diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..1df83a8f36e672cd1d0c9d5442a5e19e56d9eff7 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,6 @@ 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
+weight/alphagenome-all-folds/ocdbt.process_0/d/48900811ab32b5ad3873d40e96d63846 filter=lfs diff=lfs merge=lfs -text
+weight/alphagenome-all-folds/ocdbt.process_0/d/6b08a83c9d6a2ade925dbcdd91299ce2 filter=lfs diff=lfs merge=lfs -text
+weight/alphagenome-all-folds/ocdbt.process_0/d/bda173d4f00d3afcef0a043614df05a6 filter=lfs diff=lfs merge=lfs -text
diff --git a/.msc b/.msc
new file mode 100644
index 0000000000000000000000000000000000000000..8b364de7c6a63b65fc738ec698c037e924fa8fdd
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diff --git a/.mv b/.mv
new file mode 100644
index 0000000000000000000000000000000000000000..8c0cdda17ff30bf1377794f529c74f84c7bcb5fe
--- /dev/null
+++ b/.mv
@@ -0,0 +1 @@
+Revision:master,CreatedAt:1784688508
\ No newline at end of file
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..82699b13247f9e7114768dd2dfe83823832d4374
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,203 @@
+Copyright 2025 Onescience Authors. All rights reserved.
+
+ Apache License
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+ http://www.apache.org/licenses/
+
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diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..e89b53abbdafc989f36de9a7a024131d2379ad6b
--- /dev/null
+++ b/README.md
@@ -0,0 +1,248 @@
+---
+license: apache-2.0
+tasks:
+ - genomic-sequence-modeling
+frameworks:
+ - jax
+language:
+ - en
+ - zh
+tags:
+ - OneScience
+ - Life Sciences
+ - Genomics
+ - DNA Sequence Model
+ - Variant Effect Prediction
+ - AlphaGenome
+datasets:
+ - OneScience/alphagenome_dataset
+---
+
+
+
+ AlphaGenome
+
+
+
+# Model Introduction
+
+AlphaGenome is a DNA sequence model proposed by Google DeepMind. It can take DNA intervals up to 1 Mbp as input and predict multiple classes of genomic functional signals for track prediction and regulatory variant effect scoring.
+
+Paper: Advancing regulatory variant effect prediction with AlphaGenome
+https://www.nature.com/articles/s41586-025-10014-0
+
+# Model Description
+
+AlphaGenome is implemented based on JAX / Flax and supports genomic interval inference, variant effect scoring, track evaluation, and fine-tuning examples. This model package is accompanied by the ModelScope dataset `OneScience/alphagenome_dataset`, which can be used for quick local verification.
+
+# Applicable Scenarios
+
+| Scenario | Description |
+| :---: | :--- |
+| Genomic interval prediction | Input a reference genome FASTA, chromosome, and interval coordinates, and output predicted tracks for ATAC, DNase, CAGE, RNA-seq, ChIP, and other signals |
+| Variant effect scoring | Input a VCF or built-in example variants, compare prediction differences between reference and variant sequences, and generate a variant scoring table |
+| Track prediction evaluation | Use validation data from the AlphaGenome dataset to calculate regression evaluation metrics for different assay bundles |
+| Fine-tuning experiments | Use a custom reference genome, interval CSV, and BigWig signal files to verify the fine-tuning workflow |
+| ModelScope / OneCode runtime | After downloading the model project and accompanying dataset, quickly verify script connectivity in a biology-domain runtime environment |
+
+
+
+# Usage Instructions
+
+## 1. OneCode Usage
+
+You can experience intelligent one-click AI4S programming through the OneCode online environment:
+
+[Click to experience intelligent one-click AI4S programming](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home)
+
+## 2. Manual Installation and Usage
+
+**Hardware Requirements**
+
+- GPU or DCU runtime is recommended.
+- CPU can be used for import checks and small-configuration connectivity verification, but full training and inference are slow.
+- DCU users need to install DTK in advance. DTK 25.04.2 or later is recommended, or the OneScience-recommended version matching the current cluster.
+
+
+
+
+
+**Environment Check**
+
+- NVIDIA GPU:
+
+```bash
+nvidia-smi
+```
+
+- Hygon DCU:
+
+```bash
+hy-smi
+```
+
+### Download the Model Package
+
+```bash
+modelscope download --model OneScience/alphagenome --local_dir ./alphagenome
+cd alphagenome
+```
+
+### Install the Runtime Environment
+
+**DCU Environment**
+
+```bash
+# First activate DTK and Conda
+conda create -n onescience311 python=3.11 -y
+conda activate onescience311
+# Supports uv installation
+pip install onescience[bio-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai
+```
+
+After installation, return to the model package directory:
+
+```bash
+cd ./alphagenome
+```
+
+### Training and Inference Data Introduction
+
+The OneScience community has uploaded the data required for AlphaGenome inference, evaluation, and fine-tuning to ModelScope: [OneScience/alphagenome_dataset](https://modelscope.cn/datasets/OneScience/alphagenome_dataset). After downloading, place the data in the `data/` directory of the model package.
+
+```bash
+modelscope download --dataset OneScience/alphagenome_dataset --local_dir ./data
+```
+
+### Training Weights
+
+The repository includes `weight/alphagenome-all-folds`, and all scripts also support specifying model weights through `--model_dir`.
+
+### Prepare Weights
+
+If using local weights, place the AlphaGenome Orbax checkpoint in the following directory:
+
+```text
+weight/
+ alphagenome-all-folds/
+ _CHECKPOINT_METADATA
+ _METADATA
+ ...
+```
+
+If running in a shared runtime environment, you can also reuse a unified directory through environment variables:
+
+```bash
+export ONESCIENCE_MODELS_DIR=/path/to/onescience/models
+export ONESCIENCE_DATASETS_DIR=/path/to/onescience/datasets
+```
+
+The scripts will preferentially read:
+
+- `${ONESCIENCE_MODELS_DIR}/AlphaGenome/alphagenome-all-folds`
+- `${ONESCIENCE_DATASETS_DIR}/AlphaGenome`
+
+If the above environment variables are not set, the defaults under the current model package are read:
+
+- `weight/alphagenome-all-folds`
+- `data/`
+
+### Interval Inference
+
+```bash
+bash scripts/inference.sh
+```
+
+Equivalent Python command example:
+
+```bash
+python scripts/run_inference.py \
+ --fasta_path ./data/reference/HOMO_SAPIENS/GRCh38.p13.genome.fa \
+ --model_dir ./weight/alphagenome-all-folds \
+ --chromosome chr19 \
+ --start 10587331 \
+ --end 11635907 \
+ --output_dir ./outputs
+```
+
+Inference results will be saved to `outputs/`.
+
+### Variant Effect Scoring
+
+```bash
+bash scripts/run_variant.sh
+```
+
+When specifying VCF input, you can use:
+
+```bash
+python scripts/run_variant_scoring.py \
+ --vcf_path ./data/example.vcf \
+ --fasta_path ./data/reference/HOMO_SAPIENS/GRCh38.p13.genome.fa \
+ --model_dir ./weight/alphagenome-all-folds \
+ --output_dir ./outputs_variant
+```
+
+Scoring results will be saved as CSV files.
+
+### Track Prediction Evaluation
+
+```bash
+bash scripts/run_track.sh
+```
+
+You can also explicitly specify the data and output paths:
+
+```bash
+python scripts/run_track_prediction_eval.py \
+ --model_dir ./weight/alphagenome-all-folds \
+ --model_version ALL_FOLDS \
+ --data_dir ./data/v1/train \
+ --output_path ./outputs_track/eval_results.csv
+```
+
+### Fine-tuning Example
+
+```bash
+python scripts/run_finetuning.py \
+ --fasta_path ./data/reference/HOMO_SAPIENS/GRCh38.p13.genome.fa \
+ --regions_csv ./data/finetune_regions.csv \
+ --bigwig_paths ./data/sample_atac.bw \
+ --output_dir ./finetuned_model \
+ --num_steps 1000 \
+ --batch_size 2
+```
+
+# Data Format
+
+The ModelScope dataset `OneScience/alphagenome_dataset` is recommended to be downloaded to `data/` under the model package. The default structure is as follows:
+
+```text
+data/
+ reference/
+ HOMO_SAPIENS/
+ GRCh38.p13.genome.fa
+ GRCh38.p13.genome.fa.fai
+ v1/
+ train/
+ ...
+```
+
+Where:
+
+- `reference/HOMO_SAPIENS/GRCh38.p13.genome.fa` is the human reference genome FASTA.
+- `.fai` is the FASTA index file.
+- `v1/train/` is the data directory used for track prediction evaluation.
+- Custom fine-tuning also requires preparing an interval CSV file with column names `chromosome,start,end`, as well as one or more BigWig signal files.
+
+# Official OneScience Information
+
+| Platform | OneScience Main Repository | Skills Repository |
+| --- | --- | --- |
+| Gitee | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills |
+| GitHub | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills |
+
+# Citations and License
+
+- This repository is based on the AlphaGenome open-source model and provides DCU adaptation. The related source code uses the Apache License 2.0.
+- For scientific research, cite the original paper: [Advancing regulatory variant effect prediction with AlphaGenome](https://www.nature.com/articles/s41586-025-10014-0).
diff --git a/flax_model/__init__.py b/flax_model/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e6dbcfd0f531f21bc89b511afe8c039b35ea2abc
--- /dev/null
+++ b/flax_model/__init__.py
@@ -0,0 +1 @@
+"""Local flax_models copied from OneScience for this standalone package."""
diff --git a/flax_model/alphagenome/__init__.py b/flax_model/alphagenome/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..47379dd9b6801a986d4e5e22d7d5c8e59981c95e
--- /dev/null
+++ b/flax_model/alphagenome/__init__.py
@@ -0,0 +1,39 @@
+# Copyright 2026 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.
+
+"""AlphaGenome - 统一DNA序列基因组模型，已集成至OneScience。
+
+AlphaGenome 是 Google DeepMind 发布的统一DNA序列模型，能够分析最长100万碱基对的
+DNA序列，以单碱基对分辨率预测基因表达、剪接模式、染色质特征和接触图谱。
+
+模块结构:
+ model/ - 核心模型组件 (AlphaGenome, SequenceEncoder, 各类Head)
+ io/ - 数据输入输出 (BundleName, Dataset, Fasta)
+ finetuning/ - 微调工具 (训练步骤、数据管道)
+ evals/ - 评估指标 (track预测、回归指标)
+ _sdk/ - AlphaGenome SDK (vendored alphagenome v0.6.1)
+ 包含数据类型、gRPC客户端、变异/区间评分、可视化和ISM解释工具
+
+使用方式:
+ from flax_model.alphagenome.model import model as alphagenome_model
+ from flax_model.alphagenome.io import dataset
+ from flax_model.alphagenome.finetuning import finetune
+
+ # SDK 数据类型 (原 from alphagenome.data import genome):
+ from flax_model.alphagenome._sdk.data import genome
+ from flax_model.alphagenome._sdk.models import dna_model
+ from flax_model.alphagenome._sdk.models import dna_output
+"""
+
+__version__ = '0.1.0'
diff --git a/flax_model/alphagenome/_sdk/__init__.py b/flax_model/alphagenome/_sdk/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e32e0e966d502b11bda7201e4debd2a4ca7d9bf
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/__init__.py
@@ -0,0 +1,25 @@
+# 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.
+
+"""AlphaGenome Python SDK (vendored into OneScience).
+
+Original package: alphagenome v0.6.1
+Provides data types, gRPC client, variant/interval scoring,
+visualization, and ISM interpretation utilities for genomic models.
+
+This is a vendored copy integrated into OneScience to avoid requiring
+a separate alphagenome package installation.
+"""
+
+__version__ = '0.6.1'
diff --git a/flax_model/alphagenome/_sdk/colab_utils.py b/flax_model/alphagenome/_sdk/colab_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3a56753e6f7e5719d64ab1067eba0d4613c4aba
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/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/flax_model/alphagenome/_sdk/data/__init__.py b/flax_model/alphagenome/_sdk/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..c1bb78fc624ebb702acc55c87c095170a4bc4570
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/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/flax_model/alphagenome/_sdk/data/fold_intervals.py b/flax_model/alphagenome/_sdk/data/fold_intervals.py
new file mode 100644
index 0000000000000000000000000000000000000000..63de4108f22f8ca775e75576b99945cee0715ecf
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/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 flax_model.alphagenome._sdk.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 names of the folds for a given model version and subset."""
+ 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 intervals for a given model version and subset."""
+ 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/flax_model/alphagenome/_sdk/data/gene_annotation.py b/flax_model/alphagenome/_sdk/data/gene_annotation.py
new file mode 100644
index 0000000000000000000000000000000000000000..647bcc8d044b3a7752f92b54864e68f3f4b2030d
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/data/gene_annotation.py
@@ -0,0 +1,445 @@
+# 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 flax_model.alphagenome._sdk.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 as zero-width point
+ intervals (Start == End, 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_to_mane_select_transcript(gtf: pd.DataFrame) -> pd.DataFrame:
+ """Filter GTF entries to only the MANE select transcript.
+
+ Note that the MANE_Select tag only exists for the human GTF file.
+
+ Args:
+ gtf: pd.DataFrame of GENCODE GTF entries. Must contain columns 'tag'.
+
+ Returns:
+ pd.DataFrame of GENCODE GTF entries subset to rows representing MANE
+ select transcripts, which are transcripts that are well-supported,
+ conserved, and expressed.
+ """
+ filtered_gtf = gtf[gtf['tag'].fillna('').str.contains('MANE_Select')]
+ if filtered_gtf.empty:
+ raise ValueError(
+ 'No MANE_Select transcripts found in the GTF, possibly due to non-human'
+ ' GTF.'
+ )
+ return filtered_gtf
+
+
+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_intervals(
+ gtf: pd.DataFrame,
+ gene_symbols: Sequence[str] | None = None,
+ gene_ids: Sequence[str] | None = None,
+) -> list[genome.Interval]:
+ """Returns a list of stranded `genome.Interval`s for the given identifiers.
+
+ Args:
+ gtf: pd.DataFrame of GENCODE GTF entries. Must contain columns 'Feature',
+ 'gene_name', 'gene_id', 'Chromosome', 'Start', 'End', and 'Strand'.
+ gene_symbols: A sequence of gene names or gene symbols (e.g., ['EGFR',
+ 'TNF', 'TP53']). Matching is case-insensitive.
+ gene_ids: A sequence of Ensembl gene IDs, which can be patched (e.g.
+ ['ENSG00000141510.17']) or unpatched (e.g., ['ENSG00000141510']). Matching
+ is done on unpatched IDs.
+
+ Returns:
+ A list of `genome.Interval`s for the given identifiers. The
+ returned list of intervals is in the same order as the input gene
+ identifiers.
+
+ Raises:
+ ValueError: If neither or both gene_symbols and gene_ids are set, or if no
+ interval or multiple intervals are found for any of the given gene
+ identifiers.
+ """
+ if (gene_symbols is None) == (gene_ids is None):
+ raise ValueError('Exactly one of gene_symbols or gene_ids must be set.')
+
+ gtf_genes = gtf[gtf['Feature'] == 'gene'].copy()
+
+ if gene_symbols is not None:
+ id_col = 'gene_name'
+ input_ids = gene_symbols
+ process_fn = lambda s: s.str.upper()
+ else:
+ id_col = 'gene_id'
+ input_ids = gene_ids
+ process_fn = lambda s: s.str.split('.', n=1).str[0]
+
+ processed_input_ids = process_fn(pd.Series(input_ids, dtype=str))
+ gtf_genes['processed_id'] = process_fn(gtf_genes[id_col])
+
+ # Filter the GTF to only the genes that are in the input IDs.
+ gtf_subset = gtf_genes[
+ gtf_genes['processed_id'].isin(processed_input_ids.unique())
+ ]
+
+ dup_mask = gtf_subset['processed_id'].duplicated(keep=False)
+ if dup_mask.any():
+ offending_ids = gtf_subset.loc[dup_mask, id_col].unique()
+ raise ValueError(
+ 'Multiple intervals found for gene(s):'
+ f' {", ".join(sorted(offending_ids))}.'
+ )
+
+ # Create a lookup map from processed_id to GTF data.
+ # Use reindex to order genes by input and insert NaNs for missing genes.
+ gtf_map = gtf_subset.set_index('processed_id')
+ result_df = gtf_map.reindex(processed_input_ids)
+
+ missing_mask = result_df['Chromosome'].isnull()
+ if missing_mask.any():
+ missing_ids = pd.Series(input_ids)[missing_mask.values].unique()
+ raise ValueError(
+ f'No interval found for gene(s): {", ".join(sorted(missing_ids))}.'
+ )
+
+ # Add original identifiers to the result for the 'name' field of the interval.
+ result_df[id_col] = list(input_ids)
+
+ return [
+ genome.Interval(
+ chromosome=row.Chromosome,
+ start=row.Start,
+ end=row.End,
+ strand=row.Strand,
+ name=getattr(row, id_col),
+ )
+ for row in result_df.itertuples()
+ ]
+
+
+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').
+
+ Returns:
+ A `genome.Interval` for the given gene identifier.
+
+ 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.')
+
+ return get_gene_intervals(
+ gtf,
+ [gene_symbol] if gene_symbol else None,
+ [gene_id] if gene_id else None,
+ )[0]
diff --git a/flax_model/alphagenome/_sdk/data/genome.py b/flax_model/alphagenome/_sdk/data/genome.py
new file mode 100644
index 0000000000000000000000000000000000000000..5de418b2283f5b53dbc395c445dd181387886a63
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/data/genome.py
@@ -0,0 +1,1181 @@
+# 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 flax_model.alphagenome._sdk.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 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 up for
+ positive/unstranded intervals and rounded down for negative strand
+ intervals.
+
+ 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)
+
+ @property
+ def is_frameshift(self) -> bool:
+ """Return if the variant is a frameshift."""
+ indel_size = abs(len(self.reference_bases) - len(self.alternate_bases))
+ return indel_size > 0 and indel_size % 3 != 0
+
+ @property
+ def is_indel(self) -> bool:
+ """Return if the variant is an insertion or deletion."""
+ return self.is_insertion or self.is_deletion
+
+ @property
+ def is_structural(self) -> bool:
+ """Return if the variant is a structural variant."""
+ indel_size = abs(len(self.reference_bases) - len(self.alternate_bases))
+ return indel_size >= 50
+
+ 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/flax_model/alphagenome/_sdk/data/junction_data.py b/flax_model/alphagenome/_sdk/data/junction_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4bf5e93216e69091cfe4ef624e122187cdd0b55
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/data/junction_data.py
@@ -0,0 +1,272 @@
+# 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 flax_model.alphagenome._sdk import typing
+from flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome._sdk.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 an array of track names (not necessarily unique)."""
+ return self.metadata['name'].values
+
+ @property
+ def strands(self) -> np.ndarray:
+ """Returns an array of junction 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 name 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 name.
+ """
+ filtered = predictions.filter_by_name(name)
+ if filtered.num_tracks > 1:
+ raise ValueError(
+ f'Expected only one ontology term, got {filtered.num_tracks}.'
+ )
+ filtered_junctions = []
+ if filtered.values.size == 0:
+ return filtered_junctions
+
+ if k_threshold is None:
+ k_threshold = filtered.values.max() * 0.05
+
+ # Round k for better visualization.
+ 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/flax_model/alphagenome/_sdk/data/ontology.py b/flax_model/alphagenome/_sdk/data/ontology.py
new file mode 100644
index 0000000000000000000000000000000000000000..15b75dcc4699bbd8bfb45d130dfc7da39279656b
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/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 flax_model.alphagenome._sdk.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/flax_model/alphagenome/_sdk/data/track_data.py b/flax_model/alphagenome/_sdk/data/track_data.py
new file mode 100644
index 0000000000000000000000000000000000000000..56b22531385aa5f6a559456e63b46d17f4149f6d
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/data/track_data.py
@@ -0,0 +1,918 @@
+# 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 flax_model.alphagenome._sdk import typing
+from flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome._sdk.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
+PositionalIndex = slice | genome.Interval | int
+TrackIndex = np.ndarray | Sequence[int] | Sequence[str] | slice | int | str
+Index = PositionalIndex | tuple[PositionalIndex, TrackIndex] | TrackIndex
+
+
+@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:
+
+ * [num_tracks]
+ * [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.
+
+ 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.
+
+ 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 __getitem__(self, index: Index) -> 'TrackData':
+ """Retrieves a subset of TrackData using positional and/or track indices.
+
+ This method allows slicing `TrackData` similar to numpy arrays or pandas
+ DataFrames. The index can be a single value or a tuple.
+
+ Args:
+ index: A single index or a tuple of indices. If a single index, it's
+ treated as a positional index if the `TrackData` has positional axes,
+ otherwise as a track index. If a tuple, the first element specifies the
+ positional slice, and the second element specifies the track index.
+ Positional indices can be `int`, `slice`, or `genome.Interval`, and this
+ slice is applied to all positional axes of the `values` array. Track
+ indices can be `int`, `str` (track name), `slice`, `Sequence[int]`, or
+ `Sequence[str]` (track names).
+
+ Returns:
+ A new `TrackData` object containing the selected subset.
+
+ Raises:
+ IndexError: If a slice step is not 1 for positional indexing.
+ IndexError: If an unsupported index type is provided.
+ """
+ if isinstance(index, tuple):
+ position_index, track_index = index
+ elif self.positional_axes:
+ position_index, track_index = index, None
+ else:
+ position_index, track_index = None, index
+ if isinstance(track_index, genome.Interval):
+ raise IndexError(
+ 'Track indexing by interval is supported only when there are'
+ ' positional axes.'
+ )
+
+ tdata = self
+ match position_index:
+ case None:
+ pass
+ case int():
+ tdata = tdata.slice_by_positions(position_index, position_index + 1)
+ case slice():
+ if position_index.step is not None and position_index.step != 1:
+ raise IndexError('Slice step must be 1 for positional indexing.')
+ if position_index != slice(None):
+ tdata = tdata.slice_by_positions(
+ position_index.start, position_index.stop
+ )
+ case genome.Interval():
+ tdata = tdata.slice_by_interval(position_index)
+ case _:
+ raise IndexError(
+ f'Unsupported positional index type: {type(position_index)}'
+ )
+
+ match track_index:
+ case None:
+ pass
+ case str():
+ tdata = tdata.select_tracks_by_name([track_index])
+ case int():
+ tdata = tdata.select_tracks_by_index([track_index])
+ case slice():
+ if track_index != slice(None):
+ indices = np.arange(tdata.num_tracks)[track_index]
+ tdata = tdata.select_tracks_by_index(indices)
+ case np.ndarray() if np.issubdtype(track_index.dtype, np.character):
+ tdata = tdata.select_tracks_by_name(track_index)
+ case np.ndarray():
+ tdata = tdata.select_tracks_by_index(track_index)
+ case Sequence():
+ track_index_arr = np.asarray(track_index)
+ if np.issubdtype(track_index_arr.dtype, np.character):
+ tdata = tdata.select_tracks_by_name(track_index_arr)
+ else:
+ tdata = tdata.select_tracks_by_index(track_index_arr)
+ case _:
+ raise IndexError(f'Unsupported track index type: {type(track_index)}')
+ return tdata
+
+ 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/flax_model/alphagenome/_sdk/data/transcript.py b/flax_model/alphagenome/_sdk/data/transcript.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c74ee637b43802584fccbe1385253a0062ddbbe
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/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 flax_model.alphagenome._sdk.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 stop 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 gene 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 begins with the dinucleotide GU at its 5′ end.
+ splice_acceptor_sites: a list of splice acceptor sites. Commonly, the RNA
+ sequence that is removed ends with AG at its 3′ 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/flax_model/alphagenome/_sdk/interpretation/__init__.py b/flax_model/alphagenome/_sdk/interpretation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..524e7a78c331fc8eec07ec2956dc7630121e716b
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/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/flax_model/alphagenome/_sdk/interpretation/ism.py b/flax_model/alphagenome/_sdk/interpretation/ism.py
new file mode 100644
index 0000000000000000000000000000000000000000..466a415ff072c98e9196a72dd8e8e75ef90778e1
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/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 flax_model.alphagenome._sdk.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/flax_model/alphagenome/_sdk/models/__init__.py b/flax_model/alphagenome/_sdk/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f9e182d809b3819f44a46f61244aaca63ce7371
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/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/flax_model/alphagenome/_sdk/models/dna_client.py b/flax_model/alphagenome/_sdk/models/dna_client.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e2a2fba9d49c5b381ca05548ff4e674b0f2baf5
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/models/dna_client.py
@@ -0,0 +1,907 @@
+# 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 functools
+import random
+import time
+from typing import TypeVar
+
+from flax_model.alphagenome._sdk import tensor_utils
+from flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome._sdk.data import junction_data
+from flax_model.alphagenome._sdk.data import ontology
+from flax_model.alphagenome._sdk.data import track_data
+from flax_model.alphagenome._sdk.models import dna_model
+from flax_model.alphagenome._sdk.models import dna_output
+from flax_model.alphagenome._sdk.models import interval_scorers as interval_scorers_lib
+from flax_model.alphagenome._sdk.models import junction_data_utils
+from flax_model.alphagenome._sdk.models import track_data_utils
+from flax_model.alphagenome._sdk.models import variant_scorers as variant_scorers_lib
+from flax_model.alphagenome._sdk.protos import dna_model_pb2
+from flax_model.alphagenome._sdk.protos import dna_model_service_pb2
+from flax_model.alphagenome._sdk.protos import dna_model_service_pb2_grpc
+from flax_model.alphagenome._sdk.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_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_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
+
+
+ModelVersion = dna_model.ModelVersion
+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,
+ interval_variant: genome.Variant | None = None,
+ 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.
+ interval_variant: Optional variant to apply to the sequence. If provided,
+ the alternate allele is used for in-silico mutagenesis, otherwise the
+ unaltered reference sequence is used.
+ 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,
+ interval_variant=interval_variant.to_proto()
+ if interval_variant is not None
+ else None,
+ )
+ 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/flax_model/alphagenome/_sdk/models/dna_model.py b/flax_model/alphagenome/_sdk/models/dna_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..35a90bda76b0f09dd972312534af3983a755ac33
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/models/dna_model.py
@@ -0,0 +1,507 @@
+# 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 flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome._sdk.data import ontology
+from flax_model.alphagenome._sdk.models import dna_output
+from flax_model.alphagenome._sdk.models import interval_scorers as interval_scorers_lib
+from flax_model.alphagenome._sdk.models import variant_scorers as variant_scorers_lib
+from flax_model.alphagenome._sdk.protos import dna_model_pb2
+import anndata
+import tqdm.auto
+
+# Default maximum number of workers to use for parallel requests.
+DEFAULT_MAX_WORKERS = 5
+
+
+@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()
+
+
+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
+
+ def __lt__(self, other: 'Organism') -> bool:
+ if not isinstance(other, Organism):
+ return NotImplemented
+ return self.value < other.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] = (),
+ *,
+ interval_variant: genome.Variant | None = None,
+ 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.
+ interval_variant: Optional variant to apply to the sequence. If provided,
+ the alternate allele is used for in-silico mutagenesis, otherwise the
+ unaltered reference sequence is 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/flax_model/alphagenome/_sdk/models/dna_output.py b/flax_model/alphagenome/_sdk/models/dna_output.py
new file mode 100644
index 0000000000000000000000000000000000000000..abd76e4ee6358b11f97ddf2529610ad49f25e077
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/models/dna_output.py
@@ -0,0 +1,435 @@
+# 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, Mapping
+import dataclasses
+import enum
+from typing import Literal
+
+from flax_model.alphagenome._sdk import typing
+from flax_model.alphagenome._sdk.data import junction_data
+from flax_model.alphagenome._sdk.data import ontology
+from flax_model.alphagenome._sdk.data import track_data
+from flax_model.alphagenome._sdk.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)
+
+ output = self.map_track_data(_filter_to_strand)
+ if output.splice_junctions is not None:
+ output = dataclasses.replace(
+ output,
+ splice_junctions=output.splice_junctions.filter_to_strand(strand),
+ )
+ return output
+
+ 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, kw_only=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
+
+ @classmethod
+ def from_outputs(
+ cls,
+ outputs: Mapping[
+ OutputType, track_data.TrackData | junction_data.JunctionData
+ ],
+ ) -> 'OutputMetadata':
+ """Creates an `OutputMetadata` from a mapping of output types to data.
