2LzXN1qxUwa++RtUhh6J8513QdO99KN9ya5RttQ0izLbX5xg1NV27QGUYqD7xJNSc8ieUjdkI1Ucchdq//LVtdnQEtaqi6f4H0HjX3XEXOY3aWvgmT4lbZeDj3s/+EMsf/TEenvdj674j+M03qD31NFQffQz8n3yK8K+/9tj2I+vWwfvMM0nTzaoqND/0n06v1ygrR31M4HvatmvWwKhOHumuoRBq/vBHlG8+HtXHHNta/rN1vipqzzob1Ucfg/qrr8no4gRglTPzPPvfuAsVpseDqoMOyWj5hiuvat2P1/7pVJTvsivWjR6L8vFbwWMTnAcA3pdezmjdFC/8y6KU2b6aH36k3eXNpiZUH3s8yrfZDutGj8W60WM7fLHe/957AADflCkIL1kCo67euvC3enXaoB5VbQ3M8E+dar1P/34BjMRAqylTEfh8Ohrv+SfMhHLLkcVL4tprQwM0JruYUVffbolVwPo8tgRQ+t54ExW77mY9r6EQgrNmo+ne+xCaP7/d9bSur7YW1SedjLJNNkP1Mcei5rjjEVm0GOrxoObkP8AoK0+5bMO119lOL9t4UzTcfAvMhgaEFrYFO6iqbdl3wAoOqthx59ZA4FT7rK+De+Lr4J7WnWAQ2pQ6+xlZnxttSA7+DP34Y7dup+nuexBZHj2ODAbb/o9SX3wmPcMmwwt1nd2F4dD3c3q/I52QeKE0OHMWGq64Cg1XXoXK/Q5AzSl/Qt0llyKyapUVaG8T+AZknt2qMzzPPQcA1vdHNAilL/zW7W1mczPU74fn6Wdsy8FWHXQIjHLruyOydq1tsHfVIYfGZXrK9vMYOwgl+MWXvbJNNU2EfvwR9Vdfi7KxG6P+6muh4TC8z/437XJfB/fE1/7dW5/jWOGff477PWTW1MQHE6Vg1tej/tLLrEyckUjcd3fc927i9n5agMp99ktZUtj3+hvwvv4GKvfZFzXHn4jKffZD9WFHtGbHBIDmRx/rteyfDdddH3e/fNvtezTg0qyvR8UuE9FwRf8tXUzUUxL3PaG5c+F740003fPP1mn1F10cFzyvqgjN/QEVE3dH+TbboXz8Vqg68miYfj/Cy3+DqsL0+VB95NFovGUSKnffM+73WDqJvzsbEwbleR5NXaGh4brroZEI1DRTnttU1aTj51RaAutNvx+mx4P6y69A2fitWs8tm14vQt/EB3KmCh4ErAyl4Z9/QeCzzzPaPgD4p76LdaPHovmRR1F/xZUIzp6dsm1staDm+x9I2S68YAGCX/T+AIxs8v7veTT9699ofuRRVB93AgKzZrWe2zJqa2E2NsIoL48b0B372TCqcjvLpFlRAY1WQIt7XAm/C5r+9W+rCtv+B8A39d1e76ed2GDv2L5rQ0PWs8gTUW5rvO2OuPuRJUuy1JP1T7bPS2SDMKsCERH1RyIyd8KECRPmpslSR51zwAHWbU8cNIWXLYexdg3y998fImLbRv1+1PzxVITmxF/4dG29NRyDBmHIow9DIwacG46C+nzWCStT0fzQf1p/yBccfhgiy39LWXZryH+fRcGBB0DcbpiNjfB/PA1Nd94Fs6YGBYf+Hq7NNoOxZi1cW46H55lnoQ0NkOJiFJ9+GvJ22QUNN90cdwHEvfPOGPbyi3CUlnbPE2X3vITDqNxnv7hSQM5RozDis0/gGDgwqb3Z2Gg7HQACn09H7Vlnt16ELb3uWgS/+x7Bz9KXIpKBA6HNzZDiYmhz+6OVB919F4r+eAokL6/dtj1J/X5IYWHX12OaEIcDqgr/W28DIig89hjANOMeowYCkIIChH/9FcbqNTDr6+F7/Q3kTdwVJZdfBsC60O0aN85aXyQCs7YWzpEjO9Qf0+MBIhE4Bg2y/g+H4Rg8GEZdHRxFRZCCAhh1dTCrquAYNgwNV13d7VmVAGDIk08gvHQpmu+7HyVXXoGCQw5B9aGHtc4fNvkd+KdORXDW7OSMlm43hr30IvL22jPlPiGRGgaMiko4NxwVt4zp9UIcDtvXOjhnLqSgAM4hQ+B74w2EFi5E0QnHw/PMfxFqp0wpADg2GInCI49E8Zlnwj1us4z6aSe8aBGa7r0P4cWLYTY0QtvJojT4wQdQdPJJAKyAICksRGDGTDhHbYC87bazXcbz3HNovOGmDvWr6LTTMOiO2yBud+u05ieeQNPtd8b357FHUXjM0RARNNx4U1xAqGPIEIz8YlbrfkcNAwiHIQUFrW28r7yKhivbLqbmTZyIyIoV3V7a286wKZORv+sutvM0HAZME+GlSyFOF1xbbI6m++5H3oSdkbfrRIS+/BLIz0PBfvsBIlDDgKOoKOW2WvYBABCa+wOMmmrk77MPpLAQ4mh/fKbp98Pz5FNwjR7d+vrHrV8VUE25rvprroP/3Xcx8LprkX/gAXCNGYPAzJlouPZ6FP/5VAz4+/lAKAQUFMD/zmRIUSEKDjwQ4V9/RWT5b3BtNBbqD6D2jDPTXlwr+P0hGHjbrXCOGGENCqiuhrFuHTRiQP1+NFx1Fcya2pTLd5UUFsI1bhxKLvsH8vfe28os9+VXkKIiNN5wY+tFiYzXV1KC0iuvgHu7bdH8wEO2F7KkqAiOQYN6pHz5oH/9E8V/PtUqSb96NfwffAj/O5NhlJVBAwGUXHE5Si+/DE3//Bea//NwyvW4t9kGRl0dnCNHIH/ffTHgnLPhGDECTbfdDs9T/5dZX/79LxSf+ieEfpiH6qOP6dLjOqnaKr385vA/AACKTz8NA+++K+N9fnjZcjg3HNXuZ843ZSoCn32OouOPReHhh3epz3Z8U6ZAA0EUnXgCxOWK374qEInE7UM7vZ133kH9RZfYzhsxa2aXvoNaqGGgfJvt4rLEld58E0r+dl7r/Ypdd4sLVHIMHYoN5s2FOJ1d3n5fp4aB0Ny5yNt++245fkyn6b77ky6qD7zjdgw4+6we3a5RUwPH0KEZfw5jqSoiixaj9qyz05aI7itc48a1BmcMuu/fOOLhEwGXCzNndvyx96Tg119bWb9CYQz69z9RcOCBGS1nlJej+cmnEPruOxSddBLy99gDGgzAvdNO1iCD775H7Z9PSxl820IGDcKQxx9F7elnAimyQbt33AEjPngfQOpzBqbHg8jKVTArKpD/uwNbj1VMjwf+99+HWd+AgoMPgnvzzTN6fKk0XHc9vM+/AMDaPw2843YU7L9fyt+/qahq63e6a/Ro2zb+adPgeerpjH4z2Gn5HpzxuYn8ffaOm9d49z3wPBIfgCQFBRi18Cfb/Y+Gw/A89X/wPPV/rcfPpTfeAN/Lr7QGUid+7/aEoS+/iIL99++29YUWLkT4pwUoOulESF4eIuvKEP7lF9SddXa3bYOIelam+54NFsyHc8gQmE1NqP/HpR0+RzXg7+dj4I03pJzvfe01eB593DYws/DooxBZuxbheT+2u52Bt05CYPp0BGfMxMC77sSAM8+A6fVav5FDYRQdfxwab7o5835fdCECH30cd07MtdlmyNtlQlywZQspKEDJ1VdBvV4UnfKH1u+o4Ndfo+akntu/U/dL/GyM+GQawj//jPyDDoJzyODWdkZZOVQVrtEbZrReo6ICjoEDIYWF8H/0EbzPPY+Cg36H4jPP6JHz8Or3wzd1Khout7LUJn3mXS4Un3UmIkuXxmXJBoAN5s2Fc8SIzm1XFYFp0wBVuDbfAq6Nxnb48WkgYFUuiR7zJb0mn38K95Zbdqp/7TGbmmBUVsK10UaQ/Pwe2Qalp5EIwouXwL3Vlknncoi6yvR6UbHjznHnz6WkBKMW/dyp8y3UMT0Zy9ALOvUGYfAnERH1SyIyd+ctt5ww69pr4dp4YzhHWEFTGglD8vOtoAu3G+r1WgdeThecI4ZbJ5JXrEDktxWIrFkDo6wMRlkZJD8f+XvuCefIkdBwGI4hg+HadFNEVlmZpcyaGkRWr4Zr003g3nY7iMsJ55gxMKuqISUDAKcTgECcDsA0rcCP0lKYNTUQdx7gdEAj1gUXKSyEWV8P5wYjoR5P3A8/0+uDOARqKoJfzEb4pwXQiAH31lvBOWIEZMAAqNcDx+DB0dJeXkRWrEBo/nzk7747nGPHQIqKoM0eOEYMh2PIUGhTE9Tng+lphqN0IBxDh0BDIauPDiekoAAyaCA0EIQU5OOgP4yAhkL4/LUqqN8Ps74eGg4BInCUlML0NMOsr4ejdKB1wU8EjqFD4NpoYxgVFTCqq2DW1CI0dy4cQ4fCMXSoFaRSWIjme++zHqjDgQF//QsKDjsUZmOTdXHZMKCBADxPPAmzNsNgkbw8K4ClD5CCAhT9+c+AEYFZUwuzvh7u7baFY8gQaCQCcbmgPh8iK1ci+NXX1vt2w1HRUcEVcG2+OfIn7mqVOfL74Rg0yHqNvV5AFYHZXyCSosxu/n77wjlqFOCygg6M8jIEp8+Ae7vt4BgyGGZNLfJ2mQApLETop58Q+vqb3nxqIAUFcE+YAPc2W0Py8xFetNh635SWwKyqhmvcZgh89jnUMCAF+XBtuinc48dbWa4cTiAUQmjuXEhxMfL33BOOYUMRXrIEzhEj4N5hB5g1NQjMmAmzvBzOjTeCo6QUyMuDFBTArKlG+KcFkNJSuMePh2PwIKjPD8ewoYDDYb3Ha2ph1Nai8PDD4Cgthf/d92D6fHCOHIHQN99CBgxA/h57IPDpp+0+1rxdd0Vo3jw4hg+z3sfh+IunjqFDIXl5MMrL4RgxAmZ1NRA9Xrc+hwG4Nt4Y7m23hQZDkMJCqKfZChorKoY2NUIGDIBRVobgzFmA242Cg36H0NwfYNbXw7X5OEQWW6MLpbAw46wI2ebaeivrRFheHvJ22QWhefNgerwoOPAAhObMQeDjaXBtsTmcY8YiNHcOzMoqODfaCO4tx0PyCxD+dSkiS5YCDgfydt0FzhEjoaEgAjNmQpzObn0eHMOHW8HnXh/gdCBvZ+sHfujHH1F8xhlAMAijogJqmAgvXACzvsH6XhpQnNFFhkRSXGxbSjlv992g4QicI4bDvdVWkKIihH78sdNltxyjRsG9+ebWPryqOm1GQxk4ENqYnJnOtfnm1uc5JoNR/r77Im/CzvC+/gZMm4xHvck5ZoyVwcPrhXPMaLhGj0H4l186FsgX/d5x77wTHEOGQgoKrBG9Lieco8e0BtHLoIFQfwCIyYzkGr8FBpx/PqQg3/rcqwIOByAOmNEsEZFly6yA0Wh2EOeGG6LgyCPgGjsW4aW/IrzgJ4Tn/wSIwLXFFig4YH+4Nt8c3ldfg3o8MNasyez9npcHiR4nAYBj2DAre2s7wSHUs2TQoLRB4a5x46wy0abZs/0oKkLp1VfB8+RTtpnKOsLuQnDB4Ychb8IEK/jQ7YZz1AZwbbopzNq6tgEUP/8M/0cfIbJ4CRwbbAD3FlvAMXgQHMOHI3/iREjJAIg7D6EFP8H/9uS48qzFZ5wO9847w1FaAtfGG0OKimDW1UW/S4sQWbUaUBPuLbZAeOlSaChsHXd5mhFeshSOQQPh3mYbOAYOhFlfj9D3c9A46VYAQN5uEzHojjus0uyGiciaNfA88wyMsjIUn3468vfcA3A44Rg0EIgY1kCMqko4x4yFc/SGMMsroKYBmCbM2lqozw+jshJmXR3CS5YiOHu2dWyQQsk/LoFry/GI/LoMUEXB7w+BBoMw1q6DBgJwDBsK19ixgDsPZlUVpLQUUpAP9XoRXvgzTI8HwRkzki7IAUDBIQfDMXy4FUj9+fSk+e7ttkP+Pnsjf//9AMOAUVZu/dYZNMjar44eDefQoXAMHYLIqlXWAJjmZrg2H4eCQw5pDeDVSAT+KVOhgQDc221rBdUHQ4CacI4aZZWYrq+HUVuL4JdfwayvR/7EXeHeeWc4hw0D3G4gHLay1wcCkNISOIePAPLyEPltOaSgEIg9yW4a1vGkmtBgCI7iYkhpKbS5CXDnwTVmNCJr18E5dAgiq1ej+b4HEJo7F67x4zHgL+fAOXYM3FtvDaO8HOrzwTFiJBAJW++pklI4hw5B+NdfIQ4njIoKSGkpnEOHtma2Ur8fjtISRH77DeFFi9uChF0ueB59zHafXfD7Q+DebjsUHHggjJpqaGMTnBtuaP3Gzc+H5OfBrKyCUV0Fx7DhcAwoBtx51mMVsX6P+nwwKiuhPj+kqBBmfQOMtWsRXrAQwS+/RN4euyNv4kQAViZf9fvhGDoMzlEbwKyvhxQWwiivgHPIYCsj1Zw5cJSUJg3QyzU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\n", + "text/plain": [ + "\u003cFigure size 1440x1224 with 17 Axes\u003e" + ] + }, + "metadata": { + "image/png": { + "height": 984, + "width": 1343 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Build plot.\n", + "plot = plot_components.plot(\n", + " [\n", + " plot_components.TranscriptAnnotation(longest_transcripts),\n", + " plot_components.Tracks(\n", + " tdata=reordered_chip_histone,\n", + " ylabel_template=(\n", + " 'CHIP HISTONE: {biosample_name} ({strand})\\n{histone_mark}'\n", + " ),\n", + " filled=True,\n", + " track_colors=track_colors,\n", + " ),\n", + " ],\n", + " interval=interval,\n", + " annotations=[\n", + " plot_components.IntervalAnnotation(\n", + " tss_as_intervals, alpha=0.5, colors='blue'\n", + " )\n", + " ],\n", + " despine_keep_bottom=True,\n", + " title='Predicted histone modification markers in colon tissue',\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "P6CGieJNIqf2" + }, + "source": [ + "We can see that many of the predicted histone modification peaks overlap with\n", + "transcription start sites, especially markers associated with promoters\n", + "(H3K4me3, H3K27ac, and H3K9ac). Other markers such as H3K36me3 and H3K4me1 are\n", + "more associated with gene bodies and enhancers, so these signals are less\n", + "pronounced around the TSSs." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FHDs_Vq94SpV" + }, + "source": [ + "## ChIP-TF" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eUr7IcCW4SpV" + }, + "source": [ + "Transcription factor (TF) binding patterns are captured by the model's `CHIP_TF`\n", + "output. We will visualize these in a similar way to `CHIP_HISTONE` predictions,\n", + "with one difference: we need to modify the ontology terms we request in order to\n", + "get good coverage of both biosamples of interest and multiple TFs. Many\n", + "biosamples have predictions for two major TFs, namely CTCF and POLR2A, but some\n", + "cell-lines (HepG2 and K562) have coverage over many more TFs (501 and 269\n", + "respectively).\n", + "\n", + "Here, we will also demonstrate two helpful utilities: selecting specific tracks\n", + "from a `TrackData` object and aggregating predictions across tracks in a\n", + "`TrackData` object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "OTOi-Ktb4SpW" + }, + "outputs": [], + "source": [ + "ontology_terms = [\n", + " 'UBERON:0001159', # Sigmoid colon.\n", + " 'UBERON:0001157', # Transverse colon.\n", + " 'EFO:0002067', # K562.\n", + " 'EFO:0001187', # HepG2.\n", + "]\n", + "\n", + "output = dna_model.predict_interval(\n", + " interval=interval,\n", + " requested_outputs={dna_client.OutputType.CHIP_TF},\n", + " ontology_terms=ontology_terms,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yMSXBhrDDGu8" + }, + "source": [ + "Say we then only want to work with the K562 and HepG2 part of the output. We can\n", + "accomplish this by calling `filter_tracks`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "executionInfo": { + "elapsed": 53, + "status": "ok", + "timestamp": 1753110029714, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "dx9tlBcQCXzR", + "outputId": "e2f5c2c8-c786-483b-c2a5-287057fe31af" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "845" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ontology_terms = [\n", + " 'EFO:0002067', # K562.\n", + " 'EFO:0001187', # HepG2.\n", + "]\n", + "\n", + "output_chip_tf = output.chip_tf.filter_tracks(\n", + " (output.chip_tf.metadata['ontology_curie'].isin(ontology_terms)).values\n", + ")\n", + "len(output_chip_tf.metadata)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5CcAfBOfDdxt" + }, + "source": [ + "In these 845 tracks across the 2 cell types, the maximum predicted values vary\n", + "considerably:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "height": 424 + }, + "executionInfo": { + "elapsed": 53, + "status": "ok", + "timestamp": 1753110030011, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "4gn11NP_W3mJ", + "outputId": "99a41f4b-56d0-4fb7-eda7-f8376f0b411d" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \")\",\n \"rows\": 845,\n \"fields\": [\n {\n \"column\": \"ontology_curie\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"EFO:0001187\",\n \"EFO:0002067\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"biosample_name\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"HepG2\",\n \"K562\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n 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'block' : 'none';\n", + " })();\n", + " \u003c/script\u003e\n", + " \u003c/div\u003e\n", + " \u003c/div\u003e\n", + " \u003c/div\u003e\n" + ], + "text/plain": [ + " ontology_curie biosample_name transcription_factor max_prediction\n", + "0 EFO:0002067 K562 PKNOX1 20608.0\n", + "1 EFO:0002067 K562 POLR2G 20608.0\n", + "2 EFO:0001187 HepG2 RBFOX2 19328.0\n", + "3 EFO:0002067 K562 GABPB1 18304.0\n", + "4 EFO:0001187 HepG2 REST 15488.0\n", + ".. ... ... ... ...\n", + "840 EFO:0001187 HepG2 TCF12 183.0\n", + "841 EFO:0001187 HepG2 GPBP1L1 173.0\n", + "842 EFO:0002067 K562 ZNF778 159.0\n", + "843 EFO:0002067 K562 XRCC4 149.0\n", + "844 EFO:0002067 K562 COPS2 149.0\n", + "\n", + "[845 rows x 4 columns]" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "max_predictions = output_chip_tf.metadata[\n", + " ['ontology_curie', 'biosample_name', 'transcription_factor']\n", + "].copy()\n", + "\n", + "max_predictions.loc[:, 'max_prediction'] = output_chip_tf.values.max(axis=0)\n", + "max_predictions.sort_values('max_prediction', ascending=False).reset_index(\n", + " drop=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5bX1nHzBXnpU" + }, + "source": [ + "We can filter the tracks down to only those with a maximum value of at least\n", + "10000:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "executionInfo": { + "elapsed": 53, + "status": "ok", + "timestamp": 1753110030352, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "uv6jLzhvD64q", + "outputId": "8d5aa811-4b1e-43d7-d163-b9e34f5b769b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of tracks before filtering: 845\n", + "Number of tracks after filtering: 14\n" + ] + } + ], + "source": [ + "print(f'Number of tracks before filtering: {len(output_chip_tf.metadata)}')\n", + "\n", + "output_filtered = output_chip_tf.filter_tracks(\n", + " output_chip_tf.values.max(axis=0) \u003e 8000\n", + ")\n", + "print(f'Number of tracks after filtering: {len(output_filtered.metadata)}')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PgtgnV8OYNoK" + }, + "source": [ + "This is a more manageable number of tracks to visualize. Let's build up the plot\n", + "as before:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "height": 892 + }, + "executionInfo": { + "elapsed": 7336, + "status": "ok", + "timestamp": 1753110037967, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "jaUefFV-4SpW", + "outputId": "6635139b-c78c-4aa3-db8c-f736c4bf70a7" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "\u003cFigure size 1440x1080 with 15 Axes\u003e" + ] + }, + "metadata": { + "image/png": { + "height": 875, + "width": 1267 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Build plot.\n", + "plot_components.plot(\n", + " components=[\n", + " plot_components.TranscriptAnnotation(longest_transcripts),\n", + " plot_components.Tracks(\n", + " tdata=output_filtered,\n", + " ylabel_template=(\n", + " 'CHIP TF: {biosample_name} ({strand})\\n{transcription_factor}'\n", + " ),\n", + " filled=True,\n", + " ),\n", + " ],\n", + " interval=interval,\n", + " title='Predicted TF-binding in K562 and HepG2 cell-lines.',\n", + " despine_keep_bottom=True,\n", + " annotations=[\n", + " plot_components.IntervalAnnotation(\n", + " tss_as_intervals, alpha=0.3, colors='blue'\n", + " )\n", + " ],\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "q5fvBZnqHp2A" + }, + "source": [ + "In this figure, we see that CTCF binding sites strongly coincide with those of\n", + "the cohesin complex protein RAD21. Binding locations for the same protein (e.g.,\n", + "RBFOX2, REST) appear largely the same across these two cell types, with some\n", + "change in magnitude. Peaks in predicted POLR2G binding, a component of RNA\n", + "Polymerase II, typically occur near TSS sites.\n", + "\n", + "We can use the same process to select tracks with high peaks anywhere within the\n", + "*APOL4* gene interval:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "height": 699 + }, + "executionInfo": { + "elapsed": 1926, + "status": "ok", + "timestamp": 1753110040181, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "W2GwYd-8Zt61", + "outputId": "9159bb33-e119-4fea-9a56-ef479e453175" + }, + "outputs": [ + { + "data": { + "image/png": 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QJEmSJEmSAHuykyRJkiRJkqR5bX3v3azuua3dYYxygUPGSpIkSZKkGcQkO0mSJEmSJEmax67YdHW7Q9jFhRsuo5a1dochSZIkSZIEmGQnSZIkSZIkSfPa5TNoqNi6u/s3c83W69sdhiRJkiRJEmCSnSRJkiRJkiTNWzsGe7hm66p2h9HUBRsua3cIkiRJkiRJgEl2e11EZMM0FBEbI+K8iHhFRESTOiua1GucTirL3iMiNkTE9oi4d4sYXlDW+XVEdFaWPzkiPhER55QxZURcNF2PhSRJkiRJkqT2+u3maxmaocOy/vru39I31N/uMCRJkiRJkuhqdwDz2AfKeTdwb+C5wInAQ4A3taizBfh0i3WrATJzXUS8Hvg+8PWIeExmDtULRcThwBeBHcBLq+uANwLPAXqBG4ADJnxUkiRJkiRJkmaNyzZe3e4QWto51MvKTb/nUQed0O5QJEmSJEnSPGeSXZtk5inV+xHxaOAC4G8i4hOZeXOTapsb67XY9g8i4qvAy4F3A/9c7iOArwDLgddl5o0NVT8CvAf4I3Ak0CwGSZIkSZIkSXPAQG2Q3266pt1hjOmCDZeZZCdJkiRJktrO4WJniMz8FUVyWwAPnoJN/h2wBnhfRDyksuzJwI8y80tNYvh1Zl7T0LudJiAiDoyI10TEGRFxQ0TsjIgtEXFRRLw6Ijoayu9uKOBvj7Gvl0fEZeXQwFvKIYefOUb5xRHxgYi4LiJ6I2J9RJweEfedysdAkiRJkiRJs8O1W2+gZ2hnu8MY0282/56tA9vbHYYkSZIkSZrn7MluZolyPrCnG8rMrRHxcuBc4LSIeCnwYWA98Jo93b5aegHw78Ba4JfALcChwPOALwNPj4gXZGY21LsKOLPJ9n7fbCcR8XHg7cBtwJeABcCLgR9FxN9m5ucayi8Efg48GrgC+AxFb4UvAP4iIp6QmZdO+GglSZIkSZI0a10xg4eKrRvKGhff/Ruedo/HtTsUSZIkSZI0j5lkN0NExOOA+wD9wGUtiu0fEac0WX5lZp7ZuDAzz4+ITwJ/D1wELARemJnrpyRoNbMKeDZwdmbW6gsj4t0Uz+vzKRLuvt9Q78rxDAVcbutRFAl2NwIPzcxN5fKPASuBj0fEjzNzdaXa2ygS7L4HvKgeW0R8hyK5778j4oHVmCVJkiRJkjR3ZSaXb5r5SXZQDBlrkp0kSZIkSWonk+zapJIs1w3cG3guRU92f5+Za1tUWwa8v8nyr9K8FzSAU4C/AZYA38nMH00uYo1HZp7bYvm6iPgi8EHgJHZNspuIN5TzD9YT7Mp9rI6IzwPvBV5J+VqJiKjUeUc1kS4zz4qIC4HHAidS9L4nSZIkSZKkOW5Nz+1s6NvY7jDG5bptN7Gu9y7useigdociSZIkSZLmqY52BzCPvb+c3g28kCLh8dWZ+dkx6qzJzGgyvWKMOu+gSLADOCkivBLVPvVhgAebrPuTiHh9RLy7nP/pGNt5Qjn/nybrftpQBuBewFHAqsy8eZx1JEmSJEmSNIddPguGiq26cEOrwT8kSZIkSZKmnz3ZtUlmBkBELAUeCfwX8MWIWNOqN7SJioiHUyTx3Qx8HXgf8EXg5KnYvsYvIrqAl5V3myXHPbmcqnXOA16embdUli0FDge2t+jx8Ppyflxl2X3K+aoW4TWr01RErGyx6vjd1ZUkSZIkSdLMMduS7M7fcBknH/F0ikEbJEmSJEmS9i57smuzzNyRmb8AngV0Al+NiCW7qbZb5Ta+TvEcvwz4AHAh8PyIeOmebl8T9mHgAcBPMvNnleU9wD8DDwYOKKf6sK0nAeeUiXV1y8r5lhb7qS/ffw/rSJIkSZIkaY66u28TN+64ZfcFZ5C1veu5YfuadochSZIkSZLmKZPsZojMvBr4EnAE8NYp2OQngWOBj2bmRZlZA14ObAc+GxFHTME+NA4R8XfA24E/An9VXZeZ6zPzfZn5m8zcXE4XAE8BLgXuDbxmErvNiYQ43jqZ+eBmE8WxSZIkSZIkaRY4/daftDuESbnAIWMlSZIkSVKbmGQ3s/wL0Av8fUQcMNmNRMQzgNcDVwLvry/PzJuBt1H0WPbf4dgK0y4i3gh8BrgWeHxmbhxPvcwcBL5c3n1cZVW917llNNes17rd1dmvSR1JkiRJkiTNQVdt/gO/WP+rdocxKRfddQWDtaF2hyFJkiRJkuYhk+xmkMy8HfgPiiS4d0xmGxFxEPBfFMl6L83M/oZ9fAk4G3gy8MaGuo+JiFMj4lTg4+XiY+vLyuUap4h4C/A54PcUCXbrJriJDeV8eLjYzNwB3A7sExGHNalzbDlfVVl2XTk/rsV+mtWRJEmSJEnSHLNzqJd/v/Eb7Q5j0rYObueqLX9odxiSJEmSJGkeMslu5vlXoAf4u4g4dBL1/xO4B/DuzLymRZnXAHcBH4mIauLVvSmGlH058Pxy2SGVZS+fRDzzUkS8E/gURW+Cj8/M9ZPYzCPK+U0Ny88t509rUufpDWUAbgRuAY6LiKPHWUeSJEmSJElzzGlrzmJD37gGWpixHDJWkiRJkiS1g0l2e1lmRma2HKY1M+/MzKXldGe5bHVZb8U4tv+8suynxiizLjMPLvexqrL81Hp8raYJHu68FBHvBT4MrASemJl3jVH24RGxoMnyJwBvLe+e1rD6i+X8PdVhhSNiBUXvhH3AV+rLMzMrdT4aER2VOs8BHksxnO354zk+SZIkSZIkzT7XbLme/1k3+y//XLbxKnYO9bY7DEmSJEmSNM90tTsAaS6JiJcD/wQMARdS9EjYWGx1Zp5a3v4IcP+IOA+4rVz2p8ATytvvzcyLq5Uz8+KI+CTwNuDqiPgesAB4EbAc+NvMXN2wz08CzwROBi6NiHOAo4AXUPSc+KrMrE3ysCVJkiRJkjSD9Q3184Ubvt7uMKZEf22AS+++kpMOecTuC0uSJEmSJE0Rk+ykqVUfjrUTeEuLMucDp5a3vw48F3goxbCt3cCdwOnA5zLzwmYbyMy3R8TVwJuA1wE14DfAxzLzx03K90XEk4B3AS+h6CVvK3Am8P7MvHYiBylJkiRJkqTZ41u3/Ih1fS0HW5h1LthwmUl2kiRJkiRpr4piJElJmryIWHnCCSecsHLlynaHIkmSJEmSpIrrtt3Ee373CZK5cx04CP7zIR9k+YL92x2KJEnStNm4cWO7Q9A0WL58ebtDkDTL+H4wtcp2eJchKcejY2pDkSRJkiRJkiTNBP21AT5/w9fnVIIdQJJctOGKdochSZIkSZLmEZPsJEmSJEmSJGkO+u6tP+H2nXe2O4xpccFdl7U7BEmSJEmSNI+YZCdJkiRJkiRJc8yN22/hzNt/3u4wps3NO27jlh13tDsMSZIkSZI0T5hkJ0mSJEmSJElzyEBtkM/d8DVq1NodyrSyNztJkiRJkrS3mGQnSZIkSZIkSXPIGbf/jFt65n4vbxduuJxazu1EQkmSJEmSNDOYZCdJkiRJkiRJc8TqHbfxvdt+2u4w9oq7+jfxh603tDsMSZIkSZI0D5hkJ0mSJEmSJElzwFAO8fkbvs7QPOrd7YINl7c7BEmSJEmSNA+YZCdJkiRJkiRJc8BZt/+cm3bc2u4w9qqL7/4N/bWBdochSZIkSZLmOJPsJEmSJEmSJGmWu61nLd+59ex2h7HX9QztZOWm37c7DEmSJEmSNMeZZCdJkiRJkiRJs9hQ1vj8DacxmEPtDqUtLthwWbtDkCRJkiRJc5xJdpIkSZIkSZI0i5299pes2n5zu8Nom5Wbfs+2gR3tDkOSJEmSJM1hJtlJkiRJkiRJ0ix1x871fHPND9sdRlsN5RAX372y3WFIkiRJkqQ5zCQ7SZIkSZIkSZqFalnj3288jYEcaHcobffjtb/k6s1/pJa1dociSZIkSZLmoK52ByBJkiRJkiRJmrifrbuQa7fe0O4wZoQ7dt7JB679N+6x6GCefOhjePwhj2BZ977tDkuSJEmSJM0RJtlJkiRJkiRJ0iyzvvduvr7mjHaHMeOs693A19ecwbdu+SGPOPDPecqhj+F++x1LRLQ7NEmSJEmSNIuZZCdJkiRJkiRJs0hm8u83foO+Wn+7Q5mxBnOIi+66govuuoLDFx/KUw59LCce/HD27V7a7tAkSZIkSdIsZJKdJEmSJEmSJM0i56y/mKu3/LHdYcwat++8k6+s/h6nrTmLRx305zzl0Mdyn32PsXc7SZIkSZI0bibZSZIkSZIkSdIMlplsGtjC6h23s3rHrfzg9v9td0iz0kAOcP6Gyzh/w2UcteRPePKhj+HEgx/O0q7F7Q5NkiRJkiTNcCbZSZIkSZIkSdIMMVgb4vad67h5x22s6bmtTKy7ja2D29sd2pxyS88d/NfNp3PamjN59EEP5imHPpZ773NPe7eTJEmSJElNmWQnSZIkSZIk7aFa1hjKGrWs0REddEWnyTrarW0DO1jdcxtrykS61T23cWvPOgZzsN2hzRt9tX7OXf9rzl3/a/btWkpHdBCM/O0GQfGnHNT/jfq/MVKmOs/6f1ncq5HF0qyuS4DKuvqaYh9d0UV3RxfdHd0jt8t5V3TS1dFFd3SXZbpGlenq6GJBRzcLOxawsHNBMd/l9kIWdnSzsGMhCzsX0B1dtlmSJO1lmclgDjKYQwzUBhnKoZHzCpJ1d91BbfjTQo1aFp8ZatSKzxVZG15fI8msAQx/ngmCzugkAjrpgAg6Iugo/4soynREBx0RBB10Dn8e2PPPBR0U2+2Ijj3e1lyyfPnydoewV9SyeM3WKM6TkyyXFcsJhl+nHeVrMcrXZ/HahChvd5Trpktm0lfrp2doJz2Dvewc6qVnqJeeoZ3sHCzn5f2eod7hZT1DRdn+2sDwtqJy3kCMnFlE5UyiWDVyDlH8rXawT9cS9uneh327lhZTdzHfp+H+ks7FfnafZ7Zu3druEOaUPWmHTbKTNCX6av3csH3N+Apn1i8fAsWHLGD4QuLIRcUsiyeNFyeLcrVR26leoByuPWpfI9unUm8mGDm2+r3KrayUaYh5dL1CRPUj2siHs5EPq5U1lQ+wI/VHfyjb9SNasyWjl9VP9ooTveJicv0DdGb5IbpyUXn4fiY5fErYJN7Khe1RH0qj8aMpIyeH5Qfv4oM45bKRk8cIRp9MDpevzHe5XX6wr+xn5MN/EUP9pLHYXsfwSUJ9Xx2VZcMnsZX9jNzv2OXkodlz1upxqv87+kL/yDOVldfXqNdejiq1y99jLeun9Tlq2fCJPtVlWZ7sN5YdeQ003q5uHxi5n6OPYPgUZNQXGzH83Ix+VOpld31c9kT1RGiXdaOemeEKu6zf0yhGtxm567KG243Pb6Nmf0stY45oWLbnRzP8amx4v2jWzmeObuUzR5Ue/aXWON9/xnqH2F2b2Px1wC7tVIx63KJSpvpaHd9remR7Yz93o//eRr+P7vp32LC+UqbYVn2jDe9ZlfVjvc6iIe6RtmvPH6d6vcbHqdW2R7fxDcsp3j/LFp+OGD1vfLbH06aMdfGj+pjtsm7U33LDmlGv+13/LuqVRrXnlb+PPfmbq8beLMLRZUbH3fx4CqNf3yPPcX1pY9vbrI3fE7u+H7ZuW2ko27hmou9Jux7bru1udfmodZXHrLHMnhh5/xi5N/LaavJ6qLx2oPnjNflY6rvd9bXY6j2g8TVeo8ZQbYghagzWhhjKYhrMhtu16rLaqHXVz6/1LzKKzzDNP/NULyZX29p6u9ZReb5Ht0kjazpi1zLAqHirsQ42Oa7hdZXjrpF00EFndNAZneWXK/X7HXTQOXI7OumI4kuaUcuIkbrDt+tf1HTSSQxvtyhTrzv69nArNer9qqHtKhq0Xd7P6s91/YJ9/fEeonIRn9ouF/nrz1m1Xj1RrpgPlV9w1SrLq1981e+P/pxaN5IQM0ayTD0pZrhMcbsjYtc2vvL6qr7uhz85V/4mq9HU25nRn+F2ObNqfd4RTdqjJu+9zc5B6rNdzgUq54zJ6HOLkb+tYk2tfN7rX7h0RHGu1EkML6ufZw1/kTf8ZcxI+VHne83e+yufTYbf94nRf6fl8zLymhz9mq2fZ1VbqMbXbX+tn1t67mDNjtu5q3/TLq8btc+2wR3tDqFtOggWNCTiLejobnpOOHJtZqx2ZeQ9a9T9UeV3PZdsbIuq2+lg5HpK9W96OI2g+jffUGaqPhcFQWdH8T7YVb73ddI5vGz4PZJOOjvq9+vLOujs6Cxj7Bh+39zldiUhYZf7Zd1Wx9PqfKPZ0vr7Zg6/L1av5Y1+z6y22dX3znq73vi5bGTZSJvYeM5R/czYeM4+el2t4ZpwMuq/LN4Ha5lN3qOr79vF+/UQI2Xqy2o5xBC5y2eWzspz1Vl5/IfvV5Y1+2xTfe4a63eMuh+j6o3r3HI368e+/tFwXb3J9ff6J6zqiVyr86HqdhqX198nq3tsvP431vPa+Plm9Hlv9fy1Xq7Z+/FI9NUIRx1HNl/eeETVz/cwktRUfa2PfL5heF39s8yodZXb9b+B4VSqZtdOK+czI0kqtSafVWujygy/9svP/rXy/kib1Tnq8/3o+2X7VrZfnYxeX339dkT1s9fo5aPbuNHtW/U5GnUrR25Xn73q9YfRn5THfl1UXwWjr4k3P8+g8pjvun7Xc9AaWZ5DDjJQG2QgBxmsjSTPDeQAA7XBIqGuNsRAWW53P6w4aschY66fTaL8jD36/bryfl75TqQzOoAY/rxf/4Qx8jlg5Dphx/CVu+r1vPr3MVGey3RUzqnrZRu2NRxH5XpsvdTw+2v1+sgYbQoNr8PhhUUb8cu8YvS5aKtz1Yb7I+3J6PfdXc+bqZSpjWoPR7UrZd0aZZtWnn9VX/vD7V0O733UZ4V6mdGxjmxzqo36zo/G68CN19bqaxh+HdWXV8sO1gbZOdQ78t43C3RQT8hbOpKQ17WUpV1LRn3fOPxdZP1z9PBn59HnyfW/x10/c4/8DbT6LrVZ2ZEz6F2vMxTzypKG67mNn4Oqr9Xq+3mztrj6WWbkXDyHv5Me+ew78t5a/S575DNKw/fWu3xm3fXzSvW9utl3K1UT/V4xImDjANVXb/28rGjz6u1gfeoYPt8qylSvr1ev5TScozU8E1S2Sez67NW3sWvdXe81Xtve9er36CW7nEtGs+XV46tcQ638fVfb9+rjsaV/G6+64p0Hff9RX7iLCYqxvsiRpPGIiLs7F3Yu33fFAe0ORZIkSZIkSZIkSZr1VtxjRbtD0DRYvW51u0OQNMv4fjC1Vq9bzb5HHcAt/7tqwr/Gsic7SVNha8eCrvnRt7EkSZIkSZIkSZI0zWox1O4QJEkzgO8HM4c92UmSJEmSJEmSJEmSNIOcfvrpD253DJp6L3zhC1e2OwZJs4vvB1NrT9rhjqkMRJIkSZIkSZIkSZIkSZKkucQkO0mSJEmSJEmSJEmSJEmSWjDJTpIkSZIkSZIkSZIkSZKkFkyykyRJkiRJkiRJkiRJkiSpBZPsJEmSJEmSJEmSJEmSJElqwSQ7SZIkSZIkSZIkSZIkSZJaMMlOkiRJkiRJkiRJkiRJkqQWTLKTJEmSJEmSJEmSJEmSJKkFk+wkSZIkSZIkSZIkSZIkSWrBJDtJkiRJkiRJkiRJkiRJklowyU6SJEmSJEmSJEmSJEmSpBZMspMkSZIkSZIkSZIkSZIkqQWT7CRJkiRJkiRJkiRJkiRJasEkO0mSJEmSJEmSJEmSJEmSWjDJTpIkSZIkSZIkSZIkSZKkFkyykyRJkiRJkiRJkiRJkiSpBZPsJEmSJEmSJEmSJEmSJElqwSQ7SZIkSZIkSZIkSZIkSZJaMMlOkiRJkiRJkiRJkiRJkqQWTLKTJEmSJEmSJEmSJEmSJKkFk+wkSZIkSZIkSZIkSZIkSWrBJDtJkiRJkiRJkiRJkiRJklowyU6SJEmSJEmSJEmSJEmSpBZMspO0xyLiGxHxjXbHIUmSJEmSJEmSJEmSJE21yMx2xyBplouIlSeccMIJK1eubHcokiRJkjQvfGfNe7lpx294xdGf4rDFx7U7HEmSJEnSFNu4cWO7Q9A0WL58ebtDkDTL+H4wtcp2OCZT157sJEmSJEmSZpGewS3cvOO3QPKHrRe2OxxJkiRJkiRJmvNMspMkSZIkSZpFrt92KUkNgFXbfo2jFEiSJEmSJEnS9DLJTpIkSZIkaRZZte3Xw7c39d/B3f23tjEaSZIkSZIkSZr7TLKTJEmSJEmaJfprO7l5x29GLVu19dctSkuSJEmSJEmSpoJJdpIkSZIkSbPEzdt/w1AOjFpW7dlOkiRJkiRJkjT1TLKTJEmSJEmaJZol1K3tXcW2gbvbEI0kSZIkSZIkzQ8m2UmSJEmSJM0CQznIDdsua7ru+m2X7OVoJEmSJEmSJGn+MMlOkiRJkiRpFrh1x+/prW1vum6VSXaSJEmSJEmSNG1MspMkSZIkSZoFmg0VW7dmx1X0DfXsxWgkSZIkSZIkaf4wyU6aQhFxYES8JiLOiIgbImJnRGyJiIsi4tUR0fRvLiIeFRE/iYiNEdETEVdHxFsionOMfb08Ii6LiO3lPs6LiGeOUX5xRHwgIq6LiN6IWB8Rp0fEfafi2CVJkiRJ0yczx0yyqzHIjdsv34sRSZIkSZIkSdL8YZKdNLVeAHwJeDhwKfBp4PvAA4AvA6dHRFQrRMRzgAuAxwFnAJ8HFgCfAr7dbCcR8XHgVOCwcn+nAQ8EfhQRb2pSfiHwc+B9wFbgM8AvgOcCV0TEwyd/yJIkSZKk6bau9wa2Dd41ZpnrHTJWkiRJkiRJkqZFV7sDkOaYVcCzgbMzs1ZfGBHvBi4Dng88jyLxjojYjyJJbgg4KTOvKJe/FzgXODkiXpyZ365s61HA24EbgYdm5qZy+ceAlcDHI+LHmbm6EtfbgEcD3wNeVI8tIr4DnAn8d0Q8sBqzJEmSJGnmGKsXu7obt1/OUA7QGd17ISJJkiRJkiRJmj/syU6aQpl5bmb+qDFZLTPXAV8s755UWXUycDDw7XqCXVm+F/jH8u5fN+zmDeX8g/UEu7LOaope8BYCr6wvL3vOq9d5RzW2zDwLuBC4H3DiuA9UkiRJkrRXXT+OJLu+Wg+37PjdXohGkiRJkiRJkuYXk+ykvWegnA9Wlj2hnP9Pk/IXAD3Ao8rhXsdT56cNZQDuBRwFrMrMm8dZR5IkSZI0Q2zsv4MNfWvGVXY8Pd5JkiRJkiRJkibGJDtpL4iILuBl5d1qctx9yvmqxjqZOQjcTDGs8zHldpYChwPbM3Ntk11dX86PG88+xqjTVESsbDYBx++uriRJkiRpcq7fOv7Eueu3XUJmTmM0kiRJkiRJkjT/mGQn7R0fBh4A/CQzf1ZZvqycb2lRr758/0mWn2wdSZIkSdIMMZHe6bYN3s263ut3X1CSJEmSJEmSNG5d7Q5Amusi4u+AtwN/BP5qotXL+US7IZhI+XHvIzMf3HQDRW92J0xgn5IkSZKkcdgxuInbdv5hQnVWbfs1hy3ebWflkiRJkiRJkqRxsic7aRpFxBuBzwDXAo/PzI0NReq9yC2juf0ayu2ufLNe6ya6D0mSJEnSDHH9tkuZ6O+urt92yfQEI0mSJEmSJEnzlEl20jSJiLcAnwN+T5Fgt65JsevK+S5dDEREF3A0MAjcBJCZO4DbgX0i4rAm2zu2nK8azz7GqCNJkiRJmgEmMlRs3Ya+NWzqXzsN0UiSJEmSJEnS/GSSnTQNIuKdwKeAKykS7Na3KHpuOX9ak3WPA5YAF2dm3zjrPL2hDMCNwC3AcRFx9DjrSJIkSZLarG+oh9U7rpxU3esnkZwnSZIkSZIkSWrOJDtpikXEe4EPAyuBJ2bmXWMU/x5wF/DiiHhIZRuLgH8p7/57Q50vlvP3RMQBlTorgDcCfcBX6sszMyt1PhoRHZU6zwEeSzGc7fnjPERJkiRJ0l5w046VDOXApOpOpgc8SZIkSZIkSVJzXe0OQJTzmZwAAFc6SURBVJpLIuLlwD8BQ8CFwN9FRGOx1Zl5KkBmbo2I11Ik250XEd8GNgLPBu5TLv9OtXJmXhwRnwTeBlwdEd8DFgAvApYDf5uZqxv2+UngmcDJwKURcQ5wFPACoAd4VWbW9vgBkCRJkiRNmVVbJ58od1vPH+gZ3MKSrmVTGJEkSZIkSZIkzU8m2UlTqz4cayfwlhZlzgdOrd/JzDMj4kTgPcDzgUXADRRJdP9W9kQ3Sma+PSKuBt4EvA6oAb8BPpaZP25Svi8ingS8C3gJ8FZgK3Am8P7MvHaiBypJkiRJmj5DOcCN2y+fdP2kxg3bL+VP93/KFEYlSZIkSZIkSfOTSXaakyJiAXANsCoz/2Ic5Z9P0WvckzLznMnuNzNPAU6ZRL1fAc+YYJ2vAl+dQPmdwPvLSZIkSZI0g92y43f01Xbs0TZWbbvEJDtJkiRJkiRJmgId7Q6gUUQcHxGfjYjfR8SWiOiPiDsi4uyIeHVELGoonxGxS09fDWVWl+VWTHB5fapFxOaIuDgi3hgRYyYnRsQpDfV3N60u660YR9mTxv1g7hpXy8cqIu4dETeWZT5UWX7ebuJZ1Gx7Zd1nR8RPI2JDRPRFxK0R8cOIeERDuQeVj9mvImJt+ZzfHhHfiogTJnm4fwfcG3jfOMv/gKInuE9GxIz7u5AkSZIkzS+rtk1+qNi6m7f/hoFa7xREI0mSJEmSJEnz24zqyS4i3kfRy1YHcAlFL13bgUOBk4AvA38NPGQvhfQZYDPF0J9HA88DHgk8sbzdynlNlj0IeA5wFcUQnVWbG+5vAT7dYturx9jvpETEg4GfAAcBf5uZn2tS7AMtqg822V4H8EXgtcCtFAlsd1M8j48AHkzx/NZ9EXg4sLIsu53i8XoxcHJEvDAzz5jA8SylGHr155m5cjx1MjMj4iPAd8r9fnO8+5MkSZIkaSpl1rh+2yW7L7gbg9nPzTt+y3H7PnIKopIkSZIkSZKk+WvGJNlFxLspErluBV6QmZc2KfNM4O17MaxPZ+bqyv7vD1wOPDciTszM85tVyszzaEi0i4hXUCTZXVkOKTqWzeMoMyUi4knAGcAC4MWZ+d1m5SYYz9spEuy+DrwmM/sb9tndUP4bwEsz84aGcn8JnAZ8KSLObtzOGF4C7A+cOoGYAc6iSHj8G0yykyRJkiS1ydre69k2ePeUbGvV1l+bZCdJkiRJkiRJe2hGDItZDtd6CjAAPKNZgh1AZv4YeNrei2yX/V/DSPLcw9oVx1SJiBcDZwM14GmtEuwmuM39KIZovQ14bbPEuMwcaLj/2cYEu3L5N4DrgQOBB04gjFcD/ezaY+CYMrOvrPPoiDh+InUlSZIkSZoqq7bu+VCxdTdsv4xaDk3Z9iRJkiRJkiRpPpoRSXbAK4Fu4PuZ+fuxCpaJUO0U5TzbGkTEeRGREXHSJOu/maK3to3AiZn5y92Uf1FEvCsi3hYRT4+IhS2KPhvYB/g20BERJ5f13hgRfzaJUOsJebsMS9sizmUUwwn/JjN7JrG/X5XzJ02iriRJkiRJe2zVtqlLsts5tJXbeq6dsu1JkiRJkiRJ0nw0U4aLfUw5P2eyG4iIU8ZYvf9kt9uwj/sDJ5Z3m/a2N0X2b3E8V2bmmXu68Yj4V+BdFL3EPTUzbx5HtW833F8fEW/MzO81LH9oOR8A/gDcs2Hf3wdeNp4EuIh4OHA/4HZgzOTLikcCncAV4yzf6PJy/jjgc5PchiRJkiRJk3J3323c3X/rlG5z1bZfc9TSiXQQL0mSJEmSJEmqmilJdoeV89v2YBvvn4pAGrwlIjZTJG0dDTwPWAyckZkXTsP+6pbR/Hi+ysgQqC8DlgC3TGL776JIgnvaOBLszgI+DvwWuJsiae7lwNuB70TEMzPzp5Xyh5Tzd5R1XghcS5Es93ng+cB24BVj7TQiDgC+Xt59W+a4x7Y5qpyvHWf5RusatiNJkiRJ0l5z/RT2YjeyzUt44qGvJSJ2X1iSJEmSJEmStIuZkmS3x0OwZmbLK8URsZqGHtXG6c31zVMkhl0NnAZ8cRLbmog1mblirAKZOZnkurqfAU8FvhkRT8vMzWPs51MNi64D3h0RdwCfBT4EVJPsOsv5TuBZmVlPWrssIp4NrAL+KiLek5m3N9tnRCwFfggcC3w0M0+fwLEdWM43TaBO1cZyftAk60uSJEmSNGmrtl0y5dvcPLCODX2rOWTR0VO+bUmSJEmSJEmaDzraHUDpjnJ+RFuj2NXRmRmZ2ZGZ+2XmIzLzc5k52O7A9tBzKJLYHg6cGxGTSSj7MjAIPCgi9q0srye3XVJJsAMgM9dSDLPbATyk2UbLBLuzKYYQ/mRmvnOCce0s54smWK9uccN2JEmSJEnaK7YPbOT2nX+Ylm1fPw3Je5IkSZIkSZI0X8yUJLuLyvkT2xrFPJGZfRTDtp4O/DlwXkTcY4Lb6AW2lXeXVlZdV843t6haT8Jb3LiiTNb7KXAiRQ92b59ITKX15fzAMUu1Vq+3fsxSkiRJkiRNseu3T18i3KppGIZWkiRJkiRJkuaLmZJk9xVgAHh+RNxvrIIRsXDvhDS3lb3xvQT4KnB/4PyIGHdPghFxH+AAikS7uyqrzinn929Rtb58dcP2lgH/CzwW+OAkerCru7qcHz/J+vV6V06yviRJkiRJk7Jq6/Qlwq3rvYGtAxumbfuSJEmSJEmSNJfNiCS7zFwNnAIsAM6OiFZDiT6NoqezeS8ijoqI4yNiyWS3kZlDwCuB/wCOAy6IiBWVfRwTEYc32fdBFImRAN+uDp+bmVcBvwLuGxGvaaj3GuC+wI3A5ZXlBwC/AB4BvD8z/3GyxwRcA2wot7WLiFhSPm5Htahfr/fLPYhBkiRJkqQJ6RvqYfWOq6Z1Hw4ZK0mSJEmSJEmT09XuAOoy80MR0QW8H7g8Ii4GrgC2A4cCjwOOLZcJvkYxrOrjgfMmu5HMTOANEbETeAtwYUQ8ITOvp3jMvxwR51Mkxm0EjgKeASyjeC7e0WSzr6YYAvhLEfE8isS3+5X1eoBXlAl+dT8AHlLuoyMiTmmyzTMz88rxHE9EnAG8LiLun5nXNBR5GEUC3fnASU028RSKoW7P3d2+JEmSJEmaKjduv5wag7svuAdWbbuEBy9/1rTuQ5IkSZIkSZLmohmTZAeQmf8UEd8F/oYieeyVwCLgborhOz8CnNa2AOewzHxrRPQA76bo0e5JwEqKx/vBwIOA/SiGh/0dcDrwH5nZ32Rb10XECRQJk08HnkSRoPct4J8z8w8NVY4u5/cq6zSzmvEP4foF4HXAy4BxDzsbEcdR9GT3mczsGW89SZIkSZL21Kq90MvcLTuupndoO4s695n2fUmSJEmSJEnSXBJFR2bS3BIRPwP+DDg6M3eOs84ngDcB983Mm6YzvrkmIlaecMIJJ6xcubLdoUiSJEnSrDNYG+Azq15Mf21cp6975NmHv4P7Lztp2vcjSZIkSdozGzdubHcImgbLly9vdwiSZhnfD6ZW2Q7HZOp2TG0o0ozx98BBFL0i7lZEHAb8NfBZE+wkSZIkSXvTmp6r9kqCHcD12369V/YjSZIkSZIkSXOJSXaakzLzd8CrgN5xVllBMRzxv0xXTJIkSZIkNbM3E99u3H4Fg7WBvbY/SZIkSZIkSZoLutodgDRdMvNrEyj7a8Cf80uSJEmS9qrMGqu2XrLX9tdf28manqu41z4P2Wv7lCRJkiRJkqTZzp7sJEmSJEmS2uSOnavYMbRpr+7TIWMlSZIkSZIkaWJMspMkSZIkSWqTVW1IeLt+26Vk1vb6fiVJkiRJkiRptjLJTppCEXFyRHw2Ii6MiK0RkRFx2m7qPCoifhIRGyOiJyKujoi3RETnGHVeHhGXRcT2iNgSEedFxDPHKL84Ij4QEddFRG9ErI+I0yPivntyvJIkSZKkPdOOJLvtgxu5Y+eqvb5fSZIkSZIkSZqtTLLbSyKiMyJeGxHnl8lUA2Wi09UR8eWIeHal7EllclZGxM0R0fR5ioh9KolcGRErWpQ7MiI+HBErI2JTZd+/iIg3R8SyhvL/r0z6Wl0mcW2NiN9FxCcj4ogpfWDmnn8E3gQ8CLh9d4Uj4jnABcDjgDOAzwMLgE8B325R5+PAqcBhwJeA04AHAj+KiDc1Kb8Q+DnwPmAr8BngF8BzgSsi4uETOD5JkiRJ0hS5q+8WNvbf1pZ9X7/tkrbsV5IkSZIkSZJmo652BzAflD2S/Rh4GrAZOBu4DVgO3At4CXA88MOGqoPACuBJwP822fSLgX3Lck2fy4h4DfA5YCFwFfAtYBNwIPAY4NPAe4GDKtVeD2wHzgfuBLqBPwfeCrw6Ik7KzN+O6+Dnn7dSPLc3ACcCv2xVMCL2o0iSGwJOyswryuXvBc4FTo6IF2fmtyt1HgW8HbgReGhmbiqXfwxYCXw8In6cmasru3ob8Gjge8CLshwTKCK+A5wJ/HdEPDAdK0iSJEmS9qp29GJX3fdJh76ibfuXJEmSJEmSpNnEJLu94/9SJNhdBZyYmVuqKyNiCdCsN7FfAI8HXkvzJLvXAmuBW5rVj4iXUCRxbQKen5lnNynzaIre06oekJm9Tcq+FvhP4IPAM5rEM+9l5nBSXUTsrvjJwMHA1+oJduU2eiPiH4FzgL9mdI92byjnH6wn2JV1VkfE5ykSJl8JvL+MISp13lFNpMvMsyLiQuCx7CYhUJIkSZI09VZtbV+S3d39t3J3320cuNAO6yVJkiRJkiRpdxwudu94VDk/tTHBDiAze6rJWRV3Az8AnhMRB1dXRMSfAg8DvkLRkx0N6/cFPlvefXGzBLty37+iIUGvWYJd6fRyfmyL9ZqYJ5Tz/2my7gKgB3hUOdzreOr8tKEMFD0lHgWsysybx1lHkiRJkjTNtg3cxdreVW2NwSFjJUmSJEmSJGl8TLLbO+4u58dNou6XKIZrfXnD8tcCCfxXi3onUwxHe0lmNusFb1hm9o0zlmeV86vHWV5ju0853+VblcwcBG6m6G3yGICIWAocDmzPzLVNtnd9Oa++zlruY4w6LUXEymYTxXDHkiRJkqRxun7bpe0OwSQ7SZIkSZIkSRonh4vdO34AvBN4Q9nD3BnAysxcM4665wE3AK8BPg4QEYuBlwLnZOZNLYYlfUw5P2eyQUfEa4AjgH2ABwJPAtYA75rsNjXKsnK+S++GDcv3n2T5ydaRJEmSJE2zVdvaN1Rs3W07/8COwU0s7Tqg3aFIkiRJkiRJ0oxmkt1ekJm/jYiXAp+hSI57KUBEbKQYFvS/M/NHLepmRHwZ+HBEPC4zL6DopW5/il7uWjmsnN+2B6G/htFDyV4OvCQzb9iDbWr86tmTOcF6Eyk/oX1k5oObbqToze6ECexXkiRJkuat3qHtrNlxVbvDAJIbtl3Gnx3w1HYHIkmSJEmSJEkzmsPF7iWZeTpwFPBU4J+BH1M8/v8H+GFEfDVadEkHnAoMUAwRC/A64C7gzDF2OdkErWrMj8jMAA4CnlIuXhkRT5vsNjVKvRe5ZS3W79dQbnflm/VaN9F9SJIkSZKm2Y3bL6fGULvDAGZGj3qSJEmSJEmSNNOZZLcXZeZAZv5vZr4vM59Fkbz2ImAH8DLgOS3q3Qn8CHh+RDySYijYr2Zm/xi7u6OcHzEFcd+dmT+nSLTbCXytHLJWe+a6cn5c44qI6AKOBgaBmwAycwdwO7BPRBzWWAc4tpyvGs8+xqgjSZIkSZpG1229uN0hDLt5x2/pr+1sdxiSJEmSJEmSNKOZZNdGmTlU9nD3qXLRE8Yo/p/AYuD08v5YQ8UCXFTOnzj5CEfLzM3Ar4GDgftP1XbnsXPLebOeAR8HLAEuzsy+cdZ5ekMZgBuBW4DjIuLocdaRJEmSJE2TNTuu4rptMyfJbigH+O3Gn7Q7DEmSJEmSJEma0Uyymxm2lfNWw8UC/BxYQ9Ez3QWZed0YZQG+B2wEHhkRTxqrYEQsHG+gwOHlfHACddTc9yiG/X1xRDykvjAiFgH/Ut7994Y6Xyzn74mIAyp1VgBvBPqAr9SXZ2ZW6nw0IjoqdZ4DPBa4Fjh/Co5HkiRJkjSG3qHt/Pj2TwLZ7lBGOW/9qazdeX27w5AkSZIkSZKkGcsku70gIv5vRDy5muBUWXcP4LXl3QtabSMza8DzgOcCr9vdPjNzG/B35d3vRMRTW8T2CIre6er37xkRx7Qo+3rgocCtwO92F8N8FBH/JyJOjYhTgXeVix9ZXxYRH6+XzcytFM99J3BeRHw5Ij4KXAk8kiIJ7zvV7WfmxcAngXsBV0fEpyLi88AVwHLg7zNzdUNYnwQuBk4GLo2ID0fEN8vt9wCvKl9fkiRJkqRp9LO1X2Dr4IZ2h7GLGkOcddtH6BvqaXcokiRJkiRJkjQjdbU7gHni4cCbgXURcRFwc7n8aOAvKIaBPYsi6amlzPwN8Jvx7jQzvxERi4HPAf8TEVdSJFttAg6kSOT6M4re1Or+HPhBRFwMrALuLMs+AnggsB34q8wcGm8c88yDgJc3LDumnKDojfDv6ysy88yIOBF4D/B8YBFwA/A24N/KnuhGycy3R8TVwJsoEi5rFK+Lj2Xmj5uU7yt7M3wX8BLgrcBW4Ezg/Zl57WQPVpIkSZI0PtdsOY9rt57X7jBa2jRwB/+77gs86/C/331hSZIkSZIkSZpnokkOj6ZYRBwJPBt4EnA/4DCKZKq7gd8C3wS+We9NLCJOAn4JfCMzXzqO7V8EPBo4ukkvZvX9vwl4MkWy11JgM/B7iuS+/y57VSMijqJICHwscE+K3tF6gZsohqz9TGbeOuEHQXNaRKw84YQTTli5cmW7Q5EkSZKkGWfrwAa+fOPf0Ffb0e5QduuZf/J2Hrj/E9sdhiRJkiTNexs3bmx3CJoGy5cvb3cIkmYZ3w+mVtkOx2TqmmQnaY+ZZCdJkiRJzWXW+Naad7Om5+p2hzIu3bGIVx3zWZYvPLzdoUiSJEnSvGZSxdxkkp2kifL9YGrtSZJdx9SGIkmSJEmSpLrLN545axLsAAayl7Nu/wiDtYF2hyJJkiRJkiRJM4ZJdpIkSZIkSdNgfe/NnLf+1HaHMWHrem/g/FkYtyRJkiRJkiRNF5PsJEmSJEmSpthgrZ8f3v4xhnKw3aFMymUbz+DGbZe3OwxJkiRJkiRJmhFMspMkSZIkSZpi56//Ghv6Vrc7jD3y4zs+yfaBje0OQ5IkSZIkSZLaziQ7SZIkSZKkKbRmx1VctvGMdoexx3qGtvCjOz5OZq3doUiSJEmSJElSW5lkJ0mSJEmSNEV2Dm3jR7d/Ash2hzIlVu+4kl/f/d12hyFJkiRJkiRJbWWSnSRJkiRJ0hT537VfYNvgXe0OY0pdsP7r3Nbzh3aHIUmSJEmSJEltY5KdJEmSJEnSFLhmy3lcu/X8docx5ZIaZ93+EXqHtrc7FEmSJEmSJElqC5PsJEmSJEmS9tDWgQ38bO3n2x3GtNk6sJ6frv03MufGMLiSJEmSJEmSNBEm2UmSJEmSJO2BzBo/vv0T9NV2tDuUafXHrRdx1eaftTsMSZIkSZIkSdrrTLKTJEmSJEnaA5dtPJM1PVe3O4y94ufrvshdfbe0OwxJkiRJkiRJ2qu62h2AJEmSJEnSbLW+92bOX39qu8PYawaznzNv+1defvSn6e5Y2O5wJEmSJGleqjFIz+DWYhraTM/gFnqGtgKwtGt/lnbtz5LO/dmna38Wde4DRHsDliRpDjDJTpIkSZIkaRIGa/388PaPMZSD7Q5lr9rQt4Zz7vwyTzvsje0ORZIkSZLmlFoO0TO4hW2Dd7O+Zy09Q1vKBLpyPriVnqEt9Na2j3ubndHJks79Wdq1jCWdB5RJeAewtHMZS7r2Z5+uA0zEkyRpHEyykyRJkiRJmoTz13+NDX2r2x1GW/x209kcvfRB3Ge/R7c7FEmSJEmadTKTrYMbuGPndazduYo7dl7H5v61bB/cRFIDYMHOA6dkX0M5xLbBu9k2eHfLMh10sqRrGft07c++XQexfMERHLTwSJYvPIKuWDAlcUiS2mcoB0lqtul7yCQ7SZIkSZKkCVq940ou23hGu8Noq7Pv+DT3WHwsy7oPaXcokiRJkjSj9Q7tYN3O67lj53XF1HsdOwY3tTusYTWG2D64ke2DG1nHTcBlAATBsu5DOWjhERy44EgOXHgUBy48nK5Y2N6AJUljqjHEXb1ruKN3FWt3Xs+GvpupZY1DF92Lo5Y8gCOXPID9ug9ud5izjkl2kiRJkiRJE7BzaBs/vv2TQLY7lLbqq+3gh7d9lL9c8RE6orPd4UiSpFkgM9k8sI5bdlzNmp6r2dy/ju6OhSzsWMKCjiUs6FhcTJ2LWdCxpFy+eHha2Fm/v4QFHYv8DCJpRhrKQTb0rh5JqNt5HXf338ZsPIdMinZ788A6buCK4eX7dx/KgQuP5MAFR3LQwiM5cOERJt7NE0M5QNDhe7A0wyQ1Nvbdwdqdq1jbu4p1vTcymP27lFvXewPrem/gso1nsqz7EI5cfH+OXPoADl10DEFHGyKfXUyykyRJkiRJmoD/XfsFtg3e1e4wZoTbdl7LRRu+yeMO+at2hyJJkmaoLQPrWbPj6jKx7iq2DmyYsm13x0IWd+3HoQvvxT0W35vDFh3LYYuPY0nXsinbhyS1kpn013ayffBu7uy9aTih7s4WiQ1zyeaBO9k8cCc3Nk28O4IDFhzG4s79WNS5D4s69zFxYxbJTHYObSuSK/vXsql/7ajbxbDDyaKOfVjctR+LO/dlced+5bQvSzqXsbhrZNmS+rqufemM7nYfnjSnbB5YV/aSuop1O6+nL3dOqP6WgfVsGVjP77f+kgWxiCOW3I8jlzyAw5fcl4UdS6Yp6tktMmdfxrykmSUiVp5wwgknrFy5st2hSJIkSdKUyUwGs4/eoe3D0207/8B567/S7tBmmODIJffj4IUrOHjhCg5ZdDQHL1zBwk4vxklz1VAOsGNwMzsGN9HdsYh9uw6ak3/ztRxiKAcYzAGGauU8+8v5AIO1/pH15ZxMujsW0d2xiAUdC4vbsaiyzJ63NPdtH9jImp6rWLPjatbsuIrNA+v2egz7dR/MYYuO5R6Ljx2eL+7cd6/HIWl2qn/W2T64iR3ltH1wYzmvLtvEYPZNWxwLdh44bdve2xZ2LGFx574s6lzKoo59y+S7Yr64Yx8Wde0zvHxh55I5nZS3fPnydodALYfYOnAXm8sEuk39a0fd7qvtmJb9LuhYPJx8d8CCP+Hwxcdz+JLjOXTRMSbgSWPYuHEjANsHN7J25yruKHur2zm0bVr2FwSHLroXRy55AEcuuT/Lug+Zlv20S9kOx2TqmmQnaY+ZZCdJkiRpJqvlEDuHtrJjcDM7h7aNJM3VttM7tI3eoR2V+yNTX207QznY7vBnrWXdhwwn3h28aAWHLFzB8oVH0BkOrCDNRPUeK3YMbtzly+MdQ5vYPrCxmA9uorfJhfwFHYvZt+sg9u0+cHi+T9eB7Nd9EPt0Hci+3QeytHN/Itr7hWnfUA9bBu4se+O4ky0D69jSv57NA+vYObSNwexnqFYkzdUYmpYYOqNrl8S77o6Fw8vqQ2Iu7FjKws6lLCyHzCxuLy3XFfe7YxERk/puQJoyOwY3Dw//umbH1Wzsv63dITW1f/c9OGzxsdxj0bEctvhYDl10bxZ1Lm13WJKmSC2HGMx+Bmp9DNT6GMw+Bmq95f1eBrK6fOR2f623PF8sEulafdZph7mUZDdRizqWsqhzXxaXyXhFgt6+Za9p+w73oraocx+6YkG7w52QvZVkN1jrL5Pm7mBT/9pyvo7NA2vZ0n/ntH3WnYyuWMA9Ft2bw5fclz9ZfB8OX3xf9u2ev69/zQ+jf9y7g77a9so1yh30DV+/3MHOLQNs7L+97Ely71vWfXA5rOwDOWTR0XQwu384ZpKdpLYyyU6SJElS1aief3KAoRwkCDqji47oKuZ00Rmdk0q2yKzRW9tBz+BmdgxupmdoCz2DW9gxtJme6v3ydvGrTq9/zAQddHHQwiM5eNGKSs93K9i366BZmyQylANsG9jItsG72DZwF9sG7yZziI7opjO66Iru4dd9cbtxeTddHSNlOuvLYgFdsWDWPi6aOpnJQPayc3ArO4fK5ODadgZzgFoOkTlEjSFqWU7UqOVg5f4QtayVt+vLawzlADuHtlYS6jZTY3oTizvoZJ/u5aOT8boOZFHnvkWSWZlw1hULy8Sz+rSQ7lg4rveMwVo/WwaKpLkt/XdW5neyZeBOdg5tndZj3NuCjjLhbkklAW8pCzoWV9qXbrrKtqezvD2yrJvOjtHr61NXdNPVsYCuWEj38HzhrOqJrz6MX19tB7WsFd+iRBAElP8W86D4P0buE8NtcHV5R3TSER0E5TSL2unMGkNlWzCUg9SymBfTwPBnuPqy5usHh8tsGljLmh1XcVffmnYf2qQtX3A491h0LIcuOoZ9upazpGtZOczdfizpXEZ3x8J2h6gGxet4sHKuMVC+Jw4yWBu9rH67M7pY3LmMJV3LWNq5Pws6Fs+qv93ZLrPGzqFt9AyV52iDm9kxtIWdg1tGnTNW25xate1hdNvUWHaw1sdA9jGUA+0+1Ck3n5PsJqI7Fg4n5FWT8RaVQ5cevPAolnTt3+4wh01lkl3fUE+RQDdwRzmcaz2hbi3bBu+asv20w37dB3P44vvyJ4uP5/DFx3OPxfeyt7sZbLA2QH+th97aDvqHiiFLi8/MQZRzKrfrn6M7Kp+pix4sY/h2/dP6ZCVZ9kDeVyZh9zOYfQzW+hnIYtlguWyg1s9QQ5n68iyvKY5EU54jNHyWGIk3Gu5TJtON/Mi3b2gHvUM7xn0OPpPeDzroGP5B2KLyh2CLhn8Qtg+L6uemHUXPpIs692Fhx2ImmdM2LUyyk9RWEbHyAQ86/oQfXfQtOugs3yyDoLOcj77d7ELcrm1RNtzbta1KatSyNmqeWaPGEJnZsLy4yFysH5lDjcxiW0mSWc7JYq+V+5m1MpIc3n6SdNBJV8cCujsW0hULi9vD80WV+wvpinq58X1RMtaFr9G3h3a5eFAfyqTxgkLxYaL5xYZa1kZdNK3Pu4djX0RXeWG7flyN5Tqju/K8jFzorz83tRwaWZc1ctTF/lq5fmiXLwNGl2tcXy0zNLz/jugsv8DtLC8Qd4364qqj8gVWUbZ71Lqu6GZp1/4s6VzW9l/aa27or/UWX7wO3MXWwQ1sG7iLHYOboTyR6IjO4oQiOodvF/MiAWHUbYoyxe1iXb3Fjfrt4Yv+MfIFwKgTmNHralmrfOlW/TJucDdf2o3cH91iZ/lvVu9WSjSsL3VFN90di1kwPC2iu2MxCzsWl8sXsaBjCZ3R5QXRWSCzRl9tJ31D2+mr9Yxu7yu3i/fv2qjXWv29fNR7B5UvdWqVC6wMDr/vVb/0Gbk9sryWQ0R00BmdZZJP0e7X3zc6y9v1dcP3q+8h5d9i/e+p+EKuo7xUUP+irfrlXPN11QsIw3+T1b/Vyt9x079pYtT72XByRkd35X53Ge/e/3sZ+UzW6j278hoon+c93ic5/Flp5EvAweH2rfpZqn6/Vr6G6vczh4jK54iOerscXXTQUbY/xbJORq8beS11Dn82nM4vpovEiz4Ga8Wv74tf54/+pX71eIePmaHKYzA0qq2vf74cecyGxvVZsvj8WQyhN5HnMugoX6+dTT+rjfyddjJQ62XH0GZ2Dm6dUb+41p5b0LGY7o5FdFJtc+t/W527tNXF319j2W4WdixmwXBvT0uGe3sataxjKV0d47swP/z5bfAutg3cPfr24F1sHbiLnqHN0/rYjH2euaByTtZ4LreAhR1LWdK1jMWd+w0nDCzq2GfWfIaqnhOParNHnRcPjfrytXW5wcr1guKcn+Hz+/J25Zy/ep+slyjatl3ed2PXJMnOUe3ZSAJTR3QRdNBX2z7cs+bOoa273N45uI3e2rbh29Od/DZb1F/j9US8kV7gFtE3tIPNA3eyvU2/7J9Phq+Fle3RyPWuhU3apwUtli8cqdtkeXdlffEZoN7Dw7bhL6aqvdPurCzvrfZaO7R92j8z1JPtOqJ+bt4xfK4++py9Y/h8fuSa6egvFDtGff6PUecC1M8WoqNyrl69NjhyjbCWQ8U5UsPn3qn4vD3fdMeiXRLvlpTJWks69yvfZ5cNl1nYsWTWvM9ORpHQ1D/cE1i917DiS+q+8txk9JfVwwlxTa9VDza9ll1rkkQ3lb2MdkbX8PO4eNRz2ny+qGOp14dLmUmNIqGx3lt48SOn4gdQ1R8+FQl1W+gZ2mL7M0kzKalittunazmHLjqGQxYezaGLjmH/Bfdo25C0E02yq+UQG/tvZ13vjWzqu51NA2uHh3ftGdoyTVHOPJ3RXfR2t/h4Dl9yXw5ffDz7dh/U7rAmrJ54XLSfZds5uJne2vbye+naqO+ni2um9XPVocr32bVyfbUO4/yxX9fwj3Eal3fQWf5IpYe+oR3013rK2z301XYM3+6v9dA7vH6HI0FMs9n+fhBEpXf24lpd9Tp6/fpf9bpf/Tua6vX5+nX54pp8/bW8gE666Oyo/mhs9O1GJtlJaquIWPkn99vvhDd999HtDmVWqV407IiOkS8GKr8I9cRzZuiMrsqv7A9mv+6D2Lf7IPbrKub7dh80I4a8UXsVX8BuYGv5xevWgQ2VL1+LhLre2vZ2hzmndNA5nIhXH1KpPk3FMHSdHd0s6tin/DXOPizq2IdF5e2Fo24vmfN//7UcKk70y6ETW3VZ3ld2az7yS6wisc73s5mh8cSymgAQdDT80GD4Ms6oRIORdbWRZIPKDxYak+B97purfzE9kgxT+RK68oV1Z3SPGsZmlwS6MolO0sR1RlfZ09MSFnUW84UdS1jQuZidg1uHE+nm4ue3DjqLpLuu/cqkgP0qXy6XyXhl0sCSzmWVtqhsf7JvVCLvYK26rnekzcrecl3xxfuuSb61UUnQoxOBB4e/KJCk4rOq7YFmhwUdi9mv+xD26z6YZd0Hs19XcbuYDmHf7gOn5JrFZA3lYJHQPbiVnqGt7BzaSs/glmI+fL+YD9R6d0mmm69/i/VeQ0f3xBNNk2Kbr6sm1Y70hjnqB7f1RNzKD2+ryblFnXpP4PUOBnZNtKA8j69RGz6nH52wUav8kGHkc1njZ7L6jxxz+DOb5/ntMNuTKmayBbGIQxat4JBFx3Doontx0MKj9tqws2Ml2Q3lIHf1rWHdzhtZ13sDd/beyPrem7z+00J92OBiGOGR89nFXfuO3O7cd/jHZ4s7952WXpmHcoAdg/WE403sGNpCT9lb+I4yka4YgWETPYMmHmvifD/YM13RRSfddJSJeLG0l7v7b73yH+73kz+f6LZMspO0x0yyk4phr/btPrBIwCuT7/brPph9uw5kQceS4V+BdHV00zj0Sr3XvCL7vn2JOpnJYPbTX9tZTj30D+2kr367tpNaDo3uWaaht8BqTyKj7o9aP3KBqLFL6OpQLO1SyyH6aj1FclBDslBvbceo5XP9C1iNV7Co0jV2kZi3D4vK4Qn26Vo+PC3tOoB9upazsHNJu4MeZaDWx9aBDWwZWM/WgfVsGbizcns92wbu9sRfkiRJkqQ9FuzTtXwkCa+7koTXVSThBTGqR7d6T3C1sue36vLRPcIVPbwP5sBw76g9Q1tGJc711Xa0+wGQNAEmVew9HXSwfMERRW93i4re7hZ37jct+6on2Q3W+lnft5o7e28YTqrb0HezPYJNs8bEvO6OhaNHdih7TB/do/rQqB+JNSsrTSffD6ZW/+KiB/x/uN9PJvyldPt+LiNJ0hxSY7BMSrlzj7YzPLRtx8hwQiNDD3USw0NldRRd5EZlCLuya9yO6Nh1eDu6GMqB4eS5voZEunrXzzMjiaY+tHTjEInV4RQ7hrsQHnlMqo9DZdnw4zK6fFKjb6hnJImunPfVetr9AGjWyWJIotp2tgyMr0Z3LGKf7uXs03UA+3QdWCbhHcDSSkLePt3L92g4t/ovlQH6aztHJc1t6a/cHlg/7cPcSZIkSZIkgGT74N1sH7ybO3b+sd3BSJJKNWrc1X8Ld/XfwjVbzwNg364DOXTR0Ry8cAXdHYvLXibL72cq39nUl3dGZzGM4fD3ECP3M5NN/Wu5u/82NvfexLqdN3BX3y3TPqy9dtVb205v/3Y2tTsQSbOSPdlJ2mMRcXf3oo7lBx+zT7tDkSRJkiRJkiRJkma9qNlfzlyUHfZ6JmlifD+YWtkxyMFHL+XKH99hT3aS2mJr14KO5e0OQpoOG24qhgA1iVTSWGwrJI2HbYWk8bCtkDQethWSxsO2Qprd9lYylm2FpPGwrWgfk3NnDnuykyRpDBGxEiAzH9zuWCTNXLYVksbDtkLSeNhWSBoP2wpJ42FbIWk8bCskjYdthQQd7Q5AkiRJkiRJkiRJkiRJkqSZyiQ7SZIkSZIkSZIkSZIkSZJaMMlOkiRJkiRJkiRJkiRJkqQWTLKTJEmSJEmSJEmSJEmSJKkFk+wkSZIkSZIkSZIkSZIkSWohMrPdMUiSJEmSJEmSJEmSJEmSNCPZk50kSZIkSZIkSZIkSZIkSS2YZCdJkiRJkiRJkiRJkiRJUgsm2UmSJEmSJEmSJEmSJEmS1IJJdpIkSZIkSZIkSZIkSZIktWCSnSRJkiRJkiRJkiRJkiRJLZhkJ0mSJEmSJEmSJEmSJElSCybZSZIkSZIkSZIkSZIkSZLUgkl2kqQZLyI+EhHnRMStEbEzIjZGxG8j4v0RceAktvfYiPh+RKyNiL5y/r8R8YyGct0R8eaI+EpEXBkR/RGREfGa3Wz/mIj4rzLe/ohYFxHfiojjx6izOCI+EBHXRURvRKyPiNMj4r4TPT5pvprLbUW5j+eW5X8fEVsjoicifhcR/xQR+070+KT5ai63FS3qHxcRO8p9nTbR45Pmq/nSVkTEioj494i4qTwPuTsiLo2It0/0GKX5aD60FRHxwIj4RkTcUB7j7RHxy4h4UUT4/YI0Dm1sK46NiHdGxLmVv/s7I+KsiHj8bvbx8oi4LCK2R8SWiDgvIp45RnmvbUp7aC63FeG1TWnKzOW2okVdr21qRonMbHcMkiSNKSL6gd8A1wLrgaXAI4CHAHcAj8jMW8e5rX8E/hm4C/gxsBY4CPhz4JeZ+Y5K2f2BTeXdO4F+4EjgtZn55RbbPwH4JbAfcG4Z95HA88r6T8rMSxrqLATOAR4NXFHWOxJ4QVnnCZl56XiOT5rP5nJbEcWXXn8AdpT1rgX2AZ4K3AtYBTw6M+8az/FJ89lcbiua1O8CfgXcj6LN+EZmvnQ8xybNd/OhrYiIpwI/ALrKuFZRtBX3AZZk5mPGc3zSfDbX24qIeBZFO1EDfgjcWMb0XGA58OXMfO14jk+az9rYVnwbeFG534uAjRTv888GOoE3Z+a/NdnHx4G3A7cB3wMWAC+m+Lv/28z8XEN5r21KU2AutxVe25SmzlxuK5rU9dqmZp7MdHJycnJymtETsKjF8g8CCXxhnNt5QVn+58C+TdZ3N9xfADwdOKy8f0pZ/zVj7OO3ZZm3Nix/JDBAcbLYuJ9/KOt8F+ioLH9Oufya6nInJ6fm01xuK4DDgb8BljbZ94/LbX223c+Bk9NsmOZyW9Gk/vuAPuDvyu2c1u7H38lptkxzva0AjgG2AbcAx+0uLicnp+bTPGgrrinrnNiw/B4UyX0JHNXu58HJaaZPbWwrXgH8eZNyJ1Ikv/XV25HKukeV+7gBOKCyfAVwN9ALrGio47VNJ6cpmOZyW4HXNp2cpmyay21Fk217bdNpxk125y5JmvEys7fFqtPL+bG720YUQ5h8BOgBXpKZ25rsZ6Dhfn9m/jQz144nzog4BngQxS9HPtOwrV8DZ5WxPq1SJ4A3lHffkZm1Sp2zgAspfqFx4nhikOazudxWZObtmfmFzNzRuG/gQ+Xdk8azf2m+m8ttRUP9hwDvpfg16tXj2aekEfOgrTiF4lfgf52Zq3YXl6Tm5kFbcQywNTPPb6izDqj3SnXweGKQ5rM2thWnZuZvm5Q7HziPIrnlUQ2r69cpP5iZmyp1VgOfBxYCr6zE5bVNaYrM5bbCa5vS1JnLbUVDjF7b1Ixkkp0kaTZ7Vjkfz4erRwFHAz8BNkXEX0TEOyPizRHxyCmK5x7lfHX1glLFTeX8iZVl9wKOAlZl5s1N6vy0nD9hakKU5qW50FaMpX6yO7hHUUmaM21FRCwGvgZcCXx4iuKRVJj1bUVEdAMnUyTb/CQiHhYRb42I/xcRz4yIBVMUmzSfzfq2onQNsF9EjBo+OiIOAR5GMRzVtVMUozQftbOtaHUtoX4N8n+a1Gl2ndJrm9L0mwttxWT2IWli5kxb4bVNzWRd7Q5AkqTxioi/p+htYRnwEOAxFB8Wx/MB66Hl/E7gN8ADG7Z9AXByZm7YgxDvKuf3jIjIzGxYf0w5P76y7D7lfJfeI0rXl/Pj9iAuaV6Zo23FWF5VzpudqEpqYY63FR8u15+QmYNF5xKSJmOOthUPABYDvwa+Dbywoc4tEXFyZl6+B3FJ88ocbSsA3koxhNsvIuIsimS8g4D/A2ym6PVi5x7EJc0rM6WtiIh7UiTV9gAXVJYvpRjScXuLnjKbXaf02qY0xeZoWzEWr21KkzDH2wqvbWrGMslOkjSb/D1waOX+/wCvGOeF5kPK+RuAm4EnUQxtck/gE8BTge+yB12SZ+aqiFhF8YHwb4F/q6+LiIcDzynvHlCptqycb2mx2fry/ScblzQPzcW2oqmIeDbweuA24KOTjUmap+ZkWxERTyzLvysz7VlG2nNzsa2ox3UisBN4NXAmxcX5NwLvoOjh7r6ZeReSxmMuthVk5oVlTxanMzohdxvwFeB3k41Jmqfa3lZExELgGxTDs72jOnQbk7tO6bVNaerNxbai1X68tilN3pxsK7y2qZnO4WIlSbNGZt4jM4NimJPnUfyK4bcRccI4qneW86D49cU5mbk9M68BnktxEnfiFAzF8nqgD/hMRPw8Ij4WEd+i+PVG/cPg0AS2V/95RuOvzCW1MF/aioh4FPBNYAfw/IYTWEm7MRfbiojYn+IL70spLohJ2kNzsa2oxNUJ/ENm/ndmbszMWzLzncAPKHqqeu0exiXNG3O0rSAingxcCNwOPBhYSjE05JeBDwLnRIQ/5JfGqd1tRUR0Al8HHg18B/j4ZA9lAmW9tilN0HxpK7y2Ke2ZudhWeG1Ts4FJdpKkWScz78zMM4CnAAcCXxtHtfoJ2k2ZeVXD9nYCPyvvPmwPYzuv3MZ3gT8F3lze/xfgvWWx9ZUq9V9qLKO5/RrKSRqnOdZWjFKe3P4UqAFPy8zL9iQeaT6bY23FJykSY16RmRNJ6pe0G3Osrah+eXVGk03Wl+1RXNJ8NJfaiohYTvFl2U7guZn5m8zsycybMvNtFD1gPgp46Z7EJc1H7Wgryi/CTwNeQNE75UubDB29u+uUzXqk8dqmNE3mWFvRuB+vbUpTZI61FV7b1Ixnkp0kadbKzDUUv7a+f0QctJvi15XzzS3W1z9QLp6CuK7OzBdm5qGZuSAz75WZ/0zxq2+Ay5vEdVyLzR1bzlftaVzSfDVH2ophEfFYipPcBJ6Smb/a01gkzZm24oRyn3+MiKxPwC/L9X9ZLrtyT+OS5qs50lZcV7ndLLYpi0uar+ZIW/EoiuFjL83Mniabq3++eHCTdZLGYW+1FWWPk98CXkzRa9RLMnOwSTw7KHqu3CciDmuyj2bXKb22KU2zOdJWVPfjtU1pGsyRtsJrm5rx7MpdkjTb/Uk5390vGi4ABoFjI2JBZvY3rH9AOV89hbENi4iFwMsofpn17cqqG4FbgOMi4ujMvLmh6tPL+bnTEZc0j8z2tqK+/gnAD4F+4KmZ2TQRT9Kkzfa24gfAFU2qHAY8g+Jzx3kUnz0kTd6sbisyc2N5QfpBZQyX7s24pHlkVrcVwMJyfnCLqvXljfFKmphpbSsiYgFFDzPPoejZ5pWZWRtjP+cCfwU8jWK4tqpm1ym9tintHbO9rajvx2ub0vSa7W2F1zY149mTnSRpRouI4yPiHk2Wd0TEB4FDgIszc1O5vLusc69q+cy8i2KYk2XA+xq29WTgqRRdEv/PHsa7tOwmubqsG/h3YAXw75l5YyWuBL5Y3v1oRHRU6j0HeCzFL0/O35O4pLlurrcV5fqnAD8GeoEnehFKmri53lZk5j9l5msaJ+BjZZFLymX/tCdxSXPdXG8rSp8v5x+MiEWVekcAby3v7pLwL2nEPGgrfk3xxdujy3ORar0jgdeXd8/Zk7ikua6dbUWZRHsGxRfh/8XuvwiHkeuU74mIAyrbWgG8Eeij8iW51zalqTHX24pyndc2pT0019sKr21qNohdh0aWJGnmiIi3UHx4uoDiFwp3A4cCJwLHAOsoTsiuLcuvAG4G1mTmioZtHQL8Crg3cCFwGXBP4LkUXZO/JDO/21DnXcDx5d0HAX8GXAxcXy67KDO/XCn/TODLwC+AW4H9KH5dsQI4Gzg5M3sb9rGQ4pcaj6L4hcY5wFHACyh+0fWEzGzsXUJSxVxvKyLiPsCVwCLg+8Dvmz0OmXlK60dJ0lxvK8Y47pMohlX4Rma+dHflpfluPrQV5Rfg3wf+D8XQLD8Dlpb3lwP/lplvHs/jJc1X86SteB/wAYpe7n4M/BG4B/A8YB/gjMx83vgeMWl+amdbERFfAV4B3AV8oSzT6LzMPK9hP58A3gbcBnwPWAC8CDgQ+NvM/FxDea9tSntorrcVXtuUpsZcbyvGOO6T8NqmZgiT7CRJM1pEPAD4a+DRwBHA/sAOii+Czqb48mdjpfwKWnxgLNcvB/6R4kPi4cA24CLgXzPzkiblz6P4cNrKVzPzFZXyxwH/CjyM4hcjO4GrKH6J8bVWv+qIiMXAu4CXUFyE2krR5fH76x+GJbU219uKyknkmDIzdldGms/melsxxnGfhBeipHGbL21FRHRR/HL8lcBxFEk0V1H0ZnXaGPuXxLxqK54DvAF4CHAA0EPxxfjXgf/MzN0NRSXNa+1sK8bRTgB8oFlSS0S8HHgTcD+Kzwi/AT6WmT9ucZxe25T2wFxvK7y2KU2Nud5WtOK1Tc0kJtlJkiRJkiRJkiRJkiRJktRCR7sDkCRJkiRJkiRJkiRJkiRppjLJTpIkSZIkSZIkSZIkSZKkFkyykyRJkiRJkiRJkiRJkiSpBZPsJEmSJEmSJEmSJEmSJElqwSQ7SZIkSZIkSZIkSZIkSZJaMMlOkiRJkiRJkiRJkiRJkqQWTLKTJEmSJEmSJEmSJEmSJKkFk+wkSZIkSZIkSZIkSZIkSWrBJDtJkiRJkiRJkiRJkiRJklowyU6SJEmSJEmSJEmSJEmSpBZMspMkSZIkSZIkSZIkSZIkTbmIODkiPhsRF0bE1ojIiDhtirZ9Urm93U1H7um+uqYiYEmSJEmSpLkiIlYANwNfzcxXtDcazVcRkcD5mXnSBOqcCrwcODozV09PZFNnMsc4X0TEaoDMXNHGGE5lFr2eJEmSJEnSjPWPwJ8B24HbgOOncNurgQ+0WPdA4HnANZl5657uyJ7sJEmSJEnSnBcRx5e/lvx9RGyJiP6IuCMizo6IV0fEonbHKO1ORJxS/vL2pHbHMpNExHllwp5muIh4UPk6/lVErC3b4tsj4lsRcUKT8hERTyvb7ysjYlNE9EbEdRHx6Yg4dIL736+sd2H5HtAbEesj4rKIeEtELB2jbndE/F1EXFq+j+yIiFUR8bWIOLih7H0j4gMRcVZE3FL51fyYP3qPiMeUdVaXsd0SET+JiKe1KN8ZEX9ZHs+6iOgpY/pKRNx/T49lso9/RPy/Mu7VEbG97KXgdxHxyYg4YqzHYKq3FxHPjoifRsSGiOiLiFsj4ocR8YiGchN6bVbqdZavnasjYmdEbCxjfVSL8ufF2D0rLGooP+nXbGUbERE/393rcKLHIkmSJEkT8FbgOGA/4K+ncsOZuTozT2k2Af1lsf+cin1FptefJEmSJEnS3BUR7wPeT/Fjw0uAyyl+NXkocBJwDLAyMx9Sll+BPdmpzSLieKAnM2+pLDuF4rX8+Mw8r0mdw4BlwI2ZObCXQp20Zsc4ye2cB5yYmTElgc0AMUd7souIS4CHAyuBSyna4gcBTwEGgRdm5hmV8ouAnRQXxS8ArgI6gScAfwrcCTw2M68f5/5XANdSvA+sAjZQ/M08geJX9NcCj8zMrQ31lgM/BR4G/KaMpR84kuJ95CmZ+ftK+bcAnwKGgOuBFcAioDszB1vE9tfAF4AdwBkUv+w/guIX90uAf8zMDzbU+Q7wwrLsj4BtFL/SfxowADw9M8+d7LFM9vGPiBsonturyjLdwJ8DJwJbgZMy87fNHocWj82EtxcRHcAXgdcCt5bHfDfFe/8jgC9k5ucr5Sf02izrBHA6cDJwHcVzsBx4EcXz/fzMPKuhznll3K16WfiX6mtksq/Zhn3+LcXrcYAWr8PJHIskSZIkTUYUPx79JfCNzHzpGOX+L/A6inOzxRTXa78BfCwz+8axnwOB24EacHhmbtrT2B0uVpIkSZIkzVkR8W6KL7JvBV6QmZc2KfNM4O17OzZpLJn5x0nUWQusnYZwpsVkjlGz3jeAl2bmDdWFEfGXwGnAlyLi7Mys/9J8iGJImS9UL4aXCVRfAF4PfBJ41jj3fyuwrFkSakScBvwl8Abgow2rv0aRlPY3mfnvDfWCXUeM+Snwa+DqzNxZJk3es1VQEdEN/CvQCzw4M6+rrPsQ8FvgPRHx8foXCRHxUIoEu2uAh2VmT6XOK4H/pnjsRiXZTfBYJvv4PyAze5sc52speg/4IPCMVo9HE5PZ3tspEuy+Drym8pqq1+1uKD/R1ybAiymS0i4GnliPMSK+CFxU1jk3M7c1xl72qDAek33N1svcB/gI8PEy3lavw0kfiyRJkiRNtYj4L+BVFD8q+wGwmeIHU/8MPDEintzqR2wVrwAWAl+bigQ7cLhYSZIkSZI0R5W9v5xC0XPLM5ol2AFk5o8pev1puo2I+HZE3FUO0XZFmZTXWO4V5RBsr4hiaL3zohiCLytllkXEv0YxzF5vFMPu/SwintRkeyeV2zslIh4SEf9Tbm9TRHw/Io4syx1TxrehHNrtlxHxZy2O5bCI+HwUw+31l3V+EBEPblJ2QRRDCf6m3GdPWe+sFvE+sYxxY3lsqyLiwxGxrEnZ+lB53RHxvoi4sazzxzJhol7uDVEMB7gzIm6LYujFpteyIuLhEfG9KIZL7I9iOMD/iIg/aVa+xTaqz+FfRMTFUQyhuKnc9rF783EtYzmvcn81RS92AL+MyvCClTKnlstWNNn3CyPigvJ1tLN8bP8hIhY2Kbu6nJZExMeiGLKyLyJuiIh3RsQuPcaVj8PxzZ7zVhqPsVx2Srn8pIg4OYohEXvK19a3I+LwStkV5fGfWNlettjuERHxuYi4qTyWu6MYMvKhTeKqxvCSKIbU3F4+Jo8s1/1gjOP6Q7mP5eX9BRHxpiiGXVxTrtsYEb+IiKeP9/GaKuXz+s4o2rNt5bH9ISL+LVoP//n68jXTGxF3RsR/NnuuK6+d/aIYynN1RAxE0QsjmfnZxiSmcvk3KHp8O5CiJ7b68oHM/GDjxfDMrAH/VN49abzHnplDY/Ty+N1yPupvPSKeAPwF8L3GpLRym5mZQw3LrsvMSzNz5zhDW07RO9mqaoJdua0/UPRgthjYp7LqmHJ+TjXBrlTvcaxxGNsJHctkH/9mCXGl08t50/a0lYluLyL2A95H8WXMaxsT7MptDjTcn9Brs1Qf4ugfqzFm5uXAdyge/5NbxD4uk3nN1kUxLOzXKXp7eH+zMhXTfiySJEmSNB4R8QqKBLszgOMy89WZ+fbMfDTFj6lPAt44jk29ppz/x1TFZpKdJEmSJEmaq15JMaTc96vD+DXTYoiBewKXUQzz93WKL5kfAJwVEY9vsamTgR9TDNn3RcoEgIjYn6J3mHcBW4BPA98HHgn8b0S8vsX2HgpcWN7+UhnP84Bzohhq8zKK4QS/BpxNkWz084ioJmIQEUcDVwB/A9wIfAL4GUWyxcWxa+LgqcBnKB6/rwH/RjFUYH0Ywuq2Xw/8HHg0cGZ5bBuBd5bb3r/FsX2bYsiHc4D/Ag4A/jOKJLdPUvRM9BuKC2H9FAkT/69xI1H02PQr4OkUQ018ujzW1wBXRMRRLfbfyvPK47iN4jH4NfB84JIoegSq7nvaHtcmPg2cX97+KsVFxfo0pih6wvoOcF/gm8DngAA+BPwsdu3RiTLG/6U49p8CX6ZI8vkwxXPR6F+BPwDP3V084/Q3FL1HrQY+D/yeYtjCX1QSAzdTHP+a8n71MTm1vqGIOAG4stzmdcBnKYZCfBxwUUS06lHr7RS9gd1C8Zj9NDN/XW7jmVEMOzJKRDyMYgjHH2XmxnLxcornfV+Kv5VPAj+kGO7yJxHxmsbtTJeIOICiLfowRcLWfwP/TvHcvYriNdLoo+V0FcVzcTtFD2FnNCkLsICi97T/Q/Ea+gxFks/u1BOJdvdL9Lp64tQu5SuJkqeMc1sw0hvb1Q3LX1LOT42IQyPi1WWC6iurSZ97aD3FMKDHRUNCb0QcR5FEdWVm3l1ZdU05f0JELG7YXr3t+UXD8qk8lpaP/xhaPcaT1Wp7z6Z4fX8b6CgTdt8VEW+MFonou7HLa7Nshx4F9DDyPl3103L+hGYbjIgXlTG9LSKeHk0Snsdhd4/nP1K0My8fayilyR7LJP/OJEmSJGl33kxx/vWqJj9e+2fgbopevVuKiBMprs9ck5kXT1VgDhcrSZIkSZLmqseU83MmWf8k4JTMHE5iiohvAv9Dkez1yyZ1nkHRa97/NCz/CHA/imHt3pCZWW7vIxRJWv8WET/LzNVNtvfSsiedegz14RIuBj6RmR+srHsvRe9Cr6ZIbKn7IvAnFD3UVMt/gSLJ66sRcc/M3F72TvViYCXw8MYemqqJRRFxT4pEse0UwxX+sWHbf02RnPO6Jo/VURRDAG4uy38C+CPwKYrkqT/NzNvLdacANwB/HxGfqA8HUSaf/AdFItaJ9fLluidQJDR9hoklfj0LeFbZw2F9W2+mSHL7AvDEStlpeVybycxPlwmLJwKnZuZ54zmYiHgk8A8UQw4+LDPXlcv/gSJJ6pkUr+cPNVT9E4qkqifXL2hGxAcoetR6a0R8aIzelabC04CHZubvKsfyTeD/As8BTi9fO6dExEnAPZsNv1j25HQ6RcLN4zPz/Mq6PwEuB/4rIlY0SUJ5AvDIzPxtw/KvUjxe/5ci+a7q5ZUydZvK+G5riG0ZRYLoRyPiG7vr9ax8/t8yVpkmzszMKyv3Pw/8GcVr941lj2T17e9L8x9lPwJ4YGbeUpbrokiie3xEPCwzL2sofxhwLcXf5I7xBBkRD6doI2+nSKgcj1eX88b2djz766JIQIIiCfJxFI/LLykSmqvqvR0eR/FaWlJZNxAR/5SZ/zLRGKoyMyPijRSJpSsj4gzgDuBwivbrGor2o1rn9xHxKeCtwB8jop7gfX+Kv59vV45xOo5lt49/mUB6BMXf3wOBJ1Ekxb5rnPuY7PbqxzlAkUA6aojUiPg+8LImPQA222er1+a9gU7gphZDFF1fzo9rselvN9xfHxFvzMzvtYhjIq9Zouil8z3AhzPzihYx1O3psUiSJEnSlIiIJRTnOncBb4ldB1MA6KP5jwSr6tcip6wXOwAy08nJycnJycnJycnJycnJyWnOTRRJHgk8bYL1VpT1VgOdTdavAe5qWPaKss4ZTcp3Azsokh+WN1n/z2Xd91WWnVQuu7BJ+ceV625ujI8ikSCBr1SWHVEuWwN0N9ne18v1Lyvv71fe/xUQu3ms3lOW/VCTdQcAW4GdwMLK8vPKOk9sUufcct2rmqz7SrnunpVlnyqX/UWL+M6g+OXrvuN43uvP4TlN1nVSJPkN7386H9eyfALnNSw7pVx+Uos6p5brV1SWfalc9rom5Y8DhigSK6rLV5d17t2kzlfLdQ+YyN/VJI7xX5qUf3y57uMNy8+jyFNqto/nlHU+1mL9m8v1z2gSw6da1DmifNwub1i+gOLX1HcCXeN8DN5W7utxTZ6D1Q3LVpRlJzK9olL/kDLuO4Cl44it/np6TZN1ryzXvanFa+fPJvA6OIAieTOBF46zzkMpet3aCtyryfqDKH6xflCL+ouaPFZfA/ZpUnZtuX6wLHNviqFdn1s+16Me5xb7qz8uY74uKHoEvaUhrnUUw+B0tKjz+vKxqNa5gibvfVNxLON5/CvlLmmI6zKatCsTeK2Ma3vAtyrHeTnwMIrEvIeV95MiWXnSr02Knt8SuKhF3WPL9dc1LH8rRXLz4eXr8D4USbt9FH+fT5+C1+xiiqT1q6i8P7V6He7BsYz5d+bk5OTk5OTk5OTk5NRsYuS652lN1h3e5Nyn6TTG9pcDveV56/5TGbs92UmSJEmSpLmq/lPHnGT9K7Oht7HSrRTDvDbT2KMTFF9ALwF+lSPDR1ady8iQbo2a9T5zxxjx1XtyO6KyrL7dC7N5z2PnAi8ty30tM7dGxI8oenS7suzx50Lg0ty1158TKtsYJTM3RcRvKZICj6f4sn+8x7ayybrqsdWHB60/DyeWvfY0OoQiQe64Ftts5vzGBZk5FBEXAfeieJzWML2P61Qa6zlaFRG3AUdHxP5Z9ipY2pKZNzTZ3q3l/ICpDXMXzV4fk9l3/TVyzxZDGtaH5rwv8JOGdc3+nsnM2yLiHODJEXG/zLy2XPUsigu5n8qG3qAi4v4UPQY+jqKnt0UNm93tUJ1Z9HTZ9Cfc4/RQip7qLshx9jBXmuhz0cs4hwONiKUUQ+ceC3w0M08fR53jKIb77QZenJk3NpbJzLsofvXeVGb2FpuKoOi18UkUQx5fERFPy9G9inaW899SDLtZf085IyIGy/j/gcoQxZMRES+lSIr9AUXy9RqKxOn3UvSYeCLwwkr5oOip828o3kNOo+gF9EEUCcg/jYg3Zebnp/JYxvP412XmI8o6B1K0RR+k6KnvRVn2+DpGD42fbmiTxrW9huPcSdEz6bry/mUR8WyKxLm/ioj3ZKUH1IbjnPBrs3ET9bAbjuFTDeWuA94dEXdQDGX9IUaGZ63Wm8hr9qPAMRS9l05Fj6OtjmWXv7OJPJ+SJEmS1MSWcv7bzDxhzJKtvRxYCHx1qs9DTLKTJEmSJElz1R0UyV1H7K5gC5tbLB+k+ZCKUPQ41GhZOV/bok59+f5N1m1psmyw1brMHCyHUejew/2/CHgn8BKgPlxub0R8D/j7zLxzD7Zdj3VCx1ZZVz22+hCr/6/F/uv22c36qjtbLK8/t8sa5tPxuE6l8cR5VFluc2X55maFGXkeOlusnyrN9j+ZfddfIy/YTblmr5Fmf891pwJPprhw+85y2cvLeXWoWCLiERRJjl0Uw1f/kKIHsBpFQtRzKC7+Trf9y3nTpKIxbG6ybKznYn0leaulMonpbIqhvT+Zme/cTRUi4liK4TGXUyR4/XB3dcZSxnk7xdDO1wG/pkhoe2al2CbgYIqhdxuP62ygHzguIpa1aNd2q0xc+2+K5MS/ypFhfP8YEX9F0dvZCyLipBwZKvrlwN9SJHV+uLK5iyLiWcBNwIcj4quZuX0qjmWyj39m3g38PCIup+hh7WvlUNo7KV6X729S7VRatEO72V79OAEuqSTY1euujYhLKYb+fghN/h7G+dqsPz7LmqyDovfSarnd+TJFcuSDImLfzNzWrNDuXrMRcSJFz4en5OihoscylceyPxN8PiVJkiSpLjO3R8Q1wP0jYnmLHyzvzmvL+X9OYWiASXaSJEmSJGnuugh4AsUX6f+1l/bZLLGk/qX0PVrUOayh3FSb8P7LRIVTgFMi4kiK3rdeQdEz2wrgsU22fc14tj3FhhMDMnPrFG3z0BbL64/flob5dDyuU6kaZ7Mep6b7OWq3+nE9ZxIJWWMlip1BkSj30oh4N0XS0dOBqzKzsdfG/9/encfYVZdxGH9ehCpioyjELS4UE0RBNomgFkETEERjoIjGiFUQiVRjEBeURFBcAUWjGKPSEiCibCouoFQWsWJB6oJCiUARd6TUDRXE1z/e37WXM/fOzJ2ZK1KfT3JzZs7cs845Z9pzv+d9j6XaN+7VF5ACICKOoUJ2U5qkQtRkvtQXtFnXhlNWzZul6QTs5lMhpoVUlbDpBOy2pUKKjwIOyswvz3ZF+2XmVRGxjmpb0281VQ1z3YBp/hURf6LaZm7KzM+jvakA8eV9Abv+ZVwB7NJel7Uf9YKAlw5Yr99GxA1UJc1tWF/Jc8bbMhf7PzPXRcT3gJcCTweumU2FxkHzaz9a3YbrhkzaC+Ft2v3BCMfmz6n2rgsiYuNu9UrWV8m8cYrNAKpSXUT8maoOuRnVYn6qaQYdsztR+/P4iDh+0HTAPS2Qv1O7PszZtsxBxU1JkiRJ+gh1L/e0iFjcrUYXEZsDW2Xmtd0JI2Ih1a3gusxcMdcrZshOkiRJkiRtqJZSbe8O7LR0nCAiHpyZ/xjTeqwG7qKq02yemXd2fr5XG064MTRHVrXhc4d8eD7p8jPzNuCsiPg8VTHouRHxqFZJaBVwAPUB//L+6VogaEeqdeT1s9+Mga6iQicLqVDEXHhed0REPIiqaATr9+c49+swvfbAo1RyW0W1VdyTTsguIp5CVXq85QHexu9eqN9TTmyhfFUbLqQqyM2JzPxbRHwROIxq27gtda/19AFvfwqwthuwayYcb5N4BIMrRE1mDfDD9vVKqnreHhGx2YgtY+dMRDwcuAjYDXhfZh47jWm2By6hKm0dmJlfHcN6zacqdnXDTcupVsDbDZjm0VQo7a9M0p52GnqVDLcc8vPe+LtnOc2MtmWO938v5Nm9Zs7l/Hp/j54+ZJre+DX9I0c5NjPzHxGxgrq2LGRi2HHfNpzQqnuQiNiGCtj9mWkeS0OO2esY/mDBwVTVztOoMOwdMPfbIkmSJEldEfFS6gEpWP/A6u4Rsax9/YfMPBogM0+LiF2ANwA3RcTFwC+oBxy3oh5aXQocMWBRh7fhnFexg+GtTSRJkiRJkh7QWjWV44B5wNci4pmD3hcRLwS+Mcb1uBs4i/pg+z2dZW8NvAm4BzhjTMv/JfAtqlLamzvLfxbVuvROqjIXEbFlG9+1GTCfCjL0QhtnUuv+xhbY6vde6sP/M8cYYPxEW/5HW7vF+4iIee0J1lE8PyL274xbAmwNXJqZt8LY9+swvQDeE6e5LVBhCoBjI+I/YZwWHDyJuj8460qPEbEsIjIiFs92XjMw2X75MhUuPDIi9hs0cUTsHhEPncFyl7XhIe31T+pc71oDPDIintFZ7qHAPtNdWGauycwY8bWsb/rbgbOp6oUnRcR97g1HxMNayGhs2tPml1AhpndPM2C3IxX6mU9VJJwy4BURW0TEUyNii+68WgC4+/551PVkIyYGds+iKqItbmGz3jQbAR9u3547IGg7iu+04aIBx8mOwCIqFPXtAdMc1f29RcQRVID2t0B/wHzkbRl1/0fEkyJiwZCfvR7YFbgN+Mlk85nN/Fo1ye8C20bEYZ1pDqNCsTcBV/eNH/nYBD7VhidExEP65rUrFWi7HTivb/yCiJhQSbIdp0vbt2d39/8ox2xmXpKZhw16sf5a+fo27raZbktvvQedZ5IkSZI0wI7Aq9urdy9kQd+4Rf1vzswjqYfEvkc93HgU8BLq4a8TgVO6C2j/r1sE/I0x3We1kp0kSZIkSdpgZeb7I2JjqvrT1a1SyzXAX6i2oHtQbdCuGT6XOfEOqjrMkvaB9aVUxaCXUcGFJZl5yxiXfwQVODgxIvamtvcJwEFUZavXZGavEs7jgasi4nqqCtttVFhuf+pJ04/33puZayLizcAngWtbZa/bqepcu1MV2qZsAzlTmXlDRLyWCpL9NCIuotrZbUIFrha29XnqCLO9ELggIi6gWujtAOwHrKWeoO03lv06iUvbfD8QEdvRWh5m5gnDJsjMFRHxYeBtwHURcS5VqWpfqprVldTNydnqBbbmqjrVKJZT+/z8iPg6dTP11sw8IzPviYgDgIupsO0KqrLbXdTvalfqpu5j27hpy8zvRsTP27I3AS7MzN8PeOsp1A3kK9s58kfgmVR1xHPp3EgesyXU7/0IYM/2NPjd1JPg+1A3rC8b4/LPp7b9JmCjiDhuwHv+0+K23SBfTj2tvpx6yn33AdOc0qnGuIS67h9Pha17FgOHR8RlwK1U4OxxVLvWx1CVR4/un3Fm/iEiDqcCit+PiPNYf53bmbpOvLV/mhY6OqlvVC+E9LmI6LXT/WBm3tCWsTIilgKvof5WXdDW78nUk/7z2jb2t+U+FXgl8Azgxoj4StuenalW6fcCR/ZXdxx1W2a4/3eizsUV1PX4d1SL2d2A7am/v68aUHVymJnO71Dq+vaZdg34KfA06np+F7C4M81Ix2ZzNlXNdRGwKiIubOt2MFVx9HWdVuZ7AJ+NiMvbctZSf6v2oz4ouoa6VvdbzIjH7AyNui0w/DyTJEmSpPvIzOMY8f8N7SGvaVdSb91DNh1pxUZkyE6SJEmSJG3QMvM9EXEOFZDaiwoxPISq6PJD4ENURbZxrsPaFkw4hvoQ+ygqCLQSODEzvznm5d/cKvkdS32YvyfwJ6ot3vsy8+q+t6+hPjTfk9pfW1BBgNVUWPDszrxPbUGjo4EDgYdSAbITgfePuw1pZp4ZET8C3tLWd28qRPZrKsD0hRFneT7VUuJdwIuoSnnnA8dk5o2dZY9tvw7Z1usj4tXUvn4DdRwDDA3ZteneHhGrqEDEIVQg7Ka23ie3aouztT3VsnCu2vaO4rPAk4CXUwGVjYHLaU8tZ+aPI2IH6rzbn7oG/Av4DdVO993MvNXn6VTVxt7XE2TmRRHxYmp/H0yFn1ZSx8EC/oshu8y8MyKeTVVfPJhqo3Ivdc6exn2rno3DVm24NcNb365hfYvbh1MBL4AXtNcgy6jw0VTOoYLNu1FB4PnUOfsz4GTg1MycELbMzHMi4lfAO6lzfT61z06mzvVuG/CHUU/idx3SWecb+r4/FLiCClXt07duVwKfyczutfcvEfEc6rg+gKqeOY8KzZ0DnJSZK2e5LTPZ/9cCH6VCzi9q0/8duLkt42OdCmpTmdH8MnN1ROxMHWf7UpUP1gKfB96bmd025qMem2RmRsQrgBXAa4E3tnW7AjghM1d0pv8B9e+NXagqDr1Wrz8Bvgh8esD1eEbH7KhmsC2SJEmS9H8nMnPqd0mSJEmSJEkbsNbmdClVfW7Z/bs2DyytleEdVGCvW4VJkiRJkiRJesDbaOq3SJIkSZIkSdJQC6mKfx+5v1dEkiRJkiRJGgfbxUqSJEmSJEmascy8kPWtayVJkiRJkqQNjpXsJEmSJEmSJEmSJEmSJEkaIjLz/l4HSZIkSZIkSZIkSZIkSZL+J1nJTpIkSZIkSZIkSZIkSZKkIQzZSZIkSZIkSZIkSZIkSZI0hCE7SZIkSZIkSZIkSZIkSZKGMGQnSZIkSZIkSZIkSZIkSdIQhuwkSZIkSZIkSZIkSZIkSRrCkJ0kSZIkSZIkSZIkSZIkSUMYspMkSZIkSZIkSZIkSZIkaQhDdpIkSZIkSZIkSZIkSZIkDWHITpIkSZIkSZIkSZIkSZKkIQzZSZIkSZIkSZIkSZIkSZI0hCE7SZIkSZIkSZIkSZIkSZKGMGQnSZIkSZIkSZIkSZIkSdIQhuwkSZIkSZIkSZIkSZIkSRri3/xEyyUV9PIeAAAAAElFTkSuQmCC\n", + "text/plain": [ + "\u003cFigure size 1440x824.4 with 11 Axes\u003e" + ] + }, + "metadata": { + "image/png": { + "height": 682, + "width": 1260 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Compute max predicted values per track in the APOL4 gene interval.\n", + "max_predictions = output.chip_tf.slice_by_interval(\n", + " apol4_interval, match_resolution=True\n", + ").values.max(axis=0)\n", + "\n", + "# Filter to the 10 tracks with the highest predictions.\n", + "output_filtered = output.chip_tf.filter_tracks(\n", + " (max_predictions \u003e= np.sort(max_predictions)[-10])\n", + ")\n", + "\n", + "# Build plot.\n", + "plot_components.plot(\n", + " [\n", + " plot_components.TranscriptAnnotation(transcripts),\n", + " plot_components.Tracks(\n", + " tdata=output_filtered,\n", + " ylabel_template=(\n", + " 'CHIP TF: {biosample_name} ({strand})\\n{transcription_factor}'\n", + " ),\n", + " filled=True,\n", + " ),\n", + " ],\n", + " interval=apol4_interval,\n", + " annotations=[plot_components.IntervalAnnotation(promoter_intervals)],\n", + " despine_keep_bottom=True,\n", + " title='Predicted TF-binding in K562, HepG2, and sigmoid colon.',\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2pkxPpeeEvst" + }, + "source": [ + "We observe a strong predicted CTCF peak near the second-to-last exon (counting\n", + "from the right), which is predicted across different tissues. We also see peaks\n", + "for (phosphorylated) POL2 binding around one of the putative promoters.\n", + "\n", + "We can average the signal for a transcription factor across tissues to simplify\n", + "the plot. For example, to plot the mean CTCF signal across tissues, we first\n", + "need to construct a new `TrackData` object:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "CQwDVKAFhheC" + }, + "outputs": [], + "source": [ + "mean_ctcf = output_filtered.values[\n", + " :, output_filtered.metadata['transcription_factor'] == 'CTCF'\n", + "].mean(axis=1)\n", + "\n", + "# Construct a new TrackData object from the mean values.\n", + "tdata_mean_ctcf = track_data.TrackData(\n", + " values=mean_ctcf[:, None],\n", + " metadata=pd.DataFrame(\n", + " {'transcription_factor': ['CTCF'], 'name': ['mean'], 'strand': ['.']}\n", + " ),\n", + " interval=output_filtered.interval,\n", + " resolution=output_filtered.resolution,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NkBP0l8zn41d" + }, + "source": [ + "And then plot as usual:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "height": 210 + }, + "executionInfo": { + "elapsed": 658, + "status": "ok", + "timestamp": 1753110041466, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "pibKAGe1jKhm", + "outputId": "35234317-c22a-4f1a-e175-664fe53cc317" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "\u003cFigure size 1440x176.4 with 2 Axes\u003e" + ] + }, + "metadata": { + "image/png": { + "height": 193, + "width": 1225 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_components.plot(\n", + " [\n", + " plot_components.TranscriptAnnotation(transcripts),\n", + " plot_components.Tracks(\n", + " tdata=tdata_mean_ctcf,\n", + " ylabel_template='{name} {transcription_factor}',\n", + " filled=True,\n", + " ),\n", + " ],\n", + " interval=apol4_interval,\n", + " annotations=[plot_components.IntervalAnnotation(promoter_intervals)],\n", + " despine_keep_bottom=True,\n", + " title='Predicted CTCF binding (mean across cell types)',\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XLknUdRRGJPE" + }, + "source": [ + "## Contact maps" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fc9LMc_KGJPF" + }, + "source": [ + "Relative frequency of physical contacts between pairwise genetic positions are\n", + "captured by the model's `CONTACT_MAPS` output. This output type is provided at\n", + "even coarser resolution (2048bp), so we can only consider intervals that span\n", + "distances larger than this.\n", + "\n", + "Let's make the contact map predictions for a colon cell line:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "1KD-Ac1YLjle" + }, + "outputs": [], + "source": [ + "ontology_terms = [\n", + " 'EFO:0002824', # HCT116 colon carcinoma cell line.\n", + "]\n", + "\n", + "output = dna_model.predict_interval(\n", + " interval=interval,\n", + " requested_outputs={dna_client.OutputType.CONTACT_MAPS},\n", + " ontology_terms=ontology_terms,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6VS7O_kdkJGB" + }, + "source": [ + "And plot the predicted DNA-DNA contacts:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "height": 1000 + }, + "executionInfo": { + "elapsed": 2213, + "status": "ok", + "timestamp": 1753110044579, + "user": { + "displayName": "", + "userId": "" + }, + "user_tz": -60 + }, + "id": "riZLzUZZJFzZ", + "outputId": "cc5581d1-108c-4ea1-dbc6-bcc1aef24fe2" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "\u003cFigure size 1440x1512 with 3 Axes\u003e" + ] + }, + "metadata": { + "image/png": { + "height": 1201, + "width": 1259 + }, + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot = plot_components.plot(\n", + " [\n", + " plot_components.TranscriptAnnotation(longest_transcripts),\n", + " plot_components.ContactMaps(\n", + " tdata=output.contact_maps,\n", + " ylabel_template='{biosample_name}\\n{name}',\n", + " cmap='autumn_r',\n", + " vmax=1.0,\n", + " ),\n", + " ],\n", + " interval=interval,\n", + " title='Predicted contact maps',\n", + ")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ymPfZ4Qckesf" + }, + "source": [ + "Three prominent interacting regions, potentially representing\n", + "topologically-associated domains (TADs), are visible as blocks. This interaction\n", + "signal appears to be stronger in the first plot." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BnHh7sw2RpAH" + }, + "source": [ + "## Conclusion" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4dTTcGnTCh1I" + }, + "source": [ + "Congratulations! You have now completed the tour of the visualization library\n", + "and learned how to visualize the core modalities predicted by AlphaGenome." + ] + } + ], + "metadata": { + "colab": { + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/alphagenome/source/conftest.py b/alphagenome/source/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..d23621d92e462554a978e9566518308aa0c19d4d --- /dev/null +++ b/alphagenome/source/conftest.py @@ -0,0 +1,22 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Configure FLAGS with default values for absltest.""" + +from absl import app + +try: + app.run(lambda argv: None) +except SystemExit: + pass diff --git a/alphagenome/source/docs/Makefile b/alphagenome/source/docs/Makefile new file mode 100644 index 0000000000000000000000000000000000000000..d0c3cbf1020d5c292abdedf27627c6abe25e2293 --- /dev/null +++ b/alphagenome/source/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/alphagenome/source/docs/README.md b/alphagenome/source/docs/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c4045ce1070e7e25582953685bc3f746a880bfee --- /dev/null +++ b/alphagenome/source/docs/README.md @@ -0,0 +1,6 @@ +AlphaGenome API is a service that provides comprehensive and accurate AI +predictions for genome interpretation. + +AlphaGenome is a deep learning genomics model that takes a genomic (DNA) +sequence as input and predicts various molecular properties of DNA & RNA, many +at single base pair resolution. diff --git a/alphagenome/source/docs/make.bat b/alphagenome/source/docs/make.bat new file mode 100644 index 0000000000000000000000000000000000000000..8ad027db06dc17d3e59ce18f983651aeca4d75a5 --- /dev/null +++ b/alphagenome/source/docs/make.bat @@ -0,0 +1,49 @@ +@rem Copyright 2024 Google LLC. +@rem +@rem Licensed under the Apache License, Version 2.0 (the "License"); +@rem you may not use this file except in compliance with the License. +@rem You may obtain a copy of the License at +@rem +@rem http://www.apache.org/licenses/LICENSE-2.0 +@rem +@rem Unless required by applicable law or agreed to in writing, software +@rem distributed under the License is distributed on an "AS IS" BASIS, +@rem WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +@rem See the License for the specific language governing permissions and +@rem limitations under the License. + +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=source +set BUILDDIR=build + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +if "%1" == "" goto help + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/alphagenome/source/docs/source/_templates/autosummary/class.rst b/alphagenome/source/docs/source/_templates/autosummary/class.rst new file mode 100644 index 0000000000000000000000000000000000000000..a2c231a8ee4df82484b7bb5d01abf91f33300386 --- /dev/null +++ b/alphagenome/source/docs/source/_templates/autosummary/class.rst @@ -0,0 +1,55 @@ +{{ fullname | escape | underline }} + +.. currentmodule:: {{ module }} + +.. autoclass:: {{ objname }} + +{% block attributes %} +{% if attributes %} + + +Attributes +~~~~~~~~~~ + +.. rubric:: Table + +.. autosummary:: +{% for item in attributes %} +{%- if item not in inherited_members%} + ~{{ name }}.{{ item }} +{%- endif -%} +{%- endfor %} + +{% for item in attributes %} +.. autoattribute:: {{ [objname, item] | join(".") }} +{%- endfor %} + +{% endif %} +{% endblock %} + +{% block methods %} + +{% if methods %} + +Methods +~~~~~~~ + +.. rubric:: Table + +.. autosummary:: +{% for item in methods %} +{%- if item != '__init__' %} + ~{{ name }}.{{ item }} +{%- endif -%} + +{%- endfor %} + +{% for item in methods %} +{%- if item != '__init__'%} +.. automethod:: {{ [objname, item] | join(".") }} +{%- endif %} +{%- endfor %} +{% endif %} + +{% endblock %} + diff --git a/alphagenome/source/docs/source/api/data.md b/alphagenome/source/docs/source/api/data.md new file mode 100644 index 0000000000000000000000000000000000000000..5f4d3b9e5d9d96388af0f57625f4e4bb6968381b --- /dev/null +++ b/alphagenome/source/docs/source/api/data.md @@ -0,0 +1,89 @@ +# Data + +Classes and utilities for manipulating genomics data. + +## Fold Intervals + +``` {eval-rst} +.. currentmodule:: alphagenome +``` + +``` {eval-rst} + +.. autosummary:: + :toctree: generated + + data.fold_intervals.Subset + data.fold_intervals.get_all_folds + data.fold_intervals.get_fold_names + data.fold_intervals.get_fold_intervals +``` + +## Genome + +``` {eval-rst} +.. currentmodule:: alphagenome +``` + +``` {eval-rst} + +.. autosummary:: + :toctree: generated + + data.genome.Strand + data.genome.Interval + data.genome.Variant + data.genome.Junction +``` + +## Gene annotation + +``` {eval-rst} + +.. autosummary:: + :toctree: generated + + data.gene_annotation.TranscriptType + data.gene_annotation.extract_tss + data.gene_annotation.filter_transcript_type + data.gene_annotation.filter_protein_coding + data.gene_annotation.filter_to_longest_transcript + data.gene_annotation.filter_transcript_support_level +``` + +## Ontology + +``` {eval-rst} + +.. autosummary:: + :toctree: generated + + data.ontology.OntologyType + data.ontology.OntologyTerm +``` + +## Track data + +``` {eval-rst} + +.. autosummary:: + :toctree: generated + + data.track_data.TrackData + data.track_data.concat + data.track_data.interleave + data.track_data.metadata_to_proto + data.track_data.metadata_from_proto + data.track_data.from_protos +``` + +## Transcript + +``` {eval-rst} + +.. autosummary:: + :toctree: generated + + data.transcript.Transcript + data.transcript.TranscriptExtractor +``` diff --git a/alphagenome/source/docs/source/api/index.md b/alphagenome/source/docs/source/api/index.md new file mode 100644 index 0000000000000000000000000000000000000000..1488a9bed213ca48ada68bb510baeaebeebbe30a --- /dev/null +++ b/alphagenome/source/docs/source/api/index.md @@ -0,0 +1,46 @@ +# API + +``` {toctree} +:maxdepth: 1 +:hidden: + +data +models +interpretation +visualization +``` + + + +::::{grid} 1 1 2 3 +:gutter: 2 + +:::{grid-item-card} Data +:link: data +:link-type: doc + +Classes and utilities for manipulating genomics data. +::: + +:::{grid-item-card} Models +:link: models +:link-type: doc + +AlphaGenome client and variant scorers. +::: + +:::{grid-item-card} Interpretation +:link: interpretation +:link-type: doc + +Sequence interpretation tools (like in silico mutagenesis). +::: + +:::{grid-item-card} Visualization +:link: visualization +:link-type: doc + +Visualization and plotting tools. +::: + + diff --git a/alphagenome/source/docs/source/api/interpretation.md b/alphagenome/source/docs/source/api/interpretation.md new file mode 100644 index 0000000000000000000000000000000000000000..124ad07489cab06c4dd4f61836b43d13252b6b54 --- /dev/null +++ b/alphagenome/source/docs/source/api/interpretation.md @@ -0,0 +1,16 @@ +# Interpretation + +Sequence interpretation tools (like in silico mutagenesis). + +## ISM + +``` {eval-rst} +.. module:: alphagenome.interpretation +.. currentmodule:: alphagenome + +.. autosummary:: + :toctree: generated + + interpretation.ism.ism_variants + interpretation.ism.ism_matrix +``` diff --git a/alphagenome/source/docs/source/api/models.md b/alphagenome/source/docs/source/api/models.md new file mode 100644 index 0000000000000000000000000000000000000000..797d4e9da42fb3833b21f13ed237b54a6dcf4822 --- /dev/null +++ b/alphagenome/source/docs/source/api/models.md @@ -0,0 +1,55 @@ +# Models + +AlphaGenome client and variant scorers. + +## DNA Client + +``` {eval-rst} +.. currentmodule:: alphagenome +``` + +``` {eval-rst} + +.. autosummary:: + :toctree: generated + + models.dna_client.create + models.dna_client.ModelVersion + models.dna_client.Organism + models.dna_client.validate_sequence_length + models.dna_client.DnaClient +``` + +## DNA Output + +``` {eval-rst} + +.. autosummary:: + :toctree: generated + + models.dna_output.OutputType + models.dna_output.Output + models.dna_output.OutputMetadata + models.dna_output.VariantOutput +``` + +## Variant Scorers + +``` {eval-rst} + +.. autosummary:: + :toctree: generated + + models.variant_scorers.AggregationType + models.variant_scorers.BaseVariantScorer + models.variant_scorers.CenterMaskScorer + models.variant_scorers.ContactMapScorer + models.variant_scorers.GeneMaskLFCScorer + models.variant_scorers.GeneMaskActiveScorer + models.variant_scorers.GeneMaskSplicingScorer + models.variant_scorers.PolyadenylationScorer + models.variant_scorers.SpliceJunctionScorer + models.variant_scorers.get_recommended_scorers + models.variant_scorers.tidy_anndata + models.variant_scorers.tidy_scores +``` diff --git a/alphagenome/source/docs/source/api/visualization.md b/alphagenome/source/docs/source/api/visualization.md new file mode 100644 index 0000000000000000000000000000000000000000..062164e231b29a136ea3b619e2a7178641243de5 --- /dev/null +++ b/alphagenome/source/docs/source/api/visualization.md @@ -0,0 +1,60 @@ +# Visualization + +Visualization and plotting tools. + +## Plot + +``` {eval-rst} +.. currentmodule:: alphagenome +``` + +``` {eval-rst} + +.. autosummary:: + :toctree: generated + + visualization.plot.seqlogo + visualization.plot.plot_contact_map + visualization.plot.plot_track + visualization.plot.plot_tracks + visualization.plot.sashimi_plot + visualization.plot.pad_track +``` + +(visualization/plot-components)= + +## Plot components + +``` {eval-rst} + +.. autosummary:: + :toctree: generated + + visualization.plot_components.plot + visualization.plot_components.AbstractComponent + visualization.plot_components.Tracks + visualization.plot_components.OverlaidTracks + visualization.plot_components.ContactMaps + visualization.plot_components.ContactMapsDiff + visualization.plot_components.TranscriptAnnotation + visualization.plot_components.SeqLogo + visualization.plot_components.Sashimi + visualization.plot_components.EmptyComponent + visualization.plot_components.AbstractAnnotation + visualization.plot_components.IntervalAnnotation + visualization.plot_components.VariantAnnotation +``` + +## Plot transcripts + +``` {eval-rst} + +.. autosummary:: + :toctree: generated + + visualization.plot_transcripts.TranscriptStyle + visualization.plot_transcripts.TranscriptStylePreset + visualization.plot_transcripts.plot_transcripts + visualization.plot_transcripts.draw_interval + visualization.plot_transcripts.draw_transcript +``` diff --git a/alphagenome/source/docs/source/conf.py b/alphagenome/source/docs/source/conf.py new file mode 100644 index 0000000000000000000000000000000000000000..d1b268df9935bc069c36e687532d39a0f08ea1c3 --- /dev/null +++ b/alphagenome/source/docs/source/conf.py @@ -0,0 +1,206 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Configuration file for the Sphinx documentation builder.""" + +# +# This file only contains a selection of the most common options. For a full +# list see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +import importlib.metadata +import inspect +import os +import sys + +# The package is installed by Readthedocs before sphinx building. +import alphagenome # pylint: disable=unused-import, g-import-not-at-top +import alphagenome.models.dna_client # pylint: disable=unused-import, g-import-not-at-top + +# -- Project information ----------------------------------------------------- + +project = 'alphagenome' +project_info = importlib.metadata.metadata(project) +author = project_info['Author'] +copyright = f'2024, {author}' # pylint: disable=redefined-builtin +version = project_info['Version'] +repository_url = f'https://github.com/google-deepmind/{project}' + + +# The full version, including alpha/beta/rc tags +release = version +# Warn if links are broken +nitpicky = True + +# -- General configuration --------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + 'myst_nb', + 'sphinx_design', + 'sphinx.ext.autodoc', + 'sphinx.ext.intersphinx', + 'sphinx.ext.autosummary', + 'sphinx.ext.napoleon', + 'sphinxcontrib.bibtex', + 'sphinx_autodoc_typehints', + 'sphinx.ext.mathjax', + 'IPython.sphinxext.ipython_console_highlighting', + 'sphinx.ext.coverage', + 'sphinx_copybutton', + 'sphinx_remove_toctrees', + 'sphinx.ext.linkcode', +] + +autosummary_generate = True +autodoc_member_order = 'groupwise' +default_role = 'literal' +bibtex_reference_style = 'author_year' +napoleon_google_docstring = True +napoleon_numpy_docstring = False +napoleon_include_init_with_doc = False +napoleon_use_rtype = True +napoleon_use_param = True +myst_heading_anchors = 6 # Create heading anchors for h1-h6 +autodoc_mock_imports = [ + 'google.protobuf.runtime_version', + 'google.protobuf.internal.builder', + 'absl', + 'alphagenome.protos', +] +remove_from_toctrees = ['api/generated/*'] +bibtex_bibfiles = ['refs.bib'] + +myst_enable_extensions = [ + 'amsmath', + 'colon_fence', + 'deflist', + 'dollarmath', + 'html_image', + 'html_admonition', + 'attrs_inline', + 'attrs_block', +] + +# TODO(b/372225132): Resolve showing notebook output without executing. +# TODO(b/372226231): Resolve not modifying notebook when building docs. +myst_url_schemes = ['http', 'https', 'mailto'] +nb_output_stderr = 'remove' +nb_execution_mode = 'off' +nb_merge_streams = True +typehints_defaults = 'braces' + +source_suffix = { + '.rst': 'restructuredtext', + '.ipynb': 'myst-nb', + '.myst': 'myst-nb', +} + +intersphinx_mapping = { + 'python': ('https://docs.python.org/3', None), + 'anndata': ('https://anndata.readthedocs.io/en/stable/', None), + 'numpy': ('https://numpy.org/doc/stable/', None), + 'jax': ('https://jax.readthedocs.io/en/latest/', None), + 'pandas': ('https://pandas.pydata.org/docs/', None), + 'matplotlib': ('https://matplotlib.org/stable/', None), +} + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', 'protos'] + +# -- Options for autodoc ----------------------------------------------------- + +autodoc_default_options = { + 'member-order': 'bysource', + 'special-members': True, + 'exclude-members': '__repr__, __str__, __weakref__', +} + + +# -- Source code links ----------------------------------------------------- + + +def linkcode_resolve(domain, info): + """Resolve a GitHub URL corresponding to Python object.""" + if domain != 'py': + return None + + try: + mod = sys.modules[info['module']] + except ImportError: + return None + + obj = mod + try: + for attr in info['fullname'].split('.'): + obj = getattr(obj, attr) + except AttributeError: + return None + else: + obj = inspect.unwrap(obj) + + try: + filename = inspect.getsourcefile(obj) + except TypeError: + return None + + try: + source, lineno = inspect.getsourcelines(obj) + except OSError: + return None + + path = os.path.relpath(filename, start=os.path.dirname(alphagenome.__file__)) + return ( + f'{repository_url}/tree/main/src/{project}/' + f'{path}#L{lineno}#L{lineno + len(source) - 1}' + ) + + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. + +html_theme = 'sphinx_book_theme' +html_title = 'AlphaGenome' +pygments_style = 'default' +html_theme_options = { + 'repository_url': repository_url, + 'repository_branch': 'main', + 'use_repository_button': True, + 'launch_buttons': { + 'colab_url': 'https://colab.research.google.com', + }, + 'article_header_start': ['toggle-primary-sidebar.html', 'breadcrumbs'], + 'show_prev_next': False, +} +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +# html_static_path = ['_static'] + +# TODO: b/377291190 - Look at adding notebook support (see haiku example) diff --git a/alphagenome/source/docs/source/exploring_model_metadata.md b/alphagenome/source/docs/source/exploring_model_metadata.md new file mode 100644 index 0000000000000000000000000000000000000000..fa6e97f5af19a3c76ecb0ab060a8ec92585937f2 --- /dev/null +++ b/alphagenome/source/docs/source/exploring_model_metadata.md @@ -0,0 +1,93 @@ +# Model output metadata + +AlphaGenome returns predictions for 11 different output types, covering a +variety of modalities. Here we provide details about the human model outputs and +associated metadata to help users make informed decisions about the parameters +of their API requests (e.g., ontology term; output types). + +For further details on dataset processing and precise definitions of each output +type, including their respective units and normalization methods, please refer +to the Methods section of the AlphaGenome paper. + + + + +{#model_metadata-table1-target} +**Table 1**: Descriptions of output types predicted by AlphaGenome. + +| OutputType name | Description | Units | Resolution | Unique biosamples | Total tracks | +| :--- | :--- | :--- | :--- | :--- | :--- | +| [RNA\_SEQ](#alphagenome.models.dna_output.OutputType.RNA_SEQ) | RNA expression as measured by RNA-seq. Includes a mixture of PolyA+ RNA and Total RNA assays. Some tracks are also stranded. | Normalized read signal | 1bp | 285 | 667 | +| [CAGE](#alphagenome.models.dna_output.OutputType.CAGE) | RNA expression at transcription start-sites as measured by Cap Analysis Gene Expression (CAGE) assay. | Normalized read signal | 1bp | 264 | 546 | +| [PROCAP](#alphagenome.models.dna_output.OutputType.PROCAP) | RNA expression at transcription start-sites as measured by Precision Run-On sequencing and capping (PROCAP) assay. | Normalized read signal | 1bp | 6 | 12 | +| [DNASE](#alphagenome.models.dna_output.OutputType.DNASE) | Chromatin accessibility as measured by DNase I hypersensitive sites sequencing (DNase-seq) assay. | Normalized insertion signal | 1bp | 305 | 305 | +| [ATAC](#alphagenome.models.dna_output.OutputType.ATAC) | Chromatin accessibility as measured by the transposase-accessible chromatin (ATAC-seq) assay. | Normalized insertion signal | 1bp | 167 | 167 | +| [CHIP\_HISTONE](#alphagenome.models.dna_output.OutputType.CHIP_HISTONE) | Relative abundance of histone modification marks as measured by chromatin immunoprecipitation (ChIP-seq) for 24 different markers e.g. H3k27ac (see ENCODE [documentation](https://www.encodeproject.org/chip-seq/histone/)). | Fold-change over control | 128bp | 219 | 1116 | +| [CHIP\_TF](#alphagenome.models.dna_output.OutputType.CHIP_TF) | Relative abundance of DNA-bound transcription factors as measured by ChIP-seq targeting 43 different proteins (see ENCODE [documentation](https://www.encodeproject.org/chip-seq/transcription_factor/)). | Fold-change over control | 128bp | 163 | 1617 | +| [SPLICE\_SITES](#alphagenome.models.dna_output.OutputType.SPLICE_SITES) | Predicted location of donor or acceptor splice sites, for both the positive and negative strand, expressed as a probability (higher numbers indicate higher probability of the base being a splice site). | Predicted probability | 1bp | NA | 4 | +| [SPLICE\_JUNCTIONS](#alphagenome.models.dna_output.OutputType.SPLICE_JUNCTIONS) | Splice junction spliced read counts, as measured by RNA-Seq. Predictions are for all possible pairings of at most 512 donors and 512 acceptors from each strand in the requested interval, where the position of donors and acceptors along the input sequence is given by predictions of splice site positions. | Normalized junction signal | 1bp | 282 | 734 | +| [SPLICE\_SITE\_USAGE](#alphagenome.models.dna_output.OutputType.SPLICE_SITE_USAGE) | Fraction of transcripts using a splice site, as measured by RNA-seq. All reads that span a given splice site are considered, and we predict the fraction of these that use the site (donor or acceptor). | Fraction | 1bp | 282 | 734 | +| [CONTACT\_MAPS](#alphagenome.models.dna_output.OutputType.CONTACT_MAPS) | Relative frequency of physical contact between pairwise positions (symmetric), derived from chromatin contact maps (Micro-C and Hi-C assays). Values are coarse-grained and normalized by removing the off-diagonal power law decay (as also done in [Zhou, J. 2022](https://www.nature.com/articles/s41588-022-01065-4)). | Log-fold over genomic distance-based expectation | 2048bp | 12 | 28 | + + + +## Track metadata + +To access the metadata describing each track for human outputs use: + +```py +output_metadata = dna_model.output_metadata( + organism=dna_client.Organism.HOMO_SAPIENS +) +``` + +Each predicted output type (e.g., RNA\_SEQ) contains metadata in a +{py:class}`~pandas.DataFrame`: {py:class}`output_metadata.rna_seq +` + +Each row of the {class}`~pandas.DataFrame` corresponds to a ‘track’, and each +column contains key information for biological interpretation such as: + +* `name`: Name of the track. Example: `CL:0000047 polyA plus RNA-seq`. +* `strand` Strand of the track, either positive (`+`), negative (`-`), or + unstranded (`.`). +* `ontology_curie`: A string ID representing the ontology term corresponding + to the biosample. Example: `CL:0000100`. +* `biosample_name`: Plain text description of the biosample. Example: `motor + neuron`. + +For a full list of metadata columns available for each output type, please see +the [navigating data ontologies notebook](colabs/tissue_ontology_mapping), which +demonstrates how to access and browse track metadata. + + + +:::{note} For `SPLICE_JUNCTION` outputs the strand information is a property of +a junction rather than a track, so the metadata for this output type will show +half as many rows as reported in the above table. +::: + + + +## Additional track metadata + +Some output types contain additional columns. For example, +{py:class}`OutputMetadata.rna_seq +` and +{py:class}`OutputMetadata.splice_sites +` also contain a +`gtex_tissue` column, which is populated for the tracks that make predictions +for the tissues sampled in the +[GTEx project](https://gtexportal.org/home/samplingSitePage) +{cite:t}`gtex2020gtex`. + + +:::{note} For one tissue, +’Brain \- Cerebellar hemisphere’, we used an alternative Uberon ID to that was +provided in the +[GTEx documentation](https://gtexportal.org/home/samplingSitePage) +(‘UBERON:0002037’), to reflect Uberon’s ID for cerebellar hemisphere: +[‘UBERON:0002245'](https://www.ebi.ac.uk/ols4/ontologies/uberon/classes/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FUBERON_0002245). +::: + + diff --git a/alphagenome/source/docs/source/faqs.md b/alphagenome/source/docs/source/faqs.md new file mode 100644 index 0000000000000000000000000000000000000000..637519813a2b18b82b5d498205f092d5c04d40fb --- /dev/null +++ b/alphagenome/source/docs/source/faqs.md @@ -0,0 +1,378 @@ +# FAQ + +Frequently asked questions. + +## Model inputs + +### How do I make predictions for a specific genomic region? + +You can define any region in either the human or mouse genome, and use the API +to predict various outputs. See the [quick start colab](colabs/quick_start) for +a demonstration. + +### How do I specify a genomic region? + +Using the {class}`genome.Interval` class, +which is initialized with a chromosome, a start, and an end position. + + + +:::{note} +AlphaGenome classes such as {class}`genome.Interval` +uses 0-based indexing, consistent with the underlying Python implementations. + +This means an +{class}`genome.Interval` includes the base +pair at the `start` position up to the base pair at the `end-1` position. + +For example, to specify the first base pair of chromosome 1, use +`genome.Interval('chr1', 0, 1)`. This interval has a width of 1, and contains +only the base pair at the first position of chromosome 1. + +To interpret interval overlaps, remember that 0-based indexing excludes the base +pair at the `end` position itself, such that +`genome.Interval('chr1', 0, 1).overlaps(genome.Interval('chr1', 1, 2))` +returns `False`. +::: + + + +### What are the reference genome versions used by the model? + +We use human genome assembly hg38 (GRCh38.p13.genome.fa) and mouse assembly mm10 +(GRCm38.p6.genome.fa). For other genome builds (such as hg19, for example), the +[LiftOver](https://genome.ucsc.edu/cgi-bin/hgLiftOver) tool can be used to +convert from hg38 coordinates to the desired assembly. + +### Can I make a prediction for any arbitrary DNA sequence? + +Yes, you can make predictions for any sequence, provided it is within the range +of sequence lengths supported by the model. Note that model predictions have +only been evaluated using sequences that vary by a relatively small amount from +the reference genome (SNPs and indels), so very large differences from the human +reference genome (for example, structural variants, sequences with a large +amount of padding, synthetic sequences, or artificial DNA constructs) may result +in predictions that are not as reliable. + +### Can I make predictions for DNA from other species? + +Yes, with the caveat that the model has only been trained on mouse and human +DNA. Prediction quality is likely to degrade as evolutionary distance from these +two species increases, but note that this has not been formally benchmarked. + +### What is the longest sequence the model can take as input? + +1MB (precisely 2^20 base-pairs long). Other sequence lengths are also supported: +\~2KB, \~16KB, \~100KB, \~500KB. + +### How do I request predictions for a sequence with a length that is not in the list of supported lengths? + +You can use +{func}`genome.Interval.resize` to crop +or expand your sequence length to the nearest supported length. + +Note that `.resize` expands sequences using the actual surrounding genomic data, +not by adding padding. + +## Model outputs + +### How many tracks are there per output type and what do they represent? + +This varies from 5 to over 600. Each of the tracks refers to a particular +cell-type or tissue, as well as other properties, such as strand or a specific +transcription factor (for the `CHIP_TF` output type). See the +[output metadata documentation](project:exploring_model_metadata.md#Exploring-model-metadata) +for a full list of the output types. + +### How do I find out what tissue or cell-type an output ‘track’ refers to? + +Using the [navigating data ontologies notebook](colabs/tissue_ontology_mapping), +you can look at the output metadata where biosample names and ontology CURIEs +(IDs) for each track are described. + +### What is an ontology CURIE? + +CURIEs (Compact Uniform Resource Identifiers) are standardized, abbreviated +codes (e.g., ‘UBERON:0001114’ for liver) that uniquely identify specific +ontology terms. + +### Where are your ontology CURIEs sourced from? + +We source these from the IDs provided in the source training data. We also +restricted the ontology types to UBERON, CL, CLO and EFO, following ENCODE +practices. We recommend using EBI's +[Ontology Lookup Service](https://www.ebi.ac.uk/ols4) to understand +relationships between the ontology IDs for different tracks. + +### What is strandedness? + +DNA is double-stranded, meaning that there are two nucleotide strands that form +the double helix. By convention, one of those molecules is designated the +forward, or positive strand (5'->3'), and the other is designated the reverse, +or negative strand (3'->5'). + +Genomic assays can either be unstranded or stranded (also called +strand-specific). + +* Unstranded assays return results that do not distinguish whether a + measurement came from the positive or negative strand. Certain assays do not + generate stranded information – for example, ATAC-seq generates unstranded + accessibility information. +* Stranded (or strand-specific) assays annotate each measurement as coming + from the positive or negative strand. This is important for transcriptional + assays to distinguish between strand-specific transcripts (for example, two + transcripts that share a transcriptional start site but are on different + strands). + + + +:::{note} +Not all RNA-seq samples will be stranded, especially those that are +from older experiments. For example, GTEx RNA-seq data is unstranded. +::: + + + +For more general information about the difference between non-stranded and +stranded protocols and how to interpret them, there is a helpful tutorial +[here](https://www.ecseq.com/support/ngs/how-do-strand-specific-sequencing-protocols-work). + +### How is strandedness handled in model outputs? + +In the model output metadata, we use the following symbols to designate the +strand of a track: + +* positive: `+` +* negative: `-` +* unstranded `.