+
+ Args:
+ outputs: A mapping from `OutputType` to `TrackData` or `JunctionData`.
+
+ Returns:
+ An `OutputMetadata` object.
+ """
+ kwargs = {}
+ for field in dataclasses.fields(cls):
+ output_type = field.metadata['output_type']
+ if data := outputs.get(output_type):
+ kwargs[field.name] = data.metadata
+ return cls(**kwargs)
+
+ 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/flax_model/alphagenome/_sdk/models/interval_scorers.py b/flax_model/alphagenome/_sdk/models/interval_scorers.py
new file mode 100644
index 0000000000000000000000000000000000000000..30dd858cc1fda3cf616a36c9d979a2d04ff620b1
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/models/interval_scorers.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.
+
+"""Module containing interval scorer dataclasses for interval scoring."""
+
+import dataclasses
+import enum
+from typing import TypeVar
+
+from flax_model.alphagenome._sdk.models import dna_output
+from flax_model.alphagenome._sdk.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: [None, 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 | None
+ 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/flax_model/alphagenome/_sdk/models/junction_data_utils.py b/flax_model/alphagenome/_sdk/models/junction_data_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..bdd6d9c3d82f3621c0de9d79c082fa3c5046e98d
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/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 flax_model.alphagenome._sdk import tensor_utils
+from flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome._sdk.data import junction_data
+from flax_model.alphagenome._sdk.data import ontology
+from flax_model.alphagenome._sdk.protos import dna_model_pb2
+from flax_model.alphagenome._sdk.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/flax_model/alphagenome/_sdk/models/track_data_utils.py b/flax_model/alphagenome/_sdk/models/track_data_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a1f781e8fde70ec7308763dd94e42a9c5b22ad6
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/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 flax_model.alphagenome._sdk import tensor_utils
+from flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome._sdk.data import ontology
+from flax_model.alphagenome._sdk.data import track_data
+from flax_model.alphagenome._sdk.protos import dna_model_pb2
+from flax_model.alphagenome._sdk.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/flax_model/alphagenome/_sdk/models/variant_scorers.py b/flax_model/alphagenome/_sdk/models/variant_scorers.py
new file mode 100644
index 0000000000000000000000000000000000000000..6536ae4a695f3b3c3f1b068e70989c6795a3a2b9
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/models/variant_scorers.py
@@ -0,0 +1,878 @@
+# 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
+import math
+from typing import TypeVar
+
+from flax_model.alphagenome._sdk.models import dna_output
+from flax_model.alphagenome._sdk.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 math.prod(adata.X.shape) == 0:
+ # Scores are empty, so we return an empty dataframe.
+ return pd.DataFrame()
+ elif '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)
+
+ # Remove patch number from gene_id.
+ obs['gene_id'] = obs['gene_id'].str.split('.', expand=True).get(0)
+ for col in gene_columns:
+ if col not in obs:
+ obs[col] = None
+ 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',
+ 'biosample_life_stage',
+ 'gtex_tissue',
+ 'data_source',
+ 'endedness',
+ 'genetically_modified',
+ '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/flax_model/alphagenome/_sdk/protos/__init__.py b/flax_model/alphagenome/_sdk/protos/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5b0f23f5bd6f7398a80489c09c50b97d231de7e
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/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/flax_model/alphagenome/_sdk/protos/dna_model.proto b/flax_model/alphagenome/_sdk/protos/dna_model.proto
new file mode 100644
index 0000000000000000000000000000000000000000..0922b4af12e3be32d7be7320e402ab847ddee8ae
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/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.
+ optional 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/flax_model/alphagenome/_sdk/protos/dna_model_pb2.py b/flax_model/alphagenome/_sdk/protos/dna_model_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..b1d0ff869de8f33a47110cf315b84a123b7a640c
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/protos/dna_model_pb2.py
@@ -0,0 +1,100 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler. DO NOT EDIT!
+# source: alphagenome/protos/dna_model.proto
+# Protobuf Python Version: 4.25.1
+"""Generated protocol buffer code."""
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf.internal import builder as _builder
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from flax_model.alphagenome._sdk.protos import tensor_pb2 as alphagenome_dot_protos_dot_tensor__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\"alphagenome/protos/dna_model.proto\x12(google.gdm.gdmscience.alphagenome.v1main\x1a\x1f\x61lphagenome/protos/tensor.proto\"|\n\x08Interval\x12\x12\n\nchromosome\x18\x01 \x01(\t\x12\r\n\x05start\x18\x02 \x01(\x03\x12\x0b\n\x03\x65nd\x18\x03 \x01(\x03\x12@\n\x06strand\x18\x04 \x01(\x0e\x32\x30.google.gdm.gdmscience.alphagenome.v1main.Strand\"a\n\x07Variant\x12\x12\n\nchromosome\x18\x01 \x01(\t\x12\x10\n\x08position\x18\x02 \x01(\x03\x12\x17\n\x0freference_bases\x18\x03 \x01(\t\x12\x17\n\x0f\x61lternate_bases\x18\x04 \x01(\t\"i\n\x0cOntologyTerm\x12M\n\rontology_type\x18\x01 \x01(\x0e\x32\x36.google.gdm.gdmscience.alphagenome.v1main.OntologyType\x12\n\n\x02id\x18\x02 \x01(\x03\"~\n\tBiosample\x12\x45\n\x04type\x18\x01 \x01(\x0e\x32\x37.google.gdm.gdmscience.alphagenome.v1main.BiosampleType\x12\x0c\n\x04name\x18\x02 \x01(\t\x12\x12\n\x05stage\x18\x03 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\n\x1cOUTPUT_TYPE_SPLICE_JUNCTIONS\x10\t\x12\x1c\n\x18OUTPUT_TYPE_CONTACT_MAPS\x10\x0b\x12\x16\n\x12OUTPUT_TYPE_PROCAP\x10\x0c*\\\n\x08Organism\x12\x18\n\x14ORGANISM_UNSPECIFIED\x10\x00\x12\x1a\n\x15ORGANISM_HOMO_SAPIENS\x10\x86K\x12\x1a\n\x15ORGANISM_MUS_MUSCULUS\x10\xeaN*\x8b\x01\n\x17IntervalAggregationType\x12)\n%INTERVAL_AGGREGATION_TYPE_UNSPECIFIED\x10\x00\x12\"\n\x1eINTERVAL_AGGREGATION_TYPE_MEAN\x10\x01\x12!\n\x1dINTERVAL_AGGREGATION_TYPE_SUM\x10\x02*\xbf\x02\n\x0f\x41ggregationType\x12 \n\x1c\x41GGREGATION_TYPE_UNSPECIFIED\x10\x00\x12\x1e\n\x1a\x41GGREGATION_TYPE_DIFF_MEAN\x10\x01\x12\x1d\n\x19\x41GGREGATION_TYPE_DIFF_SUM\x10\x02\x12\"\n\x1e\x41GGREGATION_TYPE_DIFF_SUM_LOG2\x10\x03\x12\x1c\n\x18\x41GGREGATION_TYPE_L2_DIFF\x10\x04\x12\"\n\x1e\x41GGREGATION_TYPE_L2_DIFF_LOG1P\x10\x08\x12\"\n\x1e\x41GGREGATION_TYPE_DIFF_LOG2_SUM\x10\x05\x12 \n\x1c\x41GGREGATION_TYPE_ACTIVE_MEAN\x10\x06\x12\x1f\n\x1b\x41GGREGATION_TYPE_ACTIVE_SUM\x10\x07*R\n\tEndedness\x12\x19\n\x15\x45NDEDNESS_UNSPECIFIED\x10\x00\x12\x14\n\x10\x45NDEDNESS_SINGLE\x10\x01\x12\x14\n\x10\x45NDEDNESS_PAIRED\x10\x02\x42\x94\x01\n,com.google.gdm.gdmscience.alphagenome.v1mainB\rDnaModelProtoP\x01ZSgoogle.golang.org/genproto/googleapis/gdm/gdmscience/alphagenome/v1main;alphagenomeb\x06proto3')
+
+_globals = globals()
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'flax_model.alphagenome._sdk.protos.dna_model_pb2', _globals)
+if _descriptor._USE_C_DESCRIPTORS == False:
+ _globals['DESCRIPTOR']._options = None
+ _globals['DESCRIPTOR']._serialized_options = b'\n,com.google.gdm.gdmscience.alphagenome.v1mainB\rDnaModelProtoP\001ZSgoogle.golang.org/genproto/googleapis/gdm/gdmscience/alphagenome/v1main;alphagenome'
+ _globals['_STRAND']._serialized_start=5808
+ _globals['_STRAND']._serialized_end=5905
+ _globals['_ONTOLOGYTYPE']._serialized_start=5908
+ _globals['_ONTOLOGYTYPE']._serialized_end=6070
+ _globals['_BIOSAMPLETYPE']._serialized_start=6073
+ _globals['_BIOSAMPLETYPE']._serialized_end=6326
+ _globals['_OUTPUTTYPE']._serialized_start=6329
+ _globals['_OUTPUTTYPE']._serialized_end=6670
+ _globals['_ORGANISM']._serialized_start=6672
+ _globals['_ORGANISM']._serialized_end=6764
+ _globals['_INTERVALAGGREGATIONTYPE']._serialized_start=6767
+ _globals['_INTERVALAGGREGATIONTYPE']._serialized_end=6906
+ _globals['_AGGREGATIONTYPE']._serialized_start=6909
+ _globals['_AGGREGATIONTYPE']._serialized_end=7228
+ _globals['_ENDEDNESS']._serialized_start=7230
+ _globals['_ENDEDNESS']._serialized_end=7312
+ _globals['_INTERVAL']._serialized_start=113
+ _globals['_INTERVAL']._serialized_end=237
+ _globals['_VARIANT']._serialized_start=239
+ _globals['_VARIANT']._serialized_end=336
+ _globals['_ONTOLOGYTERM']._serialized_start=338
+ _globals['_ONTOLOGYTERM']._serialized_end=443
+ _globals['_BIOSAMPLE']._serialized_start=445
+ _globals['_BIOSAMPLE']._serialized_end=571
+ _globals['_GENESCORERMETADATA']._serialized_start=574
+ _globals['_GENESCORERMETADATA']._serialized_end=841
+ _globals['_TRACKMETADATA']._serialized_start=844
+ _globals['_TRACKMETADATA']._serialized_end=1523
+ _globals['_TRACKSMETADATA']._serialized_start=1525
+ _globals['_TRACKSMETADATA']._serialized_end=1616
+ _globals['_JUNCTIONMETADATA']._serialized_start=1619
+ _globals['_JUNCTIONMETADATA']._serialized_end=1916
+ _globals['_JUNCTIONSMETADATA']._serialized_start=1918
+ _globals['_JUNCTIONSMETADATA']._serialized_end=2015
+ _globals['_TRACKDATA']._serialized_start=2018
+ _globals['_TRACKDATA']._serialized_end=2280
+ _globals['_JUNCTIONDATA']._serialized_start=2283
+ _globals['_JUNCTIONDATA']._serialized_end=2582
+ _globals['_INTERVALMETADATA']._serialized_start=2585
+ _globals['_INTERVALMETADATA']._serialized_end=2839
+ _globals['_INTERVALDATA']._serialized_start=2842
+ _globals['_INTERVALDATA']._serialized_end=3000
+ _globals['_VARIANTMETADATA']._serialized_start=3003
+ _globals['_VARIANTMETADATA']._serialized_end=3254
+ _globals['_VARIANTDATA']._serialized_start=3257
+ _globals['_VARIANTDATA']._serialized_end=3413
+ _globals['_OUTPUT']._serialized_start=3416
+ _globals['_OUTPUT']._serialized_end=3732
+ _globals['_SCOREINTERVALOUTPUT']._serialized_start=3734
+ _globals['_SCOREINTERVALOUTPUT']._serialized_end=3834
+ _globals['_SCOREVARIANTOUTPUT']._serialized_start=3836
+ _globals['_SCOREVARIANTOUTPUT']._serialized_end=3933
+ _globals['_GENEMASKINTERVALSCORER']._serialized_start=3936
+ _globals['_GENEMASKINTERVALSCORER']._serialized_end=4163
+ _globals['_INTERVALSCORER']._serialized_start=4165
+ _globals['_INTERVALSCORER']._serialized_end=4278
+ _globals['_CENTERMASKSCORER']._serialized_start=4281
+ _globals['_CENTERMASKSCORER']._serialized_end=4494
+ _globals['_GENEMASKLFCSCORER']._serialized_start=4496
+ _globals['_GENEMASKLFCSCORER']._serialized_end=4595
+ _globals['_GENEMASKACTIVESCORER']._serialized_start=4597
+ _globals['_GENEMASKACTIVESCORER']._serialized_end=4699
+ _globals['_GENEMASKSPLICINGSCORER']._serialized_start=4702
+ _globals['_GENEMASKSPLICINGSCORER']._serialized_end=4836
+ _globals['_POLYADENYLATIONSCORER']._serialized_start=4838
+ _globals['_POLYADENYLATIONSCORER']._serialized_end=4861
+ _globals['_SPLICEJUNCTIONSCORER']._serialized_start=4863
+ _globals['_SPLICEJUNCTIONSCORER']._serialized_end=4885
+ _globals['_CONTACTMAPSCORER']._serialized_start=4887
+ _globals['_CONTACTMAPSCORER']._serialized_end=4905
+ _globals['_VARIANTSCORER']._serialized_start=4908
+ _globals['_VARIANTSCORER']._serialized_end=5543
+ _globals['_OUTPUTMETADATA']._serialized_start=5546
+ _globals['_OUTPUTMETADATA']._serialized_end=5806
+# @@protoc_insertion_point(module_scope)
diff --git a/flax_model/alphagenome/_sdk/protos/dna_model_pb2_grpc.py b/flax_model/alphagenome/_sdk/protos/dna_model_pb2_grpc.py
new file mode 100644
index 0000000000000000000000000000000000000000..2daafffebfc817aefe8fcb96eaec25e65b3903e8
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/protos/dna_model_pb2_grpc.py
@@ -0,0 +1,4 @@
+# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
+"""Client and server classes corresponding to protobuf-defined services."""
+import grpc
+
diff --git a/flax_model/alphagenome/_sdk/protos/dna_model_service.proto b/flax_model/alphagenome/_sdk/protos/dna_model_service.proto
new file mode 100644
index 0000000000000000000000000000000000000000..6da71c363d88e4948779e5d677ab9912afa72b60
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/protos/dna_model_service.proto
@@ -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.
+
+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;
+
+ // Optional variant applied to the reference interval. If provided, the
+ // alternate allele is used for in-silico mutagenesis, otherwise the
+ // unaltered reference sequence is used.
+ optional Variant interval_variant = 6;
+
+ // 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/flax_model/alphagenome/_sdk/protos/dna_model_service_pb2.py b/flax_model/alphagenome/_sdk/protos/dna_model_service_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..699f6cb9bbba6a50ec8cc5bec420268d8137e5ff
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/protos/dna_model_service_pb2.py
@@ -0,0 +1,57 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler. DO NOT EDIT!
+# source: alphagenome/protos/dna_model_service.proto
+# Protobuf Python Version: 4.25.1
+"""Generated protocol buffer code."""
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf.internal import builder as _builder
+# @@protoc_insertion_point(imports)
+
+_sym_db = _symbol_database.Default()
+
+
+from flax_model.alphagenome._sdk.protos import dna_model_pb2 as alphagenome_dot_protos_dot_dna__model__pb2
+from flax_model.alphagenome._sdk.protos import tensor_pb2 as alphagenome_dot_protos_dot_tensor__pb2
+
+
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n*alphagenome/protos/dna_model_service.proto\x12(google.gdm.gdmscience.alphagenome.v1main\x1a\"alphagenome/protos/dna_model.proto\x1a\x1f\x61lphagenome/protos/tensor.proto\"\xa8\x02\n\x16PredictSequenceRequest\x12\x10\n\x08sequence\x18\x01 \x01(\t\x12\x44\n\x08organism\x18\x02 \x01(\x0e\x32\x32.google.gdm.gdmscience.alphagenome.v1main.Organism\x12N\n\x0eontology_terms\x18\x03 \x03(\x0b\x32\x36.google.gdm.gdmscience.alphagenome.v1main.OntologyTerm\x12O\n\x11requested_outputs\x18\x04 \x03(\x0e\x32\x34.google.gdm.gdmscience.alphagenome.v1main.OutputType\x12\x15\n\rmodel_version\x18\x05 \x01(\t\"\xb7\x01\n\x17PredictSequenceResponse\x12\x42\n\x06output\x18\x01 \x01(\x0b\x32\x30.google.gdm.gdmscience.alphagenome.v1main.OutputH\x00\x12M\n\x0ctensor_chunk\x18\x02 \x01(\x0b\x32\x35.google.gdm.gdmscience.alphagenome.v1main.TensorChunkH\x00\x42\t\n\x07payload\"\xdc\x02\n\x16PredictIntervalRequest\x12\x44\n\x08interval\x18\x01 \x01(\x0b\x32\x32.google.gdm.gdmscience.alphagenome.v1main.Interval\x12\x44\n\x08organism\x18\x02 \x01(\x0e\x32\x32.google.gdm.gdmscience.alphagenome.v1main.Organism\x12O\n\x11requested_outputs\x18\x03 \x03(\x0e\x32\x34.google.gdm.gdmscience.alphagenome.v1main.OutputType\x12N\n\x0eontology_terms\x18\x04 \x03(\x0b\x32\x36.google.gdm.gdmscience.alphagenome.v1main.OntologyTerm\x12\x15\n\rmodel_version\x18\x05 \x01(\t\"\xb7\x01\n\x17PredictIntervalResponse\x12\x42\n\x06output\x18\x01 \x01(\x0b\x32\x30.google.gdm.gdmscience.alphagenome.v1main.OutputH\x00\x12M\n\x0ctensor_chunk\x18\x02 \x01(\x0b\x32\x35.google.gdm.gdmscience.alphagenome.v1main.TensorChunkH\x00\x42\t\n\x07payload\"\x9f\x03\n\x15PredictVariantRequest\x12\x44\n\x08interval\x18\x01 \x01(\x0b\x32\x32.google.gdm.gdmscience.alphagenome.v1main.Interval\x12\x42\n\x07variant\x18\x02 \x01(\x0b\x32\x31.google.gdm.gdmscience.alphagenome.v1main.Variant\x12\x44\n\x08organism\x18\x03 \x01(\x0e\x32\x32.google.gdm.gdmscience.alphagenome.v1main.Organism\x12O\n\x11requested_outputs\x18\x04 \x03(\x0e\x32\x34.google.gdm.gdmscience.alphagenome.v1main.OutputType\x12N\n\x0eontology_terms\x18\x05 \x03(\x0b\x32\x36.google.gdm.gdmscience.alphagenome.v1main.OntologyTerm\x12\x15\n\rmodel_version\x18\x06 \x01(\t\"\x8e\x02\n\x16PredictVariantResponse\x12L\n\x10reference_output\x18\x01 \x01(\x0b\x32\x30.google.gdm.gdmscience.alphagenome.v1main.OutputH\x00\x12L\n\x10\x61lternate_output\x18\x02 \x01(\x0b\x32\x30.google.gdm.gdmscience.alphagenome.v1main.OutputH\x00\x12M\n\x0ctensor_chunk\x18\x03 \x01(\x0b\x32\x35.google.gdm.gdmscience.alphagenome.v1main.TensorChunkH\x00\x42\t\n\x07payload\"\x8d\x02\n\x14ScoreIntervalRequest\x12\x44\n\x08interval\x18\x01 \x01(\x0b\x32\x32.google.gdm.gdmscience.alphagenome.v1main.Interval\x12\x44\n\x08organism\x18\x02 \x01(\x0e\x32\x32.google.gdm.gdmscience.alphagenome.v1main.Organism\x12R\n\x10interval_scorers\x18\x03 \x03(\x0b\x32\x38.google.gdm.gdmscience.alphagenome.v1main.IntervalScorer\x12\x15\n\rmodel_version\x18\x04 \x01(\t\"\xc2\x01\n\x15ScoreIntervalResponse\x12O\n\x06output\x18\x01 \x01(\x0b\x32=.google.gdm.gdmscience.alphagenome.v1main.ScoreIntervalOutputH\x00\x12M\n\x0ctensor_chunk\x18\x02 \x01(\x0b\x32\x35.google.gdm.gdmscience.alphagenome.v1main.TensorChunkH\x00\x42\t\n\x07payload\"\xce\x02\n\x13ScoreVariantRequest\x12\x44\n\x08interval\x18\x01 \x01(\x0b\x32\x32.google.gdm.gdmscience.alphagenome.v1main.Interval\x12\x42\n\x07variant\x18\x02 \x01(\x0b\x32\x31.google.gdm.gdmscience.alphagenome.v1main.Variant\x12\x44\n\x08organism\x18\x03 \x01(\x0e\x32\x32.google.gdm.gdmscience.alphagenome.v1main.Organism\x12P\n\x0fvariant_scorers\x18\x04 \x03(\x0b\x32\x37.google.gdm.gdmscience.alphagenome.v1main.VariantScorer\x12\x15\n\rmodel_version\x18\x05 \x01(\t\"\xc0\x01\n\x14ScoreVariantResponse\x12N\n\x06output\x18\x01 \x01(\x0b\x32<.google.gdm.gdmscience.alphagenome.v1main.ScoreVariantOutputH\x00\x12M\n\x0ctensor_chunk\x18\x02 \x01(\x0b\x32\x35.google.gdm.gdmscience.alphagenome.v1main.TensorChunkH\x00\x42\t\n\x07payload\"\xbe\x03\n\x16ScoreIsmVariantRequest\x12\x44\n\x08interval\x18\x01 \x01(\x0b\x32\x32.google.gdm.gdmscience.alphagenome.v1main.Interval\x12H\n\x0cism_interval\x18\x02 \x01(\x0b\x32\x32.google.gdm.gdmscience.alphagenome.v1main.Interval\x12\x44\n\x08organism\x18\x03 \x01(\x0e\x32\x32.google.gdm.gdmscience.alphagenome.v1main.Organism\x12P\n\x0fvariant_scorers\x18\x04 \x03(\x0b\x32\x37.google.gdm.gdmscience.alphagenome.v1main.VariantScorer\x12P\n\x10interval_variant\x18\x06 \x01(\x0b\x32\x31.google.gdm.gdmscience.alphagenome.v1main.VariantH\x00\x88\x01\x01\x12\x15\n\rmodel_version\x18\x05 \x01(\tB\x13\n\x11_interval_variant\"\xc3\x01\n\x17ScoreIsmVariantResponse\x12N\n\x06output\x18\x01 \x01(\x0b\x32<.google.gdm.gdmscience.alphagenome.v1main.ScoreVariantOutputH\x00\x12M\n\x0ctensor_chunk\x18\x02 \x01(\x0b\x32\x35.google.gdm.gdmscience.alphagenome.v1main.TensorChunkH\x00\x42\t\n\x07payload\"W\n\x0fMetadataRequest\x12\x44\n\x08organism\x18\x01 \x01(\x0e\x32\x32.google.gdm.gdmscience.alphagenome.v1main.Organism\"e\n\x10MetadataResponse\x12Q\n\x0foutput_metadata\x18\x01 \x03(\x0b\x32\x38.google.gdm.gdmscience.alphagenome.v1main.OutputMetadata2\xc4\x08\n\x0f\x44naModelService\x12\x9c\x01\n\x0fPredictSequence\x12@.google.gdm.gdmscience.alphagenome.v1main.PredictSequenceRequest\x1a\x41.google.gdm.gdmscience.alphagenome.v1main.PredictSequenceResponse\"\x00(\x01\x30\x01\x12\x9c\x01\n\x0fPredictInterval\x12@.google.gdm.gdmscience.alphagenome.v1main.PredictIntervalRequest\x1a\x41.google.gdm.gdmscience.alphagenome.v1main.PredictIntervalResponse\"\x00(\x01\x30\x01\x12\x99\x01\n\x0ePredictVariant\x12?.google.gdm.gdmscience.alphagenome.v1main.PredictVariantRequest\x1a@.google.gdm.gdmscience.alphagenome.v1main.PredictVariantResponse\"\x00(\x01\x30\x01\x12\x96\x01\n\rScoreInterval\x12>.google.gdm.gdmscience.alphagenome.v1main.ScoreIntervalRequest\x1a?.google.gdm.gdmscience.alphagenome.v1main.ScoreIntervalResponse\"\x00(\x01\x30\x01\x12\x93\x01\n\x0cScoreVariant\x12=.google.gdm.gdmscience.alphagenome.v1main.ScoreVariantRequest\x1a>.google.gdm.gdmscience.alphagenome.v1main.ScoreVariantResponse\"\x00(\x01\x30\x01\x12\x9c\x01\n\x0fScoreIsmVariant\x12@.google.gdm.gdmscience.alphagenome.v1main.ScoreIsmVariantRequest\x1a\x41.google.gdm.gdmscience.alphagenome.v1main.ScoreIsmVariantResponse\"\x00(\x01\x30\x01\x12\x88\x01\n\x0bGetMetadata\x12\x39.google.gdm.gdmscience.alphagenome.v1main.MetadataRequest\x1a:.google.gdm.gdmscience.alphagenome.v1main.MetadataResponse\"\x00\x30\x01\x42\x9b\x01\n,com.google.gdm.gdmscience.alphagenome.v1mainB\x14\x44naModelServiceProtoP\x01ZSgoogle.golang.org/genproto/googleapis/gdm/gdmscience/alphagenome/v1main;alphagenomeb\x06proto3')
+
+_globals = globals()
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'flax_model.alphagenome._sdk.protos.dna_model_service_pb2', _globals)
+if _descriptor._USE_C_DESCRIPTORS == False:
+ _globals['DESCRIPTOR']._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=3318
+ _globals['_SCOREISMVARIANTRESPONSE']._serialized_start=3321
+ _globals['_SCOREISMVARIANTRESPONSE']._serialized_end=3516
+ _globals['_METADATAREQUEST']._serialized_start=3518
+ _globals['_METADATAREQUEST']._serialized_end=3605
+ _globals['_METADATARESPONSE']._serialized_start=3607
+ _globals['_METADATARESPONSE']._serialized_end=3708
+ _globals['_DNAMODELSERVICE']._serialized_start=3711
+ _globals['_DNAMODELSERVICE']._serialized_end=4803
+# @@protoc_insertion_point(module_scope)
diff --git a/flax_model/alphagenome/_sdk/protos/dna_model_service_pb2_grpc.py b/flax_model/alphagenome/_sdk/protos/dna_model_service_pb2_grpc.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b0b75375ca376e5feb2aee29f1b5f26ef87144f
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/protos/dna_model_service_pb2_grpc.py
@@ -0,0 +1,274 @@
+# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
+"""Client and server classes corresponding to protobuf-defined services."""
+import grpc
+
+from flax_model.alphagenome._sdk.protos import dna_model_service_pb2 as alphagenome_dot_protos_dot_dna__model__service__pb2
+
+
+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,
+ )
+ 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,
+ )
+ 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,
+ )
+ 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,
+ )
+ 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,
+ )
+ 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,
+ )
+ 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,
+ )
+
+
+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,))
+
+
+ # 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)
+
+ @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)
+
+ @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)
+
+ @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)
+
+ @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)
+
+ @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)
+
+ @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)
diff --git a/flax_model/alphagenome/_sdk/protos/tensor.proto b/flax_model/alphagenome/_sdk/protos/tensor.proto
new file mode 100644
index 0000000000000000000000000000000000000000..4d86d8396c3080b5bd7ac96156a090de7edd4479
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/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/flax_model/alphagenome/_sdk/protos/tensor_pb2.py b/flax_model/alphagenome/_sdk/protos/tensor_pb2.py
new file mode 100644
index 0000000000000000000000000000000000000000..907dac9565f61b5eb9e61ba5984163dc961550c6
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/protos/tensor_pb2.py
@@ -0,0 +1,33 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler. DO NOT EDIT!
+# source: alphagenome/protos/tensor.proto
+# Protobuf Python Version: 4.25.1
+"""Generated protocol buffer code."""
+from google.protobuf import descriptor as _descriptor
+from google.protobuf import descriptor_pool as _descriptor_pool
+from google.protobuf import symbol_database as _symbol_database
+from google.protobuf.internal import builder as _builder
+# @@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, 'flax_model.alphagenome._sdk.protos.tensor_pb2', _globals)
+if _descriptor._USE_C_DESCRIPTORS == False:
+ _globals['DESCRIPTOR']._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/flax_model/alphagenome/_sdk/protos/tensor_pb2_grpc.py b/flax_model/alphagenome/_sdk/protos/tensor_pb2_grpc.py
new file mode 100644
index 0000000000000000000000000000000000000000..2daafffebfc817aefe8fcb96eaec25e65b3903e8
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/protos/tensor_pb2_grpc.py
@@ -0,0 +1,4 @@
+# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
+"""Client and server classes corresponding to protobuf-defined services."""
+import grpc
+
diff --git a/flax_model/alphagenome/_sdk/tensor_utils.py b/flax_model/alphagenome/_sdk/tensor_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..c0f10774da4765afcc656ca653f9d4a3453dd000
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/tensor_utils.py
@@ -0,0 +1,171 @@
+# 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 flax_model.alphagenome._sdk.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.
+ """
+ dtype = _TENSOR_DTYPE_TO_NUMPY_DTYPE[proto.data_type]
+ match proto.WhichOneof('payload'):
+ case 'array':
+ data = _decompress_bytes(proto.array.data, proto.array.compression_type)
+ array = np.frombuffer(data, dtype=dtype).reshape(proto.shape)
+ case 'chunk_count':
+ array = np.empty(np.prod(proto.shape) * dtype.itemsize, dtype=np.uint8)
+ bytes_received = 0
+ for chunk in chunks:
+ chunk_data = np.frombuffer(
+ _decompress_bytes(chunk.data, chunk.compression_type),
+ dtype=np.uint8,
+ )
+ array[bytes_received : bytes_received + chunk_data.nbytes] = chunk_data
+ bytes_received += chunk_data.nbytes
+
+ if bytes_received != array.nbytes:
+ raise ValueError(
+ f'Expected {array.nbytes} bytes but only received {bytes_received} '
+ 'bytes.'
+ )
+ array = array.view(dtype).reshape(proto.shape)
+ case _:
+ raise ValueError(
+ f'Unsupported payload type: {proto.WhichOneof("payload")}'
+ )
+
+ 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/flax_model/alphagenome/_sdk/typing.py b/flax_model/alphagenome/_sdk/typing.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac4c0c8ffbfce58a9b6fd95bae0636aeb06b4c3b
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/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/flax_model/alphagenome/_sdk/visualization/__init__.py b/flax_model/alphagenome/_sdk/visualization/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..602823a26de43a59ecbc53130824f87f82ebd5bc
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/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/flax_model/alphagenome/_sdk/visualization/plot.py b/flax_model/alphagenome/_sdk/visualization/plot.py
new file mode 100644
index 0000000000000000000000000000000000000000..7608a1c23abb73983066a67e08402684747c3fd8
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/visualization/plot.py
@@ -0,0 +1,569 @@
+# 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 functools
+import math
+from typing import Any, Literal
+
+from absl import logging
+from flax_model.alphagenome._sdk.data import genome
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+
+@functools.lru_cache(maxsize=8)
+def _get_text_path(
+ letter: str, letter_colors_scheme: str
+) -> tuple[mpl.text.TextPath, str]:
+ """Returns a memoized TextPath and color for the given letter and scheme."""
+ letters_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'),
+ },
+ )
+ if (
+ letters_width_color := letters_width_colors.get(letter_colors_scheme)
+ ) is None:
+ raise ValueError(
+ f'letter_colors_scheme must be one of {letters_width_colors.keys()}.'
+ f'Got "{letter_colors_scheme}".'
+ )
+ else:
+ if (letter_width_color := letters_width_color.get(letter)) is None:
+ raise ValueError(
+ f'letter must be one of {letters_width_color.keys()}. Got "{letter}".'
+ )
+ else:
+ letter_width, letter_color = letter_width_color
+ font = mpl.font_manager.FontProperties(weight='bold')
+ return (
+ mpl.text.TextPath((-letter_width, 0), letter, size=1, prop=font),
+ letter_color,
+ )
+
+
+def seqlogo(
+ letter_heights: np.ndarray,
+ alphabet: str = 'ACGT',
+ one_based: bool = True,
+ start: int = 0,
+ ax: mpl.axes.Axes | None = None,
+ letter_colors: Literal['default', 'factorbook'] = '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 if ax is not None else 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)')
+
+ globscale = 1.35
+ paths = []
+ facecolors = []
+
+ for x_offset, heights in enumerate(letter_heights):
+ last_positive_y = 0.0
+ last_negative_y = 0.0
+ x = start + x_offset + 0.5 + 0.5 * one_based
+
+ for height, letter in sorted(zip(heights, alphabet)):
+ if height == 0:
+ continue
+ # 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
+
+ base_path, letter_color = _get_text_path(letter, letter_colors)
+
+ transform = (
+ mpl.transforms.Affine2D()
+ .scale(globscale, height * globscale)
+ .translate(x, y_position)
+ )
+ paths.append(base_path.transformed(transform))
+ facecolors.append(letter_color)
+
+ if paths:
+ collection = mpl.collections.PathCollection(
+ paths,
+ facecolors=facecolors,
+ edgecolors='none',
+ linewidths=0,
+ transform=ax.transData,
+ )
+ ax.add_collection(collection)
+
+ # Make sure all letters are displayed.
+ ax.autoscale_view()
+
+
+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/flax_model/alphagenome/_sdk/visualization/plot_components.py b/flax_model/alphagenome/_sdk/visualization/plot_components.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac37f6d50f74c6aafec1d799e4d57af5a005ac72
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/visualization/plot_components.py
@@ -0,0 +1,1555 @@
+# 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 flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome._sdk.data import junction_data
+from flax_model.alphagenome._sdk.data import track_data
+from flax_model.alphagenome._sdk.data import transcript as transcript_utils
+from flax_model.alphagenome._sdk.visualization import plot as plot_lib
+from flax_model.alphagenome._sdk.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))
+ )
+ label_index = 0
+ for annotation in annotations:
+ for annotate_index in axes_to_annotate_idx:
+ if not annotation.is_variant and annotate_index in transcript_axes_idx:
+ # Do not add interval annotations to axes that involves transcripts.
+ continue
+ annotation.plot_ax(axes[annotate_index], 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[label_index])
+ label_index += 1
+
+ # 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,
+ global_ylims: tuple[float, float] | None = None,
+ yticks: Sequence[float] | None = None,
+ yticklabels: Sequence[str] | None = None,
+ 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. This
+ is inferred from the min/max data values across all tracks.
+ global_ylims: Optional global y-axis limits (min and max).
+ yticks: Optional set y-axis tick values manually. If not provided, the
+ tick values will be automatically determined. If provided, the length of
+ yticks must match the length of yticklabels.
+ yticklabels: Optional set y-axis tick labels manually. If not provided,
+ the tick values will be automatically determined. If provided, the
+ length of yticklabels must match the length of yticks.
+ 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._global_ylims = global_ylims
+ self._yticks = yticks
+ self._yticklabels = yticklabels
+ 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 (
+ self._yticks is not None
+ and self._yticklabels is not None
+ and len(self._yticks) != len(self._yticklabels)
+ ):
+ raise ValueError(
+ 'If passing yticks and yticklabels, the length of yticks must match'
+ ' the length of yticklabels.'
+ )
+
+ 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._global_ylims is not None:
+ ax.set_ylim(self._global_ylims)
+ elif 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._yticks is not None:
+ ax.set_yticks(self._yticks)
+ if self._yticklabels is not None:
+ ax.set_yticklabels(self._yticklabels)
+
+ 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/flax_model/alphagenome/_sdk/visualization/plot_transcripts.py b/flax_model/alphagenome/_sdk/visualization/plot_transcripts.py
new file mode 100644
index 0000000000000000000000000000000000000000..90604ff17c6d26dfa26d4c354b6a89d50f78b317
--- /dev/null
+++ b/flax_model/alphagenome/_sdk/visualization/plot_transcripts.py
@@ -0,0 +1,480 @@
+# 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
+from typing import Any
+
+from flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome._sdk.data import transcript as transcript_utils
+import intervaltree
+import matplotlib as mpl
+import matplotlib.figure
+import matplotlib.path
+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,
+ num_transcripts=len(transcripts),
+ **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',
+ num_transcripts: int = 1,
+ **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.
+ num_transcripts: Total number of transcripts being drawn, used for dynamic
+ arrow sizing.
+ **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)
+
+ # 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
+ )
+
+ # Draw strand arrows across the full transcript span.
+ draw_strand_arrows(
+ ax=ax,
+ transcript=transcript,
+ interval=interval,
+ y=y,
+ color=cds_color,
+ cds_height=cds_height,
+ num_transcripts=num_transcripts,
+ )
+
+
+def draw_strand_arrows(
+ ax: plt.Axes,
+ transcript: transcript_utils.Transcript,
+ interval: genome.Interval,
+ y: float,
+ color: str,
+ *,
+ cds_height: float = 0.22,
+ num_transcripts: int = 1,
+ max_arrows_per_intron: int = 5,
+) -> None:
+ """Draw strand direction arrows on intron lines.
+
+ Arrow count per intron is computed dynamically based on the intron's width
+ relative to the visible interval. Marker size is derived from the UTR height
+ so arrows are always visually smaller than UTR exons.
+
+ Args:
+ ax: Matplotlib axis.
+ transcript: The transcript being drawn.
+ interval: The visible genomic interval.
+ y: Vertical position of the transcript.
+ color: Arrow color.
+ cds_height: CDS height in data coordinates, used to scale arrows.
+ num_transcripts: Total number of transcripts being drawn.
+ max_arrows_per_intron: Maximum number of arrows per intron.
+ """
+ introns = transcript_utils.Transcript(transcript.exons).introns
+ if not introns:
+ return
+
+ fig = ax.get_figure()
+ if fig is not None:
+ _, fig_height_inches = (
+ fig.get_size_inches() # pytype: disable=attribute-error
+ )
+ ax_height_inches = ax.get_position().height * fig_height_inches
+ y_range = num_transcripts + 2
+ if y_range > 0:
+ pts_per_data = (ax_height_inches * 72) / y_range
+ markersize = min(4.0, cds_height * pts_per_data * 2)
+ else:
+ markersize = 4.0
+ else:
+ markersize = 4.0
+
+ # Custom chevron path: two line segments forming > or < shape.
+ if transcript.is_negative_strand:
+ chevron = matplotlib.path.Path(
+ [(0.5, 0.5), (-0.5, 0.0), (0.5, -0.5)],
+ [
+ matplotlib.path.Path.MOVETO,
+ matplotlib.path.Path.LINETO,
+ matplotlib.path.Path.LINETO,
+ ],
+ )
+ else:
+ chevron = matplotlib.path.Path(
+ [(-0.5, 0.5), (0.5, 0.0), (-0.5, -0.5)],
+ [
+ matplotlib.path.Path.MOVETO,
+ matplotlib.path.Path.LINETO,
+ matplotlib.path.Path.LINETO,
+ ],
+ )
+
+ arrow_positions = []
+ for intron in introns:
+ intron_to_interval_fraction = intron.width / interval.width
+ # Skip arrows for introns that are too small.
+ if intron_to_interval_fraction < 0.01:
+ continue
+ # Use sqrt scaling so large introns don't get overwhelmed with arrows.
+ num_arrows = min(
+ max(1, round(intron_to_interval_fraction**0.5 * max_arrows_per_intron)),
+ max_arrows_per_intron,
+ )
+ space = intron.width / (num_arrows + 1)
+ for i in range(1, num_arrows + 1):
+ arrow_pos = intron.start + i * space
+ # Skip arrows too close to interval edges.
+ if arrow_pos < interval.start + 10 or arrow_pos > interval.end - 10:
+ continue
+ arrow_positions.append(arrow_pos)
+
+ if arrow_positions:
+ ax.plot(
+ arrow_positions,
+ [y] * len(arrow_positions),
+ marker=chevron,
+ markersize=markersize,
+ color=color,
+ fillstyle='none',
+ markeredgewidth=0.8,
+ linestyle='none',
+ clip_on=True,
+ )
+
+
+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
diff --git a/flax_model/alphagenome/evals/__init__.py b/flax_model/alphagenome/evals/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..65cb18306da0ba67bfb07b23f1f7299ce70e84a1
--- /dev/null
+++ b/flax_model/alphagenome/evals/__init__.py
@@ -0,0 +1,14 @@
+# Copyright 2025 DeepMind Technologies Limited
+#
+# 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.
+"""AlphaGenome Evaluation Utilities."""
diff --git a/flax_model/alphagenome/evals/regression_metrics.py b/flax_model/alphagenome/evals/regression_metrics.py
new file mode 100644
index 0000000000000000000000000000000000000000..f3c2428d48b7e26b069b7b4dc59aa05763bd53c9
--- /dev/null
+++ b/flax_model/alphagenome/evals/regression_metrics.py
@@ -0,0 +1,204 @@
+# Copyright 2026 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 experiments."""
+
+from typing import Sequence
+from flax_model.alphagenome._sdk import typing
+import chex
+import jax
+import jax.numpy as jnp
+from jaxtyping import ArrayLike, Float, PyTree # pylint: disable=g-importing-member, g-multiple-import
+import numpy as np
+
+
+@chex.dataclass
+class _PearsonRState:
+ """State to compute PearsonR correlation coefficient."""
+
+ xy_sum: jax.Array
+ x_sum: jax.Array
+ xx_sum: jax.Array
+ y_sum: jax.Array
+ yy_sum: jax.Array
+ count: jax.Array
+
+ def __add__(self, other: '_PearsonRState') -> '_PearsonRState':
+ return jax.tree.map(lambda x, y: x + y, self, other)
+
+
+def _pearsonr_initialize() -> '_PearsonRState':
+ """Initialize PearsonrState with zeros."""
+ return _PearsonRState(
+ xy_sum=np.zeros(()),
+ x_sum=np.zeros(()),
+ xx_sum=np.zeros(()),
+ y_sum=np.zeros(()),
+ yy_sum=np.zeros(()),
+ count=np.zeros(()),
+ )
+
+
+def _pearsonr_update(
+ x: jax.Array,
+ y: jax.Array,
+ axis: Sequence[int] | int | None = None,
+ mask: jax.Array | None = None,
+) -> _PearsonRState:
+ """Construct PearsonrState by correlating two arrays."""
+ if mask is not None:
+ mask = jnp.astype(mask, bool)
+ return _PearsonRState(
+ xy_sum=jnp.sum(x * y, axis=axis, where=mask, dtype=jnp.float32),
+ x_sum=jnp.sum(x, axis=axis, where=mask, dtype=jnp.float32),
+ xx_sum=jnp.sum(jnp.square(x), axis=axis, where=mask, dtype=jnp.float32),
+ y_sum=jnp.sum(y, axis=axis, where=mask, dtype=jnp.float32),
+ yy_sum=jnp.sum(jnp.square(y), axis=axis, where=mask, dtype=jnp.float32),
+ count=jnp.sum(jnp.ones_like(x), axis=axis, where=mask, dtype=jnp.float32),
+ )
+
+
+def _pearsonr_result(state: _PearsonRState) -> jax.Array:
+ """Get PearsonR correlation coeficient."""
+ x_mean = state.x_sum / state.count
+ y_mean = state.y_sum / state.count
+
+ covariance = state.xy_sum - state.count * x_mean * y_mean
+
+ x_var = state.xx_sum - state.count * x_mean * x_mean
+ y_var = state.yy_sum - state.count * y_mean * y_mean
+ variance = x_var**0.5 * y_var**0.5
+ eps = jnp.finfo(variance.dtype).eps # Avoid division by zero.
+ return covariance / (variance + eps)
+
+
+@chex.dataclass
+class RegressionState:
+ """State for accumulating regression statistics."""
+
+ pearsonr: _PearsonRState
+ pearsonr_log1p: _PearsonRState
+ sq_error: jax.Array
+ abs_error: jax.Array
+ count: jax.Array
+
+ def __add__(self, other: 'RegressionState') -> 'RegressionState':
+ return jax.tree.map(
+ lambda a, b: a + b,
+ self,
+ other,
+ )
+
+
+def initialize_regression_metrics() -> RegressionState:
+ """Initialize metric state."""
+ return RegressionState(
+ pearsonr=_pearsonr_initialize(),
+ pearsonr_log1p=_pearsonr_initialize(),
+ sq_error=np.zeros(()),
+ abs_error=np.zeros(()),
+ count=np.zeros(()),
+ )
+
+
+def update_regression_metrics(
+ y_true: jax.Array,
+ y_pred: jax.Array,
+ mask: jax.Array | None = None,
+) -> RegressionState:
+ y_true = jnp.astype(y_true, jnp.float32)
+ y_pred = jnp.astype(y_pred, jnp.float32)
+ return RegressionState(
+ pearsonr=_pearsonr_update(y_true, y_pred, mask=mask, axis=(-2, -3)),
+ pearsonr_log1p=_pearsonr_update(
+ jnp.log1p(y_true), jnp.log1p(y_pred), mask=mask, axis=(-2, -3)
+ ),
+ sq_error=jnp.sum(
+ jnp.square(y_true - y_pred),
+ axis=(-2, -3),
+ where=mask,
+ dtype=jnp.float32,
+ ),
+ abs_error=jnp.sum(
+ jnp.abs(y_true - y_pred),
+ axis=(-2, -3),
+ where=mask,
+ dtype=jnp.float32,
+ ),
+ count=jnp.sum(
+ jnp.ones_like(y_true), axis=(-2, -3), where=mask, dtype=jnp.float32
+ ),
+ )
+
+
+def finalize_regression_metrics(
+ state: PyTree[RegressionState],
+) -> PyTree[jax.Array]:
+ """Compute final metrics from accumulated state."""
+
+ def _finalize(state: RegressionState) -> PyTree[jax.Array]:
+ return {
+ 'pearsonr': (
+ _pearsonr_result(state.pearsonr).mean(
+ where=state.pearsonr.count > 0
+ )
+ ),
+ 'pearsonr_log1p': (
+ _pearsonr_result(state.pearsonr_log1p).mean(
+ where=state.pearsonr_log1p.count > 0
+ )
+ ),
+ 'mse': jnp.mean(state.sq_error / state.count, where=state.count > 0),
+ 'mae': jnp.mean(state.abs_error / state.count, where=state.count > 0),
+ }
+
+ return jax.tree.map(
+ _finalize, state, is_leaf=lambda x: isinstance(x, RegressionState)
+ )
+
+
+def reduce_regression_metrics(
+ previous_metrics: PyTree[RegressionState],
+ current_metrics: PyTree[RegressionState],
+) -> RegressionState:
+ """Reduce metrics from a single device to a single scalar."""
+ return jax.tree.map(
+ lambda x, y: x + y,
+ previous_metrics,
+ current_metrics,
+ is_leaf=lambda x: isinstance(x, RegressionState),
+ )
+
+
+@typing.jaxtyped
+def crop_sequence_length(
+ x: Float[ArrayLike, '... S D'], *, target_length: int
+) -> Float[ArrayLike, '... {target_length} D']:
+ """Crops an array to match the target length along the sequence dimension."""
+ sequence_axis = -2
+ if x.shape[sequence_axis] < target_length:
+ raise ValueError(
+ f'Input length {x.shape[sequence_axis]} is shorter than the requested'
+ f' cropped length of {target_length}.'
+ )
+ elif x.shape[sequence_axis] == target_length:
+ return x
+ else:
+ ltrim = (x.shape[sequence_axis] - target_length) // 2
+ rtrim = x.shape[sequence_axis] - target_length - ltrim
+ slices = [
+ slice(None),
+ ] * len(x.shape)
+ slices[sequence_axis] = slice(ltrim, -rtrim)
+ return x[tuple(slices)]
diff --git a/flax_model/alphagenome/evals/track_prediction.py b/flax_model/alphagenome/evals/track_prediction.py
new file mode 100644
index 0000000000000000000000000000000000000000..b835e9635c454c5858ed5adb88e4e8532749f069
--- /dev/null
+++ b/flax_model/alphagenome/evals/track_prediction.py
@@ -0,0 +1,202 @@
+# Copyright 2026 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.
+
+"""Evaluates AlphaGenome track prediction performance."""
+
+from collections.abc import Iterator
+import pprint
+from typing import Callable, Sequence
+from absl import app
+from absl import logging
+from flax_model.alphagenome._sdk.data import fold_intervals
+from flax_model.alphagenome._sdk.models import dna_output
+from flax_model.alphagenome.evals import regression_metrics
+from flax_model.alphagenome.io import bundles as bundles_lib
+from flax_model.alphagenome.io import dataset
+from flax_model.alphagenome.model import dna_model
+from flax_model.alphagenome.model import model as model_lib
+from flax_model.alphagenome.model import schemas
+from flax_model.alphagenome.model.metadata import metadata as metadata_lib
+import haiku as hk
+import jax
+from jax import sharding
+from jax.experimental import mesh_utils
+from jaxtyping import PyTree # pylint: disable=g-importing-member
+import jmp
+import kagglehub
+import orbax.checkpoint as ocp
+import pandas as pd
+import tensorflow as tf
+
+
+PS = sharding.PartitionSpec
+PredictFn = Callable[
+ [
+ hk.Params,
+ hk.State,
+ jax.Array,
+ jax.Array,
+ ],
+ PyTree[jax.Array],
+]
+
+_SUBSET = fold_intervals.Subset.VALID
+_LOG_FREQUENCY = 5
+_EVAL_BUNDLES = [
+ bundles_lib.BundleName.ATAC,
+ bundles_lib.BundleName.CAGE,
+ bundles_lib.BundleName.CHIP_HISTONE,
+ bundles_lib.BundleName.CHIP_TF,
+ bundles_lib.BundleName.DNASE,
+ bundles_lib.BundleName.PROCAP,
+ bundles_lib.BundleName.RNA_SEQ,
+]
+
+
+def load_model(
+ model_version: dna_model.ModelVersion = dna_model.ModelVersion.FOLD_0,
+) -> tuple[hk.Params, hk.State, PredictFn]:
+ """Loads the model consiting of params, state and predict function."""
+ checkpoint_path = kagglehub.model_download(
+ f'google/alphagenome/jax/{model_version.name.lower()}'
+ )
+ params, state = ocp.StandardCheckpointer().restore(checkpoint_path)
+ metadata = {
+ organism: metadata_lib.load(organism) for organism in dna_model.Organism
+ }
+
+ @hk.transform_with_state
+ def forward(dna_sequence, organism_index):
+ policy = jmp.get_policy('params=float32,compute=bfloat16,output=bfloat16')
+ with hk.mixed_precision.push_policy(model_lib.AlphaGenome, policy):
+ return model_lib.AlphaGenome(metadata)(dna_sequence, organism_index)
+
+ @jax.jit(
+ in_shardings=(PS(), PS(), PS('data'), PS('data')),
+ out_shardings=PS('data'),
+ )
+ def predict(params, state, dna_sequence, organism_index) -> PyTree[jax.Array]:
+ (predictions, _), _ = forward.apply(
+ params, state, None, dna_sequence, organism_index
+ )
+ predictions = dna_model.extract_predictions(predictions)
+ return predictions
+
+ return params, state, predict
+
+
+def create_eval_step(
+ predict_fn: PredictFn, bundles: Sequence[bundles_lib.BundleName]
+):
+ """Returns the eval step function."""
+
+ @jax.jit(
+ in_shardings=(PS(), PS(), PS('data')),
+ out_shardings=PS(),
+ )
+ def eval_step(params, state, batch: schemas.DataBatch):
+ predictions = predict_fn(
+ params, state, batch.dna_sequence, batch.organism_index
+ )
+ metrics_step = {}
+ for bundle in bundles:
+ targets_true, mask = batch.get_genome_tracks(bundle)
+ targets_pred = predictions[dna_output.OutputType[bundle.name]]
+ targets_pred = regression_metrics.crop_sequence_length(
+ targets_pred, target_length=targets_true.shape[-2]
+ )
+ metrics_step[bundle.name] = regression_metrics.update_regression_metrics(
+ targets_true, targets_pred, mask
+ )
+ return metrics_step
+
+ return eval_step
+
+
+def evaluate(
+ params: hk.Params,
+ state: hk.State,
+ predict_fn: PredictFn,
+ bundles: Sequence[bundles_lib.BundleName],
+ dataset_iterator: Iterator[tuple[schemas.DataBatch, dataset.BatchMetadata]],
+):
+ """Evaluates the model."""
+ # Setup Mesh.
+ devices = mesh_utils.create_device_mesh((jax.local_device_count(),))
+ mesh = jax.sharding.Mesh(devices, axis_names=('data',))
+ sharding_rep = sharding.NamedSharding(mesh, PS())
+ sharding_data = sharding.NamedSharding(mesh, PS('data'))
+
+ # Replicate params and state.
+ params = jax.device_put(params, sharding_rep)
+ state = jax.device_put(state, sharding_rep)
+
+ eval_step = create_eval_step(predict_fn, bundles)
+ metrics = {
+ b.name: regression_metrics.initialize_regression_metrics()
+ for b in bundles
+ }
+ num_elements = 0
+
+ for i, (batch, _) in enumerate(dataset_iterator):
+ num_elements += batch.dna_sequence.shape[0]
+ if i % _LOG_FREQUENCY == 1:
+ m = pprint.pformat(
+ regression_metrics.finalize_regression_metrics(metrics)
+ )
+ logging.info('step %d: %s', i, m)
+
+ with jax.set_mesh(mesh):
+ batch = jax.device_put(batch, sharding_data)
+ step_metrics = eval_step(params, state, batch)
+
+ # Accumulate metrics.
+ step_metrics = jax.device_get(step_metrics)
+ metrics = regression_metrics.reduce_regression_metrics(
+ metrics, step_metrics
+ )
+ logging.info('num_elements: %d', num_elements)
+ return regression_metrics.finalize_regression_metrics(metrics)
+
+
+def run(
+ organism: dna_model.Organism = dna_model.Organism.HOMO_SAPIENS,
+ model_version: dna_model.ModelVersion = dna_model.ModelVersion.FOLD_0,
+) -> pd.DataFrame:
+ """Runs the track prediction experiment."""
+ logging.info('Starting track prediction experiment.')
+ params, state, predict_fn = load_model(model_version)
+ dataset_iterator = dataset.get_numpy_dataset_iterator(
+ batch_size=jax.local_device_count(),
+ organism=organism,
+ model_version=model_version,
+ bundles=_EVAL_BUNDLES,
+ subset=_SUBSET,
+ )
+ results = evaluate(params, state, predict_fn, _EVAL_BUNDLES, dataset_iterator)
+ flattened_results = {}
+ for bundle, result in results.items():
+ for metric, value in result.items():
+ logging.info('bundle: %s, metric: %s, value: %s', bundle, metric, value)
+ flattened_results[f'{bundle}_{metric}'] = value
+ df = pd.DataFrame(
+ {'metric': flattened_results.keys(), 'value': flattened_results.values()}
+ )
+ return df
+
+
+if __name__ == '__main__':
+ # Hide local GPUs from TF. TF is only used for data loading.