` + +For assays that were performed in a stranded (or strand-specific) manner, the +assay will have two tracks per cell or tissue type: one for the positive (`+`) +and another for the negative (`-`) strand. + +For unstranded assays, there will be a single track per cell or tissue type, +annotated as unstranded (`.`). + +We provide convenience operations for manipulating +{class}`~alphagenome.data.track_data.TrackData` based on strand information, +such as +{func}`~alphagenome.data.track_data.TrackData.filter_to_negative_strand`, etc. + +### How can I save the model outputs? + +For *variant effect predictions*: We recommend converting the scores into a +pandas DataFrame. This DataFrame can then be easily exported to a common file +format, such as a CSV file, for use with other tools or for record-keeping. +Specific instructions and examples for this process are provided in our 'Variant +Scoring UI' tutorial. + +For *genome track predictions (e.g., RNA-seq levels)*: The predicted track data +is provided as NumPy arrays within TrackData objects. These arrays can be +directly saved to disk using standard NumPy functions, such as `numpy.save` (for +saving a single array to a `.npy` file) or `numpy.savez_compressed` (for saving +multiple arrays into a single compressed `.npz` file). + +### What are some of the limitations of the model? + +AlphaGenome has several key limitations: + +- *Tissue-specificity and long-range interactions*: While AlphaGenome shows + improvements in these areas compared to previous models, accurately + capturing tissue-specific effects and long-range genomic interactions + remains challenging for deep learning models in genomics, requiring further + research. +- *Species scope*: The model is trained and evaluated on human and mouse DNA. + Its performance on DNA from other species has not been determined. +- *Personal genomes*: The model has not yet been benchmarked for predicting + individual (personal) human genomes. +- *Molecular scope*: AlphaGenome predicts the molecular consequences of + genetic variations. Its direct applicability to complex trait analysis is + limited, as these traits also involve broader biological processes (e.g., + gene function, development, environmental factors) beyond the model's + primary focus. +- *Unphased training and single sequence input*: The model processes a single + DNA sequence at a time and is therefore not inherently 'diploid-aware'. It + was trained using unphased data, meaning it could not learn to distinguish + between alleles inherited from the mother versus the father. Consequently, + its variant effect predictions do not inherently model heterozygous states + (i.e., the presence of both a reference and a variant allele at a site + simultaneously). + +## Visualizing predictions + +### How do I visualize the predicted output? + +You can use any tool to visualize the numerical output, but we provide a Python +[visualization library](project:api/visualization.md#Visualization) so you can +easily visualize the output immediately. You can use our +[visualization basics guide](project:visualization_library_basics.md) and see +examples of how to plot different modalities in our +[visualizing predictions tutorial](colabs/visualization_modality_tour). + +### Can I design my own visualizations to work with this library? + +Yes. The returned figures are based on matplotlib, so should be extendible. +Additionally, you can choose to work with the raw output data and design your +own visualizations. + +### Where are the plotted transcript annotations from? + +Transcript annotations are sourced from standard Gene Transfer Format (GTF) +files from GENCODE: the hg38 reference assembly (release 46) for human and the +mm10 reference assembly (release M23) for mouse. + +### Am I limited to only plotting protein-coding genes, and only the longest transcript? + +No. If you wish to include other gene types or all transcripts (not just the +longest), you can remove the respective calls to +`gene_annotation.filter_protein_coding(gtf)` and +`gene_annotation.filter_to_longest_transcript(gtf)` in your code. Note that +including more transcripts can make the plot appear busy; you can adjust the +`fig_height` parameter of the `TranscriptAnnotation` plot component to improve +legibility. + +## Variant scoring + +### How do I define a variant? + +By creating a {class}`~alphagenome.data.genome.Variant` object. + + + +:::{note} +:name: variant-position-is-1-based +As mentioned above, AlphaGenome classes such as +{class}`~alphagenome.data.genome.Variant` use 0-indexing, and Variant's +{func}`~alphagenome.data.genome.Variant.start` and +{func}`~alphagenome.data.genome.Variant.end` contain 0-indexed values. + +However, most variants in public databases, such as dbSNP, are provided as +1-indexed. + +To enable compatibility with these annotations, the +{class}`~alphagenome.data.genome.Variant` object is initialized with a +1-indexed {attr}`~alphagenome.data.genome.Variant.position` attribute, which is +then converted to 0-indexing internally. (i.e., +{func}`~alphagenome.data.genome.Variant.start` returns +{attr}`~alphagenome.data.genome.Variant.position` - 1). + +See the {class}`~alphagenome.data.genome.Variant` docstring for more details. +::: + + + +### Are there tools to help me define variants, and run inference for them? + +See the +[scoring and visualizing a single variant notebook](colabs/variant_scoring_ui) +which walks through how to define a {class}`~alphagenome.data.genome.Variant` +object and perform inference. Batch inference over many variants can be +performed using the +[batch variant scoring notebook](colabs/batch_variant_scoring) which takes a +variant call file (VCF) as input. + +### Can I pass any sequence to {class}`~alphagenome.data.genome.Variant.reference_bases` or does it have to match the reference genome sequence at the variant location? + +You can pass any sequence to +{class}`~alphagenome.data.genome.Variant.reference_bases`. Note that +{func}`~alphagenome.models.dna_client.DnaClient.predict_variant` is agnostic to +the alleles in the reference genome, but rather uses the REF/ALT alleles +specified by the user. + +### Are variant predictions for insertions and deletions (indels) supported? + +Yes. We use left-alignment to specify indels. See +{class}`~alphagenome.data.genome.Variant` for more details. For scoring indels, +we adopt SpliceAI's {cite:p}`spliceai` indel alignment strategy: inserted bases +are summarized by taking the maximum value over the inserted segment, while +deleted bases are treated as having zero signal in the `ALT` context, thereby +enabling consistent positional comparisons. + +### Which variant scorer should I use for a given modality? + +In practice, you can use most variant scoring strategies for any modality. +However, we provide a recommendation for the best strategies based on our +evaluations in the +[variant scoring documentation](project:variant_scoring.md#variant-scoring). + +### Can I write my own variant scoring strategy? + +We do not currently support users writing their own variant scoring strategy. +However, since variant scoring is simply aggregating REF and ALT track +predictions, you can write your own methods for handling these values. + +### What is the difference between a 'quantile_score' and 'raw_score'? + +The 'raw_score' is the output for a particular variant scoring strategy. +However, different tracks and modalities yield scores that are on different +scales. For instance, the +[Splice Sites Usage scorer](project:variant_scoring.md#splicing-splice-site-usage) +returns values between 0 and 1, whereas the +[Gene Expression (RNA-seq)](project:variant_scoring.md#gene-expression-rna-seq) +scorer returns negative or positive values without bounds. To facilitate +comparisons across tracks and different variant scoring strategies, we use an +empirical quantiles approach (see {cite:p}`alphagenome` for full details). +Briefly, we estimate a background distribution for each variant scorer and track +using scores for common variants (MAF>0.01 in any GnomAD v3 population). We can +then convert any 'raw score' into a 'quantile score', representing its rank +within this background distribution. E.g. a variant with a quantile score of +0.99 has a score equivalent to the 99th percentile of common variants. This +provides a measure of predicted impact that is standardized to the same scale +across different variant scorers and tracks. The maximum (or minimum) value +never exceeds 0.999990 (or -0.999990), due to the number of variants used to +compute the quantiles (~300K). Because of this, we recommend using quantile +scores as an indicator of whether the raw score is unusually large, and use the +'raw scores' as a measure of magnitude of the effect for a given scorer and +track. + +For signed variant scores (which indicate effect direction like up-regulation or +down-regulation), their [0,1] quantile probabilities – derived directly from the +rank order of the original signed raw scores – are linearly transformed to a +[-1,1] range. This rescaling ensures the quantile score reflects the +directionality of the raw score. For instance, the 0th percentile (representing +the most negative raw scores) maps to -1, the 50th percentile (raw scores around +zero) to 0, and the 100th percentile (most positive raw scores) to +1. + +Note that quantile scores are only available for the suite of recommended +scorers. + +## Other + +### What terms of use apply to AlphaGenome outputs? + +The AlphaGenome API is provided for non-commercial use only and is subject to +the AlphaGenome +[Terms of Service](https://deepmind.google.com/science/alphagenome/terms). +Outputs generated by AlphaGenome should not be used for the training of other +machine learning models. + +### How should I cite AlphaGenome? + +If you use AlphaGenome in your research, please cite using: + + + +```bibtex +@article{alphagenome, + title={{AlphaGenome}: advancing regulatory variant effect prediction with a unified {DNA} sequence model}, + author={Avsec, {\v Z}iga and Latysheva, Natasha and Cheng, Jun and Novati, Guido and Taylor, Kyle R. and Ward, Tom and Bycroft, Clare and Nicolaisen, Lauren and Arvaniti, Eirini and Pan, Joshua and Thomas, Raina and Dutordoir, Vincent and Perino, Matteo and De, Soham and Karollus, Alexander and Gayoso, Adam and Sargeant, Toby and Mottram, Anne and Wong, Lai Hong and Drot{\'a}r, Pavol and Kosiorek, Adam and Senior, Andrew and Tanburn, Richard and Applebaum, Taylor and Basu, Souradeep and Hassabis, Demis and Kohli, Pushmeet}, + year={2025}, + doi={https://doi.org/10.1101/2025.06.25.661532}, + publisher={Cold Spring Harbor Laboratory}, + journal={bioRxiv} +} +``` + + + +### Who should I contact with issues, enquiries and feedback? + +Submit bugs and any code-related issues on +[GitHub](https://github.com/google-deepmind/alphagenome). For general feedback, +questions about usage, and/or feature requests, please use the +[community forum](https://www.alphagenomecommunity.com) – it's actively +monitored by our team so you're likely to find answers and insights faster. If +you can't find what you're looking for, please get in touch with the AlphaGenome +team at and we will be happy to assist you with +questions. We're working hard to answer all inquiries but there may be a short +delay in our response due to the high volume we are receiving. diff --git a/alphagenome/source/docs/source/index.md b/alphagenome/source/docs/source/index.md new file mode 100644 index 0000000000000000000000000000000000000000..650d53f4ad5a85bbca60c44ca84d0a11ea4c1da2 --- /dev/null +++ b/alphagenome/source/docs/source/index.md @@ -0,0 +1,82 @@ +# Exploring the genome with AlphaGenome + +This API provides access to AlphaGenome, Google DeepMind’s unifying model for +deciphering the regulatory code within DNA sequences. + +AlphaGenome offers multimodal predictions, encompassing diverse functional +outputs such as gene expression, splicing patterns, chromatin features, and +contact maps (see diagram below). The model analyzes DNA sequences of up to 1 +million base pairs in length and can deliver predictions at single base-pair +resolution for most outputs. AlphaGenome achieves state-of-the-art performance +across a range of genomic prediction benchmarks, including numerous diverse +variant effect prediction tasks (detailed in {cite:p}`alphagenome`). + +The API is offered as a free service for +[non-commercial use](https://deepmind.google.com/science/alphagenome/terms). +Query rates vary based on demand – it is well suited for smaller to medium-scale +analyses such as analysing a limited number of genomic regions or variants +requiring 1000s of predictions, but is likely not suitable for large scale +analyses requiring more than 1 million predictions. + + +```{figure} /_static/model_overview.png +:width: 600px +:alt: overview of AlphaGenome +:name: overview-figure +``` + + +## Getting started + +You can get started by +[getting an API key](https://deepmind.google.com/science/alphagenome), and +following our [Quick Start Guide](./colabs/quick_start.ipynb), or watching our +[AlphaGenome 101 tutorial](https://youtu.be/Xbvloe13nak). Please also check out +our installation guide, tutorials with comprehensive overviews of plotting, +variant scoring and other use cases, and our API reference documentation. + + +::::{grid} 1 1 2 3 +:gutter: 2 + +:::{grid-item-card} +:link: installation +:link-type: doc +Installation +^^^^^^^^^^^^ + +Install `alphagenome` locally. +::: + +:::{grid-item-card} +:link: tutorials/index +:link-type: doc +Tutorials +^^^^^^^^^ +The tutorials walk through example usage of the AlphaGenome model. +::: + +:::{grid-item-card} +:link: api/index +:link-type: doc +API reference +^^^^^^^^^^^^^ + +Reference documentation for the `alphagenome` package. +::: +:::: + + +``` {toctree} +:maxdepth: 2 +:hidden: False + +../colabs/quick_start +installation +api/index +tutorials/index +user_guides/index +faqs +Community +references +``` diff --git a/alphagenome/source/docs/source/installation.md b/alphagenome/source/docs/source/installation.md new file mode 100644 index 0000000000000000000000000000000000000000..027f25a733119c1e2f2cabe5be9af65009daadf1 --- /dev/null +++ b/alphagenome/source/docs/source/installation.md @@ -0,0 +1,73 @@ +# Installation + +The easiest way to install AlphaGenome is via the published +[PyPi package](https://pypi.org/project/alphagenome). + +```bash +$ pip install -U alphagenome +``` + +This will install the latest version of the `alphagenome` package. + +You may optionally wish to create a +[Python Virtual Environment](https://docs.python.org/3/tutorial/venv.html) to +prevent conflicts with your system's Python environment. + +## Google Colab + +The tutorial notebooks include a cell with the commands necessary to install +`alphagenome` into a colab runtime. + +### Add API key to secrets + +To make model requests using the tutorial notebooks, you need to add the +AlphaGenome API key to Colab secrets: + +1. Open your Google Colab notebook and click on the 🔑 **Secrets** tab in the + left panel. +1. Create a new secret with the name `ALPHA_GENOME_API_KEY`. +1. Copy/paste your API key into the `Value` input box of + `ALPHA_GENOME_API_KEY`. +1. Toggle the button on the left to allow notebook access to the secret. + + + +```{figure} /_static/secrets.png +:width: 600px +:alt: Image of secrets tab found on left panel. +:name: secrets-screenshot +``` + + +## Running locally + +To install a local copy of `alphagenome`, clone a local copy of the repository +and run `pip install`: + +```bash +$ rm -rf ./alphagenome +$ git clone https://github.com/google-deepmind/alphagenome.git +$ pip install -e ./alphagenome +``` + +We strongly recommend using a virtual environment management system such as +[miniconda](https://docs.anaconda.com/miniconda/) or +[uv](https://docs.astral.sh/uv/pip/environments/). + +In the case of miniconda, installation would be achieved with the following: + +```bash +conda create -n alphagenome-env python=3.11 +conda activate alphagenome-env +pip install -e ./alphagenome +``` + +### Updating `alphagenome` + +Assuming the relevant virtual environment is already activated: + +```bash +cd ./alphagenome +git pull +pip install --upgrade . +``` diff --git a/alphagenome/source/docs/source/references.md b/alphagenome/source/docs/source/references.md new file mode 100644 index 0000000000000000000000000000000000000000..fc01fb4b23c1ed322c269735ab1ee5930b79af5b --- /dev/null +++ b/alphagenome/source/docs/source/references.md @@ -0,0 +1,7 @@ +# References + + +```{bibliography} +:cited: +``` + diff --git a/alphagenome/source/docs/source/refs.bib b/alphagenome/source/docs/source/refs.bib new file mode 100644 index 0000000000000000000000000000000000000000..73c001e55e50f3a70627895dc1c5715e8ef6de51 --- /dev/null +++ b/alphagenome/source/docs/source/refs.bib @@ -0,0 +1,50 @@ +@article{alphagenome, + title={{AlphaGenome}: advancing regulatory variant effect prediction with a unified {DNA} sequence model}, + author={Avsec, {\v Z}iga and Latysheva, Natasha and Cheng, Jun and Novati, Guido and Taylor, Kyle R. and Ward, Tom and Bycroft, Clare and Nicolaisen, Lauren and Arvaniti, Eirini and Pan, Joshua and Thomas, Raina and Dutordoir, Vincent and Perino, Matteo and De, Soham and Karollus, Alexander and Gayoso, Adam and Sargeant, Toby and Mottram, Anne and Wong, Lai Hong and Drot{\'a}r, Pavol and Kosiorek, Adam and Senior, Andrew and Tanburn, Richard and Applebaum, Taylor and Basu, Souradeep and Hassabis, Demis and Kohli, Pushmeet}, + year={2025}, + doi={https://doi.org/10.1101/2025.06.25.661532}, + publisher={Cold Spring Harbor Laboratory}, + journal={bioRxiv} +} + +@article{gtex2020gtex, + title={The GTEx Consortium atlas of genetic regulatory effects across human tissues}, + author={GTEx Consortium}, + journal={Science}, + volume={369}, + number={6509}, + pages={1318--1330}, + year={2020}, + publisher={American Association for the Advancement of Science} +} + +@article{zhou2022sequence, + title={Sequence-based modeling of three-dimensional genome architecture from kilobase to chromosome scale}, + author={Zhou, Jian}, + journal={Nature genetics}, + volume={54}, + number={5}, + pages={725--734}, + year={2022}, + publisher={Nature Publishing Group US New York} +} + +@article{borzoi, + title={Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation}, + author={Linder, Johannes and Srivastava, Divyanshi and Yuan, Han and Agarwal, Vikram and Kelley, David R}, + journal={Nature Genetics}, + pages={1--13}, + year={2025}, + publisher={Nature Publishing Group US New York} +} + +@article{spliceai, + title={Predicting splicing from primary sequence with deep learning}, + author={Jaganathan, Kishore and Panagiotopoulou, Sofia Kyriazopoulou and McRae, Jeremy F and Darbandi, Siavash Fazel and Knowles, David and Li, Yang I and Kosmicki, Jack A and Arbelaez, Juan and Cui, Wenwu and Schwartz, Grace B and others}, + journal={Cell}, + volume={176}, + number={3}, + pages={535--548}, + year={2019}, + publisher={Elsevier} +} diff --git a/alphagenome/source/docs/source/tutorials/index.md b/alphagenome/source/docs/source/tutorials/index.md new file mode 100644 index 0000000000000000000000000000000000000000..f74ce164b339629852c6867e87b111dc80e61acd --- /dev/null +++ b/alphagenome/source/docs/source/tutorials/index.md @@ -0,0 +1,55 @@ +# Tutorials + +``` {toctree} +:maxdepth: 1 +:hidden: + +../colabs/visualization_modality_tour +../colabs/variant_scoring_ui +../colabs/tissue_ontology_mapping +../colabs/batch_variant_scoring +../colabs/example_analysis_workflow +``` + + + +::::{grid} 1 1 2 3 +:gutter: 2 + +:::{grid-item-card} Visualizing predictions +:link: ../colabs/visualization_modality_tour +:link-type: doc + +How to visualize different output modalities. +::: + +:::{grid-item-card} Scoring and visualizing a single variant +:link: ../colabs/variant_scoring_ui +:link-type: doc + +Tool for scoring and visualizing a single variant across multiple modalities. +::: + +:::{grid-item-card} Navigating data ontologies +:link: ../colabs/tissue_ontology_mapping +:link-type: doc + +Tool for fetching ontology IDs for a given tissue. + +::: + +:::{grid-item-card} Batch variant scoring +:link: ../colabs/batch_variant_scoring +:link-type: doc + +Tool for scoring many variants at once. +::: + +:::{grid-item-card} Example analysis workflow +:link: ../colabs/example_analysis_workflow +:link-type: doc + +Example analysis of TAL1 locus. +::: + + diff --git a/alphagenome/source/docs/source/user_guides/index.md b/alphagenome/source/docs/source/user_guides/index.md new file mode 100644 index 0000000000000000000000000000000000000000..25610b4922d01187590b90f1a08cfa26203a2a18 --- /dev/null +++ b/alphagenome/source/docs/source/user_guides/index.md @@ -0,0 +1,46 @@ +# User guides + +``` {toctree} +:maxdepth: 1 +:hidden: + +../colabs/essential_commands +../exploring_model_metadata +../variant_scoring +../visualization_library_basics +``` + + + +::::{grid} 1 1 2 3 +:gutter: 2 + +:::{grid-item-card} Essential commands +:link: ../colabs/essential_commands +:link-type: doc + +Essential commands for navigating AlphaGenome. +::: + +:::{grid-item-card} Model output metadata +:link: ../exploring_model_metadata +:link-type: doc + +A summary of model outputs and associated metadata. +::: + +:::{grid-item-card} Variant scoring +:link: ../variant_scoring +:link-type: doc + +Overview of how variant scores are calculated. +::: + +:::{grid-item-card} Visualization basics +:link: ../visualization_library_basics +:link-type: doc + +Guide to visualization tools. +::: + + diff --git a/alphagenome/source/docs/source/variant_scoring.md b/alphagenome/source/docs/source/variant_scoring.md new file mode 100644 index 0000000000000000000000000000000000000000..a8a23a92e0f01e2f60abf8de73443049df020cb3 --- /dev/null +++ b/alphagenome/source/docs/source/variant_scoring.md @@ -0,0 +1,257 @@ +# How variant scoring works + +A genomic variant is a difference identified in an individual's genome sequence +when compared to the reference genome sequence. Many genomic variants likely +have no appreciable impact, but it can be challenging to identify those that do +have a particular molecular effect. AlphaGenome predictions can be used to score +variants and help bridge this gap. + +To do so, the variant is treated as a pair of sequences: reference (`REF`) and +alternate (`ALT`). The variant effect is estimated by comparing AlphaGenome +predictions for these two sequences across different modalities returned by the +model. + +## Detailed steps + +Variant scoring is implemented as follows: + +### Make `REF` and `ALT` predictions for given modality + +Variant scoring begins by generating predictions for both the reference and +alternative alleles of a variant, restricted to a given modality of interest +(ex: `RNA-SEQ`, `ATAC`, etc.). + +The model input at this stage are `REF` and `ALT` sequences, whose sequence +interval contains the variant of interest. + + + +```{figure} /_static/variant_scoring_ref_alt.png +:width: 500px +:alt: Make `REF` and `ALT` predictions for given modality. +:name: variant-scoring-1 +``` + + + +### Optional - perform indel alignment + +For insertion or deletion (indel) variants, the `ALT` allele's prediction +profile is aligned to the `REF` allele's coordinate space. Inserted bases are +summarized by taking the maximum value over the inserted segment, while deleted +bases are treated as having zero signal in the `ALT` context, thereby enabling +consistent positional comparisons. + +### Apply spatial mask + +A spatial mask defines regions of interest within the interval containing the +variant. This mask can be centered on the variant or encompass a gene (gene +body, exons, or TSS, based on annotations from a GTF file). + +At this stage, values outside of the mask are discarded. + + + +```{figure} /_static/variant_scoring_spatial_mask.png +:width: 500px +:alt: Apply spatial mask. +:name: variant-scoring-2 +``` + + + +### Aggregate spatially and compute `ALT - REF` + +Aggregation occurs at this stage, which includes the following: + +* reduction along the spatial axis, using `mean` or `sum`, etc. +* (optional) scaling, such as a $log$ or $l^2$ transform. +* difference between `ALT - REF`. + +The final outcome is a single scalar value per track. + + + +```{figure} /_static/variant_scoring_spatial_compute.png +:width: 500px +:alt: Aggregate spatially and compute `ALT - REF`. +:name: variant-scoring-3 +``` + + + + + +```{note} +Aggregation logic is encapsulated in the options listed in +{class}`~alphagenome.models.variant_scorers.AggregationType`. + +The naming of the options reflects the order of operations of each of the above +steps, with the right-most operation applied first to the model predictions. + +For example, +{class}`~alphagenome.models.variant_scorers.AggregationType.DIFF_SUM_LOG2`, +applies a log transform, then a sum, to track data. It then returns the +difference between `ALT - REF`. + +Some aggregation options may apply the exact same steps, but in a different order. + +Regardless of the order of operations, each aggregation type returns one single +scalar value per track. +``` + + + +### Optional - aggregate tracks + +After variant scoring is completed, optional track selection and additional +aggregation can be applied. + +Suggestions include additional aggregation (mean, max, sum, etc.) over: + +* All tracks +* Subsets of tracks + +Or, a single track of interest can be chosen, i.e., from a particular sample. + + + +```{figure} /_static/variant_scoring_aggregate.png +:width: 500px +:alt: Optional - aggregate tracks. +:name: variant-scoring-4 +``` + + + +## Modality-specific recommended variant scorers + +We have established a set of recommended variant scorers, available via +{func}`~alphagenome.models.variant_scorers.get_recommended_scorers`, covering +diverse genomic modalities as outlined below: + +### Gene Expression (RNA-seq) + +Variant scores quantify the impact on overall gene transcript abundance. + +* comparison: predicted RNA coverage between `REF` and `ALT` alleles +* mask: exons for a gene of interest +* aggregation: Log-fold change of gene expression level between the `ALT` and + `REF` alleles: {math}`\log(mean(ALT) + 0.001) - log(mean(REF) + 0.001)` + +### Polyadenylation Site (PAS) Usage + +This follows Borzoi's {cite:p}`borzoi` methodology for scoring polyadenylation +quantitative trait loci (paQTLs), which captures the variant's impact on RNA +isoform production. + +* comparison: predicted RNA coverage between `REF` and `ALT` alleles +* mask: local 400-bp windows around 3' cleavage junctions +* aggregation: Maximum absolute log-fold change of isoform ratios + (distal/proximal PAS usage) between `REF` and `ALT`, considering all + proximal/distal splits. + +### TSS Activity (CAGE, PRO-cap) + +Variant scores quantify local changes at TSSs. + +* comparison: predicted CAGE or PRO-cap coverage between `REF` and `ALT` + alleles +* mask: local 501-bp window centered at the variant +* aggregation: Log2-ratio of summed signals: {math}`log2[(sum(ALT) + 1) / + (sum(REF) + 1)]` + +### Chromatin Accessibility (ATAC-seq, DNase-seq) + +Variant scores quantify local accessibility changes. + +* comparison: predicted ATAC-seq or DNase-cap coverage between `REF` and `ALT` + alleles +* mask: local 501-bp window centered at the variant +* aggregation: Log2-ratio of summed signals: {math}`log2[(sum(ALT) + 1) / + (sum(REF) + 1)]` + +### Transcription Factor Binding (ChIP-TF) + +Variant scores quantify changes in TF binding intensity. + +* comparison: predicted ChIP-TF coverage between `REF` and `ALT` alleles +* mask: local 501-bp window centered at the variant +* aggregation: Log2-ratio of summed signals: {math}`log2[(sum(ALT) + 1) / + (sum(REF) + 1)]` + +### Histone Modifications (ChIP-Histone) + +Variant scores quantify changes in histone modifications. + +* comparison: predicted ChIP-Histone coverage between `REF` and `ALT` alleles +* mask: local 2001-bp window centered at the variant +* aggregation: Log2-ratio of summed signals: {math}`log2[(sum(ALT) + 1) / + (sum(REF) + 1)]` + +### Splicing (Splice Sites) + +Variant scores quantify changes in the class assignment probabilities (acceptor, +donor) at all potential splice sites within a gene body. + +* comparison: class assignment probabilities for `REF` and `ALT` alleles +* mask: gene body for a gene of interest +* aggregation: Maximum absolute difference of predicted splice site + probabilities across the gene body: {math}`max(|ALT - REF|)` + +### Splicing (Splice Site Usage) + +Variant scores quantify changes in the usage of splice sites (i.e., increased or +decreased fractions). + +* comparison: predicted splice site usage between `REF` and `ALT` alleles +* mask: gene body for a gene of interest +* aggregation: Maximum absolute difference of predicted splice site usage + across the gene body: {math}`max(|ALT - REF|)` + +### Splicing (Splice Junctions) + +Variant scores quantify changes in the predicted RNA-seq reads spanning a +junction, which is a function of both expression level, splice site usage and +splicing efficiency. + +* comparison: predicted paired junction counts between `REF` and `ALT` alleles +* mask: top-k splice sites for a gene of interest (including annotated and + predicted splice sites) +* aggregation: Maximum absolute log-fold change of predicted junction counts + across splice site pairs of interest: {math}`max(|log(ALT) - log(REF)|)` + +### 3D Genome Contact (Contact Maps) + +Variant scores quantify local contact disruption. + +* comparison: predicted contact frequencies between `REF` and `ALT` alleles +* mask: local 1MB window centered at the variant +* aggregation: Mean absolute difference of contact frequencies, for all + interactions involving the variant-containing bin. + +### Active Allele Scorers + +In addition to the differential scores described above, we also provide scoring +configurations that capture the absolute activity level associated with one of +the alleles, rather than quantifying the change between `REF` and `ALT`. This is +calculated by taking the maximum of the aggregated signals from the `REF` and +`ALT` alleles over the masked central window or gene region. + +We provide recommended active allele scorers for the following modalities: + +* Gene expression (RNA-seq): {math}`max(mean(ALT), mean(REF))` across exons + for a gene of interest +* TSS activity (CAGE, PRO-cap): {math}`max(sum(ALT), sum(REF))` within a local + 501-bp window centered at the variant +* Chromatin Accessibility (ATAC-seq, DNase-seq): {math}`max(sum(ALT), + sum(REF))` within a local 501-bp window centered at the variant +* Transcription Factor binding (ChIP-TF): {math}`max(sum(ALT), sum(REF))` + within a local 501-bp window centered at the variant +* Histone modifications (ChIP-Histone): {math}`max(sum(ALT), sum(REF))` within + a local 2001-bp window centered at the variant + +## Available variant scorers + +For more on the types of variant scorers and how they work, visit the +[API documentation](api/models.md#variant-scorers). diff --git a/alphagenome/source/docs/source/visualization_library_basics.md b/alphagenome/source/docs/source/visualization_library_basics.md new file mode 100644 index 0000000000000000000000000000000000000000..d51e1d66766a3e515a09c26dc5ff789c03b3e858 --- /dev/null +++ b/alphagenome/source/docs/source/visualization_library_basics.md @@ -0,0 +1,153 @@ +# Visualization basics + + + +AlphaGenome predicts a variety of output types with different data shapes and +biological interpretations ([table](#viz-table)). We provide +[`alphagenome.visualization`](project:api/visualization.md) to generate +matplotlib figures from model API outputs, which we outline here. + + + +```{tip} +See the {doc}`visualizing predictions tutorial ` +for worked examples of plotting different modalities. +``` + + + +## Plot + +The key function, {func}`~alphagenome.visualization.plot_components.plot`, takes +as input a list of components and returns a {class}`matplotlib.figure.Figure`. + +## Components + +A component is a light wrapper around a model output (such as predicted genomic +tracks, splice junctions, etc) and specifies plot aesthetics. Each component +maps to one vertically stacked subplot in the final figure (see blue text in the +[figure](#viz-figure)). Each component has an independent y-axis but shares a +common x-axis, corresponding to the length of the DNA interval, in base pairs +(bp). + +Several default components are available, each designed to best visually +represent different modalities and data shapes returned by the model API (see +[table](#viz-table)). + +## Annotations + +Additional figure elements specific to the DNA interval, but outside of +components -- such as locations of promoters or variants -- can be overlaid via +a list of annotations that are passed to +{func}`~alphagenome.visualization.plot_components.plot`. + +## Custom plotting + +For users interested in configuring novel components, extend the +{func}`~alphagenome.visualization.plot_components.AbstractComponent` and +{func}`~alphagenome.visualization.plot_components.AbstractAnnotation` base +classes. + +Any other data supplied by the user can be visualized using this library as is, +as long as it is provided to +[`plot_components`](project:api/visualization.md#plot-components) in the format +required e.g. {class}`~alphagenome.data.track_data.TrackData` for +{class}`~alphagenome.visualization.plot_components.Tracks`. + + + +```{figure} /_static/visualization_overview.png +:height: 600px +:alt: visualization library description/overview +:name: viz-figure + +Illustrative diagram of visualization library. Blue text indicates +[`plot_components`]() classes, and purple text indicates arguments to +[`plot_components`]() that adjust figure-wide aesthetics +``` + +```{list-table} Plotting components and annotation classes. +:widths: 10 30 10 10 30 10 +:header-rows: 1 +:name: viz-table + +* - Component name plot\_components.\* + - Description + - Example figure + - Data shape supported + - Recommended model outputs + - Good for visualising variants? +* - {class}`~alphagenome.visualization.plot_components.Tracks` + - A line-plot visualizing a scalar value at each genomic position (or + coarser resolution) e.g. predictions of RNA\_SEQ for a specific + - Colab cell + - 1D + - All except SPLICE\_JUNCTIONS; CONTACT\_MAPS + - No +* - {class}`~alphagenome.visualization.plot_components.OverlaidTracks` + - A line-plot as for Tracks, but with two separate lines on the same axis + with different colors e.g. predictions of RNA\_SEQ for the Reference and + Alternative sequence defined by a variant. + - Colab cell + - 1D x 2 + - All except SPLICE\_JUNCTIONS; CONTACT\_MAPS + - Yes +* - {class}`~alphagenome.visualization.plot_components.Sashimi` + - A series of arcs, each representing a scalar value for a pair of genomic + positions (e.g. splice junctions). The thickness of the arcs are + determined by the relative sizes of the scalars. + - Colab cell + - 2D (sparse) + - SPLICE\_JUNCTIONS + - Yes +* - {class}`~alphagenome.visualization.plot_components.SeqLogo` + - A sequence of letters (bases) with heights corresponding to a single + scalar value per genomic position (e.g. from contribution scores). + - Colab cell + - 1D \+ sequence + - ISM contribution scores + - Yes +* - {class}`~alphagenome.visualization.plot_components.ContactMaps` + - A heatmap visualizing a matrix of scalars (e.g. predicted DNA-DNA + contacts), one for each pair of genomic positions in an interval. + - Colab cell + - 2D + - CONTACT\_MAPS + - No +* - {class}`~alphagenome.visualization.plot_components.ContactMapsDiff` + - A heatmap as for ContactMaps, but with a diverging color map centered on + zero (white) to represent values derived from differences (e.g. ALT \- + REF) + - Colab cell + - 2D + - CONTACT\_MAPS + - Yes +* - {class}`~alphagenome.visualization.plot_components.TranscriptAnnotation` + - Horizontal lines representing locations of transcripts. Exons, introns, + untranslated regions, and direction of transcription are indicated by + differences in line thickness. + - Colab cell + - Interval(s) + - N/A + - No +* - {class}`~alphagenome.visualization.plot_components.VariantAnnotation` + - A semi-transparent rectangle (or vertical line if a variant) spanning + all plot components, indicating the location of an interval (or + variant). The interval (variant) is optionally labeled. + - Colab cell + - Interval(s) or Variant(s) + - N/A + - Yes +* - {class}`~alphagenome.visualization.plot_components.AbstractComponent` + - This is an abstract class, which is the parent class of most + plot\_components.\*. A user can define their own component class, + provided it adheres to the structure specified by AbstractComponent. The + workhorse method is plot\_ax(), which populates a matplotlib.axes.Axes + object with visuals defined by the input data. + - N/A + - N/A + - N/A + - N/A +``` + + diff --git a/alphagenome/source/hatch_build.py b/alphagenome/source/hatch_build.py new file mode 100644 index 0000000000000000000000000000000000000000..ba24840cd5a12a3a5abfd872ab3adfa2a7931c3f --- /dev/null +++ b/alphagenome/source/hatch_build.py @@ -0,0 +1,49 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Hatch build hook to generate Python bindings for protos.""" + +import os +from typing import Any +from grpc_tools import protoc +from hatchling.builders.hooks.plugin.interface import BuildHookInterface # pylint: disable=g-importing-member + + +_ROOT_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'src') + +# Tuple of proto message definitions to build Python bindings for. Paths must +# be relative to root directory. +_ALPHAGENOME_PROTOS = ( + 'alphagenome/protos/dna_model.proto', + 'alphagenome/protos/dna_model_service.proto', + 'alphagenome/protos/tensor.proto', +) + + +class GenerateProtos(BuildHookInterface): + """Generates Python protobuf bindings for alphagenome.protos.""" + + def initialize(self, version: str, build_data: dict[str, Any]) -> None: + del version, build_data # Unused. + + for proto_path in _ALPHAGENOME_PROTOS: + proto_args = [ + 'grpc_tools.protoc', + f'--proto_path={_ROOT_DIR}', + f'--python_out={_ROOT_DIR}', + f'--grpc_python_out={_ROOT_DIR}', + os.path.join(_ROOT_DIR, proto_path), + ] + if protoc.main(proto_args) != 0: + raise RuntimeError(f'ERROR: {proto_args}') diff --git a/alphagenome/source/pyproject.toml b/alphagenome/source/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..b6a7c1d138aaf39c8d092379a81b6bd5f2cef38f --- /dev/null +++ b/alphagenome/source/pyproject.toml @@ -0,0 +1,131 @@ +[build-system] +build-backend = 'hatchling.build' +requires = ['hatchling', 'grpcio-tools<=1.67.1', 'importlib-resources'] + + +[project] +name = 'alphagenome' +description = 'A Python SDK for interacting and visualizing genomic models.' +readme = 'README.md' +dynamic = ['version'] +license = { file = 'LICENSE' } +requires-python = '>=3.10' +authors = [ + {name = 'Google LLC'}, + {email = 'alphagenome@google.com'}, +] +keywords = [ + 'python', + 'machine learning', + 'genomics' +] +classifiers=[ + 'Development Status :: 4 - Beta', + 'Environment :: Console', + 'Intended Audience :: Science/Research', + 'License :: OSI Approved :: Apache Software License', + 'Operating System :: OS Independent', + 'Programming Language :: Python :: 3.10', + 'Programming Language :: Python :: 3.11', + 'Programming Language :: Python :: 3.12', + 'Programming Language :: Python :: 3.13', + 'Topic :: Scientific/Engineering :: Artificial Intelligence', +] +dependencies=[ + # keep-sorted start + 'absl-py', + 'anndata', + 'grpcio>=1.67.1', + 'immutabledict', + 'intervaltree', + 'jaxtyping', + 'matplotlib', + 'ml_dtypes', + 'numpy', + 'pandas', + 'protobuf>=5.28.3', + 'pyarrow', + 'scipy', + 'seaborn', + 'tqdm', + 'typeguard', + 'typing_extensions', + 'zstandard', + # keep-sorted end +] + +[project.urls] +Repository = 'https://github.com/google-deepmind/alphagenome' +Documentation = 'https://www.alphagenomedocs.com/' + +[project.optional-dependencies] +dev = [ + 'hatch', +] +docs = [ + 'ipykernel', + 'ipython', + 'myst-nb', + 'sphinx>=5.0', + 'sphinx-autodoc-typehints', + 'sphinx-book-theme', + 'sphinx-copybutton', + 'sphinx-remove-toctrees', + 'sphinx-design', + 'sphinxcontrib-bibtex>=1.0.0', +] +scripts = [ + 'absl-py', + 'pyarrow', + 'pyranges', +] + +# Calls hatch_build.py to generate Python bindings for protos. +[tool.hatch.build.hooks.custom] + +[tool.hatch.version] +path = 'src/alphagenome/__init__.py' + +[tool.hatch.envs.default] +installer = 'uv' + +[tool.setuptools.packages.find] +include = ['README.md', 'LICENSE'] +exclude = ['*_test.py', 'examples'] + +[tool.hatch.envs.hatch-test] +default-args = [] +extra-dependencies=['google-benchmark', 'typeguard==2.13.3'] +parallel = true + + +[tool.hatch.envs.hatch-test.env-vars] +MPLBACKEND = 'agg' + +[[tool.hatch.envs.hatch-test.matrix]] +# Use hatch test --all to run tests on all supported Python versions. +python = ['3.13', '3.12', '3.11', '3.10'] + +[tool.hatch.envs.check] +dependencies = [ + 'pyink>=24.3.0', + 'pylint>=2.6.0', +] +# Do not install dependencies for the check environment. +detached = true + +[tool.hatch.envs.check.scripts] +format = 'pyink . --check' +lint = 'pylint .' +all = [ + 'format', + 'lint', +] + +[tool.pyink] +# Formatting configuration to follow Google style-guide +line-length = 80 +unstable = true +pyink-indentation = 2 +pyink-use-majority-quotes = true +exclude = 'src/alphagenome/protos' diff --git a/alphagenome/source/scripts/process_gtf.py b/alphagenome/source/scripts/process_gtf.py new file mode 100644 index 0000000000000000000000000000000000000000..efbfdc90317cf0533159192cca905e11ee9b6a3d --- /dev/null +++ b/alphagenome/source/scripts/process_gtf.py @@ -0,0 +1,42 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Script to process GTF into feather file.""" + +from absl import app +from absl import flags +from absl import logging +import pyranges + + +_GTF_PATH = flags.DEFINE_string( + 'gtf_path', None, 'Path to GTF file.', required=True +) +_OUTPUT_PATH = flags.DEFINE_string( + 'output_path', None, 'Path to output feather file.', required=True +) + + +def main(_) -> None: + logging.info('Reading GTF from %s', _GTF_PATH.value) + gtf = pyranges.read_gtf(_GTF_PATH.value, as_df=True) + + gtf['gene_id_nopatch'] = gtf['gene_id'].str.split('.', expand=True)[0] + + logging.info('Writing GTF to %s', _OUTPUT_PATH.value) + gtf.to_feather(_OUTPUT_PATH.value) + + +if __name__ == '__main__': + app.run(main) diff --git a/alphagenome/source/src/__init__.py b/alphagenome/source/src/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e6e237ed8dcb6e6cc47eed4854b52979143e6191 --- /dev/null +++ b/alphagenome/source/src/__init__.py @@ -0,0 +1,4 @@ +# -*- coding: utf-8 -*- +""" +src Package Initialization File +""" diff --git a/alphagenome/source/src/alphagenome/__init__.py b/alphagenome/source/src/alphagenome/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..db6c49bf561c861f35bb49d1e42a217deacc1466 --- /dev/null +++ b/alphagenome/source/src/alphagenome/__init__.py @@ -0,0 +1,18 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""A Python SDK for interacting and visualizing genomic models.""" + + +__version__ = '0.2.0' diff --git a/alphagenome/source/src/alphagenome/colab_utils.py b/alphagenome/source/src/alphagenome/colab_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f3a56753e6f7e5719d64ab1067eba0d4613c4aba --- /dev/null +++ b/alphagenome/source/src/alphagenome/colab_utils.py @@ -0,0 +1,62 @@ +# Copyright 2025 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Utility functions for Google Colab.""" + +import os + + +def get_api_key(secret: str = 'ALPHA_GENOME_API_KEY'): + """Returns API key from environment variable or Colab secrets. + + Tries to retrieve the API key from the environment first. If not found, + attempts to retrieve it from Colab secrets (if running in Colab). + + Args: + secret: The name of the environment variable or Colab secret key to + retrieve. + + Raises: + ValueError: If the API key cannot be found in the environment or Colab + secrets. + """ + + if api_key := os.environ.get(secret): + return api_key + + try: + # pylint: disable=g-import-not-at-top, import-outside-toplevel + from google.colab import userdata # pytype: disable=import-error + # pylint: enable=g-import-not-at-top, import-outside-toplevel + + try: + api_key = userdata.get(secret) + return api_key + except ( + userdata.NotebookAccessError, + userdata.SecretNotFoundError, + userdata.TimeoutException, + ) as e: + raise ValueError( + f'Cannot find or access API key in Colab secrets with {secret=}. Make' + ' sure you have added the API key to Colab secrets and enabled' + ' access. See' + ' https://www.alphagenomedocs.com/installation.html#add-api-key-to-secrets' + ' for more details.' + ) from e + except ImportError: + # Not running in Colab. + pass + + raise ValueError(f'Cannot find API key with {secret=}.') diff --git a/alphagenome/source/src/alphagenome/colab_utils_test.py b/alphagenome/source/src/alphagenome/colab_utils_test.py new file mode 100644 index 0000000000000000000000000000000000000000..04b387c1edea6138547b45880fb229b9fba737fe --- /dev/null +++ b/alphagenome/source/src/alphagenome/colab_utils_test.py @@ -0,0 +1,68 @@ +# Copyright 2025 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import sys +from unittest import mock + +from absl.testing import absltest +from alphagenome import colab_utils + + +_TEST_SECRET_KEY = '_TEST_ALPHAGENOME_API_KEY' + + +class ColabUtilsTest(absltest.TestCase): + + def test_get_api_key_from_environment(self): + with mock.patch.dict(os.environ, {_TEST_SECRET_KEY: 'foo'}): + self.assertEqual(colab_utils.get_api_key(_TEST_SECRET_KEY), 'foo') + + def test_get_api_key_from_environment_not_found_raises_error(self): + with self.assertRaisesRegex( + ValueError, + f"Cannot find API key with secret='{_TEST_SECRET_KEY}'.", + ): + _ = colab_utils.get_api_key(_TEST_SECRET_KEY) + + def test_get_api_key_from_colab_secrets(self): + mock_colab = mock.MagicMock() + mock_colab.userdata.get.return_value = 'bar' + + with mock.patch.dict( + sys.modules, {'google': mock.MagicMock(), 'google.colab': mock_colab} + ): + self.assertEqual(colab_utils.get_api_key(), 'bar') + + def test_get_api_key_from_colab_secrets_not_found_raises_error(self): + mock_colab = mock.MagicMock() + mock_colab.userdata.NotebookAccessError = Exception + mock_colab.userdata.SecretNotFoundError = Exception + mock_colab.userdata.TimeoutException = Exception + + mock_colab.userdata.get.side_effect = Exception() + + with mock.patch.dict( + sys.modules, {'google': mock.MagicMock(), 'google.colab': mock_colab} + ): + secret = 'my_secret' + with self.assertRaisesRegex( + ValueError, + f'Cannot find or access API key in Colab secrets with {secret=}.', + ): + _ = colab_utils.get_api_key(secret) + + +if __name__ == '__main__': + absltest.main() diff --git a/alphagenome/source/src/alphagenome/data/__init__.py b/alphagenome/source/src/alphagenome/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c1bb78fc624ebb702acc55c87c095170a4bc4570 --- /dev/null +++ b/alphagenome/source/src/alphagenome/data/__init__.py @@ -0,0 +1,15 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Data classes for interacting and visualizing genomic models.""" diff --git a/alphagenome/source/src/alphagenome/data/fold_intervals.py b/alphagenome/source/src/alphagenome/data/fold_intervals.py new file mode 100644 index 0000000000000000000000000000000000000000..bed314017ca09a14f09a76f9ba23e43c0243f96f --- /dev/null +++ b/alphagenome/source/src/alphagenome/data/fold_intervals.py @@ -0,0 +1,115 @@ +# Copyright 2025 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Genomics intervals used for training model folds.""" + +import enum + +from alphagenome.models import dna_client +import immutabledict +import pandas as pd + + +_DEFAULT_EXAMPLE_REGIONS = immutabledict.immutabledict({ + dna_client.Organism.HOMO_SAPIENS: ( + 'https://github.com/calico/borzoi/raw/' + '5c9358222b5026abb733ed5fb84f3f6c77239b37/data/sequences_human.bed.gz' + ), + dna_client.Organism.MUS_MUSCULUS: ( + 'https://github.com/calico/borzoi/raw/' + '5c9358222b5026abb733ed5fb84f3f6c77239b37/data/sequences_mouse.bed.gz' + ), +}) + + +class Subset(enum.Enum): + """Subset of the data.""" + + TRAIN = 0 + VALID = 1 + TEST = 2 + + +# Fold ONE is aligned with all trained Borzoi checkpoints: 3 and 4 are held out. +_VALID_FOLD = immutabledict.immutabledict({ + 0: 'fold0', + 1: 'fold3', + 2: 'fold2', + 3: 'fold6', + -1: 'fold0', +}) + +_TEST_FOLD = immutabledict.immutabledict({ + 0: 'fold1', + 1: 'fold4', + 2: 'fold5', + 3: 'fold7', + -1: 'fold1', +}) + +_MODEL_VERSION_TO_FOLD = immutabledict.immutabledict({ + dna_client.ModelVersion.FOLD_0: 0, + dna_client.ModelVersion.FOLD_1: 1, + dna_client.ModelVersion.FOLD_2: 2, + dna_client.ModelVersion.FOLD_3: 3, + dna_client.ModelVersion.ALL_FOLDS: -1, +}) + + +def get_all_folds() -> list[str]: + """Returns the names of all data folds.""" + return [f'fold{i}' for i in range(8)] + + +def get_fold_names( + model_version: dna_client.ModelVersion, subset: Subset +) -> list[str]: + """Returns the data folds used for the model version.""" + match subset: + case Subset.VALID: + return [_VALID_FOLD[_MODEL_VERSION_TO_FOLD[model_version]]] + case Subset.TEST: + return [_TEST_FOLD[_MODEL_VERSION_TO_FOLD[model_version]]] + case Subset.TRAIN: + all_folds = get_all_folds() + if _MODEL_VERSION_TO_FOLD[model_version] == -1: + return all_folds + remove_folds = get_fold_names( + model_version, Subset.VALID + ) + get_fold_names(model_version, Subset.TEST) + for fold in remove_folds: + all_folds.remove(fold) + return all_folds + case _: + raise ValueError(f'Unknown {subset=}') + + +def get_fold_intervals( + model_version: dna_client.ModelVersion, + organism: dna_client.Organism, + subset: Subset, + example_regions_path: str | None = None, +) -> pd.DataFrame: + """Returns the training intervals for the model version.""" + if example_regions_path is None: + example_regions_path = _DEFAULT_EXAMPLE_REGIONS[organism] + + example_regions = pd.read_csv( + example_regions_path, + sep='\t', + names=['chromosome', 'start', 'end', 'fold'], + ) + return example_regions[ + example_regions.fold.isin(get_fold_names(model_version, subset)) + ] diff --git a/alphagenome/source/src/alphagenome/data/fold_intervals_test.py b/alphagenome/source/src/alphagenome/data/fold_intervals_test.py new file mode 100644 index 0000000000000000000000000000000000000000..cf7b20a855f8898fab2fea7b6ef69e836512c7bf --- /dev/null +++ b/alphagenome/source/src/alphagenome/data/fold_intervals_test.py @@ -0,0 +1,145 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from absl.testing import absltest +from absl.testing import parameterized +from alphagenome.data import fold_intervals +from alphagenome.models import dna_client +import pandas as pd + + +_dummy_intervals = pd.DataFrame({ + 'chromosome': ['chr' + str(i) for i in range(8)], + 'start': [i for i in range(0, 8000, 1000)], + 'end': [i + 500 for i in range(0, 8000, 1000)], + 'fold': ['fold' + str(i) for i in range(8)], +}) + + +class FoldIntervalsTest(parameterized.TestCase): + """Tests for the fold intervals.""" + + @parameterized.named_parameters( + dict( + testcase_name='train_fold_0', + model_version=dna_client.ModelVersion.FOLD_0, + subset=fold_intervals.Subset.TRAIN, + expected_intervals=_dummy_intervals[ + # Folds 0 and 1 are held out + _dummy_intervals.fold.isin( + ['fold' + str(i) for i in [2, 3, 4, 5, 6, 7]] + ) + ], + ), + dict( + testcase_name='train_fold_1', + model_version=dna_client.ModelVersion.FOLD_1, + subset=fold_intervals.Subset.TRAIN, + expected_intervals=_dummy_intervals[ + # Folds 3 and 4 are held out (aligns with Borzoi fold 1) + _dummy_intervals.fold.isin( + ['fold' + str(i) for i in [0, 1, 2, 5, 6, 7]] + ) + ], + ), + dict( + testcase_name='train_all_folds', + model_version=dna_client.ModelVersion.ALL_FOLDS, + subset=fold_intervals.Subset.TRAIN, + expected_intervals=_dummy_intervals, + ), + dict( + testcase_name='valid_fold_0', + model_version=dna_client.ModelVersion.FOLD_0, + subset=fold_intervals.Subset.VALID, + expected_intervals=_dummy_intervals[ + # Model fold 0 uses data fold 0 for validation. + _dummy_intervals.fold + == 'fold0' + ], + ), + dict( + testcase_name='valid_fold_3', + model_version=dna_client.ModelVersion.FOLD_3, + subset=fold_intervals.Subset.VALID, + expected_intervals=_dummy_intervals[ + # Model fold 3 uses data fold 6 for validation. + _dummy_intervals.fold + == 'fold6' + ], + ), + dict( + testcase_name='valid_all_folds', + model_version=dna_client.ModelVersion.ALL_FOLDS, + subset=fold_intervals.Subset.VALID, + expected_intervals=_dummy_intervals[ + # All folds model uses data fold 0 for validation. + _dummy_intervals.fold + == 'fold0' + ], + ), + dict( + testcase_name='test_fold_1', + model_version=dna_client.ModelVersion.FOLD_1, + subset=fold_intervals.Subset.TEST, + expected_intervals=_dummy_intervals[ + # Model fold 1 uses data fold 4 for testing. + _dummy_intervals.fold + == 'fold4' + ], + ), + dict( + testcase_name='test_fold_2', + model_version=dna_client.ModelVersion.FOLD_2, + subset=fold_intervals.Subset.TEST, + expected_intervals=_dummy_intervals[ + # Model fold 4 uses data fold 5 for testing. + _dummy_intervals.fold + == 'fold5' + ], + ), + dict( + testcase_name='test_all_folds', + model_version=dna_client.ModelVersion.ALL_FOLDS, + subset=fold_intervals.Subset.TEST, + expected_intervals=_dummy_intervals[ + # All folds model uses data fold 0 for testing. + _dummy_intervals.fold + == 'fold1' + ], + ), + ) + def test_get_fold_intervals( + self, + model_version: dna_client.ModelVersion, + subset: fold_intervals.Subset, + expected_intervals: pd.DataFrame, + ): + path = self.create_tempfile( + content=_dummy_intervals.to_csv(sep='\t', header=False) + ).full_path + intervals = fold_intervals.get_fold_intervals( + model_version=model_version, + organism=dna_client.Organism.HOMO_SAPIENS, + subset=subset, + example_regions_path=path, + ) + pd.testing.assert_frame_equal( + intervals, + expected_intervals, + ) + + +if __name__ == '__main__': + absltest.main() diff --git a/alphagenome/source/src/alphagenome/data/gene_annotation.py b/alphagenome/source/src/alphagenome/data/gene_annotation.py new file mode 100644 index 0000000000000000000000000000000000000000..4f226b399fd71640baee0ff7aa30f7e1b283f276 --- /dev/null +++ b/alphagenome/source/src/alphagenome/data/gene_annotation.py @@ -0,0 +1,364 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Utilities for working with gene annotations (e.g., GTFs).""" + +from collections.abc import Sequence +import enum + +from alphagenome.data import genome +import numpy as np +import pandas as pd + + +@enum.unique +class TranscriptType(enum.Enum): + """Valid Transcript types available in the GENCODE GTF.""" + + IG_C_GENE = 'IG_C_gene' + IG_C_PSEUDOGENE = 'IG_C_pseudogene' + IG_D_GENE = 'IG_D_gene' + IG_J_GENE = 'IG_J_gene' + IG_J_PSEUDOGENE = 'IG_J_pseudogene' + IG_V_GENE = 'IG_V_gene' + IG_V_PSEUDOGENE = 'IG_V_pseudogene' + IG_PSEUDOGENE = 'IG_pseudogene' + MT_RRNA = 'Mt_rRNA' + MT_TRNA = 'Mt_tRNA' + TEC = 'TEC' + TR_C_GENE = 'TR_C_gene' + TR_D_GENE = 'TR_D_gene' + TR_J_GENE = 'TR_J_gene' + TR_J_PSEUDOGENE = 'TR_J_pseudogene' + TR_V_GENE = 'TR_V_gene' + TR_V_PSEUDOGENE = 'TR_V_pseudogene' + ARTIFACT = 'artifact' + LNCRNA = 'lncRNA' + MIRNA = 'miRNA' + MISC_RNA = 'misc_RNA' + NON_STOP_DECAY = 'non_stop_decay' + NONSENSE_MEDIATED_DECAY = 'nonsense_mediated_decay' + PROCESSED_PSEUDOGENE = 'processed_pseudogene' + PROCESSED_TRANSCRIPT = 'processed_transcript' + PROTEIN_CODING = 'protein_coding' + PROTEIN_CODING_CDS_NOT_DEFINED = 'protein_coding_CDS_not_defined' + PROTEIN_CODING_LOF = 'protein_coding_LoF' + RRNA = 'rRNA' + RRNA_PSEUDOGENE = 'rRNA_pseudogene' + RETAINED_INTRON = 'retained_intron' + RIBOZYME = 'ribozyme' + SRNA = 'sRNA' + SCRNA = 'scRNA' + SCARNA = 'scaRNA' + SNRNA = 'snRNA' + SNORNA = 'snoRNA' + TRANSCRIBED_PROCESSED_PSEUDOGENE = 'transcribed_processed_pseudogene' + TRANSCRIBED_UNITARY_PSEUDOGENE = 'transcribed_unitary_pseudogene' + TRANSCRIBED_UNPROCESSED_PSEUDOGENE = 'transcribed_unprocessed_pseudogene' + TRANSLATED_PROCESSED_PSEUDOGENE = 'translated_processed_pseudogene' + UNITARY_PSEUDOGENE = 'unitary_pseudogene' + UNPROCESSED_PSEUDOGENE = 'unprocessed_pseudogene' + VAULT_RNA = 'vault_RNA' + + +def extract_tss(gtf: pd.DataFrame, feature: str = 'transcript') -> pd.DataFrame: + """Extract transcription start sites (TSS) from a DataFrame. + + Args: + gtf: pd.DataFrame containing gene annotation. + feature: Feature in the GTF file to use (either transcript or gene). + + Returns: + pd.DataFrame containing transcription start sites (width=0, 0-based). + """ + tss = gtf[(gtf.Feature == feature)].copy() + + # Remove the extra base to make it width=0. + # .....[)TRANSCRIPT (strand = +) + # TPIRCSNART[)..... (strand = -) + new_start = np.where(tss.Strand == '-', tss.End, tss.Start) + tss.Start = new_start + tss.End = new_start + + return tss + + +def filter_transcript_type( + gtf: pd.DataFrame, + transcript_types: tuple[TranscriptType, ...] | None = None, +) -> pd.DataFrame: + """Filter GTF entries by transcript types. + + This function takes a GTF DataFrame and a list of transcript types and returns + a new DataFrame containing only the transcripts with the specified types. + + The GTF DataFrame must contain a column named 'transcript_type' or + 'transcript_biotype'. The function will raise a ValueError if neither of these + columns is present. + + Args: + gtf: pd.DataFrame or pyranges.PyRanges. + transcript_types: List of valid transcript types to use for filtering. + + Returns: + pd.DataFrame of GENCODE GTF entries subset to rows with the requested + transcript types. + """ + if transcript_types is not None: + transcript_types_str = [x.value for x in transcript_types] + if 'transcript_type' in gtf.columns: + gtf = gtf[gtf.transcript_type.isin(transcript_types_str)] + elif 'transcript_biotype' in gtf.columns: + gtf = gtf[gtf.transcript_biotype.isin(transcript_types_str)] + else: + raise ValueError('transcript_type or transcript_biotype not in gtf.') + return gtf + + +def filter_protein_coding( + gtf: pd.DataFrame, include_gene_entries: bool = False +) -> pd.DataFrame: + """Filter GTF entries to only protein-coding genes. + + Args: + gtf: pd.DataFrame of GENCODE GTF entries. This data frame must contain a + column named 'transcript_type' or 'transcript_biotype'. + include_gene_entries: Whether to include gene entries in addition to + transcript entries. + + Returns: + pd.DataFrame of GENCODE GTF entries subset to rows with protein-coding + genes. + """ + if include_gene_entries: + if 'gene_type' in gtf.columns: + gtf = gtf[gtf.gene_type == TranscriptType.PROTEIN_CODING.value] + else: + raise ValueError('gene_type not in gtf.') + else: + gtf = filter_transcript_type(gtf, (TranscriptType.PROTEIN_CODING,)) + return gtf + + +def filter_to_longest_transcript( + gtf: pd.DataFrame, +) -> pd.DataFrame: + """Filter GTF entries to only the longest transcript per gene. + + Args: + gtf: pd.DataFrame of GENCODE GTF entries. Must contain columns 'Feature', + 'End', 'Start', 'gene_id', and 'transcript_id'. + + Returns: + pd.DataFrame of GENCODE GTF entries subset to rows with the longest + transcript per gene. + """ + lengths = gtf[gtf['Feature'] == 'transcript'].reset_index(drop=True) + lengths['transcript_length'] = lengths['End'] - lengths['Start'] + 1 + + # Identify longest transcripts per gene_id. + longest_transcripts = lengths.loc[ + lengths.groupby('gene_id')['transcript_length'].idxmax() + ] + + return gtf[gtf['transcript_id'].isin(longest_transcripts['transcript_id'])] + + +def filter_transcript_support_level( + gtf: pd.DataFrame, + transcript_support_levels: str | Sequence[str], +) -> pd.DataFrame: + """Filter GTF to only transcripts with specific GENCODE support levels. + + As documented in the [Ensembl + glossary](https://www.ensembl.org/Help/Glossary), + the transcript support level (TSL) indicates the degree of evidence that was + used to construct the transcript. + + As taken from the glossary, the levels are: + + | Transcript support level | Description | | + |---|---|---| + | 1 | A transcript where all splice junctions are supported by at least one + non-suspect mRNA. | + | 2 | A transcript where the best supporting mRNA is flagged as suspect or the + support is from multiple ESTs | + | 3 | A transcript where the only support is from a single EST | + | 4 | A transcript where the best supporting EST is flagged as suspect | + | 5 | A transcript where no single transcript supports the model structure. | + | NA | A transcript that was not analysed for TSL. | + + Args: + gtf: pd.DataFrame of GENCODE GTF entries. Must contain column + 'transcript_support_level'. + transcript_support_levels: List of valid transcript support levels to use + for filtering. This must be a subset of ['1', '2', '3', '4', '5']. Can + also be single string. + + Returns: + pd.DataFrame exactly as provided, but subset to rows with the specified + support level(s). + + Transcripts are scored by GENCODE according to how well mRNA and EST + alignments + match over its full length. Valid levels are: + '1': All splice junctions of the transcript are supported by at least one + non-suspect mRNA. + '2': The best supporting mRNA is flagged as suspect or the support is from + multiple ESTs. + '3': The only support is from a single EST. + '4': The best supporting EST is flagged as suspect. + '5': No single transcript supports the model structure. + 'NA': The transcript was not analyzed (not supported by this filter function). + + See GENCODE GTF format documentation for further details: + https://www.gencodegenes.org/pages/data_format.html + """ + if isinstance(transcript_support_levels, str): + transcript_support_levels = list(transcript_support_levels) + + supported_tsls = {'1', '2', '3', '4', '5'} + if not set(transcript_support_levels).issubset(supported_tsls): + raise ValueError( + f'transcript_support_level must be one of {supported_tsls}, but was' + f' {transcript_support_levels}' + ) + return gtf[gtf.transcript_support_level.isin(transcript_support_levels)] + + +def upgrade_annotation_ids( + old_ids: pd.Series, new_ids: pd.Series, patchless: bool = False +) -> pd.Series: + """Upgrade or add transcript id patch version to Ensembl IDs. + + This function works by + + 1. Dropping the patch version from `old_ids` and `new_ids` + 2. Merging the two on the patch-less ids. + 3. Returning the result of the merge as a pd.Series, with the 'old_ids' as + the index and the 'new_ids' as the values. + + Ensembl patch versions have two formats: ENST####._PAR_Y or + ENST####.. Both are handled. + + The function will raise a ValueError if either `old_ids` or `new_ids` result + in duplicates after dropping the patch version. + + Examples: + * If the old ids are ENST00010.1 and the new ids are ENST00010.3, + then the mapping will be ENST00010.1 -> ENST00010.3. + * If the old ids are ENST00010 and the new ids are ENST00010.3, then the + mapping will be ENST00010 -> ENST00010.3. + + Args: + old_ids: A pd.Series of Ensembl transcript or gene ids with older or missing + version/patch numbers. The index of the series is ignored. + new_ids: A pd.Series of transcript or gene ids with newer version/patch + numbers. The index of the series is ignored. + patchless: whether old_ids are missing patch. + + Returns: + A pd.Series, with the 'old_ids' as the index and the 'new_ids' as the + values. + """ + new_ids = new_ids.drop_duplicates() + + def drop_version(x): + """Drop the patch version from an Ensembl ID.""" + if not x.str.contains('.', regex=False).all(): + raise ValueError('All ids need to contain the patch version.') + + id_split = x.str.partition('.') + + # Retain anything after _ such as PAR_Y for ENST####._PAR_Y. + return id_split[0] + id_split[2].str.partition('_')[2] + + old_ids_nopatch = old_ids if patchless else drop_version(old_ids) + new_ids_nopatch = drop_version(new_ids) + assert ( + not old_ids_nopatch.duplicated().any() + ), 'old_ids not unique without version' + assert ( + not new_ids_nopatch.duplicated().any() + ), 'new_ids not unique without version' + df = pd.merge( + pd.DataFrame({'old': old_ids.values, 'no_version': old_ids_nopatch}), + pd.DataFrame({ + 'new': new_ids.values, + 'no_version': new_ids_nopatch, + }), + on='no_version', + how='left', + ) + return pd.Series( + df.set_index('old').loc[old_ids].new.values, index=old_ids.index + ) + + +def get_gene_interval( + gtf: pd.DataFrame, + gene_symbol: str | None = None, + gene_id: str | None = None, +) -> genome.Interval: + """Returns a stranded `genome.Interval` given a gene identifier. + + Either gene_symbol or gene_id must be set, but not both. + + Args: + gtf: pd.DataFrame of GENCODE GTF entries. Must contain columns 'Feature', + 'gene_name', 'gene_id', 'Chromosome', 'Start', 'End', and 'Strand'. + gene_symbol: A gene name or gene symbol (e.g., 'EGFR', 'TNF', 'TP53') + gene_id: An Ensembl gene ID, which can be patched (e.g. + 'ENSG00000141510.17') or unpatched (e.g., 'ENSG00000141510'). + + Raises: + ValueError: If neither or both gene_symbol and gene_id are set, or if no + interval or multiple intervals are found for the given gene identifier. + """ + if sum(x is not None for x in [gene_symbol, gene_id]) != 1: + raise ValueError('Exactly one of gene_symbol or gene_id must be set.') + + interval_info = pd.DataFrame() + if gene_symbol: + interval_info = gtf[ + (gtf['Feature'] == 'gene') + & (gtf['gene_name'].str.upper() == gene_symbol.upper()) + ] + + elif gene_id: + # TODO: b/377291290 - just upstream the unpatching to the GTF loading. + if '.' not in gene_id: + gene_id_unpatched = gtf['gene_id'].str.split('.', expand=True)[0] + interval_info = gtf[ + (gtf['Feature'] == 'gene') & (gene_id_unpatched == gene_id) + ] + else: + interval_info = gtf[ + (gtf['Feature'] == 'gene') & (gtf['gene_id'] == gene_id) + ] + + passed_id = gene_symbol or gene_id + if interval_info.shape[0] == 0: + raise ValueError(f'No interval found for gene "{passed_id}".') + elif interval_info.shape[0] > 1: + raise ValueError(f'Multiple intervals found for gene "{passed_id}".') + interval_info = interval_info.iloc[0] + + return genome.Interval( + chromosome=interval_info['Chromosome'], + start=interval_info['Start'], + end=interval_info['End'], + strand=interval_info['Strand'], + name=passed_id, + ) diff --git a/alphagenome/source/src/alphagenome/data/gene_annotation_test.py b/alphagenome/source/src/alphagenome/data/gene_annotation_test.py new file mode 100644 index 0000000000000000000000000000000000000000..fea70e82201c2d641806a461d7b43b5f96f4daea --- /dev/null +++ b/alphagenome/source/src/alphagenome/data/gene_annotation_test.py @@ -0,0 +1,205 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from absl.testing import absltest +from absl.testing import parameterized +from alphagenome.data import gene_annotation +import numpy as np +import pandas as pd + + +class ExtractTSSTest(absltest.TestCase): + + def test_extract_tss(self): + gtf = pd.DataFrame({ + 'Start': [101, 102, 103, 0], + 'End': [200, 200, 200, 107], + 'Strand': ['+', '+', '+', '-'], + 'transcript_id': ['T1', 'T2', 'T2', 'T4'], + }) + gtf['Feature'] = 'transcript' + tss = gene_annotation.extract_tss(gtf) + self.assertSequenceEqual(list(tss.Start), [101, 102, 103, 107]) + self.assertSequenceEqual(list(tss.End), [101, 102, 103, 107]) + self.assertSequenceEqual(list(tss.Strand), list(gtf.Strand)) + self.assertSequenceEqual(list(tss.transcript_id), list(gtf.transcript_id)) + + +class UpgradeAnnotationIdsTest(absltest.TestCase): + + def test_upgrade_annotation_ids(self): + old_ids = ['T1.1', 'T2.2', 'T3.3', 'T3.3_PAR_Y'] + new_ids = ['T0.3', 'T1.2', 'T2.2', 'T3.1', 'T3.4_PAR_Y', 'T4.2'] + upgrade_ids = gene_annotation.upgrade_annotation_ids( + pd.Series(old_ids), pd.Series(new_ids) + ) + self.assertSequenceEqual( + list(upgrade_ids), ['T1.2', 'T2.2', 'T3.1', 'T3.4_PAR_Y'] + ) + + def test_upgrade_annotation_ids_missing(self): + old_ids = ['T1.1', 'T2.2', 'T3.3_PAR_Y'] + new_ids = ['T1.2', 'T2.2'] + upgrade_ids = gene_annotation.upgrade_annotation_ids( + pd.Series(old_ids), pd.Series(new_ids) + ) + self.assertSequenceEqual(list(upgrade_ids), ['T1.2', 'T2.2', np.nan]) + + def test_upgrade_annotation_ids_missing_patch(self): + old_ids = ['T1.1', 'T2', 'T3.3_PAR_Y'] + new_ids = ['T1.2', 'T2.2'] + with self.assertRaises(ValueError): + gene_annotation.upgrade_annotation_ids( + pd.Series(old_ids), pd.Series(new_ids) + ) + + def test_upgrade_annotation_ids_not_unique(self): + old_ids = ['T1.1', 'T2.1', 'T2.2', 'T3.3_PAR_Y'] + new_ids = ['T1.2', 'T2.2'] + with self.assertRaises(AssertionError): + gene_annotation.upgrade_annotation_ids( + pd.Series(old_ids), pd.Series(new_ids) + ) + + def test_upgrade_annotation_ids_patchless(self): + old_ids = ['T1', 'T2', 'T3', 'T3PAR_Y'] + new_ids = ['T0.3', 'T1.2', 'T2.2', 'T3.1', 'T3.4_PAR_Y', 'T4.2'] + upgrade_ids = gene_annotation.upgrade_annotation_ids( + pd.Series(old_ids), pd.Series(new_ids), True + ) + self.assertSequenceEqual( + list(upgrade_ids), ['T1.2', 'T2.2', 'T3.1', 'T3.4_PAR_Y'] + ) + + +class GetGeneIntervalTest(parameterized.TestCase): + + @parameterized.parameters([ + dict(gene_symbol='TP53'), + dict(gene_symbol='tp53'), # Should handle gene symbol case differences. + dict(gene_symbol='EGFR'), + dict(gene_symbol='TNF'), + dict(gene_id='ENSG01.1'), + dict(gene_id='ENSG01'), + ]) + def test_get_gene_interval(self, gene_symbol=None, gene_id=None): + gtf = pd.DataFrame({ + 'gene_id': ['ENSG01.1', 'ENSG02.2', 'ENSG03.1'], + 'gene_name': ['TP53', 'EGFR', 'TNF'], + 'Chromosome': ['chr1', 'chr2', 'chr3'], + 'Start': [101, 150, 101], + 'End': [200, 200, 200], + 'Strand': ['+', '+', '-'], + 'Feature': 'gene', + }) + interval = gene_annotation.get_gene_interval( + gtf=gtf, gene_symbol=gene_symbol, gene_id=gene_id + ) + self.assertIsNotNone(interval) + + @parameterized.parameters([ + dict( + gene_symbol='FOO', + gene_id=None, + expected_error='No interval found for gene "FOO".', + ), + dict( + gene_id='BAR', + gene_symbol=None, + expected_error='No interval found for gene "BAR".', + ), + dict( + gene_id=None, + gene_symbol=None, + expected_error='Exactly one of gene_symbol or gene_id must be set.', + ), + dict( + gene_id='ENSG01.1', + gene_symbol=None, + expected_error='Multiple intervals found for gene "ENSG01.1".', + ), + ]) + def test_invalid_gene_raises_error( + self, gene_symbol, gene_id, expected_error + ): + gtf = pd.DataFrame({ + 'gene_id': ['ENSG01.1', 'ENSG01.1', 'ENSG03.1'], + 'gene_name': ['TP53', 'EGFR', 'TNF'], + 'Chromosome': ['chr1', 'chr2', 'chr3'], + 'Start': [101, 150, 101], + 'End': [200, 200, 200], + 'Strand': ['+', '+', '-'], + 'Feature': 'gene', + }) + with self.assertRaisesRegex(ValueError, expected_error): + gene_annotation.get_gene_interval( + gtf=gtf, gene_symbol=gene_symbol, gene_id=gene_id + ) + + +class FilterGTFTest(parameterized.TestCase): + + def setUp(self): + super().setUp() + # Gene A has 3 transcripts, A1, A2, A3. A1 and A3 are the same length but + # the first should be returned. + # Gene B has 1 transcript, B1. It is the max length transcript due to being + # the only one. + # Gene C has 2 transcripts, C1, C2. C1 is the max length transcript. + self.gtf = pd.DataFrame({ + 'gene_id': ['A', 'A', 'A', 'B', 'C', 'C'], + 'transcript_id': ['A1', 'A2', 'A3', 'B1', 'C1', 'C2'], + 'Start': [101, 150, 101, 300, 500, 515], + 'End': [200, 200, 200, 420, 560, 560], + 'Strand': ['+', '+', '+', '-', '-', '-'], + 'Feature': 'transcript', + 'transcript_support_level': ['1', '2', '3', '1', '1', '2'], + }) + + def test_filter_to_longest_transcript(self): + longest_transcript_gtf = gene_annotation.filter_to_longest_transcript( + self.gtf + ) + self.assertSequenceEqual( + list(longest_transcript_gtf.transcript_id), ['A1', 'B1', 'C1'] + ) + + @parameterized.parameters([ + dict( + support_level=['1'], + expected=['A1', 'B1', 'C1'], + ), + dict( + support_level='1', + expected=['A1', 'B1', 'C1'], + ), + dict( + support_level=['1', '2'], + expected=['A1', 'A2', 'B1', 'C1', 'C2'], + ), + ]) + def test_filter_transcript_support_level(self, support_level, expected): + support_level1_gtf = gene_annotation.filter_transcript_support_level( + self.gtf, support_level + ) + self.assertSequenceEqual(list(support_level1_gtf.transcript_id), expected) + + def test_filter_transcript_support_level_invalid(self): + # Test expected error if any invalid support level is requested. + with self.assertRaises(ValueError): + gene_annotation.filter_transcript_support_level(self.gtf, ['1', '6']) + + +if __name__ == '__main__': + absltest.main() diff --git a/alphagenome/source/src/alphagenome/data/genome.py b/alphagenome/source/src/alphagenome/data/genome.py new file mode 100644 index 0000000000000000000000000000000000000000..c5882855090d2e9093f275dc385690787f2ab090 --- /dev/null +++ b/alphagenome/source/src/alphagenome/data/genome.py @@ -0,0 +1,1167 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Utilities for working with genome-related objects such as intervals.""" + +import collections +from collections.abc import Iterable, Iterator, Mapping, Sequence +import copy +import dataclasses +import enum +import re +import sys +from typing import Any, Protocol + +from alphagenome.protos import dna_model_pb2 +import numpy as np +from typing_extensions import Self + +STRAND_POSITIVE = '+' # Also called forward strand, 5'->3' direction. +STRAND_NEGATIVE = '-' # Also called negative strand, 3'->5' direction. +STRAND_UNSTRANDED = '.' +STRAND_OPTIONS = (STRAND_POSITIVE, STRAND_NEGATIVE, STRAND_UNSTRANDED) +_INTERVAL_START_END_REGEX = re.compile(r'(-?\d+)-(-?\d+)') +VALID_VARIANT_BASES = frozenset('ACGTN') + + +class Strand(enum.IntEnum): + """Represents the strand of a DNA sequence. + + This enum defines the possible strands for a DNA sequence: + + * `POSITIVE`: The forward strand (5' to 3'). + * `NEGATIVE`: The reverse strand (3' to 5'). + * `UNSTRANDED`: The strand is not specified. + """ + + POSITIVE = enum.auto() + NEGATIVE = enum.auto() + UNSTRANDED = enum.auto() + + def __str__(self): + match self: + case Strand.POSITIVE: + return STRAND_POSITIVE + case Strand.NEGATIVE: + return STRAND_NEGATIVE + case Strand.UNSTRANDED: + return STRAND_UNSTRANDED + + @classmethod + def from_str(cls, strand: str) -> Self: + match strand: + case '+': + return cls.POSITIVE + case '-': + return cls.NEGATIVE + case '.': + return cls.UNSTRANDED + case _: + raise ValueError(f'Strand needs to be in {STRAND_OPTIONS}') + + def to_proto(self) -> dna_model_pb2.Strand: + match self: + case Strand.POSITIVE: + return dna_model_pb2.Strand.STRAND_POSITIVE + case Strand.NEGATIVE: + return dna_model_pb2.Strand.STRAND_NEGATIVE + case Strand.UNSTRANDED: + return dna_model_pb2.Strand.STRAND_UNSTRANDED + + @classmethod + def from_proto(cls, strand: dna_model_pb2.Strand) -> Self: + match strand: + case dna_model_pb2.Strand.STRAND_POSITIVE: + return cls.POSITIVE + case dna_model_pb2.Strand.STRAND_NEGATIVE: + return cls.NEGATIVE + case dna_model_pb2.Strand.STRAND_UNSTRANDED: + return cls.UNSTRANDED + case _: + raise ValueError(f'Strand needs to be in {STRAND_OPTIONS}') + + +PYRANGES_INTERVAL_COLUMNS = ('Chromosome', 'Start', 'End', 'Strand', 'Name') + + +@dataclasses.dataclass(order=True) +class Interval: + """Represents a genomic interval. + + A genomic interval is a region on a chromosome defined by a start and end + position. This class provides methods for manipulating and comparing + intervals, and for calculating coverage and overlap. + + Attributes: + chromosome: The chromosome name (e.g., 'chr1', '1'). + start: The 0-based start position. + end: The 0-based end position (must be greater than or equal to start). + strand: The strand of the interval ('+', '-', or '.'). Defaults to '.' + (unstranded). + name: An optional name for the interval. + info: An optional dictionary to store additional information. + negative_strand: True if the interval is on the negative strand, False + otherwise. + width: The width of the interval (end - start). + """ + + chromosome: str + start: int + end: int + strand: str = STRAND_UNSTRANDED + name: str = dataclasses.field(default='', compare=False, hash=False) + info: dict[str, Any] = dataclasses.field( + default_factory=dict, repr=False, compare=False, hash=False + ) + + def __post_init__(self): + if isinstance(self.start, (np.int32, np.int64)): + self.start = int(self.start) + if isinstance(self.end, (np.int32, np.int64)): + self.end = int(self.end) + if self.end < self.start: + raise ValueError('end < start. Interval: ' + str(self)) + if self.strand not in STRAND_OPTIONS: + raise ValueError( + f'Strand needs to be in {STRAND_OPTIONS}, found {self.strand}.' + ) + + @property + def negative_strand(self) -> bool: + """Returns True if interval is on the negative strand, False otherwise.""" + return self.strand == STRAND_NEGATIVE + + @property + def width(self) -> int: + """Returns the width of the interval.""" + return self.end - self.start + + def copy(self) -> Self: + """Returns a deep copy of the interval.""" + return copy.deepcopy(self) + + def __str__(self) -> str: + """Returns a string representation of the interval.""" + return f'{self.chromosome}:{self.start}-{self.end}:{self.strand}' + + @classmethod + def from_str(cls, string: str) -> Self: + """Creates an Interval from a string (e.g., 'chr1:100-200:+').""" + chromosome, interval, *strand = string.split(':', maxsplit=2) + if strand: + strand = strand[0] + else: + strand = STRAND_UNSTRANDED + # Get start and end from the interval string. + match = _INTERVAL_START_END_REGEX.fullmatch(interval) + if match: + start, end = int(match.group(1)), int(match.group(2)) + else: + raise ValueError(f'Invalid interval: {string}') + return cls( + chromosome=chromosome, start=int(start), end=int(end), strand=strand + ) + + def to_proto(self) -> dna_model_pb2.Interval: + """Converts the interval to a protobuf message.""" + return dna_model_pb2.Interval( + chromosome=self.chromosome, + start=self.start, + end=self.end, + strand=Strand.from_str(self.strand).to_proto(), + ) + + @classmethod + def from_proto(cls, proto: dna_model_pb2.Interval) -> Self: + """Creates an Interval from a protobuf message.""" + return cls( + chromosome=proto.chromosome, + start=proto.start, + end=proto.end, + strand=str(Strand.from_proto(proto.strand)), + ) + + def to_interval_dict(self) -> dict[str, str | int]: + """Converts the interval to a dictionary.""" + return dict( + chrom=self.chromosome, + start=self.start, + end=self.end, + strand=self.strand, + ) + + @classmethod + def from_interval_dict(cls, interval: Mapping[str, str | int]) -> Self: + """Creates an Interval from a dictionary.""" + return cls( + chromosome=str(interval['chrom']), + start=int(interval['start']), + end=int(interval['end']), + strand=str(interval.get('strand', STRAND_UNSTRANDED)), + ) + + @classmethod + def from_pyranges_dict( + cls, row: Mapping[str, Any], ignore_info: bool = False + ) -> 'Interval': + """Creates an Interval from a pyranges-like dictionary. + + This method constructs an `Interval` object from a dictionary that follows + the pyranges format, such as a row from a :class:`pandas.DataFrame` + converted to a dict. + + The dictionary should have the following keys: + + * 'Chromosome': The chromosome name. + * 'Start': The start position. + * 'End': The end position. + * 'Strand': The strand (optional, defaults to unstranded). + * 'Name': The interval name (optional). + + Any other keys in the dictionary will be added to the `info` attribute of + the `Interval` object, unless `ignore_info` is set to True. + + Args: + row: A dictionary containing interval data. + ignore_info: If True, any keys in the dictionary that are not part of the + standard pyranges columns ('Chromosome', 'Start', 'End', 'Strand', + 'Name') will not be added to the `info` attribute. + + Returns: + An `Interval` object created from the input dictionary. + """ + if ignore_info: + info = {} + else: + info = { + k: v for k, v in row.items() if k not in PYRANGES_INTERVAL_COLUMNS + } + + return cls( + chromosome=str(row['Chromosome']), + start=int(row['Start']), + end=int(row['End']), + strand=str(row.get('Strand', STRAND_UNSTRANDED)), + name=str(row.get('Name', '')), + info=info, + ) + + def to_pyranges_dict(self) -> dict[str, int | str]: + """Converts the interval to a pyranges-like dictionary.""" + return { + 'Chromosome': self.chromosome, + 'Start': self.start, + 'End': self.end, + 'Name': self.name, + 'Strand': self.strand, + **self.info, + } + + def swap_strand(self) -> Self: + """Swaps the strand of the interval.""" + obj = self.copy() + if obj.strand == STRAND_POSITIVE: + obj.strand = STRAND_NEGATIVE + elif obj.strand == STRAND_NEGATIVE: + obj.strand = STRAND_POSITIVE + elif obj.strand == STRAND_UNSTRANDED: + raise ValueError('Cannot swap unstranded intervals.') + return obj + + def as_unstranded(self) -> Self: + """Returns an unstranded copy of the interval.""" + obj = self.copy() + obj.strand = STRAND_UNSTRANDED + return obj + + def within_reference(self, reference_length: int = sys.maxsize) -> bool: + """Checks if the interval is within the valid reference range.""" + return self.start >= 0 and self.end <= reference_length + + def truncate(self, reference_length: int = sys.maxsize) -> Self: + """Truncates the interval to fit within the valid reference range.""" + obj = self.copy() + if reference_length <= 0: + raise ValueError('Reference length should be larger than 0.') + if self.within_reference(reference_length): + return obj + else: + obj.start = max(self.start, 0) + obj.end = min(self.end, reference_length) + return obj + + def center(self, use_strand: bool = True) -> int: + """Computes the center of the interval. + + For intervals with an odd width, the center is rounded down to the nearest + integer. + + If `use_strand` is True and the interval is on the negative strand, the + center is calculated differently to maintain consistency when stacking + sequences from different intervals oriented in the forward strand direction. + This ensures that the relative distance between the interval's upstream + boundary and its center is preserved. + + Args: + use_strand: If True, the strand of the interval is considered when + calculating the center. + + Returns: + The integer representing the center position of the interval. + + Examples: + >>> Interval('1', 1, 3, '+').center() + 2 + >>> Interval('1', 1, 3, '-').center() # Strand doesn't matter. + 2 + >>> Interval('1', 1, 4, '+').center() + 3 + >>> Interval('1', 1, 4, '-').center() + 2 + >>> Interval('1', 1, 4, '-').center() + 2 + >>> Interval('1', 1, 4, '+').center(use_strand=False) + 3 + >>> Interval('1', 1, 2, '-').center() + 1 + """ + center = (self.start + self.end) // 2 + if use_strand and self.negative_strand: + return center + else: + return center + self.width % 2 + + def shift(self, offset: int, use_strand: bool = True) -> Self: + """Shifts the interval by the given offset. + + Args: + offset: The amount to shift the interval. + use_strand: If True, the shift direction is reversed for negative strand + intervals. + + Returns: + A new shifted interval. + """ + obj = self.copy() + if use_strand and self.negative_strand: + offset = -offset + obj.start = self.start + offset + obj.end = self.end + offset + return obj + + def boundary_shift( + self, start_offset: int = 0, end_offset: int = 0, use_strand: bool = True + ) -> Self: + """Extends or shrinks the interval by adjusting the positions with padding. + + Args: + start_offset: The amount to shift the start position. + end_offset: The amount to shift the end position. + use_strand: If True, the offsets are applied in reverse for negative + strand intervals. + + Returns: + A new interval with adjusted boundaries. + """ + return self.pad(-start_offset, end_offset, use_strand=use_strand) + + def pad( + self, start_pad: int, end_pad: int, *, use_strand: bool = True + ) -> Self: + """Pads the interval by adding the specified padding to the start and end. + + Args: + start_pad: The amount of padding to add to the start. + end_pad: The amount of padding to add to the end. + use_strand: If True, padding is applied in reverse for negative strand + intervals. + + Returns: + A new padded interval. + """ + obj = self.copy() + obj.pad_inplace(start_pad, end_pad, use_strand=use_strand) + return obj + + def pad_inplace( + self, start_pad: int, end_pad: int, *, use_strand: bool = True + ): + """Pads the interval in place by adding padding to the start and end. + + Args: + start_pad: The amount of padding to add to the start. + end_pad: The amount of padding to add to the end. + use_strand: If True, padding is applied in reverse for negative strand + intervals. + """ + if use_strand and self.strand == '-': + start_pad, end_pad = end_pad, start_pad + self.start -= start_pad + self.end += end_pad + if self.width < 0: + raise ValueError('Resulting interval has negative length') + + def resize(self, width: int, use_strand: bool = True) -> Self: + """Resizes the interval to a new width, centered around the original center. + + Args: + width: The new width of the interval. + use_strand: If True, resizing considers the strand orientation. + + Returns: + A new resized interval. + """ + obj = self.copy() + obj.resize_inplace(width, use_strand) + return obj + + def resize_inplace(self, width: int, use_strand: bool = True) -> None: + """Resizes the interval in place, centered around the original center. + + Args: + width: The new width of the interval. + use_strand: If True, resizing considers the strand orientation. + """ + if width < 0: + raise ValueError(f'Width needs to be > 0. Found: {width}.') + + if width is None or self.width == width: + return + + center = self.center() + if use_strand and self.negative_strand: + self.start = center - width // 2 + self.end = center + (width + 1) // 2 + else: + self.start = center - (width + 1) // 2 + self.end = center + width // 2 + assert self.width == width + + def overlaps(self, interval: Self) -> bool: + """Checks if this interval overlaps with another interval.""" + return ( + self.chromosome == interval.chromosome + and self.start < interval.end + and interval.start < self.end + ) + + def contains(self, interval: Self) -> bool: + """Checks if this interval completely contains another interval.""" + return ( + self.chromosome == interval.chromosome + and self.start <= interval.start + and self.end >= interval.end + ) + + def intersect(self, interval: Self) -> Self | None: + """Returns the intersection of this interval with another interval.""" + output = self.copy() + if not self.overlaps(interval): + return None + output.start = max(self.start, interval.start) + output.end = min(self.end, interval.end) + return output + + def coverage( + self, intervals: Sequence[Self], *, bin_size: int = 1 + ) -> np.ndarray: + """Computes coverage track from sequence of intervals overlapping interval. + + This method calculates the coverage of this interval by a set of other + intervals. The coverage is defined as the number of intervals that overlap + each position within this interval. + + The `bin_size` parameter allows you to bin the coverage into equal-sized + windows. This can be useful for summarizing coverage over larger regions. + If `bin_size` is 1, the coverage is calculated at single-base resolution. + + Args: + intervals: A sequence of `Interval` objects that may overlap this + interval. + bin_size: The size of the bins used to calculate coverage. Must be a + positive integer that divides the width of the interval. + + Returns: + A 1D numpy array representing the coverage track. The length of the array + is `self.width // bin_size`. Each element in the array represents the + summed coverage within the corresponding bin. + + Raises: + ValueError: If `bin_size` is not a positive integer or if the interval + width is not divisible by `bin_size`. + """ + if bin_size <= 0: + raise ValueError('bin_size needs to be larger or equal to 1.') + if self.width % bin_size != 0: + raise ValueError( + f'interval width {self.width} needs to be divisible ' + f'by bin_size {bin_size}.' + ) + output = np.zeros((self.width,), dtype=np.int32) + for interval in intervals: + if not self.overlaps(interval): + continue + relative_start = max(interval.start - self.start, 0) + relative_end = min(interval.end - self.start, self.width) + output[relative_start:relative_end] += 1 + if bin_size > 1: + return output.reshape((self.width // bin_size, bin_size)).sum(axis=-1) + else: + return output + + def overlap_ranges( + self, + intervals: Sequence[Self], + ) -> np.ndarray: + """Returns overlapping ranges from intervals overlapping this interval. + + Args: + intervals: Sequence of candidate intervals to test for overlap. + + Returns: + 2D numpy array indicating the start and end of the overlapping ranges. + """ + output = [] + for interval in intervals: + if not self.overlaps(interval): + continue + relative_start = max(interval.start - self.start, 0) + relative_end = min(interval.end - self.start, self.width) + output.append([relative_start, relative_end]) + + return ( + np.asarray(output, dtype=np.int32) + if output + else np.empty((0, 2), dtype=np.int32) + ) + + def binary_mask( + self, intervals: Sequence[Self], bin_size: int = 1 + ) -> np.ndarray: + """Boolean mask True if any interval overlaps the bin: coverage > 0.""" + return self.coverage(intervals, bin_size=bin_size) > 0 + + def coverage_stranded( + self, intervals: Sequence[Self], *, bin_size: int = 1 + ) -> np.ndarray: + """Computes a coverage track from intervals overlapping this interval. + + This method considers the strand information of both self and intervals. + + Args: + intervals: Sequence of intervals possibly overlapping self. + bin_size: Resolution at which to bin the output coverage track. Coverage + within each bin (if larger than 1) will be summarized using sum(). + + Returns: + Numpy array of shape (self.width // bin_size, 2) where output[:, 0] + represents coverage for intervals on the same strand as self and + output[:, 1] represents coverage of intervals on the opposite strand. + """ + # Split intervals based on strand. + forward_intervals = [] + reverse_intervals = [] + for interval in intervals: + if interval.negative_strand: + reverse_intervals.append(interval) + else: + forward_intervals.append(interval) + + coverage = np.stack( + [ + self.coverage(forward_intervals, bin_size=bin_size), + self.coverage(reverse_intervals, bin_size=bin_size), + ], + axis=-1, + ) + if self.negative_strand: + return coverage[::-1, ::-1] + else: + return coverage + + def binary_mask_stranded( + self, intervals: Sequence[Self], bin_size: int = 1 + ) -> np.ndarray: + """Boolean mask True if any interval overlaps the bin: coverage > 0.""" + return self.coverage_stranded(intervals, bin_size=bin_size) > 0 + + +_DEFAULT_REGEX = re.compile(r'(chr(?:X|Y|M|\d+)):(\d+):([ACGTN]*)>([ACGTN]*)') +_GTEX_REGEX = re.compile( + r'(chr(?:X|Y|M|\d+))_(\d+)_([ACGTN]*)_([ACGTN]*)_?