+ tf.config.set_visible_devices([], 'GPU')
+ app.run(lambda _: run())
diff --git a/flax_model/alphagenome/finetuning/__init__.py b/flax_model/alphagenome/finetuning/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ccd981b8e01db4c49c1e3cd1df6697d1f8675888
--- /dev/null
+++ b/flax_model/alphagenome/finetuning/__init__.py
@@ -0,0 +1,15 @@
+# Copyright 2026 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 finetuning."""
diff --git a/flax_model/alphagenome/finetuning/dataset.py b/flax_model/alphagenome/finetuning/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..aab5057a6fc16ec3d58ac3dd5644f3c0f6ac0a22
--- /dev/null
+++ b/flax_model/alphagenome/finetuning/dataset.py
@@ -0,0 +1,236 @@
+# Copyright 2026 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 pipeline for reading sequence and tracks for fine-tuning."""
+
+from collections.abc import Iterator, Sequence
+import concurrent.futures
+from typing import Any, Mapping
+
+from absl import logging
+from flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome._sdk.models import dna_model
+from flax_model.alphagenome._sdk.models import dna_output
+from flax_model.alphagenome.io import fasta
+from flax_model.alphagenome.model import one_hot_encoder
+from flax_model.alphagenome.model.metadata import metadata as metadata_lib
+import numpy as np
+import pandas as pd
+import pyBigWig
+import tensorflow as tf
+
+
+class BigWigExtractor:
+ """BigWig file extractor using pyBigWig."""
+
+ def __init__(self, file_path: str):
+ self._bw = pyBigWig.open(file_path)
+ self._chromosomes = set(self._bw.chroms().keys())
+
+ def __del__(self):
+ self.close()
+
+ @property
+ def chromosomes(self) -> set[str]:
+ return self._chromosomes
+
+ def extract(self, interval: genome.Interval) -> tf.Tensor:
+ """Extracts values from a BigWig file for a given interval."""
+ if interval.chromosome not in self._chromosomes:
+ raise ValueError(
+ f'Chromosome {interval.chromosome} not found in BigWig. '
+ 'Check self._bw.chroms() for available chromosomes. '
+ )
+ start = max(0, interval.start)
+ end = min(self._bw.chroms()[interval.chromosome], interval.end)
+
+ if end <= start:
+ return tf.cast(np.zeros(interval.width), tf.bfloat16)
+
+ values = self._bw.values(interval.chromosome, start, end, numpy=True)
+ if values.shape[0] != interval.width:
+ pad_left = start - interval.start
+ pad_right = interval.end - end
+ values = np.pad(
+ values, pad_width=(pad_left, pad_right), constant_values=0
+ )
+ return tf.cast(np.nan_to_num(values, nan=0.0), tf.bfloat16)
+
+ def close(self):
+ if self._bw is not None:
+ self._bw.close()
+
+
+class MultiTrackExtractor:
+ """Multi-track BigWig file extractor.
+
+ Returns tracks per output type. Tracks are ordered as per the metadata.
+ """
+
+ def __init__(
+ self,
+ output_metadata: metadata_lib.AlphaGenomeOutputMetadata,
+ sequence_length: int,
+ max_workers=32,
+ ):
+ """Initializes the MultiTrackExtractor.
+
+ Args:
+ output_metadata: The output metadata containing the track information.
+ sequence_length: The length of the sequence to extract.
+ max_workers: The maximum number of workers to use for parallel extraction.
+ """
+ self._output_metadata = output_metadata
+ self._sequence_length = sequence_length
+ self._executor = concurrent.futures.ThreadPoolExecutor(
+ max_workers=max_workers
+ )
+
+ # group bigwig extractors by output type.
+ self._bw_extractors: dict[
+ dna_output.OutputType, Sequence[BigWigExtractor]
+ ] = {}
+ for output_type in dna_output.OutputType:
+ if (metadata := output_metadata.get(output_type)) is not None:
+ self._bw_extractors[output_type] = [
+ BigWigExtractor(file_path) for file_path in metadata['file_path']
+ ]
+
+ self._track_masks = {
+ f'{output_type.name.lower()}_mask': np.logical_not(mask).reshape(1, -1)
+ for output_type, mask in output_metadata.padding.items()
+ }
+
+ def get_output_signature(self):
+ """Returns the output signature of the dataset."""
+ signature = {}
+ for output_type, extractors in self._bw_extractors.items():
+ num_tracks = len(extractors)
+ output_name = output_type.name.lower()
+ signature[output_name] = tf.TensorSpec(
+ shape=(self._sequence_length, num_tracks),
+ dtype=tf.bfloat16,
+ )
+ signature[f'{output_name}_mask'] = tf.TensorSpec(
+ shape=(1, num_tracks),
+ dtype=tf.bool,
+ )
+ return signature
+
+ def extract(self, interval: genome.Interval) -> Mapping[str, tf.Tensor]:
+ """Extracts all tracks for all groups in parallel."""
+
+ # Submit
+ future_map = {}
+ for output_type, extractors in self._bw_extractors.items():
+ future_map[output_type] = [
+ self._executor.submit(bw.extract, interval) for bw in extractors
+ ]
+
+ # Collect
+ data = {}
+ for output_type, futures in future_map.items():
+ try:
+ results = [f.result() for f in futures]
+ data[output_type.name.lower()] = np.stack(results, axis=-1)
+ except Exception as e:
+ raise RuntimeError(
+ f"Failed to extract tracks for output group '{output_type.name}'"
+ ) from e
+ return data | self._track_masks
+
+ def close(self):
+ for bw_extractor in self._bw_extractors.values():
+ for bw in bw_extractor:
+ bw.close()
+
+
+class DataPipeline:
+ """Data pipeline for reading sequence and tracks."""
+
+ def __init__(
+ self,
+ *,
+ fasta_path: str,
+ intervals: pd.DataFrame,
+ output_metadata: metadata_lib.AlphaGenomeOutputMetadata,
+ organism: dna_model.Organism,
+ sequence_length: int,
+ ):
+ if organism != dna_model.Organism.HOMO_SAPIENS:
+ raise NotImplementedError('Only HOMO_SAPIENS is currently supported.')
+
+ self._fasta_extractor = fasta.FastaExtractor(fasta_path)
+ self._intervals = intervals
+ self._organsim = organism
+ self._organism_index = 0 # Only one organism is supported.
+ self._sequence_length = sequence_length
+ self._one_hot_encoder = one_hot_encoder.DNAOneHotEncoder()
+ self._multi_track_extractor = MultiTrackExtractor(
+ output_metadata=output_metadata,
+ sequence_length=sequence_length,
+ )
+
+ def get_element(self, idx: int) -> Mapping[str, Any]:
+ """Returns a single element of the dataset."""
+ interval = self._intervals.iloc[idx]
+ interval = genome.Interval(
+ chromosome=interval['chromosome'],
+ start=int(interval['start']),
+ end=int(interval['end']),
+ )
+ interval = interval.resize(self._sequence_length)
+ seq_str = self._fasta_extractor.extract(interval)
+ seq_one_hot = self._one_hot_encoder.encode(seq_str)
+ track_bundles = self._multi_track_extractor.extract(interval)
+ return {
+ 'dna_sequence': seq_one_hot,
+ 'organism_index': self._organism_index,
+ 'bundles': track_bundles,
+ }
+
+ def get_output_signature(self):
+ """Returns the output signature of the dataset."""
+ return {
+ 'dna_sequence': tf.TensorSpec(
+ shape=(self._sequence_length, 4), dtype=tf.float32
+ ),
+ 'organism_index': tf.TensorSpec(shape=[], dtype=tf.int32),
+ 'bundles': self._multi_track_extractor.get_output_signature(),
+ }
+
+ def get_generator(
+ self, num_epochs: int = -1, shuffle: bool = True, seed: int = 0
+ ) -> Iterator[Mapping[str, Any]]:
+ """Returns a generator for the dataset."""
+ rng = np.random.default_rng(seed=seed)
+ num_epochs = num_epochs if num_epochs > 0 else float('inf')
+ epoch_idx = 0
+ while epoch_idx < num_epochs:
+ epoch_idx += 1
+ if shuffle:
+ self._intervals = self._intervals.sample(
+ frac=1, random_state=rng
+ ).reset_index(drop=True)
+ for idx in range(len(self._intervals)):
+ try:
+ yield self.get_element(idx)
+ except Exception as e: # pylint: disable=broad-except
+ logging.warning(
+ 'Failed to get interval %s. With error: %s',
+ self._intervals.iloc[idx].to_dict(),
+ e,
+ )
+
+ def close(self):
+ self._multi_track_extractor.close()
diff --git a/flax_model/alphagenome/finetuning/dataset_test.py b/flax_model/alphagenome/finetuning/dataset_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..141c710d21ec5ac2d7817e912205a213eb949399
--- /dev/null
+++ b/flax_model/alphagenome/finetuning/dataset_test.py
@@ -0,0 +1,198 @@
+# Copyright 2026 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 flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome._sdk.models import dna_client
+from flax_model.alphagenome.finetuning import dataset
+from flax_model.alphagenome.io import fasta
+from flax_model.alphagenome.model.metadata import metadata as metadata_lib
+import chex
+import numpy as np
+import pandas as pd
+import pyBigWig
+import tensorflow as tf
+
+MOCK_CHROM_SIZES = {'chr1': 100}
+
+
+def get_mock_metadata(num_atac_tracks: int = 2, num_dnase_tracks: int = 1):
+ return metadata_lib.AlphaGenomeOutputMetadata(
+ atac=pd.DataFrame({
+ 'file_path': [f'path{i}' for i in range(num_atac_tracks)],
+ 'name': [f'atac_track{i}' for i in range(num_atac_tracks)],
+ 'strand': ['.'] * num_atac_tracks,
+ }),
+ dnase=pd.DataFrame({
+ 'file_path': [f'path{i}' for i in range(num_dnase_tracks)],
+ 'name': [f'dnase_track{i}' for i in range(num_dnase_tracks)],
+ 'strand': ['.'] * num_dnase_tracks,
+ }),
+ )
+
+
+def get_mock_bw():
+ mock_bw = mock.Mock()
+ mock_bw.chroms.return_value = MOCK_CHROM_SIZES
+
+ def mock_values(chrom, start, end, numpy=True):
+ del chrom, numpy
+ return np.full(end - start, 1.0)
+
+ mock_bw.values = mock_values
+ return mock_bw
+
+
+class MockFastaExtractor:
+
+ def __init__(self, path):
+ pass
+
+ def extract(self, interval):
+ return 'A' * interval.width
+
+
+class DataTest(parameterized.TestCase):
+
+ @parameterized.named_parameters([
+ dict(
+ testcase_name='valid_interval',
+ interval=genome.Interval('chr1', 10, 20),
+ expected_values=np.ones(10),
+ ),
+ dict(
+ testcase_name='interval_start_negative',
+ interval=genome.Interval('chr1', -10, 10),
+ expected_values=np.concatenate([np.zeros(10), np.ones(10)]),
+ ),
+ dict(
+ testcase_name='interval_end_beyond_chrom_length',
+ interval=genome.Interval('chr1', 90, 110),
+ expected_values=np.concatenate([np.ones(10), np.zeros(10)]),
+ ),
+ dict(
+ testcase_name='interval_fully_outside_negative',
+ interval=genome.Interval('chr1', -20, -10),
+ expected_values=np.zeros(10),
+ ),
+ dict(
+ testcase_name='interval_fully_outside_positive',
+ interval=genome.Interval('chr1', 110, 120),
+ expected_values=np.zeros(10),
+ ),
+ dict(
+ testcase_name='zero_length_interval',
+ interval=genome.Interval('chr1', 10, 10),
+ expected_values=np.array([]),
+ ),
+ ])
+ @mock.patch.object(pyBigWig, 'open')
+ def test_bigwig_extractor(self, mock_bigwig_open, interval, expected_values):
+ mock_bigwig_open.return_value = get_mock_bw()
+ extractor = dataset.BigWigExtractor('mock_path')
+ values = extractor.extract(interval)
+ self.assertEqual(values.shape, expected_values.shape)
+ np.testing.assert_array_equal(values, expected_values)
+ extractor.close()
+ mock_bigwig_open.assert_called_once_with('mock_path')
+
+ @mock.patch.object(pyBigWig, 'open')
+ def test_bigwig_extractor_wrong_chromosome(self, mock_bigwig_open):
+ mock_bigwig_open.return_value = get_mock_bw()
+ extractor = dataset.BigWigExtractor('mock_path')
+ with self.assertRaisesRegex(ValueError, 'Chromosome chr2 not found'):
+ extractor.extract(genome.Interval('chr2', 0, 10))
+ extractor.close()
+ mock_bigwig_open.assert_called_once_with('mock_path')
+
+ @mock.patch.object(pyBigWig, 'open')
+ def test_multi_track_extractor(self, mock_bigwig_open):
+ mock_bigwig_open.return_value = get_mock_bw()
+ extractor = dataset.MultiTrackExtractor(
+ output_metadata=get_mock_metadata(
+ num_atac_tracks=3, num_dnase_tracks=4
+ ),
+ sequence_length=10,
+ )
+ interval = genome.Interval('chr1', 10, 20)
+ result = extractor.extract(interval)
+
+ chex.assert_shape(result['atac'], (10, 3))
+ self.assertEqual(result['atac'].dtype, tf.bfloat16)
+ chex.assert_shape(result['atac_mask'], (1, 3))
+ self.assertEqual(result['atac_mask'].dtype, tf.bool)
+ chex.assert_shape(result['dnase'], (10, 4))
+ chex.assert_shape(result['dnase_mask'], (1, 4))
+ extractor.close()
+ self.assertEqual(mock_bigwig_open.call_count, 7)
+
+ @parameterized.named_parameters([
+ dict(testcase_name='one_epoch', num_epochs=1),
+ dict(testcase_name='three_epochs', num_epochs=3),
+ ])
+ @mock.patch.object(fasta, 'FastaExtractor', MockFastaExtractor)
+ @mock.patch.object(pyBigWig, 'open')
+ def test_data_pipeline(self, mock_bigwig_open, num_epochs):
+ mock_bigwig_open.return_value = get_mock_bw()
+ intervals = pd.DataFrame({
+ 'chromosome': ['chr1', 'chr1', 'chr1'],
+ 'start': [10, 50, 90],
+ 'end': [30, 70, 110],
+ })
+ sequence_length = 30
+ pipeline = dataset.DataPipeline(
+ fasta_path='mock_fasta',
+ intervals=intervals,
+ output_metadata=get_mock_metadata(
+ num_atac_tracks=2, num_dnase_tracks=1
+ ),
+ organism=dna_client.Organism.HOMO_SAPIENS,
+ sequence_length=sequence_length,
+ )
+
+ generator = pipeline.get_generator(num_epochs=num_epochs, shuffle=False)
+ elements = list(generator)
+ self.assertLen(elements, len(intervals) * num_epochs)
+
+ element = elements[0]
+ chex.assert_shape(element['dna_sequence'], (sequence_length, 4))
+ np.testing.assert_array_equal(
+ element['dna_sequence'],
+ np.tile(np.array([[1, 0, 0, 0]]), (sequence_length, 1)), # 'A'
+ )
+ self.assertEqual(element['organism_index'], 0)
+ bundles = element['bundles']
+ chex.assert_shape(bundles['atac'], (sequence_length, 2))
+ chex.assert_shape(bundles['dnase'], (sequence_length, 1))
+
+ # The third interval (non shuffled) is half out of bounds.
+ element = elements[2]
+ bundles = element['bundles']
+ expected_track = np.concatenate([np.ones(15), np.zeros(15)])
+ np.testing.assert_array_equal(
+ bundles['atac'], np.stack([expected_track] * 2, axis=-1)
+ )
+ np.testing.assert_array_equal(
+ bundles['dnase'], expected_track.reshape(-1, 1)
+ )
+
+ pipeline.close()
+ self.assertEqual(mock_bigwig_open.call_count, 3)
+
+
+if __name__ == '__main__':
+ absltest.main()
diff --git a/flax_model/alphagenome/finetuning/finetune.py b/flax_model/alphagenome/finetuning/finetune.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb4d33aa1224370d371a7a9e98ffd8b1f50158d9
--- /dev/null
+++ b/flax_model/alphagenome/finetuning/finetune.py
@@ -0,0 +1,153 @@
+# Copyright 2026 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.
+"""AlphaGenome finetuning script."""
+
+from collections.abc import Iterator, Mapping
+from typing import Any, Callable
+from flax_model.alphagenome._sdk.data import fold_intervals
+from flax_model.alphagenome._sdk.models import dna_model
+from flax_model.alphagenome.finetuning import dataset as dataset_lib
+from flax_model.alphagenome.model import model
+from flax_model.alphagenome.model import schemas
+from flax_model.alphagenome.model.metadata import metadata as metadata_lib
+import haiku as hk
+import jax
+import jmp
+import optax
+import tensorflow as tf
+
+_FASTA_PATH = (
+ 'https://storage.googleapis.com/alphagenome/reference/gencode/'
+ 'hg38/GRCh38.p13.genome.fa'
+)
+
+
+def get_dataset_iterator(
+ *,
+ batch_size: int,
+ sequence_length: int,
+ output_metadata: metadata_lib.AlphaGenomeOutputMetadata,
+ model_version: dna_model.ModelVersion,
+ subset: fold_intervals.Subset,
+ organism: dna_model.Organism = dna_model.Organism.HOMO_SAPIENS,
+ fasta_path: str = _FASTA_PATH,
+ example_regions_path: str | None = None,
+) -> Iterator[schemas.DataBatch]:
+ """Converts pipeline output dict to a DataBatch schema.
+
+ Args:
+ batch_size: The batch size of the dataset.
+ sequence_length: The sequence length of the dataset.
+ output_metadata: Metadata for the output tracks for each organism.
+ model_version: The model version to use.
+ subset: The subset of the dataset.
+ organism: The organism to use.
+ fasta_path: The path to the reference genome FASTA file.
+ example_regions_path: The path to the example regions BED file.
+
+ Returns:
+ A schemas.DataBatch object.
+ """
+ intervals = fold_intervals.get_fold_intervals(
+ model_version,
+ organism,
+ subset,
+ example_regions_path=example_regions_path,
+ )
+ pipeline = dataset_lib.DataPipeline(
+ fasta_path=fasta_path,
+ intervals=intervals,
+ output_metadata=output_metadata,
+ organism=organism,
+ sequence_length=sequence_length,
+ )
+ dataset = tf.data.Dataset.from_generator(
+ pipeline.get_generator,
+ output_signature=pipeline.get_output_signature(),
+ )
+ dataset = (
+ dataset.batch(batch_size).prefetch(tf.data.AUTOTUNE).as_numpy_iterator()
+ )
+
+ def iterator():
+ for batch in dataset:
+ yield schemas.DataBatch(
+ dna_sequence=batch['dna_sequence'],
+ organism_index=batch['organism_index'],
+ **batch['bundles'],
+ )
+
+ return iterator()
+
+
+def get_forward_fn(
+ output_metadata: Mapping[
+ dna_model.Organism, metadata_lib.AlphaGenomeOutputMetadata
+ ],
+ jmp_policy: str = 'params=float32,compute=bfloat16,output=bfloat16',
+) -> hk.TransformedWithState:
+ """Creates a Haiku transformed function for the AlphaGenome model.
+
+ Args:
+ output_metadata: Metadata for the output tracks for each organism.
+ jmp_policy: The JMP policy to use for mixed precision.
+
+ Returns:
+ A `hk.TransformedWithState` object representing the forward pass.
+ """
+ jmp_policy = jmp.get_policy(jmp_policy)
+
+ @hk.transform_with_state
+ def forward(batch: schemas.DataBatch):
+ with hk.mixed_precision.push_policy(model.AlphaGenome, jmp_policy):
+ return model.AlphaGenome(
+ output_metadata, freeze_trunk_embeddings=True
+ ).loss(batch)
+
+ return forward
+
+
+def get_train_step(
+ predict_fn: Callable[..., Any],
+ optimizer: optax.GradientTransformation,
+):
+ """Creates a jitted training step function using AlphaGenome.loss.
+
+ Args:
+ predict_fn: The Haiku transformed forward function.
+ optimizer: An Optax optimizer to apply gradients.
+
+ Returns:
+ A jitted function `train_step` that takes `params`, `state`, `opt_state`,
+ and a `batch` as input and returns the updated `params`, `next_state`,
+ `new_opt_state`, and a dictionary of `metrics`.
+ """
+
+ @jax.jit
+ def train_step(params, state, opt_state, batch):
+ def loss_fn(params, state, batch):
+ (loss, scalars, predictions), new_state = predict_fn(
+ params, state, None, batch
+ )
+ del predictions # Unused.
+ return loss, (new_state, scalars)
+
+ loss_grad_fn = jax.value_and_grad(loss_fn, has_aux=True)
+ (loss, (next_state, scalars)), grads = loss_grad_fn(params, state, batch)
+ scalars['loss'] = loss
+ updates, new_opt_state = optimizer.update(grads, opt_state, params)
+ new_params = optax.apply_updates(params, updates)
+ return new_params, next_state, new_opt_state, scalars
+
+ return train_step
diff --git a/flax_model/alphagenome/finetuning/finetune_test.py b/flax_model/alphagenome/finetuning/finetune_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..70fae700268f25f5828c011a043add20c1224b1b
--- /dev/null
+++ b/flax_model/alphagenome/finetuning/finetune_test.py
@@ -0,0 +1,179 @@
+# Copyright 2026 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
+
+from absl.testing import absltest
+from flax_model.alphagenome.finetuning import finetune
+from flax_model.alphagenome.model import dna_model
+from flax_model.alphagenome.model import model as model_lib
+from flax_model.alphagenome.model import schemas
+from flax_model.alphagenome.model.metadata import metadata as metadata_lib
+import haiku as hk
+import jax
+import jax.numpy as jnp
+import numpy as np
+import optax
+import orbax.checkpoint as ocp
+import pandas as pd
+
+
+def _create_mock_df(modality_name: str, num_tracks: int) -> pd.DataFrame | None:
+ if num_tracks == 0:
+ return None
+ return pd.DataFrame({
+ 'file_path': [f'path{i}' for i in range(num_tracks)],
+ 'name': [f'{modality_name}_track{i}' for i in range(num_tracks)],
+ 'strand': ['.'] * num_tracks,
+ })
+
+
+def get_mock_metadata(
+ num_atac_tracks: int = 0, num_dnase_tracks: int = 0, rna_seq_tracks: int = 0
+) -> metadata_lib.AlphaGenomeOutputMetadata:
+ return metadata_lib.AlphaGenomeOutputMetadata(
+ atac=_create_mock_df('atac', num_atac_tracks),
+ dnase=_create_mock_df('dnase', num_dnase_tracks),
+ rna_seq=_create_mock_df('rna_seq', rna_seq_tracks),
+ )
+
+
+class FinetuneTest(absltest.TestCase):
+
+ def test_finetune_train_step(self):
+ seq_length, batch_size, key = 131072, 1, jax.random.key(0)
+
+ # Setup base model.
+ base_metadata = {
+ dna_model.Organism.HOMO_SAPIENS: get_mock_metadata(
+ num_atac_tracks=(atac_tracks_base := 2),
+ rna_seq_tracks=(rna_seq_tracks_base := 1),
+ )
+ }
+
+ @hk.transform_with_state
+ def base_forward(batch: schemas.DataBatch):
+ return model_lib.AlphaGenome(base_metadata).loss(batch)
+
+ # Batch contains ATAC and RNA-seq data.
+ base_batch = schemas.DataBatch(
+ dna_sequence=np.zeros((batch_size, seq_length, 4), dtype=np.float32),
+ organism_index=np.zeros((batch_size,), dtype=np.int32),
+ atac=jnp.zeros(
+ (batch_size, seq_length, atac_tracks_base), dtype=np.float32
+ ),
+ atac_mask=np.ones((batch_size, 1, atac_tracks_base), dtype=bool),
+ rna_seq=np.zeros(
+ (batch_size, seq_length, rna_seq_tracks_base), dtype=np.float32
+ ),
+ rna_seq_mask=np.ones((batch_size, 1, rna_seq_tracks_base), dtype=bool),
+ )
+ base_params, base_state = jax.eval_shape(base_forward.init, key, base_batch)
+
+ # Setup fine-tuning model with different metadata.
+ ft_metadata = {
+ dna_model.Organism.HOMO_SAPIENS: get_mock_metadata(
+ num_atac_tracks=(atac_tracks_ft := 3), # More ATAC tracks.
+ rna_seq_tracks=0, # No RNA-seq tracks.
+ num_dnase_tracks=(dnase_tracks_ft := 7), # New DNase tracks.
+ )
+ }
+ batch_ft = schemas.DataBatch(
+ dna_sequence=np.zeros((batch_size, seq_length, 4), dtype=np.float32),
+ organism_index=np.zeros((batch_size,), dtype=np.int32),
+ atac=jnp.zeros(
+ (batch_size, seq_length, atac_tracks_ft), dtype=np.float32
+ ),
+ atac_mask=np.ones((batch_size, 1, atac_tracks_ft), dtype=bool),
+ dnase=jnp.zeros(
+ (batch_size, seq_length, dnase_tracks_ft), dtype=np.float32
+ ),
+ dnase_mask=np.ones((batch_size, 1, dnase_tracks_ft), dtype=bool),
+ )
+
+ forward_fn = finetune.get_forward_fn(ft_metadata)
+ params_ft, _ = jax.eval_shape(forward_fn.init, key, batch_ft)
+
+ def merge(frozen, trainable):
+ trainable = hk.data_structures.filter(
+ lambda module_name, *_: 'head' in module_name, trainable
+ )
+ merged = hk.data_structures.merge(frozen, trainable)
+ return merged
+
+ params = merge(base_params, params_ft)
+
+ optimizer = optax.adam(learning_rate=1e-3)
+ opt_state = jax.eval_shape(optimizer.init, params)
+ train_step = finetune.get_train_step(forward_fn.apply, optimizer)
+ _, _, _, scalars = jax.eval_shape(
+ train_step, params, base_state, opt_state, batch_ft
+ )
+ self.assertIn('loss', scalars)
+ self.assertIn('atac_loss', scalars)
+ self.assertIn('dnase_loss', scalars)
+ self.assertNotIn('rna_seq_loss', scalars) # RNA-seq head not in FT model.
+
+ def test_create_finetuned_dna_sequence_model(self):
+ seq_length, batch_size, key = 131072, 1, jax.random.key(0)
+ ft_metadata = {
+ dna_model.Organism.HOMO_SAPIENS: get_mock_metadata(
+ num_atac_tracks=(atac_tracks_ft := 3), # More ATAC tracks.
+ rna_seq_tracks=0, # No RNA-seq tracks.
+ num_dnase_tracks=(dnase_tracks_ft := 7), # New DNase tracks.