[a-zA-Z0-9]*' +) +_OPEN_TARGETS_REGEX = re.compile(r'((?:X|Y|M|\d+))_(\d+)_([ACGTN]*)_([ACGTN]*)') +_OPEN_TARGETS_BIGQUERY_REGEX = re.compile( + r'((?:X|Y|M|\d+)):(\d+):([ACGTN]*):([ACGTN]*)' +) +_GNOMAD_REGEX = re.compile(r'((?:X|Y|M|\d+))-(\d+)-([ACGTN]*)-([ACGTN]*)') + + +class VariantFormat(enum.Enum): + """A format for parsing a string into a Variant object. + + This is used to convert from a string to a formal Variant object. + Note that it does not perform any validation (e.g. it does not verify that the + reference allele corresponds to the reference genome or that the + position is valid). + + Example formats: + DEFAULT: chr22:1024:A>C + GTEX: chr22_1024_A_C_b38 (build suffix is optional) + OPEN_TARGETS: 22_1024_A_C (chr prefix is omitted) + OPEN_TARGETS_BIGQUERY: 22:1024:A:C (chr prefix is omitted) + GNOMAD: 22-1024-A-C (chr prefix is omitted) + """ + + DEFAULT = 'default' + GTEX = 'gtex' + OPEN_TARGETS = 'open_targets' + OPEN_TARGETS_BIGQUERY = 'open_targets_bigquery' + GNOMAD = 'gnomad' + + def to_regex(self) -> re.Pattern[str]: + """Returns a regular expression for the variant format.""" + match self: + case VariantFormat.DEFAULT: + return _DEFAULT_REGEX + case VariantFormat.GTEX: + return _GTEX_REGEX + case VariantFormat.OPEN_TARGETS: + return _OPEN_TARGETS_REGEX + case VariantFormat.OPEN_TARGETS_BIGQUERY: + return _OPEN_TARGETS_BIGQUERY_REGEX + case VariantFormat.GNOMAD: + return _GNOMAD_REGEX + + +@dataclasses.dataclass +class Variant: + """Represents a genomic variant/mutation. + + Differs from the Variant definition in a VCF file, which allows + for multiple alternative bases and contains sample information. This + `Variant` class does not include sample information or variant call + quality information. + + Attributes: + chromosome: The chromosome name (e.g., 'chr1', '1'). + position: The 1-based position of the variant on the chromosome. + reference_bases: The reference base(s) at the variant position. Most + frequently (not always!), these correspond to the sequence in the + reference genome at positions: [position, ..., position + + len(reference_bases) - 1] + alternate_bases: The alternate base(s) that replace the reference. For + example, if sequence='ACT', position=2, reference_bases='C', + alternate_bases='TG', then the actual (alternate) sequence would be ATGT. + name: An optional name for the variant (e.g., a dbSNP ID like rs206437). + info: An optional dictionary for additional variant information. + """ + + chromosome: str + position: int + reference_bases: str + alternate_bases: str + name: str = dataclasses.field(default='', compare=False, hash=False) + info: dict[str, Any] = dataclasses.field( + default_factory=dict, repr=False, compare=False, hash=False + ) + + def __post_init__(self): + """Validates the variant's position.""" + if self.position < 1: + raise ValueError(f'Position has to be >=1. Found: {self.position}.') + if not set(self.reference_bases).issubset(VALID_VARIANT_BASES): + raise ValueError( + f'Invalid reference bases: "{self.reference_bases}". Must only' + ' contain "ACGTN".' + ) + if not set(self.alternate_bases).issubset(VALID_VARIANT_BASES): + raise ValueError( + f'Invalid alternate bases: "{self.alternate_bases}". Must only' + ' contain "ACGTN".' + ) + + def __str__(self): + """Returns a string representation of the variant.""" + ref_alt = f'{self.reference_bases}>{self.alternate_bases}' + return f'{self.chromosome}:{self.position}:{ref_alt}' + + def as_truncated_str(self, max_length: int = 50): + """Truncates the variant str's ref and alt bases to the given max length.""" + + def _truncate(s: str): + if len(s) <= max_length: + return s + else: + return s[: max_length // 2] + '...' + s[-max_length // 2 :] + + return ( + f'{self.chromosome}:{self.position}:' + f'{_truncate(self.reference_bases)}>{_truncate(self.alternate_bases)}' + ) + + @property + def start(self) -> int: + """Returns the 0-based start position of the variant.""" + return self.position - 1 + + @property + def end(self) -> int: + """Returns the 0-based end position of the variant.""" + return self.start + len(self.reference_bases) + + @property + def reference_interval(self) -> Interval: + """Returns an `Interval` for the variant's reference sequence.""" + return Interval(self.chromosome, self.start, self.end) + + def reference_overlaps(self, interval: Interval) -> bool: + """Checks if the variant's reference overlaps with the interval.""" + return interval.overlaps(Interval(self.chromosome, self.start, self.end)) + + def alternate_overlaps(self, interval: Interval) -> bool: + """Checks if the variant's alternate overlaps with the interval.""" + return interval.overlaps( + Interval( + self.chromosome, self.start, self.start + len(self.alternate_bases) + ) + ) + + @property + def is_snv(self) -> bool: + """Return if the variant is a Single Nucleotide Variant (SNV).""" + return len(self.reference_bases) == 1 and len(self.alternate_bases) == 1 + + @property + def is_deletion(self) -> bool: + """Return if the variant is a deletion.""" + return len(self.reference_bases) > len(self.alternate_bases) + + @property + def is_insertion(self) -> bool: + """Return if the variant is an insertion.""" + return len(self.reference_bases) < len(self.alternate_bases) + + def copy(self) -> Self: + """Returns a deep copy of the variant.""" + return copy.deepcopy(self) + + @classmethod + def from_str( + cls, string: str, variant_format: VariantFormat = VariantFormat.DEFAULT + ) -> Self: + """Creates a `Variant` from a string representation. + + Args: + string: The string representation. + variant_format: The format of the variant string. By default, this uses + "chromosome:position:ref>alt" (for example, "chr1:1024:A>C"). See + VariantFormat for alternate formatting options. + + Returns: + A `Variant` object. + """ + result = re.fullmatch(variant_format.to_regex(), string) + if result is None: + raise ValueError(f'Invalid format for variant string: {string}') + + chromosome, position, reference, alternate = result.groups() + # Add chr prefix if not already present. + if not chromosome.startswith('chr'): + chromosome = f'chr{chromosome}' + return cls( + chromosome=chromosome, + position=int(position), + reference_bases=reference, + alternate_bases=alternate, + ) + + def to_dict(self) -> dict[str, Any]: + """Converts the variant to a dictionary.""" + return dataclasses.asdict(self) + + @classmethod + def from_dict(cls, dictionary: Mapping[str, Any] | Self) -> Self: + """Creates a `Variant` from a dictionary.""" + return cls(**dictionary) # pytype: disable=bad-return-type + + def to_proto(self) -> dna_model_pb2.Variant: + """Converts the variant to a protobuf message.""" + return dna_model_pb2.Variant( + chromosome=self.chromosome, + position=self.position, + reference_bases=self.reference_bases, + alternate_bases=self.alternate_bases, + ) + + @classmethod + def from_proto(cls, variant: dna_model_pb2.Variant) -> Self: + """Creates a `Variant` from a protobuf message.""" + return cls( + chromosome=variant.chromosome, + position=variant.position, + reference_bases=variant.reference_bases, + alternate_bases=variant.alternate_bases, + ) + + def split(self, anchor: int) -> tuple[Self | None, Self | None]: + """Splits the variant into two at the anchor point. + + If the anchor point falls within the variant's reference sequence, the + variant is split into two new variants: one upstream of the anchor and one + downstream. If the anchor is outside the variant's reference sequence, + the original variant is returned on the appropriate side, and None on the + other. + + Example: + position= 3 + ref: ...[ A C ]... + alt: .....T G T C + anchor=3 | + returns: (chr1:3:A>T, chr1:4:C>GTC) + + Args: + anchor: The 0-based anchor point to split the variant. + + Returns: + A tuple of the upstream and downstream variants. If the variant is only + on one side of the anchorpoint, then None is returned. + """ + if anchor <= self.start: + return None, self.copy() + elif anchor >= self.end: + return self.copy(), None + else: + mid = anchor - self.start + upstream, downstream = self.copy(), self.copy() + upstream.reference_bases = self.reference_bases[:mid] + upstream.alternate_bases = self.alternate_bases[:mid] + + downstream.position = anchor + 1 + downstream.reference_bases = self.reference_bases[mid:] + downstream.alternate_bases = self.alternate_bases[mid:] + return upstream, downstream + + +@dataclasses.dataclass +class Junction(Interval): + """Represents a splice junction. + + A splice junction is a point in a pre-mRNA transcript where an intron is + removed and exons are joined during RNA splicing. This class inherits from + `Interval` and adds properties and methods specific to splice junctions. + + Attributes: + chromosome: The chromosome name. + start: The 0-based start position of the junction. + end: The 0-based end position of the junction. + strand: The strand of the junction ('+' or '-'). + name: An optional name for the junction. + info: An optional dictionary to store additional information. + k: An optional integer representing the number of reads supporting the + splice junction. + + Raises: + ValueError: If the strand is unstranded. + """ + + k: int | None = None + + def __post_init__(self): + """Validates that the junction is stranded.""" + super().__post_init__() + if self.strand == STRAND_UNSTRANDED: + raise ValueError('Junctions must be stranded.') + + @property + def acceptor(self) -> int: + """Returns the acceptor site position.""" + return self.start if self.strand == STRAND_NEGATIVE else self.end + + @property + def donor(self) -> int: + """Returns the donor site position.""" + return self.end if self.strand == STRAND_NEGATIVE else self.start + + def dinucleotide_region(self) -> tuple[Interval, Interval]: + """Returns the dinucleotide regions around acceptor and donor sites.""" + return ( + Interval( + self.chromosome, self.start, self.start + 2, strand=self.strand + ), + Interval(self.chromosome, self.end - 2, self.end, strand=self.strand), + ) + + def acceptor_region(self, overhang: tuple[int, int] = (250, 250)) -> Interval: + """Returns the region around the acceptor site with overhang.""" + return Interval( + self.chromosome, self.acceptor, self.acceptor, strand=self.strand + ).pad(start_pad=overhang[0], end_pad=overhang[1]) + + def donor_region(self, overhang: tuple[int, int] = (250, 250)) -> Interval: + """Returns the region around the donor site with overhang.""" + return Interval( + self.chromosome, self.donor, self.donor, strand=self.strand + ).pad(start_pad=overhang[0], end_pad=overhang[1]) + + +class _FastaExtractorType(Protocol): + """Protocol definition for extracting intervals from a Fasta file.""" + + def extract(self, interval: Interval) -> str: + """Extract and return the DNA sequence for a given interval.""" + + +def _prefix_length(*sequences) -> int: + """Returns the length of the common prefix for a sequence of strings.""" + i = 0 + for chars in zip(*sequences, strict=False): + if all(c == chars[0] for c in chars): + i += 1 + else: + break + return i + + +def normalize_variant( + variant: Variant, extractor: _FastaExtractorType +) -> Variant: + """Normalize a Variant by left-aligning the reference and alternate bases. + + Normalization applied following algorithm described in + https://doi.org/10.1093/bioinformatics/btv112. + + Args: + variant: The Variant to normalize. + extractor: The FastaExtractor to use for extracting the reference sequence. + + Returns: + The normalized Variant. + """ + if variant.is_snv: + return variant + + chromosome = variant.chromosome + genome_sequence = extractor.extract( + Interval( + chromosome, + variant.start, + variant.start + + max(len(variant.reference_bases), len(variant.alternate_bases)), + ) + ) + + position = variant.position + alleles = [genome_sequence, variant.reference_bases, variant.alternate_bases] + + # Remove any common suffix from the alleles. + finished = False + while not finished: + suffix_length = _prefix_length(*[reversed(a) for a in alleles]) + if suffix_length > 0: + alleles = [a[:-suffix_length] for a in alleles] + + # If any alleles are empty, extend all alleles by 1 nucleotide to the left. + if not all(alleles): + position -= 1 + base = extractor.extract(Interval(chromosome, position - 1, position))[0] + alleles = [base + a for a in alleles] + else: + # Finished iff we haven't shifted alleles and don't have a common suffix. + finished = suffix_length == 0 + + # Left-align the variant to the genome, ensuring at least 1 bp of context. + max_prefix_length = len(min(alleles)) - 1 + prefix_length = _prefix_length(*alleles) + i = min(max_prefix_length, prefix_length) + + _, reference_bases, alternate_bases = alleles + return Variant( + chromosome=chromosome, + position=position + i, + reference_bases=reference_bases[i:], + alternate_bases=alternate_bases[i:], + ) + + +def _split_intervals( + intervals: Iterable[Interval], marker: int, bounds: list[tuple[int, int]] +): + """Splits intervals into start and end points with markers.""" + for i in intervals: + bounds.append((i.start, +marker)) + bounds.append((i.end, -marker)) + + +def _group_by_chromosome( + intervals: Iterable[Interval], +) -> dict[str, list[Interval]]: + """Groups intervals by chromosome.""" + interval_map = collections.defaultdict(list) + for i in intervals: + interval_map[i.chromosome].append(i) + return dict(interval_map) + + +def intersect_intervals( + lhs: Iterable[Interval], + rhs: Iterable[Interval], + *, + result_strand: str = '.', +) -> Iterator[Interval]: + """Generates the intersection of two interval sets. + + Point ranges (width == 0) are considered to intersect any range which contains + them. + + In these examples, the intersection is a point range (2,2) + ``` + 1 2 3 4 + ..|..|..|..|.. + <> (start=2, end=2) + -------> (..., end=2) + ``` + + ``` + 1 2 3 4 + ..|..|..|..|.. + <> (start=2, end=2) + <------- (start=2, end=...) + ``` + + For consistency, this means that the following results in a point + intersection: + + ``` + 1 2 3 4 + ..|..|..|..|.. + -------> (start=..., end=2) + <------- (start=2, end=...) + ``` + + Args: + lhs: A set of intervals. + rhs: A set of intervals. + result_strand: The strand for the resulting intervals. + + Yields: + The intersection of intervals. Overlapping intervals within either `lhs` + or `rhs` are implicitly unioned. + """ + + def _intersect(lhs, rhs, chrom): + """Calculates the intersection for a specific chromosome. + + Deconstructs two sets of intervals into (start, +k) and (end, -k) positions, + where k is different for each set, then sorts all interval endpoints. By + walking the sorted endpoints and accumulating the k's, we can work out when + we enter or exit an interval from either set. + + This allows emitting intervals corresponding to the intersection, and with + appropriate k's, detecting when we enter an interval in either set before + exiting the previous. + + Args: + lhs: The first list of `Interval` objects. + rhs: The second list of `Interval` objects. + chrom: The chromosome name. + + Yields: + `Interval` objects representing the intersections. + """ + bounds = [] + _split_intervals(lhs, 0x00001, bounds) + _split_intervals(rhs, 0x10000, bounds) + accum = 0 + start = None + for pos, delta in sorted(bounds, key=lambda x: (x[0], -x[1])): + old_accum = accum + accum += delta + if accum & 0xFFFF and accum & 0xFFFF0000: + if start is None: + start = pos + elif old_accum & 0xFFFF and old_accum & 0xFFFF0000: + yield Interval(chrom, start, pos, result_strand) + start = None + + lhs = _group_by_chromosome(lhs) + rhs = _group_by_chromosome(rhs) + for chromosome in set(lhs) & set(rhs): + yield from _intersect(lhs[chromosome], rhs[chromosome], chromosome) + + +def union_intervals( + lhs: Iterable[Interval], + rhs: Iterable[Interval], + *, + result_strand: str = '.', +) -> Iterator[Interval]: + """Generates the union of two interval sets. + + Args: + lhs: A non-overlapping set of intervals. + rhs: A non-overlapping set of intervals. + result_strand: The strand for the resulting intervals. + + Yields: + The union of intervals. Any position covered by an interval in `lhs` or + `rhs` is covered in the result. + """ + + def _union(lhs, rhs, chrom): + """Calculates the union for a specific chromosome, analog of _intersect.""" + bounds = [] + _split_intervals(lhs, 0x00001, bounds) + _split_intervals(rhs, 0x10000, bounds) + accum = 0 + start = end = None + for pos, delta in sorted(bounds, key=lambda x: (x[0], -x[1])): + if accum == 0 and end is not None and end < pos: + # Delay generating an interval until after the observed end point. + # This merges abutting ranges. + yield Interval(chrom, start, end, result_strand) + start = end = None + accum += delta + if accum: + if start is None: + start = pos + else: + assert start is not None + end = pos + if end is not None: + yield Interval(chrom, start, end, result_strand) + + lhs = _group_by_chromosome(lhs) + rhs = _group_by_chromosome(rhs) + for chromosome in set(lhs) | set(rhs): + yield from _union( + lhs.get(chromosome, []), rhs.get(chromosome, []), chromosome + ) + + +def merge_overlapping_intervals( + intervals: Sequence[Interval], +) -> list[Interval]: + """Merges overlapping intervals and returns a sorted list. + + Args: + intervals: A sequence of intervals with the same strand. + + Returns: + A new sorted list of merged intervals. + """ + if not intervals: + return [] + assert all(i.strand == intervals[0].strand for i in intervals) + return list(union_intervals(intervals, [], result_strand=intervals[0].strand)) diff --git a/alphagenome/source/src/alphagenome/data/genome_test.py b/alphagenome/source/src/alphagenome/data/genome_test.py new file mode 100644 index 0000000000000000000000000000000000000000..5a6f6513f59831ef4e54b579903576b38f0cc197 --- /dev/null +++ b/alphagenome/source/src/alphagenome/data/genome_test.py @@ -0,0 +1,1084 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from unittest import mock + +from absl.testing import absltest +from absl.testing import parameterized +from alphagenome.protos import dna_model_pb2 +from alphagenome.data import genome +import numpy as np + + +class StrandTest(parameterized.TestCase): + + @parameterized.parameters( + list( + zip( + genome.STRAND_OPTIONS, + ( + genome.Strand.POSITIVE, + genome.Strand.NEGATIVE, + genome.Strand.UNSTRANDED, + ), + ) + ) + ) + def test_from_str(self, strand_str, expected): + strand = genome.Strand.from_str(strand_str) + self.assertEqual(strand, expected) + + def test_invalid_strand_str(self): + with self.assertRaises(ValueError): + genome.Strand.from_str('foo') + + @parameterized.parameters( + (genome.Strand.POSITIVE,), + (genome.Strand.NEGATIVE,), + (genome.Strand.UNSTRANDED,), + ) + def test_from_proto(self, strand): + proto = genome.Strand.to_proto(strand) + round_trip = genome.Strand.from_proto(proto) + self.assertEqual(strand, round_trip) + + def test_invalid_strand_proto(self): + with self.assertRaises(ValueError): + genome.Strand.from_proto(dna_model_pb2.Strand.STRAND_UNSPECIFIED) + + +class IntervalTest(parameterized.TestCase): + + @parameterized.parameters( + dict(chromosome='chr1', start=0, end=100, strand='-'), + dict(chromosome='chr2', start=100, end=200, strand='.'), + dict(chromosome='chr3', start=100, end=200, strand='+'), + ) + def test_proto_roundtrip(self, chromosome, start, end, strand): + interval = genome.Interval(chromosome, start, end, strand) + proto = interval.to_proto() + round_trip = genome.Interval.from_proto(proto) + self.assertEqual(interval, round_trip) + + def test_init_valid(self): + genome.Interval('chr1', 10, 10) + genome.Interval( + 'chr1', + 10, + 20, + name='test', + info=dict(a=1, b='b', c=[1, 2], d=dict(e=1)), + ) + + def test_init_raises_value_error(self): + with self.assertRaises(ValueError): + genome.Interval('chr1', 5, 1) # start < end + + def test_attributes(self): + interval = genome.Interval('chr1', 10, 20, strand='-') + self.assertEqual(interval.start, 10) + self.assertEqual(interval.end, 20) + self.assertEqual(interval.chromosome, 'chr1') + self.assertEqual(interval.name, '') + self.assertIsInstance(interval.info, dict) + self.assertEmpty(interval.info) + interval.info['test'] = 10 + self.assertEqual(interval.info['test'], 10) + self.assertTrue(interval.negative_strand) + + self.assertEqual(interval.width, 10) + + def test_copy(self): + # make sure the original got unchanged + interval = genome.Interval('chr1', 10, 20, strand='-') + i2 = interval.copy() + i2.info['test'] = 10 + self.assertEqual(i2.info['test'], 10) + self.assertEmpty(interval.info) + + def test_serialization(self): + interval = genome.Interval('chr1', 0, 100, '-') + self.assertEqual(interval, genome.Interval.from_str('chr1:0-100:-')) + self.assertEqual(str(interval), 'chr1:0-100:-') + + def test_serialization_no_strand(self): + # Serialized intervals are allowed to be missing a strand, in which case + # they are treated as unstranded. Serializing an interval always adds the + # strand. + interval = genome.Interval('chr1', 0, 100) + self.assertEqual(interval, genome.Interval.from_str('chr1:0-100')) + self.assertEqual(interval, genome.Interval.from_str('chr1:0-100:.')) + self.assertEqual(str(interval), 'chr1:0-100:.') + + def test_from_to_string(self): + interval = genome.Interval('chr1', 10, 20, strand='-') + self.assertIsInstance(str(interval), str) + self.assertEqual(interval, genome.Interval.from_str(str(interval))) + + def test_from_to_string_negative_start(self): + interval = genome.Interval('chr1', -10, 20, strand='-') + self.assertIsInstance(str(interval), str) + self.assertEqual(interval, genome.Interval.from_str(str(interval))) + + def test_from_to_string_negative_end(self): + interval = genome.Interval('chr1', -20, -10, strand='-') + self.assertIsInstance(str(interval), str) + self.assertEqual(interval, genome.Interval.from_str(str(interval))) + + def test_overlaps_overlapping_at_start(self): + self.assertTrue( + genome.Interval('chr1', 1, 5).overlaps(genome.Interval('chr1', 3, 7)) + ) + + def test_overlaps_overlapping_at_end(self): + self.assertTrue( + genome.Interval('chr1', 1, 5).overlaps(genome.Interval('chr1', 0, 2)) + ) + + def test_overlaps_overlapping_contains(self): + self.assertTrue( + genome.Interval('chr1', 1, 5).overlaps(genome.Interval('chr1', 0, 6)) + ) + + def test_overlaps_nonoverlapping_at_start(self): + self.assertFalse( + genome.Interval('chr1', 1, 5).overlaps(genome.Interval('chr1', 0, 1)) + ) + + def test_overlaps_nonoverlapping_at_end(self): + self.assertFalse( + genome.Interval('chr1', 1, 5).overlaps(genome.Interval('chr1', 5, 6)) + ) + + def test_overlaps_nonoverlapping_different_chromosome(self): + self.assertFalse( + genome.Interval('chr1', 1, 5).overlaps(genome.Interval('chr2', 0, 6)) + ) + + def test_contains_small_within_larger(self): + self.assertTrue( + genome.Interval('chr1', 1, 5).contains(genome.Interval('chr1', 2, 3)) + ) + + def test_contains_large_within_small(self): + self.assertFalse( + genome.Interval('chr1', 2, 3).contains(genome.Interval('chr1', 1, 5)) + ) + + def test_contains_same_size(self): + self.assertTrue( + genome.Interval('chr1', 1, 5).contains(genome.Interval('chr1', 1, 5)) + ) + + def test_contains_overlapping_end(self): + self.assertFalse( + genome.Interval('chr1', 1, 5).contains(genome.Interval('chr1', 4, 9)) + ) + + def test_contains_overlapping_start(self): + self.assertFalse( + genome.Interval('chr1', 4, 10).contains(genome.Interval('chr1', 1, 5)) + ) + + def test_contains_non_overlapping_end(self): + self.assertFalse( + genome.Interval('chr1', 1, 5).contains(genome.Interval('chr1', 6, 9)) + ) + + def test_contains_non_overlapping_start(self): + self.assertFalse( + genome.Interval('chr1', 6, 9).contains(genome.Interval('chr1', 1, 5)) + ) + + def test_contains_different_chromosome(self): + self.assertFalse( + genome.Interval('chr1', 1, 5).contains(genome.Interval('chr2', 1, 5)) + ) + + def test_shift_dont_use_strand(self): + interval = genome.Interval('chr1', 10, 20, strand='-') + i2 = interval.shift(10, use_strand=False) + + # original unchanged + self.assertEqual(interval.start, 10) + self.assertEqual(interval.end, 20) + + self.assertEqual(i2.start, 20) + self.assertEqual(i2.end, 30) + + def test_shift_use_strand(self): + interval = genome.Interval('chr1', 10, 20, strand='-') + i2 = interval.shift(10) # use_strand = True by default. + self.assertEqual(i2.start, 0) + self.assertEqual(i2.end, 10) + + # Shift outside of the reference. + self.assertFalse(interval.shift(20, use_strand=True).within_reference()) + + i2 = interval.shift(15, use_strand=True).truncate() + self.assertEqual(i2.start, 0) + self.assertEqual(i2.end, 5) + + self.assertEqual(interval.center(), 15) + + def test_pad_does_not_mutate(self): + interval = genome.Interval('chr1', 100, 200) + self.assertIsNot(interval, interval.pad(0, 0)) + + def test_pad_unstranded(self): + self.assertEqual( + genome.Interval('chr1', 100, 200, '.').pad(20, 10, use_strand=False), + genome.Interval('chr1', 80, 210, '.'), + ) + self.assertEqual( + genome.Interval('chr1', 100, 200, '+').pad(20, 10, use_strand=False), + genome.Interval('chr1', 80, 210, '+'), + ) + self.assertEqual( + genome.Interval('chr1', 100, 200, '+').pad(20, 10, use_strand=False), + genome.Interval('chr1', 80, 210, '+'), + ) + + def test_pad_stranded(self): + self.assertEqual( + genome.Interval('chr1', 100, 200, '.').pad(20, 10, use_strand=True), + genome.Interval('chr1', 80, 210, '.'), + ) + self.assertEqual( + genome.Interval('chr1', 100, 200, '+').pad(20, 10, use_strand=True), + genome.Interval('chr1', 80, 210, '+'), + ) + # Negative strand interval padding behaves differently when use_strand=True + self.assertEqual( + genome.Interval('chr1', 100, 200, '-').pad(20, 10, use_strand=True), + genome.Interval('chr1', 90, 220, '-'), + ) + + def test_boundary_shift_positive_strand(self): + interval = genome.Interval('chr1', 10, 20, strand='+') + resized = interval.boundary_shift(1, -2) + self.assertEqual(resized.start, 11) + self.assertEqual(resized.end, 18) + + def test_boundary_shift_negative_strand(self): + interval = genome.Interval('chr1', 10, 20, strand='-') + resized = interval.boundary_shift(1, -2) + self.assertEqual(resized.start, 12) + self.assertEqual(resized.end, 19) + + def test_resize_positive_strand_odd_width(self): + interval = genome.Interval('chr1', 10, 20, strand='+') + i2 = interval.resize(11) + self.assertEqual(i2.start, 9) + self.assertEqual(i2.end, 20) + + def test_resize_positive_strand_even_width(self): + interval = genome.Interval('chr1', 10, 20, strand='+') + i2 = interval.resize(12) + self.assertEqual(i2.start, 9) + self.assertEqual(i2.end, 21) + + def test_resize_negative_strand_odd_width(self): + interval = genome.Interval('chr1', 10, 20, strand='-') + i2 = interval.resize(11) + self.assertEqual(i2.start, 10) + self.assertEqual(i2.end, 21) + + def test_resize_negative_strand_even_width(self): + interval = genome.Interval('chr1', 10, 20, strand='-') + i2 = interval.resize(12) + self.assertEqual(i2.start, 9) + self.assertEqual(i2.end, 21) + + i2 = interval.resize(9) + self.assertEqual(i2.start, 11) + self.assertEqual(i2.end, 20) + + @parameterized.parameters(('+', '-'), ('-', '+')) + def test_swap_strand(self, before, after): + interval = genome.Interval('chr1', 10, 20, strand=before) + self.assertEqual(interval.swap_strand().strand, after) + self.assertEqual(interval.strand, before) + + @parameterized.parameters(('+', '.'), ('-', '.'), ('.', '.')) + def test_unstranded(self, before, after): + interval = genome.Interval('chr1', 10, 20, strand=before) + self.assertEqual(interval.as_unstranded().strand, after) + self.assertEqual(interval.strand, before) + + def test_swap_strand_unstranded(self): + interval = genome.Interval('chr1', 10, 20, strand='.') + with self.assertRaises(ValueError): + interval.swap_strand() + + @parameterized.parameters( + (0, True), (0, False), (1, True), (1, False), (2, True), (2, False) + ) + def test_center_even(self, shift, use_strand): + """Checks even-width intervals.""" + interval = genome.Interval('chr1', 10 + shift, 20 + shift, strand='-') + self.assertEqual(interval.center(use_strand=use_strand), 15 + shift) + + @parameterized.parameters(0, 1, 2) + def test_center_odd_positive_strand(self, shift): + """Checks odd-width intervals.""" + interval = genome.Interval('chr1', 9 + shift, 20 + shift, strand='+') + self.assertEqual(interval.center(use_strand=False), 15 + shift) + self.assertEqual(interval.center(use_strand=True), 15 + shift) + + @parameterized.parameters(0, 1, 2) + def test_center_odd_negative_strand(self, shift): + interval = genome.Interval('chr1', 9 + shift, 20 + shift, strand='-') + self.assertEqual(interval.center(use_strand=False), 15 + shift) + self.assertEqual(interval.center(use_strand=True), 14 + shift) + + @parameterized.parameters( + (10, 13, '+'), # even -> odd + (10, 14, '+'), # even -> even + (11, 13, '+'), # odd -> odd + (11, 14, '+'), # odd -> even + (10, 13, '-'), # negative strand + (10, 14, '-'), + (11, 13, '-'), + (11, 14, '-'), + ) + def test_center_constant_at_resize(self, start_width, resize_width, strand): + """Tests that the center remains the same if we resize.""" + interval = genome.Interval('chr1', 10, 10 + start_width, strand=strand) + self.assertEqual(interval.resize(resize_width).center(), interval.center()) + + def test_intersect_overlaps(self): + a = genome.Interval('chr1', 1, 5) + b = genome.Interval('chr1', 3, 7) + self.assertEqual(a.intersect(b), genome.Interval('chr1', 3, 5)) + + def test_intersect_no_overlap(self): + a = genome.Interval('chr1', 1, 5) + b = genome.Interval('chr1', 7, 10) + self.assertIsNone(a.intersect(b)) + + def test_coverage(self): + a = genome.Interval('chr1', 1, 5) + b = genome.Interval('chr1', 3, 7) + c = genome.Interval('chr1', 4, 7) + d = genome.Interval('chr1', 1, 2) + np.testing.assert_array_equal(a.coverage([b, c, d]), [1, 0, 1, 2]) + np.testing.assert_array_equal(a.coverage([b, c, d], bin_size=2), [1, 3]) + np.testing.assert_array_equal(a.coverage([b, c, d], bin_size=4), [4]) + + def test_coverage_raises(self): + a = genome.Interval('chr1', 1, 5) + b = genome.Interval('chr1', 3, 7) + c = genome.Interval('chr1', 4, 7) + d = genome.Interval('chr1', 1, 2) + with self.assertRaises(ValueError): + a.coverage([b, c, d], bin_size=0) + with self.assertRaises(ValueError): + a.coverage([b, c, d], bin_size=-1) + with self.assertRaises(ValueError): + a.coverage([b, c, d], bin_size=3) + + def test_overlap_ranges(self): + a = genome.Interval('chr1', 1, 5) + b = genome.Interval('chr1', 3, 7) + c = genome.Interval('chr1', 2, 4) + d = genome.Interval('chr1', 0, 2) + e = genome.Interval('chr1', 5, 10) + f = genome.Interval('chr2', 1, 5) + np.testing.assert_array_equal( + a.overlap_ranges([b, c, d, e, f]), + np.array( + [ + (2, 4), + (1, 3), + (0, 1), + ], + dtype=np.int32, + ), + ) + np.testing.assert_array_equal( + a.overlap_ranges([f]), np.empty((0, 2), dtype=np.int32) + ) + + def test_binary_mask(self): + a = genome.Interval('chr1', 1, 5) + b = genome.Interval('chr1', 3, 7) + c = genome.Interval('chr1', 4, 7) + d = genome.Interval('chr1', 1, 2) + np.testing.assert_array_equal( + a.binary_mask([b, c, d]), [True, False, True, True] + ) + np.testing.assert_array_equal( + a.binary_mask([b, c, d], bin_size=2), [True, True] + ) + + def test_binary_mask2(self): + interval = genome.Interval('chr1', 0, 6) + intervals = [ + genome.Interval('chr1', 1, 2), + genome.Interval('chr1', 2, 3), + genome.Interval('chr1', 3, 4), + ] + mask = interval.binary_mask(intervals, bin_size=1) + self.assertSequenceEqual( + [False, True, True, True, False, False], list(mask) + ) + + mask = interval.binary_mask(intervals, bin_size=2) + self.assertSequenceEqual([True, True, False], list(mask)) + + def test_coverage_stranded(self): + a = genome.Interval('chr1', 1, 5) + a_reversed = genome.Interval('chr1', 1, 5, strand='-') + b = genome.Interval('chr1', 3, 7) + c = genome.Interval('chr1', 4, 7) + d = genome.Interval('chr1', 1, 2, strand='-') + np.testing.assert_array_equal( + a.coverage_stranded([b, c, d]), [[0, 1], [0, 0], [1, 0], [2, 0]] + ) + np.testing.assert_array_equal( + a_reversed.coverage_stranded([b, c, d]), + [[0, 2], [0, 1], [0, 0], [1, 0]], + ) + + def test_binary_mask_stranded(self): + interval = genome.Interval('chr1', 0, 6) + intervals = [ + genome.Interval('chr1', 1, 2), + genome.Interval('chr1', 2, 3), + genome.Interval('chr1', 3, 4), + ] + + mask = interval.binary_mask_stranded(intervals, bin_size=2) + np.testing.assert_array_equal( + [[True, False], [True, False], [False, False]], mask + ) + + def test_binary_mask_stranded2(self): + interval = genome.Interval('chr1', 0, 6, strand='-') + intervals = [ + genome.Interval('chr1', 1, 2, strand='+'), + genome.Interval('chr1', 2, 3, strand='+'), + genome.Interval('chr1', 3, 4, strand='-'), + ] + + mask = interval.binary_mask_stranded(intervals, bin_size=2) + np.testing.assert_array_equal( + [[False, False], [True, True], [False, True]], mask + ) + + +class VariantTest(parameterized.TestCase): + + def test_proto_roundtrip(self): + variant = genome.Variant('chr1', 10, 'C', 'T') + proto = variant.to_proto() + round_trip = genome.Variant.from_proto(proto) + self.assertEqual(variant, round_trip) + + def test_init_valid(self): + genome.Variant('chr1', 10, 'C', 'T') + genome.Variant('chr1', 10, 'C', 'T', name='test') + genome.Variant( + 'chr1', + 10, + 'C', + 'T', + name='test', + info=dict(a=1, b='b', c=[1, 2], d=dict(e=1)), + ) + + @parameterized.parameters( + ('chr1', -20, 'C', 'T', 'Position has to be >=1.'), + ('chr1', 1, 'Z', 'T', 'Invalid reference bases'), + ('chr1', 1, 'A', 'Z', 'Invalid alternate bases'), + ) + def test_init_raises_value_error( + self, chromosome, position, ref_bases, alt_bases, expected_error + ): + with self.assertRaisesRegex(ValueError, expected_error): + genome.Variant(chromosome, position, ref_bases, alt_bases) + + def test_attributes(self): + v = genome.Variant('chr1', 10, 'C', 'T') + self.assertEqual(v.start, 9) + self.assertEqual(v.chromosome, 'chr1') + self.assertEqual(v.position, 10) + self.assertEqual(v.reference_bases, 'C') + self.assertEqual(v.alternate_bases, 'T') + + def test_attributes_info_assignment(self): + v = genome.Variant('chr1', 10, 'C', 'T') + self.assertIsInstance(v.info, dict) + v.info['test'] = 10 + self.assertEqual(v.info['test'], 10) + + def test_copy(self): + # Make sure the original got unchanged. + v = genome.Variant('chr1', 10, 'C', 'T', info=dict(test=10)) + v2 = v.copy() + v.info['test'] = 20 + self.assertEqual(v2.info['test'], 10) + self.assertEqual(v.info['test'], 20) + + @parameterized.parameters( + ('chr22:1024:A>C', genome.VariantFormat.DEFAULT), + ('chr22_1024_A_C_b38', genome.VariantFormat.GTEX), + ('22_1024_A_C', genome.VariantFormat.OPEN_TARGETS), + ('22:1024:A:C', genome.VariantFormat.OPEN_TARGETS_BIGQUERY), + ('22-1024-A-C', genome.VariantFormat.GNOMAD), + ) + def test_from_str_single_base(self, string, variant_format): + expected_variant = genome.Variant( + chromosome='chr22', + position=1024, + reference_bases='A', + alternate_bases='C', + ) + actual_variant = genome.Variant.from_str( + string, variant_format=variant_format + ) + self.assertEqual(expected_variant.chromosome, actual_variant.chromosome) + self.assertEqual(expected_variant.position, actual_variant.position) + self.assertEqual( + expected_variant.reference_bases, actual_variant.reference_bases + ) + self.assertEqual( + expected_variant.alternate_bases, actual_variant.alternate_bases + ) + + @parameterized.parameters( + ('chrX:1024:AGTC>C', genome.VariantFormat.DEFAULT), + ('chrX_1024_AGTC_C', genome.VariantFormat.GTEX), + ('X_1024_AGTC_C', genome.VariantFormat.OPEN_TARGETS), + ('X:1024:AGTC:C', genome.VariantFormat.OPEN_TARGETS_BIGQUERY), + ('X-1024-AGTC-C', genome.VariantFormat.GNOMAD), + ) + def test_from_str_multiple_bases(self, string, variant_format): + expected_variant = genome.Variant( + chromosome='chrX', + position=1024, + reference_bases='AGTC', + alternate_bases='C', + ) + actual_variant = genome.Variant.from_str( + string, variant_format=variant_format + ) + self.assertEqual(expected_variant.chromosome, actual_variant.chromosome) + self.assertEqual(expected_variant.position, actual_variant.position) + self.assertEqual( + expected_variant.reference_bases, actual_variant.reference_bases + ) + self.assertEqual( + expected_variant.alternate_bases, actual_variant.alternate_bases + ) + + @parameterized.parameters( + ('chrM:1024:AGTC>', genome.VariantFormat.DEFAULT), + ('chrM_1024_AGTC__hg38', genome.VariantFormat.GTEX), + ('M_1024_AGTC_', genome.VariantFormat.OPEN_TARGETS), + ('M:1024:AGTC:', genome.VariantFormat.OPEN_TARGETS_BIGQUERY), + ('M-1024-AGTC-', genome.VariantFormat.GNOMAD), + ) + def test_from_str_deletion(self, string, variant_format): + expected_variant = genome.Variant( + chromosome='chrM', + position=1024, + reference_bases='AGTC', + alternate_bases='', + ) + actual_variant = genome.Variant.from_str( + string, variant_format=variant_format + ) + self.assertEqual(expected_variant.chromosome, actual_variant.chromosome) + self.assertEqual(expected_variant.position, actual_variant.position) + self.assertEqual( + expected_variant.reference_bases, actual_variant.reference_bases + ) + self.assertEqual( + expected_variant.alternate_bases, actual_variant.alternate_bases + ) + + @parameterized.parameters( + ('chrM:1024:>AGTC', genome.VariantFormat.DEFAULT), + ('chrM_1024__AGTC_hg38', genome.VariantFormat.GTEX), + ('M_1024__AGTC', genome.VariantFormat.OPEN_TARGETS), + ('M:1024::AGTC', genome.VariantFormat.OPEN_TARGETS_BIGQUERY), + ('M-1024--AGTC', genome.VariantFormat.GNOMAD), + ) + def test_from_str_insertion(self, string, variant_format): + expected_variant = genome.Variant( + chromosome='chrM', + position=1024, + reference_bases='', + alternate_bases='AGTC', + ) + actual_variant = genome.Variant.from_str( + string, variant_format=variant_format + ) + self.assertEqual(expected_variant.chromosome, actual_variant.chromosome) + self.assertEqual(expected_variant.position, actual_variant.position) + self.assertEqual( + expected_variant.reference_bases, actual_variant.reference_bases + ) + self.assertEqual( + expected_variant.alternate_bases, actual_variant.alternate_bases + ) + + @parameterized.parameters( + # Invalid chromosomes. + ('chrR:1024:A>C', genome.VariantFormat.DEFAULT), + ('chrR_1024_A_C', genome.VariantFormat.GTEX), + ('R_1024_A_C', genome.VariantFormat.OPEN_TARGETS), + ('R:1024:A:C', genome.VariantFormat.OPEN_TARGETS_BIGQUERY), + ('R-1024-A-C', genome.VariantFormat.GNOMAD), + # Missing chromosomes. + (':1024:A>C', genome.VariantFormat.DEFAULT), + ('_1024_A_C', genome.VariantFormat.GTEX), + ('_1024_A_C', genome.VariantFormat.OPEN_TARGETS), + (':1024:A:C', genome.VariantFormat.OPEN_TARGETS_BIGQUERY), + ('-1024-A-C', genome.VariantFormat.GNOMAD), + # Missing positions. + ('chr22::A>C', genome.VariantFormat.DEFAULT), + ('chr22__A_C', genome.VariantFormat.GTEX), + ('22__A_C', genome.VariantFormat.OPEN_TARGETS), + ('22::A:C', genome.VariantFormat.OPEN_TARGETS_BIGQUERY), + ('22--A-C', genome.VariantFormat.GNOMAD), + # Missing chr prefixes. + ('22:1024:A>C', genome.VariantFormat.DEFAULT), + ('22_1024_A_C', genome.VariantFormat.GTEX), + # Unexpected chr prefixes. + ('chr22_1024_A_C', genome.VariantFormat.OPEN_TARGETS), + ('chr22:1024:A:C', genome.VariantFormat.OPEN_TARGETS_BIGQUERY), + ('chr22-1024-A-C', genome.VariantFormat.GNOMAD), + # Unexpected builds. + ('chr22:1024:A>C_b38', genome.VariantFormat.DEFAULT), + ('22_1024_A_C_b', genome.VariantFormat.OPEN_TARGETS), + ('22:1024:A:C_hg38', genome.VariantFormat.OPEN_TARGETS_BIGQUERY), + # Mismatched formats. + ('22-1024-A-C_b38', genome.VariantFormat.GNOMAD), + ('chr22_1024_A_C', genome.VariantFormat.DEFAULT), + ('chr22:1024:A>C', genome.VariantFormat.GTEX), + ('chr22:1024:A>C', genome.VariantFormat.OPEN_TARGETS), + ('chr22:1024:A>C', genome.VariantFormat.OPEN_TARGETS_BIGQUERY), + ('chr22:1024:A>C', genome.VariantFormat.GNOMAD), + ) + def test_from_str_invalid(self, string, variant_format): + with self.assertRaisesRegex( + ValueError, f'Invalid format for variant string: {string}' + ): + genome.Variant.from_str(string, variant_format=variant_format) + + def test_from_to_string(self): + v = genome.Variant('chr1', 10, 'C', 'T') + self.assertIsInstance(str(v), str) + self.assertEqual(v, genome.Variant.from_str(str(v))) + + def test_from_to_dict(self): + v = genome.Variant('chr1', 10, 'C', 'T') + d = v.to_dict() + self.assertIsInstance(d, dict) + self.assertEqual(v, genome.Variant.from_dict(d)) + + def test_split_alternate_longer(self): + # Case #1: alternate_bases longer than reference_bases. + v = genome.Variant('chr1', 3, 'AC', 'TGTC') + # Anchor before the variant. + self.assertEqual( + v.split(2), (None, genome.Variant('chr1', 3, 'AC', 'TGTC')) + ) + # Anchor at the variant (between A and C in the reference). + # Note: start=2, end=4. + self.assertEqual( + v.split(3), + ( + genome.Variant('chr1', 3, 'A', 'T'), + genome.Variant('chr1', 4, 'C', 'GTC'), + ), + ) + # Anchor after the variant. + for split in [4, 5, 6]: + self.assertEqual( + v.split(split), (genome.Variant('chr1', 3, 'AC', 'TGTC'), None) + ) + + def test_split_reference_longer(self): + # Case #2: reference_bases longer than alternate_bases. + v = genome.Variant('chr1', 3, 'TGTC', 'AC') + # Anchor before the variant. + self.assertEqual( + v.split(2), (None, genome.Variant('chr1', 3, 'TGTC', 'AC')) + ) + # Anchor at the variant. + self.assertEqual( + v.split(3), + ( + genome.Variant('chr1', 3, 'T', 'A'), + genome.Variant('chr1', 4, 'GTC', 'C'), + ), + ) + self.assertEqual( + v.split(4), + ( + genome.Variant('chr1', 3, 'TG', 'AC'), + genome.Variant('chr1', 5, 'TC', ''), + ), + ) + self.assertEqual( + v.split(5), + ( + genome.Variant('chr1', 3, 'TGT', 'AC'), + genome.Variant('chr1', 6, 'C', ''), + ), + ) + # Anchor after the variant. + self.assertEqual( + v.split(6), (genome.Variant('chr1', 3, 'TGTC', 'AC'), None) + ) + + @parameterized.parameters( + ('A', 'C', True), + ('AC', 'A', False), + ('A', 'AC', False), + ('AC', 'GT', False), + ('', 'A', False), + ) + def test_is_snv(self, ref, alt, expected): + v = genome.Variant('chr1', 10, ref, alt) + self.assertEqual(v.is_snv, expected) + + @parameterized.parameters( + ('A', 'C', False), + ('AC', 'A', True), + ('A', 'AC', False), + ('AC', 'GT', False), + ('', 'A', False), + ('A', '', True), + ) + def test_is_deletion(self, ref, alt, expected): + v = genome.Variant('chr1', 10, ref, alt) + self.assertEqual(v.is_deletion, expected) + + @parameterized.parameters( + ('A', 'C', False), + ('AC', 'A', False), + ('A', 'AC', True), + ('AC', 'GT', False), + ('', 'A', True), + ) + def test_is_insertion(self, ref, alt, expected): + v = genome.Variant('chr1', 10, ref, alt) + self.assertEqual(v.is_insertion, expected) + + @parameterized.parameters( + ('chr1:1:AAA>GGG', 'TTTNNNN', 'chr1:1:AAA>GGG'), + ('chr1:1:ATCG>ATCC', 'ATCGNNN', 'chr1:4:G>C'), + ('chr1:1:ATCG>ATC', 'ATCG', 'chr1:3:CG>C'), + ('chr1:1:ACTG>GGTG', 'NNNN', 'chr1:1:ACTG>GGTG'), + ('chr1:1:C>AAA', 'CCC', 'chr1:1:C>AAA'), + ('chr1:5:C>', 'AAAAA', 'chr1:4:AC>A'), + ('chr1:2:A>A', 'AAAAAA', 'chr1:2:A>A'), + ('chr1:1:GAT>GT', 'GAT', 'chr1:1:GA>G'), + ('chr1:1:GAT>GT', 'GAN', 'chr1:2:AT>T'), + # Examples from paper. + ('chr1:6:CAC>C', 'GGGCACACACAGGG', 'chr1:3:GCA>G'), + ('chr2:3:GCACA>GCA', 'GGGCACACACAGGG', 'chr2:3:GCA>G'), + ('chr3:2:GGCA>GG', 'GGGCACACACAGGG', 'chr3:3:GCA>G'), + ('chr4:3:GCA>G', 'GGGCACACACAGGG', 'chr4:3:GCA>G'), + ) + def test_normalize_variant( + self, variant: str, reference_sequence: str, expected: str + ): + mock_extractor = mock.create_autospec(genome._FastaExtractorType) # pylint: disable=protected-access + variant = genome.Variant.from_str(variant) + + def _extract(interval): + return reference_sequence[interval.start : interval.end] + + mock_extractor.extract.side_effect = _extract + + result = genome.normalize_variant(variant, mock_extractor) + self.assertEqual(str(result), expected) + + +class JunctionTest(parameterized.TestCase): + + def test_init_valid(self): + genome.Junction(chromosome='chr1', start=10, end=10, strand='+') + genome.Junction(chromosome='chr1', start=10, end=20, strand='-', k=1) + + def test_unstranded_junction_raises_value_error(self): + with self.assertRaises(ValueError): + genome.Junction(chromosome='chr1', start=10, end=10) + + def test_invalid_start_end_raises_value_error(self): + with self.assertRaises(ValueError): + genome.Junction(chromosome='chr1', start=10, end=1, strand='+') + + def test_attributes(self): + junction = genome.Junction( + 'chr1', + 10, + 20, + strand='-', + info=dict(a=1, b='b', c=[1, 2], d=dict(e=1)), + k=1, + ) + self.assertEqual(junction.start, 10) + self.assertEqual(junction.end, 20) + self.assertEqual(junction.chromosome, 'chr1') + self.assertEqual(junction.name, '') + self.assertIsInstance(junction.info, dict) + self.assertEqual(junction.info['a'], 1) + self.assertTrue(junction.negative_strand) + self.assertEqual(junction.width, 10) + self.assertEqual(junction.k, 1) + + def test_copy(self): + junction = genome.Junction('chr1', 10, 20, strand='-', k=1) + junction_copy = junction.copy() + junction_copy.info['test'] = 10 + self.assertEqual(junction_copy.info['test'], 10) + self.assertEmpty(junction.info) + self.assertEqual(junction.k, junction_copy.k) + + def test_acceptor_donor(self): + junction = genome.Junction('chr1', 10, 20, strand='-') + self.assertEqual(junction.acceptor, 10) + self.assertEqual(junction.donor, 20) + + junction = genome.Junction('chr1', 10, 20, strand='+') + self.assertEqual(junction.acceptor, 20) + self.assertEqual(junction.donor, 10) + + def test_dinucleotide_region(self): + junction = genome.Junction('chr1', 10, 20, strand='-') + self.assertEqual( + junction.dinucleotide_region(), + ( + genome.Interval('chr1', 10, 12, '-'), + genome.Interval('chr1', 18, 20, '-'), + ), + ) + + def test_acceptor_donor_region(self): + junction = genome.Junction('chr1', 10, 20, strand='-') + self.assertEqual( + junction.acceptor_region(), + genome.Interval('chr1', -240, 260, '-'), + ) + self.assertEqual( + junction.donor_region(), + genome.Interval('chr1', -230, 270, '-'), + ) + + +class IntervalIntersectionTest(parameterized.TestCase): + + def _parse_intervals(self, intervals): + if not intervals: + return [] + return [genome.Interval.from_str(i) for i in intervals.split(',')] + + @parameterized.named_parameters( + ('no_overlap_1', 'chr1:0-100', 'chr2:0-100', ''), + ('no_overlap_2', 'chr1:0-100', 'chr1:200-300', ''), + ('overlap_1', 'chr1:0-100', 'chr1:50-150', 'chr1:50-100'), + ( + 'overlap_2', + 'chr1:0-100,chr2:100-200', + 'chr2:50-150,chr1:50-150', + 'chr1:50-100,chr2:100-150', + ), + ( + 'overlap_3', + 'chr1:0-100,chr1:150-200', + 'chr1:50-200', + 'chr1:50-100,chr1:150-200', + ), + ( + 'point_intervals', + 'chr1:10-10,chr1:15-15,chr1:20-20', + 'chr1:5-15', + 'chr1:10-10,chr1:15-15', + ), + ('point_intervals_2', 'chr1:10-10,chr1:10-20', 'chr1:5-15', 'chr1:10-15'), + ('point_intervals_3', 'chr1:0-10,chr1:10-10', 'chr1:5-15', 'chr1:5-10'), + ( + 'abutting_intervals', + 'chr1:10-20,chr1:20-30', + 'chr1:15-25', + 'chr1:15-25', + ), + ('abutting_intervals_2', 'chr1:0-100', 'chr1:100-200', 'chr1:100-100'), + ) + def test_intersection(self, lhs, rhs, expected): + with self.subTest('forward'): + self.assertSameElements( + self._parse_intervals(expected), + list( + genome.intersect_intervals( + self._parse_intervals(lhs), self._parse_intervals(rhs) + ) + ), + ) + with self.subTest('reverse'): + self.assertSameElements( + self._parse_intervals(expected), + list( + genome.intersect_intervals( + self._parse_intervals(rhs), self._parse_intervals(lhs) + ) + ), + ) + + +class IntervalUnionTest(parameterized.TestCase): + + def _parse_intervals(self, intervals): + if not intervals: + return [] + return [genome.Interval.from_str(i) for i in intervals.split(',')] + + @parameterized.named_parameters( + ('no_overlap_1', 'chr1:0-100', 'chr2:0-100', 'chr1:0-100,chr2:0-100'), + ('no_overlap_2', 'chr1:0-100', 'chr1:200-300', 'chr1:0-100,chr1:200-300'), + ('overlap_1', 'chr1:0-100', 'chr1:50-150', 'chr1:0-150'), + ( + 'overlap_2', + 'chr1:0-100,chr2:100-200', + 'chr2:50-150,chr1:50-150', + 'chr1:0-150,chr2:50-200', + ), + ('overlap_3', 'chr1:0-100,chr1:150-200', 'chr1:50-200', 'chr1:0-200'), + ( + 'point_intervals', + 'chr1:10-10,chr1:15-15,chr1:20-20', + 'chr1:5-15', + 'chr1:5-15,chr1:20-20', + ), + ('point_intervals_2', 'chr1:10-10,chr1:10-20', 'chr1:5-15', 'chr1:5-20'), + ('point_intervals_3', 'chr1:0-10,chr1:10-10', 'chr1:5-15', 'chr1:0-15'), + ( + 'abutting_intervals', + 'chr1:10-20,chr1:20-30', + 'chr1:15-25', + 'chr1:10-30', + ), + ('abutting_intervals_2', 'chr1:0-100', 'chr1:100-200', 'chr1:0-200'), + ) + def test_union(self, lhs, rhs, expected): + with self.subTest('forward'): + self.assertSameElements( + self._parse_intervals(expected), + list( + genome.union_intervals( + self._parse_intervals(lhs), self._parse_intervals(rhs) + ) + ), + ) + with self.subTest('reverse'): + self.assertSameElements( + self._parse_intervals(expected), + list( + genome.union_intervals( + self._parse_intervals(rhs), self._parse_intervals(lhs) + ) + ), + ) + + +class MergeIntervalsTest(parameterized.TestCase): + + def test_empty_list(self): + self.assertEmpty(genome.merge_overlapping_intervals([])) + + def test_one_interval(self): + intervals = [genome.Interval('chr1', 1, 100)] + self.assertListEqual( + genome.merge_overlapping_intervals(intervals), intervals + ) + + def test_no_overlap(self): + intervals = [ + genome.Interval('chr1', 1, 100), + genome.Interval('chr1', 101, 200), + ] + self.assertListEqual( + genome.merge_overlapping_intervals(intervals), intervals + ) + + def test_interval_within(self): + intervals = [ + genome.Interval('chr1', 1, 100), + genome.Interval('chr1', 10, 100), + ] + self.assertListEqual( + genome.merge_overlapping_intervals(intervals), [intervals[0]] + ) + + def test_single_overlap(self): + intervals = [ + genome.Interval('chr1', 1, 100), + genome.Interval('chr1', 90, 200), + genome.Interval('chr1', 300, 400), + ] + self.assertListEqual( + genome.merge_overlapping_intervals(intervals), + [genome.Interval('chr1', 1, 200), intervals[-1]], + ) + + def test_multiple_overlap(self): + intervals = [ + genome.Interval('chr1', 1, 100), + genome.Interval('chr1', 100, 200), + genome.Interval('chr1', 200, 300), + genome.Interval('chr1', 500, 700), + genome.Interval('chr1', 700, 800), + genome.Interval('chr1', 750, 801), + ] + self.assertListEqual( + genome.merge_overlapping_intervals(intervals), + [genome.Interval('chr1', 1, 300), genome.Interval('chr1', 500, 801)], + ) + + def test_multiple_overlap_unsorted(self): + intervals = [ + genome.Interval('chr1', 1, 100), + genome.Interval('chr1', 200, 300), + genome.Interval('chr1', 700, 800), + genome.Interval('chr1', 750, 801), + genome.Interval('chr1', 500, 700), + genome.Interval('chr1', 100, 200), + ] + self.assertListEqual( + genome.merge_overlapping_intervals(intervals), + [genome.Interval('chr1', 1, 300), genome.Interval('chr1', 500, 801)], + ) + + def test_info_field(self): + original_intervals = [ + genome.Interval( + 'chr1', 1, 100, info={'transcript_type': 'protein_coding'} + ), + genome.Interval('chr1', 101, 200), + ] + merged_intervals = genome.merge_overlapping_intervals(original_intervals) + self.assertListEqual(merged_intervals, original_intervals) + for interval in merged_intervals: + self.assertEmpty(interval.info) + + +if __name__ == '__main__': + absltest.main() diff --git a/alphagenome/source/src/alphagenome/data/junction_data.py b/alphagenome/source/src/alphagenome/data/junction_data.py new file mode 100644 index 0000000000000000000000000000000000000000..53233cf337b132b065061482d1fd17a90679f582 --- /dev/null +++ b/alphagenome/source/src/alphagenome/data/junction_data.py @@ -0,0 +1,268 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Splice junction data container. + +`JunctionData` stores splice junction data for a given transcript or interval. +""" + +from collections.abc import Sequence +import dataclasses +from typing import Any + +from alphagenome import typing +from alphagenome.data import genome +from alphagenome.data import ontology +from jaxtyping import Float, Shaped # pylint: disable=g-multiple-import, g-importing-member +import numpy as np +import pandas as pd + +JunctionMetadata = pd.DataFrame + + +@typing.jaxtyped +@dataclasses.dataclass(frozen=True) +class JunctionData: + """Container for storing splice junction data. + + Attributes: + junctions: A numpy array representing the splice junctions. + values: A numpy array of floats representing the values associated with each + junction for each track. + metadata: A pandas DataFrame containing metadata for each track. + interval: An optional `Interval` object representing the genomic region + containing the junctions. + uns: An optional dictionary to store additional unstructured data. + + Raises: + ValueError: If the number of tracks in `values` does not match the number + of rows in `metadata`, or if `metadata` contains duplicate names. + """ + + junctions: Shaped[np.ndarray, 'num_junctions'] + values: Float[np.ndarray, 'num_junctions num_tracks'] + metadata: JunctionMetadata + interval: genome.Interval | None = None + uns: dict[str, Any] | None = None + + def __post_init__(self): + """Validates the consistency of the data.""" + if self.values.shape[1] != len(self.metadata): + raise ValueError( + f'Number of tracks {self.values.shape[1]} and ' + f'metadata {len(self.metadata)} do not match.' + ) + + if self.metadata['name'].duplicated().any(): + raise ValueError('Metadata contain duplicated names.') + + def __len__(self): + """Returns the number of junctions.""" + return len(self.junctions) + + @property + def num_tracks(self) -> int: + """Returns the number of tracks.""" + return len(self.metadata) + + @property + def names(self) -> np.ndarray: + """Returns a list of track names (not necessarily unique).""" + return self.metadata['name'].values + + @property + def strands(self) -> np.ndarray: + """Returns a list of track strands.""" + return np.array([j.strand for j in self.junctions]) + + @property + def possible_strands(self) -> np.ndarray: + """All possible strands.""" + return np.unique(self.strands) + + @property + def ontology_terms(self) -> Sequence[ontology.OntologyTerm | None] | None: + """Returns a list of ontology terms (if available).""" + if 'ontology_curie' in self.metadata.columns: + return [ + ontology.from_curie(curie) if curie is not None else None + for curie in self.metadata['ontology_curie'].values + ] + else: + return None + + # Track order / subset wrangling: + def filter_tracks(self, mask: np.ndarray | list[bool]) -> 'JunctionData': + """Filters tracks by a boolean mask. + + Args: + mask: A boolean mask to select tracks. + + Returns: + A new `JunctionData` object with the filtered tracks. + """ + return JunctionData( + junctions=self.junctions, + values=self.values[:, mask], + metadata=self.metadata.loc[mask], + interval=self.interval, + uns=self.uns, + ) + + def filter_to_strand(self, strand: str) -> 'JunctionData': + """Filters junctions to a specific DNA strand. + + Args: + strand: The strand to filter by ('+' or '-'). + + Returns: + A new `JunctionData` object with junctions on the specified strand. + """ + mask = self.strands == strand + return JunctionData( + junctions=self.junctions[mask], + values=self.values[mask, :], + metadata=self.metadata, + interval=self.interval, + uns=self.uns, + ) + + def normalize_values(self, total_k: float = 10.0) -> 'JunctionData': + """Normalizes the values by the k value.""" + values = self.values * total_k / self.values.sum() + return JunctionData( + junctions=self.junctions, + values=values, + metadata=self.metadata, + interval=self.interval, + uns=self.uns, + ) + + def filter_to_positive_strand(self) -> 'JunctionData': + """Filters junctions to the positive DNA strand.""" + return self.filter_to_strand(genome.STRAND_POSITIVE) + + def filter_to_negative_strand(self) -> 'JunctionData': + """Filters junctions to the negative DNA strand.""" + return self.filter_to_strand(genome.STRAND_NEGATIVE) + + def filter_by_tissue(self, tissue: str) -> 'JunctionData': + """Filters tracks by GTEx tissue type. + + Args: + tissue: The GTEx tissue type to filter by. + + Returns: + A new `JunctionData` object with tracks from the specified tissue. + + Raises: + ValueError: If the metadata does not contain a 'gtex_tissue' column. + """ + if 'gtex_tissue' not in self.metadata.columns: + raise ValueError( + 'Metadata does not contain gtex_tissue column. ' + f'Got {set(self.metadata.columns)}.' + ) + return self.filter_tracks(self.metadata['gtex_tissue'] == tissue) + + def filter_by_name(self, name: str) -> 'JunctionData': + """Filters tracks by name.""" + return self.filter_tracks(self.metadata['name'] == name) + + def filter_by_ontology(self, ontology_curie: str) -> 'JunctionData': + """Filters tracks by ontology term. + + Args: + ontology_curie: The ontology term CURIE to filter by. + + Returns: + A new `JunctionData` object with tracks associated with the specified + ontology term. + + Raises: + ValueError: If the metadata does not contain an 'ontology_curie' column. + """ + if 'ontology_curie' not in self.metadata.columns: + raise ValueError( + 'Metadata does not contain ontology_curie column. ' + f'Got {set(self.metadata.columns)}.' + ) + return self.filter_tracks(self.metadata['ontology_curie'] == ontology_curie) + + def intersect_with_interval( + self, interval: genome.Interval + ) -> 'JunctionData': + """Returns the intersection of the junctions and the interval.""" + mask = np.array([j.overlaps(interval) for j in self.junctions]) + return JunctionData( + junctions=self.junctions[mask], + values=self.values[mask, :], + metadata=self.metadata, + interval=self.interval, + uns=self.uns, + ) + + +def get_junctions_to_plot( + *, + predictions: JunctionData, + name: str, + strand: str, + k_threshold: float | None = 0.0, +) -> list[genome.Junction]: + """Gets a list of junctions to plot. + + Filters the junctions in the `predictions` by ontology term and strand, and + applies a threshold on the `k` value (read count). + + Args: + predictions: A `JunctionData` object containing junction predictions. + name: The name to filter by. + strand: The strand to filter by ('+' or '-'). + k_threshold: The minimum `k` value for a junction to be included. If None, + use 5% of the maximum value. + + Returns: + A list of `Junction` objects to plot. + + Raises: + ValueError: If more than one track is found for the specified ontology term. + """ + filtered = predictions.filter_by_name(name) + if filtered.num_tracks > 1: + raise ValueError( + f'Expected only one ontology term, got {filtered.num_tracks}.' + ) + if k_threshold is None: + k_threshold = filtered.values.max() * 0.05 + # Round k for better visualization. + filtered_junctions = [] + for interval, k in zip(filtered.junctions, filtered.values, strict=True): + # Filter by threshold and strand. + if interval.strand != strand: + continue + k = k.item() + if k >= k_threshold: + k = round(k, 2) + + filtered_junctions.append( + genome.Junction( + interval.chromosome, + interval.start, + interval.end, + interval.strand, + k=k, + ) + ) + return filtered_junctions diff --git a/alphagenome/source/src/alphagenome/data/junction_data_test.py b/alphagenome/source/src/alphagenome/data/junction_data_test.py new file mode 100644 index 0000000000000000000000000000000000000000..541fe89524f9e4d1b66103f3dbdb19446531c944 --- /dev/null +++ b/alphagenome/source/src/alphagenome/data/junction_data_test.py @@ -0,0 +1,256 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from absl.testing import absltest +from absl.testing import parameterized +from alphagenome.data import genome +from alphagenome.data import junction_data +import numpy as np +import pandas as pd + + +class JunctionDataTest(parameterized.TestCase): + + def _assert_junction_data_equal( + self, + actual: junction_data.JunctionData, + expected: junction_data.JunctionData, + msg: ..., + ) -> None: + pd.testing.assert_frame_equal(actual.metadata, expected.metadata) + np.testing.assert_array_equal(actual.junctions, expected.junctions) + np.testing.assert_array_equal(actual.values, expected.values) + self.assertEqual(actual.interval, expected.interval, msg) + self.assertEqual(actual.uns, expected.uns, msg) + + def setUp(self): + super().setUp() + self.addTypeEqualityFunc( + junction_data.JunctionData, self._assert_junction_data_equal + ) + + def test_invalid_metadata_raises(self): + metadata = pd.DataFrame({'name': ['foo', 'bar', 'baz']}) + junctions = np.array([ + genome.Interval('chr1', 10, 11, '+'), + genome.Interval('chr1', 10, 11, '+'), + genome.Interval('chr1', 10, 11, '-'), + genome.Interval('chr1', 10, 11, '-'), + ]) + values = np.zeros((4, 2), dtype=np.float32) + + with self.assertRaisesRegex( + ValueError, 'Number of tracks .* do not match.' + ): + junction_data.JunctionData(junctions, values, metadata) + + def test_invalid_junctions_raises(self): + metadata = pd.DataFrame({'name': ['foo', 'bar', 'baz']}) + junctions = np.array([ + genome.Interval('chr1', 10, 11, '+'), + genome.Interval('chr1', 10, 11, '+'), + genome.Interval('chr1', 10, 11, '-'), + ]) + values = np.zeros((4, 3), dtype=np.float32) + + with self.assertRaises(TypeError): + junction_data.JunctionData(junctions, values, metadata) + + def test_duplicate_name_raises(self): + metadata = pd.DataFrame({'name': ['foo', 'foo']}) + junctions = np.array([ + genome.Interval('chr1', 10, 11, '+'), + genome.Interval('chr1', 10, 11, '+'), + ]) + values = np.zeros((2, 2), dtype=np.float32) + + with self.assertRaisesRegex( + ValueError, 'Metadata contain duplicated names' + ): + junction_data.JunctionData(junctions, values, metadata) + + def test_filter_by_ontology(self): + metadata = pd.DataFrame({ + 'name': ['foo', 'bar'], + 'ontology_curie': ['UBERON:0000005', 'UBERON:0000006'], + }) + junctions = np.array([ + genome.Interval('chr1', 10, 11, '+'), + genome.Interval('chr1', 10, 11, '-'), + ]) + values = np.arange(4).reshape((2, 2)).astype(np.float32) + filtered = junction_data.JunctionData( + junctions, values, metadata + ).filter_by_ontology('UBERON:0000005') + + expected = junction_data.JunctionData( + junctions=np.array([ + genome.Interval('chr1', 10, 11, '+'), + genome.Interval('chr1', 10, 11, '-'), + ]), + metadata=pd.DataFrame( + {'name': ['foo'], 'ontology_curie': ['UBERON:0000005']} + ), + values=np.array([[0], [2]], dtype=np.float32), + ) + self.assertEqual(filtered, expected) + + def test_filter_to_strand(self): + metadata = pd.DataFrame({ + 'name': ['foo', 'bar'], + }) + junctions = np.array([ + genome.Interval('chr1', 10, 11, '+'), + genome.Interval('chr1', 10, 11, '-'), + genome.Interval('chr2', 10, 11, '-'), + genome.Interval('chr2', 1, 2, '+'), + ]) + values = np.arange(8).reshape((4, 2)).astype(np.float32) + + with self.subTest('positive_strand'): + filtered = junction_data.JunctionData( + junctions, values, metadata + ).filter_to_positive_strand() + + expected = junction_data.JunctionData( + junctions=np.array([ + genome.Interval('chr1', 10, 11, '+'), + genome.Interval('chr2', 1, 2, '+'), + ]), + metadata=pd.DataFrame({'name': ['foo', 'bar']}), + values=np.array([[0, 1], [6, 7]], dtype=np.float32), + ) + self.assertEqual(filtered, expected) + + with self.subTest('negative_strand'): + filtered = junction_data.JunctionData( + junctions, values, metadata + ).filter_to_negative_strand() + expected = junction_data.JunctionData( + junctions=np.array([ + genome.Interval('chr1', 10, 11, '-'), + genome.Interval('chr2', 10, 11, '-'), + ]), + metadata=pd.DataFrame({'name': ['foo', 'bar']}), + values=np.array([[2, 3], [4, 5]], dtype=np.float32), + ) + self.assertEqual(filtered, expected) + + def test_filter_by_tissue(self): + metadata = pd.DataFrame({ + 'name': ['foo', 'bar'], + 'gtex_tissue': ['Brain_Cerebellum', 'Adipose_Subcutaneous'], + }) + junctions = np.array([ + genome.Interval('chr1', 10, 11, '+'), + genome.Interval('chr1', 10, 11, '-'), + ]) + values = np.arange(4).reshape((2, 2)).astype(np.float32) + filtered = junction_data.JunctionData( + junctions, values, metadata + ).filter_by_tissue('Brain_Cerebellum') + + expected = junction_data.JunctionData( + junctions=np.array([ + genome.Interval('chr1', 10, 11, '+'), + genome.Interval('chr1', 10, 11, '-'), + ]), + metadata=pd.DataFrame( + {'name': ['foo'], 'gtex_tissue': ['Brain_Cerebellum']} + ), + values=np.array([[0], [2]], dtype=np.float32), + ) + + self.assertEqual(filtered, expected) + + def test_intersect_with_interval(self): + metadata = pd.DataFrame({'name': ['foo', 'bar']}) + junctions = np.array([ + genome.Interval('chr1', 18, 20, '+'), + genome.Interval('chr1', 15, 18, '+'), + genome.Interval('chr1', 10, 11, '-'), + genome.Interval('chr1', 1, 2, '+'), + ]) + values = np.arange(8).reshape((4, 2)).astype(np.float32) + interval = genome.Interval('chr1', 8, 17) + junctions = junction_data.JunctionData(junctions, values, metadata) + filtered = junctions.intersect_with_interval(interval) + expected = junction_data.JunctionData( + junctions=np.array([ + genome.Interval('chr1', 15, 18, '+'), + genome.Interval('chr1', 10, 11, '-'), + ]), + metadata=pd.DataFrame({'name': ['foo', 'bar']}), + values=np.array([[2, 3], [4, 5]], dtype=np.float32), + ) + self.assertEqual(filtered, expected) + + @parameterized.parameters([ + dict( + name='foo', + k_threshold=0.0, + strand='+', + expected=[ + genome.Junction('chr1', 10, 11, '+', k=0), + genome.Junction('chr2', 10, 11, '+', k=4), + ], + ), + dict( + name='foo', + k_threshold=0.0, + strand='-', + expected=[ + genome.Junction('chr1', 10, 11, '-', k=2), + genome.Junction('chr2', 10, 11, '-', k=6), + ], + ), + dict( + name='bar', + k_threshold=4, + strand='+', + expected=[ + genome.Junction('chr2', 10, 11, '+', k=5), + ], + ), + ]) + def test_get_junctions_to_plot(self, name, k_threshold, strand, expected): + metadata = pd.DataFrame({ + 'name': ['foo', 'bar'], + 'ontology_curie': ['UBERON:0000005', 'UBERON:0000006'], + }) + junctions = np.array([ + genome.Interval('chr1', 10, 11, '+'), + genome.Interval('chr1', 10, 11, '-'), + genome.Interval('chr2', 10, 11, '+'), + genome.Interval('chr2', 10, 11, '-'), + ]) + values = np.arange(8).reshape((4, 2)).astype(np.float32) + predictions = junction_data.JunctionData(junctions, values, metadata) + + with self.subTest('test_junctions'): + actual = junction_data.get_junctions_to_plot( + predictions=predictions, + name=name, + strand=strand, + k_threshold=k_threshold, + ) + self.assertEqual(actual, expected) + with self.subTest('test_normalize_values'): + self.assertEqual( + predictions.normalize_values(total_k=10.0).values.sum(), 10.0 + ) + + +if __name__ == '__main__': + absltest.main() diff --git a/alphagenome/source/src/alphagenome/data/ontology.py b/alphagenome/source/src/alphagenome/data/ontology.py new file mode 100644 index 0000000000000000000000000000000000000000..909935d04b10e4337daa348a0c1907c9f0ebd49a --- /dev/null +++ b/alphagenome/source/src/alphagenome/data/ontology.py @@ -0,0 +1,122 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Handling of biological ontologies.""" + +from collections.abc import Sequence +import dataclasses +import enum + +from alphagenome.protos import dna_model_pb2 +import immutabledict + + +class OntologyType(enum.IntEnum): + """Supported ontology types. + + CLO: Cell Line Ontology. + UBERON: Uber-anatomy ontology. + CL: Cell Ontology. + EFO: Experimental Factor Ontology. + NTR: New Term Requested. + """ + + CLO = 1 + UBERON = 2 + CL = 3 + EFO = 4 + NTR = 5 + + +_ONTOLOGY_TYPE_TO_PROTO_ENUM = immutabledict.immutabledict({ + OntologyType.CLO: dna_model_pb2.OntologyType.ONTOLOGY_TYPE_CLO, + OntologyType.UBERON: dna_model_pb2.OntologyType.ONTOLOGY_TYPE_UBERON, + OntologyType.CL: dna_model_pb2.OntologyType.ONTOLOGY_TYPE_CL, + OntologyType.EFO: dna_model_pb2.OntologyType.ONTOLOGY_TYPE_EFO, + OntologyType.NTR: dna_model_pb2.OntologyType.ONTOLOGY_TYPE_NTR, +}) + + +@dataclasses.dataclass(frozen=True) +class OntologyTerm: + """A single biological ontology term. + + Attributes: + type: The ontology type. + id: The ID of the term within the ontology. + """ + + type: OntologyType + id: int + + @property + def ontology_curie(self) -> str: + """Returns the CURIE (Compact Uniform Resource Identifier) for the term.""" + return f'{self.type.name}:{self.id:07d}' + + def to_proto(self) -> dna_model_pb2.OntologyTerm: + """Converts the ontology term to a protobuf message.""" + return dna_model_pb2.OntologyTerm( + ontology_type=_ONTOLOGY_TYPE_TO_PROTO_ENUM[self.type], id=self.id + ) + + +def from_curie(ontology_curie: str) -> OntologyTerm: + """Creates an `OntologyTerm` from a CURIE. + + Args: + ontology_curie: The CURIE (Compact Uniform Resource Identifier) for the + ontology term. + + Returns: + An `OntologyTerm` object. + """ + try: + ontology_type, local_id = ontology_curie.split(':') + except ValueError as e: + raise ValueError( + f'Invalid {ontology_curie=}. CURIEs must be of the form :.' + ) from e + try: + ontology_type = OntologyType[ontology_type] + except KeyError as e: + raise ValueError(f'Cannot parse {ontology_type=}') from e + return OntologyTerm(ontology_type, int(local_id)) + + +def from_curies(ontology_curies: Sequence[str]) -> Sequence[OntologyTerm]: + """Creates a list of `OntologyTerm` objects from a list of CURIEs. + + Args: + ontology_curies: A list of CURIEs (Compact Uniform Resource Identifiers). + + Returns: + A list of `OntologyTerm` objects. + """ + return [from_curie(curie) for curie in ontology_curies] + + +def from_proto(proto: dna_model_pb2.OntologyTerm) -> OntologyTerm: + """Creates an `OntologyTerm` from a protobuf message. + + Args: + proto: An `OntologyTerm` protobuf message. + + Returns: + An `OntologyTerm` object. + """ + return OntologyTerm( + OntologyType(proto.ontology_type), + proto.id, + ) diff --git a/alphagenome/source/src/alphagenome/data/ontology_test.py b/alphagenome/source/src/alphagenome/data/ontology_test.py new file mode 100644 index 0000000000000000000000000000000000000000..f18f6c1b1fb4f3a07860be39f5b71683fa47f518 --- /dev/null +++ b/alphagenome/source/src/alphagenome/data/ontology_test.py @@ -0,0 +1,67 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from absl.testing import absltest +from absl.testing import parameterized +from alphagenome.data import ontology + + +class OntologyTest(parameterized.TestCase): + + def test_ontology_term_by_curie(self): + term = ontology.from_curie('UBERON:0000005') + self.assertEqual(term.ontology_curie, 'UBERON:0000005') + self.assertEqual(term.type, ontology.OntologyType.UBERON) + self.assertEqual(term.id, 5) + + @parameterized.parameters( + ('UBERON:0000005',), + ('CL:0000005',), + ('EFO:0000005',), + ('NTR:0000512',), + ) + def test_proto_roundtrip(self, curie: str): + term = ontology.from_curie(curie) + proto = term.to_proto() + round_trip = ontology.from_proto(proto) + self.assertEqual(term, round_trip) + + def test_ontology_terms_from_curies(self): + terms = ontology.from_curies(['UBERON:0000005', 'CL:0000005']) + self.assertEqual( + terms, + [ + ontology.OntologyTerm(ontology.OntologyType.UBERON, 5), + ontology.OntologyTerm(ontology.OntologyType.CL, 5), + ], + ) + + @parameterized.named_parameters( + ('invalid_curie', 'invalid:0000005', 'Cannot parse ontology_type'), + ('lowercase_curie', 'uberon:0000005', 'Cannot parse ontology_type'), + ('too_many_colons', 'UBERON:0000005:0000005', 'Invalid ontology_curie'), + ('missing_colon', 'foo_bar', "Invalid ontology_curie='foo_bar'"), + ( + 'invalid_id', + 'UBERON:foo', + r"invalid literal for int\(\) with base 10: 'foo'", + ), + ) + def test_invalid_curie_raises_error(self, curie: str, expected_error: str): + with self.assertRaisesRegex(ValueError, expected_error): + ontology.from_curie(curie) + + +if __name__ == '__main__': + absltest.main() diff --git a/alphagenome/source/src/alphagenome/data/track_data.py b/alphagenome/source/src/alphagenome/data/track_data.py new file mode 100644 index 0000000000000000000000000000000000000000..da59d95a300c876f67f140ea443c9cea0a29f994 --- /dev/null +++ b/alphagenome/source/src/alphagenome/data/track_data.py @@ -0,0 +1,835 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +"""Track data container analogous to AnnData.""" + +from collections.abc import Sequence +import copy +import dataclasses +import enum +from typing import Any, Union + +from alphagenome import typing +from alphagenome.data import genome +from alphagenome.data import ontology +from jaxtyping import Bool, Float32, Int32 # pylint: disable=g-multiple-import, g-importing-member +import numpy as np +import pandas as pd + +# Required columns: name, strand. +# Optional standardized columns: cell_type, assay, padding. +TrackMetadata = pd.DataFrame + + +@enum.unique +class AggregationType(enum.Enum): + """Aggregation types for downsampling/upsampling track resolutions. + + SUM: Sum pooling, where values within a bin are summed. This is recommended + for continuous tracks where the total value within a bin is meaningful (e.g. + read counts, coverage). + MAX: Max pooling, where the maximum value within a bin is selected. This is + recommended for binary tracks (e.g., gene masks, regions of interest). + """ + + SUM = 'sum' + MAX = 'max' + + +@typing.jaxtyped +@dataclasses.dataclass(frozen=True) +class TrackData: + """Container for storing track values and metadata. + + `TrackData` stores multiple genomic tracks at the same resolution, stacked + into an ND matrix of shape (positional_bins, num_tracks). It also contains + metadata information as a pandas DataFrame with `num_tracks` rows. + + Metadata DataFrame has two main required columns: + + * name: The name of the track. + * strand: The strand of the track ('+', '-', or '.'). + + Other columns are optional. + + Valid shapes of `TrackData.values` are: + + * [positional_bins] + * [positional_bins, num_tracks] + * [positional_bins, positional_bins, num_tracks] + * ... + + `TrackData` can store both model predictions and raw data. It can + optionally hold information about the `genome.Interval` from which the data + were derived and `.uns` for storing additional unstructured data. + + In addition to being a container, `TrackData` provides functionality for + common aggregation and slicing operations. + + Attributes: + values: A numpy array of floats or integers representing the track values. + Positional axes have the same length. Example valid shapes are: + [num_tracks], [positional_bins, num_tracks], and [positional_bins, + positional_bins, num_tracks]. + metadata: A pandas DataFrame containing metadata for each track. The + DataFrame must have at least two columns: 'name' and 'strand'. + resolution: The resolution of the track data in base pairs. + interval: An optional `Interval` object representing the genomic region. + uns: An optional dictionary to store additional unstructured data. + + Raises: + ValueError: If the number of tracks in `values` does not match the number + of rows in `metadata`, or if `metadata` contains duplicate (name, strand) + pairs, or if the positional axes have different lengths, or if the + interval width does not match the expected width. + """ + + # Use Union due to https://github.com/patrick-kidger/jaxtyping/issues/73. + values: Union[ + Float32[np.ndarray, '*positional_bins num_tracks'], + Int32[np.ndarray, '*positional_bins num_tracks'], + Bool[np.ndarray, '*positional_bins num_tracks'], + ] + metadata: TrackMetadata + resolution: int = 1 + interval: genome.Interval | None = None + uns: dict[str, Any] | None = ( + # Unstructured data dict, analagous to anndata.AnnData.uns. + None + ) + + def __post_init__(self): + """Validates the consistency of the data.""" + if self.values.shape[-1] != len(self.metadata): + raise ValueError( + f'value number of tracks {self.values.shape[-1]} and ' + f'metadata {len(self.metadata)} do not match.' + ) + + if self.positional_axes: + if len(set(np.array(self.values.shape)[self.positional_axes])) != 1: + raise ValueError('All positional axes must have the same length.') + + if self.interval and self.interval.width != self.width: + raise ValueError( + f'Interval width must match expected width. {self.interval.width=},' + f' {self.width=}' + ) + + if not {'name', 'strand'}.issubset(self.metadata.columns): + raise ValueError('Metadata must contain columns "name" and "strand".') + + if self.metadata[['name', 'strand']].duplicated().any(): + raise ValueError( + 'Metadata contain duplicated values for (name, strand) tuples.' + ) + + @property + def positional_axes(self) -> list[int]: + """Returns a list of the positional axes.""" + return list(range(self.values.ndim - 1)) + + @property + def num_tracks(self) -> int: + """Returns the number of tracks.""" + return self.values.shape[-1] + + @property + def width(self) -> int: + """Returns the interval width covered by the tracks.""" + if self.positional_axes: + return self.values.shape[0] * self.resolution + else: + return 0 + + @property + def names(self) -> np.ndarray: + """Returns an array of track names (not necessarily unique).""" + return self.metadata['name'].values + + @property + def strands(self) -> np.ndarray: + """Returns an array of track strands.""" + return self.metadata['strand'].values + + @property + def ontology_terms(self) -> Sequence[ontology.OntologyTerm | None] | None: + """Returns a list of ontology terms (if available).""" + if 'ontology_curie' in self.metadata.columns: + return [ + ontology.from_curie(curie) if curie is not None else None + for curie in self.metadata['ontology_curie'].values + ] + else: + return None + + def copy(self) -> 'TrackData': + """Returns a deep copy of the `TrackData` object.""" + if self.interval: + interval = self.interval.copy() + else: + interval = None + return TrackData( + self.values.copy(), + resolution=self.resolution, + metadata=self.metadata.copy(), + interval=interval, + uns=copy.deepcopy(self.uns), + ) + + def bin_index(self, relative_position: int) -> int: + """Returns the bin index for a relative position. + + Args: + relative_position: The relative position within the interval. + + Returns: + The corresponding bin index. + """ + return relative_position // self.resolution + + def slice_by_positions(self, start: int, end: int) -> 'TrackData': + """Slices the track data along the positional axes. + + The slicing follows Python slicing conventions (0 indexed, and includes + elements up to end-1). + + Args: + start: The 1-bp resolution start position for slicing. + end: The 1-bp resolution end position for slicing. + + Returns: + A new `TrackData` object with the sliced values. + + Raises: + ValueError: If (end - start) is greater than the width, or if (end - + start) is not divisible by the resolution. + """ + if (end - start) > self.width: + raise ValueError( + 'When slicing track data, (end - start) must be less than or ' + 'equal to width.' + ) + + if (end - start) % self.resolution != 0: + raise ValueError( + f'end - start needs to be to be divisible by {self.resolution=}' + ) + + sl = slice(self.bin_index(start), self.bin_index(end)) + slice_list = [slice(None)] * self.values.ndim + for i in self.positional_axes: + slice_list[i] = sl + + interval = self.interval + if interval: + interval = genome.Interval( + interval.chromosome, + interval.start + start, + interval.start + end, + strand=interval.strand, + name=interval.name, + info=interval.info, + ) + + return TrackData( + self.values[tuple(slice_list)], + resolution=self.resolution, + metadata=self.metadata, + interval=interval, + uns=self.uns, + ) + + def slice_by_interval( + self, interval: genome.Interval, match_resolution: bool = False + ) -> 'TrackData': + """Slices the track data using a `genome.Interval`. + + Args: + interval: The interval to slice to. + match_resolution: If True, the interval will first be extended to make + sure the width is divisible by resolution. + + Returns: + A new `TrackData` object sliced to the interval. + + Raises: + ValueError: If `.interval` is not specified or if the specified interval + is not fully contained within the current interval. + """ + if self.interval is None: + raise ValueError( + '.interval is needs to be specified for slice_by_interval.' + ) + if not self.interval.contains(interval): + raise ValueError( + f'Interval {self.interval=} does not fully contain {interval=}.' + ) + start = interval.start - self.interval.start + end = interval.end - self.interval.start + + if match_resolution and self.resolution != 1: + start = int(np.floor(start / self.resolution) * self.resolution) + end = int(np.ceil(end / self.resolution) * self.resolution) + return self.slice_by_positions(start, end) + + def pad(self, start_pad: int, end_pad: int) -> 'TrackData': + """Pads the track data along positional axes. + + Args: + start_pad: The amount of padding to add at the beginning. + end_pad: The amount of padding to add at the end. + + Returns: + A new `TrackData` object with padded values. + + Raises: + ValueError: If `start_pad` or `end_pad` is not divisible by the + resolution. + """ + if start_pad == 0 and end_pad == 0: + return self + if start_pad % self.resolution != 0: + raise ValueError(f'start_pad needs to be divisible by {self.resolution}') + if end_pad % self.resolution != 0: + raise ValueError(f'end_pad needs to be divisible by {self.resolution}') + + pad = [(0, 0)] * self.values.ndim + for axis in self.positional_axes: + pad[axis] = (start_pad // self.resolution, end_pad // self.resolution) + + return TrackData( + np.pad(self.values, tuple(pad)), + resolution=self.resolution, + metadata=self.metadata, + interval=None, # Padding invalidates the interval. + uns=self.uns, + ) + + def resize(self, width: int) -> 'TrackData': + """Resizes the track data by cropping or padding with a fixed center. + + Args: + width: The desired width in base pairs. + + Returns: + A new `TrackData` object with resized values. + + Raises: + ValueError: If `width` is not divisible by the resolution. + """ + if width == self.width: + return self + elif width > self.width: + if width % self.resolution != 0: + raise ValueError(f'width needs to be divisible by {self.resolution}') + pad_amount = (width - self.width) // self.resolution + pad_start = (pad_amount // 2 + pad_amount % 2) * self.resolution + pad_end = (pad_amount // 2) * self.resolution + return self.pad(pad_start, pad_end) + else: + crop_amount = (self.width - width) // self.resolution + start = (crop_amount // 2 + crop_amount % 2) * self.resolution + return self.slice_by_positions(start, start + width) + + def upsample( + self, + resolution: int, + aggregation_type: AggregationType = AggregationType.SUM, + ) -> 'TrackData': + """Upsamples the track data to a higher resolution by repeating existing values. + + Args: + resolution: The desired resolution in base pairs. + aggregation_type: The aggregation method to use for pooling the values. + + Returns: + A new `TrackData` object with upsampled values. + + Raises: + ValueError: If `resolution` is not lower than the current resolution + or not divisible by the current resolution. + """ + if resolution == self.resolution: + return self + if resolution > self.resolution: + raise ValueError(f'Resolution must be lower than {self.resolution}') + repeat = self.resolution // resolution + if self.resolution % resolution != 0: + raise ValueError(f'Resolution not divisible by {resolution}') + + values = self.values + for axis in self.positional_axes: + values = np.repeat(values, repeat, axis=axis) + match aggregation_type: + case AggregationType.SUM: + values = values / repeat + case AggregationType.MAX: + pass + return TrackData( + values, + resolution=resolution, + metadata=self.metadata, + interval=self.interval, + uns=self.uns, + ) + + def downsample( + self, + resolution: int, + aggregation_type: AggregationType = AggregationType.SUM, + ) -> 'TrackData': + """Downsamples the track data to a lower resolution using sum pooling. + + Args: + resolution: The desired resolution in base pairs. + aggregation_type: The aggregation method to use for pooling the values. + + Returns: + A new `TrackData` object with downsampled values. + + Raises: + ValueError: If `resolution` is not greater than the current resolution + or not divisible by the current resolution. + """ + if resolution == self.resolution: + return self + if resolution < self.resolution: + raise ValueError(f'Resolution must be greater than {self.resolution}') + if resolution % self.resolution != 0: + raise ValueError(f'Resolution not divisible by {resolution}') + pool_width = resolution // self.resolution + + values = self.values + for axis in self.positional_axes: + # Bring axis of interest to the front, reshape and aggregate, and reswap + values = np.swapaxes(values, 0, axis) + shape = list(values.shape) + reshaped_values = values.reshape( + [shape[0] // pool_width, pool_width] + shape[1:] + ) + match aggregation_type: + case AggregationType.SUM: + values = reshaped_values.sum(axis=1) + case AggregationType.MAX: + values = reshaped_values.max(axis=1) + values = np.swapaxes(values, 0, axis) + + return TrackData( + values, + resolution=resolution, + metadata=self.metadata, + interval=self.interval, + uns=self.uns, + ) + + def change_resolution( + self, + resolution: int, + aggregation_type: AggregationType = AggregationType.SUM, + ) -> 'TrackData': + """Changes the resolution of the track data. + + Args: + resolution: The desired resolution in base pairs. + aggregation_type: The aggregation method to use for pooling the values. + + Returns: + A new `TrackData` object with the new resolution. + """ + if resolution >= self.resolution: + return self.downsample(resolution, aggregation_type) + else: + return self.upsample(resolution, aggregation_type) + + def filter_tracks(self, mask: np.ndarray | list[bool]) -> 'TrackData': + """Filters tracks by a boolean mask. + + Args: + mask: A boolean mask to select tracks. + + Returns: + A new `TrackData` object with the filtered tracks. + """ + return TrackData( + self.values[..., mask], + resolution=self.resolution, + metadata=self.metadata.iloc[mask], + interval=self.interval, + uns=self.uns, + ) + + def filter_to_positive_strand(self) -> 'TrackData': + """Filters tracks to the positive DNA strand.""" + return self.filter_tracks(self.strands == genome.STRAND_POSITIVE) + + def filter_to_negative_strand(self) -> 'TrackData': + """Filters tracks to the negative DNA strand.""" + return self.filter_tracks(self.strands == genome.STRAND_NEGATIVE) + + def filter_to_nonnegative_strand(self) -> 'TrackData': + """Filters tracks to the non-negative DNA strands (positive and unstranded).""" + return self.filter_tracks(self.strands != genome.STRAND_NEGATIVE) + + def filter_to_nonpositive_strand(self) -> 'TrackData': + """Filters tracks to the non-positive DNA strands (negative and unstranded).""" + return self.filter_tracks(self.strands != genome.STRAND_POSITIVE) + + def filter_to_stranded(self) -> 'TrackData': + """Filters tracks to stranded tracks (excluding unstranded).""" + return self.filter_tracks(self.strands != genome.STRAND_UNSTRANDED) + + def filter_to_unstranded(self) -> 'TrackData': + """Filters tracks to unstranded tracks.""" + return self.filter_tracks(self.strands == genome.STRAND_UNSTRANDED) + + def select_tracks_by_index( + self, idx: np.ndarray | Sequence[int] + ) -> 'TrackData': + """Selects tracks by numerical index. + + Args: + idx: A list or array of numerical indices to select tracks. + + Returns: + A new `TrackData` object with the selected tracks. + """ + return TrackData( + self.values[..., idx], + resolution=self.resolution, + metadata=self.metadata.iloc[idx], + interval=self.interval, + uns=self.uns, + ) + + def select_tracks_by_name( + self, names: np.ndarray | Sequence[str] + ) -> 'TrackData': + """Selects tracks by name. + + Args: + names: A list or array of track names to select. + + Returns: + A new `TrackData` object with the selected tracks. + """ + track_idx = pd.Series(np.arange(self.num_tracks), index=self.names) + return self.select_tracks_by_index(track_idx.loc[names].values) + + def groupby(self, column: str) -> dict[str, 'TrackData']: + """Splits tracks into groups based on a metadata column. + + This method splits the tracks in the `TrackData` object into separate + `TrackData` objects based on the unique values in the specified metadata + column. It returns a dictionary where the keys are the unique values in + the column, and the values are new `TrackData` objects containing the + tracks corresponding to each key. + + Args: + column: The name of the metadata column to split by. + + Returns: + A dictionary mapping unique values in the column to `TrackData` objects + containing the corresponding tracks. + """ + output = {} + for key in self.metadata[column].unique(): + mask = (self.metadata[column] == key).values + output[key] = self.filter_tracks(mask) + return output + + def _reverse_complement_idx(self) -> np.ndarray: + """Gets indices for reverse complementing the tracks. + + Returns: + An array of indices that reorders the tracks to achieve reverse + complementation. + + Raises: + ValueError: If not all stranded tracks have both '+' and '-' strands, + or if the number of '+' and '-' stranded tracks is not equal. + """ + df_strands = pd.DataFrame({ + 'name': self.names, + 'strand': self.strands, + 'old_idx': np.arange(self.num_tracks), + }) + df_strands = df_strands[df_strands.strand != genome.STRAND_UNSTRANDED] + df_strands.sort_values(['strand', 'name'], inplace=True) + if np.all(df_strands.groupby('name').size() != 2): + raise ValueError('Not all stranded tracks have both + and - strand.') + if (df_strands.strand == genome.STRAND_POSITIVE).sum() != ( + df_strands.strand == genome.STRAND_NEGATIVE + ).sum(): + raise ValueError( + 'We need to have the exact same number of + and - stranded tracks' + ) + new_idx = df_strands.old_idx.values.reshape((2, -1))[::-1].ravel() + # Swap strands by idx. + idx = np.arange(self.num_tracks) + idx[df_strands.old_idx.values] = new_idx + return idx + + def reverse_complement(self) -> 'TrackData': + """Reverse complements the track data and interval if present. + + Returns: + A new `TrackData` object with reverse complemented tracks. + """ + if self.interval: + # Note that the interval needs to be stranded in order to perform + # this operation. + interval = self.interval.swap_strand() + else: + interval = None + + idx = self._reverse_complement_idx() + slices = [slice(None)] * self.values.ndim + slices[-1] = idx + for axis in self.positional_axes: + slices[axis] = slice(None, None, -1) + + return TrackData( + self.values[tuple(slices)], + resolution=self.resolution, + metadata=self.metadata.iloc[idx], + interval=interval, + uns=self.uns, + ) + + def _check_track_data_compatibility(self, other: 'TrackData') -> None: + """Checks if two `TrackData` objects are compatible for sum/diff. + + Args: + other: The other `TrackData` object to compare. + + Raises: + TypeError: If `other` is not a `TrackData` object. + ValueError: If the intervals, resolutions, shapes, or metadata + shapes don't match between the two objects. + """ + if not isinstance(other, TrackData): + raise TypeError( + f'Unsupported type "{type(other)}". Must be a TrackData object' + ) + if self.interval != other.interval: + raise ValueError('Intervals must match for the two TrackData objects.') + if self.resolution != other.resolution: + raise ValueError('Resolutions must match for the two TrackData objects.') + if self.values.shape != other.values.shape: + raise ValueError('Shapes must match for the two TrackData objects.') + if self.metadata.shape != other.metadata.shape: + raise ValueError( + 'Metadata shapes must match for the two TrackData objects.' + ) + + def __add__(self, other: 'TrackData') -> 'TrackData': + """Adds the values of two `TrackData` objects. + + Args: + other: The `TrackData` object to add. + + Returns: + A new `TrackData` object with the summed values. + + Raises: + ValueError: If the objects are not compatible (see + `_check_track_data_compatibility`). + TypeError: If `other` is not a `TrackData` object. + """ + self._check_track_data_compatibility(other) + new_values = self.values + other.values + return TrackData( + values=new_values, + metadata=self.metadata, + resolution=self.resolution, + interval=self.interval, + uns=self.uns, + ) + + def __sub__(self, other: 'TrackData') -> 'TrackData': + """Subtracts the values of two `TrackData` objects. + + Args: + other: The `TrackData` object to subtract. + + Returns: + A new `TrackData` object with the difference of the values. + + Raises: + ValueError: If the objects are not compatible (see + `_check_track_data_compatibility`). + TypeError: If `other` is not a `TrackData` object. + """ + self._check_track_data_compatibility(other) + new_values = self.values - other.values + return TrackData( + values=new_values, + metadata=self.metadata, + resolution=self.resolution, + interval=self.interval, + uns=self.uns, + ) + + +def concat( + track_datas: Sequence[TrackData], + extra_metadata_name_and_keys: ( + tuple[str, Sequence[str | int | float]] | None + ) = None, +) -> TrackData: + """Concatenates multiple `TrackData` objects along the track dimension. + + This function combines multiple `TrackData` objects into a single object + by concatenating their values and metadata. The resulting `TrackData` + object will have the same resolution and interval as the input objects. + + Args: + track_datas: A sequence of `TrackData` objects to concatenate. All objects + must have the same resolution, interval, and width. + extra_metadata_name_and_keys: An optional tuple specifying a new metadata + column to add. The first element is the column name, and the second is a + sequence of values to populate the column. + + Returns: + A new `TrackData` object containing the concatenated data. + + Raises: + ValueError: If the input `TrackData` objects have different resolutions, + intervals, or widths, or if the length of + `extra_metadata_name_and_keys[1]` does not match the length of + `track_datas`. + """ + if len(set(x.resolution for x in track_datas)) != 1: + raise ValueError('Track data contain multiple resolutions') + if len(set(str(x.interval) for x in track_datas)) != 1: + raise ValueError('Track data contain multiple intervals') + if len(set(x.width for x in track_datas)) != 1: + raise ValueError('Track data are of different width') + if extra_metadata_name_and_keys: + if len(extra_metadata_name_and_keys[1]) != len(track_datas): + raise ValueError( + 'Second element of new_metadata_name_and_keys must be the same' + ' length as track_datas' + ) + + concatenated_metadata = ( + pd.concat( + [x.metadata for x in track_datas], + keys=extra_metadata_name_and_keys[1] + if extra_metadata_name_and_keys + else None, + names=[extra_metadata_name_and_keys[0]] + if extra_metadata_name_and_keys + else None, + ) + .reset_index(level=0, drop=extra_metadata_name_and_keys is None) + .reset_index(drop=True) + ) + return TrackData( + np.concatenate( + [x.values for x in track_datas], axis=track_datas[0].values.ndim - 1 + ), + resolution=track_datas[0].resolution, + metadata=concatenated_metadata, + interval=track_datas[0].interval, + uns=None, + ) + + +def interleave( + track_datas: Sequence[TrackData], name_prefixes: Sequence[str] +) -> TrackData: + """Interleaves multiple `TrackData` objects by alternating rows. + + This function combines multiple `TrackData` objects into a single object + by interleaving their rows and metadata. This interleaves operation alternates + between the trackdatas, like shuffling cards, i.e., 'abcd' interleaved with + 'efgh' would be "aebfcgdh". The resulting `TrackData` object will have the + same resolution and interval as the input objects, but the number of tracks + will be the sum of the tracks in the input objects. + + Args: + track_datas: A sequence of `TrackData` objects to interleave. All objects + must have the same shape, resolution, and interval. The order in this list + will determine the interleaving order. + name_prefixes: A sequence of name prefixes to add to the track names in the + metadata to ensure uniqueness of (name, strand) pairs. + + Returns: + A new `TrackData` object containing the interleaved data. + + Raises: + ValueError: If the input `TrackData` objects have different shapes, + resolutions, or intervals. + """ + # Checks on the track data. + shapes = set(data.values.shape for data in track_datas) + if len(shapes) != 1: + raise ValueError( + 'Cannot interleave track data which have different shapes. ' + f'Detected shapes: {shapes}' + ) + + if any(data.resolution != track_datas[0].resolution for data in track_datas): + raise ValueError( + 'Cannot interleave track data which have different resolutions. ' + f'Detected shapes: {shapes}' + ) + + if any(data.interval != track_datas[0].interval for data in track_datas): + raise ValueError( + 'Cannot interleave track data which have different intervals. ' + ) + + # Interleave arrays. + shape = list(track_datas[0].values.shape) + shape[-1] = shape[-1] * len(track_datas) + interleaved_data = np.empty(tuple(shape), dtype=track_datas[0].values.dtype) + + for i, data in enumerate(track_datas): + interleaved_data[..., i :: len(track_datas)] = data.values + + # Interleave metadata. + metadatas = [] + for prefix, data in zip(name_prefixes, track_datas): + metadata = data.metadata.copy() + metadata['name'] = prefix + metadata['name'] + metadatas.append(metadata) + + # Assign a new index idx that, when sorted, will produce an interleave. + interleaved_metadata = ( + pd.concat([ + metadata.assign( + idx=np.arange( + stop=(len(metadata) * len(track_datas)), + step=len(track_datas), + ) + + i + ) + for i, metadata in enumerate(metadatas) + ]) + .sort_values('idx') + .drop('idx', axis=1) + .reset_index(drop=True) + ) + + return TrackData( + values=interleaved_data, + metadata=interleaved_metadata, + resolution=track_datas[0].resolution, + interval=track_datas[0].interval, + uns={'num_interleaved_trackdatas': len(track_datas)}, + ) diff --git a/alphagenome/source/src/alphagenome/data/track_data_test.py b/alphagenome/source/src/alphagenome/data/track_data_test.py new file mode 100644 index 0000000000000000000000000000000000000000..b5512bf7c2ca448ec342d0f0da84f5e6293f18de --- /dev/null +++ b/alphagenome/source/src/alphagenome/data/track_data_test.py @@ -0,0 +1,862 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from absl.testing import absltest +from absl.testing import parameterized +from alphagenome.data import genome +from alphagenome.data import track_data +import jaxtyping +import numpy as np +import pandas as pd + + +class TrackDataInitTest(parameterized.TestCase): + + @parameterized.product(copy=[True, False], include_ontologies=[True, False]) + def test_valid_init_properties(self, copy: bool, include_ontologies: bool): + values = np.arange(15).reshape((5, 3)).astype(np.int32) + metadata = pd.DataFrame({ + 'name': ['first', 'first', 'second'], + 'strand': ['+', '-', '.'], + 'other': ['foo', 'foo', 'bar'], + }) + if include_ontologies: + metadata['ontology_curie'] = [ + 'UBERON:0000005', + 'UBERON:0000007', + None, + ] + tdata = track_data.TrackData(values, metadata) + if copy: + tdata = tdata.copy() + self.assertListEqual(tdata.positional_axes, [0]) + self.assertEqual(tdata.num_tracks, 3) + self.assertEqual(tdata.width, 5) + self.assertEqual(tdata.resolution, 1) + self.assertListEqual(list(tdata.names), ['first', 'first', 'second']) + self.assertListEqual(list(tdata.strands), ['+', '-', '.']) + if include_ontologies: + self.assertEqual( + [ + o.ontology_curie if o is not None else None + for o in tdata.ontology_terms + ], + metadata['ontology_curie'].tolist(), + ) + else: + self.assertIsNone(tdata.ontology_terms) + + @parameterized.parameters([ + dict(num_positional=0), + dict(num_positional=1), + dict(num_positional=2), + ]) + def test_valid_positional_axis(self, num_positional: int): + values = ( + np.arange(2**num_positional * 3) + .reshape([2] * num_positional + [3]) + .astype(np.int32) + ) + metadata = pd.DataFrame({ + 'name': ['first', 'first', 'second'], + 'strand': ['+', '-', '.'], + 'other': ['foo', 'foo', 'bar'], + }) + tdata = track_data.TrackData(values, metadata) + self.assertLen(tdata.positional_axes, num_positional) + + def test_invalid_init(self): + values = np.arange(12).reshape((2, 2, 3)).astype(np.int32) + metadata = pd.DataFrame({ + 'name': ['first', 'first', 'second'], + 'strand': ['+', '-', '.'], + 'other': ['foo', 'foo', 'bar'], + }) + with self.assertRaises(ValueError): + # Missing strand + track_data.TrackData(values, metadata[['name', 'other']]) + with self.assertRaises(ValueError): + # Missing name + track_data.TrackData(values, metadata[['strand', 'other']]) + with self.assertRaises(ValueError): + # Not matching number of tracks + track_data.TrackData(values[:, :, :2], metadata) + with self.assertRaises(ValueError): + # Not matching positional axis + track_data.TrackData(values[:1], metadata) + with self.assertRaises(ValueError): + # Not matching interval width + track_data.TrackData( + values, metadata, interval=genome.Interval('chr1', 0, 3) + ) + + def test_invalid_value_types_raises(self): + values = np.array(['foo', 'bar', 'baz'], dtype=str) + metadata = pd.DataFrame({ + 'name': ['first', 'first', 'second'], + 'strand': ['+', '-', '.'], + 'other': ['foo', 'foo', 'bar'], + }) + with self.assertRaises(jaxtyping.TypeCheckError): + track_data.TrackData(values, metadata) + + +class TrackDataSlicingTest(parameterized.TestCase): + + @parameterized.parameters([ + dict(num_positional=0), + dict(num_positional=1), + dict(num_positional=2), + ]) + def test_slice_by_positions(self, num_positional: int): + # Values are [0,1,2] if num_positional=0, [[0,1,2],[3,4,5]] if + # num_positional=1, etc. + values = ( + np.arange(2**num_positional * 3) + .reshape([2] * num_positional + [3]) + .astype(np.int32) + ) + metadata = pd.DataFrame({ + 'name': ['first', 'first', 'second'], + 'strand': ['+', '-', '.'], + 'other': ['foo', 'foo', 'bar'], + }) + interval = genome.Interval('chr1', 0, 4) + tdata = track_data.TrackData( + values, metadata, resolution=2, interval=interval + ) + assert tdata.interval is not None + self.assertIsNotNone(tdata.interval) + self.assertEqual(tdata.interval.start, 0) + self.assertEqual(tdata.interval.end, 4) + # Note that track data with no positional axes has a width of 0 and so + # cannot be sliced in this way. + if num_positional: + # Slicing to the full width (no-op). + tdata2 = tdata.slice_by_positions(0, 4) + assert tdata2.interval is not None + self.assertEqual(tdata2.width, tdata.width) + # Slicing to half the width. + tdata2 = tdata.slice_by_positions(0, 2) + self.assertEqual(tdata2.values.shape, tuple([1] * num_positional + [3])) + self.assertEqual(tdata2.interval.start, 0) + self.assertEqual(tdata2.interval.end, 2) + self.assertEqual(tdata2.width, tdata2.interval.width) + self.assertEqual(tdata2.width, 2) + # Fails because (end - start) is not evenly divisible by resolution. + with self.assertRaises(ValueError): + tdata.slice_by_positions(0, 3) + with self.assertRaises(ValueError): + tdata.slice_by_positions(0, 1) + # Fails because there are no positional axes to slice into. + else: + self.assertEqual(tdata.width, 0) + with self.assertRaises(ValueError): + tdata.slice_by_positions(0, 4) + + @parameterized.parameters([ + dict(num_positional=0), + dict(num_positional=1), + dict(num_positional=2), + ]) + def test_slice_by_interval(self, num_positional: int): + # Values are [0,1,2] if num_positional=0, [[0,1,2],[3,4,5]] if + # num_positional=1, etc. + values = ( + np.arange(2**num_positional * 3) + .reshape([2] * num_positional + [3]) + .astype(np.int32) + ) + metadata = pd.DataFrame({ + 'name': ['first', 'first', 'second'], + 'strand': ['+', '-', '.'], + 'other': ['foo', 'foo', 'bar'], + }) + interval = genome.Interval('chr1', 1, 5) + tdata = track_data.TrackData( + values, metadata, resolution=2, interval=interval + ) + assert tdata.interval is not None + + if num_positional: + # Slicing to the full width (no-op). + tdata2 = tdata.slice_by_interval(interval) + assert tdata2.interval is not None + self.assertEqual(tdata2.width, tdata.width) + # Slicing to half the width. + tdata2 = tdata.slice_by_interval(genome.Interval('chr1', 1, 3)) + self.assertEqual(tdata2.values.shape, tuple([1] * num_positional + [3])) + self.assertEqual(tdata2.interval.start, 1) + self.assertEqual(tdata2.interval.end, 3) + self.assertEqual(tdata2.width, tdata2.interval.width) + self.assertEqual(tdata2.width, 2) + # Fails because (end - start) is not evenly divisible by resolution. + with self.assertRaises(ValueError): + tdata.slice_by_interval(genome.Interval('chr1', 1, 4)) + with self.assertRaises(ValueError): + tdata.slice_by_interval(genome.Interval('chr1', 1, 2)) + + # Same would work if we set match_resolution=True. + tdata2 = tdata.slice_by_interval( + genome.Interval('chr1', 1, 4), match_resolution=True + ) + self.assertEqual(tdata2.interval.start, 1) + self.assertEqual(tdata2.interval.end, 5) + tdata2 = tdata.slice_by_interval( + genome.Interval('chr1', 2, 5), match_resolution=True + ) + self.assertEqual(tdata2.interval.start, 1) + self.assertEqual(tdata2.interval.end, 5) + tdata2 = tdata.slice_by_interval( + genome.Interval('chr1', 3, 5), match_resolution=True + ) + self.assertEqual(tdata2.interval.start, 3) + self.assertEqual(tdata2.interval.end, 5) + else: + # Fails because there are no positional axes to slice into. + self.assertEqual(tdata.width, 0) + with self.assertRaises(ValueError): + tdata.slice_by_interval(interval) + + @parameterized.parameters([ + dict(num_positional=0), + dict(num_positional=1), + dict(num_positional=2), + ]) + def test_pad(self, num_positional: int): + values = ( + np.arange(2**num_positional * 3) + .reshape([2] * num_positional + [3]) + .astype(np.int32) + ) + metadata = pd.DataFrame({ + 'name': ['first', 'first', 'second'], + 'strand': ['+', '-', '.'], + 'other': ['foo', 'foo', 'bar'], + }) + tdata = track_data.TrackData(values, metadata, resolution=2) + if num_positional: + self.assertEqual(tdata.width, 4) + tdata2 = tdata.pad(0, 0) + self.assertEqual(tdata2.width, tdata.width) + tdata2 = tdata.pad(2, 0) + self.assertEqual(tdata2.values.shape, tuple([3] * num_positional + [3])) + if num_positional: + self.assertEqual(tdata2.width, 6) + with self.assertRaises(ValueError): + tdata.pad(1, 1) + + @parameterized.parameters([ + dict(num_positional=0), + dict(num_positional=1), + dict(num_positional=2), + ]) + def test_resize(self, num_positional: int): + values = ( + np.arange(2**num_positional * 3) + .reshape([2] * num_positional + [3]) + .astype(np.int32) + ) + metadata = pd.DataFrame({ + 'name': ['first', 'first', 'second'], + 'strand': ['+', '-', '.'], + 'other': ['foo', 'foo', 'bar'], + }) + tdata = track_data.TrackData( + values, + metadata, + resolution=2, + interval=(genome.Interval('chr1', 0, 4) if num_positional else None), + ) + if num_positional: + self.assertEqual(tdata.width, 4) + tdata2 = tdata.resize(4) + self.assertEqual(tdata2.width, tdata.width) + tdata2 = tdata.resize(6) + self.assertEqual(tdata2.values.shape, tuple([3] * num_positional + [3])) + if num_positional: + self.assertEqual(tdata2.width, 6) + tdata2 = tdata.resize(2) + self.assertEqual(tdata2.values.shape, tuple([1] * num_positional + [3])) + if num_positional: + self.assertEqual(tdata2.width, 2) + # Pad and crop to reach the same original array. + tdata2 = tdata.resize(6).resize(4) + if num_positional: + self.assertEqual(tdata2.width, 4) + self.assertTrue(np.all(tdata2.values == tdata.values)) + if num_positional: + with self.assertRaises(ValueError): + tdata.resize(5) + + +class TrackDataUpDownSampleTest(parameterized.TestCase): + + @parameterized.parameters([ + dict(num_positional=0, aggregation_type=track_data.AggregationType.SUM), + dict(num_positional=1, aggregation_type=track_data.AggregationType.SUM), + dict(num_positional=2, aggregation_type=track_data.AggregationType.SUM), + dict(num_positional=0, aggregation_type=track_data.AggregationType.MAX), + dict(num_positional=1, aggregation_type=track_data.AggregationType.MAX), + dict(num_positional=2, aggregation_type=track_data.AggregationType.MAX), + ]) + def test_upsample( + self, num_positional: int, aggregation_type: track_data.AggregationType + ): + values = ( + np.arange(2**num_positional * 3) + .reshape([2] * num_positional + [3]) + .astype(np.float32) + ) + metadata = pd.DataFrame({ + 'name': ['first', 'first', 'second'], + 'strand': ['+', '-', '.'], + 'other': ['foo', 'foo', 'bar'], + }) + tdata = track_data.TrackData(values, metadata, resolution=2) + tdata2 = tdata.upsample(resolution=1, aggregation_type=aggregation_type) + self.assertEqual(tdata2.width, tdata.width) + self.assertEqual(tdata2.values.shape, tuple([4] * num_positional + [3])) + if aggregation_type == track_data.AggregationType.SUM: + self.assertEqual(tdata2.values.sum(), tdata.values.sum()) + elif aggregation_type == track_data.AggregationType.MAX: + self.assertEqual(tdata2.values.max(), tdata.values.max()) + with self.assertRaises(ValueError): + tdata.upsample(resolution=4) + + @parameterized.parameters([ + dict( + num_positional=0, + aggregation_type=track_data.AggregationType.SUM, + ), + dict( + num_positional=1, + aggregation_type=track_data.AggregationType.SUM, + ), + dict( + num_positional=2, + aggregation_type=track_data.AggregationType.SUM, + ), + dict( + num_positional=0, + aggregation_type=track_data.AggregationType.MAX, + ), + dict( + num_positional=1, + aggregation_type=track_data.AggregationType.MAX, + ), + dict( + num_positional=2, + aggregation_type=track_data.AggregationType.MAX, + ), + ]) + def test_downsample( + self, num_positional: int, aggregation_type: track_data.AggregationType + ): + values = ( + np.arange(4**num_positional * 3) + .reshape([4] * num_positional + [3]) + .astype(np.float32) + ) + metadata = pd.DataFrame({ + 'name': ['first', 'first', 'second'], + 'strand': ['+', '-', '.'], + 'other': ['foo', 'foo', 'bar'], + }) + tdata = track_data.TrackData(values, metadata, resolution=1) + tdata2 = tdata.downsample(resolution=2, aggregation_type=aggregation_type) + self.assertEqual(tdata2.width, tdata.width) + self.assertEqual(tdata2.values.shape, tuple([2] * num_positional + [3])) + if aggregation_type == track_data.AggregationType.SUM: + self.assertEqual(tdata2.values.sum(), tdata.values.sum()) + elif aggregation_type == track_data.AggregationType.MAX: + self.assertEqual(tdata2.values.max(), tdata.values.max()) + tdata = track_data.TrackData(values, metadata, resolution=2) + with self.assertRaises(ValueError): + tdata.downsample(resolution=1) + + @parameterized.parameters([ + dict( + num_positional=0, + resolution=1, + aggregation_type=track_data.AggregationType.SUM, + ), + dict( + num_positional=1, + resolution=1, + aggregation_type=track_data.AggregationType.SUM, + ), + dict( + num_positional=2, + resolution=1, + aggregation_type=track_data.AggregationType.SUM, + ), + dict( + num_positional=0, + resolution=4, + aggregation_type=track_data.AggregationType.SUM, + ), + dict( + num_positional=1, + resolution=4, + aggregation_type=track_data.AggregationType.SUM, + ), + dict( + num_positional=2, + resolution=4, + aggregation_type=track_data.AggregationType.SUM, + ), + dict( + num_positional=0, + resolution=1, + aggregation_type=track_data.AggregationType.MAX, + ), + dict( + num_positional=1, + resolution=1, + aggregation_type=track_data.AggregationType.MAX, + ), + dict( + num_positional=2, + resolution=1, + aggregation_type=track_data.AggregationType.MAX, + ), + dict( + num_positional=0, + resolution=4, + aggregation_type=track_data.AggregationType.MAX, + ), + dict( + num_positional=1, + resolution=4, + aggregation_type=track_data.AggregationType.MAX, + ), + dict( + num_positional=2, + resolution=4, + aggregation_type=track_data.AggregationType.MAX, + ), + ]) + def test_change_resolution( + self, + num_positional: int, + resolution: int, + aggregation_type: track_data.AggregationType, + ): + values = ( + np.arange(2**num_positional * 3) + .reshape([2] * num_positional + [3]) + .astype(np.float32) + ) + metadata = pd.DataFrame({ + 'name': ['first', 'first', 'second'], + 'strand': ['+', '-', '.'], + 'other': ['foo', 'foo', 'bar'], + }) + tdata = track_data.TrackData(values, metadata, resolution=2) + tdata2 = tdata.change_resolution( + resolution=resolution, aggregation_type=aggregation_type + ) + self.assertEqual(tdata2.width, tdata.width) + self.assertEqual( + tdata2.values.shape, + tuple([tdata2.width // resolution] * num_positional + [3]), + ) + if aggregation_type == track_data.AggregationType.SUM: + self.assertEqual(tdata2.values.sum(), tdata.values.sum()) + elif aggregation_type == track_data.AggregationType.MAX: + self.assertEqual(tdata2.values.max(), tdata.values.max()) + + +class TrackDataTrackSubsetTest(parameterized.TestCase): + + @parameterized.parameters([ + dict(num_positional=0), + dict(num_positional=1), + dict(num_positional=2), + ]) + def test_filter_tracks(self, num_positional: int): + values = ( + np.arange(2**num_positional * 3) + .reshape([2] * num_positional + [3]) + .astype(np.int32) + ) + metadata = pd.DataFrame({ + 'name': ['first', 'first', 'second'], + 'strand': ['+', '-', '.'], + 'other': ['foo', 'foo', 'bar'], + }) + tdata = track_data.TrackData(values, metadata, resolution=2) + tdata2 = tdata.filter_tracks([False, True, False]) + self.assertEqual(tdata2.num_tracks, 1) + self.assertListEqual(list(tdata2.names), ['first']) + self.assertListEqual(list(tdata2.strands), ['-']) + self.assertEqual(tdata2.metadata['other'].iloc[0], 'foo') + self.assertEqual(tdata2.values.shape, tuple([2] * num_positional + [1])) + + @parameterized.parameters([ + dict(num_positional=0, strand='+'), + dict(num_positional=1, strand='+'), + dict(num_positional=2, strand='+'), + dict(num_positional=0, strand='-'), + dict(num_positional=1, strand='-'), + dict(num_positional=2, strand='-'), + dict(num_positional=0, strand='.'), + dict(num_positional=1, strand='.'), + dict(num_positional=2, strand='.'), + ]) + def test_filter_to_strand(self, num_positional: int, strand: str): + values = ( + np.arange(2**num_positional * 3) + .reshape([2] * num_positional + [3]) + .astype(np.int32) + ) + metadata = pd.DataFrame({ + 'name': ['first', 'first', 'second'], + 'strand': ['+', '-', '.'], + 'other': ['foo', 'foo', 'bar'], + }) + tdata = track_data.TrackData(values, metadata, resolution=2) + if strand == '+': + tdata2 = tdata.filter_to_positive_strand() + name = 'first' + other = 'foo' + elif strand == '-': + tdata2 = tdata.filter_to_negative_strand() + name = 'first' + other = 'foo' + elif strand == '.': + tdata2 = tdata.filter_to_unstranded() + name = 'second' + other = 'bar' + else: + raise ValueError(f'Invalid strand: {strand}') + self.assertEqual(tdata2.num_tracks, 1) + self.assertListEqual(list(tdata2.names), [name]) + self.assertListEqual(list(tdata2.strands), [strand]) + self.assertEqual(tdata2.metadata['other'].iloc[0], other) + self.assertEqual(tdata2.values.shape, tuple([2] * num_positional + [1])) + + @parameterized.parameters([ + dict(num_positional=0), + dict(num_positional=1), + dict(num_positional=2), + ]) + def test_filter_nonpositive_nonnegative_strand(self, num_positional: int): + values = ( + np.arange(2**num_positional * 3) + .reshape([2] * num_positional + [3]) + .astype(np.int32) + ) + metadata = pd.DataFrame({ + 'name': ['first', 'first', 'second'], + 'strand': ['+', '-', '.'], + 'other': ['foo', 'foo', 'bar'], + }) + tdata = track_data.TrackData(values, metadata, resolution=2) + tdata2 = tdata.filter_to_nonpositive_strand() + self.assertListEqual(list(tdata2.strands), ['-', '.']) + tdata2 = tdata.filter_to_nonnegative_strand() + self.assertListEqual(list(tdata2.strands), ['+', '.']) + + @parameterized.parameters([ + dict(num_positional=0), + dict(num_positional=1), + dict(num_positional=2), + ]) + def test_filter_to_stranded(self, num_positional: int): + values = ( + np.arange(2**num_positional * 3) + .reshape([2] * num_positional + [3]) + .astype(np.int32) + ) + metadata = pd.DataFrame({ + 'name': ['first', 'first', 'second'], + 'strand': ['+', '-', '.'], + 'other': ['foo', 'foo', 'bar'], + }) + tdata = track_data.TrackData(values, metadata, resolution=2) + tdata2 = tdata.filter_to_stranded() + self.assertEqual(tdata2.num_tracks, 2) + self.assertListEqual(list(tdata2.names), ['first', 'first']) + self.assertListEqual(list(tdata2.strands), ['+', '-']) + self.assertEqual(tdata2.metadata['other'].iloc[0], 'foo') + self.assertEqual(tdata2.values.shape, tuple([2] * num_positional + [2])) + + @parameterized.parameters([ + dict(num_positional=0), + dict(num_positional=1), + dict(num_positional=2), + ]) + def test_select_tracks_by_index(self, num_positional: int): + values = ( + np.arange(2**num_positional * 3) + .reshape([2] * num_positional + [3]) + .astype(np.int32) + ) + metadata = pd.DataFrame({ + 'name': ['first', 'first', 'second'], + 'strand': ['+', '-', '.'], + 'other': ['foo', 'foo', 'bar'], + }) + tdata = track_data.TrackData(values, metadata, resolution=2) + tdata2 = tdata.select_tracks_by_index([1]) + self.assertEqual(tdata2.num_tracks, 1) + self.assertListEqual(list(tdata2.names), ['first']) + self.assertListEqual(list(tdata2.strands), ['-']) + self.assertEqual(tdata2.metadata['other'].iloc[0], 'foo') + self.assertEqual(tdata2.values.shape, tuple([2] * num_positional + [1])) + + @parameterized.parameters([ + dict(num_positional=0), + dict(num_positional=1), + dict(num_positional=2), + ]) + def test_select_tracks_by_name(self, num_positional: int): + values = ( + np.arange(2**num_positional * 3) + .reshape([2] * num_positional + [3]) + .astype(np.int32) + ) + metadata = pd.DataFrame({ + 'name': ['first', 'first', 'second'], + 'strand': ['+', '-', '.'], + 'other': ['foo', 'foo', 'bar'], + }) + tdata = track_data.TrackData(values, metadata, resolution=2) + tdata2 = tdata.select_tracks_by_name(['first']) + self.assertEqual(tdata2.num_tracks, 2) + self.assertListEqual(list(tdata2.names), ['first', 'first']) + self.assertListEqual(list(tdata2.strands), ['+', '-']) + self.assertEqual(tdata2.metadata['other'].iloc[0], 'foo') + self.assertEqual(tdata2.values.shape, tuple([2] * num_positional + [2])) + tdata2 = tdata.select_tracks_by_name(['second']) + self.assertEqual(tdata2.num_tracks, 1) + self.assertListEqual(list(tdata2.names), ['second']) + self.assertListEqual(list(tdata2.strands), ['.']) + self.assertEqual(tdata2.metadata['other'].iloc[0], 'bar') + self.assertEqual(tdata2.values.shape, tuple([2] * num_positional + [1])) + + @parameterized.parameters([ + dict(num_positional=0), + dict(num_positional=1), + dict(num_positional=2), + ]) + def test_groupby(self, num_positional: int): + values = ( + np.arange(2**num_positional * 3) + .reshape([2] * num_positional + [3]) + .astype(np.int32) + ) + metadata = pd.DataFrame({ + 'name': ['first', 'first', 'second'], + 'strand': ['+', '-', '.'], + 'other': ['foo', 'foo', 'bar'], + }) + tdata = track_data.TrackData(values, metadata, resolution=2) + tdata2_dict = tdata.groupby('other') + self.assertListEqual(list(tdata2_dict.keys()), ['foo', 'bar']) + self.assertListEqual(list(tdata2_dict['foo'].names), ['first', 'first']) + self.assertListEqual(list(tdata2_dict['bar'].names), ['second']) + self.assertEqual(tdata2_dict['foo'].num_tracks, 2) + self.assertEqual(tdata2_dict['bar'].num_tracks, 1) + self.assertEqual(tdata2_dict['foo'].width, tdata.width) + self.assertEqual(tdata2_dict['bar'].width, tdata.width) + + +class TrackDataReverseComplementTest(parameterized.TestCase): + + @parameterized.parameters([ + dict(num_positional=0), + dict(num_positional=1), + dict(num_positional=2), + ]) + def test_reverse_complement(self, num_positional: int): + values = ( + np.arange(2**num_positional * 3) + .reshape([2] * num_positional + [3]) + .astype(np.int32) + ) + metadata = pd.DataFrame({ + 'name': ['first', 'first', 'second'], + 'strand': ['+', '-', '.'], + 'other': ['foo', 'foo', 'bar'], + }) + tdata = track_data.TrackData(values, metadata, resolution=2) + tdata2 = tdata.reverse_complement() + if num_positional == 0: + self.assertTrue(np.all(tdata2.values[[1, 0, 2]] == tdata.values)) + elif num_positional == 1: + self.assertTrue(np.all(tdata2.values[::-1, [1, 0, 2]] == tdata.values)) + elif num_positional == 2: + self.assertTrue( + np.all(tdata2.values[::-1, ::-1, [1, 0, 2]] == tdata.values) + ) + self.assertListEqual(list(tdata2.names), list(tdata.names)) + self.assertListEqual(list(tdata2.strands), ['-', '+', '.']) + + @parameterized.parameters([ + dict(num_positional=0), + dict(num_positional=1), + dict(num_positional=2), + ]) + def test_reverse_complement_raises(self, num_positional: int): + values = ( + np.arange(2**num_positional * 3) + .reshape([2] * num_positional + [3]) + .astype(np.int32) + ) + metadata = pd.DataFrame({ + 'name': ['first', 'third', 'second'], + 'strand': ['+', '-', '.'], + 'other': ['foo', 'foo', 'bar'], + }) + tdata = track_data.TrackData(values, metadata, resolution=2) + with self.assertRaises(ValueError): + # Not matching number of strands by name + tdata.reverse_complement() + + +class TrackDataConcatTest(parameterized.TestCase): + + @parameterized.parameters([ + dict(num_positional=0), + dict(num_positional=1), + dict(num_positional=2), + dict(num_positional=0, new_metadata_values=['tdata1', 'tdata2']), + dict(num_positional=0, new_metadata_values=['tdata1', None]), + ]) + def test_concat( + self, num_positional: int, new_metadata_values: list[str] | None = None + ): + values = ( + np.arange(2**num_positional * 3) + .reshape([2] * num_positional + [3]) + .astype(np.int32) + ) + metadata1 = pd.DataFrame({ + 'name': ['first', 'first', 'second'], + 'strand': ['+', '-', '.'], + 'other': ['foo', 'foo', 'bar'], + }) + tdata = track_data.TrackData(values, metadata1, resolution=2, uns={'a': 1}) + metadata2 = pd.DataFrame({ + 'name': ['third', 'third', 'fourth'], + 'strand': ['+', '-', '.'], + 'other': ['foo', 'foo', 'bar'], + }) + tdata2 = track_data.TrackData(values, metadata2, resolution=2, uns={'a': 1}) + with self.assertRaises(ValueError): + track_data.concat([tdata, tdata]) + tdata3 = track_data.concat( + [tdata, tdata2], + ('new_column', new_metadata_values) if new_metadata_values else None, + ) + self.assertEqual(tdata3.num_tracks, 6) + self.assertEqual(tdata3.width, tdata.width) + self.assertIsNone(tdata3.uns) + self.assertEqual(tdata3.values.shape, tuple([2] * num_positional + [6])) + self.assertLen(tdata3.metadata, 6) + if new_metadata_values: + # Expect error if the column already exists. + with self.assertRaises(ValueError): + track_data.concat([tdata, tdata2], ('name', new_metadata_values)) + self.assertSequenceEqual( + list(tdata3.metadata['new_column'].unique()), new_metadata_values + ) + else: + self.assertNotIn('new_column', tdata3.metadata.columns) + + +class TrackDataInterleaveTest(absltest.TestCase): + + def test_interleave(self): + values_1 = np.array( + [ + [0.0, 0.2, 0.3, 0.4, 0.5], + [0.1, 0.3, 0.5, 0.6, 0.7], + [0.2, 0.4, 0.7, 0.8, 0.9], + ], + dtype=np.float32, + ) + metadata_1 = pd.DataFrame({ + 'name': ['track_0', 'track_1', 'track_2', 'track_3', 'track_4'], + 'strand': '.', + 'gtex_tissue': ['BLADDER', 'LIVER', 'LIVER', 'STOMACH', 'STOMACH'], + 'padding': [False, False, False, False, True], + }) + values_2 = -1 * values_1 + metadata_2 = metadata_1.copy() + data_1 = track_data.TrackData(values=values_1, metadata=metadata_1) + data_2 = track_data.TrackData(values=values_2, metadata=metadata_2) + track_datas = [data_1, data_2] + name_prefixes = ['REF_', 'ALT_'] + interleaved_track_data = track_data.interleave(track_datas, name_prefixes) + expected_values = np.array( + [ + [0.0, -0.0, 0.2, -0.2, 0.3, -0.3, 0.4, -0.4, 0.5, -0.5], + [0.1, -0.1, 0.3, -0.3, 0.5, -0.5, 0.6, -0.6, 0.7, -0.7], + [0.2, -0.2, 0.4, -0.4, 0.7, -0.7, 0.8, -0.8, 0.9, -0.9], + ], + dtype=np.float32, + ) + np.testing.assert_array_equal( + interleaved_track_data.values, expected_values + ) + expected_metadata = pd.DataFrame({ + 'name': [ + 'REF_track_0', + 'ALT_track_0', + 'REF_track_1', + 'ALT_track_1', + 'REF_track_2', + 'ALT_track_2', + 'REF_track_3', + 'ALT_track_3', + 'REF_track_4', + 'ALT_track_4', + ], + 'strand': ['.', '.', '.', '.', '.', '.', '.', '.', '.', '.'], + 'gtex_tissue': [ + 'BLADDER', + 'BLADDER', + 'LIVER', + 'LIVER', + 'LIVER', + 'LIVER', + 'STOMACH', + 'STOMACH', + 'STOMACH', + 'STOMACH', + ], + 'padding': [ + False, + False, + False, + False, + False, + False, + False, + False, + True, + True, + ], + }) + pd.testing.assert_frame_equal( + interleaved_track_data.metadata, expected_metadata + ) + + +if __name__ == '__main__': + absltest.main() diff --git a/alphagenome/source/src/alphagenome/data/transcript.py b/alphagenome/source/src/alphagenome/data/transcript.py new file mode 100644 index 0000000000000000000000000000000000000000..234266a7d45b54df9d70f2eac6d78a20a17567d1 --- /dev/null +++ b/alphagenome/source/src/alphagenome/data/transcript.py @@ -0,0 +1,755 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Utilities for working with transcripts.""" + +import collections +import copy +import dataclasses +import functools +import sys +from typing import Any + +from alphagenome.data import genome +import pandas as pd + + +MITOCHONDRIAL_CHROMS = ['M', 'chrM', 'MT'] + + +@dataclasses.dataclass(frozen=True) +class Transcript: + """Represents transcript object containing attributes from a GTF file. + + A transcript is a region of DNA that encodes a single RNA molecule. The + Transcript dataclass contains attributes that describe the structure and + content of a transcript, namely: + + Attributes: + exons: A list of `genome.Interval`s representing exons within transcript. + Each `Transcript` must contain exons. + cds: An optional list of `genome.Interval`s representing coding sequences + (CDS) within a transcript. CDS include start codon and exclude top codon. + start_codon: An optional list of `genome.Interval`s representing a single + start codon. Start codons can be split by introns, therefore might have + more than one genomic interval. Some coding transcripts are missing start + codons, e.g., ENST00000455638.6. + stop_codon: An optional list of `genome.Interval`s representing a single + stop codon. Stop codon can be split by introns, therefore might have more + than one genomic interval. Some transcripts coding transcripts are missing + stop codons, e.g., ENST00000574051.5. + transcript_id: An optional string representing a transcript id. + gene_id: An optional string representing a protein id. + protein_id: An optional string representing a protein id which is encoded by + the transcript. + uniprot_id: An optional UniprotKB-AC id string. + info: a dictionary of additional information on a transcript. + chromosome: chromosome name on which the transcript is present. Must be the + same for all genomic intervals within a transcript. + is_mitochondrial: whether the transcript is on the mitochondria chromosome. + strand_int: strand on which transcript is present as an int. -1 for negative + strand +1 for positive strand + strand: strand (positive or negative) on which transcript is present. Must + be the same for all genomic intervals within a transcript. + is_negative_strand: a boolean value indicating whether transcript is on + negative strand. + is_positive_strand: a boolean value indicating whether transcript is on + positive strand. + transcript_interval: a genomic interval of a transcript. + selenocysteines: a list of intervals where selenocysteines are present + within a transcript. + selenocysteine_pos_in_protein: a list of 0-based positions of + selenocysteines in protein encoded by the transcript. + is_coding: a value indicating whether a `Transcript` contains coding + sequences (CDS) or not. + cds_including_stop_codon: a list of CDS and stop_codon intervals with + overlapping intervals merged. + utr5: A list of genomic intervals representing 5' untranslated region. 5' + UTR doesn't include start codon. There may be no 5' UTR present in the + transcript or UTRs can be split by introns. + utr3: A list of genomic intervals representing 3' untranslated region. 3' + UTR doesn't include stop codon. There may be no 3' UTR present in the + transcript or UTRs can be split by introns. + splice_regions: a list of splice regions within a transcript. + splice_donor_sites: a list of splice donor sites. Commonly, the RNA sequence + that is removed ends with AG at its 3′ end. + splice_acceptor_sites: a list of splice acceptor sites. Commonly, the RNA + sequence that is removed begins with the dinucleotide GU at its 5′ end. + splice_donors: a list of splice donors. The first nucleotide of the intron + (0-based). + splice_acceptors: a list of splice acceptors. The last nucleotide of the + intron (0-based). + """ + + exons: list[genome.Interval] + cds: list[genome.Interval] | None = None + start_codon: list[genome.Interval] | None = None + stop_codon: list[genome.Interval] | None = None + transcript_id: str | None = dataclasses.field(compare=False, default=None) + gene_id: str | None = dataclasses.field(compare=False, default=None) + protein_id: str | None = dataclasses.field(compare=False, default=None) + uniprot_id: str | None = dataclasses.field(compare=False, default=None) + info: dict[str, Any] = dataclasses.field( + default_factory=dict, repr=False, compare=False, hash=False + ) + + def offset_in_cds(self, genome_position: int) -> int | None: + """Return the offset within the set of CDS exons of `genome_position`. + + Args: + genome_position: A coordinate presumed to be on the same chromosome as + this transcript. + + Returns: + The offset of `genome_position` from the start of the CDS, accounting for + strand, or None, if `genome_position` does not overlap the CDS. + """ + offset = 0 + for cds_exon in self.cds_including_stop_codon[:: self.strand_int]: + if cds_exon.start <= genome_position < cds_exon.end: + if self.is_positive_strand: + return offset + genome_position - cds_exon.start + else: + return offset + cds_exon.end - genome_position - 1 + offset += cds_exon.width + return None + + @property + def chromosome(self) -> str: + """Gets the chromosome name on which the transcript is present. + + Returns: + The chromosome name. + """ + return self.exons[0].chromosome + + @property + def is_mitochondrial(self) -> bool: + """Gets whether the transcript is on the mitochondria chromosome. + + Returns: + True if the transcript is on the mitochondria chromosome, False otherwise. + """ + return self.chromosome in MITOCHONDRIAL_CHROMS + + # TODO: b/376466056 - Unify strand representations. + @property + def strand_int(self) -> int: + """Gets the strand as an integer. + + Returns: + -1 for negative strand, +1 for positive strand, 0 for unknown. + """ + return {genome.STRAND_NEGATIVE: -1, genome.STRAND_POSITIVE: +1}.get( + self.strand, 0 + ) + + @property + def strand(self) -> str: + """Gets the strand on which the transcript is present. + + Returns: + The strand (positive or negative). + """ + return self.exons[0].strand + + @property + def is_positive_strand(self) -> bool: + return self.strand == genome.STRAND_POSITIVE + + @property + def is_negative_strand(self) -> bool: + return self.strand == genome.STRAND_NEGATIVE + + @functools.cached_property + def transcript_interval(self) -> genome.Interval: + """Gets a genomic interval of a transcript. + + Returns: + A genomic interval of a transcript where transcript start is equal to the + first exon start and transcript end is equal to the last exon end. + """ + return genome.Interval( + self.chromosome, + self.exons[0].start, + self.exons[-1].end, + strand=self.strand, + ) + + @functools.cached_property + def selenocysteines(self) -> list[genome.Interval]: + if 'selenocysteines' not in self.info: + return [] + return self.info['selenocysteines'] + + @functools.cached_property + def selenocysteine_pos_in_protein(self) -> list[int]: + # 0-based + selenocystein_pos = [] + for selenocysteine in self.selenocysteines: + if selenocysteine.info['cds_offset'] is None: + raise ValueError( + 'Transcript cannot be translated due to ' + 'bad input data of selenocysteines.' + ) + selenocystein_pos.append(selenocysteine.info['cds_offset'] // 3) + return selenocystein_pos + + @functools.cached_property + def introns(self) -> list[genome.Junction]: + """Get a list of intron intervals. + + Returns: + A list of genomic intervals representing introns, where a single intron + junction is an interval spanning between two adjacent exons. + """ + intron_intervals = [] + for i in range(1, len(self.exons)): + intron_intervals.append( + genome.Junction( + self.chromosome, + self.exons[i - 1].end, + self.exons[i].start, + strand=self.strand, + ) + ) + return intron_intervals + + @functools.cached_property + def is_coding(self) -> bool: + return bool(self.cds) + + @functools.cached_property + def cds_including_stop_codon(self) -> list[genome.Interval]: + """Obtains coding sequences including stop codon. + + By default gtf files exclude stop codons from CDS while gff include stop + codons within coding sequences. + """ + if not self.is_coding: + return [] + return genome.merge_overlapping_intervals( + self.cds + (self.stop_codon or []) + ) + + @functools.cached_property + def utr5(self) -> list[genome.Interval]: + return self._get_utr(self.strand != genome.STRAND_NEGATIVE) + + @functools.cached_property + def utr3(self) -> list[genome.Interval]: + return self._get_utr(self.strand == genome.STRAND_NEGATIVE) + + def _get_utr(self, before: bool) -> list[genome.Interval]: + """Gets the UTRs located before/after first/last coding sequence.""" + utrs = [] + if not self.cds: + return utrs + + merged_cds_stop = genome.merge_overlapping_intervals( + self.cds + (self.stop_codon or []) + ) + + if before: + start, end = 0, merged_cds_stop[0].start + else: + start, end = merged_cds_stop[-1].end, sys.maxsize + + valid_interval = genome.Interval( + self.chromosome, start, end, strand=self.strand + ) + + for exon in filter(lambda x: x.overlaps(valid_interval), self.exons): + intersect = valid_interval.intersect(exon) + if intersect: + utrs.append(intersect) + return utrs + + # TODO: b/376465275 - deal with cases where exon shorter than 3 bp length + # TODO: b/376465275 - deal with cases where intron is shorther than 4 bp + @functools.cached_property + def splice_regions(self) -> list[genome.Interval]: + """Obtains and returns splice regions of a transcript. + + splice region (SO:0001630) is "within 1-3 bases of the exon or 3-8 bases of + the intron. + """ + if not self.introns: + return [] + splice_regions = [] + + for intron, prev_exon, next_exon in zip( + self.introns, self.exons[:-1], self.exons[1:] + ): + if prev_exon.width > 2: + splice_regions.append( + genome.Interval( + self.chromosome, + intron.start - 3, + intron.start, + strand=self.strand, + ) + ) + + if next_exon.width > 2: + splice_regions.append( + genome.Interval( + self.chromosome, intron.end, intron.end + 3, strand=self.strand + ) + ) + + if intron.width > 4: + splice_regions.append( + genome.Interval( + self.chromosome, + intron.start + 2, + min(intron.start + 8, intron.end - 2), + strand=self.strand, + ) + ) + splice_regions.append( + genome.Interval( + self.chromosome, + max(intron.end - 8, intron.start + 2), + intron.end - 2, + strand=self.strand, + ) + ) + return genome.merge_overlapping_intervals(splice_regions) + + @functools.cached_property + def splice_donor_sites(self) -> list[genome.Interval]: + return self._get_splice_sites(False, intron_overhang=2, exon_overhang=0) + + @functools.cached_property + def splice_acceptor_sites(self) -> list[genome.Interval]: + return self._get_splice_sites(True, intron_overhang=2, exon_overhang=0) + + @functools.cached_property + def splice_donors(self) -> list[genome.Interval]: + # To be consistent with the splice sites defined by intron start and end, + # the overhang for donor and acceptor are different. + return self._get_splice_sites( # pytype: disable=bad-return-type # enable-cached-property + False, intron_overhang=1, exon_overhang=0 + ) + + @functools.cached_property + def splice_acceptors(self) -> list[genome.Interval]: + return self._get_splice_sites( # pytype: disable=bad-return-type # enable-cached-property + True, intron_overhang=0, exon_overhang=1 + ) + + # TODO: b/376465275 - deal with cases where intron shorter than 4 bp length. + def _get_splice_sites( + self, acceptor: bool, intron_overhang: int, exon_overhang: int + ) -> list[genome.Interval]: + """Obtains splice acceptor/donor intervals. + + https://www.nature.com/scitable/topicpage/rna-splicing-introns-exons-and-spliceosome-12375/#:~:text=Introns%20are%20removed%20from%20primary,AG%20at%20its%203%E2%80%B2%20end + Introns are removed from primary transcripts by cleavage at conserved + sequences called splice sites. These sites are found at the 5′ and 3′ + ends of introns. Most commonly, the RNA sequence that is removed begins with + the dinucleotide GU at its 5′ end, and ends with AG at its 3′ end. + + if - strand, the end two bases of intron are splice donor bases, + if +, then the start two bases. + + Args: + acceptor: value indicating whether splice acceptor or donor should be + obtained. + intron_overhang: bases into the intron. + exon_overhang: bases into the exon. + + Returns: + List of intervals of splice acceptor/donor sites. + """ + if not self.introns: + return [] + splice_sites = [] + for intron in self.introns: # pylint:disable=not-an-iterable + if intron.width < 4: + continue + if self.is_negative_strand != acceptor: + splice = genome.Interval( + self.chromosome, + intron.end - intron_overhang, + intron.end + exon_overhang, + strand=self.strand, + ) + else: + splice = genome.Interval( + self.chromosome, + intron.start - exon_overhang, + intron.start + intron_overhang, + strand=self.strand, + ) + splice_sites.append(splice) + return splice_sites + + def __post_init__(self): + if not self.exons: + raise ValueError('Transcript must contain at least one exon.') + + for exon in self.exons: + if exon.strand != self.strand or exon.chromosome != self.chromosome: + raise ValueError( + 'Transcript intervals are inconsistent. All intervals of a ' + 'transcript should have same strand and chromosome.' + ) + + if self.cds: + # first exons can be part of UTR. Searching for the first coding exon. + index = 0 + for exon in self.exons: + # if overlaps, will check whether exon contains it in the latter loop. + if exon.overlaps(self.cds[0]): + break + index += 1 + if index == len(self.exons) or len(self.cds) + index > len(self.exons): + raise ValueError( + 'The number of coding exons must be the same as CDS ' + 'and CDS cannot be outside of the exon intervals.' + ) + + # checks each subsequent exon is coding + for seq, exon in zip(self.cds, self.exons[index : index + len(self.cds)]): + if not exon.contains(seq): + raise ValueError( + 'The number of coding exons must be the same as CDS ' + 'and CDS cannot be outside of the exon intervals.' + ) + if seq.strand != self.strand: + raise ValueError( + 'Transcript intervals are inconsistent. All intervals of a ' + 'transcript should have same strand and chromosome.' + ) + + for sc in self.selenocysteines: # pylint:disable=not-an-iterable + sc_pos = sc.end - 1 if sc.negative_strand else sc.start + sc.info['cds_offset'] = self.offset_in_cds(sc_pos) + + def __len__(self): + return self.transcript_interval.width + + @classmethod + def from_gtf_df( + cls, + transcript_df: pd.DataFrame, + ignore_info: bool = True, + fix_truncation: bool = False, + ) -> 'Transcript': + """Initialises Trancript object from a given transcript dataframe. + + Args: + transcript_df: Dataframe representing a transcript. The dataframe must + contain a single transcript. + ignore_info: If True, other columns in transcript_df won't be added to the + info field, except transcript_type and selenocysteines. + fix_truncation: Whether or not apply truncation fixation to CDS. + + Returns: + Initialised Transcript object. + Raises: + ValueError: if the dataframe provided is invalid (no or more than one + transcript, transcript has inconsistent strand or chromosome, + transcript doesn't contain exons, CDS are not within exons, etc.) + """ + if transcript_df.empty: + raise ValueError('transcript_df is empty') + if 'Feature' not in transcript_df: + raise ValueError('transcript_df must contain Feature column.') + + if ( + 'transcript_id' in transcript_df + and len(transcript_df.transcript_id.unique()) > 1 + ): + raise ValueError('transcript_df should only contain a single transcript.') + + # Convert rows to genome.Interval list. + transcript_df = transcript_df.sort_values(by='Start') + intervals_per_feature = collections.defaultdict(list) + exon_row = None + for _, row in transcript_df.iterrows(): + interval = genome.Interval.from_pyranges_dict( + row, ignore_info=True + ) # pytype: disable=wrong-arg-types # pandas-drop-duplicates-overloads + if row.Feature in ['CDS', 'stop_codon']: + interval.info['frame'] = int(row.Frame) + if exon_row is None and row.Feature == 'exon': + exon_row = row + intervals_per_feature[row.Feature].append(interval) + + if exon_row is None: + raise ValueError('transcript_df must contain at least one exon') + + # Seed info. + if ignore_info: + info = {} + else: + skip = list(genome.PYRANGES_INTERVAL_COLUMNS) + [ + 'Feature', + 'transcript_type', + 'Selenocysteines', + 'gene_type', + ] + info = {k: v for k, v in exon_row.items() if k not in skip} + + if 'transcript_type' in exon_row: + info['transcript_type'] = exon_row['transcript_type'] + + if 'Selenocysteine' in intervals_per_feature: + info['selenocysteines'] = intervals_per_feature['Selenocysteine'] + + if 'gene_type' in exon_row: + info['gene_type'] = exon_row['gene_type'] + + transcript_obj = cls( + exons=intervals_per_feature['exon'], + cds=intervals_per_feature.get('CDS', None), + start_codon=intervals_per_feature.get('start_codon', None), + stop_codon=intervals_per_feature.get('stop_codon', None), + transcript_id=exon_row.get('transcript_id', None), + gene_id=exon_row.get('gene_id', None), + protein_id=exon_row.get('protein_id', None), + uniprot_id=exon_row.get('uniprot_id', None), + info=info, + ) + if fix_truncation: + return Transcript.fix_truncation(transcript_obj) + return transcript_obj + + @classmethod + def fix_truncation(cls, transcript: 'Transcript') -> 'Transcript': + """Fixes CDS start and stop positions to be within coding frame. + + Args: + transcript: a transcript to fix. + + Returns: + New transcript with set start/stop codons and fixed CDS if the total + length of CDS is > 6. Returns a copy of original transcript otherwise. + """ + cds = sorted( + (transcript.cds or []) + (transcript.stop_codon or []), + key=lambda x: x.start, + ) + cdna_len = sum(seq.width for seq in cds) + if cdna_len < 7: + return copy.deepcopy(transcript) + + positive_strand = transcript.is_positive_strand + try: + frame = cds[0 if positive_strand else -1].info['frame'] + except KeyError as key_error: + raise KeyError( + 'CDS intervals are missing frame information,' + ' truncations cannot be deduced.' + ) from key_error + frame_last = (cdna_len - frame) % 3 + + cds, start_codon = cls._fix_coding_frame( + five_prime=True, + beginning=positive_strand, + frame=frame, + cds_transcript=cds, + ) + + cds, stop_codon = cls._fix_coding_frame( + five_prime=False, + beginning=not positive_strand, + frame=frame_last, + cds_transcript=cds, + ) + + return cls( + exons=[exon.copy() for exon in transcript.exons], + cds=cds, + start_codon=start_codon, + stop_codon=stop_codon, + transcript_id=transcript.transcript_id, + gene_id=transcript.gene_id, + protein_id=transcript.protein_id, + uniprot_id=transcript.uniprot_id, + info={**transcript.info, 'truncation_fixed': True}, + ) + + @classmethod + def _fix_coding_frame( + cls, + five_prime: bool, + beginning: bool, + frame: int, + cds_transcript: list[genome.Interval], + ) -> tuple[list[genome.Interval], list[genome.Interval]]: + """Fixes coding frame for a transcript and returns a new start/stop codon. + + Args: + five_prime: a value indicating whether a 5' end to be fixed. + beginning: indicates whether the beginning or the end of the transript to + be fixed. + frame: a current coding frame (0, 1, 2). For fixing 5' end, it is a + position at which the first full codon starts within a cds. For fixing + 3' end, this indicates where the last full codon ends within a cds. + cds_transcript: a list of coding sequence intervals. + + Returns: + A pair of lists where the first item is a list of CDS with fixed coding + frame and the second item is a start/stop codon. + """ + + def shorten_intervals( + cds_transcript: list[genome.Interval], + beginning: bool, + frame: int, + ) -> list[genome.Interval]: + cds = [interval.copy() for interval in cds_transcript] + index = 0 if beginning else -1 + while frame > 0: + if cds[index].width > frame: + if beginning: + cds[index].start += frame + else: + cds[index].end -= frame + frame = 0 + else: + frame -= cds[index].width + del cds[index] + return cds + + # Fix CDS coding frame. + fixed_cds = shorten_intervals(cds_transcript, beginning, frame) + + # Set codon. + codon_bases = 3 + fixed_codon = [] + for seq in fixed_cds if beginning else fixed_cds[::-1]: + if codon_bases == 0: + break + start, end = seq.start, seq.end + if seq.width > codon_bases and beginning: + end = seq.start + codon_bases + elif seq.width > codon_bases: + start = seq.end - codon_bases + interval = genome.Interval(seq.chromosome, start, end, strand=seq.strand) + fixed_codon.append(interval) + codon_bases -= interval.width + + if five_prime: + fixed_cds[0 if beginning else -1].info['frame'] = 0 + else: + # Remove newly set stop interval from cds. + fixed_cds = shorten_intervals(fixed_cds, beginning, 3) + return fixed_cds, fixed_codon + + +class _RangeExtractor: + """Range extractor from gtf df.""" + + def __init__(self, df: pd.DataFrame): + self._df_start_end = { + chromosome: (dfc, dfc['Start'].values, dfc['End'].values) + for chromosome, dfc in df.groupby('Chromosome') + } + self._df_empty = df.iloc[:0] + + def extract(self, interval: genome.Interval) -> pd.DataFrame: + """Finds all rows that contain the input Interval. + + Args: + interval: query Interval + + Returns: + a dataframe containing genome intervals that contain the query Interval + """ + if interval.chromosome not in self._df_start_end: + return self._df_empty + else: + dfc, start, end = self._df_start_end[interval.chromosome] + start_contained = (interval.start <= start) & (start <= interval.end) + end_contained = (interval.start <= end) & (end <= interval.end) + interval_contained = (interval.start >= start) & (end >= interval.end) + return dfc[start_contained | end_contained | interval_contained] + + +class TranscriptExtractor: + """Transcript extractor from gtf.""" + + def __init__(self, gtf_df: pd.DataFrame) -> None: + """Init. + + Args: + gtf_df: pd.DataFrame of GENCODE GTF entries containing transcript + annotation. Must contain columns 'Chromosome', 'Start', 'End', 'Strand', + 'Feature', and 'transcript_id'. + """ + self._transcript_extractor = _RangeExtractor( + gtf_df[gtf_df.Feature == 'transcript'][ + ['Chromosome', 'Start', 'End', 'Strand', 'transcript_id'] + ] + ) + self._transcript_indexed_gtf = gtf_df.set_index('transcript_id') + self._transcript_from_id_cache = None + + def cache_transcripts(self) -> None: + """Speed up extract() by converting GTF to dictionary of Transcripts. + + This may take ca 11 minutes on the full human genome GTF of 84k protein + coding transcripts and 15 s on chr22 (1.5k transcripts). + + Running cache_transcripts() will speed up .extract() by ca 5-10x: + (11 ms vs 65 ms tested on chr22, or 15 ms vs 160 ms on whole genome). + """ + self._transcript_from_id_cache = self._transcripts_from_gtf( + self._transcript_indexed_gtf.reset_index() + ) + + def _transcripts_from_gtf( + self, + gtf_df: pd.DataFrame, + ) -> dict[str, Transcript]: + return ( + { # pytype: disable=bad-return-type # pandas-drop-duplicates-overloads + transcript_id: ( + Transcript.fix_truncation( + Transcript.from_gtf_df(gtf_subset, ignore_info=False) + ) + ) + for transcript_id, gtf_subset in gtf_df.groupby('transcript_id') + } + ) + + def extract(self, interval: genome.Interval) -> list[Transcript]: + """Extract transcripts overlapping an interval. + + Args: + interval: Interval used to overlap with transcripts. + + Returns: + List of transcript overlapping `interval`. + """ + gtf_df_within_interval = self._transcript_extractor.extract(interval) + if gtf_df_within_interval.empty: + return [] + + transcript_ids = gtf_df_within_interval.transcript_id.dropna().unique() + if self._transcript_from_id_cache is not None: + return [ + self._transcript_from_id_cache[transcript_id] + for transcript_id in transcript_ids + ] + else: + transcript_gtfs = self._transcript_indexed_gtf.loc[ + transcript_ids + ].reset_index() + return list(self._transcripts_from_gtf(transcript_gtfs).values()) diff --git a/alphagenome/source/src/alphagenome/interpretation/__init__.py b/alphagenome/source/src/alphagenome/interpretation/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..524e7a78c331fc8eec07ec2956dc7630121e716b --- /dev/null +++ b/alphagenome/source/src/alphagenome/interpretation/__init__.py @@ -0,0 +1,15 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Library of tools for interpreting sequences and model predictions.""" diff --git a/alphagenome/source/src/alphagenome/interpretation/ism.py b/alphagenome/source/src/alphagenome/interpretation/ism.py new file mode 100644 index 0000000000000000000000000000000000000000..1c58948011ba5295d3810c062fe2a9f394ab4998 --- /dev/null +++ b/alphagenome/source/src/alphagenome/interpretation/ism.py @@ -0,0 +1,150 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""In-silico mutagenesis (ISM) functions for sequence interpretation.""" + +from collections.abc import Sequence + +from alphagenome.data import genome +import numpy as np + + +def ism_variants( + interval: genome.Interval, + sequence: str, + vocabulary: str = 'ACGT', + skip_n: bool = False, +) -> list[genome.Variant]: + """Create a list of all possible single nucleotide variants for an interval. + + Args: + interval: Interval for which to generate the variants. + sequence: Sequence extracted from the reference genome at the `interval`. + Needs to have the same length as `interval.width`. + vocabulary: Vocabulary of possible alternative bases contained in + `sequence`. + skip_n: If True, skip the N bases. + + Returns: + List of all possible single nucleotide variants for a genomic interval. + """ + if len(sequence) != interval.width: + raise ValueError('Sequence must have the same length as interval.') + variants = [] + for position in range(interval.width): + reference_base = sequence[position] + if skip_n and reference_base == 'N': + continue + for alternative_base in vocabulary: + if reference_base == alternative_base: + continue + variants.append( + genome.Variant( + chromosome=interval.chromosome, + reference_bases=reference_base, + alternate_bases=alternative_base, + position=interval.start + position + 1, + ) + ) + return variants + + +def ism_matrix( + variant_scores: Sequence[float], + variants: Sequence[genome.Variant], + interval: genome.Interval | None = None, + multiply_by_sequence: bool = True, + vocabulary: str = 'ACGT', + require_fully_filled: bool = True, +) -> np.ndarray: + """Construct the ISM (position, base) matrix from individual ISM scores. + + This function returns the relative effect of the variants compared to the + per-position average: score[position, base] - mean(score[position, :]]). + + Args: + variant_scores: Variant effect scores corresponding to the variants. These + could be obtained from the score_variants() output summarised to a single + scalar. Summarisation could be for example be obtained by selecting a + specific variant scorer output and extract a specific value from the + (variant, track) matrix. + variants: Sequence of variants used to transform into the ISM matrix. + interval: Interval for which to get the contribution scores. All variants + need to be contained within that interval. If None, it will be + automatically inferred from variants. + multiply_by_sequence: If True, only return non-zero values at + one-hot-encoded reference genome sequence bases. + vocabulary: Vocabulary of possible alternative bases contained in + `sequence`. The order determines the column order of the returned matrix. + require_fully_filled: If True, raise an error if not all positions are + covered by variants. + + Returns: + Matrix of shape (interval.width, 4) containing variant scores. + """ + if len(variants) != len(variant_scores): + raise ValueError( + 'Variants and variant_scores need to have the same length.' + ) + if interval is None: + interval = genome.Interval( + chromosome=variants[0].chromosome, + start=min(variant.start for variant in variants), + end=max(variant.end for variant in variants), + ) + scores = np.zeros((interval.width, 4), dtype=np.float32) + filled = np.zeros((interval.width, 4), dtype=bool) + base_index = {base: i for i, base in enumerate(vocabulary)} + + for variant, score in zip(variants, variant_scores, strict=True): + if len(variant.alternate_bases) != 1 or len(variant.reference_bases) != 1: + # Only looking for single nucleotide variants. + continue + if not interval.contains(variant.reference_interval): + continue + position = variant.start - interval.start + scores[position, base_index[variant.alternate_bases]] = score + filled[position, base_index[variant.alternate_bases]] = True + + # Check that all positions were covered by variants. + if (filled.sum(axis=-1) == 0).any() and require_fully_filled: + missing_positions = list( + np.where(filled.sum(axis=-1) == 0)[0] + interval.start + 1 + ) + raise ValueError( + 'No variants were found for the (1-based) positions on chromosome ' + f'{interval.chromosome}: {missing_positions}' + ) + elif (filled.sum(axis=-1) == 4).any(): + full_positions = list( + np.where(filled.sum(axis=-1) == 4)[0] + interval.start + 1 + ) + raise ValueError( + 'Some variants were found with 4 different alternative bases for the' + ' position instead of 3. List of positions on chromosome ' + f'{interval.chromosome}: {full_positions}' + ) + elif (filled.sum(axis=-1) != 3).any() and require_fully_filled: + missing_positions = list( + np.where(filled.sum(axis=-1) != 3)[0] + interval.start + 1 + ) + raise ValueError( + 'Some variants were found with only 1 or 2 alternative bases for the' + ' position instead of 3. List of positions on chromosome ' + f'{interval.chromosome}: {missing_positions}' + ) + + scores -= np.sum(scores, axis=-1, keepdims=True) / (len(vocabulary) - 1) + if multiply_by_sequence: + scores = scores * (~filled) + return scores diff --git a/alphagenome/source/src/alphagenome/interpretation/ism_test.py b/alphagenome/source/src/alphagenome/interpretation/ism_test.py new file mode 100644 index 0000000000000000000000000000000000000000..ba1877a09c2b96ad9d08295d0ee4cf2522690b34 --- /dev/null +++ b/alphagenome/source/src/alphagenome/interpretation/ism_test.py @@ -0,0 +1,174 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from absl.testing import absltest +from absl.testing import parameterized +from alphagenome.data import genome +from alphagenome.interpretation import ism +import numpy as np + + +class IsmTest(parameterized.TestCase): + + def test_ism_variants(self): + sequence = 'ACG' + interval = genome.Interval('chr1', 0, len(sequence)) + variants = ism.ism_variants(interval, sequence=sequence) + expected = [ + genome.Variant('chr1', 1, 'A', 'C'), + genome.Variant('chr1', 1, 'A', 'G'), + genome.Variant('chr1', 1, 'A', 'T'), + genome.Variant('chr1', 2, 'C', 'A'), + genome.Variant('chr1', 2, 'C', 'G'), + genome.Variant('chr1', 2, 'C', 'T'), + genome.Variant('chr1', 3, 'G', 'A'), + genome.Variant('chr1', 3, 'G', 'C'), + genome.Variant('chr1', 3, 'G', 'T'), + ] + self.assertEqual(variants, expected) + + def test_ism_variants_length_mismatch(self): + sequence = 'ACG' + interval = genome.Interval('chr1', 0, 2) + with self.assertRaises(ValueError): + ism.ism_variants(interval, sequence=sequence) + + @parameterized.parameters([True, False]) + def test_ism_variants_with_interval_with_n(self, skip_n): + sequence = 'NCG' + interval = genome.Interval('chr1', 0, len(sequence)) + variants = ism.ism_variants(interval, sequence=sequence, skip_n=skip_n) + expected_n = [ + genome.Variant('chr1', 1, 'N', 'A'), + genome.Variant('chr1', 1, 'N', 'C'), + genome.Variant('chr1', 1, 'N', 'G'), + genome.Variant('chr1', 1, 'N', 'T'), + ] + expected = [ + genome.Variant('chr1', 2, 'C', 'A'), + genome.Variant('chr1', 2, 'C', 'G'), + genome.Variant('chr1', 2, 'C', 'T'), + genome.Variant('chr1', 3, 'G', 'A'), + genome.Variant('chr1', 3, 'G', 'C'), + genome.Variant('chr1', 3, 'G', 'T'), + ] + self.assertEqual(variants, expected if skip_n else expected_n + expected) + + @parameterized.parameters(dict(multiply_by_sequence=[True, False])) + def test_ism_scores(self, multiply_by_sequence): + # 'ACG' + variants = [ + genome.Variant('chr1', 1, 'A', 'C'), + genome.Variant('chr1', 1, 'A', 'G'), + genome.Variant('chr1', 1, 'A', 'T'), + genome.Variant('chr1', 2, 'C', 'A'), + genome.Variant('chr1', 2, 'C', 'G'), + genome.Variant('chr1', 2, 'C', 'T'), + genome.Variant('chr1', 3, 'G', 'A'), + genome.Variant('chr1', 3, 'G', 'C'), + genome.Variant('chr1', 3, 'G', 'T'), + ] + ism_matrix = ism.ism_matrix( + variant_scores=[1] * len(variants), + variants=variants, + multiply_by_sequence=multiply_by_sequence, + ) + if multiply_by_sequence: + expect = np.array( + [ + [-1, 0, 0, 0], + [0, -1, 0, 0], + [0, 0, -1, 0], + ], + dtype=np.float32, + ) + np.testing.assert_array_equal(ism_matrix, expect) + else: + expect = ( + np.array( + [ + [0, 1, 1, 1], + [1, 0, 1, 1], + [1, 1, 0, 1], + ], + dtype=np.float32, + ) + - 1 + ) + np.testing.assert_array_equal(ism_matrix, expect) + + def test_ism_scores_subset(self): + variants = [ + genome.Variant('chr1', 1, 'A', 'C'), + genome.Variant('chr1', 1, 'A', 'G'), + genome.Variant('chr1', 1, 'A', 'T'), + genome.Variant('chr1', 2, 'C', 'A'), + genome.Variant('chr1', 2, 'C', 'G'), + genome.Variant('chr1', 2, 'C', 'T'), + genome.Variant('chr1', 3, 'G', 'A'), + genome.Variant('chr1', 3, 'G', 'C'), + genome.Variant('chr1', 3, 'G', 'T'), + ] + ism_matrix = ism.ism_matrix( + variant_scores=[1] * len(variants), + variants=variants, + interval=genome.Interval('chr1', 1, 3), + multiply_by_sequence=False, + ) + + expect = ( + np.array( + [ + [1, 0, 1, 1], + [1, 1, 0, 1], + ], + dtype=np.float32, + ) + - 1 + ) + np.testing.assert_array_equal(ism_matrix, expect) + + def test_ism_scores_missing_variants(self): + variants = [ + genome.Variant('chr1', 1, 'A', 'C'), + genome.Variant('chr1', 1, 'A', 'G'), + genome.Variant('chr1', 1, 'A', 'T'), + genome.Variant('chr1', 2, 'C', 'A'), + genome.Variant('chr1', 2, 'C', 'G'), + genome.Variant('chr1', 2, 'C', 'T'), + genome.Variant('chr1', 3, 'G', 'A'), + genome.Variant('chr1', 3, 'G', 'C'), + genome.Variant('chr1', 3, 'G', 'T'), + ] + with self.assertRaises(ValueError): + # Out of bounds. + ism.ism_matrix( + variant_scores=[1] * len(variants), + variants=variants, + interval=genome.Interval('chr1', 1, 4), + multiply_by_sequence=False, + ) + + output = ism.ism_matrix( + variant_scores=[1] * len(variants), + variants=variants, + interval=genome.Interval('chr1', 1, 4), + multiply_by_sequence=False, + require_fully_filled=False, + ) + np.testing.assert_array_equal(output[-1], 0) + + +if __name__ == '__main__': + absltest.main() diff --git a/alphagenome/source/src/alphagenome/models/__init__.py b/alphagenome/source/src/alphagenome/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6f9e182d809b3819f44a46f61244aaca63ce7371 --- /dev/null +++ b/alphagenome/source/src/alphagenome/models/__init__.py @@ -0,0 +1,15 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Models library for interacting with AlphaGenome models.""" diff --git a/alphagenome/source/src/alphagenome/models/dna_client.py b/alphagenome/source/src/alphagenome/models/dna_client.py new file mode 100644 index 0000000000000000000000000000000000000000..2e7b18fdc7d72f60e7a66fcf4e52573266ff1bb9 --- /dev/null +++ b/alphagenome/source/src/alphagenome/models/dna_client.py @@ -0,0 +1,925 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Client implementation for interacting with a DNA model server.""" + +from collections.abc import Container, Iterable, Iterator, Mapping, Sequence +import concurrent.futures +import enum +import functools +import random +import time +from typing import TypeVar + +from alphagenome import tensor_utils +from alphagenome.data import genome +from alphagenome.data import junction_data +from alphagenome.data import ontology +from alphagenome.data import track_data +from alphagenome.models import dna_model +from alphagenome.models import dna_output +from alphagenome.models import interval_scorers as interval_scorers_lib +from alphagenome.models import junction_data_utils +from alphagenome.models import track_data_utils +from alphagenome.models import variant_scorers as variant_scorers_lib +from alphagenome.protos import dna_model_pb2 +from alphagenome.protos import dna_model_service_pb2 +from alphagenome.protos import dna_model_service_pb2_grpc +from alphagenome.protos import tensor_pb2 +import anndata +import grpc +import numpy as np +import pandas as pd +import tqdm.auto + + +# Supported DNA sequence lengths. +SEQUENCE_LENGTH_2KB = 2**11 # 2_048 +SEQUENCE_LENGTH_16KB = 2**14 # 16_384 +SEQUENCE_LENGTH_100KB = 2**17 # 131_072 +SEQUENCE_LENGTH_500KB = 2**19 # 524_288 +SEQUENCE_LENGTH_1MB = 2**20 # 1_048_576 + +SUPPORTED_SEQUENCE_LENGTHS = { + 'SEQUENCE_LENGTH_2KB': SEQUENCE_LENGTH_2KB, + 'SEQUENCE_LENGTH_16KB': SEQUENCE_LENGTH_16KB, + 'SEQUENCE_LENGTH_100KB': SEQUENCE_LENGTH_100KB, + 'SEQUENCE_LENGTH_500KB': SEQUENCE_LENGTH_500KB, + 'SEQUENCE_LENGTH_1MB': SEQUENCE_LENGTH_1MB, +} + +# Maximum width of an in silico mutagenesis (ISM) interval request. +# This controls the behavior of `score_ism_variants` in the client. +# When a user inputs an ISM interval that is wider than this value, +# the client will automatically split the interval into chunks of this width. +MAX_ISM_INTERVAL_WIDTH = 10 + +# Maximum number of variant scorers per request. +MAX_VARIANT_SCORERS_PER_REQUEST = 20 + +_VALID_SEQUENCE_CHARACTERS = frozenset('ACGTN') + +RetryableFunction = TypeVar('RetryableFunction') + + +def retry_rpc( + function: RetryableFunction, + *, + max_attempts: int = 5, + initial_backoff: float = 1.25, + backoff_multiplier: float = 1.5, + retry_status_codes: Container[grpc.StatusCode] = frozenset( + [grpc.StatusCode.RESOURCE_EXHAUSTED, grpc.StatusCode.UNAVAILABLE] + ), + jitter: float = 0.2, +) -> RetryableFunction: + """Decorator that retries when an RPC fails. + + gRPC currently doesn't support retries for streaming RPCs. This decorator is + an implementation of the retry logic from https://grpc.io/docs/guides/retry. + + Arguments: + function: Callable to retry. + max_attempts: Maximum number of attempts to make. + initial_backoff: Initial backoff time in seconds. + backoff_multiplier: Backoff multiplier to apply on each retry. + retry_status_codes: Set of status codes to retry on. + jitter: Jitter to apply to the backoff time. For example, a jitter of 0.2 + and an initial backoff of 1.5 will result in a backoff time between 1.2 + and 1.8 seconds. + + Returns: + A decorator that retries the function on retryable RPC errors. + """ + if backoff_multiplier < 1.0: + raise ValueError('Backoff multiplier must be >= 1.0.') + + @functools.wraps(function) + def wrapper(*args, **kwargs): + attempt = 0 + current_backoff = initial_backoff + ( + random.uniform(-jitter, jitter) * initial_backoff + ) + while True: + try: + attempt += 1 + return function(*args, **kwargs) + except grpc.RpcError as e: + error_code = e.code() # pytype: disable=attribute-error + if error_code not in retry_status_codes or attempt >= max_attempts: + raise e + time.sleep(current_backoff) + current_backoff *= backoff_multiplier + + return wrapper + + +@enum.unique +class ModelVersion(enum.Enum): + """Enumeration of all available model versions. + + A fold is a part of the genome that is held out from training, and is used for + model validation. + + Folds are numbered from 0 to 3, and represent disjoint subsets of the genome. + + A model version that is designated FOLD_#, where # is an integer from 0 to 3, + indicates that the model was trained with that particular fold held out. + + The ALL_FOLDS version refers to the distilled model that was trained on + an ensemble of teacher models trained on all folds. + """ + + ALL_FOLDS = enum.auto() # Default model version if None explicitly set. + FOLD_0 = enum.auto() + FOLD_1 = enum.auto() + FOLD_2 = enum.auto() + FOLD_3 = enum.auto() + + +Output = dna_output.Output +OutputMetadata = dna_output.OutputMetadata +OutputType = dna_output.OutputType +VariantOutput = dna_output.VariantOutput +Organism = dna_model.Organism + +PredictResponse = TypeVar( + 'PredictResponse', + dna_model_service_pb2.PredictSequenceResponse, + dna_model_service_pb2.PredictIntervalResponse, +) + + +def _read_tensor_chunks( + responses: Iterator[ + PredictResponse + | dna_model_service_pb2.PredictVariantResponse + | dna_model_service_pb2.ScoreVariantResponse + | dna_model_service_pb2.ScoreIntervalResponse + ], + chunk_count: int, +) -> Iterable[tensor_pb2.TensorChunk]: + """Helper to read a sequence of tensor chunks from a response iterator.""" + for _ in range(chunk_count): + try: + response = next(responses) + except StopIteration as e: + raise ValueError('Expected tensor_chunk, got end of stream') from e + if response.WhichOneof('payload') != 'tensor_chunk': + raise ValueError( + f'Expected tensor_chunk, got "{response.WhichOneof("payload")}"' + ' payload' + ) + yield response.tensor_chunk + + +def _make_output_data( + output: dna_model_pb2.Output, + responses: Iterator[ + PredictResponse | dna_model_service_pb2.PredictVariantResponse + ], + interval: genome.Interval | None = None, +) -> track_data.TrackData | np.ndarray | junction_data.JunctionData: + """Helper to create an output data object from an output proto.""" + match output.WhichOneof('payload'): + case 'track_data': + return track_data_utils.from_protos( + output.track_data, + _read_tensor_chunks(responses, output.track_data.values.chunk_count), + interval=interval, + ) + case 'data': + chunks = _read_tensor_chunks(responses, output.data.chunk_count) + values = tensor_utils.unpack_proto(output.data, chunks) + return tensor_utils.upcast_floating(values) + case 'junction_data': + return junction_data_utils.from_protos( + output.junction_data, + _read_tensor_chunks( + responses, output.junction_data.values.chunk_count + ), + interval=interval, + ) + case _: + raise ValueError( + f'Unsupported output type: {output.WhichOneof("payload")}' + ) + + +def construct_output_metadata( + responses: Iterator[dna_model_service_pb2.MetadataResponse], +) -> OutputMetadata: + """Constructs an OutputMetadata from a stream of responses.""" + metadata = {} + for response in responses: + for metadata_proto in response.output_metadata: + match metadata_proto.WhichOneof('payload'): + case 'tracks': + metadata[metadata_proto.output_type] = ( + track_data_utils.metadata_from_proto(metadata_proto.tracks) + ) + case 'junctions': + metadata[metadata_proto.output_type] = ( + junction_data_utils.metadata_from_proto(metadata_proto.junctions) + ) + case _: + raise ValueError( + 'Unsupported metadata type:' + f' {metadata_proto.WhichOneof("payload")}' + ) + + return OutputMetadata( + atac=metadata.get(dna_model_pb2.OUTPUT_TYPE_ATAC), + cage=metadata.get(dna_model_pb2.OUTPUT_TYPE_CAGE), + dnase=metadata.get(dna_model_pb2.OUTPUT_TYPE_DNASE), + rna_seq=metadata.get(dna_model_pb2.OUTPUT_TYPE_RNA_SEQ), + chip_histone=metadata.get(dna_model_pb2.OUTPUT_TYPE_CHIP_HISTONE), + chip_tf=metadata.get(dna_model_pb2.OUTPUT_TYPE_CHIP_TF), + splice_sites=metadata.get(dna_model_pb2.OUTPUT_TYPE_SPLICE_SITES), + splice_site_usage=metadata.get( + dna_model_pb2.OUTPUT_TYPE_SPLICE_SITE_USAGE + ), + splice_junctions=metadata.get(dna_model_pb2.OUTPUT_TYPE_SPLICE_JUNCTIONS), + contact_maps=metadata.get(dna_model_pb2.OUTPUT_TYPE_CONTACT_MAPS), + procap=metadata.get(dna_model_pb2.OUTPUT_TYPE_PROCAP), + ) + + +def _construct_output( + output_dict: Mapping[ + dna_model_pb2.OutputType, + track_data.TrackData | np.ndarray | junction_data.JunctionData, + ], +): + """Helper to construct an Output dataclass from a mapping of output types.""" + output = Output( + atac=output_dict.get(dna_model_pb2.OUTPUT_TYPE_ATAC), + cage=output_dict.get(dna_model_pb2.OUTPUT_TYPE_CAGE), + dnase=output_dict.get(dna_model_pb2.OUTPUT_TYPE_DNASE), + rna_seq=output_dict.get(dna_model_pb2.OUTPUT_TYPE_RNA_SEQ), + chip_histone=output_dict.get(dna_model_pb2.OUTPUT_TYPE_CHIP_HISTONE), + chip_tf=output_dict.get(dna_model_pb2.OUTPUT_TYPE_CHIP_TF), + splice_sites=output_dict.get(dna_model_pb2.OUTPUT_TYPE_SPLICE_SITES), + splice_site_usage=output_dict.get( + dna_model_pb2.OUTPUT_TYPE_SPLICE_SITE_USAGE + ), + splice_junctions=output_dict.get( + dna_model_pb2.OUTPUT_TYPE_SPLICE_JUNCTIONS + ), + contact_maps=output_dict.get(dna_model_pb2.OUTPUT_TYPE_CONTACT_MAPS), + procap=output_dict.get(dna_model_pb2.OUTPUT_TYPE_PROCAP), + ) + return output + + +def _make_output( + responses: Iterator[PredictResponse], + *, + interval: genome.Interval | None = None, +) -> Output: + """Helper to load an Output dataclass from a stream of response protos.""" + outputs = {} + + for response in responses: + match response.WhichOneof('payload'): + case 'output': + outputs[response.output.output_type] = _make_output_data( + response.output, responses, interval=interval + ) + case 'tensor_chunk': + raise ValueError('Received tensor chunk before output proto.') + case _: + raise ValueError( + f'Unsupported response type: {response.WhichOneof("payload")}' + ) + + return _construct_output(outputs) + + +def _make_variant_output( + responses: Iterator[dna_model_service_pb2.PredictVariantResponse], +) -> VariantOutput: + """Helper to load an Output dataclass from a stream of response protos.""" + ref_outputs = {} + alt_outputs = {} + + for response in responses: + match response.WhichOneof('payload'): + case 'reference_output': + ref_outputs[response.reference_output.output_type] = _make_output_data( + response.reference_output, responses + ) + case 'alternate_output': + alt_outputs[response.alternate_output.output_type] = _make_output_data( + response.alternate_output, responses + ) + case 'tensor_chunk': + raise ValueError('Received tensor chunk before output proto.') + case _: + raise ValueError( + f'Unsupported response type: {response.WhichOneof("payload")}' + ) + + return VariantOutput( + reference=_construct_output(ref_outputs), + alternate=_construct_output(alt_outputs), + ) + + +def _construct_anndata_from_proto( + scorer_metadata: Sequence[dna_model_pb2.GeneScorerMetadata] | None, + track_metadata: Sequence[dna_model_pb2.TrackMetadata], + values: np.ndarray, + interval: genome.Interval, + variant: dna_model_pb2.Variant | None = None, +) -> anndata.AnnData: + """Helper to construct `AnnData` from a scoring proto.""" + obs = None + if scorer_metadata: + metadata = [] + for gene_proto in scorer_metadata: + scorer_metadata = { + 'gene_id': gene_proto.gene_id, + } + if gene_proto.HasField('strand'): + scorer_metadata['strand'] = str( + genome.Strand.from_proto(gene_proto.strand) + ) + + if gene_proto.HasField('name'): + scorer_metadata['gene_name'] = gene_proto.name + if gene_proto.HasField('type'): + scorer_metadata['gene_type'] = gene_proto.type + if gene_proto.HasField('junction_start'): + scorer_metadata['junction_Start'] = gene_proto.junction_start + if gene_proto.HasField('junction_end'): + scorer_metadata['junction_End'] = gene_proto.junction_end + + metadata.append(scorer_metadata) + + obs = pd.DataFrame(metadata) + obs.index = obs.index.map(str) + + var = track_data_utils.metadata_from_proto( + dna_model_pb2.TracksMetadata(metadata=track_metadata) + ) + var.index = var.index.map(str) + + uns = {'interval': interval} + if variant is not None: + uns['variant'] = genome.Variant.from_proto(variant) + layers = None + if values.shape[0] > 1: + layers = {'quantiles': values[1]} + + return anndata.AnnData( + X=values[0], + obs=obs, + var=var, + uns=uns, + layers=layers, + ) + + +def _construct_score_variant( + output: dna_model_pb2.ScoreVariantOutput, + responses: Iterator[ + dna_model_service_pb2.ScoreVariantResponse + | dna_model_service_pb2.ScoreIsmVariantResponse + ], + interval: genome.Interval, +) -> anndata.AnnData: + """Returns `AnnData` of variant scores from a ScoreVariantOutput proto.""" + # dna_model_pb2.ScoreVariantOutput currently has ONLY VariantData with values + # and metadata. + chunks = _read_tensor_chunks( + responses, output.variant_data.values.chunk_count + ) + values = tensor_utils.unpack_proto(output.variant_data.values, chunks) + values = tensor_utils.upcast_floating(values) + anndata_scores = _construct_anndata_from_proto( + output.variant_data.metadata.gene_metadata, + output.variant_data.metadata.track_metadata, + values, + interval=interval, + variant=output.variant_data.metadata.variant, + ) + return anndata_scores + + +def _make_score_variant_output( + responses: Iterator[ + dna_model_service_pb2.ScoreVariantResponse + | dna_model_service_pb2.ScoreIsmVariantResponse + ], + interval: genome.Interval, +) -> list[anndata.AnnData]: + """Returns a list of `AnnData` variant scores from a stream of responses.""" + + anndata_scores = [] + for response in responses: + match response.WhichOneof('payload'): + case 'output': + anndata_scores.append( + _construct_score_variant( + response.output, responses, interval=interval + ) + ) + case 'tensor_chunk': + raise ValueError('Received tensor chunk before output proto.') + case _: + raise ValueError( + f'Unsupported response type: {response.WhichOneof("payload")}' + ) + return anndata_scores + + +def _construct_score_interval( + output: dna_model_pb2.ScoreIntervalOutput, + responses: Iterator[dna_model_service_pb2.ScoreIntervalResponse], + interval: genome.Interval, +) -> anndata.AnnData: + """Returns `AnnData` of interval scores from a ScoreIntervalOutput proto.""" + chunks = _read_tensor_chunks( + responses, output.interval_data.values.chunk_count + ) + values = tensor_utils.unpack_proto(output.interval_data.values, chunks) + values = tensor_utils.upcast_floating(values) + anndata_scores = _construct_anndata_from_proto( + output.interval_data.metadata.gene_metadata, + output.interval_data.metadata.track_metadata, + values, + interval=interval, + ) + return anndata_scores + + +def _make_interval_output( + responses: Iterator[dna_model_service_pb2.ScoreIntervalResponse], + interval: genome.Interval, +) -> list[anndata.AnnData]: + """Returns a list of `AnnData` interval scores from a stream of responses.""" + + anndata_scores = [] + for response in responses: + match response.WhichOneof('payload'): + case 'output': + anndata_scores.append( + _construct_score_interval( + response.output, + responses, + interval=interval, + ) + ) + case 'tensor_chunk': + raise ValueError('Received tensor chunk before output proto.') + case _: + raise ValueError( + f'Unsupported response type: {response.WhichOneof("payload")}' + ) + return anndata_scores + + +def _convert_ontologies_to_protos( + ontology_terms: Iterable[ontology.OntologyTerm | str] | None, +) -> Sequence[dna_model_pb2.OntologyTerm] | None: + """Convert ontology terms or curies to unique list of OntologyTerm protos.""" + if ontology_terms is None: + return None + else: + protos = [] + for term in dict.fromkeys(ontology_terms): + if isinstance(term, ontology.OntologyTerm): + protos.append(term.to_proto()) + else: + protos.append(ontology.from_curie(term).to_proto()) + return protos + + +def validate_sequence_length(length: int): + """Validate that the input sequence length is supported by the model.""" + if length not in SUPPORTED_SEQUENCE_LENGTHS.values(): + raise ValueError( + f'Sequence length {length} not supported by the model.' + f' Supported lengths: {[*SUPPORTED_SEQUENCE_LENGTHS.values()]}' + ) + + +class DnaClient(dna_model.DnaModel): + """Client for interacting with a DNA model server. + + Attributes: + channel: gRPC channel to the DNA model server. + metadata: Metadata to send with each request. + model_version: Optional model version to use for the DNA model server. If + none provided, the default model version will be used. + """ + + def __init__( + self, + *, + channel: grpc.Channel, + model_version: ModelVersion | None = None, + metadata: Sequence[tuple[str, str]] = (), + ): + self._channel = channel + self._metadata = metadata + self._model_version = ( + model_version.name if model_version is not None else None + ) + + @retry_rpc + def predict_sequence( + self, + sequence: str, + *, + organism: Organism = Organism.HOMO_SAPIENS, + requested_outputs: Iterable[OutputType], + ontology_terms: Iterable[ontology.OntologyTerm | str] | None, + interval: genome.Interval | None = None, + ) -> Output: + """Generate predictions for a given DNA sequence. + + Args: + sequence: DNA sequence to make prediction for. + organism: Organism to use for the prediction. + requested_outputs: Iterable of OutputTypes indicating which subsets of + predictions to return. + ontology_terms: Iterable of ontology terms or curies to generate + predictions for. If None returns all ontologies. + interval: Optional interval from which the sequence was derived. This is + used as the interval in the output TrackData. + + Returns: + Output for the provided DNA sequence. + """ + if not (unique_values := set(sequence)).issubset( + _VALID_SEQUENCE_CHARACTERS + ): + invalid_characters = ','.join(unique_values - _VALID_SEQUENCE_CHARACTERS) + raise ValueError( + 'Invalid sequence, must only contain the characters "ACGTN". Found' + f' invalid characters: "{invalid_characters}"' + ) + validate_sequence_length(len(sequence)) + requested_outputs = [o.to_proto() for o in dict.fromkeys(requested_outputs)] + request = dna_model_service_pb2.PredictSequenceRequest( + sequence=sequence, + organism=organism.to_proto(), + ontology_terms=_convert_ontologies_to_protos(ontology_terms), + requested_outputs=requested_outputs, + model_version=self._model_version, + ) + responses = dna_model_service_pb2_grpc.DnaModelServiceStub( + self._channel + ).PredictSequence(iter([request]), metadata=self._metadata) + return _make_output(responses, interval=interval) + + @retry_rpc + def predict_interval( + self, + interval: genome.Interval, + *, + organism: Organism = Organism.HOMO_SAPIENS, + requested_outputs: Iterable[OutputType], + ontology_terms: Iterable[ontology.OntologyTerm | str] | None, + ) -> Output: + """Generate predictions for a given DNA interval. + + Args: + interval: DNA interval to make prediction for. + organism: Organism to use for the prediction. + requested_outputs: Iterable of OutputTypes indicating which subsets of + predictions to return. + ontology_terms: Iterable of ontology terms or curies to generate + predictions for. If None returns all ontologies. + + Returns: + Output for the provided DNA interval. + """ + validate_sequence_length(interval.width) + requested_outputs = [o.to_proto() for o in dict.fromkeys(requested_outputs)] + request = dna_model_service_pb2.PredictIntervalRequest( + interval=interval.to_proto(), + organism=organism.to_proto(), + ontology_terms=_convert_ontologies_to_protos(ontology_terms), + requested_outputs=requested_outputs, + model_version=self._model_version, + ) + responses = dna_model_service_pb2_grpc.DnaModelServiceStub( + self._channel + ).PredictInterval(iter([request]), metadata=self._metadata) + return _make_output(responses) + + @retry_rpc + def predict_variant( + self, + interval: genome.Interval, + variant: genome.Variant, + *, + organism: Organism = Organism.HOMO_SAPIENS, + requested_outputs: Iterable[OutputType], + ontology_terms: Iterable[ontology.OntologyTerm | str] | None, + ) -> VariantOutput: + """Generate predictions for a given DNA variant. + + Args: + interval: DNA interval to make prediction for. + variant: DNA variant to make prediction for. + organism: Organism to use for the prediction. + requested_outputs: Iterable of OutputTypes indicating which subsets of + predictions to return. + ontology_terms: Iterable of ontology terms or curies to generate + predictions for. If None returns all ontologies. + + Returns: + Variant output for the provided DNA interval and variant. + """ + validate_sequence_length(interval.width) + requested_outputs = [o.to_proto() for o in dict.fromkeys(requested_outputs)] + request = dna_model_service_pb2.PredictVariantRequest( + interval=interval.to_proto(), + variant=variant.to_proto(), + organism=organism.to_proto(), + ontology_terms=_convert_ontologies_to_protos(ontology_terms), + requested_outputs=requested_outputs, + model_version=self._model_version, + ) + responses = dna_model_service_pb2_grpc.DnaModelServiceStub( + self._channel + ).PredictVariant(iter([request]), metadata=self._metadata) + return _make_variant_output(responses) + + @retry_rpc + def score_interval( + self, + interval: genome.Interval, + interval_scorers: Sequence[interval_scorers_lib.IntervalScorerTypes] = (), + *, + organism: Organism = Organism.HOMO_SAPIENS, + ) -> list[anndata.AnnData]: + """Generate interval scores for a single given interval. + + Args: + interval: Interval to make prediction for. + interval_scorers: Sequence of interval scorers to use for scoring. If no + interval scorers are provided, the recommended interval scorers for the + organism will be used. + organism: Organism to use for the prediction. + + Returns: + List of `AnnData` interval scores. + """ + if not interval_scorers: + interval_scorers = list( + interval_scorers_lib.RECOMMENDED_INTERVAL_SCORERS.values() + ) + + validate_sequence_length(interval.width) + if len(interval_scorers) != len(set(interval_scorers)): + raise ValueError( + f'Duplicate interval scorers requested: {interval_scorers}.' + ) + if len(interval_scorers) > MAX_VARIANT_SCORERS_PER_REQUEST: + raise ValueError( + 'Too many interval scorers requested, ' + f'maximum allowed: {MAX_VARIANT_SCORERS_PER_REQUEST}.' + ) + if any( + scorer.width is not None and scorer.width > interval.width + for scorer in interval_scorers + ): + raise ValueError('Interval scorers must have widths <= interval width.') + request = dna_model_service_pb2.ScoreIntervalRequest( + interval=interval.to_proto(), + interval_scorers=[scorer.to_proto() for scorer in interval_scorers], + organism=organism.to_proto(), + model_version=self._model_version, + ) + responses = dna_model_service_pb2_grpc.DnaModelServiceStub( + self._channel + ).ScoreInterval(iter([request]), metadata=self._metadata) + interval_outputs = _make_interval_output(responses, interval) + for ix in range(len(interval_scorers)): + interval_outputs[ix].uns['interval_scorer'] = interval_scorers[ix] + return interval_outputs + + @retry_rpc + def score_variant( + self, + interval: genome.Interval, + variant: genome.Variant, + variant_scorers: Sequence[variant_scorers_lib.VariantScorerTypes] = (), + *, + organism: Organism = Organism.HOMO_SAPIENS, + ) -> list[anndata.AnnData]: + """Generate variant scores for a single given DNA variant. + + Args: + interval: DNA interval to make prediction for. + variant: DNA variant to make prediction for. + variant_scorers: Sequence of variant scorers to use for scoring. If no + variant scorers are provided, the recommended variant scorers for the + organism will be used. + organism: Organism to use for the prediction. + + Returns: + List of `AnnData` variant scores. + """ + if not variant_scorers: + variant_scorers = variant_scorers_lib.get_recommended_scorers( + organism.to_proto() + ) + + validate_sequence_length(interval.width) + for variant_scorer in variant_scorers: + supported_organisms = variant_scorers_lib.SUPPORTED_ORGANISMS[ + variant_scorer.base_variant_scorer + ] + if organism.to_proto() not in supported_organisms: + supported_organisms = [ + dna_model_pb2.Organism.Name(o).removeprefix('ORGANISM_') + for o in supported_organisms + ] + raise ValueError( + f'Unsupported organism: {organism.name} for scorer' + f' {variant_scorer}. Supported organisms:' + f' {supported_organisms}' + ) + if len(variant_scorers) != len(set(variant_scorers)): + raise ValueError( + f'Duplicate variant scorers requested: {variant_scorers}.' + ) + if len(variant_scorers) > MAX_VARIANT_SCORERS_PER_REQUEST: + raise ValueError( + 'Too many variant scorers requested, ' + f'maximum allowed: {MAX_VARIANT_SCORERS_PER_REQUEST}.' + ) + request = dna_model_service_pb2.ScoreVariantRequest( + interval=interval.to_proto(), + variant=variant.to_proto(), + organism=organism.to_proto(), + variant_scorers=[vs.to_proto() for vs in variant_scorers], + model_version=self._model_version, + ) + responses = dna_model_service_pb2_grpc.DnaModelServiceStub( + self._channel + ).ScoreVariant(iter([request]), metadata=self._metadata) + variant_outputs = _make_score_variant_output(responses, interval) + for ix in range(len(variant_scorers)): + variant_outputs[ix].uns['variant_scorer'] = variant_scorers[ix] + return variant_outputs + + def score_ism_variants( + self, + interval: genome.Interval, + ism_interval: genome.Interval, + variant_scorers: Sequence[variant_scorers_lib.VariantScorerTypes] = (), + *, + organism: Organism = Organism.HOMO_SAPIENS, + progress_bar: bool = True, + max_workers: int = dna_model.DEFAULT_MAX_WORKERS, + ) -> list[list[anndata.AnnData]]: + """Generate in-silico mutagenesis (ISM) variant scores for a given interval. + + Args: + interval: DNA interval to make the prediction for. + ism_interval: Interval to perform ISM. + variant_scorers: Sequence of variant scorers to use for scoring each + variant. If no variant scorers are provided, the recommended variant + scorers for the organism will be used. + organism: Organism to use for the prediction. + progress_bar: If True, show a progress bar. + max_workers: Number of parallel workers to use. + + Returns: + List of variant scores for each variant in the ISM interval. + """ + if not variant_scorers: + variant_scorers = variant_scorers_lib.get_recommended_scorers( + organism.to_proto() + ) + + validate_sequence_length(interval.width) + if ism_interval.negative_strand: + raise ValueError('ISM interval must be on the positive strand.') + + ism_intervals = [] + for position in range(0, ism_interval.width, MAX_ISM_INTERVAL_WIDTH): + ism_intervals.append( + genome.Interval( + ism_interval.chromosome, + ism_interval.start + position, + min( + ism_interval.start + position + MAX_ISM_INTERVAL_WIDTH, + ism_interval.end, + ), + ) + ) + + @retry_rpc + def _score_ism_variant( + ism_interval: genome.Interval, + ) -> list[anndata.AnnData]: + request = dna_model_service_pb2.ScoreIsmVariantRequest( + interval=interval.to_proto(), + ism_interval=ism_interval.to_proto(), + organism=organism.to_proto(), + variant_scorers=[vs.to_proto() for vs in variant_scorers], + model_version=self._model_version, + ) + responses = dna_model_service_pb2_grpc.DnaModelServiceStub( + self._channel + ).ScoreIsmVariant(iter([request]), metadata=self._metadata) + + return _make_score_variant_output(responses, interval) + + with concurrent.futures.ThreadPoolExecutor( + max_workers=max_workers + ) as executor: + futures = [ + executor.submit(_score_ism_variant, ism_interval) + for ism_interval in ism_intervals + ] + ism_scores = [] + for future in tqdm.auto.tqdm( + concurrent.futures.as_completed(futures), + total=len(futures), + disable=not progress_bar, + ): + ism_scores.extend(future.result()) + + # Group scores by variant. + ism_scores_by_variant: dict[str, list[anndata.AnnData]] = {} + for variant_score in ism_scores: + ism_scores_by_variant.setdefault( + str(variant_score.uns['variant']), [] + ).append(variant_score) + + return list(ism_scores_by_variant.values()) + + def output_metadata( + self, organism: Organism = Organism.HOMO_SAPIENS + ) -> OutputMetadata: + """Get the metadata for a given organism. + + Args: + organism: Organism to get metadata for. + + Returns: + OutputMetadata for the provided organism. + """ + request = dna_model_service_pb2.MetadataRequest( + organism=organism.to_proto() + ) + responses = dna_model_service_pb2_grpc.DnaModelServiceStub( + self._channel + ).GetMetadata(request, metadata=self._metadata) + return construct_output_metadata(responses) + + +def create( + api_key: str, + *, + model_version: ModelVersion | None = None, + timeout: float | None = None, + address: str | None = None, +) -> DnaClient: + """Creates a model client for a given API key. + + Args: + api_key: API key to use for authentication. + model_version: Optional model version to use for the DNA model server. If + none is provided, the default model will be used. + timeout: Optional timeout for waiting for the channel to be ready. + address: Optional server address to connect to. + + Returns: + `DnaClient` instance. + """ + options = [ + ('grpc.max_send_message_length', -1), + ('grpc.max_receive_message_length', -1), + ] + address = address or 'dns:///gdmscience.googleapis.com:443' + channel = grpc.secure_channel( + address, grpc.ssl_channel_credentials(), options=options + ) + grpc.channel_ready_future(channel).result(timeout) + + return DnaClient( + channel=channel, + model_version=model_version, + metadata=[('x-goog-api-key', api_key)], + ) diff --git a/alphagenome/source/src/alphagenome/models/dna_client_test.py b/alphagenome/source/src/alphagenome/models/dna_client_test.py new file mode 100644 index 0000000000000000000000000000000000000000..f17f2e56cf84a85f8b91dd5951a3bb5e895cb547 --- /dev/null +++ b/alphagenome/source/src/alphagenome/models/dna_client_test.py @@ -0,0 +1,1976 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from __future__ import annotations + +from collections.abc import Sequence +import dataclasses +import functools +import time +from unittest import mock + +from absl.testing import absltest +from absl.testing import parameterized +from alphagenome import tensor_utils +from alphagenome.data import genome +from alphagenome.data import junction_data +from alphagenome.data import track_data +from alphagenome.interpretation import ism +from alphagenome.models import dna_client +from alphagenome.models import interval_scorers +from alphagenome.models import junction_data_utils +from alphagenome.models import track_data_utils +from alphagenome.models import variant_scorers +from alphagenome.protos import dna_model_pb2 +from alphagenome.protos import dna_model_service_pb2 +from alphagenome.protos import dna_model_service_pb2_grpc +import anndata +import grpc +import numpy as np +import pandas as pd + + +class _FakeRpcError(grpc.RpcError): + """Fake RPC error for testing.""" + + def __init__(self, code: grpc.StatusCode): + self._code = code + + def code(self) -> grpc.StatusCode: + return self._code + + +def _generate_prediction_protos( + predictions: dna_client.Output, + requested_outputs: Sequence[dna_model_pb2.OutputType], + bytes_per_chunk: int, + response_proto_type: ..., +): + """Helper to generate proto responses for the provided predictions.""" + for output_type in requested_outputs: + prediction = predictions.get(dna_client.OutputType(output_type)) + if prediction is not None: + if isinstance(prediction, track_data.TrackData): + proto, chunks = track_data_utils.to_protos( + prediction, bytes_per_chunk=bytes_per_chunk + ) + yield response_proto_type( + output=dna_model_pb2.Output( + output_type=output_type, track_data=proto + ) + ) + elif isinstance(prediction, junction_data.JunctionData): + proto, chunks = junction_data_utils.to_protos( + prediction, bytes_per_chunk=bytes_per_chunk + ) + yield response_proto_type( + output=dna_model_pb2.Output( + output_type=output_type, junction_data=proto + ) + ) + else: + raise ValueError(f'Unsupported prediction type: {type(prediction)}') + + for chunk in chunks: + yield response_proto_type(tensor_chunk=chunk) + + +def _generate_variant_protos( + predictions: dna_client.VariantOutput, + requested_outputs: Sequence[dna_model_pb2.OutputType], + bytes_per_chunk: int, +): + """Helper to generate variant proto responses for the provided predictions.""" + for output_type in requested_outputs: + prediction = predictions.reference.get(dna_client.OutputType(output_type)) + if prediction is not None: + if isinstance(prediction, track_data.TrackData): + proto, chunks = track_data_utils.to_protos( + prediction, bytes_per_chunk=bytes_per_chunk + ) + yield dna_model_service_pb2.PredictVariantResponse( + reference_output=dna_model_pb2.Output( + output_type=output_type, track_data=proto + ) + ) + elif isinstance(prediction, junction_data.JunctionData): + proto, chunks = junction_data_utils.to_protos( + prediction, bytes_per_chunk=bytes_per_chunk + ) + yield dna_model_service_pb2.PredictVariantResponse( + reference_output=dna_model_pb2.Output( + output_type=output_type, junction_data=proto + ) + ) + else: + raise ValueError(f'Unsupported prediction type: {type(prediction)}') + + for chunk in chunks: + yield dna_model_service_pb2.PredictVariantResponse(tensor_chunk=chunk) + + for output_type in requested_outputs: + prediction = predictions.alternate.get(dna_client.OutputType(output_type)) + if prediction is not None: + if isinstance(prediction, track_data.TrackData): + proto, chunks = track_data_utils.to_protos( + prediction, bytes_per_chunk=bytes_per_chunk + ) + yield dna_model_service_pb2.PredictVariantResponse( + alternate_output=dna_model_pb2.Output( + output_type=output_type, track_data=proto + ) + ) + elif isinstance(prediction, junction_data.JunctionData): + proto, chunks = junction_data_utils.to_protos( + prediction, bytes_per_chunk=bytes_per_chunk + ) + yield dna_model_service_pb2.PredictVariantResponse( + alternate_output=dna_model_pb2.Output( + output_type=output_type, junction_data=proto + ) + ) + else: + raise ValueError(f'Unsupported prediction type: {type(prediction)}') + + for chunk in chunks: + yield dna_model_service_pb2.PredictVariantResponse(tensor_chunk=chunk) + + +def _generate_variant_scoring_protos( + scores: list[anndata.AnnData], + bytes_per_chunk: int, + response_proto_type: ..., + variant: genome.Variant | None = None, +): + """Helper to generate variant scoring responses for the provided scores.""" + for score in scores: + track_metadata_protos = [ + dna_model_pb2.TrackMetadata( + name=name, + strand=genome.Strand.from_str(strand).to_proto(), + ) + for name, strand in score.var[['name', 'strand']].values + ] + gene_metadata_protos = [] + if score.obs is not None and 'gene_id' in score.obs: + for _, row in score.obs.iterrows(): + if (strand := row.get('strand')) is not None: + strand = genome.Strand.from_str(strand).to_proto() + gene_metadata_protos.append( + dna_model_pb2.GeneScorerMetadata( + gene_id=row['gene_id'], + strand=strand, + name=row.get('gene_name'), + type=row.get('gene_type'), + junction_start=row.get('junction_Start'), + junction_end=row.get('junction_End'), + ) + ) + + if 'quantiles' in score.layers: + score_tensor = np.stack([score.X, score.layers['quantiles']]) + else: + score_tensor = score.X[np.newaxis] + + tensor_proto, chunks = tensor_utils.pack_tensor( + score_tensor, bytes_per_chunk=bytes_per_chunk + ) + yield response_proto_type( + output=dna_model_pb2.ScoreVariantOutput( + variant_data=dna_model_pb2.VariantData( + values=tensor_proto, + metadata=dna_model_pb2.VariantMetadata( + variant=variant.to_proto() if variant else None, + track_metadata=track_metadata_protos, + gene_metadata=gene_metadata_protos, + ), + ) + ) + ) + for chunk in chunks: + yield response_proto_type(tensor_chunk=chunk) + + +def _generate_interval_scoring_protos( + scores: list[anndata.AnnData], + bytes_per_chunk: int, + response_proto_type: ..., + interval: genome.Interval, +): + """Helper to generate interval scoring responses for the provided scores.""" + for score in scores: + track_metadata_protos = [ + dna_model_pb2.TrackMetadata( + name=name, + strand=genome.Strand.from_str(strand).to_proto(), + ) + for name, strand in score.var[['name', 'strand']].values + ] + gene_metadata_protos = [] + if score.obs is not None and 'gene_id' in score.obs: + for _, row in score.obs.iterrows(): + if (strand := row.get('strand')) is not None: + strand = genome.Strand.from_str(strand).to_proto() + gene_metadata_protos.append( + dna_model_pb2.GeneScorerMetadata( + gene_id=row['gene_id'], + strand=strand, + name=row.get('gene_name'), + type=row.get('gene_type'), + ) + ) + + tensor_proto, chunks = tensor_utils.pack_tensor( + score.X[np.newaxis], bytes_per_chunk=bytes_per_chunk + ) + yield response_proto_type( + output=dna_model_pb2.ScoreIntervalOutput( + interval_data=dna_model_pb2.IntervalData( + values=tensor_proto, + metadata=dna_model_pb2.IntervalMetadata( + interval=interval.to_proto(), + track_metadata=track_metadata_protos, + gene_metadata=gene_metadata_protos, + ), + ) + ) + ) + for chunk in chunks: + yield response_proto_type(tensor_chunk=chunk) + + +def _create_example_model_predictions( + *, + sequence_length: int = 0, + value: float = 0.0, + interval: genome.Interval | None = None, +) -> dna_client.Output: + """Helper to generate example model predictions.""" + if sequence_length == 0: + if interval is None: + raise ValueError('Interval must be provided for zero length sequences.') + sequence_length = interval.width + + return dna_client.Output( + atac=track_data.TrackData( + values=np.full((sequence_length, 32), value, dtype=np.float32), + metadata=pd.DataFrame({ + 'name': [f'{i}' for i in range(32)], + 'strand': ['.'] * 32, + }), + interval=interval, + ), + rna_seq=track_data.TrackData( + values=np.full((sequence_length, 64), value, dtype=np.float32), + metadata=pd.DataFrame({ + 'name': [f'{i}' for i in range(64)], + 'strand': ['.'] * 64, + }), + interval=interval, + ), + splice_junctions=junction_data.JunctionData( + junctions=np.array([ + genome.Interval('chr1', 0, 1000, strand='+'), + genome.Interval('chr1', 1500, 2000, strand='-'), + ]), + values=np.full([2, 3], value, dtype=np.float32), + metadata=pd.DataFrame({'name': [f'{i}' for i in range(3)]}), + interval=interval, + ), + ) + + +def _create_mock_channel_and_stream( + response_callable, *, bidi_stream: bool = True +) -> ...: + mock_stream = mock.create_autospec( + grpc.StreamStreamMultiCallable, side_effect=response_callable + ) + mock_channel = mock.create_autospec(grpc.Channel) + if bidi_stream: + mock_channel.stream_stream.return_value = mock_stream + else: + mock_channel.unary_stream.return_value = mock_stream + return mock_channel, mock_stream + + +class ClientTest(parameterized.TestCase): + + def _assert_output_equal( + self, + actual: dna_client.Output, + expected: dna_client.Output, + msg: ..., + ) -> None: + """Helper function to compare Output objects.""" + self.assertEqual(actual.atac, expected.atac, msg) + self.assertEqual(actual.cage, expected.cage, msg) + self.assertEqual(actual.dnase, expected.dnase, msg) + self.assertEqual(actual.rna_seq, expected.rna_seq, msg) + self.assertEqual(actual.chip_histone, expected.chip_histone, msg) + self.assertEqual(actual.chip_tf, expected.chip_tf, msg) + + self.assertEqual(actual.splice_sites, expected.splice_sites, msg) + self.assertEqual(actual.splice_site_usage, expected.splice_site_usage, msg) + self.assertEqual(actual.splice_junctions, expected.splice_junctions) + self.assertEqual(actual.contact_maps, expected.contact_maps, msg) + + def _assert_track_metadata_equal( + self, + actual: track_data.TrackMetadata, + expected: track_data.TrackMetadata, + msg: ..., + ) -> None: + if actual is not None and expected is not None: + pd.testing.assert_frame_equal(actual, expected) + else: + self.assertEqual(actual, expected, msg) + + def _assert_output_metadata_equal( + self, + actual: dna_client.OutputMetadata, + expected: dna_client.OutputMetadata, + msg: ..., + ) -> None: + """Helper function to compare OutputMetadata objects.""" + self._assert_track_metadata_equal(actual.atac, expected.atac, msg) + self._assert_track_metadata_equal(actual.cage, expected.cage, msg) + self._assert_track_metadata_equal(actual.dnase, expected.dnase, msg) + self._assert_track_metadata_equal(actual.rna_seq, expected.rna_seq, msg) + self._assert_track_metadata_equal( + actual.chip_histone, expected.chip_histone, msg + ) + self._assert_track_metadata_equal(actual.chip_tf, expected.chip_tf, msg) + self._assert_track_metadata_equal( + actual.splice_sites, expected.splice_sites, msg + ) + self._assert_track_metadata_equal( + actual.splice_site_usage, expected.splice_site_usage, msg + ) + self._assert_track_metadata_equal( + actual.splice_junctions, expected.splice_junctions, msg + ) + + def _assert_track_data_equal( + self, + actual: track_data.TrackData, + expected: track_data.TrackData, + msg: ..., + ) -> None: + """Helper function to compare TrackData objects.""" + pd.testing.assert_frame_equal(actual.metadata, expected.metadata) + self.assertEqual(actual.interval, expected.interval, msg) + np.testing.assert_array_equal(actual.values, expected.values) + self.assertEqual(actual.uns, expected.uns, msg) + + def _assert_junction_data_equal( + self, + actual: junction_data.JunctionData, + expected: junction_data.JunctionData, + msg: ..., + ) -> None: + pd.testing.assert_frame_equal(actual.metadata, expected.metadata) + np.testing.assert_array_equal(actual.junctions, expected.junctions) + np.testing.assert_array_equal(actual.values, expected.values) + self.assertEqual(actual.interval, expected.interval, msg) + self.assertEqual(actual.uns, expected.uns, msg) + + def _assert_anndata_equal( + self, + actual: anndata.AnnData, + expected: anndata.AnnData, + ) -> None: + """Helper function to compare AnnData objects.""" + pd.testing.assert_frame_equal(actual.obs, expected.obs, check_like=True) + pd.testing.assert_frame_equal(actual.var, expected.var, check_like=True) + self.assertEqual(actual.uns['interval'], expected.uns['interval']) + if 'variant' in expected.uns: + self.assertEqual(actual.uns['variant'], expected.uns['variant']) + np.testing.assert_array_equal(actual.X, expected.X) + if 'quantiles' in expected.layers: + np.testing.assert_array_equal( + actual.layers['quantiles'], expected.layers['quantiles'] + ) + else: + self.assertNotIn('quantiles', actual.layers) + + def setUp(self): + super().setUp() + self.addTypeEqualityFunc(dna_client.Output, self._assert_output_equal) + self.addTypeEqualityFunc( + track_data.TrackData, self._assert_track_data_equal + ) + self.addTypeEqualityFunc( + junction_data.JunctionData, self._assert_junction_data_equal + ) + self.addTypeEqualityFunc( + dna_client.OutputMetadata, self._assert_output_metadata_equal + ) + + @parameterized.product( + ( + dict( + requested_output_types=[*dna_client.OutputType], + sequence_length=2048, + expected=dna_client.Output( + atac=track_data.TrackData( + values=np.zeros((2048, 32), dtype=np.float32), + metadata=pd.DataFrame({ + 'name': [f'{i}' for i in range(32)], + 'strand': ['.'] * 32, + }), + ), + rna_seq=track_data.TrackData( + values=np.zeros((2048, 64), dtype=np.float32), + metadata=pd.DataFrame({ + 'name': [f'{i}' for i in range(64)], + 'strand': ['.'] * 64, + }), + ), + splice_junctions=junction_data.JunctionData( + junctions=np.array([ + genome.Interval('chr1', 0, 1000, strand='+'), + genome.Interval('chr1', 1500, 2000, strand='-'), + ]), + values=np.zeros([2, 3], dtype=np.float32), + metadata=pd.DataFrame( + {'name': [f'{i}' for i in range(3)]} + ), + ), + ), + ), + dict( + requested_output_types={dna_client.OutputType.RNA_SEQ}, + sequence_length=16_384, + expected=dna_client.Output( + rna_seq=track_data.TrackData( + values=np.zeros((16_384, 64), dtype=np.float32), + metadata=pd.DataFrame({ + 'name': [f'{i}' for i in range(64)], + 'strand': ['.'] * 64, + }), + ), + ), + ), + dict( + requested_output_types={dna_client.OutputType.CONTACT_MAPS}, + sequence_length=2048, + expected=dna_client.Output(), + ), + ), + bytes_per_chunk=[0, 128], + model_version=[None, dna_client.ModelVersion.FOLD_0], + with_interval=[True, False], + ) + def test_predict_sequence( + self, + requested_output_types: set[dna_client.OutputType] | None, + sequence_length: int, + expected: dna_client.Output, + bytes_per_chunk: int, + model_version: dna_client.ModelVersion | None, + with_interval: bool, + ): + mock_predictions = _create_example_model_predictions( + sequence_length=sequence_length + ) + + expected_request = dna_model_service_pb2.PredictSequenceRequest( + sequence='A' * sequence_length, + organism=dna_model_pb2.ORGANISM_HOMO_SAPIENS, + requested_outputs=[o.to_proto() for o in requested_output_types], + ontology_terms=[ + dna_model_pb2.OntologyTerm( + ontology_type=dna_model_pb2.ONTOLOGY_TYPE_UBERON, id=4 + ) + ], + model_version=model_version.name if model_version else None, + ) + + interval = None + if with_interval: + interval = genome.Interval('chr1', 0, sequence_length) + expected_dict = {} + for field in dataclasses.fields(expected): + output_type = field.metadata['output_type'] + if output := expected.get(output_type): + expected_dict[field.name] = dataclasses.replace( + output, interval=interval + ) + expected = dna_client.Output(**expected_dict) + + def _mock_generate(requests, metadata): + del metadata + for request in requests: + self.assertEqual(request, expected_request) + yield from _generate_prediction_protos( + mock_predictions, + request.requested_outputs, + bytes_per_chunk, + dna_model_service_pb2.PredictSequenceResponse, + ) + + mock_channel, mock_stream = _create_mock_channel_and_stream(_mock_generate) + model = dna_client.DnaClient( + channel=mock_channel, model_version=model_version + ) + + outputs = model.predict_sequence( + 'A' * sequence_length, + organism=dna_client.Organism.HOMO_SAPIENS, + ontology_terms=['UBERON:00004'], + requested_outputs=requested_output_types, + interval=interval, + ) + + self.assertEqual(outputs, expected) + mock_stream.assert_called_once() + + @parameterized.product( + sequence_length=[dna_client.SEQUENCE_LENGTH_2KB], + num_predictions=[1, 4], + num_workers=[1, 4], + bytes_per_chunk=[0, 128], + with_interval=[True, False], + ) + def test_predict_sequences( + self, + sequence_length: int, + num_predictions: int, + num_workers: int, + bytes_per_chunk: int, + with_interval: bool, + ): + interval = None + if with_interval: + interval = genome.Interval('chr1', 0, sequence_length) + + mock_predictions = _create_example_model_predictions( + sequence_length=sequence_length, interval=interval + ) + + def _mock_generate(requests, metadata): + del metadata + for request in requests: + yield from _generate_prediction_protos( + mock_predictions, + request.requested_outputs, + bytes_per_chunk, + dna_model_service_pb2.PredictSequenceResponse, + ) + + mock_channel, mock_stream = _create_mock_channel_and_stream(_mock_generate) + model = dna_client.DnaClient(channel=mock_channel) + + outputs = model.predict_sequences( + ['A' * sequence_length] * num_predictions, + organism=dna_client.Organism.HOMO_SAPIENS, + ontology_terms=None, + requested_outputs=[*dna_client.OutputType], + progress_bar=False, + max_workers=num_workers, + intervals=[interval] * num_predictions if with_interval else None, + ) + for output in outputs: + self.assertEqual(output, mock_predictions) + + self.assertEqual(mock_stream.call_count, num_predictions) + + def test_predict_sequences_error_raises(self): + mock_channel, mock_stream = _create_mock_channel_and_stream( + _FakeRpcError(grpc.StatusCode.INTERNAL) + ) + model = dna_client.DnaClient(channel=mock_channel) + with self.assertRaises(grpc.RpcError): + model.predict_sequences( + ['A' * dna_client.SEQUENCE_LENGTH_2KB], + ontology_terms=None, + requested_outputs=[*dna_client.OutputType], + ) + + mock_stream.assert_called_once() + + def test_invalid_sequence_raises_error(self): + model = dna_client.DnaClient(channel=mock.create_autospec(grpc.Channel)) + with self.assertRaisesRegex( + ValueError, + 'Invalid sequence, must only contain the characters "ACGTN". Found' + ' invalid characters: "[wozers,]*"', + ): + model.predict_sequence( + 'wowzers', + ontology_terms=None, + requested_outputs=[dna_client.OutputType.ATAC], + ) + + @parameterized.product( + ( + dict( + requested_output_types={dna_client.OutputType.ATAC}, + interval=genome.Interval('chr1', 0, 2048), + expected=dna_client.Output( + atac=track_data.TrackData( + values=np.zeros((2048, 32), dtype=np.float32), + metadata=pd.DataFrame({ + 'name': [f'{i}' for i in range(32)], + 'strand': ['.'] * 32, + }), + interval=genome.Interval('chr1', 0, 2048), + ) + ), + ), + ), + organism=[ + dna_client.Organism.HOMO_SAPIENS, + dna_client.Organism.MUS_MUSCULUS, + ], + bytes_per_chunk=[0, 128], + model_version=[None, dna_client.ModelVersion.FOLD_0], + ) + def test_predict_interval( + self, + requested_output_types, + interval, + organism, + expected, + bytes_per_chunk, + model_version, + ): + mock_predictions = _create_example_model_predictions(interval=interval) + + def _mock_generate(requests, metadata): + del metadata + for request in requests: + yield from _generate_prediction_protos( + mock_predictions, + request.requested_outputs, + bytes_per_chunk, + dna_model_service_pb2.PredictIntervalResponse, + ) + + mock_channel, mock_stream = _create_mock_channel_and_stream(_mock_generate) + model = dna_client.DnaClient( + channel=mock_channel, model_version=model_version + ) + + outputs = model.predict_interval( + interval=interval, + organism=organism, + ontology_terms=['UBERON:00004'], + requested_outputs=requested_output_types, + ) + self.assertEqual(expected, outputs) + + mock_stream.assert_called_once() + + @parameterized.product( + intervals=[ + [genome.Interval('chr1', 0, 2048)], + [ + genome.Interval('chr1', 0, 2048), + genome.Interval('chr1', 2048, 4096), + ], + ], + num_workers=[1, 4], + bytes_per_chunk=[0, 128], + ) + def test_predict_intervals(self, intervals, num_workers, bytes_per_chunk): + examples = { + (interval.start, interval.end): _create_example_model_predictions( + interval=interval, value=interval.start + ) + for interval in intervals + } + + def _mock_generate(requests, metadata): + del metadata + for request in requests: + yield from _generate_prediction_protos( + examples[(request.interval.start, request.interval.end)], + request.requested_outputs, + bytes_per_chunk, + dna_model_service_pb2.PredictIntervalResponse, + ) + + mock_channel, mock_stream = _create_mock_channel_and_stream(_mock_generate) + model = dna_client.DnaClient(channel=mock_channel) + + outputs = model.predict_intervals( + intervals=intervals, + ontology_terms=None, + requested_outputs=[*dna_client.OutputType], + max_workers=num_workers, + progress_bar=False, + ) + for interval, output in zip(intervals, outputs): + self.assertEqual(output, examples[(interval.start, interval.end)]) + + self.assertLen(intervals, mock_stream.call_count) + + def test_predict_intervals_error_raises(self): + mock_channel, mock_stream = _create_mock_channel_and_stream( + _FakeRpcError(grpc.StatusCode.INTERNAL) + ) + model = dna_client.DnaClient(channel=mock_channel) + with self.assertRaises(grpc.RpcError): + model.predict_intervals( + [genome.Interval('chr1', 0, 2048)], + ontology_terms=None, + requested_outputs=[*dna_client.OutputType], + ) + + mock_stream.assert_called_once() + + @parameterized.product( + ( + dict( + requested_output_types={dna_client.OutputType.ATAC}, + interval=genome.Interval('chr1', 0, 2048), + variant=genome.Variant('chr1', 1000, 'A', 'C'), + expected=( + dna_client.Output( + atac=track_data.TrackData( + values=np.zeros((2048, 32), dtype=np.float32), + metadata=pd.DataFrame({ + 'name': [f'{i}' for i in range(32)], + 'strand': ['.'] * 32, + }), + interval=genome.Interval('chr1', 0, 2048), + ) + ), + dna_client.Output( + atac=track_data.TrackData( + values=np.ones((2048, 32), dtype=np.float32), + metadata=pd.DataFrame({ + 'name': [f'{i}' for i in range(32)], + 'strand': ['.'] * 32, + }), + interval=genome.Interval('chr1', 0, 2048), + ) + ), + ), + ), + ), + organism=[ + dna_client.Organism.HOMO_SAPIENS, + dna_client.Organism.MUS_MUSCULUS, + ], + bytes_per_chunk=[0, 128], + model_version=[None, dna_client.ModelVersion.FOLD_1], + ) + def test_predict_variant( + self, + requested_output_types, + interval, + variant, + organism, + expected, + bytes_per_chunk, + model_version, + ): + mock_predictions = dna_client.VariantOutput( + reference=_create_example_model_predictions(interval=interval, value=0), + alternate=_create_example_model_predictions(interval=interval, value=1), + ) + expected = dna_client.VariantOutput( + reference=expected[0], + alternate=expected[1], + ) + + def _mock_generate(requests, metadata): + del metadata + for request in requests: + yield from _generate_variant_protos( + mock_predictions, request.requested_outputs, bytes_per_chunk + ) + + mock_channel, mock_stream = _create_mock_channel_and_stream(_mock_generate) + model = dna_client.DnaClient( + channel=mock_channel, model_version=model_version + ) + + outputs = model.predict_variant( + interval=interval, + variant=variant, + organism=organism, + ontology_terms=['UBERON:00004'], + requested_outputs=requested_output_types, + ) + + self.assertEqual(expected.reference, outputs.reference) + self.assertEqual(expected.alternate, outputs.alternate) + + mock_stream.assert_called_once() + + @parameterized.product( + intervals=[ + genome.Interval('chr1', 0, 2048), + [ + genome.Interval('chr1', 0, 2048), + genome.Interval('chr1', 2048, 4096), + ], + ], + variants=[ + [ + genome.Variant('chr1', 1000, 'A', 'C'), + genome.Variant('chr1', 2000, 'A', 'C'), + ], + ], + num_workers=[1, 4], + bytes_per_chunk=[0, 128], + ) + def test_predict_variants( + self, intervals, variants, num_workers, bytes_per_chunk + ): + if isinstance(intervals, genome.Interval): + intervals = [intervals] * len(variants) + examples = { + (interval.start, interval.end): _create_example_model_predictions( + interval=interval, value=interval.start + ) + for interval in intervals + } + + def _mock_generate(requests, metadata): + del metadata + for request in requests: + key = (request.interval.start, request.interval.end) + yield from _generate_variant_protos( + dna_client.VariantOutput( + reference=examples[key], alternate=examples[key] + ), + request.requested_outputs, + bytes_per_chunk, + ) + + mock_channel, mock_stream = _create_mock_channel_and_stream(_mock_generate) + model = dna_client.DnaClient(channel=mock_channel) + + outputs = model.predict_variants( + intervals=intervals, + variants=variants, + ontology_terms=None, + requested_outputs=[*dna_client.OutputType], + max_workers=num_workers, + progress_bar=False, + ) + for interval, output in zip(intervals, outputs, strict=True): + expected = examples[(interval.start, interval.end)] + self.assertEqual(output.reference, expected) + self.assertEqual(output.alternate, expected) + + self.assertLen(intervals, mock_stream.call_count) + + def test_predict_variants_error_raises(self): + mock_channel, mock_stream = _create_mock_channel_and_stream( + _FakeRpcError(grpc.StatusCode.INTERNAL) + ) + model = dna_client.DnaClient(channel=mock_channel) + with self.assertRaises(grpc.RpcError): + model.predict_variants( + [genome.Interval('chr1', 0, 2048)], + [genome.Variant('chr1', 1000, 'A', 'C')], + ontology_terms=None, + requested_outputs=[*dna_client.OutputType], + ) + + mock_stream.assert_called_once() + + def test_predict_variants_mismatch_lengths_raises(self): + mock_channel, mock_stream = _create_mock_channel_and_stream(None) + model = dna_client.DnaClient(channel=mock_channel) + with self.assertRaisesRegex( + ValueError, 'Intervals and variants must have the same length' + ): + model.predict_variants( + [genome.Interval('chr1', 0, 2048), genome.Interval('chr1', 0, 2048)], + [genome.Variant('chr1', 1000, 'A', 'C')], + ontology_terms=None, + requested_outputs=[*dna_client.OutputType], + ) + mock_stream.assert_not_called() + + def test_output_metadata(self): + + def _mock_generate(requests, metadata): + del metadata + del requests + yield dna_model_service_pb2.MetadataResponse( + output_metadata=[ + dna_model_pb2.OutputMetadata( + output_type=dna_model_pb2.OUTPUT_TYPE_ATAC, + tracks=dna_model_pb2.TracksMetadata( + metadata=[ + dna_model_pb2.TrackMetadata( + name=f'track_{i}', + strand=dna_model_pb2.STRAND_UNSTRANDED, + ) + for i in range(32) + ] + ), + ), + dna_model_pb2.OutputMetadata( + output_type=dna_model_pb2.OUTPUT_TYPE_SPLICE_JUNCTIONS, + junctions=dna_model_pb2.JunctionsMetadata( + metadata=[ + dna_model_pb2.JunctionMetadata(name=f'junction_{i}') + for i in range(16) + ] + ), + ), + ] + ) + + mock_channel, mock_stream = _create_mock_channel_and_stream( + _mock_generate, bidi_stream=False + ) + model = dna_client.DnaClient(channel=mock_channel) + + outputs = model.output_metadata(organism=dna_client.Organism.HOMO_SAPIENS) + expected = dna_client.OutputMetadata( + atac=track_data.TrackMetadata( + {'name': [f'track_{i}' for i in range(32)], 'strand': ['.'] * 32} + ), + splice_junctions=junction_data.JunctionMetadata( + {'name': [f'junction_{i}' for i in range(16)]} + ), + ) + self.assertEqual(expected, outputs) + mock_stream.assert_called_once() + + def test_output_type_to_proto(self): + expected = [ + dna_model_pb2.OUTPUT_TYPE_ATAC, + dna_model_pb2.OUTPUT_TYPE_CAGE, + dna_model_pb2.OUTPUT_TYPE_DNASE, + dna_model_pb2.OUTPUT_TYPE_RNA_SEQ, + dna_model_pb2.OUTPUT_TYPE_CHIP_HISTONE, + dna_model_pb2.OUTPUT_TYPE_CHIP_TF, + dna_model_pb2.OUTPUT_TYPE_SPLICE_SITES, + dna_model_pb2.OUTPUT_TYPE_SPLICE_SITE_USAGE, + dna_model_pb2.OUTPUT_TYPE_SPLICE_JUNCTIONS, + dna_model_pb2.OUTPUT_TYPE_CONTACT_MAPS, + dna_model_pb2.OUTPUT_TYPE_PROCAP, + ] + + for output_type, expected_proto in zip( + dna_client.OutputType, expected, strict=True + ): + self.assertEqual(output_type.to_proto(), expected_proto) + + def test_organism_to_proto(self): + expected = [ + dna_model_pb2.ORGANISM_HOMO_SAPIENS, + dna_model_pb2.ORGANISM_MUS_MUSCULUS, + ] + + for organism, expected_proto in zip( + dna_client.Organism, expected, strict=True + ): + self.assertEqual(organism.to_proto(), expected_proto) + + @parameterized.product( + ( + dict( + interval=genome.Interval('chr1', 0, 2048), + scorers=[ + interval_scorers.GeneMaskScorer( + requested_output=dna_client.OutputType.ATAC, + width=2_001, + aggregation_type=( + interval_scorers.IntervalAggregationType.MEAN + ), + ), + interval_scorers.GeneMaskScorer( + requested_output=dna_client.OutputType.DNASE, + width=501, + aggregation_type=( + interval_scorers.IntervalAggregationType.SUM + ), + ), + ], + ), + ), + expected=[ + ( + anndata.AnnData( + X=np.zeros((5, 10)), + var=pd.DataFrame({ + 'name': [f'{i}' for i in range(10)], + 'strand': ['.'] * 10, + }), + uns={ + 'interval': genome.Interval('chr1', 0, 2048), + 'interval_scorer': interval_scorers.GeneMaskScorer( + requested_output=dna_client.OutputType.ATAC, + width=2_001, + aggregation_type=( + interval_scorers.IntervalAggregationType.MEAN + ), + ), + }, + ), + anndata.AnnData( + X=np.ones((5, 10)), + obs=pd.DataFrame({ + 'gene_id': [f'{i}' for i in range(5)], + 'strand': ['-'] * 5, + 'gene_name': [f'gene_{i}' for i in range(5)], + 'gene_type': [f'gene_type_{i}' for i in range(5)], + }), + var=pd.DataFrame({ + 'name': [f'{i}' for i in range(10)], + 'strand': ['.'] * 10, + }), + uns={ + 'interval': genome.Interval('chr1', 0, 2048), + 'interval_scorer': interval_scorers.GeneMaskScorer( + requested_output=dna_client.OutputType.DNASE, + width=501, + aggregation_type=( + interval_scorers.IntervalAggregationType.SUM + ), + ), + }, + ), + ), + ], + organism=[ + dna_client.Organism.HOMO_SAPIENS, + dna_client.Organism.MUS_MUSCULUS, + ], + bytes_per_chunk=[0, 128], + ) + def test_score_interval( + self, + interval, + scorers, + expected, + organism, + bytes_per_chunk, + ): + + def _mock_generate(requests, metadata): + del metadata + for request in requests: + yield from _generate_interval_scoring_protos( + scores=expected, + bytes_per_chunk=bytes_per_chunk, + response_proto_type=dna_model_service_pb2.ScoreIntervalResponse, + interval=genome.Interval.from_proto(request.interval), + ) + + mock_channel, mock_stream = _create_mock_channel_and_stream(_mock_generate) + model = dna_client.DnaClient(channel=mock_channel) + outputs = model.score_interval( + interval=interval, + organism=organism, + interval_scorers=scorers, + ) + + for expected_anndata, output_anndata in zip(expected, outputs): + self._assert_anndata_equal(expected_anndata, output_anndata) + + mock_stream.assert_called_once() + + @parameterized.product(organism=list(dna_client.Organism)) + def test_recommended_interval_scorers(self, organism): + def _mock_generate(requests, metadata): + del metadata + for request in requests: + yield from _generate_interval_scoring_protos( + scores=[ + anndata.AnnData( + np.zeros((0, 0)), + var=pd.DataFrame(columns=['name', 'strand']), + ) + for _ in range(len(request.interval_scorers)) + ], + bytes_per_chunk=1024, + response_proto_type=dna_model_service_pb2.ScoreIntervalResponse, + interval=genome.Interval.from_proto(request.interval), + ) + + mock_channel, mock_stream = _create_mock_channel_and_stream(_mock_generate) + model = dna_client.DnaClient(channel=mock_channel) + scores = model.score_interval( + interval=genome.Interval('chr1', 0, 2**20), organism=organism + ) + + self.assertSameElements( + [score.uns['interval_scorer'] for score in scores], + interval_scorers.RECOMMENDED_INTERVAL_SCORERS.values(), + ) + mock_stream.assert_called_once() + + def test_duplicate_interval_scorers_raises_error(self): + model = dna_client.DnaClient(channel=mock.create_autospec(grpc.Channel)) + with self.assertRaisesRegex( + ValueError, 'Duplicate interval scorers requested' + ): + model.score_interval( + interval=genome.Interval('chr1', 0, 2048), + interval_scorers=[ + interval_scorers.GeneMaskScorer( + requested_output=dna_client.OutputType.DNASE, + width=501, + aggregation_type=( + interval_scorers.IntervalAggregationType.SUM + ), + ) + ] + * 2, + ) + + def test_invalid_width_raises_error(self): + model = dna_client.DnaClient(channel=mock.create_autospec(grpc.Channel)) + with self.assertRaisesRegex(ValueError, 'must have widths <= interval'): + model.score_interval( + interval=genome.Interval('chr1', 0, 2048), + interval_scorers=[ + interval_scorers.GeneMaskScorer( + requested_output=dna_client.OutputType.DNASE, + width=10_001, + aggregation_type=( + interval_scorers.IntervalAggregationType.SUM + ), + ) + ], + ) + + def test_exceed_max_interval_scorers_raises_error(self): + model = dna_client.DnaClient(channel=mock.create_autospec(grpc.Channel)) + with self.assertRaisesRegex( + ValueError, 'Too many interval scorers requested' + ): + scorers = [] + for output_type in interval_scorers.SUPPORTED_OUTPUT_TYPES[ + interval_scorers.BaseIntervalScorer.GENE_MASK + ]: + for width in interval_scorers.SUPPORTED_WIDTHS[ + interval_scorers.BaseIntervalScorer.GENE_MASK + ]: + scorers.append( + interval_scorers.GeneMaskScorer( + requested_output=output_type, + width=width, + aggregation_type=( + interval_scorers.IntervalAggregationType.SUM + ), + ) + ) + model.score_interval( + interval=genome.Interval('chr1', 0, 2048), interval_scorers=scorers + ) + + @parameterized.product( + ( + dict( + interval=genome.Interval('chr1', 0, 2048), + scorers=[ + interval_scorers.GeneMaskScorer( + requested_output=dna_client.OutputType.ATAC, + width=2_001, + aggregation_type=( + interval_scorers.IntervalAggregationType.MEAN + ), + ), + interval_scorers.GeneMaskScorer( + requested_output=dna_client.OutputType.DNASE, + width=501, + aggregation_type=( + interval_scorers.IntervalAggregationType.SUM + ), + ), + ], + ), + ), + expected=[ + ( + anndata.AnnData( + X=np.zeros((5, 10)), + var=pd.DataFrame({ + 'name': [f'{i}' for i in range(10)], + 'strand': ['.'] * 10, + }), + uns={ + 'interval': genome.Interval('chr1', 0, 2048), + 'interval_scorer': interval_scorers.GeneMaskScorer( + requested_output=dna_client.OutputType.ATAC, + width=2_001, + aggregation_type=( + interval_scorers.IntervalAggregationType.MEAN + ), + ), + }, + ), + anndata.AnnData( + X=np.ones((5, 10)), + obs=pd.DataFrame({ + 'gene_id': [f'{i}' for i in range(5)], + 'strand': ['-'] * 5, + 'gene_name': [f'gene_{i}' for i in range(5)], + 'gene_type': [f'gene_type_{i}' for i in range(5)], + }), + var=pd.DataFrame({ + 'name': [f'{i}' for i in range(10)], + 'strand': ['.'] * 10, + }), + uns={ + 'interval': genome.Interval('chr1', 0, 2048), + 'interval_scorer': interval_scorers.GeneMaskScorer( + requested_output=dna_client.OutputType.DNASE, + width=501, + aggregation_type=( + interval_scorers.IntervalAggregationType.SUM + ), + ), + }, + ), + ), + ], + organism=[ + dna_client.Organism.HOMO_SAPIENS, + dna_client.Organism.MUS_MUSCULUS, + ], + bytes_per_chunk=[0, 128], + ) + def test_score_intervals( + self, + interval, + scorers, + expected, + organism, + bytes_per_chunk, + ): + def _mock_generate(requests, metadata): + del metadata + for request in requests: + yield from _generate_interval_scoring_protos( + scores=expected, + bytes_per_chunk=bytes_per_chunk, + response_proto_type=dna_model_service_pb2.ScoreIntervalResponse, + interval=genome.Interval.from_proto(request.interval), + ) + + mock_channel, mock_stream = _create_mock_channel_and_stream(_mock_generate) + model = dna_client.DnaClient(channel=mock_channel) + all_outputs = model.score_intervals( + intervals=[interval, interval], + organism=organism, + interval_scorers=scorers, + ) + + for outputs in all_outputs: + for expected_anndata, output_anndata in zip(expected, outputs): + self._assert_anndata_equal(expected_anndata, output_anndata) + + self.assertEqual(mock_stream.call_count, 2) + + @parameterized.product( + ( + dict( + interval=genome.Interval('chr1', 0, 2048), + variant=genome.Variant('chr1', 1000, 'A', 'C'), + scorers=[ + variant_scorers.CenterMaskScorer( + requested_output=dna_client.OutputType.ATAC, + width=10_001, + aggregation_type=( + variant_scorers.AggregationType.DIFF_MEAN + ), + ), + variant_scorers.GeneMaskLFCScorer( + requested_output=dna_client.OutputType.RNA_SEQ, + ), + ], + ), + ), + expected=[ + ( + anndata.AnnData( + X=np.zeros((5, 10)), + var=pd.DataFrame({ + 'name': [f'{i}' for i in range(10)], + 'strand': ['.'] * 10, + }), + uns={ + 'variant': genome.Variant('chr1', 1000, 'A', 'C'), + 'interval': genome.Interval('chr1', 0, 2048), + 'variant_scorer': variant_scorers.CenterMaskScorer( + requested_output=dna_client.OutputType.ATAC, + width=10_001, + aggregation_type=( + variant_scorers.AggregationType.DIFF_MEAN + ), + ), + }, + ), + anndata.AnnData( + X=np.ones((5, 10)), + obs=pd.DataFrame({ + 'gene_id': [f'{i}' for i in range(5)], + 'strand': ['-'] * 5, + 'gene_name': [f'gene_{i}' for i in range(5)], + 'gene_type': [f'gene_type_{i}' for i in range(5)], + }), + var=pd.DataFrame({ + 'name': [f'{i}' for i in range(10)], + 'strand': ['.'] * 10, + }), + uns={ + 'variant': genome.Variant('chr1', 1000, 'A', 'C'), + 'interval': genome.Interval('chr1', 0, 2048), + 'variant_scorer': variant_scorers.GeneMaskLFCScorer( + requested_output=dna_client.OutputType.RNA_SEQ, + ), + }, + layers={'quantiles': np.zeros((5, 10))}, + ), + ), + ( + anndata.AnnData( + X=np.zeros((5, 10)), + var=pd.DataFrame({ + 'name': [f'{i}' for i in range(10)], + 'strand': ['.'] * 10, + }), + uns={ + 'variant': genome.Variant('chr1', 1000, 'A', 'C'), + 'interval': genome.Interval('chr1', 0, 2048), + 'variant_scorer': variant_scorers.CenterMaskScorer( + requested_output=dna_client.OutputType.ATAC, + width=10_001, + aggregation_type=( + variant_scorers.AggregationType.DIFF_MEAN + ), + ), + }, + ), + anndata.AnnData( + X=np.ones((5, 10)), + var=pd.DataFrame({ + 'name': [f'{i}' for i in range(10)], + 'strand': ['.'] * 10, + }), + uns={ + 'variant': genome.Variant('chr1', 1000, 'A', 'C'), + 'interval': genome.Interval('chr1', 0, 2048), + 'variant_scorer': variant_scorers.GeneMaskLFCScorer( + requested_output=dna_client.OutputType.RNA_SEQ, + ), + }, + ), + ), + ], + organism=[ + dna_client.Organism.HOMO_SAPIENS, + dna_client.Organism.MUS_MUSCULUS, + ], + bytes_per_chunk=[0, 128], + ) + def test_score_variant( + self, + interval, + variant, + scorers, + expected, + organism, + bytes_per_chunk, + ): + + def _mock_generate(requests, metadata): + del metadata + for request in requests: + yield from _generate_variant_scoring_protos( + scores=expected, + bytes_per_chunk=bytes_per_chunk, + response_proto_type=dna_model_service_pb2.ScoreVariantResponse, + variant=genome.Variant.from_proto(request.variant), + ) + + mock_channel, mock_stream = _create_mock_channel_and_stream(_mock_generate) + model = dna_client.DnaClient(channel=mock_channel) + outputs = model.score_variant( + interval=interval, + variant=variant, + organism=organism, + variant_scorers=scorers, + ) + + for expected_anndata, output_anndata in zip(expected, outputs): + self._assert_anndata_equal(expected_anndata, output_anndata) + + mock_stream.assert_called_once() + + def test_duplicate_variant_scorers_raises_error(self): + model = dna_client.DnaClient(channel=mock.create_autospec(grpc.Channel)) + with self.assertRaisesRegex( + ValueError, 'Duplicate variant scorers requested' + ): + model.score_variant( + interval=genome.Interval('chr1', 0, 2048), + variant=genome.Variant('chr1', 1000, 'A', 'C'), + variant_scorers=[ + variant_scorers.GeneMaskLFCScorer( + requested_output=dna_client.OutputType.RNA_SEQ, + ), + variant_scorers.GeneMaskLFCScorer( + requested_output=dna_client.OutputType.RNA_SEQ, + ), + ], + ) + + def test_invalid_organism_raises_error(self): + model = dna_client.DnaClient(channel=mock.create_autospec(grpc.Channel)) + with self.assertRaisesWithLiteralMatch( + ValueError, + 'Unsupported organism: MUS_MUSCULUS for scorer PolyadenylationScorer().' + " Supported organisms: ['HOMO_SAPIENS']", + ): + model.score_variant( + interval=genome.Interval('chr1', 0, 2048), + variant=genome.Variant('chr1', 1000, 'A', 'C'), + variant_scorers=[variant_scorers.PolyadenylationScorer()], + # The PaQTL scorer doesn't currently support mouse. + organism=dna_client.Organism.MUS_MUSCULUS, + ) + + def test_exceed_max_variant_scorers_raises_error(self): + model = dna_client.DnaClient(channel=mock.create_autospec(grpc.Channel)) + with self.assertRaisesRegex( + ValueError, 'Too many variant scorers requested' + ): + variant_scorers_to_use = [] + for output_type in variant_scorers.SUPPORTED_OUTPUT_TYPES[ + variant_scorers.BaseVariantScorer.CENTER_MASK + ]: + for width in variant_scorers.SUPPORTED_WIDTHS[ + variant_scorers.BaseVariantScorer.CENTER_MASK + ]: + variant_scorers_to_use.append( + variant_scorers.CenterMaskScorer( + requested_output=output_type, + width=width, + aggregation_type=variant_scorers.AggregationType.DIFF_MEAN, + ) + ) + model.score_variant( + interval=genome.Interval('chr1', 0, 2048), + variant=genome.Variant('chr1', 1000, 'A', 'C'), + variant_scorers=variant_scorers_to_use, + ) + + @parameterized.product(organism=list(dna_client.Organism)) + def test_recommended_variant_scorers(self, organism): + def _mock_generate(requests, metadata): + del metadata + for request in requests: + yield from _generate_variant_scoring_protos( + scores=[ + anndata.AnnData( + np.zeros((0, 0)), + var=pd.DataFrame(columns=['name', 'strand']), + ) + for _ in range(len(request.variant_scorers)) + ], + bytes_per_chunk=1024, + response_proto_type=dna_model_service_pb2.ScoreVariantResponse, + variant=genome.Variant.from_proto(request.variant), + ) + + mock_channel, mock_stream = _create_mock_channel_and_stream(_mock_generate) + model = dna_client.DnaClient(channel=mock_channel) + scores = model.score_variant( + interval=genome.Interval('chr1', 0, 2048), + variant=genome.Variant('chr1', 1000, 'A', 'C'), + organism=organism, + ) + + self.assertSameElements( + [score.uns['variant_scorer'] for score in scores], + variant_scorers.get_recommended_scorers(organism.to_proto()), + ) + mock_stream.assert_called_once() + + @parameterized.product( + ( + dict( + interval=genome.Interval('chr1', 0, 2048), + scorers=[ + variant_scorers.CenterMaskScorer( + requested_output=dna_client.OutputType.ATAC, + width=10_001, + aggregation_type=( + variant_scorers.AggregationType.DIFF_MEAN + ), + ), + variant_scorers.GeneMaskLFCScorer( + requested_output=dna_client.OutputType.RNA_SEQ, + ), + variant_scorers.SpliceJunctionScorer(), + ], + ), + ), + expected=[( + anndata.AnnData( + X=np.ones((5, 10)), + obs=pd.DataFrame({'gene_id': [f'{i}' for i in range(5)]}), + var=pd.DataFrame({ + 'name': [f'{i}' for i in range(10)], + 'strand': ['.'] * 10, + }), + uns={ + 'interval': genome.Interval('chr1', 0, 2048), + 'variant_scorer': variant_scorers.CenterMaskScorer( + requested_output=dna_client.OutputType.ATAC, + width=10_001, + aggregation_type=( + variant_scorers.AggregationType.DIFF_MEAN + ), + ), + }, + ), + anndata.AnnData( + X=np.ones((5, 10)), + obs=pd.DataFrame({'gene_id': [f'{i}' for i in range(5)]}), + var=pd.DataFrame({ + 'name': [f'{i}' for i in range(10)], + 'strand': ['.'] * 10, + }), + uns={ + 'interval': genome.Interval('chr1', 0, 2048), + 'variant_scorer': variant_scorers.GeneMaskLFCScorer( + requested_output=dna_client.OutputType.RNA_SEQ, + ), + }, + ), + anndata.AnnData( + X=np.ones((5, 10)), + obs=pd.DataFrame({ + 'gene_id': [f'{i}' for i in range(5)], + 'junction_Start': list(range(5)), + 'junction_End': list(range(1, 6)), + }), + var=pd.DataFrame({ + 'name': [f'{i}' for i in range(10)], + 'strand': ['.'] * 10, + }), + uns={ + 'interval': genome.Interval('chr1', 0, 2048), + 'variant_scorer': variant_scorers.SpliceJunctionScorer(), + }, + ), + )], + variants=[ + [ + genome.Variant('chr1', 100, 'G', 'T'), + ], + [ + genome.Variant('chr1', 100, 'G', 'T'), + genome.Variant('chr1', 1000, 'A', 'C'), + genome.Variant('chr1', 1001, 'A', 'C'), + genome.Variant('chr1', 1002, 'A', 'C'), + ], + ], + organism=[ + dna_client.Organism.HOMO_SAPIENS, + dna_client.Organism.MUS_MUSCULUS, + ], + bytes_per_chunk=[0, 128], + ) + def test_score_variants( + self, + interval: genome.Interval, + variants: list[genome.Variant], + scorers: list[dna_client.VariantScorerTypes], + expected: list[anndata.AnnData], + organism: dna_client.Organism, + bytes_per_chunk: int, + ): + position_to_expected_scores = {} + for variant in variants: + scores = [] + for score in expected: + score = score.copy() + score.uns['variant'] = variant + score.X *= variant.position + scores.append(score) + position_to_expected_scores[variant.position] = scores + + def _mock_generate(requests, metadata): + del metadata + for request in requests: + yield from _generate_variant_scoring_protos( + scores=position_to_expected_scores[request.variant.position], + bytes_per_chunk=bytes_per_chunk, + response_proto_type=dna_model_service_pb2.ScoreVariantResponse, + variant=genome.Variant.from_proto(request.variant), + ) + + mock_channel, mock_stream = _create_mock_channel_and_stream(_mock_generate) + model = dna_client.DnaClient(channel=mock_channel) + all_scores = model.score_variants( + intervals=interval, + variants=variants, + organism=organism, + variant_scorers=scorers, + ) + + for scores in all_scores: + self.assertLen(scores, len(expected)) + expected = position_to_expected_scores[scores[0].uns['variant'].position] + for expected_anndata, output_anndata in zip(expected, scores): + self._assert_anndata_equal(output_anndata, expected_anndata) + + self.assertLen(variants, mock_stream.call_count) + + @parameterized.product( + ( + dict( + interval=genome.Interval('chr1', 0, 2048), + ism_interval=genome.Interval('chr1', 10, 11), + scorers=[ + variant_scorers.CenterMaskScorer( + requested_output=dna_client.OutputType.ATAC, + width=501, + aggregation_type=( + variant_scorers.AggregationType.DIFF_MEAN + ), + ), + variant_scorers.PolyadenylationScorer(), + ], + example_scores=[ + anndata.AnnData( + X=np.zeros((5, 10)), + obs=pd.DataFrame({'gene_id': [f'{i}' for i in range(5)]}), + var=pd.DataFrame({ + 'name': [f'{i}' for i in range(10)], + 'strand': ['.'] * 10, + }), + uns={ + 'interval': genome.Interval('chr1', 0, 2048), + 'variant_scorer': variant_scorers.CenterMaskScorer( + requested_output=dna_client.OutputType.ATAC, + width=10_001, + aggregation_type=( + variant_scorers.AggregationType.DIFF_MEAN + ), + ), + }, + ), + ], + expected_variants=[ + genome.Variant('chr1', 11, 'A', 'C'), + genome.Variant('chr1', 11, 'A', 'G'), + genome.Variant('chr1', 11, 'A', 'T'), + ], + ), + dict( + interval=genome.Interval('chr1', 0, 2048), + ism_interval=genome.Interval('chr1', 10, 21), + scorers=[ + variant_scorers.CenterMaskScorer( + requested_output=dna_client.OutputType.ATAC, + width=501, + aggregation_type=( + variant_scorers.AggregationType.DIFF_MEAN + ), + ), + variant_scorers.PolyadenylationScorer(), + ], + example_scores=[ + anndata.AnnData( + X=np.zeros((5, 10)), + obs=pd.DataFrame({'gene_id': [f'{i}' for i in range(5)]}), + var=pd.DataFrame({ + 'name': [f'{i}' for i in range(10)], + 'strand': ['.'] * 10, + }), + uns={ + 'interval': genome.Interval('chr1', 0, 2048), + 'variant_scorer': variant_scorers.CenterMaskScorer( + requested_output=dna_client.OutputType.ATAC, + width=10_001, + aggregation_type=( + variant_scorers.AggregationType.DIFF_MEAN + ), + ), + }, + ), + anndata.AnnData( + X=np.ones((5, 10)), + obs=pd.DataFrame({'gene_id': [f'{i}' for i in range(5)]}), + var=pd.DataFrame({ + 'name': [f'{i}' for i in range(10)], + 'strand': ['.'] * 10, + }), + uns={ + 'interval': genome.Interval('chr1', 0, 2048), + 'variant_scorer': ( + variant_scorers.PolyadenylationScorer() + ), + }, + layers={'quantiles': np.zeros((5, 10))}, + ), + ], + expected_variants=[ + genome.Variant('chr1', 11, 'A', 'C'), + genome.Variant('chr1', 11, 'A', 'G'), + genome.Variant('chr1', 11, 'A', 'T'), + genome.Variant('chr1', 12, 'A', 'C'), + genome.Variant('chr1', 12, 'A', 'G'), + genome.Variant('chr1', 12, 'A', 'T'), + genome.Variant('chr1', 13, 'A', 'C'), + genome.Variant('chr1', 13, 'A', 'G'), + genome.Variant('chr1', 13, 'A', 'T'), + genome.Variant('chr1', 14, 'A', 'C'), + genome.Variant('chr1', 14, 'A', 'G'), + genome.Variant('chr1', 14, 'A', 'T'), + genome.Variant('chr1', 15, 'A', 'C'), + genome.Variant('chr1', 15, 'A', 'G'), + genome.Variant('chr1', 15, 'A', 'T'), + genome.Variant('chr1', 16, 'A', 'C'), + genome.Variant('chr1', 16, 'A', 'G'), + genome.Variant('chr1', 16, 'A', 'T'), + genome.Variant('chr1', 17, 'A', 'C'), + genome.Variant('chr1', 17, 'A', 'G'), + genome.Variant('chr1', 17, 'A', 'T'), + genome.Variant('chr1', 18, 'A', 'C'), + genome.Variant('chr1', 18, 'A', 'G'), + genome.Variant('chr1', 18, 'A', 'T'), + genome.Variant('chr1', 19, 'A', 'C'), + genome.Variant('chr1', 19, 'A', 'G'), + genome.Variant('chr1', 19, 'A', 'T'), + genome.Variant('chr1', 20, 'A', 'C'), + genome.Variant('chr1', 20, 'A', 'G'), + genome.Variant('chr1', 20, 'A', 'T'), + genome.Variant('chr1', 21, 'A', 'C'), + genome.Variant('chr1', 21, 'A', 'G'), + genome.Variant('chr1', 21, 'A', 'T'), + ], + ), + ), + bytes_per_chunk=[0, 128], + ) + def test_score_ism_variant( + self, + interval, + ism_interval, + scorers, + bytes_per_chunk: int, + example_scores, + expected_variants, + ): + + def _mock_generate(requests, metadata): + del metadata + for request in requests: + ism_interval = genome.Interval.from_proto(request.ism_interval) + for variant in ism.ism_variants(ism_interval, 'A' * ism_interval.width): + yield from _generate_variant_scoring_protos( + scores=example_scores, + bytes_per_chunk=bytes_per_chunk, + response_proto_type=dna_model_service_pb2.ScoreIsmVariantResponse, + variant=variant, + ) + + mock_channel, _ = _create_mock_channel_and_stream(_mock_generate) + + model = dna_client.DnaClient(channel=mock_channel) + ism_scores = model.score_ism_variants( + interval=interval, + ism_interval=ism_interval, + variant_scorers=scorers, + ) + self.assertLen(ism_scores, len(expected_variants)) + ism_scores = sorted(ism_scores, key=lambda x: str(x[0].uns['variant'])) + for expected_variant, outputs in zip( + expected_variants, ism_scores, strict=True + ): + for output, example_output in zip(outputs, example_scores, strict=True): + expected = example_output.copy() + expected.uns['variant'] = expected_variant + self._assert_anndata_equal(expected, output) + + def test_score_negative_strand_ism_interval_raises_error(self): + model = dna_client.DnaClient(channel=mock.create_autospec(grpc.Channel)) + with self.assertRaisesRegex( + ValueError, 'ISM interval must be on the positive strand.' + ): + model.score_ism_variants( + interval=genome.Interval('chr1', 0, 2048), + ism_interval=genome.Interval( + 'chr1', 10, 11, strand=genome.STRAND_NEGATIVE + ), + variant_scorers=[], + ) + + def test_create(self): + with ( + mock.patch.object(grpc, 'secure_channel') as mock_secure_channel, + mock.patch.object(grpc, 'channel_ready_future'), + ): + _ = dna_client.create('foo') + mock_secure_channel.assert_called_once_with( + 'dns:///gdmscience.googleapis.com:443', + mock.ANY, + options=mock.ANY, + ) + + def test_missing_chunk_raises_error(self): + def _mock_generate(requests, metadata): + del metadata + for request in requests: + for response in _generate_prediction_protos( + _create_example_model_predictions( + sequence_length=len(request.sequence) + ), + request.requested_outputs, + bytes_per_chunk=128, + response_proto_type=dna_model_service_pb2.PredictSequenceResponse, + ): + if response.WhichOneof('payload') != 'tensor_chunk': + yield response + + mock_channel, mock_stream = _create_mock_channel_and_stream(_mock_generate) + model = dna_client.DnaClient(channel=mock_channel) + + with self.assertRaisesRegex( + ValueError, 'Expected tensor_chunk, got "output" payload' + ): + _ = model.predict_sequence( + sequence='A' * 2048, + requested_outputs=[ + dna_client.OutputType.ATAC, + dna_client.OutputType.RNA_SEQ, + ], + ontology_terms=None, + ) + + mock_stream.assert_called_once() + + def test_missing_end_of_stream_chunk_raises_error(self): + def _mock_generate(requests, metadata): + del metadata + for request in requests: + for response in _generate_prediction_protos( + _create_example_model_predictions( + sequence_length=len(request.sequence) + ), + request.requested_outputs, + bytes_per_chunk=128, + response_proto_type=dna_model_service_pb2.PredictSequenceResponse, + ): + if response.WhichOneof('payload') != 'tensor_chunk': + yield response + + mock_channel, mock_stream = _create_mock_channel_and_stream(_mock_generate) + model = dna_client.DnaClient(channel=mock_channel) + + with self.assertRaisesRegex( + ValueError, 'Expected tensor_chunk, got end of stream' + ): + _ = model.predict_sequence( + sequence='A' * 2048, + requested_outputs=[dna_client.OutputType.ATAC], + ontology_terms=None, + ) + + mock_stream.assert_called_once() + + +class RpcRetryTest(parameterized.TestCase): + + def test_retry_rpc(self): + mock_channel, _ = _create_mock_channel_and_stream([ + _FakeRpcError(grpc.StatusCode.UNAVAILABLE), + 'bar', + ]) + + @functools.partial(dna_client.retry_rpc, initial_backoff=0.01) + def foo(channel): + return channel.stream_stream('method')(iter([])) + + self.assertEqual(foo(mock_channel), 'bar') + + @parameterized.named_parameters( + { + 'testcase_name': 'normal', + 'initial_backoff': 1.25, + 'backoff_multiplier': 1.5, + 'max_attempts': 5, + 'expected_backoff': [1.25, 1.875, 2.8125, 4.21875], + }, + { + 'testcase_name': 'no_backoff_multiplier', + 'initial_backoff': 1, + 'backoff_multiplier': 1.0, + 'max_attempts': 2, + 'expected_backoff': [1.0], + }, + { + 'testcase_name': 'no_retries', + 'initial_backoff': 1, + 'backoff_multiplier': 1.0, + 'max_attempts': 1, + 'expected_backoff': [], + }, + ) + @mock.patch.object(time, 'sleep', autospec=True) + def test_retry_fails_after_max_attempts( + self, + mock_sleep: mock.Mock, + initial_backoff: float, + backoff_multiplier: float, + max_attempts: int, + expected_backoff: Sequence[float], + ): + mock_channel, _ = _create_mock_channel_and_stream( + _FakeRpcError(grpc.StatusCode.UNAVAILABLE) + ) + + @functools.partial( + dna_client.retry_rpc, + max_attempts=max_attempts, + initial_backoff=initial_backoff, + backoff_multiplier=backoff_multiplier, + jitter=0.0, + ) + def function(channel: grpc.Channel): + return dna_model_service_pb2_grpc.DnaModelServiceStub( + channel + ).PredictSequence(iter([dna_model_service_pb2.PredictSequenceRequest()])) + + with self.assertRaises(grpc.RpcError): + _ = function(mock_channel) + + mock_sleep.assert_has_calls( + [mock.call(backoff) for backoff in expected_backoff] + ) + + def test_invalid_backoff_multiplier_raises_error(self): + with self.assertRaisesRegex( + ValueError, 'Backoff multiplier must be >= 1.0.' + ): + + @functools.partial(dna_client.retry_rpc, backoff_multiplier=0.5) + def foo(): + return 'bar' + + _ = foo() + + +if __name__ == '__main__': + absltest.main() diff --git a/alphagenome/source/src/alphagenome/models/dna_model.py b/alphagenome/source/src/alphagenome/models/dna_model.py new file mode 100644 index 0000000000000000000000000000000000000000..fa4707b695fe607a0c2744cbcad37cd22929e65d --- /dev/null +++ b/alphagenome/source/src/alphagenome/models/dna_model.py @@ -0,0 +1,475 @@ +# Copyright 2025 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Abstract base class for AlphaGenome DNA models.""" + +import abc +from collections.abc import Iterable, Sequence +import concurrent.futures +import enum + +from alphagenome.data import genome +from alphagenome.data import ontology +from alphagenome.models import dna_output +from alphagenome.models import interval_scorers as interval_scorers_lib +from alphagenome.models import variant_scorers as variant_scorers_lib +from alphagenome.protos import dna_model_pb2 +import anndata +import tqdm.auto + +# Default maximum number of workers to use for parallel requests. +DEFAULT_MAX_WORKERS = 5 + + +class Organism(enum.Enum): + """Enumeration of all the available organisms.""" + + HOMO_SAPIENS = dna_model_pb2.ORGANISM_HOMO_SAPIENS + MUS_MUSCULUS = dna_model_pb2.ORGANISM_MUS_MUSCULUS + + def to_proto(self) -> dna_model_pb2.Organism: + return self.value + + +class DnaModel(metaclass=abc.ABCMeta): + """Abstract base class for AlphaGenome DNA models.""" + + @abc.abstractmethod + def predict_sequence( + self, + sequence: str, + *, + organism: Organism = Organism.HOMO_SAPIENS, + requested_outputs: Iterable[dna_output.OutputType], + ontology_terms: Iterable[ontology.OntologyTerm | str] | None, + interval: genome.Interval | None = None, + ) -> dna_output.Output: + """Generate predictions for a given DNA sequence. + + Args: + sequence: DNA sequence to make prediction for. + organism: Organism to use for the prediction. + requested_outputs: Iterable of OutputTypes indicating which subsets of + predictions to return. + ontology_terms: Iterable of ontology terms or curies to generate + predictions for. If None returns all ontologies. + interval: Optional interval from which the sequence was derived. This is + used as the interval in the output TrackData. + + Returns: + Output for the provided DNA sequence. + """ + + def predict_sequences( + self, + sequences: Sequence[str], + *, + organism: Organism = Organism.HOMO_SAPIENS, + requested_outputs: Iterable[dna_output.OutputType], + ontology_terms: Iterable[ontology.OntologyTerm | str] | None, + progress_bar: bool = True, + max_workers: int = DEFAULT_MAX_WORKERS, + intervals: Sequence[genome.Interval] | None = None, + ) -> list[dna_output.Output]: + """Generate predictions for a given DNA sequence. + + Args: + sequences: DNA sequences to make predictions for. + organism: Organism to use for the prediction. + requested_outputs: Iterable of OutputTypes indicating which subsets of + predictions to return. + ontology_terms: Iterable of ontology terms or curies to generate + predictions for. If None returns all ontologies. + progress_bar: If True, show a progress bar. + max_workers: Number of parallel workers to use. + intervals: Optional intervals from which the sequences were derived. This + is used as the interval in the output TrackData. Must be the same length + as `sequences` if provided. + + Returns: + Outputs for the provided DNA sequences. + """ + with concurrent.futures.ThreadPoolExecutor( + max_workers=max_workers + ) as executor: + futures = [ + executor.submit( + self.predict_sequence, + sequence=sequence, + organism=organism, + ontology_terms=ontology_terms, + requested_outputs=requested_outputs, + interval=interval, + ) + for sequence, interval in zip( + sequences, intervals or [None] * len(sequences), strict=True + ) + ] + futures_as_completed = tqdm.auto.tqdm( + concurrent.futures.as_completed(futures), + total=len(futures), + disable=not progress_bar, + ) + for future in futures_as_completed: + if (exception := future.exception()) is not None: + executor.shutdown(wait=False, cancel_futures=True) + raise exception + + return [future.result() for future in futures] + + @abc.abstractmethod + def predict_interval( + self, + interval: genome.Interval, + *, + organism: Organism = Organism.HOMO_SAPIENS, + requested_outputs: Iterable[dna_output.OutputType], + ontology_terms: Iterable[ontology.OntologyTerm | str] | None, + ) -> dna_output.Output: + """Generate predictions for a given DNA interval. + + Args: + interval: DNA interval to make prediction for. + organism: Organism to use for the prediction. + requested_outputs: Iterable of OutputTypes indicating which subsets of + predictions to return. + ontology_terms: Iterable of ontology terms or curies to generate + predictions for. If None returns all ontologies. + + Returns: + Output for the provided DNA interval. + """ + + def predict_intervals( + self, + intervals: Sequence[genome.Interval], + *, + organism: Organism = Organism.HOMO_SAPIENS, + requested_outputs: Iterable[dna_output.OutputType], + ontology_terms: Iterable[ontology.OntologyTerm | str] | None, + progress_bar: bool = True, + max_workers: int = DEFAULT_MAX_WORKERS, + ) -> list[dna_output.Output]: + """Generate predictions for a sequence of DNA intervals. + + Args: + intervals: DNA intervals to make predictions for. + organism: Organism to use for the predictions. + requested_outputs: Iterable of OutputTypes indicating which subsets of + predictions to return. + ontology_terms: Iterable of ontology terms or curies to generate + predictions for. If None returns all ontologies. + progress_bar: If True, show a progress bar. + max_workers: Number of parallel workers to use. + + Returns: + Outputs for the provided DNA intervals. + """ + with concurrent.futures.ThreadPoolExecutor( + max_workers=max_workers + ) as executor: + futures = [ + executor.submit( + self.predict_interval, + interval=interval, + organism=organism, + ontology_terms=ontology_terms, + requested_outputs=requested_outputs, + ) + for interval in intervals + ] + futures_as_completed = tqdm.auto.tqdm( + concurrent.futures.as_completed(futures), + total=len(futures), + disable=not progress_bar, + ) + for future in futures_as_completed: + if (exception := future.exception()) is not None: + executor.shutdown(wait=False, cancel_futures=True) + raise exception + + return [future.result() for future in futures] + + @abc.abstractmethod + def predict_variant( + self, + interval: genome.Interval, + variant: genome.Variant, + *, + organism: Organism = Organism.HOMO_SAPIENS, + requested_outputs: Iterable[dna_output.OutputType], + ontology_terms: Iterable[ontology.OntologyTerm | str] | None, + ) -> dna_output.VariantOutput: + """Generate predictions for a given DNA variant. + + Args: + interval: DNA interval to make prediction for. + variant: DNA variant to make prediction for. + organism: Organism to use for the prediction. + requested_outputs: Iterable of OutputTypes indicating which subsets of + predictions to return. + ontology_terms: Iterable of ontology terms or curies to generate + predictions for. If None returns all ontologies. + + Returns: + Variant output for the provided DNA interval and variant. + """ + + def predict_variants( + self, + intervals: genome.Interval | Sequence[genome.Interval], + variants: Sequence[genome.Variant], + *, + organism: Organism = Organism.HOMO_SAPIENS, + requested_outputs: Iterable[dna_output.OutputType], + ontology_terms: Iterable[ontology.OntologyTerm | str] | None, + progress_bar: bool = True, + max_workers: int = DEFAULT_MAX_WORKERS, + ) -> list[dna_output.VariantOutput]: + """Generate predictions for a given DNA variant. + + Args: + intervals: DNA interval(s) to make predictions for. + variants: DNA variants to make prediction for. + organism: Organism to use for the prediction. + requested_outputs: Iterable of OutputTypes indicating which subsets of + predictions to return. + ontology_terms: Iterable of ontology terms or curies to generate + predictions for. If None returns all ontologies. + progress_bar: If True, show a progress bar. + max_workers: Number of parallel workers to use. + + Returns: + Variant outputs for each DNA interval and variant pair. + """ + if not isinstance(intervals, Sequence): + intervals = [intervals] * len(variants) + elif len(intervals) != len(variants): + raise ValueError( + 'Intervals and variants must have the same length.' + f'Got {len(intervals)} intervals and {len(variants)} variants.' + ) + with concurrent.futures.ThreadPoolExecutor( + max_workers=max_workers + ) as executor: + futures = [ + executor.submit( + self.predict_variant, + interval=interval, + variant=variant, + organism=organism, + ontology_terms=ontology_terms, + requested_outputs=requested_outputs, + ) + for interval, variant in zip(intervals, variants, strict=True) + ] + futures_as_completed = tqdm.auto.tqdm( + concurrent.futures.as_completed(futures), + total=len(futures), + disable=not progress_bar, + ) + for future in futures_as_completed: + if (exception := future.exception()) is not None: + executor.shutdown(wait=False, cancel_futures=True) + raise exception + + return [future.result() for future in futures] + + @abc.abstractmethod + def score_interval( + self, + interval: genome.Interval, + interval_scorers: Sequence[interval_scorers_lib.IntervalScorerTypes] = (), + *, + organism: Organism = Organism.HOMO_SAPIENS, + ) -> list[anndata.AnnData]: + """Generate interval scores for a single given interval. + + Args: + interval: Interval to make prediction for. + interval_scorers: Sequence of interval scorers to use for scoring. If no + interval scorers are provided, the recommended interval scorers for the + organism will be used. + organism: Organism to use for the prediction. + + Returns: + List of `AnnData` interval scores. + """ + + def score_intervals( + self, + intervals: Sequence[genome.Interval], + interval_scorers: Sequence[interval_scorers_lib.IntervalScorerTypes] = (), + *, + organism: Organism = Organism.HOMO_SAPIENS, + progress_bar: bool = True, + max_workers: int = DEFAULT_MAX_WORKERS, + ) -> list[list[anndata.AnnData]]: + """Generate interval scores for a sequence of intervals. + + Args: + intervals: Sequence of DNA intervals to make prediction for. + interval_scorers: Sequence of interval scorers to use for scoring. If no + interval scorers are provided, the recommended interval scorers for the + organism will be used. + organism: Organism to use for the prediction. + progress_bar: If True, show a progress bar. + max_workers: Number of parallel workers to use. + + Returns: + List of `AnnData` lists corresponding to score_interval + outputs for each interval. + """ + with concurrent.futures.ThreadPoolExecutor( + max_workers=max_workers + ) as executor: + futures = [ + executor.submit( + self.score_interval, + interval=interval, + interval_scorers=interval_scorers, + organism=organism, + ) + for interval in intervals + ] + futures_as_completed = tqdm.auto.tqdm( + concurrent.futures.as_completed(futures), + total=len(futures), + disable=not progress_bar, + ) + for future in futures_as_completed: + if (exception := future.exception()) is not None: + executor.shutdown(wait=False, cancel_futures=True) + raise exception + + results = [future.result() for future in futures] + return results + + @abc.abstractmethod + def score_variant( + self, + interval: genome.Interval, + variant: genome.Variant, + variant_scorers: Sequence[variant_scorers_lib.VariantScorerTypes] = (), + *, + organism: Organism = Organism.HOMO_SAPIENS, + ) -> list[anndata.AnnData]: + """Generate variant scores for a single given DNA variant. + + Args: + interval: DNA interval to make prediction for. + variant: DNA variant to make prediction for. + variant_scorers: Sequence of variant scorers to use for scoring. If no + variant scorers are provided, the recommended variant scorers for the + organism will be used. + organism: Organism to use for the prediction. + + Returns: + List of `AnnData` variant scores. + """ + + def score_variants( + self, + intervals: genome.Interval | Sequence[genome.Interval], + variants: Sequence[genome.Variant], + variant_scorers: Sequence[variant_scorers_lib.VariantScorerTypes] = (), + *, + organism: Organism = Organism.HOMO_SAPIENS, + progress_bar: bool = True, + max_workers: int = DEFAULT_MAX_WORKERS, + ) -> list[list[anndata.AnnData]]: + """Generate variant scores for a sequence of variants. + + Args: + intervals: DNA interval(s) to make prediction for. If a single interval is + provided, then the same interval will be used for all variants. + variants: Sequence of DNA variants to make prediction for. + variant_scorers: Sequence of variant scorers to use for scoring. If no + variant scorers are provided, the recommended variant scorers for the + organism will be used. + organism: Organism to use for the prediction. + progress_bar: If True, show a progress bar. + max_workers: Number of parallel workers to use. + + Returns: + List of `AnnData` lists corresponding to score_variant outputs for each + variant. + """ + if not isinstance(intervals, Sequence): + intervals = [intervals] * len(variants) + if len(intervals) != len(variants): + raise ValueError( + 'Intervals and variants must have the same length.' + f'Got {len(intervals)} intervals and {len(variants)} variants.' + ) + with concurrent.futures.ThreadPoolExecutor( + max_workers=max_workers + ) as executor: + futures = [ + executor.submit( + self.score_variant, + interval=interval, + variant=variant, + variant_scorers=variant_scorers, + organism=organism, + ) + for interval, variant in zip(intervals, variants, strict=True) + ] + futures_as_completed = tqdm.auto.tqdm( + concurrent.futures.as_completed(futures), + total=len(futures), + disable=not progress_bar, + ) + for future in futures_as_completed: + if (exception := future.exception()) is not None: + executor.shutdown(wait=False, cancel_futures=True) + raise exception + + return [future.result() for future in futures] + + @abc.abstractmethod + def score_ism_variants( + self, + interval: genome.Interval, + ism_interval: genome.Interval, + variant_scorers: Sequence[variant_scorers_lib.VariantScorerTypes] = (), + *, + organism: Organism = Organism.HOMO_SAPIENS, + ) -> list[list[anndata.AnnData]]: + """Generate in-silico mutagenesis (ISM) variant scores for a given interval. + + Args: + interval: DNA interval to make the prediction for. + ism_interval: Interval to perform ISM. + variant_scorers: Sequence of variant scorers to use for scoring each + variant. If no variant scorers are provided, the recommended variant + scorers for the organism will be used. + organism: Organism to use for the prediction. + + Returns: + List of variant scores for each variant in the ISM interval. + """ + + @abc.abstractmethod + def output_metadata( + self, organism: Organism = Organism.HOMO_SAPIENS + ) -> dna_output.OutputMetadata: + """Get the metadata for a given organism. + + Args: + organism: Organism to get metadata for. + + Returns: + OutputMetadata for the provided organism. + """ diff --git a/alphagenome/source/src/alphagenome/models/dna_model_test.py b/alphagenome/source/src/alphagenome/models/dna_model_test.py new file mode 100644 index 0000000000000000000000000000000000000000..4309e94392107c41e51b0698f96f0bc26a0a2e9e --- /dev/null +++ b/alphagenome/source/src/alphagenome/models/dna_model_test.py @@ -0,0 +1,319 @@ +# Copyright 2025 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from collections.abc import Callable, Iterable, Sequence +from unittest import mock + +from absl.testing import absltest +from absl.testing import parameterized +from alphagenome.data import genome +from alphagenome.data import ontology +from alphagenome.models