+ )
+ }
+ batch_ft = schemas.DataBatch(
+ dna_sequence=np.zeros((batch_size, seq_length, 4), dtype=np.float32),
+ organism_index=np.zeros((batch_size,), dtype=np.int32),
+ atac=jnp.zeros(
+ (batch_size, seq_length, atac_tracks_ft), dtype=np.float32
+ ),
+ atac_mask=np.ones((batch_size, 1, atac_tracks_ft), dtype=bool),
+ dnase=jnp.zeros(
+ (batch_size, seq_length, dnase_tracks_ft), dtype=np.float32
+ ),
+ dnase_mask=np.ones((batch_size, 1, dnase_tracks_ft), dtype=bool),
+ )
+
+ forward_fn = finetune.get_forward_fn(ft_metadata)
+ params_ft, state_ft = jax.eval_shape(forward_fn.init, key, batch_ft)
+
+ checkpointer = ocp.StandardCheckpointer()
+ ckpt_dir = self.create_tempdir().full_path
+ ckpt_path = os.path.join(ckpt_dir, 'checkpoint')
+ checkpointer.save(
+ ckpt_path,
+ jax.tree_util.tree_map(
+ lambda x: jnp.empty(x.shape, x.dtype), (params_ft, state_ft)
+ ),
+ )
+ checkpointer.wait_until_finished()
+
+ ft_organism_settings = {
+ k: dna_model.OrganismSettings(metadata=v)
+ for k, v in ft_metadata.items()
+ }
+ finetuned_dna_sequence_model = dna_model.create(
+ ckpt_path,
+ organism_settings=ft_organism_settings,
+ device=jax.devices('cpu')[0],
+ )
+ self.assertIsInstance(
+ finetuned_dna_sequence_model, dna_model.AlphaGenomeModel
+ )
+
+
+if __name__ == '__main__':
+ absltest.main()
diff --git a/flax_model/alphagenome/io/__init__.py b/flax_model/alphagenome/io/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..742f89aa058f809017fa691ed903899d8188ab64
--- /dev/null
+++ b/flax_model/alphagenome/io/__init__.py
@@ -0,0 +1,15 @@
+# Copyright 2026 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 reading and writing data."""
diff --git a/flax_model/alphagenome/io/bundles.py b/flax_model/alphagenome/io/bundles.py
new file mode 100644
index 0000000000000000000000000000000000000000..d40424058457ad1195fb0237413a8e87f666b740
--- /dev/null
+++ b/flax_model/alphagenome/io/bundles.py
@@ -0,0 +1,91 @@
+# Copyright 2026 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.
+
+"""Bundle names and their associated keys and resolutions."""
+
+import enum
+import tensorflow as tf
+
+
+class BundleName(enum.Enum):
+ """Bundle names."""
+
+ ATAC = 'atac'
+ DNASE = 'dnase'
+ PROCAP = 'procap'
+ CAGE = 'cage'
+ RNA_SEQ = 'rna_seq'
+ CHIP_TF = 'chip_tf'
+ CHIP_HISTONE = 'chip_histone'
+ CONTACT_MAPS = 'contact_maps'
+ SPLICE_SITES_CLASSIFICATION = 'splice_sites'
+ SPLICE_SITES_USAGE = 'splice_site_usage'
+ SPLICE_SITES_JUNCTION = 'splice_junctions'
+ SPLICE_SITES_POSITIONS = 'splice_site_positions'
+
+ def get_dtypes(self) -> dict[str, tf.DType]:
+ """Returns the keys and dtypes for the given bundle."""
+ match self:
+ case BundleName.ATAC:
+ return {'atac': tf.bfloat16, 'atac_mask': tf.bool}
+ case BundleName.DNASE:
+ return {'dnase': tf.bfloat16, 'dnase_mask': tf.bool}
+ case BundleName.PROCAP:
+ return {'procap': tf.bfloat16, 'procap_mask': tf.bool}
+ case BundleName.CAGE:
+ return {'cage': tf.bfloat16, 'cage_mask': tf.bool}
+ case BundleName.RNA_SEQ:
+ return {
+ 'rna_seq': tf.bfloat16,
+ 'rna_seq_mask': tf.bool,
+ 'rna_seq_strand': tf.int32,
+ }
+ case BundleName.CHIP_TF:
+ return {'chip_tf': tf.float32, 'chip_tf_mask': tf.bool}
+ case BundleName.CHIP_HISTONE:
+ return {'chip_histone': tf.float32, 'chip_histone_mask': tf.bool}
+ case BundleName.CONTACT_MAPS:
+ return {'contact_maps': tf.float32}
+ case BundleName.SPLICE_SITES_CLASSIFICATION:
+ return {'splice_sites': tf.bool}
+ case BundleName.SPLICE_SITES_USAGE:
+ return {'splice_site_usage': tf.float16}
+ case BundleName.SPLICE_SITES_JUNCTION:
+ return {'splice_junctions': tf.float32}
+ case BundleName.SPLICE_SITES_POSITIONS:
+ return {'splice_site_positions': tf.int32}
+ case _:
+ raise ValueError(f'Unknown bundle name: {self}')
+
+ def get_resolution(self) -> int:
+ """Returns the resolutions for the given bundle."""
+ match self:
+ case (
+ BundleName.ATAC
+ | BundleName.DNASE
+ | BundleName.PROCAP
+ | BundleName.CAGE
+ | BundleName.RNA_SEQ
+ | BundleName.SPLICE_SITES_CLASSIFICATION
+ | BundleName.SPLICE_SITES_USAGE
+ | BundleName.SPLICE_SITES_JUNCTION
+ | BundleName.SPLICE_SITES_POSITIONS
+ ):
+ return 1
+ case BundleName.CHIP_TF | BundleName.CHIP_HISTONE:
+ return 128
+ case BundleName.CONTACT_MAPS:
+ return 2_048
+ case _:
+ raise ValueError(f'Unknown bundle name: {self}')
diff --git a/flax_model/alphagenome/io/dataset.py b/flax_model/alphagenome/io/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..5229862cfc10c4d6ba5e2f4755808c8bd839e943
--- /dev/null
+++ b/flax_model/alphagenome/io/dataset.py
@@ -0,0 +1,255 @@
+# Copyright 2026 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.
+
+"""Reading AlphaGenome data from TFRecords.
+
+This module provides functions to load AlphaGenome datasets from TFRecord files.
+Each TFRecord file contains data from a particular bundle (see `bundles.py`)
+for a set of intervals.
+
+We recommend using `get_numpy_dataset_iterator` to load the data for a
+particular organism, fold split, and subset. The iterator yields batches of
+data, where each batch is a tuple of `schemas.DataBatch` and a metadata
+dictionary containing the chromosome, start, and end of each interval.
+"""
+
+from collections.abc import Iterator, Mapping
+import functools
+import os
+import re
+from typing import Any, Sequence
+
+from flax_model.alphagenome._sdk.data import fold_intervals
+from flax_model.alphagenome._sdk.models import dna_model
+from flax_model.alphagenome.io import bundles as bundles_lib
+from flax_model.alphagenome.model import dna_model as research_dna_model
+from flax_model.alphagenome.model import schemas
+from etils import epath
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+
+_DEFAULT_PATH = 'gs:///alphagenome-datasets/v1/train/'
+
+BatchMetadata = Mapping[str, Any]
+
+_DNA_SEQUENCE_DTYPE = tf.float32
+_DNA_SEQUENCE_FEATURE_SPEC = {
+ 'dna_sequence': tf.io.FixedLenFeature([], tf.string)
+}
+_INTERVAL_FEATURE_SPEC = {
+ 'interval/chromosome': tf.io.FixedLenFeature([], tf.string),
+ 'interval/start': tf.io.FixedLenFeature([], tf.int64),
+ 'interval/end': tf.io.FixedLenFeature([], tf.int64),
+}
+_FILENAME_REGEX = re.compile(
+ r'data_(?P.+)_(?P\d+)-(?P\d+)\.gz\.tfrecord'
+)
+
+
+def get_tfrecords_df(
+ *,
+ organism: dna_model.Organism | None = None,
+ bundle: bundles_lib.BundleName | None = None,
+ fold_split: dna_model.ModelVersion | None = None,
+ subset: fold_intervals.Subset | None = None,
+ chromosome: str | None = None,
+ path: str | os.PathLike[str] | None = None,
+) -> pd.DataFrame:
+ """Return a dataframe with metadata about the TFRecord files.
+
+ Args:
+ organism: The organism to load. If None, all organisms are loaded.
+ bundle: The bundle to load. If None, all bundles are loaded.
+ fold_split: The fold split to load. If None, all fold splits are loaded.
+ subset: The subset to load. If None, all subsets are loaded.
+ chromosome: The chromosome to load. If None, all chromosomes are loaded.
+ path: The path to the TFRecord files. If None, the default path is used.
+ """
+ organism_pattern = organism.name if organism is not None else '*'
+ fold_split_pattern = fold_split.name if fold_split is not None else '*'
+ subset_pattern = subset.name if subset is not None else '*'
+ chromosome_pattern = chromosome if chromosome is not None else '*'
+ bundle_pattern = bundle.value.upper() if bundle is not None else '*'
+ glob_pattern = '/'.join([
+ fold_split_pattern,
+ organism_pattern,
+ subset_pattern,
+ bundle_pattern,
+ f'data_{chromosome_pattern}_*-*.gz.tfrecord',
+ ])
+ tfrecord_paths = epath.Path(path or _DEFAULT_PATH).glob(glob_pattern)
+
+ def _parse_path(tfrecord_path: epath.Path):
+ base_name = tfrecord_path.name
+ match_ = _FILENAME_REGEX.match(base_name)
+ if not match_:
+ raise ValueError(f'Could not parse metadata for file: {base_name}')
+
+ parsed = match_.groupdict()
+ metadata = {
+ 'organism': tfrecord_path.parts[-4],
+ 'bundle': tfrecord_path.parts[-2],
+ 'fold_split': tfrecord_path.parts[-5],
+ 'subset': tfrecord_path.parts[-3],
+ 'chromosome': parsed['chr'],
+ 'shard': int(parsed['shard']),
+ 'num_shards': int(parsed['num_shards']),
+ 'path': str(tfrecord_path),
+ }
+ return pd.DataFrame(metadata, index=[0])
+
+ if not tfrecord_paths:
+ return pd.DataFrame()
+
+ return pd.concat(
+ [_parse_path(epath.Path(p)) for p in tfrecord_paths]
+ ).reset_index(drop=True)
+
+
+def _get_parse_function(bundle: bundles_lib.BundleName):
+ """Get parse function for a given output type."""
+ feature_spec = (
+ _DNA_SEQUENCE_FEATURE_SPEC
+ | _INTERVAL_FEATURE_SPEC
+ | {
+ key: tf.io.FixedLenFeature([], tf.string)
+ for key in bundle.get_dtypes().keys()
+ }
+ )
+ output_dtypes = bundle.get_dtypes() | {'dna_sequence': _DNA_SEQUENCE_DTYPE}
+
+ def _parse(proto):
+ example = tf.io.parse_single_example(proto, feature_spec)
+ for key, dtype in output_dtypes.items():
+ example[key] = tf.io.parse_tensor(example[key], dtype)
+ return example
+
+ return _parse
+
+
+def _get_tfrecords_dataset(
+ paths: Sequence[str | os.PathLike[str]], bundle: bundles_lib.BundleName
+) -> tf.data.Dataset:
+ """Returns a dataset for a given output type from a sequence of paths."""
+ parser = _get_parse_function(bundle)
+
+ def _get(p):
+ ds = tf.data.TFRecordDataset(p, compression_type='GZIP')
+ ds = ds.map(parser, num_parallel_calls=tf.data.AUTOTUNE)
+ return ds
+
+ ds = _get(paths[0])
+ for p in paths[1:]:
+ ds_next = _get(p)
+ ds = ds.concatenate(ds_next)
+ return ds
+
+
+def create_dataset(
+ *,
+ organism: dna_model.Organism,
+ fold_split: dna_model.ModelVersion,
+ subset: fold_intervals.Subset,
+ bundles: Sequence[bundles_lib.BundleName] | None = None,
+ path: str | os.PathLike[str] | None = None,
+) -> tf.data.Dataset:
+ """Returns AlphaGenome dataset for a given organism, fold and subset.
+
+ Args:
+ organism: The organism to load.
+ fold_split: The fold split to load.
+ subset: The subset to load.
+ bundles: The bundles to load. If None, all bundles are loaded.
+ path: The path to the TFRecord files. If None, the default path is used.
+ """
+ bundles = bundles or [None]
+ records = []
+ for bundle in bundles:
+ records.append(
+ get_tfrecords_df(
+ organism=organism,
+ bundle=bundle,
+ fold_split=fold_split,
+ subset=subset,
+ path=path,
+ )
+ )
+ df = pd.concat(records)
+ num_paths = df.groupby('bundle').agg('path').count()
+ if num_paths.nunique() != 1:
+ raise ValueError(
+ f'Number of TFRecord files per bundle is not the same: {num_paths}'
+ )
+ dataset_per_bundle = []
+ bundles = df['bundle'].unique()
+ for bundle in bundles:
+ dft = df[df['bundle'] == bundle].sort_values('shard')
+ if dft.empty:
+ raise ValueError(f'No data found for {bundle=}.')
+ dataset_per_bundle.append(
+ _get_tfrecords_dataset(
+ dft['path'].tolist(), bundles_lib.BundleName(bundle.lower())
+ )
+ )
+
+ # Zip datasets across bundles.
+ return tf.data.Dataset.zip(*dataset_per_bundle)
+
+
+def _parse_batch(
+ element,
+ bundles: Sequence[bundles_lib.BundleName] | None,
+ organism_index: int,
+ batch_size: int,
+) -> tuple[schemas.DataBatch, BatchMetadata]:
+ """Parses a raw dataset element into Input and Target schemas."""
+
+ if bundles is None:
+ bundles = list(bundles_lib.BundleName)
+ if len(bundles) == 1:
+ element = (element,)
+
+ merged_data = functools.reduce(lambda x, y: x | y, element)
+ organism_index = np.full((batch_size,), organism_index, dtype=np.int32)
+ metadata = {
+ 'interval/chromosome': merged_data.pop('interval/chromosome'),
+ 'interval/start': merged_data.pop('interval/start'),
+ 'interval/end': merged_data.pop('interval/end'),
+ }
+ batch = schemas.DataBatch(organism_index=organism_index, **merged_data)
+ return batch, metadata
+
+
+def get_numpy_dataset_iterator(
+ *,
+ batch_size: int,
+ organism: dna_model.Organism = dna_model.Organism.HOMO_SAPIENS,
+ model_version: dna_model.ModelVersion = dna_model.ModelVersion.FOLD_0,
+ subset: fold_intervals.Subset = fold_intervals.Subset.VALID,
+ bundles: Sequence[bundles_lib.BundleName] | None = None,
+ path: str | os.PathLike[str] | None = None,
+) -> Iterator[tuple[schemas.DataBatch, BatchMetadata]]:
+ """Yields numpy batches of data from the dataset."""
+ ds = create_dataset(
+ organism=organism,
+ fold_split=model_version,
+ subset=subset,
+ bundles=bundles,
+ path=path,
+ )
+ organism_index = research_dna_model.convert_to_organism_index(organism)
+ ds = ds.batch(batch_size, drop_remainder=True).prefetch(tf.data.AUTOTUNE)
+ for element in ds.as_numpy_iterator():
+ yield _parse_batch(element, bundles, organism_index, batch_size)
diff --git a/flax_model/alphagenome/io/dataset_test.py b/flax_model/alphagenome/io/dataset_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..f0d728711e7b1a2f8cf084a482161f5875177350
--- /dev/null
+++ b/flax_model/alphagenome/io/dataset_test.py
@@ -0,0 +1,254 @@
+# Copyright 2026 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 pathlib
+
+from absl.testing import absltest
+from absl.testing import parameterized
+from flax_model.alphagenome._sdk.data import fold_intervals
+from flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome._sdk.models import dna_model
+from flax_model.alphagenome.io import bundles
+from flax_model.alphagenome.io import dataset
+from flax_model.alphagenome.model import schemas
+import jax
+import jax.numpy as jnp
+import numpy as np
+import tensorflow as tf
+
+
+def _bytes_feature(value):
+ """Returns a bytes_list from a string / byte."""
+ if isinstance(value, type(tf.constant(0))):
+ value = value.numpy()
+ return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
+
+
+def _int64_feature(value):
+ """Returns an int64_list from a bool / enum / int / uint."""
+ return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
+
+
+def to_tf_example(data):
+ """Creates a tf.train.Example message from a data dictionary."""
+ features = {}
+ for key, value in data.items():
+ if isinstance(value, int):
+ features[key] = _int64_feature(value)
+ elif isinstance(value, str):
+ features[key] = _bytes_feature(value.encode('utf-8'))
+ elif isinstance(value, np.ndarray):
+ features[key] = _bytes_feature(tf.io.serialize_tensor(value).numpy())
+ else:
+ raise ValueError(f'Unsupported data type for key {key}: {type(value)}')
+ return tf.train.Example(
+ features=tf.train.Features(feature=features)
+ ).SerializeToString()
+
+
+def _write_tfrecord(path: pathlib.Path, data_list):
+ """Writes a list of data to a TFRecord file."""
+ path.parent.mkdir(parents=True, exist_ok=True)
+ options = tf.io.TFRecordOptions(compression_type='GZIP')
+ with tf.io.TFRecordWriter(str(path), options=options) as writer:
+ for data in data_list:
+ writer.write(to_tf_example(data))
+
+
+class LoadDataTest(parameterized.TestCase):
+
+ def setUp(self):
+ super().setUp()
+ self.tmpdir = self.create_tempdir().full_path
+ self._num_channels = 7
+ self._seq_len = int(2**5)
+ self._num_shards = 2
+ self._intervals = {
+ 'chr1': {
+ '1': [
+ genome.Interval(
+ start=10, end=10 + self._seq_len, chromosome='chr1'
+ ),
+ genome.Interval(
+ start=100, end=100 + self._seq_len, chromosome='chr1'
+ ),
+ ],
+ '2': [
+ genome.Interval(
+ start=200, end=200 + self._seq_len, chromosome='chr1'
+ ),
+ ],
+ },
+ 'chr3': {
+ '1': [
+ genome.Interval(
+ start=30, end=30 + self._seq_len, chromosome='chr3'
+ ),
+ ],
+ '2': [
+ genome.Interval(
+ start=130, end=130 + self._seq_len, chromosome='chr3'
+ ),
+ genome.Interval(
+ start=230, end=230 + self._seq_len, chromosome='chr3'
+ ),
+ ],
+ },
+ }
+ self._organism = dna_model.Organism.HOMO_SAPIENS
+ self._fold_split = dna_model.ModelVersion.FOLD_0
+ self._subset = fold_intervals.Subset.TRAIN
+ self._bundles = [
+ bundles.BundleName.ATAC,
+ bundles.BundleName.RNA_SEQ,
+ ]
+ self._create_dummy_files()
+
+ def _create_dummy_files(self):
+ """Creates data for two bundles in two chromosomes."""
+ for bundle in self._bundles:
+ for chromosome, shard_intervals_map in self._intervals.items():
+ for shard_idx, intervals in shard_intervals_map.items():
+ data_list = []
+ for interval in intervals:
+ data = {
+ 'dna_sequence': np.zeros((self._seq_len, 4), dtype=np.float32),
+ 'interval/chromosome': interval.chromosome,
+ 'interval/start': interval.start,
+ 'interval/end': interval.end,
+ f'{bundle.value}': np.zeros(
+ (self._seq_len, self._num_channels), dtype=jnp.bfloat16
+ ),
+ f'{bundle.value}_mask': np.ones(
+ (1, self._num_channels), dtype=bool
+ ),
+ }
+ if bundle == bundles.BundleName.RNA_SEQ:
+ data['rna_seq_strand'] = np.zeros(
+ (1, self._num_channels), dtype=np.int32
+ )
+ data_list.append(data)
+
+ path = (
+ pathlib.Path(self.tmpdir)
+ / self._fold_split.name
+ / self._organism.name
+ / self._subset.name
+ / bundle.name
+ / f'data_{chromosome}_{shard_idx}-{self._num_shards}.gz.tfrecord'
+ )
+ _write_tfrecord(path, data_list)
+
+ def test_get_tfrecords_df(self):
+ df = dataset.get_tfrecords_df(path=self.tmpdir)
+ self.assertLen(df, 8)
+ self.assertSameElements(df.organism, ['HOMO_SAPIENS'])
+ self.assertSameElements(df.bundle, ['ATAC', 'RNA_SEQ'])
+ self.assertSameElements(df.fold_split, ['FOLD_0'])
+ self.assertSameElements(df.subset, ['TRAIN'])
+ self.assertSameElements(df.chromosome, ['chr1', 'chr3'])
+ self.assertSameElements(df.shard, [1, 2])
+
+ @parameterized.parameters(True, False)
+ def test_create_dataset(self, shuffle_dataset: bool):
+ ds = dataset.create_dataset(
+ organism=self._organism,
+ fold_split=self._fold_split,
+ subset=self._subset,
+ bundles=self._bundles,
+ path=self.tmpdir,
+ )
+ if shuffle_dataset:
+ ds = ds.shuffle(buffer_size=100)
+ ds_iterator = ds.as_numpy_iterator()
+ intervals = []
+
+ # 6 intervals in total: 3 in chr1, 3 in chr3
+ for _ in range(6):
+ data = next(ds_iterator)
+ self.assertLen(data, 2) # 2 bundles
+ data_atac, data_rna_seq = (
+ (data[0], data[1])
+ if bundles.BundleName.ATAC.value in data[0]
+ else (data[1], data[0])
+ )
+
+ self.assertIn('atac', data_atac)
+ self.assertIn('rna_seq', data_rna_seq)
+ self.assertEqual(
+ data_atac['interval/chromosome'], data_rna_seq['interval/chromosome']
+ )
+ self.assertEqual(
+ data_atac['interval/start'], data_rna_seq['interval/start']
+ )
+ self.assertEqual(data_atac['interval/end'], data_rna_seq['interval/end'])
+ intervals.append(
+ genome.Interval(
+ start=data_atac['interval/start'],
+ end=data_atac['interval/end'],
+ chromosome=data_atac['interval/chromosome'].decode('utf-8'),
+ )
+ )
+ self.assertEqual(data_atac['atac'].dtype, tf.bfloat16)
+ self.assertEqual(data_rna_seq['rna_seq'].dtype, tf.bfloat16)
+
+ def _interval_to_tuple(interval):
+ # Convert interval to tuple to check equality.
+ return (interval.chromosome, interval.start, interval.end)
+
+ all_intervals = jax.tree.reduce(
+ lambda x, y: x + y,
+ self._intervals,
+ is_leaf=lambda x: isinstance(x, list),
+ )
+ self.assertEqual(
+ set(_interval_to_tuple(i) for i in intervals),
+ set(_interval_to_tuple(i) for i in all_intervals),
+ )
+
+ with self.subTest('Dataset length is correct'):
+ with self.assertRaises(StopIteration):
+ next(ds_iterator)
+
+ @parameterized.parameters(
+ ([bundles.BundleName.ATAC],),
+ ([bundles.BundleName.ATAC, bundles.BundleName.RNA_SEQ],),
+ )
+ def test_get_numpy_dataset_iterator(self, requested_bundles):
+ batch_size = 2
+ ds_iterator = dataset.get_numpy_dataset_iterator(
+ batch_size=batch_size,
+ organism=self._organism,
+ model_version=self._fold_split,
+ subset=self._subset,
+ bundles=requested_bundles,
+ path=self.tmpdir,
+ )
+
+ num_batches = 0
+ for batch, metadata in ds_iterator:
+ self.assertIsInstance(batch, schemas.DataBatch)
+ self.assertEqual(batch.dna_sequence.shape[0], batch_size)
+ self.assertEqual(batch.organism_index.shape[0], batch_size)
+ for bundle in requested_bundles:
+ self.assertIsNotNone(getattr(batch, bundle.value))
+ self.assertIsNotNone(getattr(batch, f'{bundle.value}_mask'))
+ self.assertLen(metadata, 3)
+ self.assertEqual(metadata['interval/start'].shape[0], batch_size)
+ num_batches += 1
+
+ self.assertEqual(num_batches, 6 // batch_size)
+
+
+if __name__ == '__main__':
+ absltest.main()
diff --git a/flax_model/alphagenome/io/fasta.py b/flax_model/alphagenome/io/fasta.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b80dc3f69f9bbca5dbbd7ef26392cc956f477f9
--- /dev/null
+++ b/flax_model/alphagenome/io/fasta.py
@@ -0,0 +1,75 @@
+# Copyright 2026 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.
+
+"""Extractors for working with FASTA files."""
+
+import os
+
+from flax_model.alphagenome._sdk.data import genome
+import fsspec
+import pyfaidx
+
+_REVERSE_COMPLEMENT_TRANSLATION = str.maketrans('ATCGN', 'TAGCN')
+
+
+def reverse_complement(sequence: str) -> str:
+ """Returns the reverse complement of a DNA sequence string."""
+ return sequence.translate(_REVERSE_COMPLEMENT_TRANSLATION)[::-1]
+
+
+class FastaExtractor:
+ """FASTA file extractor."""
+
+ def __init__(self, fasta_path: str | os.PathLike[str]):
+ self._faidx = pyfaidx.Faidx(
+ fsspec.open(fasta_path),
+ as_raw=True,
+ mutable=False,
+ build_index=False,
+ sequence_always_upper=True,
+ )
+
+ def extract(self, interval: genome.Interval) -> str:
+ """Returns the FASTA sequence in some given interval as a string.
+
+ Args:
+ interval: the interval to query.
+
+ Returns:
+ sequence of requested interval.
+ """
+ if (chromosome := self._faidx.index.get(interval.chromosome)) is None:
+ raise ValueError(f'Chromosome "{interval.chromosome}" not found.')
+
+ chromosome_length = chromosome.rlen
+
+ if interval.start >= chromosome_length or interval.end < 0:
+ raise ValueError(f'Interval fully out of bounds. {interval=}')
+ elif interval.within_reference(chromosome_length):
+ sequence = self._faidx.fetch(
+ interval.chromosome, interval.start + 1, interval.end
+ )
+ else:
+ start_padding = 'N' * max(-interval.start, 0)
+ end_padding = 'N' * max(interval.end - chromosome_length, 0)
+ trimmed_interval = interval.truncate(chromosome_length)
+ sequence = self._faidx.fetch(
+ interval.chromosome, trimmed_interval.start + 1, trimmed_interval.end
+ )
+ sequence = start_padding + sequence + end_padding
+
+ if interval.negative_strand:
+ return reverse_complement(sequence)
+ else:
+ return sequence
diff --git a/flax_model/alphagenome/io/fasta_test.py b/flax_model/alphagenome/io/fasta_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..f212517b90e8f8240945d95d936de7f2ac91862a
--- /dev/null
+++ b/flax_model/alphagenome/io/fasta_test.py
@@ -0,0 +1,55 @@
+# Copyright 2026 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 pathlib
+
+from absl.testing import absltest
+from absl.testing import parameterized
+from flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome.io import fasta
+
+
+def _get_test_fasta_path() -> str | os.PathLike[str]:
+ return pathlib.Path(__file__).parent / 'testdata' / 'example.fa'
+
+
+class FastaExtractorTest(parameterized.TestCase):
+
+ @parameterized.parameters([
+ dict(interval='chr1:0-9:+', expected='TAATATGCT'),
+ dict(interval='chr1:0-9:-', expected='AGCATATTA'),
+ dict(interval='chr2:70-78', expected='CAGATACA'),
+ dict(interval='chr1:-10-1', expected='NNNNNNNNNNT'),
+ dict(interval='chr1:50-55:.', expected='ANNNN'),
+ dict(interval='chr1:50-55:-', expected='NNNNT'),
+ ])
+ def test_extract(self, interval: str, expected: str):
+ extractor = fasta.FastaExtractor(_get_test_fasta_path())
+ interval = genome.Interval.from_str(interval)
+ self.assertEqual(extractor.extract(interval), expected)
+
+ def test_extract_fully_out_of_bounds_raises(self):
+ extractor = fasta.FastaExtractor(_get_test_fasta_path())
+ with self.assertRaisesRegex(ValueError, 'Interval fully out of bounds.'):
+ extractor.extract(genome.Interval('chr1', 100, 1000))
+
+ def test_invalid_chromosome_raises(self):
+ extractor = fasta.FastaExtractor(_get_test_fasta_path())
+ with self.assertRaisesRegex(ValueError, 'Chromosome "foo" not found.'):
+ extractor.extract(genome.Interval('foo', 0, 10))
+
+
+if __name__ == '__main__':
+ absltest.main()
diff --git a/flax_model/alphagenome/io/genome.py b/flax_model/alphagenome/io/genome.py
new file mode 100644
index 0000000000000000000000000000000000000000..e4ee9cfbe3f08fbc82ea50540ec8a180baa06f7a
--- /dev/null
+++ b/flax_model/alphagenome/io/genome.py
@@ -0,0 +1,139 @@
+# Copyright 2026 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."""
+
+from flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome.io import fasta
+
+
+def insert_reference_variant(
+ sequence: str,
+ interval: genome.Interval,
+ variant: genome.Variant,
+) -> str:
+ """Replace reference genome sequence with variant REF sequences.
+
+ Args:
+ sequence: Reference genome DNA sequence.
+ interval: Interval from which the reference genome sequence was extracted.
+ variant: A variant.
+
+ Returns:
+ Sequence of the same length as input `sequence`, but with variant.REF
+ integrated in case it mismatches it.
+ """
+ if interval.negative_strand:
+ raise ValueError('Interval must not be on the negative strand.')
+
+ if not variant.reference_overlaps(interval):
+ return sequence
+
+ # Relative start/end must be within the interval boundaries.
+ relative_start = max(variant.start - interval.start, 0)
+ relative_end = min(variant.end - interval.start, interval.width)
+
+ # Remove reference bases outside the interval.
+ clip_start = max(interval.start - variant.start, 0)
+ clip_end = min(interval.end - variant.start, variant.end - variant.start)
+ variant_reference_bases = variant.reference_bases[clip_start:clip_end]
+
+ return (
+ sequence[:relative_start]
+ + variant_reference_bases
+ + sequence[relative_end:]
+ )
+
+
+def insert_alternate_variant(
+ sequence: str, interval: genome.Interval, variant: genome.Variant
+) -> str:
+ """Replace reference genome sequence with a single variant ALT sequence.
+
+ Args:
+ sequence: Reference genome DNA sequence.
+ interval: Interval corresponding to reference sequence.
+ variant: Variant to integrate.
+
+ Returns:
+ DNA sequence with integrated variant. Length of the sequence might be
+ be different from the input sequence.
+ """
+ if interval.negative_strand:
+ raise ValueError('Interval must not be on the negative strand.')
+
+ if not (
+ variant.reference_overlaps(interval)
+ or (not variant.reference_bases and variant.alternate_overlaps(interval))
+ ):
+ return sequence
+
+ if variant.end > interval.end:
+ # Variant reference is partially outside of the interval at the end.
+ variant, _ = variant.split(interval.end)
+ assert isinstance(variant, genome.Variant)
+
+ if variant.start < interval.start:
+ # Variant reference is partially outside of the interval at the start.
+ _, variant = variant.split(interval.start)
+ assert isinstance(variant, genome.Variant)
+
+ relative_start = variant.start - interval.start
+ relative_end = relative_start + len(variant.reference_bases)
+ return (
+ sequence[:relative_start]
+ + variant.alternate_bases
+ + sequence[relative_end:]
+ )
+
+
+def extract_variant_sequences(
+ interval: genome.Interval,
+ variant: genome.Variant,
+ extractor: fasta.FastaExtractor,
+) -> tuple[str, str]:
+ """Extracts the interval and inserts variant to generate ref/alt sequences.
+
+ Args:
+ interval: Interval of interest from which to query the sequence.
+ variant: Variant to insert.
+ extractor: FASTA extractor for extracting the sequence.
+
+ Returns:
+ Tuple of reference and alternate sequences.
+ """
+
+ # If the variant is a deletion, extend the interval to account for the
+ # shrinking sequence.
+ interval_length = interval.width
+ extended_length = max(
+ 0, len(variant.reference_bases) - len(variant.alternate_bases)
+ )
+ extended_unstranded_interval = interval.as_unstranded().boundary_shift(
+ end_offset=extended_length
+ )
+
+ sequence = extractor.extract(extended_unstranded_interval)
+ reference_sequence = insert_reference_variant(
+ sequence, extended_unstranded_interval, variant
+ )[:interval_length]
+ alternate_sequence = insert_alternate_variant(
+ sequence, extended_unstranded_interval, variant
+ )[:interval_length]
+
+ if interval.negative_strand:
+ reference_sequence = fasta.reverse_complement(reference_sequence)
+ alternate_sequence = fasta.reverse_complement(alternate_sequence)
+
+ return reference_sequence, alternate_sequence
diff --git a/flax_model/alphagenome/io/genome_test.py b/flax_model/alphagenome/io/genome_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..80add4f3a1c4de33e36a359ce678c401a926f04c
--- /dev/null
+++ b/flax_model/alphagenome/io/genome_test.py
@@ -0,0 +1,239 @@
+# Copyright 2026 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 flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome.io import fasta
+from flax_model.alphagenome.io import genome as genome_io
+
+
+class VariantTest(parameterized.TestCase):
+
+ @parameterized.named_parameters([
+ dict(
+ testcase_name='Modification',
+ sequence='AAAA',
+ interval=genome.Interval('chr1', 0, 4),
+ variant=genome.Variant('chr1', 3, 'A', 'G'),
+ expected='AAGA',
+ ),
+ dict(
+ testcase_name='Insertion',
+ sequence='AAAA',
+ interval=genome.Interval('chr2', 0, 4),
+ variant=genome.Variant('chr2', 2, 'A', 'GGGG'),
+ expected='AGGGGAA',
+ ),
+ dict(
+ testcase_name='Deletion',
+ sequence='AAAA',
+ interval=genome.Interval('chr3', 0, 4),
+ variant=genome.Variant('chr3', 2, 'AA', ''),
+ expected='AA',
+ ),
+ dict(
+ testcase_name='NoOverlap',
+ sequence='AAAA',
+ interval=genome.Interval('chr4', 0, 4),
+ variant=genome.Variant('chr4', 6, 'A', 'G'),
+ expected='AAAA',
+ ),
+ dict(
+ testcase_name='OverlapStart',
+ sequence='AAAA',
+ interval=genome.Interval('chr4', 4, 8),
+ variant=genome.Variant('chr4', 1, 'TTTTT', 'GGGGG'),
+ expected='GAAA',
+ ),
+ dict(
+ testcase_name='OverlapEnd',
+ sequence='AAAA',
+ interval=genome.Interval('chr4', 0, 4),
+ variant=genome.Variant('chr4', 4, 'TTTTTT', 'GG'),
+ expected='AAAG',
+ ),
+ dict(
+ testcase_name='DifferentChromosomes',
+ sequence='AAAA',
+ interval=genome.Interval('chr1', 0, 4),
+ variant=genome.Variant('chr2', 1, 'A', 'G'),
+ expected='AAAA',
+ ),
+ ])
+ def test_insert_alternate_variant(
+ self,
+ sequence: str,
+ interval: genome.Interval,
+ variant: genome.Variant,
+ expected: str,
+ ):
+ alternate_sequence = genome_io.insert_alternate_variant(
+ sequence, interval, variant
+ )
+ self.assertEqual(alternate_sequence, expected)
+
+ def test_insert_alternate_variant_negative_strand_raises(self):
+ with self.assertRaisesRegex(
+ ValueError, 'Interval must not be on the negative strand.'
+ ):
+ _ = genome_io.insert_alternate_variant(
+ sequence='AAAA',
+ interval=genome.Interval('chr1', 0, 4, '-'),
+ variant=genome.Variant('chr1', 2, 'A', 'G'),
+ )
+
+ @parameterized.named_parameters([
+ dict(
+ testcase_name='Normal',
+ sequence='AAAA',
+ interval=genome.Interval('chr1', 0, 4),
+ variant=genome.Variant('chr1', 2, 'A', 'G'),
+ expected='AAAA',
+ ),
+ dict(
+ testcase_name='ModifiedReference',
+ sequence='AAAA',
+ interval=genome.Interval('chr2', 0, 4),
+ variant=genome.Variant('chr2', 2, 'GGG', ''),
+ expected='AGGG',
+ ),
+ dict(
+ testcase_name='NoOverlap',
+ sequence='AAAA',
+ interval=genome.Interval('chr4', 0, 4),
+ variant=genome.Variant('chr4', 6, 'G', ''),
+ expected='AAAA',
+ ),
+ dict(
+ testcase_name='OverlapStart',
+ sequence='AAAA',
+ interval=genome.Interval('chr4', 4, 8),
+ variant=genome.Variant('chr4', 1, 'GGGGG', ''),
+ expected='GAAA',
+ ),
+ dict(
+ testcase_name='OverlapEnd',
+ sequence='AAAA',
+ interval=genome.Interval('chr4', 0, 4),
+ variant=genome.Variant('chr4', 4, 'GGG', ''),
+ expected='AAAG',
+ ),
+ dict(
+ testcase_name='DifferentChromosomes',
+ sequence='AAAA',
+ interval=genome.Interval('chr1', 0, 4),
+ variant=genome.Variant('chr2', 1, 'G', ''),
+ expected='AAAA',
+ ),
+ ])
+ def test_insert_reference_variant(
+ self,
+ sequence: str,
+ interval: genome.Interval,
+ variant: genome.Variant,
+ expected: str,
+ ):
+ reference_sequence = genome_io.insert_reference_variant(
+ sequence, interval, variant
+ )
+ self.assertEqual(reference_sequence, expected)
+
+ def test_insert_reference_variant_negative_strand_raises(self):
+ with self.assertRaisesRegex(
+ ValueError, 'Interval must not be on the negative strand.'
+ ):
+ _ = genome_io.insert_reference_variant(
+ sequence='AAAA',
+ interval=genome.Interval('chr1', 0, 4, '-'),
+ variant=genome.Variant('chr1', 2, 'A', 'G'),
+ )
+
+ @parameterized.named_parameters([
+ dict(
+ testcase_name='Normal',
+ sequence='AAAA',
+ interval=genome.Interval('chr1', 0, 4),
+ variant=genome.Variant('chr1', 2, 'A', 'G'),
+ expected_reference='AAAA',
+ expected_alternate='AGAA',
+ ),
+ dict(
+ testcase_name='ModifiedReference',
+ sequence='AAAA',
+ interval=genome.Interval('chr2', 0, 4),
+ variant=genome.Variant('chr2', 2, 'G', 'G'),
+ expected_reference='AGAA',
+ expected_alternate='AGAA',
+ ),
+ dict(
+ testcase_name='Insertion',
+ sequence='AAAA',
+ interval=genome.Interval('chr3', 0, 4),
+ variant=genome.Variant('chr3', 2, 'A', 'GGGG'),
+ expected_reference='AAAA',
+ expected_alternate='AGGG',
+ ),
+ dict(
+ testcase_name='Deletion',
+ sequence='GATACA',
+ interval=genome.Interval('chr3', 0, 4),
+ variant=genome.Variant('chr3', 2, 'AT', ''),
+ expected_reference='GATA',
+ expected_alternate='GACA',
+ ),
+ dict(
+ testcase_name='NoOverlap',
+ sequence='GGGG',
+ interval=genome.Interval('chr4', 0, 4),
+ variant=genome.Variant('chr4', 6, 'T', 'T'),
+ expected_reference='GGGG',
+ expected_alternate='GGGG',
+ ),
+ dict(
+ testcase_name='NegativeStrand',
+ sequence='AAAAA',
+ interval=genome.Interval('chr1', 0, 5, '-'),
+ variant=genome.Variant('chr1', 2, 'A', 'G'),
+ expected_reference='TTTTT',
+ expected_alternate='TTTCT',
+ ),
+ ])
+ def test_extract_variant_sequences(
+ self,
+ sequence: str,
+ interval: genome.Interval,
+ variant: genome.Variant,
+ expected_reference: str,
+ expected_alternate: str,
+ ):
+ def _extract(interval: genome.Interval) -> str:
+ self.assertLen(sequence, interval.width, f'{interval=}')
+ return sequence
+
+ extractor = mock.create_autospec(fasta.FastaExtractor)
+ extractor.extract.side_effect = _extract
+ reference_sequence, alternate_sequence = (
+ genome_io.extract_variant_sequences(interval, variant, extractor)
+ )
+ self.assertLen(reference_sequence, interval.width)
+ self.assertLen(alternate_sequence, interval.width)
+ self.assertEqual(reference_sequence, expected_reference)
+ self.assertEqual(alternate_sequence, expected_alternate)
+
+
+if __name__ == '__main__':
+ absltest.main()
diff --git a/flax_model/alphagenome/io/splicing.py b/flax_model/alphagenome/io/splicing.py
new file mode 100644
index 0000000000000000000000000000000000000000..d532282972606ae8674c895cfe07f730f24ce366
--- /dev/null
+++ b/flax_model/alphagenome/io/splicing.py
@@ -0,0 +1,153 @@
+# Copyright 2026 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.
+
+"""Extractors for working with splicing data."""
+
+from flax_model.alphagenome._sdk.data import genome
+import numpy as np
+import pandas as pd
+
+
+class PositionExtractor:
+ """Extractor focused on single position, rather than an interval.
+
+ Interval is considered semi-open [start, end) rows where:
+ - chromosome == interval.chromosome,
+ - position >= interval.start,
+ - position < interval.end,
+ are returned.
+
+ Note: This code doesn't consider stand information.
+ """
+
+ def __init__(
+ self,
+ df: pd.DataFrame,
+ position_column: str,
+ chromosome_column: str = 'Chromosome',
+ ):
+ """Init.
+
+ Args:
+ df: dataframe to query with `position_column` and `chromosome_column`.
+ position_column: Which column in the dataframe to use as 0-based position.
+ chromosome_column: Which column in the dataframe to use as chromosome.
+ """
+ self._positions = {
+ chromosome: (values, values[position_column].values)
+ for chromosome, values in df.groupby(chromosome_column, observed=False)
+ }
+
+ self._empty = df.iloc[:0]
+
+ def extract(self, interval: genome.Interval) -> pd.DataFrame:
+ if interval.chromosome not in self._positions:
+ return self._empty
+ else:
+ dfc, position = self._positions[interval.chromosome]
+ return dfc[(position >= interval.start) & (position < interval.end)]
+
+
+class SpliceSiteAnnotationExtractor:
+ """Generate binary masks of overlapping splice sites."""
+
+ def __init__(
+ self,
+ junction_starts: pd.DataFrame,
+ junction_ends: pd.DataFrame,
+ ):
+ """Init.
+
+ Args:
+ junction_starts: DataFrame of splice junctions starts with columns:
+ Chromosome, Start, Strand and an optional set of tissue columns. The
+ `Start` column must be 0-based position of the first nucleotide of the
+ intron.
+ junction_ends: DataFrame of splice junctions ends with columns:
+ Chromosome, End, Strand and an optional set of tissue columns. The `End`
+ column must be the 1-based position of the last nucleotide of the
+ intron.
+ """
+ self._tissues = [
+ c for c in junction_starts if c not in ['Chromosome', 'Strand', 'Start']
+ ]
+ ends_tissues = [
+ c for c in junction_ends if c not in ['Chromosome', 'Strand', 'End']
+ ]
+ if self._tissues != ends_tissues:
+ raise ValueError('Tissues mismatch:', self._tissues, ends_tissues)
+
+ # Junctions describe intron intervals as [Start, End). Start point to the
+ # first nucleotide of the intron and End point to the fist nucleotide of the
+ # the next exon. We label DONOR the last nucleotide of the exon before the
+ # intron (so we move Start back by 1) and ACCEPTOR the first nucleotide of
+ # the exon after the intron (so End are not moved).
+ junction_starts.update(junction_starts['Start'] - 1)
+
+ self._start_position_extractor = PositionExtractor(junction_starts, 'Start')
+ self._end_position_extractor = PositionExtractor(junction_ends, 'End')
+
+ def extract(self, interval: genome.Interval) -> np.ndarray:
+ """Extract splice site masks."""
+ splice_junctions_starts = self._start_position_extractor.extract(interval)
+ splice_junctions_ends = self._end_position_extractor.extract(interval)
+ tissues = self._tissues
+ splice_junctions_starts = splice_junctions_starts[
+ ['Chromosome', 'Strand', 'Start'] + tissues
+ ]
+ splice_junctions_ends = splice_junctions_ends[
+ ['Chromosome', 'Strand', 'End'] + tissues
+ ]
+
+ donors_fw = splice_junctions_starts[splice_junctions_starts.Strand == '+']
+ accept_fw = splice_junctions_ends[splice_junctions_ends.Strand == '+']
+ donors_rw = splice_junctions_ends[splice_junctions_ends.Strand == '-']
+ accept_rw = splice_junctions_starts[splice_junctions_starts.Strand == '-']
+
+ donor_idx_fw = (donors_fw.Start.values - interval.start).astype(np.int32)
+ accept_idx_fw = (accept_fw.End.values - interval.start).astype(np.int32)
+ donor_idx_rw = (donors_rw.End.values - interval.start).astype(np.int32)
+ accept_idx_rw = (accept_rw.Start.values - interval.start).astype(np.int32)
+
+ if tissues:
+ donor_theta_fw = donors_fw[tissues].to_numpy()
+ accept_theta_fw = accept_fw[tissues].to_numpy()
+ donor_theta_rw = donors_rw[tissues].to_numpy()
+ accept_theta_rw = accept_rw[tissues].to_numpy()
+ else:
+ donor_theta_fw = True
+ accept_theta_fw = True
+ donor_theta_rw = True
+ accept_theta_rw = True
+
+ if interval.negative_strand:
+ # Then model sees negative strand data as positive strand, swap arrays.
+ donor_idx_fw, donor_idx_rw = donor_idx_rw, donor_idx_fw
+ accept_idx_fw, accept_idx_rw = accept_idx_rw, accept_idx_fw
+ donor_theta_fw, donor_theta_rw = donor_theta_rw, donor_theta_fw
+ accept_theta_fw, accept_theta_rw = accept_theta_rw, accept_theta_fw
+ # Sequence is flipped, so indices must start at end of sequence.
+ donor_idx_fw = interval.width - 1 - donor_idx_fw
+ accept_idx_fw = interval.width - 1 - accept_idx_fw
+ donor_idx_rw = interval.width - 1 - donor_idx_rw
+ accept_idx_rw = interval.width - 1 - accept_idx_rw
+
+ splice_sites = np.zeros((interval.width, 5), dtype=bool)
+ splice_sites[donor_idx_fw, 0] = np.any(donor_theta_fw > 0)
+ splice_sites[accept_idx_fw, 1] = np.any(accept_theta_fw > 0)
+ splice_sites[donor_idx_rw, 2] = np.any(donor_theta_rw > 0)
+ splice_sites[accept_idx_rw, 3] = np.any(accept_theta_rw > 0)
+ splice_sites[:, 4] = np.logical_not(np.any(splice_sites[:, :4], axis=1))
+
+ return splice_sites
diff --git a/flax_model/alphagenome/io/splicing_test.py b/flax_model/alphagenome/io/splicing_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..70d734a6627eb2bcf832b5c219d2e75809d74eb7
--- /dev/null
+++ b/flax_model/alphagenome/io/splicing_test.py
@@ -0,0 +1,115 @@
+# Copyright 2026 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 flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome.io import splicing
+import numpy as np
+import pandas as pd
+
+
+class SpliceSiteAnnotationExtractorTest(parameterized.TestCase):
+
+ @parameterized.product(interval_strand=['+', '-'], with_tissues=[True, False])
+ def test_extract(self, interval_strand, with_tissues):
+ interval_start = 100
+ interval_end = 116
+
+ pos_first_bp_intron_0based = np.asarray([101, 106, 107])
+ neg_first_bp_intron_0based = np.asarray([101, 106, 107])
+ pos_last_bp_intron_1based = np.asarray([104, 109, 115])
+ neg_last_bp_intron_1based = np.asarray([104, 109, 115])
+ junction_starts = pd.DataFrame({
+ 'Chromosome': 'chr1',
+ 'Start': [*pos_first_bp_intron_0based, *neg_first_bp_intron_0based],
+ 'Strand': ['+', '+', '+', '-', '-', '-'],
+ })
+ junction_ends = pd.DataFrame({
+ 'Chromosome': 'chr1',
+ 'End': [*pos_last_bp_intron_1based, *neg_last_bp_intron_1based],
+ 'Strand': ['+', '+', '+', '-', '-', '-'],
+ })
+ if with_tissues:
+ junction_starts['Tissue_0'] = [1, 3, 5, 2, 4, 6]
+ junction_starts['Tissue_1'] = [2, 4, 6, 3, 5, 7]
+ junction_ends['Tissue_0'] = [1, 3, 5, 2, 4, 6]
+ junction_ends['Tissue_1'] = [2, 4, 6, 3, 5, 7]
+
+ interval = genome.Interval(
+ 'chr1', interval_start, interval_end, interval_strand
+ )
+ extractor = splicing.SpliceSiteAnnotationExtractor(
+ junction_starts, junction_ends
+ )
+ result = extractor.extract(interval)
+
+ expected_sites = np.zeros((interval.width, 5), dtype=bool)
+
+ # Move all to the exon bp, and to 0-based coordinates within the interval.
+ pos_last_exon_before = pos_first_bp_intron_0based - interval_start - 1
+ pos_first_exon_after = pos_last_bp_intron_1based - interval_start
+ neg_first_exon_after = neg_last_bp_intron_1based - interval_start
+ neg_last_exon_before = neg_first_bp_intron_0based - interval_start - 1
+ if interval_strand == '+':
+ expected_sites[pos_last_exon_before, 0] = True
+ expected_sites[pos_first_exon_after, 1] = True
+ expected_sites[neg_first_exon_after, 2] = True
+ expected_sites[neg_last_exon_before, 3] = True
+ elif interval_strand == '-':
+ pos_last_exon_before = interval.width - 1 - pos_last_exon_before
+ pos_first_exon_after = interval.width - 1 - pos_first_exon_after
+ neg_last_exon_before = interval.width - 1 - neg_last_exon_before
+ neg_first_exon_after = interval.width - 1 - neg_first_exon_after
+ expected_sites[neg_first_exon_after, 0] = True
+ expected_sites[neg_last_exon_before, 1] = True
+ expected_sites[pos_last_exon_before, 2] = True
+ expected_sites[pos_first_exon_after, 3] = True
+ expected_sites[:, 4] = np.logical_not(np.any(expected_sites, axis=1))
+
+ np.testing.assert_array_equal(result, expected_sites)
+
+ def test_tissue_mismatch_raises(self):
+ junction_starts = pd.DataFrame({
+ 'Chromosome': 'chr1',
+ 'Start': [101, 106, 107],
+ 'Strand': ['+', '+', '+'],
+ 'Tissue_0': [1, 3, 5],
+ })
+ junction_ends = pd.DataFrame({
+ 'Chromosome': 'chr1',
+ 'End': [104, 109, 115],
+ 'Strand': ['+', '+', '+'],
+ 'Tissue_1': [1, 3, 5],
+ })
+ with self.assertRaisesRegex(ValueError, 'Tissues mismatch'):
+ _ = splicing.SpliceSiteAnnotationExtractor(junction_starts, junction_ends)
+
+
+class PositionExtractorTest(parameterized.TestCase):
+
+ def test_extract(self):
+ df = pd.DataFrame({'Chromosome': ['chr1'], 'position': [1]})
+ ex = splicing.PositionExtractor(df, position_column='position')
+ # Inside.
+ self.assertLen(ex.extract(genome.Interval('chr1', 0, 2)), 1)
+ self.assertLen(ex.extract(genome.Interval('chr1', 1, 2)), 1)
+ # Outside.
+ self.assertEmpty(ex.extract(genome.Interval('chr2', 0, 2)))
+ self.assertEmpty(ex.extract(genome.Interval('chr1', 0, 1)))
+ self.assertEmpty(ex.extract(genome.Interval('chr1', 2, 3)))
+
+
+if __name__ == '__main__':
+ absltest.main()
diff --git a/flax_model/alphagenome/io/testdata/example.fa b/flax_model/alphagenome/io/testdata/example.fa
new file mode 100644
index 0000000000000000000000000000000000000000..5749f6dbe712dfb34091d117e61238f5bdd53c13
--- /dev/null
+++ b/flax_model/alphagenome/io/testdata/example.fa
@@ -0,0 +1,4 @@
+>chr1
+TAATATGCTATATATGATACAGATACAGATACAGATACAGATACAAAAAAA
+>chr2
+GATACAGATACAGATACAGATACAGATACAGATACAGATACAGATACAGATACAGATACAGATACAGATACAGATACA
diff --git a/flax_model/alphagenome/io/testdata/example.fa.fai b/flax_model/alphagenome/io/testdata/example.fa.fai
new file mode 100644
index 0000000000000000000000000000000000000000..ac37ccd191379748be3a808d957c9e28255f534a
--- /dev/null
+++ b/flax_model/alphagenome/io/testdata/example.fa.fai
@@ -0,0 +1,2 @@
+chr1 51 6 51 52
+chr2 78 64 78 79
diff --git a/flax_model/alphagenome/model/__init__.py b/flax_model/alphagenome/model/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d498393731fba16d4e87d90cf5a91feb70d8e059
--- /dev/null
+++ b/flax_model/alphagenome/model/__init__.py
@@ -0,0 +1,15 @@
+# Copyright 2026 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.
+
+"""AlphaGenome Model."""
diff --git a/flax_model/alphagenome/model/attention.py b/flax_model/alphagenome/model/attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..c29a4cd94620777d1618b485da3389195fa4f8cc
--- /dev/null
+++ b/flax_model/alphagenome/model/attention.py
@@ -0,0 +1,283 @@
+# Copyright 2026 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.
+
+"""Attention layers."""
+
+import math
+from flax_model.alphagenome._sdk import typing
+from flax_model.alphagenome.model import layers
+import chex
+from einshape import jax_einshape as einshape # pylint: disable=g-importing-member
+import haiku as hk
+import jax
+import jax.numpy as jnp
+from jaxtyping import Array, Float, Int # pylint: disable=g-importing-member, g-multiple-import
+
+
+_MAX_RELATIVE_DISTANCE = 8192 # 1Mb / 128bp.
+
+
+def _shift(
+ x: jnp.ndarray,
+ query_length: int,
+ key_length: int,
+) -> jnp.ndarray:
+ """Shifts the diagonal of a 2D array."""
+ chex.assert_axis_dimension(x, -2, query_length)
+ chex.assert_axis_dimension(x, -1, query_length + key_length)
+ *batch_shapes, n_rows, n_diags = x.shape
+ x = x.reshape(batch_shapes + [n_diags, n_rows])
+ x = x[..., 1:, :]
+ x = x.reshape(batch_shapes + [n_rows, n_diags - 1])
+ return x[..., :key_length]
+
+
+def _central_mask_features(
+ *, distances: jnp.ndarray, feature_size: int, seq_length: int
+) -> jnp.ndarray:
+ """Positional features using exponentially-spaced central mask."""
+ if feature_size > seq_length:
+ raise ValueError(f'{feature_size=} must be <= than {seq_length=}. ')
+ center_widths = jnp.arange(feature_size) + jnp.geomspace(
+ 1, seq_length - feature_size + 1, feature_size, endpoint=False
+ )
+ center_widths = jax.lax.broadcast_to_rank(center_widths, distances.ndim)
+ outputs = (center_widths > distances[..., None]).astype(distances.dtype)
+ chex.assert_shape(outputs, expected_shapes=distances.shape + (feature_size,))
+ return outputs
+
+
+def apply_rope(
+ x: Float[Array, 'B S H C'],
+ positions: Int[Array, 'B S'] | None,
+ max_position: int,
+) -> Float[Array, 'B S H C']:
+ """Applies Rotary Position Embeddings to the input tensor."""