import dna_model +from alphagenome.models import dna_output +from alphagenome.models import interval_scorers as interval_scorers_lib +from alphagenome.models import variant_scorers as variant_scorers_lib +import anndata + + +class _MockDnaModel(dna_model.DnaModel): + """Mock DNA model for testing.""" + + def __init__( + self, + *, + predict_sequence: Callable[..., dna_output.Output] | None = None, + predict_interval: Callable[..., dna_output.Output] | None = None, + predict_variant: Callable[..., dna_output.VariantOutput] | None = None, + score_interval: Callable[..., list[anndata.AnnData]] | None = None, + score_variant: Callable[..., list[anndata.AnnData]] | None = None, + score_ism_variants: ( + Callable[..., list[list[anndata.AnnData]]] | None + ) = None, + output_metadata: Callable[..., dna_output.OutputMetadata] | None = None, + ): + self._predict_sequence_callable = predict_sequence + self._predict_interval_callable = predict_interval + self._predict_variant_callable = predict_variant + self._score_interval_callable = score_interval + self._score_variant_callable = score_variant + self._output_metadata_callable = output_metadata + self._score_ism_variants_callable = score_ism_variants + + def predict_sequence( + self, + sequence: str, + *, + organism: dna_model.Organism = dna_model.Organism.HOMO_SAPIENS, + requested_outputs: Iterable[dna_output.OutputType], + ontology_terms: Iterable[ontology.OntologyTerm | str] | None, + interval: genome.Interval | None = None, + ) -> dna_output.Output: + if self._predict_sequence_callable is None: + raise NotImplementedError('predict_sequence is not implemented.') + return self._predict_sequence_callable( + sequence, + organism=organism, + requested_outputs=requested_outputs, + ontology_terms=ontology_terms, + interval=interval, + ) + + def predict_interval( + self, + interval: genome.Interval, + *, + organism: dna_model.Organism = dna_model.Organism.HOMO_SAPIENS, + requested_outputs: Iterable[dna_output.OutputType], + ontology_terms: Iterable[ontology.OntologyTerm | str] | None, + ) -> dna_output.Output: + if self._predict_interval_callable is None: + raise NotImplementedError('predict_interval is not implemented.') + return self._predict_interval_callable( + interval, + organism=organism, + requested_outputs=requested_outputs, + ontology_terms=ontology_terms, + ) + + def predict_variant( + self, + interval: genome.Interval, + variant: genome.Variant, + *, + organism: dna_model.Organism = dna_model.Organism.HOMO_SAPIENS, + requested_outputs: Iterable[dna_output.OutputType], + ontology_terms: Iterable[ontology.OntologyTerm | str] | None, + ) -> dna_output.VariantOutput: + if self._predict_variant_callable is None: + raise NotImplementedError('predict_variant is not implemented.') + return self._predict_variant_callable( + interval, + variant, + organism=organism, + requested_outputs=requested_outputs, + ontology_terms=ontology_terms, + ) + + def score_variant( + self, + interval: genome.Interval, + variant: genome.Variant, + variant_scorers: Sequence[variant_scorers_lib.VariantScorerTypes] = (), + *, + organism: dna_model.Organism = dna_model.Organism.HOMO_SAPIENS, + ) -> list[anndata.AnnData]: + if self._score_variant_callable is None: + raise NotImplementedError('score_variant is not implemented.') + return self._score_variant_callable( + interval, + variant, + variant_scorers=variant_scorers, + organism=organism, + ) + + def score_interval( + self, + interval: genome.Interval, + interval_scorers: Sequence[interval_scorers_lib.IntervalScorerTypes] = (), + *, + organism: dna_model.Organism = dna_model.Organism.HOMO_SAPIENS, + ) -> list[anndata.AnnData]: + if self._score_interval_callable is None: + raise NotImplementedError('score_interval is not implemented.') + return self._score_interval_callable( + interval, + interval_scorers=interval_scorers, + organism=organism, + ) + + def output_metadata( + self, organism: dna_model.Organism = dna_model.Organism.HOMO_SAPIENS + ) -> dna_output.OutputMetadata: + if self._output_metadata_callable is None: + raise NotImplementedError('output_metadata is not implemented.') + return self._output_metadata_callable(organism) + + def score_ism_variants( + self, + interval: genome.Interval, + ism_interval: genome.Interval, + variant_scorers: Sequence[variant_scorers_lib.VariantScorerTypes] = (), + *, + organism: dna_model.Organism = dna_model.Organism.HOMO_SAPIENS, + ) -> list[list[anndata.AnnData]]: + if self._score_ism_variants_callable is None: + raise NotImplementedError('score_ism_variants is not implemented.') + return self._score_ism_variants_callable( + interval, + ism_interval, + variant_scorers=variant_scorers, + organism=organism, + ) + + +class DnaModelTest(parameterized.TestCase): + + def test_predict_sequences(self): + mock_predict_sequence = mock.Mock(return_value=dna_output.Output()) + model = _MockDnaModel(predict_sequence=mock_predict_sequence) + + expected_sequences = [letter * 10 for letter in 'ACGT'] + + model.predict_sequences( + sequences=expected_sequences, + requested_outputs=[dna_output.OutputType.RNA_SEQ], + ontology_terms=None, + progress_bar=False, + ) + for sequence in expected_sequences: + mock_predict_sequence.assert_any_call( + sequence, + organism=dna_model.Organism.HOMO_SAPIENS, + requested_outputs=[dna_output.OutputType.RNA_SEQ], + ontology_terms=None, + interval=None, + ) + + def test_predict_intervals(self): + mock_predict_interval = mock.Mock(return_value=dna_output.Output()) + model = _MockDnaModel(predict_interval=mock_predict_interval) + + expected_intervals = [ + genome.Interval(chromosome, 0, 1000) + for chromosome in ['chr1', 'chr2', 'chr3', 'chr4', 'chr5', 'chr6'] + ] + + model.predict_intervals( + intervals=expected_intervals, + requested_outputs=[dna_output.OutputType.ATAC], + ontology_terms=None, + organism=dna_model.Organism.MUS_MUSCULUS, + progress_bar=False, + ) + for interval in expected_intervals: + mock_predict_interval.assert_any_call( + interval, + organism=dna_model.Organism.MUS_MUSCULUS, + requested_outputs=[dna_output.OutputType.ATAC], + ontology_terms=None, + ) + + @parameterized.product(single_interval=[True, False]) + def test_predict_variants(self, single_interval: bool): + mock_predict_variant = mock.Mock( + return_value=dna_output.VariantOutput( + reference=dna_output.Output(), alternate=dna_output.Output() + ) + ) + model = _MockDnaModel(predict_variant=mock_predict_variant) + + expected_variants = [ + genome.Variant('chr1', i * 2048, 'A', 'C') for i in range(1, 10) + ] + + if single_interval: + expected_intervals = [ + expected_variants[0].reference_interval.resize(2048) + ] * len(expected_variants) + else: + expected_intervals = [ + variant.reference_interval.resize(2048) + for variant in expected_variants + ] + + model.predict_variants( + intervals=expected_intervals[0] + if single_interval + else expected_intervals, + variants=expected_variants, + requested_outputs=[dna_output.OutputType.CAGE], + ontology_terms=[ontology.from_curie('UBERON:00001')], + progress_bar=False, + ) + for variant, interval in zip(expected_variants, expected_intervals): + mock_predict_variant.assert_any_call( + interval, + variant, + organism=dna_model.Organism.HOMO_SAPIENS, + requested_outputs=[dna_output.OutputType.CAGE], + ontology_terms=[ontology.from_curie('UBERON:00001')], + ) + + def test_score_intervals(self): + mock_score_interval = mock.Mock(return_value=[anndata.AnnData()]) + model = _MockDnaModel(score_interval=mock_score_interval) + + expected_intervals = [ + genome.Interval(chromosome, 0, 1000) + for chromosome in ['chr1', 'chr2', 'chr3', 'chr4', 'chr5', 'chr6'] + ] + + model.score_intervals( + intervals=expected_intervals, + interval_scorers=list( + interval_scorers_lib.RECOMMENDED_INTERVAL_SCORERS.values() + ), + progress_bar=False, + ) + for interval in expected_intervals: + mock_score_interval.assert_any_call( + interval, + interval_scorers=list( + interval_scorers_lib.RECOMMENDED_INTERVAL_SCORERS.values() + ), + organism=dna_model.Organism.HOMO_SAPIENS, + ) + + @parameterized.product(single_interval=[True, False]) + def test_score_variants(self, single_interval: bool): + mock_score_variant = mock.Mock(return_value=[anndata.AnnData()]) + model = _MockDnaModel(score_variant=mock_score_variant) + + expected_variants = [ + genome.Variant('chr1', i * 2048, 'A', 'C') for i in range(1, 10) + ] + + if single_interval: + expected_intervals = [ + expected_variants[0].reference_interval.resize(2048) + ] * len(expected_variants) + else: + expected_intervals = [ + variant.reference_interval.resize(2048) + for variant in expected_variants + ] + + model.score_variants( + intervals=expected_intervals[0] + if single_interval + else expected_intervals, + variants=expected_variants, + variant_scorers=list( + variant_scorers_lib.RECOMMENDED_VARIANT_SCORERS.values() + ), + progress_bar=False, + ) + for variant, interval in zip(expected_variants, expected_intervals): + mock_score_variant.assert_any_call( + interval, + variant, + variant_scorers=list( + variant_scorers_lib.RECOMMENDED_VARIANT_SCORERS.values() + ), + organism=dna_model.Organism.HOMO_SAPIENS, + ) + + +if __name__ == '__main__': + absltest.main() diff --git a/alphagenome/source/src/alphagenome/models/dna_output.py b/alphagenome/source/src/alphagenome/models/dna_output.py new file mode 100644 index 0000000000000000000000000000000000000000..d4c9e9d55ba5520251b3a3d591954206843d8641 --- /dev/null +++ b/alphagenome/source/src/alphagenome/models/dna_output.py @@ -0,0 +1,407 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Module for AlphaGenome model outputs.""" + +from collections.abc import Callable, Iterable +import dataclasses +import enum +from typing import Literal + +from alphagenome import typing +from alphagenome.data import junction_data +from alphagenome.data import ontology +from alphagenome.data import track_data +from alphagenome.protos import dna_model_pb2 +import pandas as pd + + +class OutputType(enum.Enum): + """Enumeration of all the available types of outputs. + + Attributes: + ATAC: ATAC-seq tracks capturing chromatin accessibility. + CAGE: CAGE (Cap Analysis of Gene Expression) tracks capturing gene + expression. + DNASE: DNase I hypersensitive site tracks capturing chromatin accessibility. + RNA_SEQ: RNA sequencing tracks capturing gene expression. + CHIP_HISTONE: ChIP-seq tracks capturing histone modifications. + CHIP_TF: ChIP-seq tracks capturing transcription factor binding. + SPLICE_SITES: Splice site tracks capturing donor and acceptor splice sites. + SPLICE_SITE_USAGE: Splice site usage tracks capturing the fraction of the + time that each splice site is used. + SPLICE_JUNCTIONS: Splice junction tracks capturing split read RNA-seq counts + for each junction. + CONTACT_MAPS: Contact map tracks capturing 3D DNA-DNA contact probabilities. + PROCAP: Precision Run-On sequencing and capping, used to measure gene + expression. + """ + + ATAC = dna_model_pb2.OUTPUT_TYPE_ATAC + CAGE = dna_model_pb2.OUTPUT_TYPE_CAGE + DNASE = dna_model_pb2.OUTPUT_TYPE_DNASE + RNA_SEQ = dna_model_pb2.OUTPUT_TYPE_RNA_SEQ + CHIP_HISTONE = dna_model_pb2.OUTPUT_TYPE_CHIP_HISTONE + CHIP_TF = dna_model_pb2.OUTPUT_TYPE_CHIP_TF + SPLICE_SITES = dna_model_pb2.OUTPUT_TYPE_SPLICE_SITES + SPLICE_SITE_USAGE = dna_model_pb2.OUTPUT_TYPE_SPLICE_SITE_USAGE + SPLICE_JUNCTIONS = dna_model_pb2.OUTPUT_TYPE_SPLICE_JUNCTIONS + CONTACT_MAPS = dna_model_pb2.OUTPUT_TYPE_CONTACT_MAPS + PROCAP = dna_model_pb2.OUTPUT_TYPE_PROCAP + + def __lt__(self, other: 'OutputType'): + """Compares if an other `OutputType` enum value is less than this one.""" + return self.value < other.value + + def to_proto(self) -> dna_model_pb2.OutputType: + """Converts the `OutputType` enum to a protobuf enum.""" + return self.value + + def __repr__(self) -> str: + """Returns name of the `OutputType` enum as the string representation.""" + return self.name + + +@typing.jaxtyped +@dataclasses.dataclass(frozen=True) +class Output: + """Model outputs for a single prediction. + + Attributes: + atac: TrackData of type OutputType.ATAC. + cage: TrackData of type OutputType.CAGE. + dnase: TrackData of type OutputType.DNASE. + rna_seq: TrackData of type OutputType.RNA_SEQ. + chip_histone: TrackData of type OutputType.CHIP_HISTONE. + chip_tf: TrackData of type OutputType.CHIP_TF. + splice_sites: TrackData of type OutputType.SPLICE_SITES. + splice_site_usage: TrackData of type OutputType.SPLICE_SITE_USAGE. + splice_junctions: TrackData of type OutputType.SPLICE_JUNCTIONS. + contact_maps: TrackData of type OutputType.CONTACT_MAPS. + procap: TrackData of type OutputType.PROCAP. + """ + + atac: track_data.TrackData | None = dataclasses.field( + default=None, metadata={'output_type': OutputType.ATAC} + ) + cage: track_data.TrackData | None = dataclasses.field( + default=None, metadata={'output_type': OutputType.CAGE} + ) + dnase: track_data.TrackData | None = dataclasses.field( + default=None, metadata={'output_type': OutputType.DNASE} + ) + rna_seq: track_data.TrackData | None = dataclasses.field( + default=None, metadata={'output_type': OutputType.RNA_SEQ} + ) + chip_histone: track_data.TrackData | None = dataclasses.field( + default=None, + metadata={'output_type': OutputType.CHIP_HISTONE}, + ) + chip_tf: track_data.TrackData | None = dataclasses.field( + default=None, metadata={'output_type': OutputType.CHIP_TF} + ) + + splice_sites: track_data.TrackData | None = dataclasses.field( + default=None, + metadata={'output_type': OutputType.SPLICE_SITES}, + ) + splice_site_usage: track_data.TrackData | None = dataclasses.field( + default=None, + metadata={'output_type': OutputType.SPLICE_SITE_USAGE}, + ) + splice_junctions: junction_data.JunctionData | None = dataclasses.field( + default=None, + metadata={'output_type': OutputType.SPLICE_JUNCTIONS}, + ) + + contact_maps: track_data.TrackData | None = dataclasses.field( + default=None, + metadata={'output_type': OutputType.CONTACT_MAPS}, + ) + procap: track_data.TrackData | None = dataclasses.field( + default=None, + metadata={'output_type': OutputType.PROCAP}, + ) + + def get( + self, output_type: OutputType + ) -> track_data.TrackData | junction_data.JunctionData | None: + """Gets the track data for the specified output type. + + Args: + output_type: The type of output to retrieve. + + Returns: + The track data for the specified output type, or None if no such data + exists. + """ + for field in dataclasses.fields(self): + if field.metadata['output_type'] == output_type: + return getattr(self, field.name) + + def map_track_data( + self, + fn: Callable[ + [track_data.TrackData, OutputType], + track_data.TrackData | None, + ], + ) -> 'Output': + """Applies a transformation function to each `TrackData`. + + Args: + fn: The function to apply to each `TrackData`. It should take a + `TrackData` object and an `OutputType` enum as input and return a + `TrackData` object or None. + + Returns: + A new `Output` object with the transformed track data. + """ + output_dict = {} + for field in dataclasses.fields(self): + output_type = field.metadata['output_type'] + value = self.get(output_type) + if isinstance(value, track_data.TrackData): + output_dict[field.name] = fn(value, output_type) + else: + output_dict[field.name] = value + return Output(**output_dict) + + def filter_to_strand(self, strand: Literal['+', '-', '.']) -> 'Output': + """Filters tracks by DNA strand. + + Args: + strand: The strand to filter by ('+', '-', or '.'). + + Returns: + A new `Output` object with only the tracks on the specified strand. + """ + + def _filter_to_strand( + tdata: track_data.TrackData, output_type: OutputType + ) -> track_data.TrackData | None: + del output_type # Unused. + return tdata.filter_tracks(tdata.strands == strand) + + return self.map_track_data(_filter_to_strand) + + def filter_ontology_terms( + self, ontology_terms: Iterable[ontology.OntologyTerm] + ) -> 'Output': + """Filters tracks to specific ontology terms. + + Args: + ontology_terms: An iterable of `OntologyTerm` objects to filter to. + + Returns: + A new `Output` object with only the tracks associated with the specified + ontology terms. + """ + + def _filter_ontology( + tdata: track_data.TrackData, output_type: OutputType + ) -> track_data.TrackData | None: + del output_type # Unused. + if track_ontologies := tdata.ontology_terms: + return tdata.filter_tracks( + [o in ontology_terms for o in track_ontologies] + ) + else: + return tdata + + return self.map_track_data(_filter_ontology) + + def filter_output_type(self, output_types: Iterable[OutputType]) -> 'Output': + """Filters tracks to specific output type. + + Args: + output_types: An iterable of `OutputType` enums to filter by. + + Returns: + A new `Output` object with only the tracks of the specified output types. + """ + output_dict = {} + for field in dataclasses.fields(self): + output_type = field.metadata['output_type'] + if output_type in output_types: + output_dict[field.name] = self.get(output_type) + else: + output_dict[field.name] = None + + return Output(**output_dict) + + def resize(self, width: int) -> 'Output': + """Resizes all track data to a specified width. + + Args: + width: The desired width in base pairs. + + Returns: + A new `Output` object with resized track data. + """ + return self.map_track_data(lambda tdata, _: tdata.resize(width)) + + def __add__(self, other: 'Output') -> 'Output': + """Adds the values of two `Output` objects element-wise. + + Args: + other: The `Output` object to add. + + Returns: + A new `Output` object with the summed values. + """ + + def add_track_data( + track_data1, + output_type: OutputType, + ): + """Adds two `TrackData` objects for a specific output type.""" + track_data2 = other.get(output_type) + if track_data1 is None or track_data2 is None: + return None + return track_data1 + track_data2 + + return self.map_track_data(add_track_data) + + def __sub__(self, other: 'Output') -> 'Output': + """Subtracts the values of two `Output` objects element-wise. + + Args: + other: The `Output` object to subtract. + + Returns: + A new `Output` object with the difference of the values. + """ + + def sub_track_data(track_data1, output_type: OutputType): + """Subtracts two `TrackData` objects for a specific output type.""" + track_data2 = other.get(output_type) + if track_data1 is None or track_data2 is None: + return None + return track_data1 - track_data2 + + return self.map_track_data(sub_track_data) + + +@dataclasses.dataclass(frozen=True) +class OutputMetadata: + """Metadata detailing the content of model output. + + Attributes: + atac: Metadata for ATAC-seq tracks. + cage: Metadata for CAGE tracks. + dnase: Metadata for DNase I hypersensitive site tracks. + rna_seq: Metadata for RNA sequencing tracks. + chip_histone: Metadata for ChIP-seq tracks capturing histone modifications. + chip_tf: Metadata for ChIP-seq tracks capturing transcription factor + binding. + splice_sites: Metadata for splice site tracks. + splice_site_usage: Metadata for splice site usage tracks. + splice_junctions: Metadata for splice junction tracks. + contact_maps: Metadata for contact map tracks. + procap: Metadata for procap tracks. + """ + + atac: track_data.TrackMetadata | None = dataclasses.field( + default=None, metadata={'output_type': OutputType.ATAC} + ) + cage: track_data.TrackMetadata | None = dataclasses.field( + default=None, metadata={'output_type': OutputType.CAGE} + ) + dnase: track_data.TrackMetadata | None = dataclasses.field( + default=None, metadata={'output_type': OutputType.DNASE} + ) + rna_seq: track_data.TrackMetadata | None = dataclasses.field( + default=None, metadata={'output_type': OutputType.RNA_SEQ} + ) + chip_histone: track_data.TrackMetadata | None = dataclasses.field( + default=None, metadata={'output_type': OutputType.CHIP_HISTONE} + ) + chip_tf: track_data.TrackMetadata | None = dataclasses.field( + default=None, metadata={'output_type': OutputType.CHIP_TF} + ) + splice_sites: track_data.TrackMetadata | None = dataclasses.field( + default=None, metadata={'output_type': OutputType.SPLICE_SITES} + ) + splice_site_usage: track_data.TrackMetadata | None = dataclasses.field( + default=None, metadata={'output_type': OutputType.SPLICE_SITE_USAGE} + ) + splice_junctions: junction_data.JunctionMetadata | None = dataclasses.field( + default=None, metadata={'output_type': OutputType.SPLICE_JUNCTIONS} + ) + contact_maps: track_data.TrackMetadata | None = dataclasses.field( + default=None, metadata={'output_type': OutputType.CONTACT_MAPS} + ) + procap: track_data.TrackMetadata | None = dataclasses.field( + default=None, metadata={'output_type': OutputType.PROCAP} + ) + + def get(self, output: OutputType) -> track_data.TrackMetadata | None: + """Gets the track metadata for a given output type. + + Args: + output: The `OutputType` enum value. + + Returns: + The corresponding track metadata, or None if it doesn't exist. + """ + match output: + case OutputType.ATAC: + return self.atac + case OutputType.CAGE: + return self.cage + case OutputType.DNASE: + return self.dnase + case OutputType.RNA_SEQ: + return self.rna_seq + case OutputType.CHIP_HISTONE: + return self.chip_histone + case OutputType.CHIP_TF: + return self.chip_tf + case OutputType.SPLICE_SITES: + return self.splice_sites + case OutputType.SPLICE_JUNCTIONS: + return self.splice_junctions + case OutputType.SPLICE_SITE_USAGE: + return self.splice_site_usage + case OutputType.CONTACT_MAPS: + return self.contact_maps + case OutputType.PROCAP: + return self.procap + + def concatenate(self) -> track_data.TrackMetadata: + """Concatenates all metadata into a single DataFrame. + + Returns: + A pandas DataFrame containing all the track metadata, with an additional + column 'output_type' specifying the type of each track. + """ + df_list = [] + for output_type in OutputType: + if (df := self.get(output_type)) is not None: + assert isinstance(df, pd.DataFrame) + df_list.append(df.assign(output_type=output_type)) + return pd.concat(df_list) + + +@typing.jaxtyped +@dataclasses.dataclass(frozen=True) +class VariantOutput: + """Model outputs for a variant prediction. + + Attributes: + reference: The model output for the reference sequence. + alternate: The model output for the alternate sequence. + """ + + reference: Output + alternate: Output diff --git a/alphagenome/source/src/alphagenome/models/dna_output_test.py b/alphagenome/source/src/alphagenome/models/dna_output_test.py new file mode 100644 index 0000000000000000000000000000000000000000..98df3c596a6014985fa71fd96ddf7bccb64fd69d --- /dev/null +++ b/alphagenome/source/src/alphagenome/models/dna_output_test.py @@ -0,0 +1,404 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from absl.testing import absltest +from absl.testing import parameterized +from alphagenome.data import ontology +from alphagenome.data import track_data +from alphagenome.models import dna_output +import numpy as np +import pandas as pd + + +_EXAMPLE_ONTOLOGY_TERM = 'UBERON:0002037' + +_ATAC_METADATA = pd.DataFrame( + dict( + name=['foo', 'bar', 'baz', 'buz', 'fux'], + strand=['.'] * 5, + padding=[False, False, False, False, True], + ontology_curie=[ + _EXAMPLE_ONTOLOGY_TERM, + _EXAMPLE_ONTOLOGY_TERM, + _EXAMPLE_ONTOLOGY_TERM, + 'UBERON:0000005', + None, + ], + extra=[1] * 5, + ) +) +_CAGE_METADATA = pd.DataFrame( + dict( + name=['foo', 'bar', 'baz'], + strand=['.'] * 3, + padding=[False] * 3, + ontology_curie=[_EXAMPLE_ONTOLOGY_TERM] * 3, + extra=[1] * 3, + ) +) +_DNASE_METADATA = pd.DataFrame( + dict(name=['foo'], strand=['.'], padding=[False], extra=[1]) +) +_RNA_SEQ_METADATA = pd.DataFrame( + dict( + name=['foo', 'foo', 'baz', 'buz'], + strand=['+', '-', '.', '.'], + ontology_curie=[_EXAMPLE_ONTOLOGY_TERM] * 4, + padding=[False] * 4, + extra=[1] * 4, + ) +) +_CHIP_HISTONE_METADATA = pd.DataFrame( + dict( + name=['foo', 'bar', 'baz', 'buz', 'quz'], + strand=['.'] * 5, + padding=[False] * 5, + extra=[1] * 5, + ) +) +_SPLICING_METADATA = pd.DataFrame( + dict( + name=['foo', 'bar', 'foo', 'bar'], + strand=['+'] * 2 + ['-'] * 2, + padding=[False] * 4, + extra=[1] * 4, + ) +) + + +def _create_track_data( + sequence_length: int, + metadata: pd.DataFrame, + *, + ontology_terms: list[ontology.OntologyTerm | None] | None = None, +) -> track_data.TrackData: + metadata = metadata[~metadata['padding']] + if ontology_terms is not None: + if 'ontology_curie' in metadata.columns: + metadata = metadata[ + metadata['ontology_curie'].isin( + [o.ontology_curie for o in ontology_terms if o is not None] + ) + ] + else: + metadata = metadata[slice(0, 0)] + return track_data.TrackData( + np.zeros( + (sequence_length, len(metadata)), + dtype=np.float32, + ), + metadata, + ) + + +def _get_output(sequence_length: int = 10): + return dna_output.Output( + atac=_create_track_data(sequence_length, _ATAC_METADATA), + cage=_create_track_data(sequence_length, _CAGE_METADATA), + dnase=_create_track_data(sequence_length, _DNASE_METADATA), + rna_seq=_create_track_data(sequence_length, _RNA_SEQ_METADATA), + chip_histone=None, + chip_tf=None, + splice_sites=None, + splice_site_usage=None, + splice_junctions=None, + contact_maps=None, + ) + + +def _get_rnaseq_atac_output(): + """Manually specified RNA-seq and ATAC Outputs for testing diff and add.""" + atac_track_data_1 = track_data.TrackData( + values=np.array( + [ + [0.0, 0.2, 0.3, 0.4], + [0.1, 0.3, 0.5, 0.6], + [0.2, 0.4, 0.7, 0.8], + [0.5, 0.1, 0.2, 0.6], + [0.2, 0.4, 0.7, 0.8], + ], + dtype=np.float32, + ), + metadata=pd.DataFrame({ + 'name': ['track_0', 'track_1', 'track_2', 'track_3'], + 'strand': '.', + 'tissue': ['HEART', 'BRAIN', 'BANANA', 'STOMACH'], + 'padding': [False, False, False, False], + }), + ) + + atac_track_data_2 = track_data.TrackData( + values=-1 * atac_track_data_1.values, + metadata=atac_track_data_1.metadata, + ) + + rna_seq_track_data_1 = track_data.TrackData( + values=np.array( + [ + [1.0, 1.1, 1.2, 1.3, 1.4], + [1.5, 1.6, 1.7, 1.8, 1.9], + [1.2, 1.4, 1.7, 1.8, 1.9], + [1.3, 1.4, 1.7, 1.8, 1.9], + [2.0, 2.1, 2.2, 2.3, 2.4], + ], + dtype=np.float32, + ), + metadata=pd.DataFrame({ + 'name': ['track_0', 'track_1', 'track_2', 'track_3', 'track_4'], + 'strand': ['+', '-', '-', '+', '.'], + 'tissue': ['BLADDER', 'PANCREAS', 'SKIN', 'PANCREAS', ''], + 'padding': [False, False, False, False, True], + }), + ) + + rna_seq_track_data_2 = track_data.TrackData( + values=-1 * rna_seq_track_data_1.values, + metadata=rna_seq_track_data_1.metadata, + ) + + # Build the 2 Output objects. + output_1 = dna_output.Output( + atac=atac_track_data_1, + rna_seq=rna_seq_track_data_1, + ) + output_2 = dna_output.Output( + atac=atac_track_data_2, + rna_seq=rna_seq_track_data_2, + ) + output_3 = dna_output.Output( + atac=atac_track_data_2, + rna_seq=None, + ) + return output_1, output_2, output_3 + + +class OutputTest(parameterized.TestCase): + + def setUp(self): + super().setUp() + self.output = _get_output() + # Additional manually specified outputs for testing diff and add. + self.output1, self.output2, self.output3 = _get_rnaseq_atac_output() + + def test_get(self): + atac_output = self.output.get(dna_output.OutputType.ATAC) + self.assertIsNotNone(atac_output) + self.assertLen( + _ATAC_METADATA[~_ATAC_METADATA['padding']], + atac_output.num_tracks, + ) + self.assertIsNone(self.output.get(dna_output.OutputType.CONTACT_MAPS)) + + def test_map_track_data(self): + + def _fn(tdata, output_type): + del output_type + return tdata.select_tracks_by_index([0]) + + filtered_output = self.output.map_track_data(_fn) + + atac_output = filtered_output.get(dna_output.OutputType.ATAC) + self.assertIsNotNone(atac_output) + self.assertEqual(atac_output.num_tracks, 1) + + cage_output = filtered_output.get(dna_output.OutputType.CAGE) + self.assertIsNotNone(cage_output) + self.assertEqual(cage_output.num_tracks, 1) + + dnase_output = filtered_output.get(dna_output.OutputType.DNASE) + self.assertIsNotNone(dnase_output) + self.assertEqual(dnase_output.num_tracks, 1) + + rna_seq_output = filtered_output.get(dna_output.OutputType.RNA_SEQ) + self.assertIsNotNone(rna_seq_output) + self.assertEqual(rna_seq_output.num_tracks, 1) + + self.assertIsNone(self.output.get(dna_output.OutputType.CONTACT_MAPS)) + + def test_filter_to_strand_plus(self): + filtered_output = self.output.filter_to_strand('+') + + atac_output = filtered_output.get(dna_output.OutputType.ATAC) + self.assertIsNotNone(atac_output) + self.assertEqual(atac_output.num_tracks, 0) + + cage_output = filtered_output.get(dna_output.OutputType.CAGE) + self.assertIsNotNone(cage_output) + self.assertEqual(cage_output.num_tracks, 0) + + dnase_output = filtered_output.get(dna_output.OutputType.DNASE) + self.assertIsNotNone(dnase_output) + self.assertEqual(dnase_output.num_tracks, 0) + + rna_seq_output = filtered_output.get(dna_output.OutputType.RNA_SEQ) + self.assertIsNotNone(rna_seq_output) + self.assertEqual(rna_seq_output.num_tracks, 1) + + self.assertIsNone(filtered_output.get(dna_output.OutputType.CONTACT_MAPS)) + + def test_filter_to_strand_minus(self): + filtered_output = self.output.filter_to_strand('-') + + atac_output = filtered_output.get(dna_output.OutputType.ATAC) + self.assertIsNotNone(atac_output) + self.assertEqual(atac_output.num_tracks, 0) + + cage_output = filtered_output.get(dna_output.OutputType.CAGE) + self.assertIsNotNone(cage_output) + self.assertEqual(cage_output.num_tracks, 0) + + dnase_output = filtered_output.get(dna_output.OutputType.DNASE) + self.assertIsNotNone(dnase_output) + self.assertEqual(dnase_output.num_tracks, 0) + + rna_seq_output = filtered_output.get(dna_output.OutputType.RNA_SEQ) + self.assertIsNotNone(rna_seq_output) + self.assertEqual(rna_seq_output.num_tracks, 1) + + self.assertIsNone(filtered_output.get(dna_output.OutputType.CONTACT_MAPS)) + + def test_filter_to_strand_unstranded(self): + filtered_output = self.output.filter_to_strand('.') + + atac_output = filtered_output.get(dna_output.OutputType.ATAC) + self.assertIsNotNone(atac_output) + self.assertLen( + _ATAC_METADATA[~_ATAC_METADATA['padding']], atac_output.num_tracks + ) + + cage_output = filtered_output.get(dna_output.OutputType.CAGE) + self.assertIsNotNone(cage_output) + self.assertLen( + _CAGE_METADATA[~_CAGE_METADATA['padding']], + cage_output.num_tracks, + ) + + dnase_output = filtered_output.get(dna_output.OutputType.DNASE) + self.assertIsNotNone(dnase_output) + self.assertLen( + _DNASE_METADATA[~_DNASE_METADATA['padding']], + dnase_output.num_tracks, + ) + + rna_seq_output = filtered_output.get(dna_output.OutputType.RNA_SEQ) + self.assertIsNotNone(rna_seq_output) + self.assertEqual(rna_seq_output.num_tracks, 2) + + self.assertIsNone(filtered_output.get(dna_output.OutputType.CONTACT_MAPS)) + + def test_filter_output_type(self): + output = self.output.filter_output_type({dna_output.OutputType.ATAC}) + self.assertIsInstance( + output.get(dna_output.OutputType.ATAC), track_data.TrackData + ) + self.assertIsNone(output.get(dna_output.OutputType.CAGE)) + self.assertIsNone(output.get(dna_output.OutputType.DNASE)) + self.assertIsNone(output.get(dna_output.OutputType.CONTACT_MAPS)) + + def test_filter_ontology_terms(self): + filtered_output = self.output.filter_ontology_terms( + {ontology.from_curie(_EXAMPLE_ONTOLOGY_TERM)} + ) + dnase_output = filtered_output.get(dna_output.OutputType.DNASE) + self.assertIsNotNone(dnase_output) + self.assertLen( + _DNASE_METADATA, + dnase_output.num_tracks, + ) + atac_output = filtered_output.get(dna_output.OutputType.ATAC) + self.assertIsNotNone(atac_output) + self.assertEqual( + atac_output.num_tracks, + np.sum(_ATAC_METADATA['ontology_curie'] == _EXAMPLE_ONTOLOGY_TERM), + ) + + def test_output_diff(self): + output_diff = self.output1 - self.output2 + self.assertIsInstance(output_diff.rna_seq, track_data.TrackData) + self.assertIsInstance(output_diff.atac, track_data.TrackData) + + # To fix failing build (since these fields can be None). Can't be list comp. + assert self.output1.rna_seq is not None + assert self.output2.rna_seq is not None + assert output_diff.rna_seq is not None + assert self.output1.atac is not None + assert self.output2.atac is not None + assert output_diff.atac is not None + + manual_diff_rnaseq = ( + self.output1.rna_seq.values - self.output2.rna_seq.values + ) + np.testing.assert_array_equal( + output_diff.rna_seq.values, manual_diff_rnaseq + ) + + manual_diff_atac = self.output1.atac.values - self.output2.atac.values + np.testing.assert_array_equal(output_diff.atac.values, manual_diff_atac) + + # output3 has None for rna_seq. Where None is passed, just return None + # rather than trying to do arithmetic. + output_diff = self.output1 - self.output3 + self.assertIsNone(output_diff.rna_seq) + self.assertIsNotNone(output_diff.atac) + + output_diff = self.output3 - self.output1 + self.assertIsNone(output_diff.rna_seq) + self.assertIsNotNone(output_diff.atac) + + def test_output_add(self): + output_sum = self.output1 + self.output2 + self.assertIsInstance(output_sum.rna_seq, track_data.TrackData) + self.assertIsInstance(output_sum.atac, track_data.TrackData) + + # To fix failing build (since these fields can be None). Can't be list comp. + assert self.output1.rna_seq is not None + assert self.output2.rna_seq is not None + assert output_sum.rna_seq is not None + assert self.output1.atac is not None + assert self.output2.atac is not None + assert output_sum.atac is not None + + manual_sum_rnaseq = ( + self.output1.rna_seq.values + self.output2.rna_seq.values + ) + np.testing.assert_array_equal(output_sum.rna_seq.values, manual_sum_rnaseq) + + manual_sum_atac = self.output1.atac.values + self.output2.atac.values + np.testing.assert_array_equal(output_sum.atac.values, manual_sum_atac) + + # output3 has None for rna_seq. Where None is passed, just return None + # rather than trying to do arithmetic. + output_sum = self.output1 + self.output3 + self.assertIsNone(output_sum.rna_seq) + self.assertIsNotNone(output_sum.atac) + + output_sum = self.output3 + self.output1 + self.assertIsNone(output_sum.rna_seq) + self.assertIsNotNone(output_sum.atac) + + def test_output_metadata_concatenate(self): + output_metadata = dna_output.OutputMetadata( + atac=track_data.TrackMetadata( + {'name': [f'track_{i}' for i in range(32)], 'strand': ['.'] * 32} + ), + dnase=track_data.TrackMetadata( + {'name': [f'track_{i}' for i in range(10)], 'strand': ['.'] * 10} + ), + ) + df = output_metadata.concatenate() + self.assertLen(df, 42) + + +if __name__ == '__main__': + absltest.main() diff --git a/alphagenome/source/src/alphagenome/models/interval_scorers.py b/alphagenome/source/src/alphagenome/models/interval_scorers.py new file mode 100644 index 0000000000000000000000000000000000000000..48e8c0abd5046903d75df7762a58ba61b9f12609 --- /dev/null +++ b/alphagenome/source/src/alphagenome/models/interval_scorers.py @@ -0,0 +1,146 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Module containing interval scorer dataclasses for interval scoring.""" + + +import dataclasses +import enum +from typing import TypeVar + +from alphagenome.models import dna_output +from alphagenome.protos import dna_model_pb2 +import immutabledict + + +class IntervalAggregationType(enum.Enum): + """Enum indicating the type of interval scorer aggregation. + + Attributes: + MEAN: Mean across positions. + SUM: Sum across positions. + + Methods: + to_proto: Converts the aggregation type to its corresponding proto enum. + """ + + MEAN = dna_model_pb2.IntervalAggregationType.INTERVAL_AGGREGATION_TYPE_MEAN + SUM = dna_model_pb2.IntervalAggregationType.INTERVAL_AGGREGATION_TYPE_SUM + + def to_proto(self) -> dna_model_pb2.IntervalAggregationType: + return self.value + + def __repr__(self) -> str: + return self.name + + +class BaseIntervalScorer(enum.Enum): + """Enum indicating the type of interval scoring. + + Attributes: + GENE_MASK: Gene-mask scoring (e.g., aggregation of predictions within genes) + """ + + GENE_MASK = enum.auto() + + +SUPPORTED_OUTPUT_TYPES = immutabledict.immutabledict({ + BaseIntervalScorer.GENE_MASK: [ + dna_output.OutputType.ATAC, + dna_output.OutputType.CAGE, + dna_output.OutputType.CHIP_HISTONE, + dna_output.OutputType.CHIP_TF, + dna_output.OutputType.DNASE, + dna_output.OutputType.RNA_SEQ, + dna_output.OutputType.SPLICE_SITES, + dna_output.OutputType.SPLICE_SITE_USAGE, + ], +}) + +SUPPORTED_WIDTHS = immutabledict.immutabledict({ + BaseIntervalScorer.GENE_MASK: [501, 2001, 10_001, 100_001, 200_001], +}) + + +@dataclasses.dataclass(frozen=True) +class GeneMaskScorer: + """Interval scorer for gene-mask scoring. + + Attributes: + requested_output: The requested output type (e.g. ATAC, DNASE, etc.) + width: The width of the target interval to include in the aggregation. + aggregation_type: The type of aggregation to perform. + base_interval_scorer: The base interval scorer. + name: The name of the scorer (a composite of the above attributes that + uniquely identifies a scorer combination). + + Methods: + to_proto: Converts the scorer to its corresponding proto message. + + Raises: + ValueError: If the requested output is not supported. + """ + + requested_output: dna_output.OutputType + width: int + aggregation_type: IntervalAggregationType + + @property + def base_interval_scorer(self) -> BaseIntervalScorer: + return BaseIntervalScorer.GENE_MASK + + @property + def name(self) -> str: + return str(self) + + def __post_init__(self): + if ( + self.requested_output + not in SUPPORTED_OUTPUT_TYPES[self.base_interval_scorer] + ): + raise ValueError( + f'Unsupported requested output: {self.requested_output}. Supported' + f' output types: {SUPPORTED_OUTPUT_TYPES[self.base_interval_scorer]}' + ) + if self.width not in SUPPORTED_WIDTHS[self.base_interval_scorer]: + raise ValueError( + f'Unsupported width: {self.width}. Supported widths:' + f' {SUPPORTED_WIDTHS[self.base_interval_scorer]}' + ) + + def to_proto(self) -> dna_model_pb2.IntervalScorer: + return dna_model_pb2.IntervalScorer( + gene_mask=dna_model_pb2.GeneMaskIntervalScorer( + requested_output=self.requested_output.to_proto(), + width=self.width, + aggregation_type=self.aggregation_type.to_proto(), + ) + ) + + +# TypeVar for all interval scorer types. +IntervalScorerTypes = TypeVar( + 'IntervalScorerTypes', + bound=GeneMaskScorer, +) + +# A dict of interval scorers, with our recommended settings for a wide range +# of different use cases and settings. +RECOMMENDED_INTERVAL_SCORERS = { + 'RNA_SEQ': GeneMaskScorer( + requested_output=dna_output.OutputType.RNA_SEQ, + width=200_001, + aggregation_type=IntervalAggregationType.MEAN, + ), +} diff --git a/alphagenome/source/src/alphagenome/models/interval_scorers_test.py b/alphagenome/source/src/alphagenome/models/interval_scorers_test.py new file mode 100644 index 0000000000000000000000000000000000000000..ded6dfd64eceb6f2ab4a56dbd4d1e62c1c36193c --- /dev/null +++ b/alphagenome/source/src/alphagenome/models/interval_scorers_test.py @@ -0,0 +1,79 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from absl.testing import absltest +from absl.testing import parameterized +from alphagenome.models import dna_output +from alphagenome.models import interval_scorers + +from alphagenome.protos import dna_model_pb2 + + +class IntervalScorersTest(parameterized.TestCase): + + def test_aggregation_type_to_proto(self): + expected = [ + dna_model_pb2.IntervalAggregationType.INTERVAL_AGGREGATION_TYPE_MEAN, + dna_model_pb2.IntervalAggregationType.INTERVAL_AGGREGATION_TYPE_SUM, + ] + + for interval_scorer_aggregation_type, expected_proto in zip( + interval_scorers.IntervalAggregationType, expected, strict=True + ): + self.assertEqual( + interval_scorer_aggregation_type.to_proto(), expected_proto + ) + + def test_interval_scorers_to_proto(self): + aggregation_type = ( + dna_model_pb2.IntervalAggregationType.INTERVAL_AGGREGATION_TYPE_MEAN + ) + expected = [ + dna_model_pb2.IntervalScorer( + gene_mask=dna_model_pb2.GeneMaskIntervalScorer( + requested_output=dna_model_pb2.OUTPUT_TYPE_RNA_SEQ, + width=10_001, + aggregation_type=aggregation_type, + ) + ), + ] + scorers = [ + interval_scorers.GeneMaskScorer( + requested_output=dna_output.OutputType.RNA_SEQ, + width=10_001, + aggregation_type=interval_scorers.IntervalAggregationType.MEAN, + ), + ] + for interval_scorer, expected_proto in zip(scorers, expected): + self.assertEqual(interval_scorer.to_proto(), expected_proto) + + def test_unsupported_output_type_raises_error(self): + with self.assertRaisesRegex(ValueError, 'Unsupported requested output'): + _ = interval_scorers.GeneMaskScorer( + requested_output=dna_output.OutputType.CONTACT_MAPS, + width=10_001, + aggregation_type=interval_scorers.IntervalAggregationType.MEAN, + ) + + def test_unsupported_width_raises_error(self): + with self.assertRaisesRegex(ValueError, 'Unsupported width'): + _ = interval_scorers.GeneMaskScorer( + requested_output=dna_output.OutputType.RNA_SEQ, + width=1234, + aggregation_type=interval_scorers.IntervalAggregationType.MEAN, + ) + + +if __name__ == '__main__': + absltest.main() diff --git a/alphagenome/source/src/alphagenome/models/junction_data_utils.py b/alphagenome/source/src/alphagenome/models/junction_data_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..61f4547d29eec61313060e284b149ea0b047a2fe --- /dev/null +++ b/alphagenome/source/src/alphagenome/models/junction_data_utils.py @@ -0,0 +1,216 @@ +# Copyright 2025 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Utils for converting JunctionData to/from protos.""" + +from collections.abc import Iterable, Sequence + +from alphagenome import tensor_utils +from alphagenome.data import genome +from alphagenome.data import junction_data +from alphagenome.data import ontology +from alphagenome.protos import dna_model_pb2 +from alphagenome.protos import tensor_pb2 +import numpy as np +import pandas as pd + + +def to_protos( + data: junction_data.JunctionData, + *, + bytes_per_chunk: int = 0, + compression_type: tensor_pb2.CompressionType = ( + tensor_pb2.CompressionType.COMPRESSION_TYPE_NONE + ), +) -> tuple[dna_model_pb2.JunctionData, Sequence[tensor_pb2.TensorChunk]]: + """Converts the `JunctionData` to protobuf messages. + + Args: + data: The `JunctionData` object to convert to protos. + bytes_per_chunk: The maximum number of bytes per tensor chunk. + compression_type: The compression type to use for the tensor chunks. + + Returns: + A tuple containing the `JunctionData` protobuf message and a sequence of + `TensorChunk` protobuf messages. + """ + tensor, chunks = tensor_utils.pack_tensor( + data.values, + bytes_per_chunk=bytes_per_chunk, + compression_type=compression_type, + ) + + return ( + dna_model_pb2.JunctionData( + junctions=[j.to_proto() for j in data.junctions], + values=tensor, + metadata=metadata_to_proto(data.metadata).metadata, + interval=data.interval.to_proto() if data.interval else None, + ), + chunks, + ) + + +def from_protos( + proto: dna_model_pb2.JunctionData, + chunks: Iterable[tensor_pb2.TensorChunk] = (), + *, + interval: genome.Interval | None = None, +) -> junction_data.JunctionData: + """Converts a `JunctionData` protobuf to a `JunctionData` object. + + Args: + proto: A `JunctionData` protobuf message. + chunks: A sequence of `TensorChunk` protobuf messages. + interval: Optional `Interval` object representing the genomic region + containing the junctions. Only used if the proto does not have an + interval. + + Returns: + A `JunctionData` object. + """ + values = tensor_utils.unpack_proto(proto.values, chunks) + values = tensor_utils.upcast_floating(values) + + metadata = metadata_from_proto( + dna_model_pb2.JunctionsMetadata(metadata=proto.metadata) + ) + + if proto.HasField('interval'): + interval = genome.Interval.from_proto(proto.interval) + + return junction_data.JunctionData( + junctions=np.array( + [genome.Interval.from_proto(j) for j in proto.junctions] + ), + values=values, + metadata=metadata, + interval=interval, + ) + + +def metadata_to_proto( + metadata: junction_data.JunctionMetadata, +) -> dna_model_pb2.JunctionsMetadata: + """Converts junction metadata to a JunctionsMetadata. + + Args: + metadata: A pandas DataFrame containing junction metadata. + + Returns: + A `JunctionsMetadata` protobuf message. + """ + names = metadata['name'] + default_values = [None] * len(names) + + columns = zip( + metadata['name'], + metadata.get('ontology_curie', default_values), + metadata.get('biosample_type', default_values), + metadata.get('biosample_name', default_values), + metadata.get('biosample_life_stage', default_values), + metadata.get('gtex_tissue', default_values), + metadata.get('data_source', default_values), + metadata.get('Assay title', default_values), + strict=True, + ) + + metadata_protos = [] + + for ( + name, + ontology_curie, + biosample_type, + biosample_name, + biosample_life_stage, + gtex_tissue, + data_source, + assay, + ) in columns: + if biosample_type is not None: + biosample_proto = dna_model_pb2.Biosample( + name=biosample_name, + type=dna_model_pb2.BiosampleType.Value( + f'BIOSAMPLE_TYPE_{biosample_type.upper()}' + ), + stage=biosample_life_stage, + ) + else: + biosample_proto = None + + metadata_protos.append( + dna_model_pb2.JunctionMetadata( + name=name, + ontology_term=ontology.from_curie(ontology_curie).to_proto() + if ontology_curie + else None, + biosample=biosample_proto, + gtex_tissue=gtex_tissue, + data_source=data_source, + assay=assay, + ) + ) + + return dna_model_pb2.JunctionsMetadata(metadata=metadata_protos) + + +def metadata_from_proto( + proto: dna_model_pb2.JunctionsMetadata, +) -> junction_data.JunctionMetadata: + """Create JunctionMetadata from a dna_model_pb2.JunctionsMetadata. + + Args: + proto: A `JunctionsMetadata` protobuf message. + + Returns: + A pandas DataFrame containing junction metadata. + """ + metadata = [] + for junction_proto in proto.metadata: + junction_metadata = { + 'name': junction_proto.name, + } + + if junction_proto.HasField('ontology_term'): + junction_metadata['ontology_curie'] = ontology.from_proto( + junction_proto.ontology_term + ).ontology_curie + + if junction_proto.HasField('biosample'): + junction_metadata['biosample_name'] = junction_proto.biosample.name + junction_metadata['biosample_type'] = ( + dna_model_pb2.BiosampleType.Name(junction_proto.biosample.type) + .removeprefix('BIOSAMPLE_TYPE_') + .lower() + ) + if junction_proto.biosample.HasField('stage'): + junction_metadata['biosample_life_stage'] = ( + junction_proto.biosample.stage + ) + + if junction_proto.HasField('gtex_tissue'): + junction_metadata['gtex_tissue'] = junction_proto.gtex_tissue + + if junction_proto.HasField('data_source'): + junction_metadata['data_source'] = junction_proto.data_source + + if junction_proto.HasField('assay'): + junction_metadata['Assay title'] = junction_proto.assay + + metadata.append(junction_metadata) + + if metadata: + return pd.DataFrame(metadata) + else: + return pd.DataFrame(columns=['name']) diff --git a/alphagenome/source/src/alphagenome/models/junction_data_utils_test.py b/alphagenome/source/src/alphagenome/models/junction_data_utils_test.py new file mode 100644 index 0000000000000000000000000000000000000000..63d6f6385fbae6d0ef638ddf61ca271c3500069f --- /dev/null +++ b/alphagenome/source/src/alphagenome/models/junction_data_utils_test.py @@ -0,0 +1,92 @@ +# Copyright 2025 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from absl.testing import absltest +from absl.testing import parameterized +from alphagenome.data import genome +from alphagenome.data import junction_data +from alphagenome.models import junction_data_utils +import numpy as np +import pandas as pd + + +class JunctionDataUtilsTest(parameterized.TestCase): + + def _assert_junction_data_equal( + self, + actual: junction_data.JunctionData, + expected: junction_data.JunctionData, + msg: ..., + ) -> None: + pd.testing.assert_frame_equal(actual.metadata, expected.metadata) + np.testing.assert_array_equal(actual.junctions, expected.junctions) + np.testing.assert_array_equal(actual.values, expected.values) + self.assertEqual(actual.interval, expected.interval, msg) + self.assertEqual(actual.uns, expected.uns, msg) + + def setUp(self): + super().setUp() + self.addTypeEqualityFunc( + junction_data.JunctionData, self._assert_junction_data_equal + ) + + @parameterized.parameters([None, genome.Interval('chr1', 0, 100)]) + def test_proto_roundtrip(self, interval: genome.Interval | None): + junctions = np.array([ + genome.Interval('chr1', 10, 11, '+'), + genome.Interval('chr1', 10, 11, '+'), + genome.Interval('chr1', 10, 11, '-'), + genome.Interval('chr1', 10, 11, '-'), + ]) + metadata = pd.DataFrame({ + 'name': ['foo', 'bar'], + 'ontology_curie': ['UBERON:0000005', 'UBERON:0000006'], + 'biosample_name': ['Liver', 'Kidney'], + 'biosample_type': ['tissue', 'cell_line'], + 'biosample_life_stage': ['adult', 'adult'], + 'gtex_tissue': ['liver', 'kidney'], + 'data_source': ['encode', 'encode'], + 'Assay title': ['polyA plus RNA-seq', 'polyA plus RNA-seq'], + }) + values = np.arange(8).reshape((4, 2)).astype(np.float32) + junctions = junction_data.JunctionData( + junctions, values, metadata, interval + ) + proto, chunks = junction_data_utils.to_protos(junctions) + round_trip = junction_data_utils.from_protos(proto, chunks) + self.assertEqual(junctions, round_trip) + + def test_from_protos_with_interval(self): + metadata = pd.DataFrame({ + 'name': ['foo', 'bar'], + 'ontology_curie': ['UBERON:0000005', 'UBERON:0000006'], + }) + junctions = np.array([ + genome.Interval('chr1', 10, 11, '+'), + genome.Interval('chr1', 10, 11, '-'), + ]) + values = np.arange(4).reshape((2, 2)).astype(np.float32) + proto, chunks = junction_data_utils.to_protos( + junction_data.JunctionData(junctions, values, metadata) + ) + interval = genome.Interval('chr1', 0, 100) + round_trip = junction_data_utils.from_protos( + proto, chunks, interval=interval + ) + expected = junction_data.JunctionData(junctions, values, metadata, interval) + self.assertEqual(round_trip, expected) + + +if __name__ == '__main__': + absltest.main() diff --git a/alphagenome/source/src/alphagenome/models/track_data_utils.py b/alphagenome/source/src/alphagenome/models/track_data_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..23d5b535f38174a758b5002c89fe8e80d34dc165 --- /dev/null +++ b/alphagenome/source/src/alphagenome/models/track_data_utils.py @@ -0,0 +1,260 @@ +# Copyright 2025 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +"""Track data utils for converting to/from protos.""" + +from collections.abc import Iterable, Sequence + +from alphagenome import tensor_utils +from alphagenome.data import genome +from alphagenome.data import ontology +from alphagenome.data import track_data +from alphagenome.protos import dna_model_pb2 +from alphagenome.protos import tensor_pb2 +import pandas as pd + + +def to_protos( + data: track_data.TrackData, + *, + bytes_per_chunk: int = 0, + compression_type: tensor_pb2.CompressionType = ( + tensor_pb2.CompressionType.COMPRESSION_TYPE_NONE + ), +) -> tuple[dna_model_pb2.TrackData, Sequence[tensor_pb2.TensorChunk]]: + """Serializes `TrackData` to protobuf messages. + + Args: + data: The `TrackData` object to serialize. + bytes_per_chunk: The maximum number of bytes per tensor chunk. + compression_type: The compression type to use for the tensor chunks. + + Returns: + A tuple containing the `TrackData` protobuf message and a sequence of + `TensorChunk` protobuf messages. + """ + tensor, chunks = tensor_utils.pack_tensor( + data.values, + bytes_per_chunk=bytes_per_chunk, + compression_type=compression_type, + ) + return ( + dna_model_pb2.TrackData( + values=tensor, + metadata=metadata_to_proto(data.metadata).metadata, + resolution=data.resolution, + interval=data.interval.to_proto() if data.interval else None, + ), + chunks, + ) + + +def from_protos( + proto: dna_model_pb2.TrackData, + chunks: Iterable[tensor_pb2.TensorChunk] = (), + *, + interval: genome.Interval | None = None, +) -> track_data.TrackData: + """Creates a `TrackData` object from protobuf messages. + + Args: + proto: A `TrackData` protobuf message. + chunks: A sequence of `TensorChunk` protobuf messages. + interval: Optional `Interval` object representing the genomic region + containing the tracks. Only used if the proto does not have an interval. + + Returns: + A `TrackData` object. + """ + metadata = metadata_from_proto( + dna_model_pb2.TracksMetadata(metadata=proto.metadata) + ) + + values = tensor_utils.unpack_proto(proto.values, chunks) + values = tensor_utils.upcast_floating(values) + resolution = proto.resolution if proto.HasField('resolution') else 1 + if proto.HasField('interval'): + interval = genome.Interval.from_proto(proto.interval) + + return track_data.TrackData( + values, metadata, resolution=resolution, interval=interval + ) + + +def metadata_to_proto( + metadata: track_data.TrackMetadata, +) -> dna_model_pb2.TracksMetadata: + """Converts track metadata to a `TracksMetadata` protobuf message. + + Args: + metadata: A pandas DataFrame containing track metadata, with the following + required columns: name, strand. + + Returns: + A `TracksMetadata` protobuf message. + """ + names = metadata['name'] + default_values = [None] * len(names) + + columns = zip( + metadata['name'], + metadata['strand'], + metadata.get('ontology_curie', default_values), + metadata.get('biosample_type', default_values), + metadata.get('biosample_name', default_values), + metadata.get('biosample_life_stage', default_values), + metadata.get('transcription_factor', default_values), + metadata.get('histone_mark', default_values), + metadata.get('gtex_tissue', default_values), + metadata.get('Assay title', default_values), + metadata.get('data_source', default_values), + metadata.get('genetically_modified', default_values), + metadata.get('endedness', default_values), + metadata.get('nonzero_mean', default_values), + strict=True, + ) + + metadata_protos = [] + for ( + name, + strand, + ontology_curie, + biosample_type, + biosample_name, + biosample_life_stage, + transcription_factor, + histone_mark, + gtex_tissue, + assay, + data_source, + genetically_modified, + endedness, + nonzero_mean, + ) in columns: + if biosample_type: + biosample = dna_model_pb2.Biosample( + name=biosample_name, + type=dna_model_pb2.BiosampleType.Value( + f'BIOSAMPLE_TYPE_{biosample_type.upper()}' + ), + stage=biosample_life_stage, + ) + else: + biosample = None + if endedness is not None: + match endedness: + case 'paired': + endedness = dna_model_pb2.Endedness.ENDEDNESS_PAIRED + case 'single': + endedness = dna_model_pb2.Endedness.ENDEDNESS_SINGLE + case _: + raise ValueError(f'Unknown endedness: {endedness}') + + metadata_protos.append( + dna_model_pb2.TrackMetadata( + name=name, + strand=genome.Strand.from_str(strand).to_proto() + if strand + else None, + ontology_term=ontology.from_curie(ontology_curie).to_proto() + if ontology_curie + else None, + biosample=biosample, + transcription_factor_code=transcription_factor + if isinstance(transcription_factor, str) + else None, + histone_mark_code=histone_mark + if isinstance(histone_mark, str) + else None, + gtex_tissue=gtex_tissue, + assay=assay, + data_source=data_source, + genetically_modified=genetically_modified, + endedness=endedness, + nonzero_mean=nonzero_mean, + ) + ) + + return dna_model_pb2.TracksMetadata(metadata=metadata_protos) + + +def metadata_from_proto( + proto: dna_model_pb2.TracksMetadata, +) -> track_data.TrackMetadata: + """Creates track metadata from a `TracksMetadata` protobuf message. + + Args: + proto: A `TracksMetadata` protobuf message. + + Returns: + A pandas DataFrame containing track metadata. + """ + metadata = [] + for track_proto in proto.metadata: + track_metadata = { + 'name': track_proto.name, + 'strand': str(genome.Strand.from_proto(track_proto.strand)), + } + + if track_proto.HasField('assay'): + track_metadata['Assay title'] = track_proto.assay + + if track_proto.HasField('ontology_term'): + track_metadata['ontology_curie'] = ontology.from_proto( + track_proto.ontology_term + ).ontology_curie + + if track_proto.HasField('biosample'): + track_metadata['biosample_name'] = track_proto.biosample.name + track_metadata['biosample_type'] = ( + dna_model_pb2.BiosampleType.Name(track_proto.biosample.type) + .removeprefix('BIOSAMPLE_TYPE_') + .lower() + ) + if track_proto.biosample.HasField('stage'): + track_metadata['biosample_life_stage'] = track_proto.biosample.stage + + if track_proto.HasField('transcription_factor_code'): + track_metadata['transcription_factor'] = ( + track_proto.transcription_factor_code + ) + + if track_proto.HasField('histone_mark_code'): + track_metadata['histone_mark'] = track_proto.histone_mark_code + + if track_proto.HasField('gtex_tissue'): + track_metadata['gtex_tissue'] = track_proto.gtex_tissue + + if track_proto.HasField('data_source'): + track_metadata['data_source'] = track_proto.data_source + + if track_proto.HasField('endedness'): + track_metadata['endedness'] = ( + dna_model_pb2.Endedness.Name(track_proto.endedness) + .removeprefix('ENDEDNESS_') + .lower() + ) + + if track_proto.HasField('genetically_modified'): + track_metadata['genetically_modified'] = track_proto.genetically_modified + + if track_proto.HasField('nonzero_mean'): + track_metadata['nonzero_mean'] = track_proto.nonzero_mean + + metadata.append(track_metadata) + if metadata: + return pd.DataFrame(metadata) + else: + return pd.DataFrame(columns=['name', 'strand']) diff --git a/alphagenome/source/src/alphagenome/models/track_data_utils_test.py b/alphagenome/source/src/alphagenome/models/track_data_utils_test.py new file mode 100644 index 0000000000000000000000000000000000000000..56c445a29ef54159b342b57125c33a06234291b5 --- /dev/null +++ b/alphagenome/source/src/alphagenome/models/track_data_utils_test.py @@ -0,0 +1,228 @@ +# Copyright 2025 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from absl.testing import absltest +from absl.testing import parameterized +from alphagenome import tensor_utils +from alphagenome.data import genome +from alphagenome.data import track_data +from alphagenome.protos import dna_model_pb2 +from alphagenome.models import track_data_utils +import ml_dtypes +import numpy as np +import pandas as pd + + +class TrackDataUtilsTest(parameterized.TestCase): + + @parameterized.product( + include_ontologies=[True, False], + bytes_per_chunk=[4, 8], + dtypes=[np.float32, np.int32], + resolution=[1, 128], + include_interval=[True, False], + include_biosample=[True, False], + include_transcription_factor=[True, False], + include_histone_mark=[True, False], + include_gtex_tissue=[True, False], + include_endedness=[True, False], + include_genetically_modified=[True, False], + include_data_source=[True, False], + include_nonzero_mean=[True, False], + override_interval=[True, False], + ) + def test_proto_roundtrip( + self, + include_ontologies: bool, + bytes_per_chunk: int, + dtypes: np.dtype, + resolution: int, + include_interval: bool, + include_biosample: bool, + include_transcription_factor: bool, + include_histone_mark: bool, + include_gtex_tissue: bool, + include_endedness: bool, + include_genetically_modified: bool, + include_data_source: bool, + include_nonzero_mean: bool, + override_interval: bool, + ): + df = pd.DataFrame({ + 'name': [f'track_{i}' for i in range(7)], + 'strand': ['.', '+', '-', '.', '+', '-', '.'], + 'Assay title': ['foo', 'bar', 'baz', 'qux', 'quux', 'wuz', 'buz'], + }) + if include_ontologies: + df['ontology_curie'] = [ + 'UBERON:0000005', + 'UBERON:0000007', + 'UBERON:0000007', + 'UBERON:0000007', + None, + None, + None, + ] + if include_biosample: + df['biosample_name'] = [f'biosample_{i}' for i in range(6)] + [None] + df['biosample_type'] = [ + 'primary_cell', + 'in_vitro_differentiated_cells', + 'cell_line', + 'tissue', + 'technical_sample', + 'organoid', + None, + ] + df['biosample_life_stage'] = [ + 'adult', + 'adult', + None, + 'newborn', + 'child', + 'embryonic', + None, + ] + if include_transcription_factor: + df['transcription_factor'] = [ + 'BCLAF1', + 'BRF2', + 'CBFA2T3', + 'CEBPB', + 'COPS2', + 'POLR2AphosphoS5', + None, + ] + if include_histone_mark: + df['histone_mark'] = [ + 'H2AFZ', + 'H2AK5AC', + 'H2BK120AC', + 'H2BK12AC', + 'H2BK20AC', + 'H2BK5AC', + None, + ] + if include_gtex_tissue: + df['gtex_tissue'] = [ + 'Liver', + '', + '', + 'Brain_Hippocampus', + 'Pancreas', + 'Artery_Aorta', + None, + ] + if include_data_source: + df['data_source'] = [ + 'encode', + 'encode', + 'encode', + 'fantom', + 'fantom', + 'gtex', + None, + ] + if include_endedness: + df['endedness'] = [ + 'paired', + 'single', + 'paired', + 'single', + 'paired', + 'single', + None, + ] + if include_genetically_modified: + df['genetically_modified'] = [ + True, + False, + True, + False, + True, + False, + None, + ] + if include_nonzero_mean: + df['nonzero_mean'] = [1.1, None, 1.1, 1.1, 1.1, 0.6, None] + + values = np.array( + [ + [0.0, 0.2, 0.3, 0.4, 0.5, 0.1, 0.1], + [0.1, 0.3, 0.5, 0.6, 0.7, 0.1, 0.1], + [0.2, 0.4, 0.7, 0.8, 0.9, 0.1, 0.1], + ], + dtype=dtypes, + ) + expected_interval = ( + genome.Interval('chr1', 0, values.shape[0] * resolution) + if include_interval or override_interval + else None + ) + + metadata = track_data.TrackData( + values=values, + metadata=df, + resolution=resolution, + interval=genome.Interval('chr1', 0, values.shape[0] * resolution) + if include_interval + else None, + ) + + proto, chunks = track_data_utils.to_protos( + metadata, bytes_per_chunk=bytes_per_chunk + ) + roundtrip = track_data_utils.from_protos( + proto, + chunks, + interval=genome.Interval('chr1', 0, values.shape[0] * resolution) + if override_interval + else None, + ) + + np.testing.assert_array_equal(roundtrip.values, values) + pd.testing.assert_frame_equal(roundtrip.metadata, df) + self.assertEqual(roundtrip.resolution, resolution) + self.assertEqual(roundtrip.interval, expected_interval) + + @parameterized.product( + bytes_per_chunk=[0, 4, 8], dtype=[ml_dtypes.bfloat16, np.float16] + ) + def test_from_low_precision_proto(self, bytes_per_chunk, dtype): + values = np.array( + [ + [0.0, 0.2, 0.3, 0.4, 0.5], + [0.1, 0.3, 0.5, 0.6, 0.7], + [0.2, 0.4, 0.7, 0.8, 0.9], + ], + dtype=dtype, + ) + metadata = pd.DataFrame({ + 'name': ['track_0', 'track_1', 'track_2', 'track_3', 'track_4'], + 'strand': ['.'] * 5, + }) + tensor, chunks = tensor_utils.pack_tensor( + values, bytes_per_chunk=bytes_per_chunk + ) + proto = dna_model_pb2.TrackData( + values=tensor, + metadata=track_data_utils.metadata_to_proto(metadata).metadata, + ) + roundtrip = track_data_utils.from_protos(proto, chunks) + np.testing.assert_array_equal(roundtrip.values, values.astype(np.float32)) + pd.testing.assert_frame_equal(roundtrip.metadata, metadata) + + +if __name__ == '__main__': + absltest.main() diff --git a/alphagenome/source/src/alphagenome/models/variant_scorers.py b/alphagenome/source/src/alphagenome/models/variant_scorers.py new file mode 100644 index 0000000000000000000000000000000000000000..9cdbf31c0c40c8a1bc0760509343f9a4e4bc2096 --- /dev/null +++ b/alphagenome/source/src/alphagenome/models/variant_scorers.py @@ -0,0 +1,872 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Module containing variant scorer dataclasses for variant scoring.""" + + +from collections.abc import Sequence +import dataclasses +import enum +import itertools +from typing import TypeVar + +from alphagenome.models import dna_output +from alphagenome.protos import dna_model_pb2 +import anndata +import immutabledict +import pandas as pd + + +class AggregationType(enum.Enum): + """Enum indicating the type of variant scorer aggregation. + + Attributes: + DIFF_MEAN: Difference of means, i.e., mean(`ALT`) - mean(`REF`). + DIFF_SUM: Difference of sums, i.e., sum(`ALT`) - sum(`REF`). + DIFF_SUM_LOG2: Log scales predictions, then takes the sum and then the + difference, i.e., sum(log2(`ALT`)) - sum(log2(`REF`)). + DIFF_LOG2_SUM: Takes the sum of predictions, applies a log transform, then + takes the difference between predictions, i.e., log2(sum(`ALT`)) - + log2(sum(`REF`)). + L2_DIFF: Takes the difference of `ALT` and `REF` predictions, then computes + the L2 norm, i.e., l2_norm(`ALT` - `REF`). + L2_DIFF_LOG1P: Log scales the predictions + 1, takes the difference of `ALT` + and `REF` predictions, then computes the L2 norm, i.e., + l2_norm(log1p(`ALT`) - log1p(`REF`)). + ACTIVE_MEAN: Maximum of means, i.e., max(mean(`ALT`), mean(`REF`)). + ACTIVE_SUM: Maximum of sums, i.e., max(sum(`ALT), sum(`REF`)). + + Methods: + to_proto: Converts the aggregation type to its corresponding proto enum. + """ + + DIFF_MEAN = dna_model_pb2.AggregationType.AGGREGATION_TYPE_DIFF_MEAN + DIFF_SUM = dna_model_pb2.AggregationType.AGGREGATION_TYPE_DIFF_SUM + DIFF_SUM_LOG2 = dna_model_pb2.AggregationType.AGGREGATION_TYPE_DIFF_SUM_LOG2 + DIFF_LOG2_SUM = dna_model_pb2.AggregationType.AGGREGATION_TYPE_DIFF_LOG2_SUM + L2_DIFF = dna_model_pb2.AggregationType.AGGREGATION_TYPE_L2_DIFF + L2_DIFF_LOG1P = dna_model_pb2.AggregationType.AGGREGATION_TYPE_L2_DIFF_LOG1P + ACTIVE_MEAN = dna_model_pb2.AggregationType.AGGREGATION_TYPE_ACTIVE_MEAN + ACTIVE_SUM = dna_model_pb2.AggregationType.AGGREGATION_TYPE_ACTIVE_SUM + + def to_proto(self) -> dna_model_pb2.AggregationType: + return self.value + + def __repr__(self) -> str: + return self.name + + +class BaseVariantScorer(enum.Enum): + """Enum indicating the type of variant scoring. + + Attributes: + CENTER_MASK: Center mask scorer. + CONTACT_MAP: Contact map scorer, a center mask scorer that handles 2D + tensors. + GENE_MASK_LFC: Gene-mask scoring based on log fold change. + GENE_MASK_ACTIVE: Gene-mask scoring based on active allele. + GENE_MASK_SPLICING: Gene-mask scoring for splicing. + PA_QTL: Polyadenylation QTL scoring. + SPLICE_JUNCTION: Splice junction scoring. + """ + + CENTER_MASK = enum.auto() + CONTACT_MAP = enum.auto() + GENE_MASK_LFC = enum.auto() + GENE_MASK_ACTIVE = enum.auto() + GENE_MASK_SPLICING = enum.auto() + PA_QTL = enum.auto() + SPLICE_JUNCTION = enum.auto() + + +SUPPORTED_ORGANISMS = immutabledict.immutabledict({ + BaseVariantScorer.CENTER_MASK: dna_model_pb2.Organism.values(), + BaseVariantScorer.CONTACT_MAP: dna_model_pb2.Organism.values(), + BaseVariantScorer.GENE_MASK_LFC: dna_model_pb2.Organism.values(), + BaseVariantScorer.GENE_MASK_ACTIVE: dna_model_pb2.Organism.values(), + BaseVariantScorer.GENE_MASK_SPLICING: dna_model_pb2.Organism.values(), + BaseVariantScorer.PA_QTL: [dna_model_pb2.Organism.ORGANISM_HOMO_SAPIENS], + BaseVariantScorer.SPLICE_JUNCTION: dna_model_pb2.Organism.values(), +}) + +SUPPORTED_OUTPUT_TYPES = immutabledict.immutabledict({ + BaseVariantScorer.CENTER_MASK: [ + dna_output.OutputType.ATAC, + dna_output.OutputType.CAGE, + dna_output.OutputType.DNASE, + dna_output.OutputType.PROCAP, + dna_output.OutputType.RNA_SEQ, + dna_output.OutputType.CHIP_HISTONE, + dna_output.OutputType.CHIP_TF, + dna_output.OutputType.SPLICE_SITES, + dna_output.OutputType.SPLICE_SITE_USAGE, + ], + BaseVariantScorer.CONTACT_MAP: [dna_output.OutputType.CONTACT_MAPS], + BaseVariantScorer.GENE_MASK_LFC: [ + dna_output.OutputType.ATAC, + dna_output.OutputType.CAGE, + dna_output.OutputType.DNASE, + dna_output.OutputType.PROCAP, + dna_output.OutputType.RNA_SEQ, + dna_output.OutputType.SPLICE_SITES, + dna_output.OutputType.SPLICE_SITE_USAGE, + ], + BaseVariantScorer.GENE_MASK_ACTIVE: [ + dna_output.OutputType.ATAC, + dna_output.OutputType.CAGE, + dna_output.OutputType.DNASE, + dna_output.OutputType.PROCAP, + dna_output.OutputType.RNA_SEQ, + dna_output.OutputType.SPLICE_SITES, + dna_output.OutputType.SPLICE_SITE_USAGE, + ], + BaseVariantScorer.GENE_MASK_SPLICING: [ + dna_output.OutputType.SPLICE_SITES, + dna_output.OutputType.SPLICE_SITE_USAGE, + ], +}) + +SUPPORTED_WIDTHS = immutabledict.immutabledict({ + BaseVariantScorer.CENTER_MASK: [None, 501, 2001, 10_001, 100_001, 200_001], + BaseVariantScorer.GENE_MASK_SPLICING: [None, 101, 1_001, 10_001], +}) + +SUPPORTED_AGGREGATIONS = immutabledict.immutabledict({ + BaseVariantScorer.CENTER_MASK: [ + AggregationType.DIFF_MEAN, + AggregationType.DIFF_SUM, + AggregationType.DIFF_SUM_LOG2, + AggregationType.DIFF_LOG2_SUM, + AggregationType.L2_DIFF, + AggregationType.L2_DIFF_LOG1P, + AggregationType.ACTIVE_MEAN, + AggregationType.ACTIVE_SUM, + ], +}) + + +@dataclasses.dataclass(frozen=True) +class CenterMaskScorer: + """Variant scorer for center mask scorers. + + Variant scorer that aggregates ALT and REF values using a spatial mask + centered around the variant before computing the difference. This scorer + aggregates over the spatial axis and returns one score per output track. + + Attributes: + requested_output: The requested output type (e.g., ATAC, DNASE, etc.) + width: The width of the mask around the variant. If None, the score is + computed over the entire sequence. + aggregation_type: The aggregation type. + base_variant_scorer: The base variant scorer. + name: The name of the scorer (a composite of the above attributes that + uniquely identifies a scorer combination). + is_signed: Whether this variant scorer is directional (such that scores may + be negative or positive) or non-directional (scores are always positive). + + Methods: + to_proto: Converts the scorer to its corresponding proto message. + + Raises: + ValueError: If the requested output, width, or aggregation type is not + supported. + """ + + requested_output: dna_output.OutputType + width: int | None + aggregation_type: AggregationType + + @property + def base_variant_scorer(self) -> BaseVariantScorer: + return BaseVariantScorer.CENTER_MASK + + @property + def name(self) -> str: + return str(self) + + @property + def is_signed(self) -> bool: + # The 'active' scorers track the maximum of the ALT and REF, and are + # non-directional. All other scorers track ALT - REF, with L2_DIFF + # nondirectional and the remaining directional. + return self.aggregation_type in [ + AggregationType.DIFF_MEAN, + AggregationType.DIFF_SUM, + AggregationType.DIFF_SUM_LOG2, + AggregationType.DIFF_LOG2_SUM, + ] + + def __post_init__(self): + if ( + self.requested_output + not in SUPPORTED_OUTPUT_TYPES[self.base_variant_scorer] + ): + raise ValueError( + f'Unsupported requested output: {self.requested_output}. Supported' + f' output types: {SUPPORTED_OUTPUT_TYPES[self.base_variant_scorer]}' + ) + if self.width not in SUPPORTED_WIDTHS[self.base_variant_scorer]: + raise ValueError( + f'Unsupported width: {self.width}. Supported widths:' + f' {SUPPORTED_WIDTHS[self.base_variant_scorer]}' + ) + if ( + self.aggregation_type + not in SUPPORTED_AGGREGATIONS[self.base_variant_scorer] + ): + raise ValueError( + f'Unsupported aggregation type: {self.aggregation_type}. Supported' + ' aggregation types:' + f' {SUPPORTED_AGGREGATIONS[self.base_variant_scorer]}' + ) + + def to_proto(self) -> dna_model_pb2.VariantScorer: + return dna_model_pb2.VariantScorer( + center_mask=dna_model_pb2.CenterMaskScorer( + requested_output=self.requested_output.to_proto(), + width=self.width, + aggregation_type=self.aggregation_type.to_proto(), + ) + ) + + +@dataclasses.dataclass(frozen=True) +class ContactMapScorer: + """Variant scorer for contact map scorers. + + Variant scorer that quantifies local contact disruption between ALT and REF + alleles. Uses a 1MB window centered around the variant to calculate the + mean absolute difference of contact frequencies, for all interactions + involving the variant-containing bin. + + Attributes: + requested_output: The requested output type (e.g., CONTACT_MAPS). + base_variant_scorer: The base variant scorer. + name: The name of the scorer (a composite of the above attributes that + uniquely identifies a scorer combination). + is_signed: Whether this variant scorer is directional (such that scores may + be negative or positive) or non-directional (scores are always positive). + + Methods: + to_proto: Converts the scorer to its corresponding proto message. + """ + + @property + def base_variant_scorer(self) -> BaseVariantScorer: + return BaseVariantScorer.CONTACT_MAP + + @property + def name(self) -> str: + return str(self) + + @property + def is_signed(self) -> bool: + return False + + def to_proto(self) -> dna_model_pb2.VariantScorer: + return dna_model_pb2.VariantScorer( + contact_map=dna_model_pb2.ContactMapScorer() + ) + + @property + def requested_output(self) -> dna_output.OutputType: + return dna_output.OutputType.CONTACT_MAPS + + +@dataclasses.dataclass(frozen=True) +class GeneMaskLFCScorer: + """Variant scorer for gene-mask log fold change scoring. + + Variant scorer that quantifies impact on overall gene transcript abundance. + Calculates the log fold change of gene expression level between ALT and REF + alleles using a gene exon mask. + + Attributes: + requested_output: The requested output type (e.g. ATAC, DNASE, etc.) + base_variant_scorer: The base variant scorer. + name: The name of the scorer (a composite of the above attributes that + uniquely identifies a scorer combination). + is_signed: Whether this variant scorer is directional (such that scores may + be negative or positive) or non-directional (scores are always positive). + + Methods: + to_proto: Converts the scorer to its corresponding proto message. + + Raises: + ValueError: If the requested output is not supported. + """ + + requested_output: dna_output.OutputType + + @property + def base_variant_scorer(self) -> BaseVariantScorer: + return BaseVariantScorer.GENE_MASK_LFC + + @property + def name(self) -> str: + return str(self) + + @property + def is_signed(self) -> bool: + return True + + def __post_init__(self): + if ( + self.requested_output + not in SUPPORTED_OUTPUT_TYPES[self.base_variant_scorer] + ): + raise ValueError( + f'Unsupported requested output: {self.requested_output}. Supported' + f' output types: {SUPPORTED_OUTPUT_TYPES[self.base_variant_scorer]}' + ) + + def to_proto(self) -> dna_model_pb2.VariantScorer: + return dna_model_pb2.VariantScorer( + gene_mask=dna_model_pb2.GeneMaskLFCScorer( + requested_output=self.requested_output.to_proto(), + ) + ) + + +@dataclasses.dataclass(frozen=True) +class GeneMaskActiveScorer: + """Variant scorer for gene-mask active allele scoring. + + Variant scorer that captures absolute activity level associated with one of + the alleles, rather than quantifying the difference between the ALT and REF. + Active allele gene scores are calculated by taking the maximum of the + aggregated ALT and REF signals across exons for a gene of interest. + + Attributes: + requested_output: The requested output type (e.g. ATAC, DNASE, etc.) + base_variant_scorer: The base variant scorer. + name: The name of the scorer (a composite of the above attributes that + uniquely identifies a scorer combination). + is_signed: Whether this variant scorer is directional (such that scores may + be negative or positive) or non-directional (scores are always positive). + + Methods: + to_proto: Converts the scorer to its corresponding proto message. + + Raises: + ValueError: If the requested output is not supported. + """ + + requested_output: dna_output.OutputType + + @property + def base_variant_scorer(self) -> BaseVariantScorer: + return BaseVariantScorer.GENE_MASK_ACTIVE + + @property + def name(self) -> str: + return str(self) + + @property + def is_signed(self) -> bool: + return False + + def __post_init__(self): + if ( + self.requested_output + not in SUPPORTED_OUTPUT_TYPES[self.base_variant_scorer] + ): + raise ValueError( + f'Unsupported requested output: {self.requested_output}. Supported' + f' output types: {SUPPORTED_OUTPUT_TYPES[self.base_variant_scorer]}' + ) + + def to_proto(self) -> dna_model_pb2.VariantScorer: + return dna_model_pb2.VariantScorer( + gene_mask_active=dna_model_pb2.GeneMaskActiveScorer( + requested_output=self.requested_output.to_proto(), + ) + ) + + +@dataclasses.dataclass(frozen=True) +class GeneMaskSplicingScorer: + """Variant scorer for gene-mask scoring for splicing. + + Variant scorer that quantifies changes in class assignment probabilties + (dna_output.OutputType.SPLICE_SITES) or changes in the usage of splice sites + (dna_output.OutputType.SPLICE_SITE_USAGE) between ALT and REF alleles using + a gene exon mask. + + Attributes: + requested_output: The requested output type (e.g. ATAC, DNASE, etc.) + width: The width of the mask around the variant. + base_variant_scorer: The base variant scorer. + name: The name of the scorer (a composite of the above attributes that + uniquely identifies a scorer combination). + is_signed: Whether this variant scorer is directional (such that scores may + be negative or positive) or non-directional (scores are always positive). + + Methods: + to_proto: Converts the scorer to its corresponding proto message. + + Raises: + ValueError: If the requested output or width is not supported. + """ + + requested_output: dna_output.OutputType + width: int | None + + @property + def base_variant_scorer(self) -> BaseVariantScorer: + return BaseVariantScorer.GENE_MASK_SPLICING + + @property + def name(self) -> str: + return str(self) + + @property + def is_signed(self) -> bool: + return False + + def __post_init__(self): + if ( + self.requested_output + not in SUPPORTED_OUTPUT_TYPES[self.base_variant_scorer] + ): + raise ValueError( + f'Unsupported requested output: {self.requested_output}. Supported' + f' output types: {SUPPORTED_OUTPUT_TYPES[self.base_variant_scorer]}' + ) + if self.width not in SUPPORTED_WIDTHS[self.base_variant_scorer]: + raise ValueError( + f'Unsupported width: {self.width}. Supported widths:' + f' {SUPPORTED_WIDTHS[self.base_variant_scorer]}' + ) + + def to_proto(self) -> dna_model_pb2.VariantScorer: + return dna_model_pb2.VariantScorer( + gene_mask_splicing=dna_model_pb2.GeneMaskSplicingScorer( + requested_output=self.requested_output.to_proto(), + width=self.width, + ) + ) + + +@dataclasses.dataclass(frozen=True) +class PolyadenylationScorer: + """Variant scorer for polyadenylation QTLs (paQTLs). + + Variant scorer for polyadenylation quantitative trait loci (paQTLs) to capture + a variant's impact on RNA isoform production. + + The scoring approach quantifies the maximum log fold change in expression + between the set of proximal polyadenylation sites (PAS)s vs. the set of + distal PASs, where the sets of proximal and distal PASs are the PASs upstream + or downstream of any given 3' cleavage site respectively. + + Attributes: + base_variant_scorer: The base variant scorer. + name: The name of the scorer (a composite of the above attributes that + uniquely identifies a scorer combination). + is_signed: Whether this variant scorer is directional (such that scores may + be negative or positive) or non-directional (scores are always positive). + + Methods: + to_proto: Converts the scorer to its corresponding proto message. + """ + + @property + def requested_output(self) -> dna_output.OutputType: + """Returns the scorer's underlying output type.""" + return dna_output.OutputType.RNA_SEQ + + @property + def base_variant_scorer(self) -> BaseVariantScorer: + return BaseVariantScorer.PA_QTL + + @property + def name(self) -> str: + return str(self) + + @property + def is_signed(self) -> bool: + return False + + def to_proto(self) -> dna_model_pb2.VariantScorer: + return dna_model_pb2.VariantScorer( + pa_qtl=dna_model_pb2.PolyadenylationScorer() + ) + + +@dataclasses.dataclass(frozen=True) +class SpliceJunctionScorer: + """Variant scorer for splice junction scoring. + + Attributes: + base_variant_scorer: The base variant scorer. + name: The name of the scorer (a composite of the above attributes that + uniquely identifies a scorer combination). + is_signed: Whether this variant scorer is directional (such that scores may + be negative or positive) or non-directional (scores are always positive). + + Methods: + to_proto: Converts the scorer to its corresponding proto message. + """ + + @property + def requested_output(self) -> dna_output.OutputType: + """Returns the scorer's underlying output type.""" + return dna_output.OutputType.SPLICE_JUNCTIONS + + @property + def base_variant_scorer(self) -> BaseVariantScorer: + return BaseVariantScorer.SPLICE_JUNCTION + + @property + def name(self) -> str: + return str(self) + + @property + def is_signed(self) -> bool: + return False + + def to_proto(self) -> dna_model_pb2.VariantScorer: + return dna_model_pb2.VariantScorer( + splice_junction=dna_model_pb2.SpliceJunctionScorer() + ) + + +# TypeVar for all variant scorer types. +VariantScorerTypes = TypeVar( + 'VariantScorerTypes', + CenterMaskScorer, + ContactMapScorer, + GeneMaskLFCScorer, + GeneMaskActiveScorer, + GeneMaskSplicingScorer, + PolyadenylationScorer, + SpliceJunctionScorer, +) + +# A dict of variant scorers, with our recommended settings for a wide range +# of different use cases and settings. +RECOMMENDED_VARIANT_SCORERS = immutabledict.immutabledict({ + 'ATAC': CenterMaskScorer( + requested_output=dna_output.OutputType.ATAC, + width=501, + aggregation_type=AggregationType.DIFF_LOG2_SUM, + ), + 'CONTACT_MAPS': ContactMapScorer(), + 'DNASE': CenterMaskScorer( + requested_output=dna_output.OutputType.DNASE, + width=501, + aggregation_type=AggregationType.DIFF_LOG2_SUM, + ), + 'CHIP_TF': CenterMaskScorer( + requested_output=dna_output.OutputType.CHIP_TF, + width=501, + aggregation_type=AggregationType.DIFF_LOG2_SUM, + ), + 'CHIP_HISTONE': CenterMaskScorer( + requested_output=dna_output.OutputType.CHIP_HISTONE, + width=2001, + aggregation_type=AggregationType.DIFF_LOG2_SUM, + ), + 'CAGE': CenterMaskScorer( + requested_output=dna_output.OutputType.CAGE, + width=501, + aggregation_type=AggregationType.DIFF_LOG2_SUM, + ), + 'PROCAP': CenterMaskScorer( + requested_output=dna_output.OutputType.PROCAP, + width=501, + aggregation_type=AggregationType.DIFF_LOG2_SUM, + ), + # Expression log fold change. + 'RNA_SEQ': GeneMaskLFCScorer( + requested_output=dna_output.OutputType.RNA_SEQ + ), + 'RNA_SEQ_ACTIVE': GeneMaskActiveScorer( + requested_output=dna_output.OutputType.RNA_SEQ + ), + 'SPLICE_SITES': GeneMaskSplicingScorer( + requested_output=dna_output.OutputType.SPLICE_SITES, + width=None, + ), + 'SPLICE_SITE_USAGE': GeneMaskSplicingScorer( + requested_output=dna_output.OutputType.SPLICE_SITE_USAGE, + width=None, + ), + 'SPLICE_JUNCTIONS': SpliceJunctionScorer(), + 'POLYADENYLATION': PolyadenylationScorer(), + 'ATAC_ACTIVE': CenterMaskScorer( + requested_output=dna_output.OutputType.ATAC, + width=501, + aggregation_type=AggregationType.ACTIVE_SUM, + ), + 'DNASE_ACTIVE': CenterMaskScorer( + requested_output=dna_output.OutputType.DNASE, + width=501, + aggregation_type=AggregationType.ACTIVE_SUM, + ), + 'CHIP_TF_ACTIVE': CenterMaskScorer( + requested_output=dna_output.OutputType.CHIP_TF, + width=501, + aggregation_type=AggregationType.ACTIVE_SUM, + ), + 'CHIP_HISTONE_ACTIVE': CenterMaskScorer( + requested_output=dna_output.OutputType.CHIP_HISTONE, + width=2001, + aggregation_type=AggregationType.ACTIVE_SUM, + ), + 'CAGE_ACTIVE': CenterMaskScorer( + requested_output=dna_output.OutputType.CAGE, + width=501, + aggregation_type=AggregationType.ACTIVE_SUM, + ), + 'PROCAP_ACTIVE': CenterMaskScorer( + requested_output=dna_output.OutputType.PROCAP, + width=501, + aggregation_type=AggregationType.ACTIVE_SUM, + ), +}) + + +def get_recommended_scorers( + organism: dna_model_pb2.Organism, +) -> list[VariantScorerTypes]: + """Returns the recommended variant scorers for a given organism.""" + return [ + scorer + for scorer in RECOMMENDED_VARIANT_SCORERS.values() + if organism in SUPPORTED_ORGANISMS[scorer.base_variant_scorer] + ] + + +def tidy_anndata( + adata: anndata.AnnData, + match_gene_strand: bool = True, + include_extended_metadata: bool = True, +) -> pd.DataFrame: + """Formats an :class:`~anndata.AnnData` score as a tidy DataFrame. + + This function converts the score output from an AnnData object into a + long-format pandas DataFrame, where each row represents: + + - For non-gene-centric variant scorers: Score for a variant-track pair. + - For non-gene-centric interval scorers: Score for an interval-track pair. + - For gene-centric variant/interval scoring: Score for a + variant/interval-gene-track combination. + + Args: + adata: An AnnData object containing scores. + match_gene_strand: If True (and using gene-centric scoring), rows with + mismatched gene and track strands are removed. + include_extended_metadata: If True, includes additional columns derived from + metadata specific to the output type, such as biosample name and type, + gtex tissue, transcription factor, and histone mark, if available. If + False, only includes minimal metadata columns required to unique identify + a track withing a given output type: track_name and track_strand. + + Returns: + A pandas DataFrame with one score per row. The DataFrame includes + columns for variant ID (if applicable), scored interval, gene information + (if applicable), output type, variant/interval scorer, track name, + ontology term, assay type, track strand, and raw score. Additional metadata + such as biosample name and type, gtex tissue are also returned (where + available). See :func:`full_path_to.tidy_scores` for more details on the + returned columns. + + Raises: + ValueError: If the input is not an AnnData object. + """ + if not isinstance(adata, anndata.AnnData): + raise ValueError('Invalid input type. Must be an AnnData object.') + + # Columns to include from the gene metadata. If the column did not + # exist in the original metadata (or if the scores do not have a concept + # of a gene), a column of all Nones is added. + gene_columns = [ + 'gene_id', + 'gene_name', + 'gene_type', + 'gene_strand', + 'junction_Start', + 'junction_End', + ] + if 'gene_id' in adata.obs and 'strand' in adata.obs: + # Scores are for a gene-based scorer. + obs = adata.obs.rename({'strand': 'gene_strand'}, axis=1) + obs['gene_id'] = obs['gene_id'].str.split('.', expand=True)[0] # Depatch. + for col in gene_columns: + if col not in obs: + obs[col] = None + elif adata.X.shape[0] == 0: + # Scores are empty, so we return an empty dataframe. + return pd.DataFrame() + elif adata.X.shape[0] == 1 and adata.obs.empty: + # Scores are for a non-gene-based scorer. + obs = pd.DataFrame([[None] * len(gene_columns)], columns=gene_columns) + else: + raise ValueError('Scores contain gene metadata but no gene_id/strand.') + + # Columns to include from the track metadata. Only added if the column + # appears in the original metadata. + var = adata.var.rename( + {'strand': 'track_strand', 'name': 'track_name'}, axis=1 + ) + track_columns = ['track_name', 'track_strand'] + if include_extended_metadata: + extended_metadata_columns = [ + 'Assay title', + 'ontology_curie', + 'biosample_name', + 'biosample_type', + 'gtex_tissue', + 'transcription_factor', + 'histone_mark', + ] + track_columns.extend([c for c in extended_metadata_columns if c in var]) + + # Construct score dataframe. + df = pd.merge(var, obs, how='cross') + df['scored_interval'] = adata.uns['interval'] + + # Add id and scorer information depending on the type of score. + if 'variant_scorer' in adata.uns: + df['variant_id'] = adata.uns['variant'] + df['output_type'] = adata.uns['variant_scorer'].requested_output.name + df['variant_scorer'] = str(adata.uns['variant_scorer']) + id_columns = ['variant_id', 'scored_interval'] + scorer_columns = ['output_type', 'variant_scorer'] + elif 'interval_scorer' in adata.uns: + df['output_type'] = adata.uns['interval_scorer'].requested_output.name + df['interval_scorer'] = str(adata.uns['interval_scorer']) + id_columns = ['scored_interval'] + scorer_columns = ['output_type', 'interval_scorer'] + else: + raise ValueError( + 'Invalid scores, expected either variant_scorer or interval_scorer.' + ) + + # Formatting final touches. + df = df.loc[:, id_columns + gene_columns + scorer_columns + track_columns] + df['raw_score'] = adata.X.T.reshape((-1,)) + if 'quantiles' in adata.layers: + df['quantile_score'] = adata.layers['quantiles'].T.reshape((-1,)) + + # Remove entries where DNA strands are mismatched. Note that we still retain + # all tracks that have strand '.' which indicates the data is unstranded. + if match_gene_strand: + mismatched_strands = (df['gene_strand'] == '+') & ( + df['track_strand'] == '-' + ) | (df['gene_strand'] == '-') & (df['track_strand'] == '+') + df = df[~mismatched_strands].reset_index(drop=True) + return df.reset_index(drop=True) + + +def tidy_scores( + scores: Sequence[anndata.AnnData] | Sequence[Sequence[anndata.AnnData]], + match_gene_strand: bool = True, + include_extended_metadata: bool = True, +) -> pd.DataFrame | None: + """Formats scores into a tidy (long) pandas DataFrame. + + This function reformats variant scores into a more readable DataFrame with one + score per row. This function supports both scores generated from variant + scorers (e.g., the output of `score_variant` or `score_variants`) or interval + scorers (e.g., the output of `score_interval` or `score_intervals`). + + It handles both variant/interval-centric scoring (producing one score row per + variant/interval-track pair) and gene-centric scoring (one score row per + variant/interval-gene-track combination). + + The function accepts these score input types: + + - A sequence of AnnData objects (e.g., output of `score_variant` with one or + more scorers). + - A nested sequence of AnnData objects (e.g., output of `score_variants` + with multiple variants). + + Scores from multiple scorers or multiple variants/intervals are concatenated + together into a single pandas DataFrame, containing the union of all + applicable columns across scorers. + + Args: + scores: Scoring output as either a sequence of AnnData objects, or a nested + sequence of AnnData objects (for example, the outputs of `score_variant` + and `score_variants`, respectively). + match_gene_strand: If True (and using gene-centric scoring), rows with + mismatched gene and track strands are removed. + include_extended_metadata: Argument passed to `tidy_anndata` to include + additional metadata columns where available. + + Returns: + pd.DataFrame with columns: + + - variant_id (when applicable): Variant of interest (e.g. + chr22:36201698:A>C). + - scored_interval: Genomic interval scored (e.g. chr22:36100000-36300000). + - gene_id: ENSEMBL gene identifier without version number. + (e.g. ENSG00000100342), or None if not applicable. + - gene_name: HGNC gene symbol (e.g. APOL1), or None if not applicable. + - gene_type: Gene biotype (e.g. protein_coding, lncRNA), or None if not + applicable. + - gene_strand: Strand of the gene ('+', '-', '.'), or None if not + applicable. + - output_type: Type of the output from the model (e.g. RNA_SEQ, DNASE). + - interval_scorer (when applicable): Name of the interval scorer used. + - variant_scorer (when applicable): Name of the variant scorer used. + - track_name: Name of the output track (e.g. UBERON:0036149 total + RNA-seq). + - track_strand: Strand of the track ('+', '-', or '.'). + - ontology_curie: Ontology term for the cell type or tissue of the track + (e.g. UBERON:0036149), or NaN if not applicable. + - gtex_tissue: Name of the gtex tissue (e.g. Liver), or NaN if not + applicable. + - Assay title: Subtype of the assay (e.g., total RNA-seq), or NaN if not + applicable. + - biosample_name: Name of the biosample (e.g. liver), or NaN if not + applicable. + - biosample_type: Type of biosample (e.g. 