+ if positions is None:
+ positions = jnp.arange(x.shape[1]).astype(x.dtype).reshape(1, x.shape[1])
+ num_freq = x.shape[-1] // 2
+ inv_freq = 1.0 / (
+ jnp.arange(num_freq)
+ + jnp.geomspace(1, max_position - num_freq + 1, num_freq)
+ ).astype(x.dtype)
+ theta = jnp.einsum('bs,f->bsf', positions, inv_freq)
+ theta = jnp.repeat(theta, 2, axis=-1)[..., None, :] # [b, s, 1, c]
+ x_rotated = jnp.stack([-x[..., 1::2], x[..., ::2]], axis=-1).reshape(
+ x.shape
+ ) # [b, s, h, c]
+ return x * jnp.cos(theta) + x_rotated * jnp.sin(theta)
+
+
+class MLPBlock(hk.Module):
+ """MLP block for sequence representations."""
+
+ @typing.jaxtyped
+ def __call__(self, x: Float[Array, 'B S D']) -> Float[Array, 'B S D']:
+ h = layers.RMSBatchNorm()(x)
+ h = hk.Linear(x.shape[-1] * 2)(h)
+ h = jax.nn.relu(h)
+ h = hk.Linear(x.shape[-1])(h)
+ return layers.RMSBatchNorm()(h)
+
+
+class PairMLPBlock(hk.Module):
+ """MLP block for pairwise representations."""
+
+ @typing.jaxtyped
+ def __call__(
+ self, pair_input: Float[Array, 'B S S F']
+ ) -> Float[Array, 'B S S F']:
+ x = layers.LayerNorm(rms_norm=True)(pair_input)
+ hidden_channels = 2 * pair_input.shape[-1]
+ x = hk.Linear(hidden_channels)(x)
+ x = jax.nn.relu(x)
+ x = hk.Linear(pair_input.shape[-1])(x)
+ return x
+
+
+class MHABlock(hk.Module):
+ """Multi-Head Attention block with residual connection."""
+
+ @typing.jaxtyped
+ def __call__(
+ self, x: Float[Array, 'B S D'], attention_bias: Float[Array, 'B H S S']
+ ) -> Float[Array, 'B S D']:
+ batch_size, seq_len, _ = x.shape
+ h = layers.RMSBatchNorm()(x)
+ q = layers.LayerNorm(name='norm_q')(
+ hk.Linear(8 * 128, with_bias=False, name='q_layer')(h).reshape(
+ batch_size, seq_len, 8, 128
+ )
+ )
+ k = layers.LayerNorm(name='norm_k')(
+ hk.Linear(128, with_bias=False, name='k_layer')(h).reshape(
+ batch_size, seq_len, 1, 128
+ )
+ )
+ v = layers.LayerNorm(name='norm_v')(
+ hk.Linear(192, with_bias=False, name='v_layer')(h).reshape(
+ batch_size, seq_len, 1, 192
+ )
+ )
+ q = apply_rope(q, None, max_position=_MAX_RELATIVE_DISTANCE)
+ k = apply_rope(k, None, max_position=_MAX_RELATIVE_DISTANCE)
+
+ logits_dtype = jnp.float32
+ attention_logits = jnp.einsum(
+ 'bshc,bS1c->bhsS',
+ q,
+ k,
+ precision=jax.lax.Precision.DEFAULT,
+ # precision=jax.lax.DotAlgorithmPreset.BF16_BF16_F32,
+ preferred_element_type=logits_dtype,
+ )
+ attention_logits = attention_logits / math.sqrt(128.0)
+ attention_logits = (attention_logits + attention_bias).astype(logits_dtype)
+ logits_soft_cap = 5.0
+ attention_logits = (
+ jnp.tanh(attention_logits / logits_soft_cap) * logits_soft_cap
+ )
+ attention_weights = jax.nn.softmax(attention_logits, axis=-1)
+
+ y = jnp.einsum(
+ 'bhsS,bS1c->bshc',
+ attention_weights,
+ v,
+ # precision=jax.lax.DotAlgorithmPreset.BF16_BF16_F32,
+ precision=jax.lax.Precision.DEFAULT,
+ ).astype(q.dtype)
+ y = hk.Linear(x.shape[-1], name='linear_embedding')(
+ y.reshape(batch_size, seq_len, -1)
+ )
+ return layers.RMSBatchNorm()(y)
+
+
+class AttentionBiasBlock(hk.Module):
+ """Generates attention bias for Multi-Head Attention."""
+
+ @typing.jaxtyped
+ def __call__(self, x: Float[Array, 'B s s D']) -> Float[Array, 'B H S S']:
+ x = jax.nn.gelu(layers.RMSBatchNorm()(x))
+ # 8 = number of heads in sequence MHA.
+ x = hk.Linear(8, with_bias=False)(x)
+ for axis in [1, 2]:
+ x = jnp.repeat(x, repeats=16, axis=axis) # [B S S H]
+ return jnp.moveaxis(x, 3, 1)
+
+
+class RowAttentionBlock(hk.Module):
+ """Self-attention block applied along rows of pairwise representations."""
+
+ @typing.jaxtyped
+ def __call__(
+ self, pair_input: Float[Array, 'B s s F']
+ ) -> Float[Array, 'B s s F']:
+ x = layers.LayerNorm(rms_norm=True)(pair_input)
+ k = hk.Linear(128, with_bias=False, name='linear_k')(x)
+ q = hk.Linear(128, with_bias=False, name='linear_q')(x)
+ v = hk.Linear(128, name='linear_v')(x)
+ x = jnp.einsum(
+ 'bpqf,bpkf->bpqk',
+ q,
+ k,
+ # precision=jax.lax.DotAlgorithmPreset.BF16_BF16_F32,
+ precision=jax.lax.Precision.DEFAULT,
+ preferred_element_type=jnp.float32,
+ ) / math.sqrt(128)
+ x = jax.nn.softmax(x, axis=-1)
+ x = jnp.einsum(
+ 'bpqk,bpkf->bpqf',
+ x,
+ v,
+ # precision=jax.lax.DotAlgorithmPreset.BF16_BF16_F32,
+ precision=jax.lax.Precision.DEFAULT,
+ ).astype(q.dtype)
+ return x
+
+
+class SequenceToPairBlock(hk.Module):
+ """Converts sequence representations to pairwise representations."""
+
+ @typing.jaxtyped
+ def __call__(
+ self, x: Float[Array, 'B S D']
+ ) -> Float[Array, 'B S//16 S//16 F']:
+ x = layers.LayerNorm(rms_norm=True, name='norm_seq2pair')(
+ layers.pool(x, by=16, reduce='mean')
+ )
+ batch_size, seq_len = x.shape[0], x.shape[1]
+ q = hk.Linear(32 * 128, with_bias=False, name='linear_q')(x).reshape(
+ batch_size, seq_len, 32, 128
+ )
+ k = hk.Linear(32 * 128, with_bias=False, name='linear_k')(x).reshape(
+ batch_size, seq_len, 32, 128
+ )
+
+ relative_positions = jnp.arange(-seq_len, seq_len).astype(jnp.float32)
+ pos_features = _central_mask_features(
+ distances=jnp.abs(relative_positions),
+ feature_size=32,
+ seq_length=_MAX_RELATIVE_DISTANCE // 16,
+ ).astype(x.dtype)
+ pos_features = jnp.concatenate(
+ [pos_features, jnp.sign(relative_positions)[..., None] * pos_features],
+ axis=-1,
+ ).astype(x.dtype)
+ pos_encoding = hk.Linear(32 * 128, name='linear_pos_features')(
+ pos_features
+ ).reshape(2 * seq_len, 32, 128)
+
+ q_bias = hk.get_parameter(
+ 'q_r_bias', (1, 1, 32, 128), init=jnp.zeros
+ ).astype(x.dtype)
+ k_bias = hk.get_parameter(
+ 'k_r_bias', (1, 1, 32, 128), init=jnp.zeros
+ ).astype(x.dtype)
+
+ rel_q_a = _shift(
+ jnp.einsum('bqhc,phc->bhqp', q + q_bias, pos_encoding), seq_len, seq_len
+ )
+ rel_k_a = _shift(
+ jnp.einsum('bkhc,phc->bhkp', k + k_bias, pos_encoding), seq_len, seq_len
+ )
+
+ a = jnp.einsum('bqhc,bkhc->bqkh', q, k)
+ a += 0.5 * (
+ einshape('bhqp->bqph', rel_q_a) + einshape('bhkp->bpkh', rel_k_a)
+ )
+
+ y_q = hk.Linear(128, with_bias=False, name='linear_y_q')(jax.nn.gelu(x))
+ y_k = hk.Linear(128, with_bias=False, name='linear_y_k')(jax.nn.gelu(x))
+ pair_act = (
+ hk.Linear(128, name='linear_pair')(a)
+ + y_q[:, :, None, :]
+ + y_k[:, None, :, :]
+ )
+ return pair_act
+
+
+class PairUpdateBlock(hk.Module):
+ """Updates pairwise representations."""
+
+ @typing.jaxtyped
+ def __call__(
+ self,
+ sequence_input: Float[Array, 'B S C'],
+ pair_input: Float[Array, 'B S//16 S//16 F'] | None,
+ ):
+ y = SequenceToPairBlock()(sequence_input)
+ x = y if pair_input is None else (pair_input + y)
+ x += RowAttentionBlock()(x)
+ x += PairMLPBlock()(x)
+ return x
diff --git a/flax_model/alphagenome/model/attention_test.py b/flax_model/alphagenome/model/attention_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..19fba85012c9bfb7d3553532ff924796fa144792
--- /dev/null
+++ b/flax_model/alphagenome/model/attention_test.py
@@ -0,0 +1,78 @@
+# Copyright 2026 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 flax_model.alphagenome.model import attention
+import chex
+import haiku as hk
+import jax
+import jax.numpy as jnp
+
+
+class ConvolutionsTest(parameterized.TestCase):
+
+ def setUp(self):
+ super().setUp()
+ self._batch_size = 4
+ self._sequence_length = 4096
+ self._pair_sequence_length = self._sequence_length // 16
+ self._hidden_size = 64
+
+ def test_mlp_block_output_shape(self):
+ """Tests that MLPBlock produces the expected output shape."""
+ rng = jax.random.PRNGKey(42)
+
+ def _forward(x):
+ module = attention.MLPBlock()
+ return module(x)
+
+ init, apply = hk.transform_with_state(_forward)
+ x = jnp.zeros((self._batch_size, self._sequence_length, self._hidden_size))
+ params, state = init(rng, x)
+ output, _ = apply(params, state, rng, x)
+ chex.assert_shape(
+ output, (self._batch_size, self._sequence_length, self._hidden_size)
+ )
+
+ def test_pair_mlp_block_output_shape(self):
+ """Tests that PairMLPBlock produces the expected output shape."""
+ rng = jax.random.PRNGKey(42)
+
+ def _forward(x):
+ module = attention.PairMLPBlock()
+ return module(x)
+
+ init, apply = hk.transform_with_state(_forward)
+ x = jnp.zeros((
+ self._batch_size,
+ self._pair_sequence_length,
+ self._pair_sequence_length,
+ self._hidden_size,
+ ))
+ params, state = init(rng, x)
+ output, _ = apply(params, state, rng, x)
+ chex.assert_shape(
+ output,
+ (
+ self._batch_size,
+ self._pair_sequence_length,
+ self._pair_sequence_length,
+ self._hidden_size,
+ ),
+ )
+
+
+if __name__ == "__main__":
+ absltest.main()
diff --git a/flax_model/alphagenome/model/augmentation.py b/flax_model/alphagenome/model/augmentation.py
new file mode 100644
index 0000000000000000000000000000000000000000..43ad3f3dc262fa8784509c6720ac1f5c3ddb7ae0
--- /dev/null
+++ b/flax_model/alphagenome/model/augmentation.py
@@ -0,0 +1,122 @@
+# Copyright 2026 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 augmenting predictions, e.g. reverse complementation."""
+
+from collections.abc import Mapping
+from typing import TypeAlias
+
+from flax_model.alphagenome._sdk import typing
+from flax_model.alphagenome._sdk.models import dna_output
+import chex
+import jax
+import jax.numpy as jnp
+from jaxtyping import Array, Bool, Float, Int32, PyTree # pylint: disable=g-importing-member, g-multiple-import
+
+_Predictions: TypeAlias = PyTree[Float[Array, 'B ...'] | Int32[Array, 'B ...']]
+
+
+@typing.jaxtyped
+def reverse_complement_output_type(
+ predictions: _Predictions,
+ *,
+ output_type: dna_output.OutputType,
+ strand_reindexing: Int32[Array, '_'] | None,
+ sequence_length: Int32[Array, ''] | int,
+) -> _Predictions:
+ """Reverse complement predictions for a given output type."""
+ match output_type: # pytype: disable=incomplete-match
+ case dna_output.OutputType.SPLICE_JUNCTIONS:
+ splice_junction_predictions = predictions['predictions']
+ chex.assert_rank(splice_junction_predictions, 4)
+ # Only need to strand flip for junction outputs. Index flipping is done
+ # with splice_site_positions.
+ chex.assert_equal_shape(
+ (splice_junction_predictions, strand_reindexing), dims=-1
+ )
+ predictions['predictions'] = splice_junction_predictions[
+ ..., strand_reindexing
+ ]
+
+ splice_site_positions = predictions['splice_site_positions']
+ chex.assert_rank(splice_site_positions, 3)
+ padding_predictions = splice_site_positions < 0
+ splice_site_positions = sequence_length - 1 - splice_site_positions
+ splice_site_positions = jnp.where(
+ padding_predictions, -1, splice_site_positions
+ )
+ splice_site_positions = splice_site_positions[:, jnp.array([2, 3, 0, 1])]
+
+ predictions['splice_site_positions'] = splice_site_positions
+ case dna_output.OutputType.CONTACT_MAPS:
+ chex.assert_rank(predictions, 4)
+ # Contact maps are unstranded, so no need to strand flip.
+ return predictions[:, ::-1, ::-1]
+ case _:
+ chex.assert_rank(predictions, 3)
+ predictions = predictions[:, ::-1]
+ if strand_reindexing is not None:
+ predictions = predictions[..., strand_reindexing]
+ # For DNASE, +ve strand predictions are not aligned with -ve strand
+ # predictions after reverse-complementation:
+ # Input positions:
+ # [ 0 ][ 1 ][ 2 ]
+ # Track values:
+ # [ A ][ B ][ C ][ D ]
+ # In this example, output predictions are determined by the track value
+ # at the start of the position. Thus, when read forwards, the + strand
+ # will give ABC and the - strand will give DCB. If we want predictions to
+ # align after reverse-complementation (for example, to perform TTA or to
+ # return aligned, unstranded predictions for both + and -), we need to
+ # shift predictions by 1 after rc.
+ if output_type == dna_output.OutputType.DNASE:
+ predictions = jnp.roll(predictions, 1, axis=1)
+ predictions = predictions.at[:, 0].set(0)
+
+ return predictions
+
+
+@typing.jaxtyped
+def reverse_complement(
+ predictions: Mapping[dna_output.OutputType, _Predictions],
+ reverse_complement_mask: Bool[Array, 'B'],
+ *,
+ strand_reindexing: Mapping[dna_output.OutputType, Int32[Array, '_']],
+ sequence_length: int,
+) -> Mapping[dna_output.OutputType, _Predictions]:
+ """Reverse complement predictions."""
+
+ def _reverse_complement(predictions: _Predictions):
+ result = {}
+ for output_type in dna_output.OutputType:
+ if (prediction := predictions.get(output_type)) is not None:
+ result[output_type] = reverse_complement_output_type(
+ prediction,
+ output_type=output_type,
+ strand_reindexing=strand_reindexing.get(output_type),
+ sequence_length=sequence_length,
+ )
+ return result
+
+ # Add single batch dimension to vmap over.
+ predictions = jax.tree.map(lambda x: jnp.expand_dims(x, axis=1), predictions)
+ predictions = jax.vmap(
+ lambda prediction, should_reverse: jax.lax.cond(
+ should_reverse,
+ _reverse_complement,
+ lambda prediction: prediction,
+ prediction,
+ )
+ )(predictions, reverse_complement_mask)
+ return jax.tree.map(lambda x: jnp.squeeze(x, axis=1), predictions)
diff --git a/flax_model/alphagenome/model/augmentation_test.py b/flax_model/alphagenome/model/augmentation_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..074c287cd99f4364be7e5ef507a163b0b628d274
--- /dev/null
+++ b/flax_model/alphagenome/model/augmentation_test.py
@@ -0,0 +1,232 @@
+# Copyright 2026 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 Sequence
+import functools
+
+from absl.testing import absltest
+from absl.testing import parameterized
+from flax_model.alphagenome._sdk.models import dna_output
+from flax_model.alphagenome.model import augmentation
+import chex
+import jax
+from jax import numpy as jnp
+from jaxtyping import PyTree # pylint: disable=g-importing-member
+import ml_dtypes
+import numpy as np
+
+
+class AugmentationTest(chex.TestCase, parameterized.TestCase):
+
+ @chex.variants(with_jit=True, without_jit=True)
+ @parameterized.parameters(
+ dict(
+ output_type=dna_output.OutputType.SPLICE_JUNCTIONS,
+ predictions={
+ 'predictions': np.array(
+ [[
+ [[0, 1, 2, 3], [4, 5, 6, 7]],
+ [[8, 9, 10, 11], [12, 13, 14, 15]],
+ ]],
+ dtype=np.float32,
+ ),
+ 'splice_site_positions': np.array(
+ [[[0, 1], [-1, 3], [4, -1], [6, 7]]],
+ dtype=np.int32,
+ ),
+ },
+ strand_reindexing=np.array([3, 2, 1, 0], dtype=np.int32),
+ expected={
+ 'predictions': np.array(
+ [[
+ [[3, 2, 1, 0], [7, 6, 5, 4]],
+ [[11, 10, 9, 8], [15, 14, 13, 12]],
+ ]],
+ dtype=np.float32,
+ ),
+ 'splice_site_positions': np.array(
+ [[[-1, -1], [-3, -4], [3, 2], [-1, 0]]],
+ dtype=np.int32,
+ ),
+ },
+ sequence_length=4,
+ ),
+ dict(
+ output_type=dna_output.OutputType.CONTACT_MAPS,
+ predictions=np.array(
+ [[
+ [[0, 1], [2, 3], [4, 5], [6, 7]],
+ [[8, 9], [10, 11], [12, 13], [14, 15]],
+ [[16, 17], [18, 19], [20, 21], [22, 23]],
+ [[24, 25], [26, 27], [28, 29], [30, 31]],
+ ]],
+ dtype=ml_dtypes.bfloat16,
+ ),
+ strand_reindexing=None,
+ expected=np.array(
+ [[
+ [[30, 31], [28, 29], [26, 27], [24, 25]],
+ [[22, 23], [20, 21], [18, 19], [16, 17]],
+ [[14, 15], [12, 13], [10, 11], [8, 9]],
+ [[6, 7], [4, 5], [2, 3], [0, 1]],
+ ]],
+ dtype=ml_dtypes.bfloat16,
+ ),
+ sequence_length=-1, # Unused.
+ ),
+ dict(
+ output_type=dna_output.OutputType.RNA_SEQ,
+ predictions=np.array(
+ [[[0, 1], [2, 3], [4, 5], [6, 7]]],
+ dtype=ml_dtypes.bfloat16,
+ ),
+ strand_reindexing=np.array([1, 0], dtype=np.int32),
+ expected=np.array(
+ [[[7, 6], [5, 4], [3, 2], [1, 0]]],
+ dtype=ml_dtypes.bfloat16,
+ ),
+ sequence_length=-1, # Unused.
+ ),
+ dict(
+ output_type=dna_output.OutputType.DNASE,
+ predictions=np.array(
+ [[[0, 1], [2, 3], [4, 5], [6, 7]]],
+ dtype=ml_dtypes.bfloat16,
+ ),
+ strand_reindexing=np.array([1, 0], dtype=np.int32),
+ expected=np.array(
+ [[[0, 0], [7, 6], [5, 4], [3, 2]]],
+ dtype=ml_dtypes.bfloat16,
+ ),
+ sequence_length=-1, # Unused.
+ ),
+ )
+ def test_reverse_complement_output_type(
+ self,
+ output_type: dna_output.OutputType,
+ predictions: PyTree[np.ndarray],
+ strand_reindexing: np.ndarray,
+ expected: PyTree[np.ndarray],
+ sequence_length: int,
+ ):
+ output = self.variant(
+ augmentation.reverse_complement_output_type,
+ static_argnames=['output_type'],
+ )(
+ jax.tree.map(jnp.asarray, predictions),
+ output_type=output_type,
+ strand_reindexing=jnp.asarray(strand_reindexing)
+ if strand_reindexing is not None
+ else None,
+ sequence_length=sequence_length,
+ )
+ jax.tree.map(np.testing.assert_array_equal, output, expected)
+
+ @chex.variants(with_jit=True, without_jit=True)
+ @parameterized.product(
+ mask=[[True, False], [False, False], [True, True]],
+ )
+ def test_reverse_complement(self, mask: Sequence[bool]):
+ sequence_length = 64
+ example = {
+ dna_output.OutputType.ATAC: (
+ jnp.arange(sequence_length * 2, dtype=jnp.bfloat16).reshape(
+ 1, sequence_length, 2
+ )
+ ),
+ dna_output.OutputType.DNASE: (
+ jnp.arange(sequence_length * 2, dtype=jnp.bfloat16).reshape(
+ 1, sequence_length, 2
+ )
+ ),
+ dna_output.OutputType.SPLICE_JUNCTIONS: {
+ 'predictions': (
+ jnp.arange(32, dtype=jnp.bfloat16).reshape(1, 4, 4, 2)
+ ),
+ 'splice_site_positions': (
+ jnp.arange(16, dtype=jnp.int32).reshape(1, 4, 4)
+ ),
+ },
+ dna_output.OutputType.CONTACT_MAPS: (
+ jnp.arange(400, dtype=jnp.bfloat16).reshape(1, 10, 10, 4)
+ ),
+ }
+ strand_reindexing = {
+ dna_output.OutputType.ATAC: jnp.array([1, 0], dtype=jnp.int32),
+ dna_output.OutputType.DNASE: jnp.array([1, 0], dtype=jnp.int32),
+ dna_output.OutputType.SPLICE_JUNCTIONS: jnp.array(
+ [0, 1], dtype=jnp.int32
+ ),
+ }
+ splice_site_positions_reversed = (
+ sequence_length
+ - 1
+ - example[dna_output.OutputType.SPLICE_JUNCTIONS][
+ 'splice_site_positions'
+ ]
+ )
+ expected_reversed = {
+ dna_output.OutputType.ATAC: example[dna_output.OutputType.ATAC][
+ :, ::-1, strand_reindexing[dna_output.OutputType.ATAC]
+ ],
+ dna_output.OutputType.DNASE: jnp.pad(
+ example[dna_output.OutputType.DNASE][
+ :, :0:-1, strand_reindexing[dna_output.OutputType.DNASE]
+ ],
+ ((0, 0), (1, 0), (0, 0)),
+ ),
+ dna_output.OutputType.SPLICE_JUNCTIONS: {
+ 'predictions': example[dna_output.OutputType.SPLICE_JUNCTIONS][
+ 'predictions'
+ ],
+ 'splice_site_positions': splice_site_positions_reversed[
+ :,
+ (2, 3, 0, 1),
+ ],
+ },
+ dna_output.OutputType.CONTACT_MAPS: example[
+ dna_output.OutputType.CONTACT_MAPS
+ ][:, ::-1, ::-1],
+ }
+ batched_example = jax.tree.map(
+ lambda x: jnp.repeat(x, len(mask), axis=0), example
+ )
+ expected = jax.tree.map(
+ lambda *x: jnp.concatenate(x, axis=0),
+ *[expected_reversed if m else example for m in mask],
+ )
+
+ result = self.variant(
+ augmentation.reverse_complement,
+ static_argnames=['sequence_length'],
+ )(
+ batched_example,
+ jnp.asarray(mask, dtype=bool),
+ strand_reindexing=strand_reindexing,
+ sequence_length=sequence_length,
+ )
+
+ for output_type in dna_output.OutputType:
+ if (prediction := result.get(output_type)) is not None:
+ jax.tree.map(
+ functools.partial(
+ np.testing.assert_array_equal, err_msg=f'{output_type=}'
+ ),
+ prediction,
+ expected.get(output_type),
+ )
+
+
+if __name__ == '__main__':
+ absltest.main()
diff --git a/flax_model/alphagenome/model/convolutions.py b/flax_model/alphagenome/model/convolutions.py
new file mode 100644
index 0000000000000000000000000000000000000000..711f63f275038ff5693f95bf44da3729e3979a09
--- /dev/null
+++ b/flax_model/alphagenome/model/convolutions.py
@@ -0,0 +1,152 @@
+# Copyright 2026 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.
+
+"""Convolutional layers."""
+
+from flax_model.alphagenome._sdk import typing
+from flax_model.alphagenome.model import layers
+import chex
+import haiku as hk
+import jax
+import jax.numpy as jnp
+from jaxtyping import Array, Float # pylint: disable=g-importing-member, g-multiple-import
+
+
+class ConvBlock(hk.Module):
+ """A convolutional block with GELU activation and RMS Batch Normalization."""
+
+ def __init__(self, num_channels: int, width: int, name: str | None = None):
+ """Initializes the convolutional block.
+
+ Args:
+ num_channels: The number of output channels.
+ width: The width of the convolution. If 1, a linear layer is used instead.
+ name: The name of the module.
+ """
+ super().__init__(name=name)
+ self._num_channels = num_channels
+ self._width = width
+
+ @typing.jaxtyped
+ def __call__(
+ self, x: Float[Array, 'B S D']
+ ) -> Float[Array, 'B S {self._num_channels}']:
+ x = layers.gelu(layers.RMSBatchNorm()(x))
+ if self._width == 1:
+ return hk.Linear(self._num_channels)(x)
+ else:
+ return StandardizedConv1D(
+ num_channels=self._num_channels, width=self._width
+ )(x)
+
+
+class StandardizedConv1D(hk.Module):
+ """Standardized 1D Convolution with scaled weight standardization."""
+
+ def __init__(
+ self,
+ num_channels: int,
+ width: int,
+ name: str | None = None,
+ ):
+ super().__init__(name=name)
+ self._num_channels = num_channels
+ self._width = width
+
+ @typing.jaxtyped
+ def __call__(
+ self, x: Float[Array, 'B S D']
+ ) -> Float[Array, 'B S {self._num_channels}']:
+ input_channels = x.shape[-1]
+ fan_in = self._width * input_channels
+ kernel_shape = (self._width, input_channels, self._num_channels)
+ w = hk.get_parameter('w', shape=kernel_shape, dtype=x.dtype, init=jnp.zeros)
+
+ # Weight standardization
+ w -= jnp.mean(w, axis=(0, 1), keepdims=True)
+ var_w = jnp.var(w, axis=(0, 1), keepdims=True)
+ scale = hk.get_parameter(
+ 'scale',
+ shape=[1, 1, self._num_channels],
+ init=jnp.ones,
+ dtype=w.dtype,
+ )
+ scale = scale * jax.lax.rsqrt(jnp.maximum(fan_in * var_w, 1e-4))
+ w_standardized = w * scale
+
+ out = jax.lax.conv_general_dilated(
+ lhs=x,
+ rhs=w_standardized,
+ window_strides=[1],
+ padding='SAME',
+ dimension_numbers=jax.lax.ConvDimensionNumbers(
+ lhs_spec=(0, 2, 1), rhs_spec=(2, 1, 0), out_spec=(0, 2, 1)
+ ),
+ )
+ bias = hk.get_parameter(
+ 'bias', shape=(self._num_channels,), dtype=x.dtype, init=jnp.zeros
+ )
+ bias = jnp.broadcast_to(bias, out.shape)
+ return out + bias
+
+
+class DnaEmbedder(hk.Module):
+ """Encodes one-hot encoded DNA sequences into a fixed-length embedding."""