'tissue' or 'primary cell'), or + NaN if not applicable. + - transcription_factor: Name of the transcription factor (e.g. 'CTCF'), or + NaN if not applicable. + - histone_mark: Name of the histological mark (e.g. 'H3K4ME3'), or NaN if + not applicable. + - raw_score: Raw variant score. + - quantile_score (when applicable): Quantile score. + + Raises: + ValueError: If the input is not a valid type (sequence of AnnData or nested + sequence of AnnDatas). + """ + if isinstance(scores, Sequence): + tidied_anndata = [ + tidy_anndata(adata, match_gene_strand, include_extended_metadata) + for adata in itertools.chain.from_iterable(scores) + ] + if not tidied_anndata: + return None + + concatenated_df = pd.concat(tidied_anndata, axis=0, ignore_index=True) + # Put the raw_score (and quantile_score when applicable) columns last. + last_columns = ['raw_score'] + if 'quantile_score' in concatenated_df.columns: + last_columns.append('quantile_score') + return concatenated_df[ + [col for col in concatenated_df.columns if col not in last_columns] + + last_columns + ] + else: + raise ValueError( + 'Invalid input type. Must be sequence of AnnDatas or nested sequence of' + ' AnnDatas.' + ) diff --git a/alphagenome/source/src/alphagenome/models/variant_scorers_test.py b/alphagenome/source/src/alphagenome/models/variant_scorers_test.py new file mode 100644 index 0000000000000000000000000000000000000000..fbcec87d3a2f20e79aff8107f7aa4f6a8449a0e8 --- /dev/null +++ b/alphagenome/source/src/alphagenome/models/variant_scorers_test.py @@ -0,0 +1,899 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from absl.testing import absltest +from absl.testing import parameterized +from alphagenome.models import dna_output +from alphagenome.models import interval_scorers +from alphagenome.models import variant_scorers +from alphagenome.protos import dna_model_pb2 +import anndata +import numpy as np +import pandas as pd + + +class VariantScorersTest(parameterized.TestCase): + + def test_aggregation_type_to_proto(self): + expected = [ + dna_model_pb2.AggregationType.AGGREGATION_TYPE_DIFF_MEAN, + dna_model_pb2.AggregationType.AGGREGATION_TYPE_DIFF_SUM, + dna_model_pb2.AggregationType.AGGREGATION_TYPE_DIFF_SUM_LOG2, + dna_model_pb2.AggregationType.AGGREGATION_TYPE_DIFF_LOG2_SUM, + dna_model_pb2.AggregationType.AGGREGATION_TYPE_L2_DIFF, + dna_model_pb2.AggregationType.AGGREGATION_TYPE_L2_DIFF_LOG1P, + dna_model_pb2.AggregationType.AGGREGATION_TYPE_ACTIVE_MEAN, + dna_model_pb2.AggregationType.AGGREGATION_TYPE_ACTIVE_SUM, + ] + + for variant_scorer_aggregation_type, expected_proto in zip( + variant_scorers.AggregationType, expected, strict=True + ): + self.assertEqual( + variant_scorer_aggregation_type.to_proto(), expected_proto + ) + + def test_variant_scorers_to_proto(self): + expected = [ + dna_model_pb2.VariantScorer( + center_mask=dna_model_pb2.CenterMaskScorer( + width=10_001, + aggregation_type=( + dna_model_pb2.AggregationType.AGGREGATION_TYPE_DIFF_MEAN + ), + requested_output=dna_model_pb2.OUTPUT_TYPE_RNA_SEQ, + ) + ), + dna_model_pb2.VariantScorer( + center_mask=dna_model_pb2.CenterMaskScorer( + width=10_001, + aggregation_type=( + dna_model_pb2.AggregationType.AGGREGATION_TYPE_ACTIVE_MEAN + ), + requested_output=dna_model_pb2.OUTPUT_TYPE_PROCAP, + ) + ), + dna_model_pb2.VariantScorer( + center_mask=dna_model_pb2.CenterMaskScorer( + width=None, + aggregation_type=( + dna_model_pb2.AggregationType.AGGREGATION_TYPE_DIFF_LOG2_SUM + ), + requested_output=dna_model_pb2.OUTPUT_TYPE_ATAC, + ) + ), + dna_model_pb2.VariantScorer( + gene_mask=dna_model_pb2.GeneMaskLFCScorer( + requested_output=dna_model_pb2.OUTPUT_TYPE_RNA_SEQ, + ) + ), + dna_model_pb2.VariantScorer( + gene_mask_splicing=dna_model_pb2.GeneMaskSplicingScorer( + width=10_001, + requested_output=dna_model_pb2.OUTPUT_TYPE_SPLICE_SITE_USAGE, + ) + ), + dna_model_pb2.VariantScorer( + pa_qtl=dna_model_pb2.PolyadenylationScorer() + ), + dna_model_pb2.VariantScorer( + splice_junction=dna_model_pb2.SpliceJunctionScorer() + ), + ] + scorers = [ + variant_scorers.CenterMaskScorer( + width=10_001, + aggregation_type=(variant_scorers.AggregationType.DIFF_MEAN), + requested_output=dna_output.OutputType.RNA_SEQ, + ), + variant_scorers.CenterMaskScorer( + width=10_001, + aggregation_type=(variant_scorers.AggregationType.ACTIVE_MEAN), + requested_output=dna_output.OutputType.PROCAP, + ), + variant_scorers.CenterMaskScorer( + width=None, + aggregation_type=(variant_scorers.AggregationType.DIFF_LOG2_SUM), + requested_output=dna_output.OutputType.ATAC, + ), + variant_scorers.GeneMaskLFCScorer( + requested_output=dna_output.OutputType.RNA_SEQ, + ), + variant_scorers.GeneMaskSplicingScorer( + width=10_001, + requested_output=dna_output.OutputType.SPLICE_SITE_USAGE, + ), + variant_scorers.PolyadenylationScorer(), + variant_scorers.SpliceJunctionScorer(), + ] + for variant_scorer, expected_proto in zip(scorers, expected): + self.assertEqual(variant_scorer.to_proto(), expected_proto) + + def test_unsupported_output_type_raises_error(self): + with self.assertRaisesRegex(ValueError, 'Unsupported requested output'): + _ = variant_scorers.CenterMaskScorer( + width=10_001, + aggregation_type=variant_scorers.AggregationType.DIFF_MEAN, + requested_output=dna_output.OutputType.CONTACT_MAPS, + ) + + def test_unsupported_width_raises_error(self): + with self.assertRaisesRegex(ValueError, 'Unsupported width'): + _ = variant_scorers.CenterMaskScorer( + width=1, + aggregation_type=variant_scorers.AggregationType.DIFF_MEAN, + requested_output=dna_output.OutputType.RNA_SEQ, + ) + + +class TestTidyScores(absltest.TestCase): + """Test tidying scores.""" + + def setUp(self): + """Set up test data.""" + super().setUp() + + # Possible scorers. + self.gene_scorer = variant_scorers.GeneMaskLFCScorer( + requested_output=dna_output.OutputType.RNA_SEQ, + ) + self.center_mask_scorer = variant_scorers.CenterMaskScorer( + width=10_001, + aggregation_type=variant_scorers.AggregationType.DIFF_MEAN, + requested_output=dna_output.OutputType.DNASE, + ) + self.paqtl_scorer = variant_scorers.PolyadenylationScorer() + self.junction_scorer = variant_scorers.SpliceJunctionScorer() + self.splicing_scorer_ssu = variant_scorers.GeneMaskSplicingScorer( + dna_output.OutputType.SPLICE_SITE_USAGE, width=101 + ) + self.splicing_scorer_ss = variant_scorers.GeneMaskSplicingScorer( + dna_output.OutputType.SPLICE_SITES, width=101 + ) + self.gene_interval_scorer = interval_scorers.GeneMaskScorer( + requested_output=dna_output.OutputType.RNA_SEQ, + width=10_001, + aggregation_type=interval_scorers.IntervalAggregationType.MEAN, + ) + + # Gene-centric scoring output. + self.adata_gene_centric = anndata.AnnData( + X=np.array([[1, 2], [3, 4], [5, 6]]), + # 3 genes in vicinity of the variant. + obs=pd.DataFrame({ + 'gene_id': [ + 'ENSG00000100342', + 'ENSG00000123456', + 'ENSG00000123457', + ], + 'gene_name': ['GENE1', 'GENE2', 'GENE3'], + 'gene_type': ['protein_coding', 'lncRNA', 'protein_coding'], + 'strand': ['+', '-', '-'], + }), + # Two RNA_SEQ tracks. + var=pd.DataFrame({ + 'name': [ + 'UBERON:0002107 total RNA-seq', + 'UBERON:0006566 gtex Heart_Left_Ventricle polyA plus RNA-seq', + ], + 'strand': ['+', '-'], + 'Assay title': ['total RNA-seq', 'polyA plus RNA-seq'], + 'ontology_curie': ['UBERON:0002107', 'UBERON:0006566'], + 'biosample_name': ['liver', 'left ventricle myocardium'], + 'biosample_type': ['tissue', 'tissue'], + 'gtex_tissue': ['Liver', 'Heart_Left_Ventricle'], + }), + uns={ + 'variant': 'chr1:100:A>C', + 'interval': 'chr1:90-110', + 'variant_scorer': self.gene_scorer, + }, + layers={ + 'quantiles': np.array([[1, 0], [-1, 0], [1, -1]]), + }, + ) + + # Variant-centric scoring output. + self.adata_variant_centric = anndata.AnnData( + # 4 DNASE tracks. + X=np.array([[5, 6, 7, 8]]), + obs=pd.DataFrame(index=[0]), + var=pd.DataFrame({ + 'name': [ + 'UBERON:0001 dnase', + 'UBERON:0002 dnase', + 'UBERON:0003 dnase', + 'UBERON:0004 dnase', + ], + 'strand': ['-', '+', '-', '.'], + 'Assay title': ['DNase-seq'] * 4, + 'ontology_curie': [ + 'UBERON:0001', + 'UBERON:0002', + 'UBERON:0003', + 'UBERON:0004', + ], + 'biosample_name': [ + 'T-cell', + 'endodermal cell', + 'natural killer cell', + 'CD4-positive, alpha-beta memory T cell', + ], + 'biosample_type': [ + 'primary_cell', + 'in_vitro_differentiated_cells', + 'primary_cell', + 'primary_cell', + ], + }), + uns={ + 'variant': 'chr2:200:G>T', + 'interval': 'chr2:190-210', + 'variant_scorer': self.center_mask_scorer, + }, + ) + # PolyadenylationScorer RNA_SEQ scoring output. This output has the same + # format as a gene-centric scoring output so it is not really a special + # case. + self.adata_paqtl = anndata.AnnData( + X=np.array([ + [ + 5, + 6, + 7, + ], + [8, 9, 10], + ]), + obs=pd.DataFrame({ + 'gene_id': ['ENSG00000100342', 'ENSG00000123456'], + 'gene_name': ['GENE1', 'GENE2'], + 'gene_type': ['protein_coding', 'lncRNA'], + 'strand': ['+', '-'], + }), + var=pd.DataFrame({ + 'name': [ + 'CL:0000047 polyA plus RNA-seq', + 'CL:0000047 polyA plus RNA-seq', + 'UBERON:0018115 polyA plus RNA-seq', + ], + 'strand': ['+', '-', '.'], + 'Assay title': [ + 'polyA plus RNA-seq', + 'polyA plus RNA-seq', + 'polyA plus RNA-seq', + ], + 'ontology_curie': [ + 'CL:0000047', + 'CL:0000047', + 'UBERON:0018115', + ], + 'biosample_name': [ + 'neuronal stem cell', + 'neuronal stem cell', + 'left renal pelvis', + ], + 'biosample_type': [ + 'in_vitro_differentiated_cells', + 'in_vitro_differentiated_cells', + 'tissue', + ], + 'gtex_tissue': ['', '', ''], + }), + uns={ + 'variant': 'chr3:300:A>G', + 'interval': 'chr3:290-310', + 'variant_scorer': self.paqtl_scorer, + }, + ) + # SpliceJunctionScorer SPLICE_JUNCTIONS scoring output. + self.adata_junction = anndata.AnnData( + X=np.array([ + [5, 6, 7, 8], + [9, 10, 11, 12], + ]), + obs=pd.DataFrame({ + 'gene_id': ['ENSG00000100342', 'ENSG00000123456'], + 'gene_name': ['GENE1', 'GENE2'], + 'gene_type': ['protein_coding', 'lncRNA'], + 'strand': ['+', '-'], + 'junction_Start': [0, 1], + 'junction_End': [1, 2], + }), + var=pd.DataFrame({ + 'name': [ + 'delta_psi5:Brain_Cerebellum', + 'delta_psi3:Brain_Cerebellum', + 'delta_counts:Brain_Cerebellum', + 'ref_counts:Brain_Cerebellum', + ], + 'strand': ['.', '.', '.', '.'], + 'gtex_tissue': [ + 'Brain_Cerebellum', + 'Brain_Cerebellum', + 'Brain_Cerebellum', + 'Brain_Cerebellum', + ], + }), + uns={ + 'variant': 'chr3:300:A>G', + 'interval': 'chr3:290-310', + 'variant_scorer': self.junction_scorer, + }, + ) + # GeneMaskSplicingScorer SPLICE_SITE_USAGE scoring output. + self.adata_splicing_ssu = anndata.AnnData( + X=np.array([[1, 2], [3, 4]]), + obs=pd.DataFrame({ + 'gene_id': ['ENSG00000100342', 'ENSG00000123456'], + 'gene_name': ['GENE1', 'GENE2'], + 'gene_type': ['protein_coding', 'lncRNA'], + 'strand': ['+', '-'], + }), + var=pd.DataFrame({ + 'name': [ + 'usage_Brain_Cerebellum', + 'usage_Brain_Cerebellum', + ], + 'strand': ['+', '-'], + 'ontology_curie': ['UBERON:0002037', 'UBERON:0002037'], + 'biosample_name': ['cerebellum', 'cerebellum'], + 'gtex_tissue': [ + 'Brain_Cerebellum', + 'Brain_Cerebellum', + ], + }), + uns={ + 'variant': 'chr3:300:A>G', + 'interval': 'chr3:290-310', + 'variant_scorer': self.splicing_scorer_ssu, + }, + ) + # GeneMaskSplicingScorer SPLICE_SITES scoring output. + self.adata_splicing_ss = anndata.AnnData( + X=np.array([[1, 2, 3, 4, 5]]), + obs=pd.DataFrame({ + 'gene_id': ['ENSG00000100342'], + 'gene_name': ['GENE1'], + 'gene_type': ['protein_coding'], + 'strand': ['+'], + }), + var=pd.DataFrame({ + # There are only ever these 4 tracks for this scorer. + 'name': [ + 'donor', + 'acceptor', + 'donor', + 'acceptor', + 'other', + ], + 'strand': ['+', '+', '-', '-', '.'], + }), + uns={ + 'variant': 'chr3:300:A>G', + 'interval': 'chr3:290-310', + 'variant_scorer': self.splicing_scorer_ss, + }, + ) + + self.extended_metadata_columns = set([ + 'Assay title', + 'ontology_curie', + 'biosample_name', + 'biosample_type', + 'gtex_tissue', + 'transcription_factor', + 'histone_mark', + ]) + # Interval scoring output. + self.adata_interval_centric = anndata.AnnData( + X=np.array([[1, 2], [3, 4], [5, 6]]), + # 3 genes in vicinity of the interval. + obs=pd.DataFrame({ + 'gene_id': [ + 'ENSG00000100342', + 'ENSG00000123456', + 'ENSG00000123457', + ], + 'gene_name': ['GENE1', 'GENE2', 'GENE3'], + 'gene_type': ['protein_coding', 'lncRNA', 'protein_coding'], + 'strand': ['+', '-', '-'], + }), + # Two RNA_SEQ tracks. + var=pd.DataFrame({ + 'name': [ + 'UBERON:0002107 total RNA-seq', + 'UBERON:0006566 gtex Heart_Left_Ventricle polyA plus RNA-seq', + ], + 'strand': ['+', '-'], + 'Assay title': ['total RNA-seq', 'polyA plus RNA-seq'], + 'ontology_curie': ['UBERON:0002107', 'UBERON:0006566'], + 'biosample_name': ['liver', 'left ventricle myocardium'], + 'biosample_type': ['tissue', 'tissue'], + 'gtex_tissue': ['Liver', 'Heart_Left_Ventricle'], + }), + uns={ + 'interval': 'chr1:90-110', + 'interval_scorer': self.gene_interval_scorer, + }, + ) + + def test_tidy_anndata_gene_centric(self): + """Test tidying a single gene centric AnnData object.""" + df = variant_scorers.tidy_anndata( + self.adata_gene_centric, match_gene_strand=False + ) + self.assertLen(df, 6) # 3 genes by 2 tracks. + + df = variant_scorers.tidy_anndata(self.adata_gene_centric) + self.assertLen(df, 3) # Removes mismatched strands. + expected = pd.DataFrame({ + 'variant_id': ['chr1:100:A>C', 'chr1:100:A>C', 'chr1:100:A>C'], + 'scored_interval': ['chr1:90-110', 'chr1:90-110', 'chr1:90-110'], + 'gene_id': ['ENSG00000100342', 'ENSG00000123456', 'ENSG00000123457'], + 'gene_name': ['GENE1', 'GENE2', 'GENE3'], + 'gene_type': ['protein_coding', 'lncRNA', 'protein_coding'], + 'gene_strand': ['+', '-', '-'], + 'junction_Start': [None] * 3, + 'junction_End': [None] * 3, + 'output_type': ['RNA_SEQ', 'RNA_SEQ', 'RNA_SEQ'], + 'variant_scorer': [str(self.gene_scorer)] * 3, + 'track_name': [ + 'UBERON:0002107 total RNA-seq', + 'UBERON:0006566 gtex Heart_Left_Ventricle polyA plus RNA-seq', + 'UBERON:0006566 gtex Heart_Left_Ventricle polyA plus RNA-seq', + ], + 'track_strand': ['+', '-', '-'], + 'Assay title': [ + 'total RNA-seq', + 'polyA plus RNA-seq', + 'polyA plus RNA-seq', + ], + 'ontology_curie': [ + 'UBERON:0002107', + 'UBERON:0006566', + 'UBERON:0006566', + ], + 'biosample_name': [ + 'liver', + 'left ventricle myocardium', + 'left ventricle myocardium', + ], + 'biosample_type': ['tissue', 'tissue', 'tissue'], + 'gtex_tissue': [ + 'Liver', + 'Heart_Left_Ventricle', + 'Heart_Left_Ventricle', + ], + 'raw_score': [1, 4, 6], + 'quantile_score': [1, 0, -1], + }) + pd.testing.assert_frame_equal(df, expected) + # Test without extended metadata. + df = variant_scorers.tidy_anndata( + self.adata_gene_centric, include_extended_metadata=False + ) + pd.testing.assert_frame_equal( + df, + expected[ + [c for c in df.columns if c not in self.extended_metadata_columns] + ], + ) + + def test_tidy_anndata_variant_centric(self): + """Test tidying a single center mask AnnData object.""" + df = variant_scorers.tidy_anndata(self.adata_variant_centric) + self.assertLen(df, 4) # 4 tracks. + # Strand filtering has no effect on variant-centric scoring. + df = variant_scorers.tidy_anndata( + self.adata_variant_centric, match_gene_strand=False + ) + self.assertLen(df, 4) + + expected = pd.DataFrame({ + 'variant_id': [ + 'chr2:200:G>T', + 'chr2:200:G>T', + 'chr2:200:G>T', + 'chr2:200:G>T', + ], + 'scored_interval': [ + 'chr2:190-210', + 'chr2:190-210', + 'chr2:190-210', + 'chr2:190-210', + ], + 'gene_id': [None, None, None, None], + 'gene_name': [None, None, None, None], + 'gene_type': [None, None, None, None], + 'gene_strand': [None, None, None, None], + 'junction_Start': [None] * 4, + 'junction_End': [None] * 4, + 'output_type': ['DNASE', 'DNASE', 'DNASE', 'DNASE'], + 'variant_scorer': [str(self.center_mask_scorer)] * 4, + 'track_name': [ + 'UBERON:0001 dnase', + 'UBERON:0002 dnase', + 'UBERON:0003 dnase', + 'UBERON:0004 dnase', + ], + 'track_strand': ['-', '+', '-', '.'], + 'Assay title': ['DNase-seq', 'DNase-seq', 'DNase-seq', 'DNase-seq'], + 'ontology_curie': [ + 'UBERON:0001', + 'UBERON:0002', + 'UBERON:0003', + 'UBERON:0004', + ], + 'biosample_name': [ + 'T-cell', + 'endodermal cell', + 'natural killer cell', + 'CD4-positive, alpha-beta memory T cell', + ], + 'biosample_type': [ + 'primary_cell', + 'in_vitro_differentiated_cells', + 'primary_cell', + 'primary_cell', + ], + 'raw_score': [5, 6, 7, 8], + }) + pd.testing.assert_frame_equal(df, expected) + # Test without extended metadata. + df = variant_scorers.tidy_anndata( + self.adata_variant_centric, include_extended_metadata=False + ) + pd.testing.assert_frame_equal( + df, + expected[ + [c for c in df.columns if c not in self.extended_metadata_columns] + ], + ) + + def test_tidy_anndata_paqtl(self): + """Test tidying a single paQTL scoring output AnnData object.""" + df = variant_scorers.tidy_anndata(self.adata_paqtl) + + expected = pd.DataFrame({ + 'variant_id': [ + 'chr3:300:A>G', + 'chr3:300:A>G', + 'chr3:300:A>G', + 'chr3:300:A>G', + ], + 'scored_interval': [ + 'chr3:290-310', + 'chr3:290-310', + 'chr3:290-310', + 'chr3:290-310', + ], + 'gene_id': [ + 'ENSG00000100342', + 'ENSG00000123456', + 'ENSG00000100342', + 'ENSG00000123456', + ], + 'gene_name': ['GENE1', 'GENE2', 'GENE1', 'GENE2'], + 'gene_type': ['protein_coding', 'lncRNA', 'protein_coding', 'lncRNA'], + 'gene_strand': ['+', '-', '+', '-'], + 'junction_Start': [None] * 4, + 'junction_End': [None] * 4, + 'output_type': ['RNA_SEQ', 'RNA_SEQ', 'RNA_SEQ', 'RNA_SEQ'], + 'variant_scorer': [str(self.paqtl_scorer)] * 4, + 'track_name': [ + 'CL:0000047 polyA plus RNA-seq', + 'CL:0000047 polyA plus RNA-seq', + 'UBERON:0018115 polyA plus RNA-seq', + 'UBERON:0018115 polyA plus RNA-seq', + ], + 'track_strand': ['+', '-', '.', '.'], + 'Assay title': [ + 'polyA plus RNA-seq', + 'polyA plus RNA-seq', + 'polyA plus RNA-seq', + 'polyA plus RNA-seq', + ], + 'ontology_curie': [ + 'CL:0000047', + 'CL:0000047', + 'UBERON:0018115', + 'UBERON:0018115', + ], + 'biosample_name': [ + 'neuronal stem cell', + 'neuronal stem cell', + 'left renal pelvis', + 'left renal pelvis', + ], + 'biosample_type': [ + 'in_vitro_differentiated_cells', + 'in_vitro_differentiated_cells', + 'tissue', + 'tissue', + ], + 'gtex_tissue': ['', '', '', ''], + 'raw_score': [5, 9, 7, 10], + }) + pd.testing.assert_frame_equal(df, expected) + # Test without extended metadata. + df = variant_scorers.tidy_anndata( + self.adata_paqtl, include_extended_metadata=False + ) + pd.testing.assert_frame_equal( + df, + expected[ + [c for c in df.columns if c not in self.extended_metadata_columns] + ], + ) + + def test_tidy_anndata_interval_centric(self): + """Test tidying a single gene centric interval scoring AnnData object.""" + df = variant_scorers.tidy_anndata( + self.adata_interval_centric, match_gene_strand=False + ) + self.assertLen(df, 6) # 3 genes by 2 tracks. + + df = variant_scorers.tidy_anndata(self.adata_interval_centric) + self.assertLen(df, 3) # Removes mismatched strands. + expected = pd.DataFrame({ + 'scored_interval': ['chr1:90-110', 'chr1:90-110', 'chr1:90-110'], + 'gene_id': ['ENSG00000100342', 'ENSG00000123456', 'ENSG00000123457'], + 'gene_name': ['GENE1', 'GENE2', 'GENE3'], + 'gene_type': ['protein_coding', 'lncRNA', 'protein_coding'], + 'gene_strand': ['+', '-', '-'], + 'junction_Start': [None] * 3, + 'junction_End': [None] * 3, + 'output_type': ['RNA_SEQ', 'RNA_SEQ', 'RNA_SEQ'], + 'interval_scorer': [str(self.gene_interval_scorer)] * 3, + 'track_name': [ + 'UBERON:0002107 total RNA-seq', + 'UBERON:0006566 gtex Heart_Left_Ventricle polyA plus RNA-seq', + 'UBERON:0006566 gtex Heart_Left_Ventricle polyA plus RNA-seq', + ], + 'track_strand': ['+', '-', '-'], + 'Assay title': [ + 'total RNA-seq', + 'polyA plus RNA-seq', + 'polyA plus RNA-seq', + ], + 'ontology_curie': [ + 'UBERON:0002107', + 'UBERON:0006566', + 'UBERON:0006566', + ], + 'biosample_name': [ + 'liver', + 'left ventricle myocardium', + 'left ventricle myocardium', + ], + 'biosample_type': ['tissue', 'tissue', 'tissue'], + 'gtex_tissue': [ + 'Liver', + 'Heart_Left_Ventricle', + 'Heart_Left_Ventricle', + ], + 'raw_score': [1, 4, 6], + }) + pd.testing.assert_frame_equal(df, expected) + # Test without extended metadata. + df = variant_scorers.tidy_anndata( + self.adata_interval_centric, include_extended_metadata=False + ) + pd.testing.assert_frame_equal( + df, + expected[ + [c for c in df.columns if c not in self.extended_metadata_columns] + ], + ) + + def test_tidy_anndata_junction(self): + """Test tidying a single splice junction scoring output AnnData object.""" + df = variant_scorers.tidy_anndata(self.adata_junction) + expected = pd.DataFrame({ + 'variant_id': [ + 'chr3:300:A>G', + 'chr3:300:A>G', + 'chr3:300:A>G', + 'chr3:300:A>G', + 'chr3:300:A>G', + 'chr3:300:A>G', + 'chr3:300:A>G', + 'chr3:300:A>G', + ], + 'scored_interval': [ + 'chr3:290-310', + 'chr3:290-310', + 'chr3:290-310', + 'chr3:290-310', + 'chr3:290-310', + 'chr3:290-310', + 'chr3:290-310', + 'chr3:290-310', + ], + 'gene_id': [ + 'ENSG00000100342', + 'ENSG00000123456', + 'ENSG00000100342', + 'ENSG00000123456', + 'ENSG00000100342', + 'ENSG00000123456', + 'ENSG00000100342', + 'ENSG00000123456', + ], + 'gene_name': [ + 'GENE1', + 'GENE2', + 'GENE1', + 'GENE2', + 'GENE1', + 'GENE2', + 'GENE1', + 'GENE2', + ], + 'gene_type': [ + 'protein_coding', + 'lncRNA', + 'protein_coding', + 'lncRNA', + 'protein_coding', + 'lncRNA', + 'protein_coding', + 'lncRNA', + ], + 'gene_strand': ['+', '-', '+', '-', '+', '-', '+', '-'], + 'junction_Start': [0, 1, 0, 1, 0, 1, 0, 1], + 'junction_End': [1, 2, 1, 2, 1, 2, 1, 2], + 'output_type': [ + 'SPLICE_JUNCTIONS', + 'SPLICE_JUNCTIONS', + 'SPLICE_JUNCTIONS', + 'SPLICE_JUNCTIONS', + 'SPLICE_JUNCTIONS', + 'SPLICE_JUNCTIONS', + 'SPLICE_JUNCTIONS', + 'SPLICE_JUNCTIONS', + ], + 'variant_scorer': [str(self.junction_scorer)] * 8, + 'track_name': [ + 'delta_psi5:Brain_Cerebellum', + 'delta_psi5:Brain_Cerebellum', + 'delta_psi3:Brain_Cerebellum', + 'delta_psi3:Brain_Cerebellum', + 'delta_counts:Brain_Cerebellum', + 'delta_counts:Brain_Cerebellum', + 'ref_counts:Brain_Cerebellum', + 'ref_counts:Brain_Cerebellum', + ], + 'track_strand': ['.', '.', '.', '.', '.', '.', '.', '.'], + 'gtex_tissue': ['Brain_Cerebellum'] * 8, + 'raw_score': [5, 9, 6, 10, 7, 11, 8, 12], + }) + pd.testing.assert_frame_equal(df, expected) + # Test without extended metadata. + df = variant_scorers.tidy_anndata( + self.adata_junction, include_extended_metadata=False + ) + pd.testing.assert_frame_equal( + df, + expected[ + [c for c in df.columns if c not in self.extended_metadata_columns] + ], + ) + + def test_tidy_anndata_splicing_ssu(self): + """Test tidying a single splicing ssu scoring output AnnData object.""" + df = variant_scorers.tidy_anndata(self.adata_splicing_ssu) + expected = pd.DataFrame({ + 'variant_id': [ + 'chr3:300:A>G', + 'chr3:300:A>G', + ], + 'scored_interval': [ + 'chr3:290-310', + 'chr3:290-310', + ], + 'gene_id': [ + 'ENSG00000100342', + 'ENSG00000123456', + ], + 'gene_name': [ + 'GENE1', + 'GENE2', + ], + 'gene_type': ['protein_coding', 'lncRNA'], + 'gene_strand': ['+', '-'], + 'junction_Start': [None] * 2, + 'junction_End': [None] * 2, + 'output_type': [ + 'SPLICE_SITE_USAGE', + 'SPLICE_SITE_USAGE', + ], + 'variant_scorer': [str(self.splicing_scorer_ssu)] * 2, + 'track_name': [ + 'usage_Brain_Cerebellum', + 'usage_Brain_Cerebellum', + ], + 'track_strand': [ + '+', + '-', + ], + 'ontology_curie': ['UBERON:0002037', 'UBERON:0002037'], + 'biosample_name': ['cerebellum', 'cerebellum'], + 'gtex_tissue': [ + 'Brain_Cerebellum', + 'Brain_Cerebellum', + ], + 'raw_score': [1, 4], + }) + pd.testing.assert_frame_equal(df, expected) + + def test_tidy_anndata_splicing_sites(self): + """Test tidying a single splicing sites scoring output AnnData object.""" + df = variant_scorers.tidy_anndata(self.adata_splicing_ss) + expected = pd.DataFrame({ + 'variant_id': ['chr3:300:A>G', 'chr3:300:A>G', 'chr3:300:A>G'], + 'scored_interval': ['chr3:290-310', 'chr3:290-310', 'chr3:290-310'], + 'gene_id': ['ENSG00000100342', 'ENSG00000100342', 'ENSG00000100342'], + 'gene_name': ['GENE1', 'GENE1', 'GENE1'], + 'gene_type': ['protein_coding', 'protein_coding', 'protein_coding'], + 'gene_strand': ['+', '+', '+'], + 'junction_Start': [None] * 3, + 'junction_End': [None] * 3, + 'output_type': ['SPLICE_SITES', 'SPLICE_SITES', 'SPLICE_SITES'], + 'variant_scorer': [str(self.splicing_scorer_ss)] * 3, + 'track_name': ['donor', 'acceptor', 'other'], + 'track_strand': ['+', '+', '.'], + 'raw_score': [1, 2, 5], + }) + pd.testing.assert_frame_equal(df, expected) + # Test without extended metadata. + df = variant_scorers.tidy_anndata( + self.adata_splicing_ss, include_extended_metadata=False + ) + pd.testing.assert_frame_equal( + df, + expected[ + [c for c in df.columns if c not in self.extended_metadata_columns] + ], + ) + + def test_tidy_lists_of_anndata(self): + # List of AnnDatas. + anndata_list = [self.adata_gene_centric, self.adata_variant_centric] + df_list = variant_scorers.tidy_scores(anndata_list) + self.assertLen(df_list, 7) + + # List of list of AnnDatas. + anndata_list = [[self.adata_gene_centric], [self.adata_variant_centric]] + df_nested_list = variant_scorers.tidy_scores(anndata_list) + self.assertLen(df_nested_list, 7) + # The exact nesting pattern doesn't matter, the result is equivalent. + pd.testing.assert_frame_equal(df_list, df_nested_list) + + # Longer list of list of AnnDatas. + anndata_list = [ + [self.adata_gene_centric, self.adata_variant_centric], + [self.adata_paqtl, self.adata_junction, self.adata_interval_centric], + ] + df_longer_nested_list = variant_scorers.tidy_scores(anndata_list) + self.assertLen(df_longer_nested_list, 22) + + # Scores are empty. + anndata_list = [anndata.AnnData(X=np.ones((0, 3)))] + df_empty_list = variant_scorers.tidy_scores(anndata_list) + self.assertIsNone(df_empty_list) + + +if __name__ == '__main__': + absltest.main() diff --git a/alphagenome/source/src/alphagenome/protos/__init__.py b/alphagenome/source/src/alphagenome/protos/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f5b0f23f5bd6f7398a80489c09c50b97d231de7e --- /dev/null +++ b/alphagenome/source/src/alphagenome/protos/__init__.py @@ -0,0 +1,15 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Protocol buffers for interacting with genomic models.""" diff --git a/alphagenome/source/src/alphagenome/protos/dna_model.proto b/alphagenome/source/src/alphagenome/protos/dna_model.proto new file mode 100644 index 0000000000000000000000000000000000000000..e59ca66743f1183776d45bf13ca7cd1dd0a793c9 --- /dev/null +++ b/alphagenome/source/src/alphagenome/protos/dna_model.proto @@ -0,0 +1,559 @@ +// Copyright 2024 Google LLC. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +syntax = "proto3"; + +package google.gdm.gdmscience.alphagenome.v1main; + +import "alphagenome/protos/tensor.proto"; + +option go_package = "google.golang.org/genproto/googleapis/gdm/gdmscience/alphagenome/v1main;alphagenome"; +option java_multiple_files = true; +option java_outer_classname = "DnaModelProto"; +option java_package = "com.google.gdm.gdmscience.alphagenome.v1main"; + +// Message that represents a genomic interval. +message Interval { + // The chromosome name, e.g., "chr1". + string chromosome = 1; + + // 0-based start position. + int64 start = 2; + + // 0-based end position. Must be greater than or equal to start. + int64 end = 3; + + // The strand of the interval. + Strand strand = 4; +} + +// Message that represents a genomic variant/mutation. +message Variant { + // The chromosome name, e.g., "chr1". + string chromosome = 1; + + // The 1-based position of the variant on the chromosome. + int64 position = 2; + + // The reference base(s) at the variant position. Normally this corresponds to + // the sequence in the reference genome. Must only contain characters "ACGTN". + string reference_bases = 3; + + // The alternate base(s) that replace the reference. For example: + // If sequence="ACT", position=2, reference_bases="C", alternate_bases="TG", + // then the alternate sequence would be "ATGT'. + string alternate_bases = 4; +} + +// A single biological ontology term. +message OntologyTerm { + // The type of ontology this term belongs to. + OntologyType ontology_type = 1; + + // The ID of the term within the ontology. + int64 id = 2; +} + +// Message that represents a biological sample that is used in an experiment. +message Biosample { + // Biosample type matching ENCODE conventions. + BiosampleType type = 1; + + // The name of the biosample, e.g. "T-cell". + string name = 2; + + // Optional stage of the biosample, e.g. "adult". + optional string stage = 3; +} + +// Message containing metadata for a gene score. +message GeneScorerMetadata { + // ENSEMBL gene identifier without version number, e.g. "ENSG00000100342". + string gene_id = 1; + + // Optional HGNC gene symbol, e.g. "APOL1". + optional string name = 3; + + // Optional strand of the gene. + optional Strand strand = 2; + + // Optional gene biotype, e.g. "protein_coding" or "lncRNA". + optional string type = 4; + + // Optional start position for junction scores. + optional int64 junction_start = 5; + + // Optional end position for junction scores. + optional int64 junction_end = 6; +} + +// Message containing metadata for a track. +message TrackMetadata { + // Name of the track. + string name = 1; + + // Strand for the track. + Strand strand = 2; + + // Ontology term for the track. + OntologyTerm ontology_term = 3; + + // Biosample for the track. + Biosample biosample = 4; + + // Optional assay for the track. e.g. "polyA plus RNA-seq". + optional string assay = 5; + + // Optional histone mark for the track. + optional string histone_mark_code = 9; + + // Optional transcription factor for the track. + optional string transcription_factor_code = 10; + + // Optional GTEx tissue for the track. + optional string gtex_tissue = 8; + + // Optional data source for the track. e.g. "encode". + optional string data_source = 11; + + // Optional sequence reading endedness. + optional Endedness endedness = 12; + + // Optional boolean indicating whether the track is genetically modified. + optional bool genetically_modified = 13; + + // Optional mean of the non-zero values in the track. + optional float nonzero_mean = 14; +} + +// Container for TrackMetadata. +message TracksMetadata { + // Ordered list of metadata for each track. + repeated TrackMetadata metadata = 1; +} + +// Message containing metadata for a splice junctions. +message JunctionMetadata { + // Name of the junction track, e.g. "Brain_Cerebellum". + string name = 1; + + // Ontology term for the junction track. + OntologyTerm ontology_term = 2; + + // Biosample for the junction track. + Biosample biosample = 3; + + // Optional GTEx tissue for the junction track. + optional string gtex_tissue = 4; + + // Optional data source for the track. e.g. "encode". + optional string data_source = 5; + + // Optional assay for the track. e.g. "polyA plus RNA-seq". + optional string assay = 6; +} + +// Container for list of JunctionMetadata. +message JunctionsMetadata { + // Ordered list of metadata for each junction. + repeated JunctionMetadata metadata = 1; +} + +// Message for storing track values and metadata. +message TrackData { + // Values for the track. + Tensor values = 1; + + // Metadata for the track. The number of metadata elements must match the last + // dimension of the values tensor. + repeated TrackMetadata metadata = 2; + + // Resolution of the track data in base pairs. + optional int64 resolution = 3; + + // Optional Interval representing the genomic region. + Interval interval = 4; +} + +// Message for storing splice junction values and metadata. +message JunctionData { + // Values for the splice junction for each track. + Tensor values = 1; + + // Metadata for the splice junction. The number of elements must match the + // last dimension of the values tensor. + repeated JunctionMetadata metadata = 2; + + // Predicted splice junctions. The number of elements must match the first + // dimensions of the values tensor. + repeated Interval junctions = 3; + + // Optional Interval representing the genomic region. + Interval interval = 4; +} + +// Message containing metadata for an interval prediction. +message IntervalMetadata { + // Genomic interval. + Interval interval = 1; + + // Track metadata for the interval prediction. + repeated TrackMetadata track_metadata = 2; + + // Gene metadata for the interval prediction. + repeated GeneScorerMetadata gene_metadata = 3; +} + +// Message for storing interval values and metadata. +message IntervalData { + // Values for the interval. + Tensor values = 1; + + // Metadata for the interval. + IntervalMetadata metadata = 2; +} + +// Message containing metadata for a variant prediction. +message VariantMetadata { + // Genomic variant. + Variant variant = 1; + + // Track metadata for the variant prediction. + repeated TrackMetadata track_metadata = 2; + + // Gene metadata for the variant prediction. + repeated GeneScorerMetadata gene_metadata = 3; +} + +// Message for storing variant values and metadata. +message VariantData { + // Values for the variant. + Tensor values = 1; + + // Metadata for the variant. + VariantMetadata metadata = 2; +} + +// Message for storing model outputs for a single output type. +message Output { + // The type of output. + OutputType output_type = 1; + + // The payload for the output. + oneof payload { + // Track data for the output. Used in all output types except + // OUTPUT_TYPE_SPLICE_JUNCTIONS. + TrackData track_data = 2; + + // Raw predictions with no metadata. + Tensor data = 3; + + // Junction data for the output. This is only set for + // OUTPUT_TYPE_SPLICE_JUNCTIONS. + JunctionData junction_data = 4; + } +} + +// Message for storing score interval results. +message ScoreIntervalOutput { + // Score interval data. + IntervalData interval_data = 1; +} + +// Message for storing score variant results. +message ScoreVariantOutput { + // Score variant data. + VariantData variant_data = 1; +} + +// Interval scorer message for gene-mask scoring. +message GeneMaskIntervalScorer { + // Requested output type. + OutputType requested_output = 1; + + // Requested width. + int64 width = 2; + + // Requested aggregation type. + IntervalAggregationType aggregation_type = 3; +} + +// Container message for all interval scorers. +message IntervalScorer { + // The interval scorer payload. + oneof scorer { + // Gene mask scorer. + GeneMaskIntervalScorer gene_mask = 1; + } +} + +// Variant scorer message for center mask scoring. +message CenterMaskScorer { + // The width of the mask around the variant. + optional int64 width = 1; + + // The aggregation type. + AggregationType aggregation_type = 2; + + // Requested output type. + OutputType requested_output = 3; +} + +// Variant scorer message for gene-mask scoring based on log fold change. +message GeneMaskLFCScorer { + // Requested output type. + OutputType requested_output = 1; +} + +// Variant scorer message for gene-mask scoring based on active allele counts. +message GeneMaskActiveScorer { + // Requested output type. + OutputType requested_output = 1; +} + +// Variant scorer message for gene-mask scoring for splicing. +message GeneMaskSplicingScorer { + // The width of the mask around the variant. + optional int64 width = 1; + + // Requested output type. + OutputType requested_output = 2; +} + +// Variant scorer message for polyadenylation QTLs (paQTLs). +message PolyadenylationScorer {} + +// Variant scorer message for splice junction scoring. +message SpliceJunctionScorer {} + +// Variant scorer message for contact map scoring. +message ContactMapScorer {} + +// Container message for all variant scorers. +message VariantScorer { + // The variant scorer payload. + oneof scorer { + // Center mask scorer. + CenterMaskScorer center_mask = 1; + + // Gene mask scorer based on log fold change. + GeneMaskLFCScorer gene_mask = 2; + + // Gene mask scorer for splicing. + GeneMaskSplicingScorer gene_mask_splicing = 3; + + // Polyadenylation scorer. + PolyadenylationScorer pa_qtl = 4; + + // Splice junction scorer. + SpliceJunctionScorer splice_junction = 5; + + // Contact map scorer. + ContactMapScorer contact_map = 6; + + // Gene mask scorer based on active allele counts. + GeneMaskActiveScorer gene_mask_active = 7; + } +} + +// Message containing metadata for an output type. +message OutputMetadata { + // The output type. + OutputType output_type = 1; + + // The payload for the output metadata. + oneof payload { + // Track metadata for the output. Used in all output types except + // OUTPUT_TYPE_SPLICE_JUNCTIONS. + TracksMetadata tracks = 2; + + // Junction metadata for the output. This is only set for + // OUTPUT_TYPE_SPLICE_JUNCTIONS. + JunctionsMetadata junctions = 3; + } +} + +// Defines the possible strands for a DNA sequence. +enum Strand { + // Unspecified strand. + STRAND_UNSPECIFIED = 0; + + // The forward strand (5' to 3') + STRAND_POSITIVE = 1; + + // The reverse strand (3' to 5') + STRAND_NEGATIVE = 2; + + // The strand is not specified. + STRAND_UNSTRANDED = 3; +} + +// Supported ontology types. +enum OntologyType { + // Unspecified ontology type. + ONTOLOGY_TYPE_UNSPECIFIED = 0; + + // Cell Line Ontology. + ONTOLOGY_TYPE_CLO = 1; + + // Uber-anatomy ontology. + ONTOLOGY_TYPE_UBERON = 2; + + // Cell Ontology. + ONTOLOGY_TYPE_CL = 3; + + // Experimental Factor Ontology. + ONTOLOGY_TYPE_EFO = 4; + + // New Term Requested. + ONTOLOGY_TYPE_NTR = 5; +} + +// Biosample type matching ENCODE conventions. See +// https://www.encodeproject.org/profiles/biosample_type. +enum BiosampleType { + // Unspecified biosample type. + BIOSAMPLE_TYPE_UNSPECIFIED = 0; + + // Primary cell sample. + BIOSAMPLE_TYPE_PRIMARY_CELL = 1; + + // In-vitro differentiated cell sample. + BIOSAMPLE_TYPE_IN_VITRO_DIFFERENTIATED_CELLS = 2; + + // Cell line sample. + BIOSAMPLE_TYPE_CELL_LINE = 3; + + // Tissue sample. + BIOSAMPLE_TYPE_TISSUE = 4; + + // Technical sample. + BIOSAMPLE_TYPE_TECHNICAL_SAMPLE = 5; + + // Organoid sample. + BIOSAMPLE_TYPE_ORGANOID = 6; +} + +// Enumeration of all the available types of outputs. +enum OutputType { + // Unspecified output type. + OUTPUT_TYPE_UNSPECIFIED = 0; + + // ATAC-seq tracks capturing chromatin accessibility. + OUTPUT_TYPE_ATAC = 1; + + // CAGE (Cap Analysis of Gene Expression) tracks capturing gene expression. + OUTPUT_TYPE_CAGE = 2; + + // DNase I hypersensitive site tracks capturing chromatin accessibility. + OUTPUT_TYPE_DNASE = 3; + + // RNA sequencing tracks capturing gene expression. + OUTPUT_TYPE_RNA_SEQ = 4; + + // ChIP-seq tracks capturing histone modifications. + OUTPUT_TYPE_CHIP_HISTONE = 5; + + // ChIP-seq tracks capturing transcription factor binding. + OUTPUT_TYPE_CHIP_TF = 6; + + // Splice site tracks capturing donor and acceptor splice sites. + OUTPUT_TYPE_SPLICE_SITES = 7; + + // Splice site usage tracks capturing the fraction of the time that each + // splice site is used. + OUTPUT_TYPE_SPLICE_SITE_USAGE = 8; + + // Splice junction tracks capturing split read RNA-seq counts for each + // junction. + OUTPUT_TYPE_SPLICE_JUNCTIONS = 9; + + // Contact map tracks capturing 3D chromatin contact probabilities. + OUTPUT_TYPE_CONTACT_MAPS = 11; + + // Precision Run-On sequencing and capping, used to measure gene expression. + OUTPUT_TYPE_PROCAP = 12; +} + +// Enumeration of all the available organisms. Values relate to the NCBI +// taxonomy ID. +enum Organism { + // Unspecified organism. + ORGANISM_UNSPECIFIED = 0; + + // Human (Homo sapiens). + ORGANISM_HOMO_SAPIENS = 9606; + + // Mouse (Mus musculus). + ORGANISM_MUS_MUSCULUS = 10090; +} + +// Enum indicating the type of interval scorer aggregation. +enum IntervalAggregationType { + // Unspecified aggregation type. + INTERVAL_AGGREGATION_TYPE_UNSPECIFIED = 0; + + // Mean across positions. + INTERVAL_AGGREGATION_TYPE_MEAN = 1; + + // Sum across positions. + INTERVAL_AGGREGATION_TYPE_SUM = 2; +} + +// Enum indicating the type of variant scorer aggregation. +enum AggregationType { + // Unspecified aggregation type. + AGGREGATION_TYPE_UNSPECIFIED = 0; + + // Difference of means, i.e., mean(ALT) - mean(REF). + AGGREGATION_TYPE_DIFF_MEAN = 1; + + // Difference of sums, i.e., sum(ALT) - sum(REF). + AGGREGATION_TYPE_DIFF_SUM = 2; + + // Log scales predictions, then takes the sum and then the difference, i.e. + // sum(log2(ALT)) - sum(log2(REF)). + AGGREGATION_TYPE_DIFF_SUM_LOG2 = 3; + + // Takes the difference of ALT and REF predictions, then computes the L2 norm, + // i.e., l2_norm(ALT - REF). + AGGREGATION_TYPE_L2_DIFF = 4; + + // Log scales the predictions + 1, takes the difference of `ALT` and `REF` + // predictions, then computes the L2 norm, i.e., l2_norm(log1p(`ALT`) - + // log1p(`REF`)). + AGGREGATION_TYPE_L2_DIFF_LOG1P = 8; + + // Takes the sum of predictions, applies a log transform, then takes the + // difference between predictions, i.e., log2(sum(ALT)) - log2(sum(REF)). + AGGREGATION_TYPE_DIFF_LOG2_SUM = 5; + + // Maximum of means, i.e., max(mean(ALT), mean(REF)). + AGGREGATION_TYPE_ACTIVE_MEAN = 6; + + // Maximum of sums, i.e., max(sum(ALT), sum(REF)). + AGGREGATION_TYPE_ACTIVE_SUM = 7; +} + +// Enum indicating the endedness of a track experiment. +enum Endedness { + // Unspecified endedness. + ENDEDNESS_UNSPECIFIED = 0; + + // Single end. + ENDEDNESS_SINGLE = 1; + + // Paired end. + ENDEDNESS_PAIRED = 2; +} diff --git a/alphagenome/source/src/alphagenome/protos/dna_model_pb2.py b/alphagenome/source/src/alphagenome/protos/dna_model_pb2.py new file mode 100644 index 0000000000000000000000000000000000000000..c8335da87eaf79490cd4d2a4afffe3e4a8cf4a01 --- /dev/null +++ b/alphagenome/source/src/alphagenome/protos/dna_model_pb2.py @@ -0,0 +1,110 @@ +# -*- coding: utf-8 -*- +# Generated by the protocol buffer compiler. DO NOT EDIT! +# NO CHECKED-IN PROTOBUF GENCODE +# source: alphagenome/protos/dna_model.proto +# Protobuf Python Version: 5.27.2 +"""Generated protocol buffer code.""" +from google.protobuf import descriptor as _descriptor +from google.protobuf import descriptor_pool as _descriptor_pool +from google.protobuf import runtime_version as _runtime_version +from google.protobuf import symbol_database as _symbol_database +from google.protobuf.internal import builder as _builder +_runtime_version.ValidateProtobufRuntimeVersion( + _runtime_version.Domain.PUBLIC, + 5, + 27, + 2, + '', + 'alphagenome/protos/dna_model.proto' +) +# @@protoc_insertion_point(imports) + +_sym_db = _symbol_database.Default() + + +from alphagenome.protos import tensor_pb2 as alphagenome_dot_protos_dot_tensor__pb2 + + +DESCRIPTOR = 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b/alphagenome/source/src/alphagenome/protos/dna_model_pb2_grpc.py new file mode 100644 index 0000000000000000000000000000000000000000..fe84ef6c6ad5adcc729255910c3891ead33275ae --- /dev/null +++ b/alphagenome/source/src/alphagenome/protos/dna_model_pb2_grpc.py @@ -0,0 +1,24 @@ +# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! +"""Client and server classes corresponding to protobuf-defined services.""" +import grpc +import warnings + + +GRPC_GENERATED_VERSION = '1.67.1' +GRPC_VERSION = grpc.__version__ +_version_not_supported = False + +try: + from grpc._utilities import first_version_is_lower + _version_not_supported = first_version_is_lower(GRPC_VERSION, GRPC_GENERATED_VERSION) +except ImportError: + _version_not_supported = True + +if _version_not_supported: + raise RuntimeError( + f'The grpc package installed is at version {GRPC_VERSION},' + + f' but the generated code in alphagenome/protos/dna_model_pb2_grpc.py depends on' + + f' grpcio>={GRPC_GENERATED_VERSION}.' + + f' Please upgrade your grpc module to grpcio>={GRPC_GENERATED_VERSION}' + + f' or downgrade your generated code using grpcio-tools<={GRPC_VERSION}.' + ) diff --git a/alphagenome/source/src/alphagenome/protos/dna_model_service.proto b/alphagenome/source/src/alphagenome/protos/dna_model_service.proto new file mode 100644 index 0000000000000000000000000000000000000000..81bcc69b35d0d7bfdf11f18fe14bef5acb41ddcf --- /dev/null +++ b/alphagenome/source/src/alphagenome/protos/dna_model_service.proto @@ -0,0 +1,263 @@ +// Copyright 2024 Google LLC. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +syntax = "proto3"; + +package google.gdm.gdmscience.alphagenome.v1main; + +import "alphagenome/protos/dna_model.proto"; +import "alphagenome/protos/tensor.proto"; + +option go_package = "google.golang.org/genproto/googleapis/gdm/gdmscience/alphagenome/v1main;alphagenome"; +option java_multiple_files = true; +option java_outer_classname = "DnaModelServiceProto"; +option java_package = "com.google.gdm.gdmscience.alphagenome.v1main"; + +// Service for making predictions with DNA models. +service DnaModelService { + // Makes a prediction for DNA sequence. + rpc PredictSequence(stream PredictSequenceRequest) + returns (stream PredictSequenceResponse) {} + + // Make prediction for a single genomic interval. + rpc PredictInterval(stream PredictIntervalRequest) + returns (stream PredictIntervalResponse) {} + + // Make variant effect predictions for a genomic interval. + rpc PredictVariant(stream PredictVariantRequest) + returns (stream PredictVariantResponse) {} + + // Score a genomic interval. + rpc ScoreInterval(stream ScoreIntervalRequest) + returns (stream ScoreIntervalResponse) {} + + // Score a variant for a genomic interval. + rpc ScoreVariant(stream ScoreVariantRequest) + returns (stream ScoreVariantResponse) {} + + // Score ISM variant effect predictions for a genomic interval. + rpc ScoreIsmVariant(stream ScoreIsmVariantRequest) + returns (stream ScoreIsmVariantResponse) {} + + // Get metadata for the model. + rpc GetMetadata(MetadataRequest) returns (stream MetadataResponse) {} +} + +// Request message for predicting a sequence. +message PredictSequenceRequest { + // DNA sequence to make prediction for. Must only contain characters "ACGTN". + string sequence = 1; + + // Organism to use for the prediction. + Organism organism = 2; + + // Ontology terms to generate predictions for. If empty returns all + // ontologies. + repeated OntologyTerm ontology_terms = 3; + + // Output types to generate predictions for. + repeated OutputType requested_outputs = 4; + + // Model version to use. + string model_version = 5; +} + +// Response message for predicting a sequence. +message PredictSequenceResponse { + // The payload for the response. + oneof payload { + // Output for a single output type. + Output output = 1; + + // Tensor chunk related to the last output message received. It is an error + // to receive a tensor chunk without a previous output message. + TensorChunk tensor_chunk = 2; + } +} + +// Request message for predicting an interval. +message PredictIntervalRequest { + // DNA interval to make prediction for. + Interval interval = 1; + + // Organism to use for the prediction. + Organism organism = 2; + + // Output types to generate predictions for. + repeated OutputType requested_outputs = 3; + + // Ontology terms to generate predictions for. If empty returns all + // ontologies. + repeated OntologyTerm ontology_terms = 4; + + // Model version to use. + string model_version = 5; +} + +// Response message for predicting an interval. +message PredictIntervalResponse { + // The payload for the response. + oneof payload { + // Output for a single output type. + Output output = 1; + + // Tensor chunk related to the last output message received. It is an error + // to receive a tensor chunk without a previous output message. + TensorChunk tensor_chunk = 2; + } +} + +// Request message for predicting a variant. +message PredictVariantRequest { + // DNA interval to make prediction for. + Interval interval = 1; + + // DNA variant to make prediction for. + Variant variant = 2; + + // Organism to use for the prediction. + Organism organism = 3; + + // Output types to generate predictions for. + repeated OutputType requested_outputs = 4; + + // Ontology terms to generate predictions for. If empty returns all + // ontologies. + repeated OntologyTerm ontology_terms = 5; + + // Model version to use. + string model_version = 6; +} + +// Response message for predicting a variant. +message PredictVariantResponse { + // The payload for the response. + oneof payload { + // Reference output for a single output type. + Output reference_output = 1; + + // Alternate output for a single output type. + Output alternate_output = 2; + + // Tensor chunk related to the last reference or alternate output message + // received. It is an error to receive a tensor chunk without a previous + // reference or alternate output message. + TensorChunk tensor_chunk = 3; + } +} + +// Request message for scoring an interval. +message ScoreIntervalRequest { + // DNA interval to make prediction for. + Interval interval = 1; + + // Organism to use for the prediction. + Organism organism = 2; + + // Interval scorers to use for the prediction. + repeated IntervalScorer interval_scorers = 3; + + // Model version to use. + string model_version = 4; +} + +// Response message for scoring an interval. +message ScoreIntervalResponse { + // The payload for the response. + oneof payload { + // Score interval output. + ScoreIntervalOutput output = 1; + + // Tensor chunk related to the last score interval output message received. + // It is an error to receice a tensor chunk without a previous output + // message. + TensorChunk tensor_chunk = 2; + } +} + +// Request message for scoring a variant. +message ScoreVariantRequest { + // DNA interval to make prediction for. + Interval interval = 1; + + // DNA variant to make prediction for. + Variant variant = 2; + + // Organism to use for the prediction. + Organism organism = 3; + + // Variant scorers to use for the prediction. + repeated VariantScorer variant_scorers = 4; + + // Model version to use. + string model_version = 5; +} + +// Response message for scoring a variant. +message ScoreVariantResponse { + // The payload for the response. + oneof payload { + // Score variant output. + ScoreVariantOutput output = 1; + + // Tensor chunk related to the last score variant output message received. + // It is an error to receive a tensor chunk without a previous output + // message. + TensorChunk tensor_chunk = 2; + } +} + +// Request message for scoring an in-silico mutagenesis (ISM) interval. +message ScoreIsmVariantRequest { + // DNA interval to make the prediction for. + Interval interval = 1; + + // ISM interval to make the prediction for. + Interval ism_interval = 2; + + // Organism to use for the prediction. + Organism organism = 3; + + // Variant scorers to use for the prediction. + repeated VariantScorer variant_scorers = 4; + + // Model version to use. + string model_version = 5; +} + +// Response message for scoring an in-silico mutagenesis (ISM) interval. +message ScoreIsmVariantResponse { + // The payload for the response. + oneof payload { + // Score variant output. + ScoreVariantOutput output = 1; + + // Tensor chunk related to the last score variant output message received. + // It is an error to receive a tensor chunk without a previous output + // message. + TensorChunk tensor_chunk = 2; + } +} + +// Request message for getting metadata for an organism. +message MetadataRequest { + // The organism we should return metadata for. + Organism organism = 1; +} + +// Response message for getting metadata for an organism. +message MetadataResponse { + // The metadata for each output type. + repeated OutputMetadata output_metadata = 1; +} diff --git a/alphagenome/source/src/alphagenome/protos/dna_model_service_pb2.py b/alphagenome/source/src/alphagenome/protos/dna_model_service_pb2.py new file mode 100644 index 0000000000000000000000000000000000000000..a55487c36d00aa180ddd95882764e3f80a819d92 --- /dev/null +++ b/alphagenome/source/src/alphagenome/protos/dna_model_service_pb2.py @@ -0,0 +1,67 @@ +# -*- coding: utf-8 -*- +# Generated by the protocol buffer compiler. DO NOT EDIT! +# NO CHECKED-IN PROTOBUF GENCODE +# source: alphagenome/protos/dna_model_service.proto +# Protobuf Python Version: 5.27.2 +"""Generated protocol buffer code.""" +from google.protobuf import descriptor as _descriptor +from google.protobuf import descriptor_pool as _descriptor_pool +from google.protobuf import runtime_version as _runtime_version +from google.protobuf import symbol_database as _symbol_database +from google.protobuf.internal import builder as _builder +_runtime_version.ValidateProtobufRuntimeVersion( + _runtime_version.Domain.PUBLIC, + 5, + 27, + 2, + '', + 'alphagenome/protos/dna_model_service.proto' +) +# @@protoc_insertion_point(imports) + +_sym_db = _symbol_database.Default() + + +from alphagenome.protos import dna_model_pb2 as alphagenome_dot_protos_dot_dna__model__pb2 +from alphagenome.protos import tensor_pb2 as alphagenome_dot_protos_dot_tensor__pb2 + + +DESCRIPTOR = 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+ +_globals = globals() +_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) +_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'alphagenome.protos.dna_model_service_pb2', _globals) +if not _descriptor._USE_C_DESCRIPTORS: + _globals['DESCRIPTOR']._loaded_options = None + _globals['DESCRIPTOR']._serialized_options = b'\n,com.google.gdm.gdmscience.alphagenome.v1mainB\024DnaModelServiceProtoP\001ZSgoogle.golang.org/genproto/googleapis/gdm/gdmscience/alphagenome/v1main;alphagenome' + _globals['_PREDICTSEQUENCEREQUEST']._serialized_start=158 + _globals['_PREDICTSEQUENCEREQUEST']._serialized_end=454 + _globals['_PREDICTSEQUENCERESPONSE']._serialized_start=457 + _globals['_PREDICTSEQUENCERESPONSE']._serialized_end=640 + _globals['_PREDICTINTERVALREQUEST']._serialized_start=643 + _globals['_PREDICTINTERVALREQUEST']._serialized_end=991 + _globals['_PREDICTINTERVALRESPONSE']._serialized_start=994 + _globals['_PREDICTINTERVALRESPONSE']._serialized_end=1177 + _globals['_PREDICTVARIANTREQUEST']._serialized_start=1180 + _globals['_PREDICTVARIANTREQUEST']._serialized_end=1595 + _globals['_PREDICTVARIANTRESPONSE']._serialized_start=1598 + _globals['_PREDICTVARIANTRESPONSE']._serialized_end=1868 + _globals['_SCOREINTERVALREQUEST']._serialized_start=1871 + _globals['_SCOREINTERVALREQUEST']._serialized_end=2140 + _globals['_SCOREINTERVALRESPONSE']._serialized_start=2143 + _globals['_SCOREINTERVALRESPONSE']._serialized_end=2337 + _globals['_SCOREVARIANTREQUEST']._serialized_start=2340 + _globals['_SCOREVARIANTREQUEST']._serialized_end=2674 + _globals['_SCOREVARIANTRESPONSE']._serialized_start=2677 + _globals['_SCOREVARIANTRESPONSE']._serialized_end=2869 + _globals['_SCOREISMVARIANTREQUEST']._serialized_start=2872 + _globals['_SCOREISMVARIANTREQUEST']._serialized_end=3215 + _globals['_SCOREISMVARIANTRESPONSE']._serialized_start=3218 + _globals['_SCOREISMVARIANTRESPONSE']._serialized_end=3413 + _globals['_METADATAREQUEST']._serialized_start=3415 + _globals['_METADATAREQUEST']._serialized_end=3502 + _globals['_METADATARESPONSE']._serialized_start=3504 + _globals['_METADATARESPONSE']._serialized_end=3605 + _globals['_DNAMODELSERVICE']._serialized_start=3608 + _globals['_DNAMODELSERVICE']._serialized_end=4700 +# @@protoc_insertion_point(module_scope) diff --git a/alphagenome/source/src/alphagenome/protos/dna_model_service_pb2_grpc.py b/alphagenome/source/src/alphagenome/protos/dna_model_service_pb2_grpc.py new file mode 100644 index 0000000000000000000000000000000000000000..db4c1d8bef798051ed04c405d78db18ab9912744 --- /dev/null +++ b/alphagenome/source/src/alphagenome/protos/dna_model_service_pb2_grpc.py @@ -0,0 +1,365 @@ +# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! +"""Client and server classes corresponding to protobuf-defined services.""" +import grpc +import warnings + +from alphagenome.protos import dna_model_service_pb2 as alphagenome_dot_protos_dot_dna__model__service__pb2 + +GRPC_GENERATED_VERSION = '1.67.1' +GRPC_VERSION = grpc.__version__ +_version_not_supported = False + +try: + from grpc._utilities import first_version_is_lower + _version_not_supported = first_version_is_lower(GRPC_VERSION, GRPC_GENERATED_VERSION) +except ImportError: + _version_not_supported = True + +if _version_not_supported: + raise RuntimeError( + f'The grpc package installed is at version {GRPC_VERSION},' + + f' but the generated code in alphagenome/protos/dna_model_service_pb2_grpc.py depends on' + + f' grpcio>={GRPC_GENERATED_VERSION}.' + + f' Please upgrade your grpc module to grpcio>={GRPC_GENERATED_VERSION}' + + f' or downgrade your generated code using grpcio-tools<={GRPC_VERSION}.' + ) + + +class DnaModelServiceStub(object): + """Service for making predictions with DNA models. + """ + + def __init__(self, channel): + """Constructor. + + Args: + channel: A grpc.Channel. + """ + self.PredictSequence = channel.stream_stream( + '/google.gdm.gdmscience.alphagenome.v1main.DnaModelService/PredictSequence', + request_serializer=alphagenome_dot_protos_dot_dna__model__service__pb2.PredictSequenceRequest.SerializeToString, + response_deserializer=alphagenome_dot_protos_dot_dna__model__service__pb2.PredictSequenceResponse.FromString, + _registered_method=True) + self.PredictInterval = channel.stream_stream( + '/google.gdm.gdmscience.alphagenome.v1main.DnaModelService/PredictInterval', + request_serializer=alphagenome_dot_protos_dot_dna__model__service__pb2.PredictIntervalRequest.SerializeToString, + response_deserializer=alphagenome_dot_protos_dot_dna__model__service__pb2.PredictIntervalResponse.FromString, + _registered_method=True) + self.PredictVariant = channel.stream_stream( + '/google.gdm.gdmscience.alphagenome.v1main.DnaModelService/PredictVariant', + request_serializer=alphagenome_dot_protos_dot_dna__model__service__pb2.PredictVariantRequest.SerializeToString, + response_deserializer=alphagenome_dot_protos_dot_dna__model__service__pb2.PredictVariantResponse.FromString, + _registered_method=True) + self.ScoreInterval = channel.stream_stream( + '/google.gdm.gdmscience.alphagenome.v1main.DnaModelService/ScoreInterval', + request_serializer=alphagenome_dot_protos_dot_dna__model__service__pb2.ScoreIntervalRequest.SerializeToString, + response_deserializer=alphagenome_dot_protos_dot_dna__model__service__pb2.ScoreIntervalResponse.FromString, + _registered_method=True) + self.ScoreVariant = channel.stream_stream( + '/google.gdm.gdmscience.alphagenome.v1main.DnaModelService/ScoreVariant', + request_serializer=alphagenome_dot_protos_dot_dna__model__service__pb2.ScoreVariantRequest.SerializeToString, + response_deserializer=alphagenome_dot_protos_dot_dna__model__service__pb2.ScoreVariantResponse.FromString, + _registered_method=True) + self.ScoreIsmVariant = channel.stream_stream( + '/google.gdm.gdmscience.alphagenome.v1main.DnaModelService/ScoreIsmVariant', + request_serializer=alphagenome_dot_protos_dot_dna__model__service__pb2.ScoreIsmVariantRequest.SerializeToString, + response_deserializer=alphagenome_dot_protos_dot_dna__model__service__pb2.ScoreIsmVariantResponse.FromString, + _registered_method=True) + self.GetMetadata = channel.unary_stream( + '/google.gdm.gdmscience.alphagenome.v1main.DnaModelService/GetMetadata', + request_serializer=alphagenome_dot_protos_dot_dna__model__service__pb2.MetadataRequest.SerializeToString, + response_deserializer=alphagenome_dot_protos_dot_dna__model__service__pb2.MetadataResponse.FromString, + _registered_method=True) + + +class DnaModelServiceServicer(object): + """Service for making predictions with DNA models. + """ + + def PredictSequence(self, request_iterator, context): + """Makes a prediction for DNA sequence. + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def PredictInterval(self, request_iterator, context): + """Make prediction for a single genomic interval. + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def PredictVariant(self, request_iterator, context): + """Make variant effect predictions for a genomic interval. + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def ScoreInterval(self, request_iterator, context): + """Score a genomic interval. + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def ScoreVariant(self, request_iterator, context): + """Score a variant for a genomic interval. + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def ScoreIsmVariant(self, request_iterator, context): + """Score ISM variant effect predictions for a genomic interval. + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + def GetMetadata(self, request, context): + """Get metadata for the model. + """ + context.set_code(grpc.StatusCode.UNIMPLEMENTED) + context.set_details('Method not implemented!') + raise NotImplementedError('Method not implemented!') + + +def add_DnaModelServiceServicer_to_server(servicer, server): + rpc_method_handlers = { + 'PredictSequence': grpc.stream_stream_rpc_method_handler( + servicer.PredictSequence, + request_deserializer=alphagenome_dot_protos_dot_dna__model__service__pb2.PredictSequenceRequest.FromString, + response_serializer=alphagenome_dot_protos_dot_dna__model__service__pb2.PredictSequenceResponse.SerializeToString, + ), + 'PredictInterval': grpc.stream_stream_rpc_method_handler( + servicer.PredictInterval, + request_deserializer=alphagenome_dot_protos_dot_dna__model__service__pb2.PredictIntervalRequest.FromString, + response_serializer=alphagenome_dot_protos_dot_dna__model__service__pb2.PredictIntervalResponse.SerializeToString, + ), + 'PredictVariant': grpc.stream_stream_rpc_method_handler( + servicer.PredictVariant, + request_deserializer=alphagenome_dot_protos_dot_dna__model__service__pb2.PredictVariantRequest.FromString, + response_serializer=alphagenome_dot_protos_dot_dna__model__service__pb2.PredictVariantResponse.SerializeToString, + ), + 'ScoreInterval': grpc.stream_stream_rpc_method_handler( + servicer.ScoreInterval, + request_deserializer=alphagenome_dot_protos_dot_dna__model__service__pb2.ScoreIntervalRequest.FromString, + response_serializer=alphagenome_dot_protos_dot_dna__model__service__pb2.ScoreIntervalResponse.SerializeToString, + ), + 'ScoreVariant': grpc.stream_stream_rpc_method_handler( + servicer.ScoreVariant, + request_deserializer=alphagenome_dot_protos_dot_dna__model__service__pb2.ScoreVariantRequest.FromString, + response_serializer=alphagenome_dot_protos_dot_dna__model__service__pb2.ScoreVariantResponse.SerializeToString, + ), + 'ScoreIsmVariant': grpc.stream_stream_rpc_method_handler( + servicer.ScoreIsmVariant, + request_deserializer=alphagenome_dot_protos_dot_dna__model__service__pb2.ScoreIsmVariantRequest.FromString, + response_serializer=alphagenome_dot_protos_dot_dna__model__service__pb2.ScoreIsmVariantResponse.SerializeToString, + ), + 'GetMetadata': grpc.unary_stream_rpc_method_handler( + servicer.GetMetadata, + request_deserializer=alphagenome_dot_protos_dot_dna__model__service__pb2.MetadataRequest.FromString, + response_serializer=alphagenome_dot_protos_dot_dna__model__service__pb2.MetadataResponse.SerializeToString, + ), + } + generic_handler = grpc.method_handlers_generic_handler( + 'google.gdm.gdmscience.alphagenome.v1main.DnaModelService', rpc_method_handlers) + server.add_generic_rpc_handlers((generic_handler,)) + server.add_registered_method_handlers('google.gdm.gdmscience.alphagenome.v1main.DnaModelService', rpc_method_handlers) + + + # This class is part of an EXPERIMENTAL API. +class DnaModelService(object): + """Service for making predictions with DNA models. + """ + + @staticmethod + def PredictSequence(request_iterator, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.stream_stream( + request_iterator, + target, + '/google.gdm.gdmscience.alphagenome.v1main.DnaModelService/PredictSequence', + alphagenome_dot_protos_dot_dna__model__service__pb2.PredictSequenceRequest.SerializeToString, + alphagenome_dot_protos_dot_dna__model__service__pb2.PredictSequenceResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def PredictInterval(request_iterator, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.stream_stream( + request_iterator, + target, + '/google.gdm.gdmscience.alphagenome.v1main.DnaModelService/PredictInterval', + alphagenome_dot_protos_dot_dna__model__service__pb2.PredictIntervalRequest.SerializeToString, + alphagenome_dot_protos_dot_dna__model__service__pb2.PredictIntervalResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def PredictVariant(request_iterator, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.stream_stream( + request_iterator, + target, + '/google.gdm.gdmscience.alphagenome.v1main.DnaModelService/PredictVariant', + alphagenome_dot_protos_dot_dna__model__service__pb2.PredictVariantRequest.SerializeToString, + alphagenome_dot_protos_dot_dna__model__service__pb2.PredictVariantResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def ScoreInterval(request_iterator, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.stream_stream( + request_iterator, + target, + '/google.gdm.gdmscience.alphagenome.v1main.DnaModelService/ScoreInterval', + alphagenome_dot_protos_dot_dna__model__service__pb2.ScoreIntervalRequest.SerializeToString, + alphagenome_dot_protos_dot_dna__model__service__pb2.ScoreIntervalResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def ScoreVariant(request_iterator, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.stream_stream( + request_iterator, + target, + '/google.gdm.gdmscience.alphagenome.v1main.DnaModelService/ScoreVariant', + alphagenome_dot_protos_dot_dna__model__service__pb2.ScoreVariantRequest.SerializeToString, + alphagenome_dot_protos_dot_dna__model__service__pb2.ScoreVariantResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def ScoreIsmVariant(request_iterator, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.stream_stream( + request_iterator, + target, + '/google.gdm.gdmscience.alphagenome.v1main.DnaModelService/ScoreIsmVariant', + alphagenome_dot_protos_dot_dna__model__service__pb2.ScoreIsmVariantRequest.SerializeToString, + alphagenome_dot_protos_dot_dna__model__service__pb2.ScoreIsmVariantResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) + + @staticmethod + def GetMetadata(request, + target, + options=(), + channel_credentials=None, + call_credentials=None, + insecure=False, + compression=None, + wait_for_ready=None, + timeout=None, + metadata=None): + return grpc.experimental.unary_stream( + request, + target, + '/google.gdm.gdmscience.alphagenome.v1main.DnaModelService/GetMetadata', + alphagenome_dot_protos_dot_dna__model__service__pb2.MetadataRequest.SerializeToString, + alphagenome_dot_protos_dot_dna__model__service__pb2.MetadataResponse.FromString, + options, + channel_credentials, + insecure, + call_credentials, + compression, + wait_for_ready, + timeout, + metadata, + _registered_method=True) diff --git a/alphagenome/source/src/alphagenome/protos/tensor.proto b/alphagenome/source/src/alphagenome/protos/tensor.proto new file mode 100644 index 0000000000000000000000000000000000000000..4d86d8396c3080b5bd7ac96156a090de7edd4479 --- /dev/null +++ b/alphagenome/source/src/alphagenome/protos/tensor.proto @@ -0,0 +1,104 @@ +// Copyright 2024 Google LLC. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +syntax = "proto3"; + +package google.gdm.gdmscience.alphagenome.v1main; + +option go_package = "google.golang.org/genproto/googleapis/gdm/gdmscience/alphagenome/v1main;alphagenome"; +option java_multiple_files = true; +option java_outer_classname = "TensorProto"; +option java_package = "com.google.gdm.gdmscience.alphagenome.v1main"; + +// Protocol buffer representing an arbitrarily large multi-dimensional array of +// data, laid out in row-major format. To support tensors larger than the +// maximum protocol buffer message size, the tensor payload may be split into +// multiple chunks. +message Tensor { + // The shape of the tensor. If empty, the data will be treated as a scalar. + repeated int32 shape = 1; + + // Data type for the elements in this tensor. + DataType data_type = 2; + + // The payload for the tensor. + oneof payload { + // The raw data for the tensor. If present, the tensor is not split into + // chunks. + TensorChunk array = 3; + + // The number of chunks the tensor is split into. + int64 chunk_count = 4; + } +} + +// A single chunk of a tensor. +message TensorChunk { + // Flattened tensor data. Data is laid out in row-major order. + bytes data = 1; + + // How the data is compressed. Compression is applied to each chunk + // independently. + CompressionType compression_type = 2; +} + +// The data type of the tensor. +enum DataType { + // Unspecified data type. + DATA_TYPE_UNSPECIFIED = 0; + + // 16-bit "Brain Floating Point". See + // https://en.wikipedia.org/wiki/Bfloat16_floating-point_format for more + // details. + DATA_TYPE_BFLOAT16 = 1; + + // 16-bit floating point. + DATA_TYPE_FLOAT16 = 11; + + // 32-bit floating point. + DATA_TYPE_FLOAT32 = 2; + + // 64-bit floating point. + DATA_TYPE_FLOAT64 = 3; + + // 8-bit signed integer. + DATA_TYPE_INT8 = 4; + + // 32-bit signed integer. + DATA_TYPE_INT32 = 5; + + // 64-bit signed integer. + DATA_TYPE_INT64 = 6; + + // 8-bit unsigned integer. + DATA_TYPE_UINT8 = 7; + + // 32-bit unsigned integer. + DATA_TYPE_UINT32 = 8; + + // 64-bit unsigned integer. + DATA_TYPE_UINT64 = 9; + + // 8-bit boolean. + DATA_TYPE_BOOL = 10; +} + +// Compression type for the tensor data. +enum CompressionType { + // No compression. + COMPRESSION_TYPE_NONE = 0; + + // ZSTD compression. + COMPRESSION_TYPE_ZSTD = 1; +} diff --git a/alphagenome/source/src/alphagenome/protos/tensor_pb2.py b/alphagenome/source/src/alphagenome/protos/tensor_pb2.py new file mode 100644 index 0000000000000000000000000000000000000000..4bff29e3ee503c3498aa7007a22e1893e3ce578e --- /dev/null +++ b/alphagenome/source/src/alphagenome/protos/tensor_pb2.py @@ -0,0 +1,43 @@ +# -*- coding: utf-8 -*- +# Generated by the protocol buffer compiler. DO NOT EDIT! +# NO CHECKED-IN PROTOBUF GENCODE +# source: alphagenome/protos/tensor.proto +# Protobuf Python Version: 5.27.2 +"""Generated protocol buffer code.""" +from google.protobuf import descriptor as _descriptor +from google.protobuf import descriptor_pool as _descriptor_pool +from google.protobuf import runtime_version as _runtime_version +from google.protobuf import symbol_database as _symbol_database +from google.protobuf.internal import builder as _builder +_runtime_version.ValidateProtobufRuntimeVersion( + _runtime_version.Domain.PUBLIC, + 5, + 27, + 2, + '', + 'alphagenome/protos/tensor.proto' +) +# @@protoc_insertion_point(imports) + +_sym_db = _symbol_database.Default() + + + + +DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x1f\x61lphagenome/protos/tensor.proto\x12(google.gdm.gdmscience.alphagenome.v1main\"\xc8\x01\n\x06Tensor\x12\r\n\x05shape\x18\x01 \x03(\x05\x12\x45\n\tdata_type\x18\x02 \x01(\x0e\x32\x32.google.gdm.gdmscience.alphagenome.v1main.DataType\x12\x46\n\x05\x61rray\x18\x03 \x01(\x0b\x32\x35.google.gdm.gdmscience.alphagenome.v1main.TensorChunkH\x00\x12\x15\n\x0b\x63hunk_count\x18\x04 \x01(\x03H\x00\x42\t\n\x07payload\"p\n\x0bTensorChunk\x12\x0c\n\x04\x64\x61ta\x18\x01 \x01(\x0c\x12S\n\x10\x63ompression_type\x18\x02 \x01(\x0e\x32\x39.google.gdm.gdmscience.alphagenome.v1main.CompressionType*\x95\x02\n\x08\x44\x61taType\x12\x19\n\x15\x44\x41TA_TYPE_UNSPECIFIED\x10\x00\x12\x16\n\x12\x44\x41TA_TYPE_BFLOAT16\x10\x01\x12\x15\n\x11\x44\x41TA_TYPE_FLOAT16\x10\x0b\x12\x15\n\x11\x44\x41TA_TYPE_FLOAT32\x10\x02\x12\x15\n\x11\x44\x41TA_TYPE_FLOAT64\x10\x03\x12\x12\n\x0e\x44\x41TA_TYPE_INT8\x10\x04\x12\x13\n\x0f\x44\x41TA_TYPE_INT32\x10\x05\x12\x13\n\x0f\x44\x41TA_TYPE_INT64\x10\x06\x12\x13\n\x0f\x44\x41TA_TYPE_UINT8\x10\x07\x12\x14\n\x10\x44\x41TA_TYPE_UINT32\x10\x08\x12\x14\n\x10\x44\x41TA_TYPE_UINT64\x10\t\x12\x12\n\x0e\x44\x41TA_TYPE_BOOL\x10\n*G\n\x0f\x43ompressionType\x12\x19\n\x15\x43OMPRESSION_TYPE_NONE\x10\x00\x12\x19\n\x15\x43OMPRESSION_TYPE_ZSTD\x10\x01\x42\x92\x01\n,com.google.gdm.gdmscience.alphagenome.v1mainB\x0bTensorProtoP\x01ZSgoogle.golang.org/genproto/googleapis/gdm/gdmscience/alphagenome/v1main;alphagenomeb\x06proto3') + +_globals = globals() +_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals) +_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'alphagenome.protos.tensor_pb2', _globals) +if not _descriptor._USE_C_DESCRIPTORS: + _globals['DESCRIPTOR']._loaded_options = None + _globals['DESCRIPTOR']._serialized_options = b'\n,com.google.gdm.gdmscience.alphagenome.v1mainB\013TensorProtoP\001ZSgoogle.golang.org/genproto/googleapis/gdm/gdmscience/alphagenome/v1main;alphagenome' + _globals['_DATATYPE']._serialized_start=395 + _globals['_DATATYPE']._serialized_end=672 + _globals['_COMPRESSIONTYPE']._serialized_start=674 + _globals['_COMPRESSIONTYPE']._serialized_end=745 + _globals['_TENSOR']._serialized_start=78 + _globals['_TENSOR']._serialized_end=278 + _globals['_TENSORCHUNK']._serialized_start=280 + _globals['_TENSORCHUNK']._serialized_end=392 +# @@protoc_insertion_point(module_scope) diff --git a/alphagenome/source/src/alphagenome/protos/tensor_pb2_grpc.py b/alphagenome/source/src/alphagenome/protos/tensor_pb2_grpc.py new file mode 100644 index 0000000000000000000000000000000000000000..cf3dcb648759e62095dbd01f8e900e4b2475db6e --- /dev/null +++ b/alphagenome/source/src/alphagenome/protos/tensor_pb2_grpc.py @@ -0,0 +1,24 @@ +# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT! +"""Client and server classes corresponding to protobuf-defined services.""" +import grpc +import warnings + + +GRPC_GENERATED_VERSION = '1.67.1' +GRPC_VERSION = grpc.__version__ +_version_not_supported = False + +try: + from grpc._utilities import first_version_is_lower + _version_not_supported = first_version_is_lower(GRPC_VERSION, GRPC_GENERATED_VERSION) +except ImportError: + _version_not_supported = True + +if _version_not_supported: + raise RuntimeError( + f'The grpc package installed is at version {GRPC_VERSION},' + + f' but the generated code in alphagenome/protos/tensor_pb2_grpc.py depends on' + + f' grpcio>={GRPC_GENERATED_VERSION}.' + + f' Please upgrade your grpc module to grpcio>={GRPC_GENERATED_VERSION}' + + f' or downgrade your generated code using grpcio-tools<={GRPC_VERSION}.' + ) diff --git a/alphagenome/source/src/alphagenome/tensor_utils.py b/alphagenome/source/src/alphagenome/tensor_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..e52265974cd36b317f1801da56385569eddb503d --- /dev/null +++ b/alphagenome/source/src/alphagenome/tensor_utils.py @@ -0,0 +1,160 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Utility functions for converting NumPy arrays to Tensor protocol buffers.""" + +from collections.abc import Iterable, Sequence + +from alphagenome.protos import tensor_pb2 +import immutabledict +import ml_dtypes +import numpy as np +import zstandard + + +_TENSOR_DTYPE_TO_NUMPY_DTYPE = immutabledict.immutabledict({ + tensor_pb2.DataType.DATA_TYPE_BFLOAT16: np.dtype(ml_dtypes.bfloat16), + tensor_pb2.DataType.DATA_TYPE_FLOAT16: np.dtype(np.float16), + tensor_pb2.DataType.DATA_TYPE_FLOAT32: np.dtype(np.float32), + tensor_pb2.DataType.DATA_TYPE_FLOAT64: np.dtype(np.float64), + tensor_pb2.DataType.DATA_TYPE_INT8: np.dtype(np.int8), + tensor_pb2.DataType.DATA_TYPE_INT32: np.dtype(np.int32), + tensor_pb2.DataType.DATA_TYPE_INT64: np.dtype(np.int64), + tensor_pb2.DataType.DATA_TYPE_UINT8: np.dtype(np.uint8), + tensor_pb2.DataType.DATA_TYPE_UINT32: np.dtype(np.uint32), + tensor_pb2.DataType.DATA_TYPE_UINT64: np.dtype(np.uint64), + tensor_pb2.DataType.DATA_TYPE_BOOL: np.dtype(bool), +}) + +_NUMPY_DTYPE_TO_TENSOR_DTYPE = immutabledict.immutabledict( + {value: key for key, value in _TENSOR_DTYPE_TO_NUMPY_DTYPE.items()} +) + + +def _compress_bytes( + array: np.ndarray, compression_type: tensor_pb2.CompressionType +): + """Compresses a c-contiguous array to the specified compression type.""" + assert array.flags.c_contiguous + array = array.view(np.uint8) + match compression_type: + case tensor_pb2.CompressionType.COMPRESSION_TYPE_ZSTD: + return zstandard.compress(array.data) + case tensor_pb2.CompressionType.COMPRESSION_TYPE_NONE: + return bytes(array.data) + + +def _decompress_bytes( + data: bytes, compression_type: tensor_pb2.CompressionType +): + """Decompress bytes using the specified compression type.""" + match compression_type: + case tensor_pb2.CompressionType.COMPRESSION_TYPE_ZSTD: + return zstandard.decompress(data) + case tensor_pb2.CompressionType.COMPRESSION_TYPE_NONE: + return data + + +def pack_tensor( + value: ..., + *, + bytes_per_chunk: int = 0, + compression_type: tensor_pb2.CompressionType = ( + tensor_pb2.CompressionType.COMPRESSION_TYPE_NONE + ), +) -> tuple[tensor_pb2.Tensor, Sequence[tensor_pb2.TensorChunk]]: + """Encodes the value as a Tensor and optional sequence of chunks. + + Args: + value: An array-like object to pack. For example, scalar (float, int, bool, + etc.), NumPy array, or nested lists of scalars. + bytes_per_chunk: The number of bytes to include in each chunk. If 0, the + entire value will be packed into the Tensor proto, otherwise the value + will be split into chunks of this size. + compression_type: The type of compression to apply to the data. This is + applied to each chunk separately. + + Returns: + Tuple of Tensor protocol buffer and, if items_per_chunk is greater than 0, a + sequence of TensorChunk protos. + """ + packed = tensor_pb2.Tensor() + value = np.ascontiguousarray(value) + + packed.shape[:] = value.shape + packed.data_type = _NUMPY_DTYPE_TO_TENSOR_DTYPE[value.dtype] + + chunks = [] + if bytes_per_chunk > 0: + items_per_chunk = bytes_per_chunk // value.itemsize + if bytes_per_chunk < value.itemsize: + raise ValueError(f'{bytes_per_chunk=} must be >= {value.itemsize=}.') + for chunk in np.split( + value.ravel(), range(items_per_chunk, value.size, items_per_chunk) + ): + chunks.append( + tensor_pb2.TensorChunk( + data=_compress_bytes(chunk, compression_type), + compression_type=compression_type, + ) + ) + packed.chunk_count = len(chunks) + else: + packed.array.data = _compress_bytes(value, compression_type) + packed.array.compression_type = compression_type + + return packed, chunks + + +def unpack_proto( + proto: tensor_pb2.Tensor, + chunks: Iterable[tensor_pb2.TensorChunk] = (), +) -> np.ndarray: + """Converts a Tensor proto and any chunks into a NumPy array. + + Args: + proto: Tensor proto to unpack. + chunks: Optional sequence of TensorChunk protos to unpack. + + Returns: + NumPy array of the unpacked data. + """ + match proto.WhichOneof('payload'): + case 'array': + data = _decompress_bytes(proto.array.data, proto.array.compression_type) + case 'chunk_count': + data = b''.join([ + _decompress_bytes(chunk.data, chunk.compression_type) + for chunk in chunks + ]) + case _: + raise ValueError( + f'Unsupported payload type: {proto.WhichOneof("payload")}' + ) + + array = np.frombuffer( + data, dtype=_TENSOR_DTYPE_TO_NUMPY_DTYPE[proto.data_type] + ).reshape(proto.shape) + return array + + +def upcast_floating(x: np.ndarray) -> np.ndarray: + """Helper to upcast low-precision floating point arrays to float32.""" + dtype = np.result_type(x) + if ( + np.issubdtype(dtype, np.floating) or dtype == ml_dtypes.bfloat16 + ) and dtype.itemsize < 4: + return x.astype(np.float32) + else: + return x diff --git a/alphagenome/source/src/alphagenome/tensor_utils_benchmark_test.py b/alphagenome/source/src/alphagenome/tensor_utils_benchmark_test.py new file mode 100644 index 0000000000000000000000000000000000000000..9cd3db912873e833a430453172d119829395e8f5 --- /dev/null +++ b/alphagenome/source/src/alphagenome/tensor_utils_benchmark_test.py @@ -0,0 +1,107 @@ +# Copyright 2025 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Benchmark for tensor_utils.""" + +import functools +import itertools +import tracemalloc + +from absl import flags +from alphagenome import tensor_utils +from alphagenome.protos import tensor_pb2 +import google_benchmark +import numpy as np + +_MB_TO_BYTES = 2**20 + +_TENSOR_SIZE_IN_MB = flags.DEFINE_integer( + 'tensor_size_in_mb', 128, 'Size of the tensor to benchmark.' +) + +_COMPRESSION_TYPE = flags.DEFINE_enum( + 'compression_type', + 'zstd', + ['none', 'zstd'], + 'Compression type to benchmark.', +) + + +@google_benchmark.register +@google_benchmark.option.use_real_time() +@google_benchmark.option.unit(google_benchmark.kMillisecond) +@google_benchmark.option.range_multiplier(2) +@google_benchmark.option.range(1, limit=10) +def pack_tensor_benchmark_chunks(state): + """Benchmark for packing tensors.""" + rng = np.random.default_rng(seed=42) + num_elements = (_TENSOR_SIZE_IN_MB.value * _MB_TO_BYTES) // np.dtype( + np.float32 + ).itemsize + values = rng.random(num_elements, dtype=np.float32) + compression_type = tensor_pb2.CompressionType.Value( + f'COMPRESSION_TYPE_{_COMPRESSION_TYPE.value.upper()}' + ) + + uncompressed_bytes = 0 + compressed_bytes = 0 + tracemalloc.start() + + while state: + packed, chunks = tensor_utils.pack_tensor( + values, + bytes_per_chunk=state.range(0) * _MB_TO_BYTES, + compression_type=compression_type, + ) + uncompressed_bytes += values.nbytes + compressed_bytes += functools.reduce( + lambda x, y: x + len(y.data), itertools.chain(chunks, [packed.array]), 0 + ) + + _, peak_mem = tracemalloc.get_traced_memory() + tracemalloc.stop() + + state.counters['compressed_bytes'] = google_benchmark.Counter( + compressed_bytes + ) + state.counters['uncompressed_bytes'] = google_benchmark.Counter( + uncompressed_bytes + ) + state.counters['peak_mem'] = google_benchmark.Counter(peak_mem) + + +@google_benchmark.register +@google_benchmark.option.use_real_time() +@google_benchmark.option.unit(google_benchmark.kMillisecond) +def unpack_tensor_benchmark_chunks(state): + """Benchmark for unpacking tensors.""" + rng = np.random.default_rng(seed=42) + num_elements = (_TENSOR_SIZE_IN_MB.value * _MB_TO_BYTES) // np.dtype( + np.float32 + ).itemsize + compression_type = tensor_pb2.CompressionType.Value( + f'COMPRESSION_TYPE_{_COMPRESSION_TYPE.value.upper()}' + ) + packed, chunks = tensor_utils.pack_tensor( + rng.random(num_elements, dtype=np.float32), + bytes_per_chunk=1 * _MB_TO_BYTES, + compression_type=compression_type, + ) + + while state: + tensor_utils.unpack_proto(packed, chunks) + + +if __name__ == '__main__': + google_benchmark.main() diff --git a/alphagenome/source/src/alphagenome/tensor_utils_test.py b/alphagenome/source/src/alphagenome/tensor_utils_test.py new file mode 100644 index 0000000000000000000000000000000000000000..dd89ac066cf9e280a5b2327f58c9d688fbb4284c --- /dev/null +++ b/alphagenome/source/src/alphagenome/tensor_utils_test.py @@ -0,0 +1,219 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math + +from absl.testing import absltest +from absl.testing import parameterized +from alphagenome import tensor_utils +from alphagenome.protos import tensor_pb2 +import ml_dtypes +import numpy as np +import zstandard + + +class TensorUtilsTest(parameterized.TestCase): + + @parameterized.product( + ( + dict( + array=np.array([[1, 2], [3, 4]], dtype=ml_dtypes.bfloat16), + expected_dtype=tensor_pb2.DataType.DATA_TYPE_BFLOAT16, + ), + dict( + array=np.array([[1, 2], [3, 4]], dtype=np.float16), + expected_dtype=tensor_pb2.DataType.DATA_TYPE_FLOAT16, + ), + dict( + array=np.array([[1, 2, 3, 4]], dtype=np.float32), + expected_dtype=tensor_pb2.DataType.DATA_TYPE_FLOAT32, + ), + dict( + array=np.array([[1], [2], [3], [4]], dtype=np.float64), + expected_dtype=tensor_pb2.DataType.DATA_TYPE_FLOAT64, + ), + dict( + array=np.array([[1, 2], [3, 4]], dtype=np.int8), + expected_dtype=tensor_pb2.DataType.DATA_TYPE_INT8, + ), + dict( + array=np.array([1, 2, 3, 4], dtype=np.int32), + expected_dtype=tensor_pb2.DataType.DATA_TYPE_INT32, + ), + dict( + array=np.array([1, 2, 3, 4], dtype=np.int64), + expected_dtype=tensor_pb2.DataType.DATA_TYPE_INT64, + ), + dict( + array=np.array([1, 2, 3, 4], dtype=np.uint8), + expected_dtype=tensor_pb2.DataType.DATA_TYPE_UINT8, + ), + dict( + array=np.array([1, 2, 3, 4], dtype=np.uint32), + expected_dtype=tensor_pb2.DataType.DATA_TYPE_UINT32, + ), + dict( + array=np.array([1, 2, 3, 4], dtype=np.uint64), + expected_dtype=tensor_pb2.DataType.DATA_TYPE_UINT64, + ), + dict( + array=np.array([True, False, True, False], dtype=bool), + expected_dtype=tensor_pb2.DataType.DATA_TYPE_BOOL, + ), + ), + compression_type=[ + tensor_pb2.CompressionType.COMPRESSION_TYPE_NONE, + tensor_pb2.CompressionType.COMPRESSION_TYPE_ZSTD, + ], + ) + def test_pack_tensor(self, array, expected_dtype, compression_type): + packed, chunks = tensor_utils.pack_tensor( + array, compression_type=compression_type + ) + + self.assertEmpty(chunks) + self.assertSequenceEqual(packed.shape, array.shape) + self.assertEqual(packed.data_type, expected_dtype) + + expected = ( + zstandard.compress(array.tobytes()) + if compression_type == tensor_pb2.CompressionType.COMPRESSION_TYPE_ZSTD + else array.tobytes() + ) + self.assertEqual(expected, packed.array.data) + + @parameterized.product( + dtype=( + ml_dtypes.bfloat16, + np.float16, + np.float32, + np.float64, + np.int8, + np.int32, + np.int64, + np.uint8, + np.uint32, + np.uint64, + bool, + ), + bytes_per_chunk=[10, 13, 200], + compression_type=[ + tensor_pb2.CompressionType.COMPRESSION_TYPE_NONE, + tensor_pb2.CompressionType.COMPRESSION_TYPE_ZSTD, + ], + ) + def test_pack_tensor_chunks(self, dtype, bytes_per_chunk, compression_type): + if dtype == bool: + data = np.ones(1000, dtype=bool) + else: + data = np.arange(1000, dtype=dtype) + data = data.reshape((10, -1)) + packed, chunks = tensor_utils.pack_tensor( + data, bytes_per_chunk=bytes_per_chunk, compression_type=compression_type + ) + self.assertEmpty(packed.array.data) + self.assertLen(chunks, packed.chunk_count) + + items_per_chunk = bytes_per_chunk // data.itemsize + self.assertEqual(packed.chunk_count, math.ceil(data.size / items_per_chunk)) + first_chunk = data.flatten()[:items_per_chunk].tobytes() + expected = ( + zstandard.compress(first_chunk) + if compression_type == tensor_pb2.CompressionType.COMPRESSION_TYPE_ZSTD + else first_chunk + ) + self.assertEqual(expected, chunks[0].data) + + @parameterized.product( + array=( + np.array([[1, 2], [3, 4]], dtype=ml_dtypes.bfloat16), + np.array([[1, 2], [3, 4]], dtype=np.float16), + np.array([[1, 2, 3, 4]], dtype=np.float32), + np.array([[1], [2], [3], [4]], dtype=np.float64), + np.array([[1, 2], [3, 4]], dtype=np.int8), + np.array([1, 2, 3, 4], dtype=np.int32), + np.array([1, 2, 3, 4], dtype=np.int64), + np.array([1, 2, 3, 4], dtype=np.uint8), + np.array([1, 2, 3, 4], dtype=np.uint32), + np.array([1, 2, 3, 4], dtype=np.uint64), + np.array([True, False, True, False], dtype=bool), + np.arange(100)[::2], + ), + bytes_per_chunk=[8, 12, 15], + compression_type=[ + tensor_pb2.CompressionType.COMPRESSION_TYPE_NONE, + tensor_pb2.CompressionType.COMPRESSION_TYPE_ZSTD, + ], + ) + def test_unpack_proto(self, array, bytes_per_chunk, compression_type): + tensor, chunks = tensor_utils.pack_tensor( + array, + bytes_per_chunk=bytes_per_chunk, + compression_type=compression_type, + ) + self.assertLen(chunks, tensor.chunk_count) + round_trip = tensor_utils.unpack_proto(tensor, chunks) + np.testing.assert_array_equal(array, round_trip) + + def test_pack_proto_invalid_chunk_size_raises(self): + with self.assertRaisesRegex( + ValueError, 'bytes_per_chunk=1 must be >= value.itemsize=8' + ): + tensor_utils.pack_tensor( + np.array([[1, 2], [3, 4]], dtype=np.int64), bytes_per_chunk=1 + ) + + def test_missing_chunk_raises(self): + tensor, chunks = tensor_utils.pack_tensor( + np.arange(128, dtype=np.float32), bytes_per_chunk=32 + ) + chunks = chunks[::2] + with self.assertRaisesWithLiteralMatch( + ValueError, 'cannot reshape array of size 64 into shape (128,)' + ): + tensor_utils.unpack_proto(tensor, chunks) + + @parameterized.named_parameters( + ( + 'float16', + np.array([1, 2, 3], dtype=np.float16), + np.array([1, 2, 3], dtype=np.float32), + ), + ( + 'bfloat16', + np.array([1, 2, 3], dtype=ml_dtypes.bfloat16), + np.array([1, 2, 3], dtype=np.float32), + ), + ( + 'float32', + np.array([1, 2, 3], dtype=np.float32), + np.array([1, 2, 3], dtype=np.float32), + ), + ( + 'float64', + np.array([1, 2, 3], dtype=np.float64), + np.array([1, 2, 3], dtype=np.float64), + ), + ( + 'int', + np.array([1, 2, 3], dtype=np.int32), + np.array([1, 2, 3], dtype=np.int32), + ), + ) + def test_upcast_floating(self, array, expected): + np.testing.assert_array_equal(tensor_utils.upcast_floating(array), expected) + + +if __name__ == '__main__': + absltest.main() diff --git a/alphagenome/source/src/alphagenome/typing.py b/alphagenome/source/src/alphagenome/typing.py new file mode 100644 index 0000000000000000000000000000000000000000..ac4c0c8ffbfce58a9b6fd95bae0636aeb06b4c3b --- /dev/null +++ b/alphagenome/source/src/alphagenome/typing.py @@ -0,0 +1,39 @@ +# Copyright 2025 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Utility functions for type annotations.""" + +import importlib.metadata +from typing import TypeVar + +import jaxtyping +import typeguard + +_T = TypeVar('_T') + + +def jaxtyped(fn: _T) -> _T: + """Wrapper around jaxtyping.jaxtyped that uses typeguard iff typeguard < 3.""" + try: + major, *_ = importlib.metadata.version('typeguard').split('.') + except importlib.metadata.PackageNotFoundError: + major = -1 + + # Only use jaxtyping if typeguard is < 3. See + # https://docs.kidger.site/jaxtyping/api/runtime-type-checking/#runtime-type-checking + # for more details. + if int(major) < 3: + return jaxtyping.jaxtyped(fn, typechecker=typeguard.typechecked) + else: + return fn diff --git a/alphagenome/source/src/alphagenome/typing_test.py b/alphagenome/source/src/alphagenome/typing_test.py new file mode 100644 index 0000000000000000000000000000000000000000..344055545cc458ccbc14b4af937aadedc2818989 --- /dev/null +++ b/alphagenome/source/src/alphagenome/typing_test.py @@ -0,0 +1,48 @@ +# Copyright 2025 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import importlib.metadata +from unittest import mock + +from absl.testing import absltest +from absl.testing import parameterized +from alphagenome import typing +import jaxtyping +from jaxtyping import Float32 # pylint: disable=g-multiple-import, g-importing-member +import numpy as np + + +class TypingTest(parameterized.TestCase): + + @parameterized.parameters((2,), (3,)) + def test_wrapped(self, typeguard_version: int): + with mock.patch.object( + importlib.metadata, 'version' + ) as mock_typeguard_version: + mock_typeguard_version.return_value = f'{typeguard_version}.0.0' + + @typing.jaxtyped + def foo(x: Float32[np.ndarray, 'B']): + return x + + expected = np.zeros((1,), dtype=np.float32) + self.assertEqual(expected, foo(expected)) + + if typeguard_version < 3: + with self.assertRaises(jaxtyping.TypeCheckError): + _ = foo(np.zeros((6, 6, 6), dtype=bool)) + + +if __name__ == '__main__': + absltest.main() diff --git a/alphagenome/source/src/alphagenome/visualization/__init__.py b/alphagenome/source/src/alphagenome/visualization/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..602823a26de43a59ecbc53130824f87f82ebd5bc --- /dev/null +++ b/alphagenome/source/src/alphagenome/visualization/__init__.py @@ -0,0 +1,15 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Library of tools for visualizing with genomic model predictions.""" diff --git a/alphagenome/source/src/alphagenome/visualization/plot.py b/alphagenome/source/src/alphagenome/visualization/plot.py new file mode 100644 index 0000000000000000000000000000000000000000..63f33a051bfd846480ca709f0ed68697a03b7377 --- /dev/null +++ b/alphagenome/source/src/alphagenome/visualization/plot.py @@ -0,0 +1,551 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Plotting functions.""" + +from collections.abc import Callable, Mapping, Sequence +import math +from typing import Any + +from absl import logging +from alphagenome.data import genome +import matplotlib as mpl +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns + + +def seqlogo( + letter_heights: np.ndarray, + alphabet: str = 'ACGT', + one_based: bool = True, + start: int = 0, + ax: mpl.axis.Axis | None = None, + letter_colors: str = 'default', +): + """Sequence logo plot. + + Args: + letter_heights: 2D array with shape (sequence length, len(alphabet)) shape. + Positive values indicate that a logo element extends above the horizontal + axis, and negative values indicate that the element extends below. + alphabet: Alphabet corresponding to the channel axis of `letter_heights`. + one_based: If True, plot letters at 1-based coordinates: first letter will + be centered at 1. If False, the first letter will be between 0 and 1. + start: Interval start. + ax: matplotlib axis to add figures to. + letter_colors: Name of the color scheme to use for coloring each letter. + Must be one of 'default', a scheme consistent with weblogo + (https://github.com/gecrooks/weblogo/blob/master/weblogo/logo.py#L136) or + 'factorbook', a scheme consistent with Factorbook + (https://www.factorbook.org/). + """ + ax = ax or plt.gca() + + if letter_heights.ndim != 2: + raise ValueError( + 'Expecting a 2D matrix of shape (sequence_length, len(alphabed))' + ) + if letter_heights.shape[1] != len(alphabet): + raise ValueError('Last axis needs to match len(alphabet)') + + for x_offset, heights in enumerate(letter_heights): + last_positive_y = 0.0 + last_negative_y = 0.0 + for height, letter in sorted(zip(heights, alphabet)): + # Start with lowest height and keep track of the last y coordinate. + if height > 0: + y_position = last_positive_y + last_positive_y += height + else: + y_position = last_negative_y + last_negative_y += height + _add_letter_to_axis( + ax, + letter, + x=start + x_offset + 0.5 + 0.5 * one_based, + y=y_position, + height=height, + letter_colors=letter_colors, + ) + + # Make sure all letters are displayed. + ax.autoscale_view() # pytype: disable=attribute-error + + +def _add_letter_to_axis(ax, letter, x, y, height=1, letter_colors='default'): + """Add a letter with unit width and stretched by height to the axis.""" + globscale = 1.35 + letter_width_colors = dict( + # Consistent with `classic` scheme from + # https://github.com/gecrooks/weblogo/blob/master/weblogo/logo.py#L136 + default={ + 'A': (0.35, 'darkgreen'), + 'C': (0.366, 'blue'), + 'G': (0.384, 'orange'), + 'T': (0.305, 'red'), + }, + # Consistent with factorbook scheme from https://www.factorbook.org/ + factorbook={ + 'A': (0.305, 'red'), + 'C': (0.366, 'blue'), + 'G': (0.384, 'orange'), + 'T': (0.35, 'darkgreen'), + }, + ) + try: + letter_width_color = letter_width_colors[letter_colors] + except KeyError as e: + raise ValueError( + f'Letter_colors must be one of {letter_width_colors.keys()}.' + ) from e + + font = mpl.font_manager.FontProperties(weight='bold') + letter_width, letter_color = letter_width_color[letter] + + letter_transform = ( + mpl.transforms.Affine2D() + .scale(globscale, height * globscale) + .translate(x, y) + ) + p = mpl.patches.PathPatch( + mpl.text.TextPath((-letter_width, 0), letter, size=1, prop=font), + lw=0, + fc=letter_color, + transform=letter_transform + + ax.transData, # pytype: disable=attribute-error + ) + ax.add_patch(p) # pytype: disable=attribute-error + return p + + +def plot_contact_map( + contact_map: pd.DataFrame, + vmin: float | None = None, + vmax: float | None = None, + square: bool = True, + cbar_shrink: float = 0.4, + ax=None, + **kwargs, +): + """Visualize a contact map. + + A contact map is a 2D array of values, where each value represents the + probability that two DNA bases are in contact. + + The array is symmetric, i.e., the (i, j) values and the (j, i) values of + `contact_map` are equal. + + Args: + contact_map: Contact map to visualize. + vmin: Minimum value for the colorbar. + vmax: Maximum value for the colorbar. + square: If `True`, plots the contact map as a square. If `False`, plots the + contact map as a rectangle with a shorter height than width. + cbar_shrink: Fraction by which to multiply the size of the colorbar. + ax: Matplotlib axis to add the heatmap to. + **kwargs: Additional keyword arguments passed to `sns.heatmap`. + """ + + def tuple2string(interval_tuple): + chromosome, start, end = interval_tuple + return f'{chromosome}:{start:,}-{end:,}' + + if isinstance(contact_map, pd.DataFrame): + interval_string = ( + tuple2string(contact_map.index[0]) + + ' - ' + + tuple2string(contact_map.index[-1]) + ) + contact_map = contact_map.values + else: + interval_string = '' + + if vmin is None: + vmin = np.nanmin(contact_map[contact_map > 0]) + if vmax is None: + vmax = np.nanmax(contact_map) + fall_cmap = mpl.colors.LinearSegmentedColormap.from_list( + 'fall', + colors=[ + 'white', + (245 / 256, 166 / 256, 35 / 256), + (208 / 256, 2 / 256, 27 / 256), + 'black', + ], + ) + log_norm = mpl.colors.LogNorm(vmin=vmin, vmax=vmax) + cbar_min = math.floor(math.log10(log_norm.vmin)) + cbar_max = 1 + math.ceil(math.log10(log_norm.vmax)) + cbar_ticks = [math.pow(10, i) for i in range(cbar_min, cbar_max)] + + if ax is None: + ax = plt.gca() + sns.heatmap( + contact_map + 1e-10, + cmap=fall_cmap, + norm=log_norm, + cbar_kws={'ticks': cbar_ticks, 'shrink': cbar_shrink}, + vmin=log_norm.vmin, + vmax=log_norm.vmax, + xticklabels=False, + yticklabels=False, + square=square, + ax=ax, + **kwargs, + ) + + ax.set_xlabel(interval_string) + + +def plot_track( + arr: np.ndarray, + ax: plt.Axes, + x: np.ndarray | None = None, + legend: bool = False, + ylim: float | None = None, + color: str | Sequence[str] | None = None, + filled: bool = False, +) -> None: + """Plot a single track on the axis after inferring track type from array. + + This function infers the type of track plot depending on channel + dimensionality of `arr`: + + - If `arr` is a single-dimensional array, plot a single track line. + - If `arr` is a 2D array, and interpreting the second dimension as the + channel dimension: + + - Plot a sequence logo if the number of channels is 4 (which suggests one + channel per DNA base). + - Plot two overlapping line plots if the number of channels is 2 (which + suggests one channel per DNA strand). + + Args: + arr: array of values to plot. + ax: matplotlib axis to use for plotting the values on. + x: optional array of values to use for setting the x axis values. If absent, + then sequential values starting from 1 to the length of the array arr will + be used. + legend: whether to draw a legend or not on the double stranded DNA plot. + ylim: y axis limit. + color: color(s) used for line plots. + filled: whether to fill the space between the x axis and the line (or leave + it as whitespace). + + Returns: + None. Adds a plot to the provided axis. + """ + sequence_length = len(arr) + + if x is None: + x = np.arange(1, sequence_length + 1) # One-based indexing. + + if arr.ndim == 1 or arr.shape[1] == 1: + if color is not None: + if isinstance(color, (list, tuple)): + logging.warning( + 'A sequence of colors was passed to plot_track but the second ' + 'array dimension is 1 suggesting single stranded DNA. Using the ' + 'first entry %s as the line color.', + color[0], + ) + color = color[0] + + # In the case of a boolean array, we fill between values and the x axis and + # simplify the y axis components. + if arr.dtype == bool: + ax.fill_between(x, arr, step='mid', color=color) + ax.yaxis.set_ticks_position('none') + ax.set_yticklabels([]) + ax.spines['left'].set_visible(False) + ax.set_ylim([0, 1.5]) + + else: + if filled: + ax.fill_between(x, np.ravel(arr), color=color) + ax.plot(x, np.ravel(arr), color=color) + + elif arr.shape[1] == 4: + seqlogo(arr, ax=ax) + + elif arr.shape[1] == 2: + if color is not None: + if not isinstance(color, Sequence) or len(color) != 2: + raise ValueError( + 'Must pass sequence of 2 colors if plotting 2 dimensional array.' + ) + color_1, color_2 = color + else: + color_1, color_2 = None, None + ax.plot(x, arr[:, 0], label='pos', color=color_1) + ax.plot(x, arr[:, 1], label='neg', color=color_2) + if legend: + ax.legend() + else: + raise ValueError( + f'Do not know how to plot array with shape[1] != {arr.shape[1]}. ' + 'Valid values are: 1, 2, or 4.' + ) + if ylim is not None: + ax.set_ylim(ylim) + + +def plot_tracks( + tracks: Mapping[str, Any], + x: np.ndarray | None = None, + title: str | None = None, + legend: bool = False, + fig_width: float = 20, + fig_track_height: float | Mapping[str, float] = 1.5, + ylim: str | Mapping[str, tuple[int, int]] | tuple[int, int] | None = 'auto', + yticks_min_max_only: bool = False, + ylab: bool = True, + color: str | Mapping[str, str] | None = None, + horizontal_ylab: bool = True, + filled_tracks: Sequence[str] | None = None, + despine: bool = True, + despine_keep_bottom: bool = False, + plot_track_fn: Callable[..., Any] = plot_track, +) -> mpl.figure.Figure: + """Plot multiple tracks as subplots within one matplotlib figure. + + Custom plotting functions may be passed as `plot_track_fn`. Otherwise, + `plot_track` will be used by default. + + Args: + tracks: dictionary of tracks to plot. Example input: tracks = {'a': [1,2,3], + 'b': [4,5,4]}. The arrays in the dict must have the same length. + x: optional array of x axis values. If absent, these will be inferred to be + from 1 to the length of the arrays in tracks. + title: Optional title to be added to the first subplot in the figure. + legend: Whether to plot a legend or not. + fig_width: Figure width. + fig_track_height: Either a scalar specifying the total height of the figure, + or a dictionary specifying the height of the subplot for each track. + ylim: y axis limit. Must be either "same" (shared y limit across subplots) + or "auto" (free scales, indicating separate limits per subplot). + yticks_min_max_only: whether the only y axis ticks should be the min and max + values. + ylab: y axis label. + color: Either a string or a dict indicating a color per track. + horizontal_ylab: Whether to display the y axis label horizontally (instead + of the default vertical orientation). + filled_tracks: List of track names that should have filled line plots drawn + (instead of the default whitespace between the values and x axis). + despine: Whether to remove top, right, and bottom spines. + despine_keep_bottom: Whether to remove top and right spines, but keep the + bottom spine. + plot_track_fn: Optional custom function for plotting tracks. If absent, + defaults to `plot_track`. + + Returns: + Matplotlib figure. + """ + # Set figure height (either total height, or per subplot if passed a dict). + if isinstance(fig_track_height, dict): + fig_height = sum(fig_track_height.values()) + gridspec_kw = { + 'height_ratios': [ + height / fig_height for height in fig_track_height.values() + ] + } + else: + fig_height = fig_track_height + gridspec_kw = dict() + + # Generate figure axes. + fig, axes = plt.subplots( + nrows=len(tracks), + ncols=1, + figsize=(fig_width, fig_height), + gridspec_kw=gridspec_kw, + sharex=True, + ) + if len(tracks) == 1: + axes = [axes] + + # Set y axis limits. + if ylim == 'same': + ylim = ( + 0, + max(v.max() for v in tracks.values() if isinstance(v, np.ndarray)), + ) + elif ylim == 'auto': + ylim = None + elif isinstance(ylim, str): + raise ValueError('Only "same" and "auto" are valid strings for ylim.') + + # Iterate over tracks and plot one track per axis in the figure. + for i, (ax, (track, arr)) in enumerate(zip(axes, tracks.items())): + # Only set title in the first subplot. + if i == 0 and title is not None: + ax.set_title(title) + + # Only set x axis ticks in the final subplot. + if i != len(tracks) - 1: + ax.xaxis.set_ticks_position('none') + + track_ylim = ylim[track] if isinstance(ylim, dict) else ylim + track_color = color[track] if isinstance(color, dict) else color + + # If the array is in fact a callable, then it is intended to be used as a + # custom plotting function (most commonly, for plotting transcripts). + if hasattr(arr, '__call__'): + # TODO: b/377225766 - Allow custom plot functions as dictionary elements. + arr(ax, ylim=ylim, color=color) + else: + filled_tracks = filled_tracks or [] + plot_track_fn( + arr, + ax=ax, + x=x, + legend=legend, + ylim=track_ylim, + color=track_color, + filled=track in filled_tracks, + ) + + # Set y axis label. + if ylab: + if horizontal_ylab: + ax.set_ylabel( + track, + rotation=0, + multialignment='center', + va='center', + ha='right', + labelpad=5, + ) + else: + ax.set_ylabel(track) + + # Only draw the y axis ticks on the min and max value locations. + if yticks_min_max_only: + if track_ylim is None: + track_ylim = ax.get_yticks()[[0, -1]] + # TODO: b/377226499 - Preventing negative values might be sub-optimal. + minimum = max(track_ylim[0], 0) + maximum = track_ylim[1] + ax.spines['left'].set_bounds(minimum, maximum) + ax.set_yticks([minimum, maximum]) + + if despine: + ax.spines['top'].set_visible(False) + ax.spines['right'].set_visible(False) + if i != len(tracks) - 1 and despine_keep_bottom: + ax.spines['bottom'].set_visible(True) + else: + ax.spines['bottom'].set_visible(False) + + # Enable default tick locator for the final subplot. + axes[-1].xaxis.set_major_locator(mpl.ticker.AutoLocator()) + + # No height for whitespace between subplots. + fig.subplots_adjust(hspace=0) + + return fig + + +def sashimi_plot( + junctions: Sequence[genome.Junction], + ax: plt.Axes, + interval: genome.Interval | None = None, + filter_threshold: float = 0.01, + annotate_counts: bool = True, + rng: np.random.Generator | None = None, +): + """Plot splice junctions as a Sashimi plot. + + Sashimi plots (first described [here](https://arxiv.org/abs/1306.3466) + visualize splice junctions from an RNA-seq experiment. + + According to the authors, + * genomic reads are converted into read densities + * junction reads are plotted as arcs whose width is determined by the number + of reads aligned to the junction spanning the exons connected by the arc. + + Args: + junctions: Splice junctions to plot. + ax: Matplotlib axis to add the plot to. + interval: Interval to plot, used to filter text annotations. + filter_threshold: Junctions with number of reads below this threshold will + be filtered out. + annotate_counts: Whether to annotate the junctions with read counts. + rng: Optional random number generator to use for jittering junction paths. + If unset will use NumPy's default random number generator. + """ + rng = rng or np.random.default_rng() + total = np.sum([junction.k for junction in junctions]) + # Random jitter position to avoid overlap. + jitters = rng.uniform(low=0.05, high=0.15, size=len(junctions)) + for junction, jt in zip(junctions, jitters): + if junction.k < filter_threshold: + continue + k = junction.k / total + verts = [ + (junction.start, 0.0), + (junction.start, jt), + (junction.end, jt), + (junction.end, 0.0), + ] + + path = mpl.path.Path( + verts, + [ + mpl.path.Path.MOVETO, + mpl.path.Path.CURVE4, + mpl.path.Path.CURVE4, + mpl.path.Path.CURVE4, + ], + ) + patch = mpl.patches.PathPatch(path, facecolor='none', lw=min(k * 30, 5)) + ax.add_patch(patch) + if annotate_counts: + text_pos = junction.center() + if interval is not None: + if text_pos < interval.start or text_pos > interval.end: + continue + ax.text(text_pos, 0.8 * jt, junction.k, horizontalalignment='center') + ax.set_ylim(0, 0.15) + + +def pad_track(track: np.ndarray, new_len: int, value: int = 0) -> np.ndarray: + """Pad a track with `value` to the desired length. + + If new_len - len(track) is an even number, the same amount of padding is added + to both sides. + + If new_len - len(track) is an odd number, the extra padded element will be + added at the end of the array. + + Args: + track: Track to pad. + new_len: Desired length of the padded track. + value: Value to use for padding. + + Returns: + Padded track. + """ + assert track.ndim == 2 + assert new_len >= len(track) + + out = np.empty((new_len, track.shape[1])) + out[:] = value + delta = new_len - len(track) + i = delta // 2 + j = i + len(track) + out[i:j] = track + return out diff --git a/alphagenome/source/src/alphagenome/visualization/plot_components.py b/alphagenome/source/src/alphagenome/visualization/plot_components.py new file mode 100644 index 0000000000000000000000000000000000000000..d91440057b4726e303297ed1ca752ef44762125f --- /dev/null +++ b/alphagenome/source/src/alphagenome/visualization/plot_components.py @@ -0,0 +1,1522 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +"""Module containing the main plotting code for AlphaGenome model outputs. + +Three main elements are: + + * `plot` function: The primary function for visualizing model outputs. + * Component classes: Implement the visualization components (e.g., tracks, + contact maps). + * Annotation classes: Implement the visualization annotations (e.g., + intervals, variants). +""" + +import abc +from collections.abc import Mapping, Sequence + +from alphagenome.data import genome +from alphagenome.data import junction_data +from alphagenome.data import track_data +from alphagenome.data import transcript as transcript_utils +from alphagenome.visualization import plot as plot_lib +from alphagenome.visualization import plot_transcripts +from jaxtyping import Float32 # pylint: disable=g-importing-member +import matplotlib +from matplotlib import colors as plt_colors +import matplotlib.pyplot as plt +import numpy as np + + +# String, RGB or RGBA color. +_ColorType = ( + str | tuple[float, float, float] | tuple[float, float, float, float] +) + + +def plot( + components: Sequence['AbstractComponent'], + interval: genome.Interval, + fig_width: int = 20, + fig_height_scale: float = 1.0, + title: str | None = None, + despine: bool = True, + despine_keep_bottom: bool = False, + annotations: Sequence['AbstractAnnotation'] | None = None, + annotation_offset_range: tuple[float, float] = (0.1, 0.6), + hspace: float = 0.3, + xlabel: str | None = None, +) -> matplotlib.figure.Figure: + """Plots AlphaGenome model outputs as individual panels of 'components'. + + This function generates a visualization of AlphaGenome model outputs + using a combination of components (e.g., tracks, contact maps) and + annotations (e.g., intervals, variants). + + Args: + components: A sequence of components to visualize. + interval: The genomic interval to focus on (similar to setting xlim in + matplotlib). + fig_width: The total figure width. + fig_height_scale: Height of the individual track unit. Total plot height is + determined as a sum of the individual component heights. + title: An optional title for the overall plot. + despine: Whether to remove top, right, and bottom spines from the axes. + despine_keep_bottom: Whether to remove top and right spines, but keep the + bottom spine. Does not apply to transcript components, which are always + despined. + annotations: Sequence of annotations to visualise across all components. + annotation_offset_range: Relative positions in y-axis to place labels for + annotations. Each set of labels in the 'annotations' list are spaced + evenly within this range. + hspace: Vertical whitespace between subplots to avoid tick label overlap + (relative fraction). + xlabel: If a non-empty string is provided, it is used as the x-axis label. + If an empty string is provided, the x-axis label is removed. If None, the + default x-axis label is used. + + Returns: + A matplotlib figure. + """ + + # If we are adding text labels for any of the annotation components, + # we need to add an extra empty component at the top. + add_label_axis = False + if annotations is not None: + add_label_axis = any(annot.has_labels for annot in annotations) + components = ( + [EmptyComponent()] + list(components) + if add_label_axis + else list(components) + ) + + num_axes = sum(component.num_axes for component in components) + fig_height = sum( + component.total_height * fig_height_scale for component in components + ) + + offset = 0 + gridspec_kw = {'height_ratios': []} + for component in components: + for i in range(component.num_axes): + gridspec_kw['height_ratios'].append( + component.get_ax_height(i) * fig_height_scale / fig_height + ) + offset += 1 + + fig, axes = plt.subplots( + nrows=num_axes, + ncols=1, + figsize=(fig_width, fig_height), + gridspec_kw=gridspec_kw, + sharex=True, + ) + # Handle the case of a single axis (e.g. single REF / ALT plot). + if not isinstance(axes, np.ndarray): + axes = [axes] + + offset = 0 + for component in components: + for i in range(component.num_axes): + ax = axes[offset] + component.plot_ax(ax, axis_index=i, interval=interval) + + if offset == 0 and title is not None: + ax.set_title(title) + + if despine: + ax.spines['top'].set_visible(False) + ax.spines['right'].set_visible(False) + if ( + i != num_axes - 1 + and despine_keep_bottom + and (not isinstance(component, TranscriptAnnotation)) + ): + ax.spines['bottom'].set_visible(True) + else: + ax.spines['bottom'].set_visible(False) + + # Remove tick marks which become visible due to a subplots_adjust() call. + if offset != num_axes - 1: + ax.tick_params(axis='x', which='both', bottom=False, labelbottom=False) + + ax.set_xlim(interval.start, interval.end) + + offset += 1 + + # Add annotations across all plotted components. + if annotations is not None: + # Add small vertical offset for each set of labels to avoid overlap. + num_labelled_annotations = sum( + annotation.has_labels for annotation in annotations + ) + height_offsets = np.linspace( + annotation_offset_range[0], + annotation_offset_range[1], + num_labelled_annotations, + ) + # Identify which axes are transcripts and which we want to annotate. + transcript_axes_idx = [ + i + for i, comp in enumerate(components) + if isinstance(comp, TranscriptAnnotation) + ] + axes_to_annotate_idx = ( + range(1, len(axes)) if add_label_axis else range(len(axes)) + ) + for i, annotation in enumerate(annotations): + for j in axes_to_annotate_idx: + if not annotation.is_variant and j in transcript_axes_idx: + # Do not add interval annotations to axes that involves transcripts. + continue + annotation.plot_ax(axes[j], interval, hspace) + if annotation.has_labels: + # Add labels to the empty axis at the top. All labels are added to + # the top axis, in the order they are supplied in annotations list. + annotation.plot_labels(axes[0], interval, height_offsets[i]) + + # Enable default tick locator for the final subplot. + axes[-1].xaxis.set_major_locator(matplotlib.ticker.AutoLocator()) + + if xlabel is not None: + axes[-1].set_xlabel(xlabel) + else: + axes[-1].set_xlabel(f'Chromosome position; interval={interval}') + + # Slight whitespace between subplots to avoid tick label overlap. + fig.subplots_adjust(hspace=hspace) + + return fig + + +class AbstractComponent(abc.ABC): + """Abstract base class for plot components.""" + + @abc.abstractmethod + def get_ax_height(self, axis_index: int) -> float: + """Returns the plot height for the individual axis. + + Args: + axis_index: The index of the axis. + + Returns: + The height of the axis. + """ + + @property + def total_height(self) -> float: + """Returns the total figure height.""" + return sum(self.get_ax_height(i) for i in range(self.num_axes)) + + @property + @abc.abstractmethod + def num_axes(self) -> int: + """Returns the number of matplotlib axes required by the component.""" + + @abc.abstractmethod + def plot_ax( + self, + ax: matplotlib.axes.Axes, + axis_index: int, + interval: genome.Interval, + ): + """Plots the component on the given axis. + + Args: + ax: The matplotlib axis to plot on. + axis_index: The index of the axis. + interval: The genomic interval to plot. + """ + + +class Tracks(AbstractComponent): + """Component for visualizing tracks.""" + + def __init__( + self, + tdata: track_data.TrackData, + cmap: str = 'viridis', + truncate_cmap: bool = True, + track_height: float = 1.0, + filled: bool = False, + ylabel_template: str = '{name}:{strand}', + ylabel_horizontal: bool = True, + shared_y_scale: bool = False, + global_ylims: tuple[float, float] | None = None, + max_num_tracks: int = 50, + track_colors: Sequence[_ColorType] | str | None = None, + **kwargs, + ): + """Initializes the `Tracks` component. + + Args: + tdata: The `TrackData` object to visualize. + cmap: The colormap to use for the tracks. + truncate_cmap: Whether to slightly truncate the colormap to avoid extreme + values. + track_height: The height of each track. + filled: Whether to fill the area under the tracks. + ylabel_template: A template for the y-axis labels. + ylabel_horizontal: Whether to make the y-axis labels horizontal. + shared_y_scale: Whether to use the same y-axis scale for all tracks. + global_ylims: Optional global y-axis limits (min and max). + max_num_tracks: The maximum number of tracks to plot. + track_colors: An optional sequence of colors to use for the tracks. If a + string is passed, it is used as a single color for all tracks. + **kwargs: Additional keyword arguments to pass to the plotting function. + + Raises: + ValueError: If the number of tracks exceeds `max_num_tracks` or if the + track data has more than one positional axis or if the interval is not + set within the track data. + """ + if tdata.num_tracks > max_num_tracks: + raise ValueError( + f'Too many tracks to plot: {tdata.num_tracks} > {max_num_tracks}.' + ) + self._tdata = tdata + self._num_tracks = tdata.values.shape[-1] + self._track_height = track_height + self._filled = filled + self._ylabel_horizontal = ylabel_horizontal + self._ylabel_template = ylabel_template + self._shared_y_scale = shared_y_scale + self._global_ylims = global_ylims + self._kwargs = kwargs + + if len(self._tdata.positional_axes) != 1: + raise ValueError( + 'Only track_data with 1 positional axis is supported in Tracks.' + ) + if self._tdata.interval is None: + raise ValueError('.interval needs to be set in track_data.') + + # Set up color per set of interleaved tracks. + if getattr(self._tdata, 'uns') is not None: + self._num_tdata = self._tdata.uns['num_interleaved_trackdatas'] + else: + self._num_tdata = 1 + + cmap = plt.get_cmap(cmap) + num_track_sets = self._num_tracks // self._num_tdata + if truncate_cmap: + # We do *1.2 to make the color change more gradual. This means that the + # the upper range of the cmap is never displayed, which often tends to + # achieve nicer results aesthetically. + self._colors = cmap(np.linspace(0, 1, round(num_track_sets * 1.2))) + else: + self._colors = cmap(np.linspace(0, 1, num_track_sets)) + + if track_colors is not None: + if isinstance(track_colors, str): + self._colors = [track_colors] * num_track_sets + elif len(track_colors) == 1: + self._colors = track_colors * num_track_sets + elif len(track_colors) != num_track_sets: + raise ValueError( + f'track_colors argument (length: {len(track_colors)}) must be' + ' either a single color, or the same number of track sets provided' + f' in the tdata ({num_track_sets}).' + ) + else: + self._colors = track_colors + + def _get_ylimits(self, tdata: track_data.TrackData): + """Computes y-axis limits for track sets.""" + return [ + ( + tdata.values[:, i : i + self._num_tdata].min(), + tdata.values[:, i : i + self._num_tdata].max(), + ) + for i in range(0, self._num_tracks, self._num_tdata) + ] + + def get_ax_height(self, axis_index: int) -> float: + """Returns the height of the axis.""" + return self._track_height + + @property + def num_axes(self) -> int: + """Returns the number of matplotlib axes required by the component.""" + return self._tdata.num_tracks + + def plot_ax( + self, + ax: matplotlib.axes.Axes, + axis_index: int, + interval: genome.Interval, + ): + """Plots the tracks on the given axis. + + Args: + ax: The matplotlib axis to plot on. + axis_index: The index of the axis. + interval: The genomic interval to plot. + """ + tdata = self._tdata + assert tdata.interval is not None + + # If an interval is passed, zoom in on that specific sub-interval. + if interval is not None: + tdata = tdata.slice_by_interval(interval, match_resolution=True) + del interval + x = ( + np.arange(tdata.values.shape[0]) * tdata.resolution + + tdata.interval.start + + tdata.resolution / 2 + ) + arr = tdata.values[:, axis_index] + + # Same shared y-axis limits across all tracks. + if self._shared_y_scale: + if self._global_ylims is not None: + ax.set_ylim(self._global_ylims) + else: + ax.set_ylim(tdata.values.min(), tdata.values.max()) + else: + ax.set_ylim(arr.min(), arr.max()) + + # Set the colour of this track. + track_color = self._colors[axis_index // self._num_tdata] + + # Draw the line-plot, or filled line-plot if filled=True. + if self._filled: + ax.fill_between(x, np.ravel(arr), color=track_color, **self._kwargs) + else: + ax.plot(x, arr, c=track_color, **self._kwargs) + + if self._ylabel_template: + _set_ylabel(ax, self._get_ylabel(axis_index), self._ylabel_horizontal) + + def _get_ylabel(self, axis_index: int) -> str: + """Returns the y-axis label for the given axis index.""" + row = self._tdata.metadata.iloc[axis_index] + return self._ylabel_template.format(**row.to_dict()) + + +class OverlaidTracks(AbstractComponent): + """Component for visualizing overlaid track pairs, such as REF/ALT tracks.""" + + def __init__( + self, + tdata: Mapping[str, track_data.TrackData], + colors: Mapping[str, str] | None = None, + cmap: str | None = 'viridis', + track_height: float = 1.0, + ylabel_template: str = '{name}:{strand}', + ylabel_horizontal: bool = True, + shared_y_scale: bool = False, + alpha: float = 0.8, + order_tdata_by_mean: bool = True, + max_num_tracks: int = 50, + legend_loc: str = 'upper right', + **kwargs, + ): + """Initializes the `OverlaidTracks` component. + + Args: + tdata: A dictionary mapping track names to `TrackData` objects. + colors: An optional dictionary mapping track names to colors. + cmap: The colormap to use if `colors` is not provided. + track_height: The height of each track. + ylabel_template: A template for the y-axis labels. + ylabel_horizontal: Whether to make the y-axis labels horizontal. + shared_y_scale: Whether to use the same y-axis scale for all tracks. + alpha: The transparency of the tracks. + order_tdata_by_mean: Whether to order the tracks by their mean value (in + descending order). + max_num_tracks: The maximum number of tracks to plot. + legend_loc: The location of the legend (such as 'upper left' or 'best'). + See the matplotlib Axes legend documentation for more details and + options. If None, no legend is shown. + **kwargs: Additional keyword arguments to pass to the plotting function. + + Raises: + ValueError: If the shapes or metadata of the track data do not match, + or if the number of tracks exceeds `max_num_tracks`, or if + the track data has more than one positional axis, or if the + interval is not set, or if colors are passed but do not match + the track data names. + """ + self._tdata = tdata + self._colors = colors + self._cmap = cmap + self._track_height = track_height + self._ylabel_template = ylabel_template + self._ylabel_horizontal = ylabel_horizontal + self._shared_y_scale = shared_y_scale + self._alpha = alpha + self._order_tdata_by_mean = order_tdata_by_mean + self._kwargs = kwargs + self._first_tdata = list(tdata.values())[0] + self._legend_loc = legend_loc + + if len(set(data.values.shape for data in tdata.values())) != 1: + raise ValueError('Shapes of track data values must be the same.') + + if not all( + self._first_tdata.metadata.equals(data.metadata) + for data in tdata.values() + ): + raise ValueError('Metadata of track data must be the same.') + + if self._first_tdata.num_tracks > max_num_tracks: + raise ValueError( + f'Too many tracks to plot: {self._first_tdata.num_tracks} >' + f' {max_num_tracks}.' + ) + + # If a colors dict is passed, then there should be a matching color for each + # track data name. + if self._colors is not None: + if self._tdata.keys() != self._colors.keys(): + raise ValueError( + f'If passing colors, each tdata name {list(self._tdata.keys())} ' + 'must have an associated color.' + ) + # Otherwise, we define a color from a cmap. + else: + cmap = plt.get_cmap(self._cmap) + colors = cmap(np.linspace(0, 1, len(tdata))) + self._colors = dict(zip(self._tdata.keys(), colors)) + + if len(self._first_tdata.positional_axes) != 1: + raise ValueError( + 'Only track_data with 1 positional axis is supported in' + ' OverlaidTracks.' + ) + if self._first_tdata.interval is None: + raise ValueError('.interval needs to be set in track_data.') + + if self._shared_y_scale: + # We compute min and max over all the track data arrays in the tdata dict. + all_values = np.stack([arr.values for arr in self._tdata.values()]) + self._vmin = np.min(all_values) + self._vmax = np.max(all_values) + + def get_ax_height(self, axis_index: int) -> float: + """Returns the height of the axis.""" + return self._track_height + + @property + def num_axes(self) -> int: + """Returns the number of matplotlib axes required by the component.""" + return self._first_tdata.num_tracks + + def plot_ax( + self, + ax: matplotlib.axes.Axes, + axis_index: int, + interval: genome.Interval, + ): + """Plots the overlaid tracks on the given axis. + + Args: + ax: The matplotlib axis to plot on. + axis_index: The index of the axis. + interval: The genomic interval to plot. + """ + + def _maybe_slice_tdata(td): + """Slices the track data to the interval if passed.""" + # If an interval is passed, zoom in on that specific sub-interval. + if interval is not None: + return td.slice_by_interval(interval, match_resolution=True) + else: + return td + + def _make_ordered_dict_by_mean(tdata): + """Reorders track data dict by mean (descending) for better plotting.""" + mean_tuples = [ + (name, np.mean(td.values, dtype=np.float64)) + for name, td in tdata.items() + ] + sorted_mean_tuples = sorted( + mean_tuples, key=lambda item: item[1], reverse=True + ) + + # Extract names in the sorted order and return ordered tdata. + sorted_names = [name for name, _ in sorted_mean_tuples] + ordered_tdata = {name: tdata[name] for name in sorted_names} + return ordered_tdata + + # Maybe slice the track data to the interval, and reorder by mean. + tdata_sliced = { + name: _maybe_slice_tdata(td) for name, td in self._tdata.items() + } + self._tdata_ordered = ( + _make_ordered_dict_by_mean(tdata_sliced) + if self._order_tdata_by_mean + else tdata_sliced + ) + + for name, tdata in self._tdata_ordered.items(): + assert tdata.interval is not None + x = ( + np.arange(tdata.values.shape[0]) * tdata.resolution + + tdata.interval.start + + tdata.resolution / 2 + ) + arr = tdata.values[:, axis_index] + + if self._shared_y_scale: + ax.set_ylim(self._vmin, self._vmax) + + # Plot the two tracks on the same axis. We plot the larger values first so + # that the overlap is more visible. + ax.plot( + x, + arr, + alpha=self._alpha, + **self._kwargs, + c=self._colors[name], + ) + if axis_index == 0 and self._legend_loc is not None: + ax.legend(self._tdata_ordered.keys(), loc=self._legend_loc) + + if self._ylabel_template: + _set_ylabel(ax, self._get_ylabel(axis_index), self._ylabel_horizontal) + + def _get_ylabel(self, axis_index: int) -> str: + """Returns the y-axis label for the given track.""" + # Metadata equality has already been checked so here we grab the first one. + metadata = self._tdata[list(self._tdata.keys())[0]].metadata + row = metadata.iloc[axis_index] + return self._ylabel_template.format(**row.to_dict()) + + +class ContactMaps(AbstractComponent): + """Component for visualizing contact maps. + + The `vmin` and `vmax` parameters control the color scaling of the heatmap. + Values outside this range will be clipped to `vmin` or `vmax`. + """ + + def __init__( + self, + tdata: track_data.TrackData, + track_height: float = 10.0, + vmin: float | None = -1.0, + vmax: float | None = 2.0, + norm: matplotlib.colors.TwoSlopeNorm | None = None, + ylabel_horizontal: bool = True, + ylabel_template: str = '{name}', + cmap: matplotlib.colors.LinearSegmentedColormap | None = None, + max_num_tracks: int = 10, + **kwargs, + ): + """Initializes the `ContactMaps` component. + + Args: + tdata: The `TrackData` object containing the contact maps. + track_height: The height of each contact map. + vmin: The minimum value for the color scale. + vmax: The maximum value for the color scale. + norm: An optional normalization for the color scale. + ylabel_horizontal: Whether to make the y-axis labels horizontal. + ylabel_template: A template for the y-axis labels. + cmap: The colormap to use for the contact maps. + max_num_tracks: The maximum number of tracks to plot. + **kwargs: Additional keyword arguments to pass to the plotting function. + + Raises: + ValueError: If the number of tracks exceeds `max_num_tracks`, or if the + track data does not have 2 positional axes, or if the contact maps are + not square, or if the interval is not set in the track data. + """ + if tdata.num_tracks > max_num_tracks: + raise ValueError( + f'Too many tracks to plot: {tdata.num_tracks} > {max_num_tracks}.' + ) + self._tdata = tdata + self._resolution = tdata.resolution + self._track_height = track_height + self._vmin = vmin + self._vmax = vmax + self._norm = norm + self._ylabel_horizontal = ylabel_horizontal + self._ylabel_template = ylabel_template + # TODO: b/377292012 - Add orca_color_map. + self._cmap = ( + cmap if cmap is not None else matplotlib.pyplot.get_cmap('autumn_r') + ) + self._kwargs = kwargs + if len(self._tdata.positional_axes) != 2: + raise ValueError( + 'Only track_data with 2 positional axes is supported in ContactMaps.' + ) + if self._tdata.values.shape[0] != self._tdata.values.shape[1]: + raise ValueError('Contact maps must be square.') + + if self._tdata.interval is None: + raise ValueError('.interval needs to be set in track_data.') + + def get_ax_height(self, axis_index: int) -> float: + """Returns the height of the axis.""" + return self._track_height + + @property + def num_axes(self) -> int: + """Returns the number of matplotlib axes required by the component.""" + return self._tdata.num_tracks + + def _get_bin_positions( + self, interval: genome.Interval, resolution: int + ) -> np.ndarray: + """Gets the positions of contact map bins in chromosome coordinates.""" + bin_indices = np.arange(interval.width // resolution) + return interval.start + (resolution * bin_indices) + + def _plot_pcolormesh( + self, + ax: matplotlib.axes.Axes, + x: np.ndarray, + y: np.ndarray, + arr: np.ndarray, + vmin: float | None = None, + vmax: float | None = None, + cmap: matplotlib.colors.Colormap | None = None, + norm: matplotlib.colors.Normalize | None = None, + **kwargs, + ) -> matplotlib.collections.QuadMesh: + """Plots the contact map heatmap using `pcolormesh`. + + Note that upsampling the contact maps to single base pair resolution and + using something like .imshow() is infeasible since the upsampling blows up + memory. + + Args: + ax: The matplotlib axis to plot on. + x: The x-axis coordinates. + y: The y-axis coordinates. + arr: The contact map data. + vmin: The minimum value for the color scale. + vmax: The maximum value for the color scale. + cmap: The colormap to use. + norm: An optional normalization for the color scale. + **kwargs: Additional keyword arguments to pass to `pcolormesh`. + + Returns: + The `matplotlib.collections.QuadMesh` object representing the plot. + """ + if not norm: # Cannot pass both norm and vmin/vmax simultaneously. + return ax.pcolormesh( + x, + y, + arr, + vmin=vmin, + vmax=vmax, + cmap=cmap, + **kwargs, + ) + else: + return ax.pcolormesh( + x, + y, + arr, + norm=norm, + **kwargs, + ) + + def plot_ax( + self, + ax: matplotlib.axes.Axes, + axis_index: int, + interval: genome.Interval, + ): + """Plots the contact map on the given axis. + + Args: + ax: The matplotlib axis to plot on. + axis_index: The index of the axis. + interval: The genomic interval to plot. + """ + tdata = self._tdata + assert tdata.interval is not None + + # If an interval is passed, zoom in on that specific sub-interval. + if interval is not None: + tdata = tdata.slice_by_interval(interval, match_resolution=True) + del interval + + arr = tdata.values[:, :, axis_index] + x = self._get_bin_positions(tdata.interval, self._resolution) + + # We shift the bin edges by half a step since pcolormesh will misalign + # the x-axis by half a step, since the plot values are centered within each + # bin, rather than at the bin edges. + half_bin_width = self._resolution // 2 + x = x + half_bin_width + + # The -1 reverse ordering ensures that contact maps are plotted with + # the diagonal going down from left to right. + y = np.arange(arr.shape[0])[::-1] + + if not self._vmin: + self._vmin = np.min(arr) + + if not self._vmax: + self._vmax = np.max(arr) + + self._plot_pcolormesh( + ax=ax, + x=x, + y=y, + arr=arr, + vmin=self._vmin, + vmax=self._vmax, + cmap=self._cmap, + norm=self._norm, + **self._kwargs, + ) + + if self._ylabel_template: + _set_ylabel(ax, self._get_ylabel(axis_index), self._ylabel_horizontal) + + def _get_ylabel(self, axis_index: int) -> str: + """Returns the y-axis label for the given contact map.""" + row = self._tdata.metadata.iloc[axis_index] + return self._ylabel_template.format(**row.to_dict()) + + +class ContactMapsDiff(ContactMaps): + """Component for visualizing contact map differences. + + This component visualizes the difference between two contact maps. It uses + a diverging red-blue color map with the center white color pinned to a + value of zero, with negative values being blue and positive values being red. + + The `vmin` and `vmax` parameters control the color scaling of the heatmap. + Values outside this range will be clipped to `vmin` or `vmax`. + """ + + def __init__( + self, + tdata: track_data.TrackData, + track_height: float = 10.0, + vmin: float | None = -1.0, + vmax: float | None = 1.0, + ylabel_horizontal: bool = True, + ylabel_template: str = '{name}', + cmap: matplotlib.colors.LinearSegmentedColormap | str | None = 'RdBu_r', + max_num_tracks: int = 10, + **kwargs, + ): + """Initializes the `ContactMapsDiff` component. + + Args: + tdata: The `TrackData` object containing the contact map differences. + track_height: The height of each contact map. + vmin: The minimum value for the color scale. + vmax: The maximum value for the color scale. + ylabel_horizontal: Whether to make the y-axis labels horizontal. + ylabel_template: A template for the y-axis labels. + cmap: The colormap to use for the contact maps. + max_num_tracks: The maximum number of tracks to plot. + **kwargs: Additional keyword arguments to pass to the plotting function. + """ + self._norm = plt_colors.TwoSlopeNorm(vmin=vmin, vcenter=0, vmax=vmax) + + super().__init__( + tdata=tdata, + track_height=track_height, + vmin=vmin, + vmax=vmax, + ylabel_horizontal=ylabel_horizontal, + ylabel_template=ylabel_template, + cmap=cmap, + max_num_tracks=max_num_tracks, + **kwargs, + ) + + +def _set_ylabel(ax: matplotlib.axes.Axes, ylabel: str, horizontal: bool): + """Sets the y-axis label. + + Args: + ax: The matplotlib axis to set the label on. + ylabel: The label text. + horizontal: Whether to make the label horizontal. + """ + if ylabel: + if horizontal: + ax.set_ylabel( + ylabel, + rotation=0, + multialignment='center', + va='center', + ha='right', + labelpad=5, + ) + else: + ax.set_ylabel(ylabel) + + +class TranscriptAnnotation(AbstractComponent): + """Visualizes transcript annotations.""" + + def __init__( + self, + transcripts: Sequence[transcript_utils.Transcript], + adaptive_fig_height: bool = True, + fig_height: float = 1.0, + transcript_style: plot_transcripts.TranscriptStyle = ( + plot_transcripts.TranscriptStylePreset.MINIMAL.value + ), + plot_labels_once: bool = True, + label_name: str = 'gene_name', + **kwargs, + ): + """Initializes the `TranscriptAnnotation` component. + + Args: + transcripts: A sequence of `Transcript` objects to visualize. + adaptive_fig_height: Whether to adjust the figure height based on the + number of transcripts. + fig_height: The base figure height. + transcript_style: The style to use for plotting transcripts. The options + are defined in `plot_transcripts.TranscriptStylePreset`. + plot_labels_once: Whether to plot labels only once per transcript. + label_name: The attribute of the transcript to use for labels. + **kwargs: Additional keyword arguments to pass to the plotting function. + """ + self._transcripts = transcripts + self._adaptive_fig_height = adaptive_fig_height + self._fig_height = fig_height + self._kwargs = kwargs + self._kwargs['label_name'] = label_name + self._kwargs['transcript_style'] = transcript_style + self._kwargs['plot_labels_once'] = plot_labels_once + + self._num_transcripts = len(self._transcripts) + # TODO(b/377291518): adaptive fig height should in theory scale with the + # number of transcripts in the sub-interval, not the total number of + # transcripts passed, but this is a bit tricky to implement in the code and + # this approach seems to work well enough for now. + if self._adaptive_fig_height: + self._fig_height = max(0.05 * self._num_transcripts * self._fig_height, 1) + + def get_ax_height(self, axis_index: int) -> float: + """Returns the height of the axis.""" + return self._fig_height + + @property + def num_axes(self) -> int: + """Returns the number of matplotlib axes required by the component.""" + return 1 + + def plot_ax( + self, ax: matplotlib.axes.Axes, axis_index: int, interval: genome.Interval + ): + """Plots the transcript annotations on the given axis. + + Args: + ax: The matplotlib axis to plot on. + axis_index: The index of the axis. + interval: The genomic interval to plot. + """ + # Update transcripts to only those overlapping interval. + transcripts = [ + t for t in self._transcripts if t.transcript_interval.overlaps(interval) + ] + ax.set_yticklabels([]) + ax.set_yticks([]) + ax.spines['left'].set_visible(False) + plot_transcripts.plot_transcripts(ax, transcripts, interval, **self._kwargs) + + +class SeqLogo(AbstractComponent): + """Visualizes a sequence logo.""" + + def __init__( + self, + scores: Float32[np.ndarray, 'S A'], + scores_interval: genome.Interval, + fig_height: float = 1.0, + alphabet: str = 'ACGT', + max_width: int = 1000, + ylabel: str = '', + ylabel_horizontal: bool = True, + ylim: tuple[float, float] | None = None, + **kwargs, + ): + """Initializes the `SeqLogo` component. + + Args: + scores: A numpy array of shape (sequence_length, alphabet_size) containing + the sequence logo scores. + scores_interval: The genomic interval corresponding to the scores. + fig_height: The height of the figure. + alphabet: The alphabet used in the sequence logo. + max_width: The maximum width of the sequence logo to plot. + ylabel: An optional label for the y-axis. + ylabel_horizontal: Whether to make the y-axis label horizontal. + ylim: An optional range to set for the y-axis. + **kwargs: Additional keyword arguments to pass to the plotting function. + """ + self._scores = scores + self._scores_interval = scores_interval + self._fig_height = fig_height + self._alphabet = alphabet + self._max_width = max_width + self._ylabel = ylabel + self._ylabel_horizontal = ylabel_horizontal + self._ylim = ylim + self._kwargs = kwargs + + def get_ax_height(self, axis_index: int) -> float: + """Returns the height of the axis.""" + return self._fig_height + + @property + def num_axes(self) -> int: + """Returns the number of matplotlib axes required by the component.""" + return 1 + + def plot_ax( + self, ax: matplotlib.axes.Axes, axis_index: int, interval: genome.Interval + ): + """Plots the sequence logo on the given axis. + + Args: + ax: The matplotlib axis to plot on. + axis_index: The index of the axis. + interval: The genomic interval to plot. + """ + intersection = self._scores_interval.intersect(interval) + if intersection is None or intersection.width > self._max_width: + return + relative_start = intersection.start - self._scores_interval.start + scores = self._scores[ + relative_start : (relative_start + intersection.width) + ] + + plot_lib.seqlogo( + scores, + ax=ax, + alphabet=self._alphabet, + start=intersection.start, + one_based=False, + **self._kwargs, + ) + + _set_ylabel(ax, self._ylabel, self._ylabel_horizontal) + if self._ylim is not None: + ax.set_ylim(self._ylim) + + +class Sashimi(AbstractComponent): + """Visualizes splice junctions as a Sashimi plot.""" + + def __init__( + self, + junction_track: junction_data.JunctionData, + fig_height: float = 1.0, + filter_threshold: float | None = None, + ylabel_template: str = '{name}', + ylabel_horizontal: bool = True, + annotate_counts: bool = True, + normalize_values: bool = True, + interval_contained: bool = True, + rng: np.random.Generator | None = None, + ): + """Initializes the `Sashimi` component. + + Args: + junction_track: A `JunctionData` object to visualize. + fig_height: The height of the figure. + filter_threshold: The minimum value for a junction to be included in the + plot. This is typically based on the normalized read count. If None, + filter out junction values below 5% of the maximum value. + ylabel_template: A template for the y-axis labels. + ylabel_horizontal: Whether to make the y-axis label horizontal. + annotate_counts: Whether to annotate the junctions with read counts. + normalize_values: Whether to normalize the values to a constant sum. + interval_contained: Whether to only plot junctions contained in the + interval. + rng: Optional random number generator to use for jittering junction paths. + If unset will use NumPy's default random number generator. + """ + if normalize_values: + self._junction_track = junction_track.normalize_values() + else: + self._junction_track = junction_track + self._fig_height = fig_height + self._filter_threshold = filter_threshold + self._ylabel_template = ylabel_template + self._ylabel_horizontal = ylabel_horizontal + self._annotate_counts = annotate_counts + self._interval_contained = interval_contained + self._rng = rng or np.random.default_rng() + + def get_ax_height(self, axis_index: int) -> float: + """Returns the height of the axis.""" + return self._fig_height + + @property + def num_axes(self) -> int: + """Returns the number of matplotlib axes required by the component.""" + # Metadata for JunctionData do not have strand information. + # Strand information is in JunctionData.intervals. + return self._junction_track.num_tracks * len( + self._junction_track.possible_strands + ) + + def _get_strand_and_metadata_index(self, axis_index: int) -> tuple[str, int]: + """Returns the strand and metadata index for the given axis index.""" + if len(self._junction_track.possible_strands) == 1: + strand = self._junction_track.possible_strands[0] + metadata_index = axis_index + else: + strand = '+' if axis_index % 2 == 0 else '-' + metadata_index = axis_index // 2 + return strand, metadata_index + + def plot_ax( + self, ax: matplotlib.axes.Axes, axis_index: int, interval: genome.Interval + ): + """Plots the Sashimi plot on the given axis. + + Args: + ax: The matplotlib axis to plot on. + axis_index: The index of the axis. + interval: The genomic interval to plot. + """ + strand, metadata_index = self._get_strand_and_metadata_index(axis_index) + track_name = self._junction_track.metadata.iloc[metadata_index]['name'] + junction_track = self._junction_track.intersect_with_interval(interval) + junctions = junction_data.get_junctions_to_plot( + predictions=junction_track, + strand=strand, + name=track_name, + k_threshold=self._filter_threshold, + ) + if self._interval_contained: + junctions = [j for j in junctions if interval.contains(j)] + else: + junctions = [j for j in junctions if j.overlaps(interval)] + + plot_lib.sashimi_plot( + junctions, + ax=ax, + interval=interval, + filter_threshold=0, + annotate_counts=self._annotate_counts, + rng=self._rng, + ) + ax.set_yticklabels([]) + ax.set_yticks([]) + ax.spines['left'].set_visible(False) + if self._ylabel_template: + _set_ylabel(ax, self._get_ylabel(axis_index), self._ylabel_horizontal) + + def _get_ylabel(self, axis_index: int) -> str: + """Returns the y-axis label for the given axis index.""" + strand, metadata_index = self._get_strand_and_metadata_index(axis_index) + row = self._junction_track.metadata.iloc[metadata_index] + row = row.to_dict() + row['strand'] = strand + return self._ylabel_template.format(**row) + + +class EmptyComponent(AbstractComponent): + """An empty plotting component.""" + + def __init__(self, fig_height: float = 1.0): + """Initializes the `EmptyComponent`. + + Args: + fig_height: The height of the figure. + """ + self._fig_height = fig_height + + def get_ax_height(self, axis_index: int) -> float: + """Returns the height of the axis.""" + return self._fig_height + + @property + def num_axes(self) -> int: + """Returns the number of matplotlib axes required by the component.""" + return 1 + + def plot_ax( + self, ax: matplotlib.axes.Axes, axis_index: int, interval: genome.Interval + ): + """Plot an empty axis, removing all labels and spines from the axis. + + Args: + ax: The matplotlib axis to plot on. + axis_index: The index of the axis. + interval: The genomic interval to plot. + """ + ax.set_yticklabels([]) + ax.set_yticks([]) + ax.spines['left'].set_visible(False) + ax.spines['right'].set_visible(False) + + +class AbstractAnnotation(abc.ABC): + """Abstract base class for plot annotations. + + Annotations are visual elements that can be added to plots to highlight + specific features or regions. This class defines the common interface + for all annotations. + + Attributes: + annotations: A sequence of `Variant` or `Interval` objects representing the + annotations. + colors: An optional string or sequence of strings specifying the colors of + the annotations. + labels: An optional sequence of strings to use as labels for the + annotations. + use_default_labels: Whether to use default labels for the annotations if + `labels` is not provided. + """ + + def __init__( + self, + annotations: Sequence[genome.Variant] | Sequence[genome.Interval], + colors: str | Sequence[str] | None, + labels: Sequence[str] | None, + use_default_labels: bool, + ): + """Initializes the `AbstractAnnotation` class. + + Args: + annotations: A sequence of `Variant` or `Interval` objects. + colors: An optional string or sequence of strings specifying colors. + labels: An optional sequence of strings to use as labels. + use_default_labels: Whether to use default labels if `labels` is not + provided. + + Raises: + ValueError: If the length of `colors` or `labels` does not match the + length of `annotations`. + """ + self._annotations = annotations + self._colors = colors + self._labels = labels + self._use_default_labels = use_default_labels + + # Pre-processing / validation of inputs. + num_annotations = len(self._annotations) + if (self._colors is not None) and (not isinstance(self._colors, str)): + if len(self._colors) != num_annotations: + raise ValueError( + 'Colors must have the same length as intervals/variants or just' + ' a single color string.' + ) + if self._labels is not None: + if len(self._labels) != num_annotations: + raise ValueError( + 'Labels must have the same length as intervals/variants.' + ) + + @abc.abstractmethod + def plot_ax( + self, ax: matplotlib.axes.Axes, interval: genome.Interval, hspace: float + ): + """Adds the annotation to an individual axis. + + Args: + ax: The matplotlib axis to add the annotation to. + interval: The genomic interval to plot. + hspace: The vertical space between subplots. + """ + raise NotImplementedError + + @abc.abstractmethod + def plot_labels( + self, + ax: matplotlib.axes.Axes, + interval: genome.Interval, + label_height_factor: float, + ): + """Adds labels for the annotation to an axis. + + Args: + ax: The matplotlib axis to add the labels to. + interval: The genomic interval to plot. + label_height_factor: A scaling factor for the label height. + """ + raise NotImplementedError + + @property + def is_variant(self) -> bool: + """Returns True if the annotation is a variant annotation.""" + return isinstance(self._annotations[0], genome.Variant) + + @property + def has_labels(self) -> bool: + """Returns True if the annotation has labels.""" + return (self._labels is not None) | ( + self.is_variant and self._use_default_labels + ) + + def add_label( + self, + ax: matplotlib.axes.Axes, + label_x_position: float, + label: str, + angle: float, + label_height_factor: float, + label_position: str = 'left', + ): + """Adds a single angled label to an axis. + + Args: + ax: The matplotlib axis to add the label to. + label_x_position: The x position of the label. + label: The label text. + angle: The angle of the label. + label_height_factor: A scaling factor for the label height. + label_position: The (horizontal) placement of the label, relative to the x + position. Can be any position string accepted by the horizontalalignment + argument of matplotlib.axes.Axes.text. + """ + ylims = ax.get_ylim() + label_height = ylims[0] + label_height_factor * np.diff(ylims)[0] + ax.text( + label_x_position, + label_height, + label, + color='black', + fontsize=10, + rotation=angle, + ha=label_position, + va='bottom', + ) + ax.axvline( + label_x_position, ymax=label_height * 0.95, color='black', alpha=0.1 + ) + + +class IntervalAnnotation(AbstractAnnotation): + """Visualizes intervals as rectangles across all plot components. + + A rectangle is drawn for each interval and overlaid on top of the final plot, + spanning all plot components. + """ + + def __init__( + self, + intervals: Sequence[genome.Interval], + colors: str | Sequence[str] = 'darkgray', + alpha: float = 0.2, + labels: Sequence[str] | None = None, + use_default_labels: bool = True, + label_angle: float = 15, + ): + """Initializes the `IntervalAnnotation` class. + + Args: + intervals: A sequence of `Interval` objects to annotate. + colors: An optional string or sequence of strings specifying the colors of + the interval annotation. + alpha: The transparency of the interval annotation. + labels: An optional sequence of strings to use as labels for the + intervals. + use_default_labels: Whether to use default labels for the intervals if + `labels` is not provided. + label_angle: The angle of the interval labels. + """ + super().__init__(intervals, colors, labels, use_default_labels) + self._label_angle = label_angle + self._alpha = alpha + self._intervals = intervals + + def plot_ax( + self, + ax: matplotlib.axes.Axes, + interval: genome.Interval, + hspace: float = 0.0, + ): + """Adds the interval annotation to an individual axis. + + Args: + ax: The matplotlib axis to add the annotation to. + interval: The genomic interval to plot. + hspace: The vertical space between subplots. + """ + for i, interval_i in enumerate(self._intervals): + if isinstance(self._colors, str): + color = self._colors + else: + color = self._colors[i] + # Only plot the piece of the annotation that intersects with the plotting + # interval. + intersection = interval_i.intersect(interval) + if intersection is None: + continue + ax.axvspan( + interval_i.start, + interval_i.end, + # Set y-maximum to be the top of the axis, including hspace. + ymax=(1 + hspace) * 1.03, + alpha=self._alpha, + facecolor=color, + # Set edgecolor to None for intervals to avoid horizongal lines + # between axes, but keep it for variants. As variants are typically + # a very thin rectangle, removing the edges results in the rectangle + # not being visible. + edgecolor=None, + clip_on=False, + ) + + def plot_labels( + self, + ax: matplotlib.axes.Axes, + interval: genome.Interval, + label_height_factor: float, + ): + """Adds interval labels to an axis. + + Args: + ax: The matplotlib axis to add the labels to. + interval: The genomic interval to plot. + label_height_factor: A scaling factor for the label height. + """ + # Only add labels if they are provided. + if self.has_labels: + for i, interval_i in enumerate(self._intervals): + label = self._labels[i] + # Place label in the middle of the rectangle. + # Only plot the piece of the annotation that intersects with the + # plotting interval. + intersection = interval_i.intersect(interval) + if intersection is None: + continue + + self.add_label( + ax, + label_x_position=np.mean((interval_i.start, interval_i.end)), + label=label, + angle=self._label_angle, + label_height_factor=label_height_factor, + ) + + +class VariantAnnotation(AbstractAnnotation): + """Visualizes variants as thin line-like rectangles across plot components.""" + + def __init__( + self, + variants: Sequence[genome.Variant], + colors: str | Sequence[str] = 'orange', + alpha: float = 0.8, + labels: Sequence[str] | None = None, + use_default_labels: bool = True, + label_angle: float = 15, + label_position: str = 'left', + ): + """Initializes the `VariantAnnotation` class. + + Args: + variants: A sequence of `Variant` objects to annotate. + colors: An optional string or sequence of strings specifying the colors of + the variant annotation. + alpha: The transparency of the variant annotation. + labels: An optional sequence of strings to use as labels for the variants. + use_default_labels: Whether to use default labels for the variants if + `labels` is not provided. + label_angle: The angle of the variant labels. + label_position: The (horizontal) placement of the variant label, relative + to the variant position. Can be any position string accepted by the + horizontalalignment argument of matplotlib.axes.Axes.text. + """ + super().__init__(variants, colors, labels, use_default_labels) + self._label_angle = label_angle + self._alpha = alpha + self._variants = variants + self._label_position = label_position + + def plot_ax( + self, + ax: matplotlib.axes.Axes, + interval: genome.Interval, + hspace: float = 0.0, + ): + """Adds a variant annotation to an individual axis. + + Args: + ax: The matplotlib axis to add the annotation to. + interval: The genomic interval to plot. + hspace: The vertical space between subplots. + """ + for i, variant in enumerate(self._variants): + if isinstance(self._colors, str): + color = self._colors + else: + color = self._colors[i] + interval_i = variant.reference_interval + # Only plot the piece of the annotation that intersects with the plotting + # interval. + intersection = interval_i.intersect(interval) + if intersection is None: + continue + ax.axvspan( + interval_i.start, + interval_i.end, + # Set y-maximum to be the top of the axis, including hspace. + ymax=(1 + hspace) * 1.03, + alpha=self._alpha, + facecolor=color, + # Set edgecolor to None for intervals to avoid horizongal lines + # between axes, but keep it for variants. As variants are typically + # a very thin rectanle, removing the edges results in the rectangle + # not being visible. + edgecolor=color, + clip_on=False, + ) + + def plot_labels( + self, + ax: matplotlib.axes.Axes, + interval: genome.Interval, + label_height_factor: float, + ): + """Adds variant labels to an axis. + + Args: + ax: The matplotlib axis to add the labels to. + interval: The genomic interval to plot. + label_height_factor: A scaling factor for the label height. + """ + # Only add labels if they are provided. + if self.has_labels: + for i, variant in enumerate(self._variants): + interval_i = variant.reference_interval + # Using truncated string method for genome.Variant class to get default + # labels for variants. + label = ( + variant.as_truncated_str(max_length=20) + if self._use_default_labels + else self._labels[i] + ) + # Place label in the middle of the rectangle. + # Only plot the piece of the annotation that intersects with the + # plotting interval. + intersection = interval_i.intersect(interval) + if intersection is None: + continue + + self.add_label( + ax, + label_x_position=np.mean((interval_i.start, interval_i.end)), + label=label, + angle=self._label_angle, + label_height_factor=label_height_factor, + label_position=self._label_position, + ) diff --git a/alphagenome/source/src/alphagenome/visualization/plot_components_test.py b/alphagenome/source/src/alphagenome/visualization/plot_components_test.py new file mode 100644 index 0000000000000000000000000000000000000000..f72df1b217eb5e4fd52ed6fa2cb8be891c4eb711 --- /dev/null +++ b/alphagenome/source/src/alphagenome/visualization/plot_components_test.py @@ -0,0 +1,242 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from absl.testing import absltest +from absl.testing import parameterized +from alphagenome.data import genome +from alphagenome.data import junction_data +from alphagenome.data import track_data +from alphagenome.data import transcript as transcript_utils +from alphagenome.visualization import plot_components +import matplotlib +import numpy as np +import pandas as pd + + +_ATAC_METADATA = pd.DataFrame( + dict( + name=['foo', 'bar', 'baz', 'buz', 'fux'], + strand=['.'] * 5, + padding=[False] * 5, + ) +) + +_CONTACT_MAP_METADATA = pd.DataFrame( + dict( + name=['H1', 'HFF'], + strand=['.'] * 2, + padding=[False] * 2, + ) +) + +_RNA_SEQ_METADATA = pd.DataFrame( + dict( + name=['foo', 'boo', 'bar', 'far'], + strand=['.'] * 4, + padding=[False] * 4, + ) +) + +_SPLICING_METADATA = pd.DataFrame( + dict( + name=['foo', 'bar'], + ontology_curie=['ontology_curie_1', 'ontology_curie_2'], + padding=[False] * 2, + ) +) + + +class PlotComponentsTest(parameterized.TestCase): + + @parameterized.parameters([ + dict(subinterval=genome.Interval('chr1', 100, 4196), logo_ylim=[0, 1]), + dict(subinterval=genome.Interval('chr1', 0, 2**20), logo_ylim=None), + ]) + def test_track_component(self, subinterval, logo_ylim): + interval = genome.Interval('chr1', 0, 2**20) + + # Mock 1D track data. + atac_values = np.stack( + [np.arange(interval.width) * 0.0001] * len(_ATAC_METADATA), axis=1 + ).astype(np.float32) + atac_tdata = track_data.TrackData( + values=atac_values, + metadata=_ATAC_METADATA, + interval=interval, + resolution=1, + ) + + rna_seq_values_ref = np.stack( + [np.arange(interval.width) * 0.0001] * len(_RNA_SEQ_METADATA), axis=1 + ).astype(np.float32) + rna_seq_tdata_ref = track_data.TrackData( + values=rna_seq_values_ref, + metadata=_RNA_SEQ_METADATA, + interval=interval, + resolution=1, + ) + # Mimic the case where the ALT allele increases expression by 50%. + rna_seq_values_alt = rna_seq_values_ref * 1.5 + rna_seq_tdata_alt = track_data.TrackData( + values=rna_seq_values_alt, + metadata=_RNA_SEQ_METADATA, + interval=interval, + resolution=1, + ) + + # Mock contact maps. + contact_maps_shape = ( + interval.width // 2048, + interval.width // 2048, + len(_CONTACT_MAP_METADATA), + ) + + contact_maps_1 = track_data.TrackData( + values=np.random.normal(0, 1, contact_maps_shape).astype(np.float32), + metadata=_CONTACT_MAP_METADATA, + interval=interval, + resolution=2048, + ) + + contact_maps_2 = track_data.TrackData( + values=contact_maps_1.values * 0.5, + metadata=_CONTACT_MAP_METADATA, + interval=interval, + resolution=2048, + ) + contact_maps_diff = track_data.TrackData( + values=contact_maps_1.values - contact_maps_2.values, + metadata=_CONTACT_MAP_METADATA, + interval=interval, + resolution=2048, + ) + + # Mock contribution scores. + contrib = np.array( + [ + [1.0, 0.0, 0.0, 0.0], + [0.0, 1.0, 0.0, 0.0], + [0.0, 0.0, 1.0, 0.0], + [0.0, 0.0, 0.0, 1.0], + ] + * (interval.width // 4) + ).astype(np.float32) + + # Get actual transcripts. + transcripts = [ + transcript_utils.Transcript( + exons=[genome.Interval('chr1', 0, 100)], info=dict(gene_name='gene') + ) + ] + + # Mock junctions. + junctions = np.array([ + genome.Junction('chr1', 102, 108, '+'), + genome.Junction('chr1', 80, 100, '+'), + genome.Junction('chr1', 104, 108, '+'), + genome.Junction('chr1', 110, 120, '+'), + ]) + junctions = junction_data.JunctionData( + junctions, + values=np.zeros((4, 2), dtype=np.float32), + metadata=_SPLICING_METADATA, + ) + + # Mock variants within subinterval to annotate. + annotated_variants = [ + genome.Variant( + chromosome=subinterval.chromosome, + position=pos, + reference_bases='A', + alternate_bases='G', + ) + for pos in ( + subinterval.start + + subinterval.width / np.array([2, 3, 10], dtype=np.float32) + ) + ] + + # Mock intervals within subinterval to annotate. + annotated_intervals = [ + interval.resize(int(interval.width // 10)), + interval.resize(int(interval.width // 100)).shift( + -int(interval.width // 10) + ), + ] + annotated_intervals_labels = [ + f'my_interval_{i+1}' for i in range(len(annotated_intervals)) + ] + + fig = plot_components.plot( + [ + plot_components.TranscriptAnnotation(transcripts, fig_height=3), + plot_components.TranscriptAnnotation( + transcripts, adaptive_fig_height=False + ), + plot_components.TranscriptAnnotation( + transcripts, adaptive_fig_height=True, fig_height=1 + ), + plot_components.Sashimi(junctions), + plot_components.Tracks(atac_tdata, filled=False, linestyle='--'), + plot_components.Tracks(atac_tdata, filled=True), + plot_components.Tracks(atac_tdata, shared_y_scale=True), + plot_components.Tracks(atac_tdata, shared_y_scale=False), + plot_components.Tracks( + rna_seq_tdata_ref, cmap='viridis', truncate_cmap=True + ), + plot_components.Tracks( + rna_seq_tdata_ref, cmap='Set2', truncate_cmap=False + ), + plot_components.OverlaidTracks( + {'REF': rna_seq_tdata_ref, 'ALT': rna_seq_tdata_alt} + ), + plot_components.OverlaidTracks( + {'REF': rna_seq_tdata_ref, 'ALT': rna_seq_tdata_alt}, + colors={'REF': 'blue', 'ALT': 'red'}, + ), + plot_components.OverlaidTracks( + {'REF': rna_seq_tdata_ref, 'ALT': rna_seq_tdata_alt}, + cmap='Set2', + ylabel_template='{name}', + ), + plot_components.ContactMaps( + tdata=contact_maps_1, cmap=matplotlib.pyplot.get_cmap('autumn') + ), + plot_components.ContactMapsDiff( + tdata=contact_maps_diff, + ), + plot_components.SeqLogo( + contrib, + subinterval, + ylabel='contribution scores', + ylim=logo_ylim, + ), + ], + interval=subinterval, + annotations=[ + plot_components.IntervalAnnotation( + annotated_intervals, + labels=annotated_intervals_labels, + colors='darkgray', + ), + plot_components.VariantAnnotation( + annotated_variants, use_default_labels=True, colors='purple' + ), + ], + ) + self.assertIsInstance(fig, matplotlib.figure.Figure) + + +if __name__ == '__main__': + absltest.main() diff --git a/alphagenome/source/src/alphagenome/visualization/plot_transcripts.py b/alphagenome/source/src/alphagenome/visualization/plot_transcripts.py new file mode 100644 index 0000000000000000000000000000000000000000..160449454453394a8fcdab04217c73cb539d3eea --- /dev/null +++ b/alphagenome/source/src/alphagenome/visualization/plot_transcripts.py @@ -0,0 +1,394 @@ +# Copyright 2024 Google LLC. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Visualize transcripts/gene annotation in matplotlib.""" + +from collections.abc import Sequence +import dataclasses +import enum +import math +from typing import Any + +from alphagenome.data import genome +from alphagenome.data import transcript as transcript_utils +import intervaltree +import matplotlib as mpl +import matplotlib.pyplot as plt + + +@dataclasses.dataclass +class TranscriptStyle: + """Style specification for a transcript plot. + + CDS = protein coding sequence. + UTR = untranslated region. + + Attributes: + cds_height: CDS height. + utr_height: UTR height. + cds_color: CDS color. + utr5_color: 5' UTR color. + utr3_color: 3' UTR color. + first_noncoding_exon_color: Color of the first non-coding exon. This helps + to indicate transcript directionality. + label_color: Label color. + xlim_pad: Controls amount of whitespace on the region flanks. + """ + + cds_height: float + utr_height: float + cds_color: str + utr5_color: str + utr3_color: str + first_noncoding_exon_color: str + label_color: str + xlim_pad: float + + +class TranscriptStylePreset(enum.Enum): + """Style enum for transcript plots. + + Attributes: + STANDARD: Standard transcript style. + MINIMAL: Minimal transcript style. + """ + + STANDARD = TranscriptStyle( + cds_height=0.7, + utr_height=0.35, + cds_color='#7f7f7f', # Grey from tab10 palette. + utr5_color='#ff7f0e', # Orange. + utr3_color='#1f77b4', # Blue. + first_noncoding_exon_color='#2ca02c', # Green. + label_color='#7f7f7f', + xlim_pad=0.01, + ) + + MINIMAL = TranscriptStyle( + cds_height=0.4, + utr_height=0.22, + cds_color='black', + utr5_color='black', + utr3_color='black', + # TODO(b/377291432): find a nice way of specifying strand orientation. + first_noncoding_exon_color='black', + label_color='black', + xlim_pad=0.05, + ) + + +def plot_transcripts( + ax: plt.Axes, + transcripts: Sequence[transcript_utils.Transcript], + interval: genome.Interval, + zero_origin: bool = False, + label_name: str | None = None, + transcript_style: TranscriptStyle = TranscriptStylePreset.STANDARD.value, + plot_labels_once: bool = False, + **kwargs, +) -> mpl.figure.Figure: + """Plot transcripts. + + Loops over each transcript in `transcripts` and calls `draw_transcript`. + + Args: + ax: Matplotlib axis onto which to plot transcript annotations. + transcripts: Sequence of transcripts returned by a + transcript.TranscriptExtractor. + interval: Genomic interval at which to visualize the transcripts. + zero_origin: If True, the beginning of the interval will start with 0. + label_name: Which label in transcript.info to draw next to the transcript. + transcript_style: specification of transcript styling details. + plot_labels_once: If True, labels will only be plotted once. + **kwargs: kwargs passed to draw_transcript. + + Returns: + Matplotlib figure object. + """ + if not transcripts: + return + + # Slightly pad x limits for nicer spacing, and shift x axis limits if needed. + shift = -interval.start if zero_origin else 0 + xlim_pad = interval.width * transcript_style.xlim_pad + ax.set_xlim( + [interval.start + shift - xlim_pad, interval.end + shift + xlim_pad] + ) + + # Get typical label width and transcript heights. + text_width = _get_text_width(transcripts[0].info[label_name], ax=ax) + heights = _get_placement_heights( + transcripts, extend_fraction=1.0, front_padding=text_width + ) + + labels_already_drawn = [] + for transcript in transcripts: + # Add transcript labels. + if label_name is not None: + label = transcript.info[label_name] + else: + label = None + + draw_transcript( + ax=ax, + transcript=transcript, + interval=interval, + y=heights[transcript.transcript_id], + cds_height=transcript_style.cds_height, + utr_height=transcript_style.utr_height, + cds_color=transcript_style.cds_color, + utr5_color=transcript_style.utr5_color, + utr3_color=transcript_style.utr3_color, + first_noncoding_exon_color=transcript_style.first_noncoding_exon_color, + label_color=transcript_style.label_color, + shift=shift, + label=None + if (label in labels_already_drawn and plot_labels_once) + else label, + **kwargs, + ) + + labels_already_drawn.append(label) + + ax.set_ylim([min(heights.values()) - 1, max(heights.values()) + 1]) + + +def draw_transcript( + ax: plt.Axes, + transcript: transcript_utils.Transcript, + interval: genome.Interval, + y: float, + cds_height: float = 0.7, + utr_height: float = 0.35, + cds_color: str = '#7f7f7f', # Grey from tab10 palette. + utr5_color: str = '#ff7f0e', # Orange. + utr3_color: str = '#1f77b4', # Blue. + first_noncoding_exon_color: str = '#2ca02c', # Green. + shift: int = 0, + label: str | None = None, + label_color: str = '#7f7f7f', + **kwargs, +) -> None: + """Draw an individual transcript as rectangular components on an axis. + + CDS = protein coding sequence. + UTR = untranslated region. + + This function is used by `plot_transcripts`. + + Args: + ax: Matplotlib axis onto which to draw the transcript. + transcript: Transcript to draw. + interval: Genomic interval at which to visualize the transcript. + y: Vertical position at which to draw the transcript. + cds_height: CDS height. + utr_height: UTR height. + cds_color: CDS color in hex string format. + utr5_color: 5' UTR color in hex string format. + utr3_color: 3' UTR color in hex string format. + first_noncoding_exon_color: Color of the first non-coding exon. This helps + to indicate transcript directionality. Hex string format. + shift: X-axis shift. + label: Optional label to draw next to the transcript. + label_color: Label color. + **kwargs: Additional keyword arguments passed to matplotlib plotting + functions. + """ + ax.set_yticklabels([]) + ax.set_yticks([]) + + def draw_exons_and_introns(exons, color, exon_height): + if not exons: + return + # 1. Draw all exons. + for exon in exons: + # TODO: b/377291432 - Skip drawing an exon if it will be drawn below + # separately to avoid overlap if alpha<1 and overlaps in vector format. + draw_interval( + ax=ax, + interval=exon, + y=y, + shift=shift, + height=exon_height, + color=color, + **kwargs, + ) + + # 2. Draw all introns. + for intron in transcript_utils.Transcript(exons).introns: + ax.plot([intron.start, intron.end], [y, y], color=color, linewidth=0.5) + # Draw max_num_arrows arrows, at least one arrow per intron. + intron_to_interval_fraction = intron.width / interval.width + # If this fraction is too small we do not draw arrow + if intron_to_interval_fraction < 0.01: + continue + max_num_arrows = 10 + num_arrows = min( + math.ceil(intron_to_interval_fraction * max_num_arrows), + max_num_arrows, + ) + space = intron.width // num_arrows + + for i in range(num_arrows): + arrow_start = intron.start - space // 2 + i * space + # Arrows are at most 10bp from the exons. + if ( + arrow_start + space - 10 > interval.end + or arrow_start < interval.start + 10 + ): + continue + style = '<-' if transcript.is_negative_strand else '->' + ax.annotate( + '', + xy=(arrow_start + space, y), + xytext=(arrow_start + space - 0.001, y), + arrowprops=dict( + arrowstyle=f'{style},head_width=0.3', linewidth=0.5, color=color + ), + ) + + # First draw all exons and introns with UTR height. + draw_exons_and_introns( + transcript.exons, color=cds_color, exon_height=utr_height + ) + draw_interval( + ax=ax, + interval=transcript.exons[0], + y=y, + shift=shift, + label=label, + height=utr_height, + color=cds_color, + label_color=label_color, + **kwargs, + ) + + # Draw the first non-coding exon with a special color. + first_exon_index = 0 if transcript.is_negative_strand else -1 + draw_interval( + ax=ax, + interval=transcript.exons[first_exon_index], + y=y, + height=utr_height, + shift=shift, + color=first_noncoding_exon_color, + **kwargs, + ) + + # Add UTRs for coding transcripts. + if transcript.cds is not None: + draw_exons_and_introns( + transcript.utr5, color=utr5_color, exon_height=utr_height + ) + draw_exons_and_introns( + transcript.cds, color=cds_color, exon_height=cds_height + ) + draw_exons_and_introns( + transcript.utr3, color=utr3_color, exon_height=utr_height + ) + + +def draw_interval( + ax: plt.Axes, + interval: genome.Interval, + y: float, + label: str | None = None, + height: float = 0.5, + shift: int = 0, + label_color: str = '#7f7f7f', + **kwargs, +): + """Draw rectangle patch on the axis given a genomic interval. + + Args: + ax: Matplotlib axis onto which to draw the interval. + interval: Genomic interval to draw. + y: Vertical position at which to draw the interval. + label: Optional label to draw next to the interval. + height: Height of the interval. + shift: X-axis shift. + label_color: Label color in hex string format. + **kwargs: Additional keyword arguments passed to matplotlib plotting + functions. + """ + xy = (interval.start + shift, y - height / 2) + ax.add_patch( + mpl.patches.Rectangle( + xy=xy, + width=interval.width, + height=height, + clip_on=True, + linewidth=0, + **kwargs, + ) + ) + + # Add center-aligned text label. + if label is not None: + ax.text( + x=max(xy[0], ax.get_xlim()[0]), + y=y, + s=label, + color=label_color, + horizontalalignment='right', + verticalalignment='center', + ) + + +def _get_placement_heights( + transcripts: Sequence[transcript_utils.Transcript], + extend_fraction: float = 1.0, + front_padding: float = 0.0, +) -> dict[Any, int]: + """Get heights at which to place the transcripts.""" + # TODO: b/376672690 - Implement simpler packing algorithm. + levels = [intervaltree.IntervalTree()] + # Sort transcripts by length and start placing longest transcripts first. + sorted_transcripts = sorted( + transcripts, key=lambda x: x.transcript_interval.width, reverse=True + ) + transcript_levels = {} + for transcript in sorted_transcripts: + placed = False + level_idx = 0 + while not placed: + if level_idx >= len(levels): + levels.append(intervaltree.IntervalTree()) + if levels[level_idx].overlaps( + transcript.transcript_interval.start - front_padding, + transcript.transcript_interval.end, + ): + # Overlaps an existing interval -> increase the level. + level_idx += 1 + else: + # Doesn't overlap. Remember the interval. + levels[level_idx].addi( + transcript.transcript_interval.start - front_padding, + int(transcript.transcript_interval.end * extend_fraction), + ) + transcript_levels[transcript.transcript_id] = level_idx + placed = True + return { + transcript_id: len(levels) - 1 - level_idx + for transcript_id, level_idx in transcript_levels.items() + } + + +def _get_text_width(label: str, ax: plt.Axes, **kwargs) -> float: + """Get text width in data coordinates.""" + text = ax.text(0, 0, label, **kwargs) + plt.gcf().canvas.draw() + bb = text.get_window_extent().transformed(ax.transData.inverted()) + text.remove() # Remove text. + return bb.x1 - bb.x0