+
+ @typing.jaxtyped
+ def __call__(
+ self, dna_sequence: Float[Array, 'B S 4']
+ ) -> Float[Array, 'B S 768']:
+ x = hk.Conv1D(output_channels=768, kernel_shape=15)(dna_sequence)
+ return x + ConvBlock(num_channels=768, width=5)(x)
+
+
+class DownResBlock(hk.Module):
+ """Down resolution convolution."""
+
+ @typing.jaxtyped
+ def __call__(self, x: Float[Array, 'B S D']) -> Float[Array, 'B S D+128']:
+ num_out_channels = x.shape[-1] + 128
+ out = ConvBlock(num_channels=num_out_channels, width=5)(x)
+ out = out + jnp.pad(
+ x, [(0, 0), (0, 0), (0, 128)]
+ ) # Padding for residual connection
+ return out + ConvBlock(num_channels=out.shape[-1], width=5)(out)
+
+
+class UpResBlock(hk.Module):
+ """Upsampling residual block."""
+
+ @typing.jaxtyped
+ def __call__(
+ self, x: Float[Array, 'B S D'], unet_skip: Float[Array, 'B S_skip D_skip']
+ ) -> Float[Array, 'B S_up D_skip']:
+ chex.assert_rank(x, 3)
+ chex.assert_rank(unet_skip, 3)
+ num_channels = unet_skip.shape[-1]
+ out = (
+ ConvBlock(num_channels=num_channels, width=5, name='conv_in')(x)
+ + x[:, :, :num_channels]
+ )
+ out = jnp.repeat(out, 2, axis=1) # Upsampling
+ residual_scale = hk.get_parameter(
+ 'residual_scale', (), init=jnp.ones
+ ).astype(out.dtype)
+ out *= residual_scale
+ out += ConvBlock(
+ num_channels=num_channels, width=1, name='pointwise_conv_unet_skip'
+ )(unet_skip)
+ return out + ConvBlock(num_channels=num_channels, width=5, name='conv_out')(
+ out
+ )
diff --git a/flax_model/alphagenome/model/convolutions_test.py b/flax_model/alphagenome/model/convolutions_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..4412430c636da1b74c205bc0061664ba0f8caf53
--- /dev/null
+++ b/flax_model/alphagenome/model/convolutions_test.py
@@ -0,0 +1,137 @@
+# Copyright 2026 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 flax_model.alphagenome.model import convolutions
+import chex
+import haiku as hk
+import jax
+import jax.numpy as jnp
+
+
+class ConvolutionsTest(parameterized.TestCase):
+
+ def setUp(self):
+ super().setUp()
+ self._batch_size = 4
+ self._sequence_length = 200
+ self._input_channels = 768
+ self._output_channels = 256
+ self._dna_embedder_output_channels = 768
+ self._unet_skip_channels = 512
+ self._down_block_added_channels = 128
+ self._rng = jax.random.PRNGKey(42)
+
+ @parameterized.named_parameters(
+ dict(testcase_name='width_1', width=1),
+ dict(testcase_name='width_5', width=5),
+ )
+ def test_conv_block_output_shape(self, width: int):
+ def _conv_block(x):
+ return convolutions.ConvBlock(
+ num_channels=self._output_channels, width=width
+ )(x)
+
+ conv_block = hk.transform_with_state(_conv_block)
+ x = jnp.zeros(
+ (self._batch_size, self._sequence_length, self._input_channels)
+ )
+ params, state = conv_block.init(self._rng, x)
+ out, state = conv_block.apply(params, state, self._rng, x)
+ chex.assert_shape(
+ out, (self._batch_size, self._sequence_length, self._output_channels)
+ )
+ self.assertNotEmpty(state)
+
+ def test_standardized_conv1d_output_shape(self):
+ def _standardized_conv1d(x):
+ return convolutions.StandardizedConv1D(
+ num_channels=self._output_channels, width=5
+ )(x)
+
+ standardized_conv1d = hk.transform_with_state(_standardized_conv1d)
+ x = jnp.zeros(
+ (self._batch_size, self._sequence_length, self._input_channels)
+ )
+ params, state = standardized_conv1d.init(self._rng, x)
+ out, state = standardized_conv1d.apply(params, state, self._rng, x)
+ chex.assert_shape(
+ out, (self._batch_size, self._sequence_length, self._output_channels)
+ )
+
+ def test_dna_embedder_output_shape(self):
+ def _dna_embedder(x):
+ return convolutions.DnaEmbedder()(x)
+
+ dna_embedder = hk.transform_with_state(_dna_embedder)
+ x = jnp.zeros((self._batch_size, self._sequence_length, 4))
+ params, state = dna_embedder.init(self._rng, x)
+ out, state = dna_embedder.apply(params, state, self._rng, x)
+ chex.assert_shape(
+ out,
+ (
+ self._batch_size,
+ self._sequence_length,
+ self._dna_embedder_output_channels,
+ ),
+ )
+ self.assertNotEmpty(state)
+
+ def test_down_res_block_output_shape(self):
+ def _down_res_block(x):
+ return convolutions.DownResBlock()(x)
+
+ down_res_block = hk.transform_with_state(_down_res_block)
+ x = jnp.zeros(
+ (self._batch_size, self._sequence_length, self._input_channels)
+ )
+ params, state = down_res_block.init(self._rng, x)
+ out, state = down_res_block.apply(params, state, self._rng, x)
+ chex.assert_shape(
+ out,
+ (
+ self._batch_size,
+ self._sequence_length,
+ self._input_channels + self._down_block_added_channels,
+ ),
+ )
+ self.assertNotEmpty(state)
+
+ def test_up_res_block_output_shape(self):
+ def _up_res_block(x, unet_skip):
+ return convolutions.UpResBlock()(x, unet_skip)
+
+ up_res_block = hk.transform_with_state(_up_res_block)
+ x = jnp.zeros(
+ (self._batch_size, self._sequence_length, self._input_channels)
+ )
+ unet_skip = jnp.zeros(
+ (self._batch_size, self._sequence_length * 2, self._unet_skip_channels)
+ )
+ params, state = up_res_block.init(self._rng, x, unet_skip)
+ out, state = up_res_block.apply(params, state, self._rng, x, unet_skip)
+ chex.assert_shape(
+ out,
+ (
+ self._batch_size,
+ self._sequence_length * 2,
+ self._unet_skip_channels,
+ ),
+ )
+ self.assertNotEmpty(state)
+
+
+if __name__ == '__main__':
+ absltest.main()
diff --git a/flax_model/alphagenome/model/dna_model.py b/flax_model/alphagenome/model/dna_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..dfe267580814d07c19c7fbd53766098ebd7dfebb
--- /dev/null
+++ b/flax_model/alphagenome/model/dna_model.py
@@ -0,0 +1,1326 @@
+# Copyright 2026 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.
+
+"""Implementation of alphagenome DNAModel interface."""
+
+from collections.abc import Callable, Iterable, Mapping, Sequence
+import dataclasses
+import functools
+import os
+import threading
+from typing import TypeAlias
+
+from flax_model.alphagenome._sdk import tensor_utils
+from flax_model.alphagenome._sdk import typing
+from flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome._sdk.data import junction_data
+from flax_model.alphagenome._sdk.data import ontology
+from flax_model.alphagenome._sdk.data import track_data
+from flax_model.alphagenome._sdk.interpretation import ism
+from flax_model.alphagenome._sdk.models import dna_model
+from flax_model.alphagenome._sdk.models import dna_output
+from flax_model.alphagenome._sdk.models import interval_scorers as interval_scorers_lib
+from flax_model.alphagenome._sdk.models import variant_scorers as variant_scorers_lib
+from flax_model.alphagenome.io import fasta
+from flax_model.alphagenome.io import genome as genome_io
+from flax_model.alphagenome.io import splicing as splicing_io
+from flax_model.alphagenome.model import augmentation
+from flax_model.alphagenome.model import model
+from flax_model.alphagenome.model import one_hot_encoder
+from flax_model.alphagenome.model import splicing
+from flax_model.alphagenome.model.interval_scoring import gene_mask as interval_gene_mask
+from flax_model.alphagenome.model.metadata import metadata as metadata_lib
+from flax_model.alphagenome.model.variant_scoring import center_mask
+from flax_model.alphagenome.model.variant_scoring import contact_map
+from flax_model.alphagenome.model.variant_scoring import gene_mask
+from flax_model.alphagenome.model.variant_scoring import gene_mask_extractor
+from flax_model.alphagenome.model.variant_scoring import polyadenylation
+from flax_model.alphagenome.model.variant_scoring import splice_junction
+from flax_model.alphagenome.model.variant_scoring import variant_scoring
+import anndata
+import chex
+import haiku as hk
+import huggingface_hub
+import jax
+import jax.numpy as jnp
+from jaxtyping import Array, Bool, Float, Float32, Int32, PyTree, Shaped # pylint: disable=g-importing-member, g-multiple-import
+import jmp
+import kagglehub
+from kagglehub import auth as kaggle_auth
+import numpy as np
+import orbax.checkpoint as ocp
+import pandas as pd
+
+
+AlphaGenomeOutputMetadata: TypeAlias = metadata_lib.AlphaGenomeOutputMetadata
+ModelVersion: TypeAlias = dna_model.ModelVersion
+Organism: TypeAlias = dna_model.Organism
+Output: TypeAlias = dna_output.Output
+OutputMetadata: TypeAlias = dna_output.OutputMetadata
+OutputType: TypeAlias = dna_output.OutputType
+VariantOutput: TypeAlias = dna_output.VariantOutput
+Organism: TypeAlias = dna_model.Organism
+
+BatchPrediction: TypeAlias = PyTree[
+ Float[Array, 'B ...'] | Int32[Array, 'B ...']
+]
+
+ApplyFn = Callable[
+ [
+ hk.Params,
+ hk.State,
+ Float32[Array, 'B S 4'],
+ Int32[Array, 'B'],
+ ],
+ BatchPrediction,
+]
+JunctionsApplyFn = Callable[
+ [
+ hk.Params,
+ hk.State,
+ Float32[Array, 'B S D'],
+ Int32[Array, 'B 4 K'],
+ Int32[Array, 'B'],
+ ],
+ BatchPrediction,
+]
+
+
+def extract_predictions(
+ predictions: BatchPrediction,
+) -> Mapping[dna_output.OutputType, BatchPrediction]:
+ """Extracts predictions from the model predictions."""
+ results = {}
+ for output_type in dna_output.OutputType:
+ match output_type:
+ case dna_output.OutputType.ATAC:
+ prediction = predictions.get('atac', {}).get('predictions_1bp')
+ case dna_output.OutputType.CAGE:
+ prediction = predictions.get('cage', {}).get('predictions_1bp')
+ case dna_output.OutputType.DNASE:
+ prediction = predictions.get('dnase', {}).get('predictions_1bp')
+ case dna_output.OutputType.RNA_SEQ:
+ prediction = predictions.get('rna_seq', {}).get('predictions_1bp')
+ case dna_output.OutputType.CHIP_HISTONE:
+ prediction = predictions.get('chip_histone', {}).get(
+ 'predictions_128bp'
+ )
+ case dna_output.OutputType.CHIP_TF:
+ prediction = predictions.get('chip_tf', {}).get('predictions_128bp')
+ case dna_output.OutputType.SPLICE_SITES:
+ prediction = predictions.get('splice_sites_classification', {}).get(
+ 'predictions'
+ )
+ case dna_output.OutputType.SPLICE_SITE_USAGE:
+ prediction = predictions.get('splice_sites_usage', {}).get(
+ 'predictions'
+ )
+ case dna_output.OutputType.SPLICE_JUNCTIONS:
+ if (
+ splice_site_preds := predictions.get('splice_sites_junction')
+ ) is not None:
+ prediction = {
+ 'predictions': splice_site_preds.get('predictions'),
+ 'splice_site_positions': splice_site_preds.get(
+ 'splice_site_positions'
+ ),
+ }
+ else:
+ prediction = None
+ case dna_output.OutputType.CONTACT_MAPS:
+ prediction = predictions.get('contact_maps', {}).get('predictions')
+ case dna_output.OutputType.PROCAP:
+ prediction = predictions.get('procap', {}).get('predictions_1bp')
+ case _:
+ raise ValueError(f'Unsupported output type: {output_type}')
+ if prediction is not None:
+ results[output_type] = prediction
+ return results
+
+
+@typing.jaxtyped
+def _predict(
+ params: hk.Params,
+ state: hk.State,
+ sequences: Float32[Array, 'B S 4'],
+ organism_indices: Int32[Array, 'B'],
+ *,
+ strand_reindexing: Mapping[dna_output.OutputType, Int32[Array, '_']],
+ negative_strand_mask: Bool[Array, 'B'],
+ apply_fn: ApplyFn,
+) -> Mapping[dna_output.OutputType, BatchPrediction]:
+ """Maps predictions to output types and optionally reverse complements."""
+ predictions = apply_fn(params, state, sequences, organism_indices)
+ predictions = extract_predictions(predictions)
+ predictions = augmentation.reverse_complement(
+ predictions,
+ negative_strand_mask,
+ strand_reindexing=strand_reindexing,
+ sequence_length=sequences.shape[1],
+ )
+ return predictions
+
+
+@typing.jaxtyped
+def _predict_variant(
+ params: hk.Params,
+ state: hk.State,
+ reference_sequences: Float32[Array, 'B S 4'],
+ alternate_sequences: Float32[Array, 'B S 4'],
+ splice_sites: Bool[Array, 'B S 5'] | None,
+ organism_indices: Int32[Array, 'B'],
+ strand_reindexing: Mapping[dna_output.OutputType, Int32[Array, '_']],
+ negative_strand_mask: Bool[Array, 'B'],
+ *,
+ apply_fn: ApplyFn,
+ junctions_apply_fn: JunctionsApplyFn,
+ num_splice_sites: int,
+ splice_site_threshold: float,
+) -> tuple[
+ Mapping[dna_output.OutputType, BatchPrediction],
+ Mapping[dna_output.OutputType, BatchPrediction],
+]:
+ """Computes and reverse-complements variant predictions."""
+ chex.assert_equal_shape([reference_sequences, alternate_sequences])
+ sequence_length = reference_sequences.shape[1]
+
+ reference_predictions = apply_fn(
+ params,
+ state,
+ reference_sequences,
+ organism_indices,
+ )
+ alternate_predictions = apply_fn(
+ params,
+ state,
+ alternate_sequences,
+ organism_indices,
+ )
+ # Get union of splice site positions across ref and alt.
+ ref_and_alt_splice_site_positions = splicing.generate_splice_site_positions(
+ ref=reference_predictions['splice_sites_classification']['predictions'],
+ alt=alternate_predictions['splice_sites_classification']['predictions'],
+ splice_sites=splice_sites,
+ k=num_splice_sites,
+ pad_to_length=num_splice_sites,
+ threshold=splice_site_threshold,
+ )
+ reference_predictions['splice_sites_junction'] = junctions_apply_fn(
+ params,
+ state,
+ reference_predictions['embeddings_1bp'],
+ ref_and_alt_splice_site_positions,
+ organism_indices,
+ )
+ alternate_predictions['splice_sites_junction'] = junctions_apply_fn(
+ params,
+ state,
+ alternate_predictions['embeddings_1bp'],
+ ref_and_alt_splice_site_positions,
+ organism_indices,
+ )
+
+ def _extract_and_rc(predictions):
+ return augmentation.reverse_complement(
+ extract_predictions(predictions),
+ negative_strand_mask,
+ strand_reindexing=strand_reindexing,
+ sequence_length=sequence_length,
+ )
+
+ return (
+ _extract_and_rc(reference_predictions),
+ _extract_and_rc(alternate_predictions),
+ )
+
+
+@typing.jaxtyped
+def _filter_predictions(
+ predictions: Mapping[dna_output.OutputType, BatchPrediction],
+ *,
+ track_masks: Mapping[dna_output.OutputType, Bool[Array, '_']],
+) -> Mapping[dna_output.OutputType, BatchPrediction]:
+ """Filters predictions by mapping of track masks."""
+ result = {}
+ for output_type, mask in track_masks.items():
+ if (prediction := predictions.get(output_type)) is not None:
+ # JAX dynamic slicing does not work with transfer_guard.
+ with jax.transfer_guard('allow'):
+ if output_type == dna_output.OutputType.SPLICE_JUNCTIONS:
+ result[output_type] = {
+ 'predictions': prediction['predictions'][
+ ..., jnp.tile(mask, reps=2)
+ ],
+ 'splice_site_positions': prediction['splice_site_positions'],
+ }
+ else:
+ result[output_type] = prediction[..., mask]
+ return result
+
+
+@functools.partial(jax.jit, static_argnames=['transfer_to_host'])
+@typing.jaxtyped
+def _upcast_single_batch_predictions(
+ x: PyTree[Float[Array, '1 ...'] | Int32[Array, '1 ...']],
+ *,
+ transfer_to_host: bool = True,
+) -> PyTree[
+ Float32[Array | np.ndarray, '...'] | Int32[Array | np.ndarray, '...']
+]:
+ """Helper to upcast and optionally transfer predictions to host."""
+ x = jax.tree.map(lambda x: tensor_utils.upcast_floating(x[0]), x)
+ # return jax.device_put(x, jax.memory.Space.Host) if transfer_to_host else x
+ return jax.device_put(x, jax.devices("cpu")[0]) if transfer_to_host else x
+
+
+
+@typing.jaxtyped
+def _filter_variant_predictions(
+ reference_predictions: Mapping[dna_output.OutputType, BatchPrediction],
+ alternate_predictions: Mapping[dna_output.OutputType, BatchPrediction],
+ *,
+ track_masks: Mapping[dna_output.OutputType, Bool[Array, '_']],
+) -> tuple[
+ Mapping[dna_output.OutputType, BatchPrediction],
+ Mapping[dna_output.OutputType, BatchPrediction],
+]:
+ """Filters variant predictions to a set of output types."""
+ return (
+ _filter_predictions(reference_predictions, track_masks=track_masks),
+ _filter_predictions(alternate_predictions, track_masks=track_masks),
+ )
+
+
+class _DeviceContextManager:
+ """Context manager for managing a jax.Device."""
+
+ def __init__(self, device: jax.Device):
+ self._device = device
+ self._lock = threading.Lock()
+
+ def __enter__(self) -> jax.Device:
+ self._lock.acquire()
+ return self._device
+
+ def __exit__(self, exc_type, exc_value, traceback):
+ del exc_type, exc_value, traceback
+ self._lock.release()
+
+
+class AlphaGenomeModel(dna_model.DnaModel):
+ """Abstract class for DNA sequence models."""
+
+ def __init__(
+ self,
+ *,
+ params: hk.Params,
+ state: hk.State,
+ apply_fn: ApplyFn,
+ junctions_apply_fn: JunctionsApplyFn,
+ metadata: Mapping[dna_model.Organism, AlphaGenomeOutputMetadata],
+ fasta_extractors: (
+ Mapping[dna_model.Organism, fasta.FastaExtractor] | None
+ ) = None,
+ splice_site_extractors: (
+ Mapping[dna_model.Organism, splicing_io.SpliceSiteAnnotationExtractor]
+ | None
+ ) = None,
+ gtfs: Mapping[dna_model.Organism, pd.DataFrame] | None = None,
+ pas_gtfs: Mapping[dna_model.Organism, pd.DataFrame] | None = None,
+ num_splice_sites: int = 512,
+ splice_site_threshold: float = 0.1,
+ device: jax.Device | None = None,
+ ):
+ """Initializes the AlphaGenomeModel.
+
+ Args:
+ params: Model parameters.
+ state: Model state.
+ apply_fn: A function that takes model parameters, state, DNA sequence and
+ organism index; and returns the model's predictions.
+ junctions_apply_fn: A function that takes model parameters, state,
+ embeddings and splice site positions; and returns the model's junctions
+ predictions.
+ metadata: A mapping of organism to OutputMetadata.
+ fasta_extractors: Optional mapping of organism to FastaExtractor. If not
+ provided, functions that require sequence extraction will fail.
+ splice_site_extractors: Optional mapping of organism to
+ SpliceSiteAnnotationExtractor. If not provided, reference splice sits
+ will not be used in variant predictions.
+ gtfs: Optional mapping of organism to GENCODE GTF Pandas dataframe. If not
+ provided, variant scorers that require GTFs will not be available.
+ pas_gtfs: Optional mapping of organism to polyadenylation annotation
+ dataframe. If not provided, variant scorers that require polyadenylation
+ annotations will not be available.
+ num_splice_sites: The maximum number of splice sites that are extracted
+ from the splice site classification predictions.
+ splice_site_threshold: The threshold to use for splice site prediction.
+ device: Optional device to use for model prediction. If None, the first
+ local device will be used.
+ """
+ if device is None:
+ device = jax.default_device.value or jax.local_devices()[0]
+ if device.platform not in {'gpu', 'tpu'}:
+ raise ValueError(
+ 'Cannot find any GPU or TPU devices. We strondly recommend running'
+ ' on GPU or TPU, but if you wish to run on CPU, please explicitly'
+ ' pass the JAX device to run the model.'
+ )
+ self._device_context = _DeviceContextManager(device)
+ self._params = jax.device_put(params, device)
+ self._state = jax.device_put(state, device)
+ self._metadata = metadata
+ self._one_hot_encoder = one_hot_encoder.DNAOneHotEncoder()
+ self._fasta_extractors = fasta_extractors or {}
+ self._splice_site_extractors = splice_site_extractors or {}
+
+ self._predict = jax.jit(functools.partial(_predict, apply_fn=apply_fn))
+ self._predict_variant = jax.jit(
+ functools.partial(
+ _predict_variant,
+ apply_fn=jax.jit(apply_fn),
+ junctions_apply_fn=jax.jit(junctions_apply_fn),
+ num_splice_sites=num_splice_sites,
+ splice_site_threshold=splice_site_threshold,
+ )
+ )
+
+ # Metadata for each organism without padding.
+ self._output_metadata_by_organism = {}
+
+ gtfs = gtfs or {}
+ pas_gtfs = pas_gtfs or {}
+
+ for organism, organism_metadata in self._metadata.items():
+ masks = jax.tree.map(np.logical_not, organism_metadata.padding)
+ output_metadata = {
+ output_type.name.lower(): m[masks[output_type]]
+ for output_type in dna_output.OutputType
+ if (m := organism_metadata.get(output_type)) is not None
+ }
+ self._output_metadata_by_organism[organism] = dna_output.OutputMetadata(
+ **output_metadata
+ )
+
+ self._variant_scorers = {}
+
+ for organism in metadata.keys():
+ self._variant_scorers[organism]: dict[
+ variant_scorers_lib.BaseVariantScorer, variant_scoring.VariantScorer
+ ] = {
+ variant_scorers_lib.BaseVariantScorer.CENTER_MASK: (
+ center_mask.CenterMaskVariantScorer()
+ ),
+ variant_scorers_lib.BaseVariantScorer.CONTACT_MAP: (
+ contact_map.ContactMapScorer()
+ ),
+ }
+ if (gtf := gtfs.get(organism)) is not None:
+ gene_scorer = gene_mask.GeneVariantScorer(
+ gene_mask_extractor=gene_mask_extractor.GeneMaskExtractor(
+ gtf=gtf,
+ gene_query_type=(
+ gene_mask_extractor.GeneQueryType.INTERVAL_CONTAINED
+ ),
+ gene_mask_type=gene_mask_extractor.GeneMaskType.EXONS,
+ ),
+ )
+ self._variant_scorers[organism][
+ variant_scorers_lib.BaseVariantScorer.GENE_MASK_LFC
+ ] = gene_scorer
+ self._variant_scorers[organism][
+ variant_scorers_lib.BaseVariantScorer.GENE_MASK_ACTIVE
+ ] = gene_scorer
+ self._variant_scorers[organism][
+ variant_scorers_lib.BaseVariantScorer.GENE_MASK_SPLICING
+ ] = gene_scorer
+ self._variant_scorers[organism][
+ variant_scorers_lib.BaseVariantScorer.SPLICE_JUNCTION
+ ] = splice_junction.SpliceJunctionVariantScorer(gtf)
+ if (pas_gtf := pas_gtfs.get(organism)) is not None:
+ self._variant_scorers[organism][
+ variant_scorers_lib.BaseVariantScorer.PA_QTL
+ ] = polyadenylation.PolyadenylationVariantScorer(gtf, pas_gtf)
+
+ self._interval_scorers = {}
+
+ for organism in metadata.keys():
+ self._interval_scorers[organism] = {}
+ if (gtf := gtfs.get(organism)) is not None:
+ self._interval_scorers[organism][
+ interval_scorers_lib.BaseIntervalScorer.GENE_MASK
+ ] = interval_gene_mask.GeneIntervalScorer(
+ gene_mask_extractor=gene_mask_extractor.GeneMaskExtractor(
+ gtf=gtf,
+ gene_query_type=(
+ gene_mask_extractor.GeneQueryType.INTERVAL_CONTAINED
+ ),
+ gene_mask_type=gene_mask_extractor.GeneMaskType.EXONS,
+ ),
+ )
+
+ def _get_fasta_extractor(
+ self, organism: dna_model.Organism
+ ) -> fasta.FastaExtractor:
+ """Returns the FastaExtractor for a given organism."""
+ if extractor := self._fasta_extractors.get(organism):
+ return extractor
+ else:
+ raise ValueError(f'FastaExtractor not found for {organism.name=}')
+
+ 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:
+ """Predicts the sequence."""
+ if ontology_terms is not None:
+ ontology_terms = set(
+ ontology.from_curie(o) if isinstance(o, str) else o
+ for o in ontology_terms
+ )
+ metadata = self._metadata[organism]
+ track_masks = metadata_lib.create_track_masks(
+ metadata,
+ requested_outputs=set(requested_outputs),
+ requested_ontologies=ontology_terms,
+ )
+
+ with self._device_context as device, jax.transfer_guard('disallow'):
+ organism_index = jax.device_put(
+ np.full((1,), convert_to_organism_index(organism), dtype=np.int32),
+ device,
+ )
+ sequence = jax.device_put(
+ np.asarray(self._one_hot_encoder.encode(sequence))[np.newaxis], device
+ )
+ predictions = self._predict(
+ self._params,
+ self._state,
+ sequence,
+ organism_index,
+ negative_strand_mask=jax.device_put(np.asarray([False]), device),
+ strand_reindexing=jax.device_put(metadata.strand_reindexing, device),
+ )
+ predictions = _filter_predictions(
+ predictions, track_masks=jax.device_put(track_masks, device)
+ )
+ predictions = _upcast_single_batch_predictions(predictions)
+ return _construct_output_from_predictions(
+ predictions,
+ track_masks=track_masks,
+ metadata=metadata,
+ 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 ontology_terms is not None:
+ ontology_terms = set(
+ ontology.from_curie(o) if isinstance(o, str) else o
+ for o in ontology_terms
+ )
+ sequence = self._get_fasta_extractor(organism).extract(interval)
+ metadata = self._metadata[organism]
+ track_masks = metadata_lib.create_track_masks(
+ metadata,
+ requested_outputs=set(requested_outputs),
+ requested_ontologies=ontology_terms,
+ )
+
+ with self._device_context as device, jax.transfer_guard('disallow'):
+ organism_index = jax.device_put(
+ np.full((1,), convert_to_organism_index(organism), dtype=np.int32),
+ device,
+ )
+ sequence = jax.device_put(
+ np.asarray(self._one_hot_encoder.encode(sequence))[np.newaxis], device
+ )
+ predictions = self._predict(
+ self._params,
+ self._state,
+ sequence,
+ organism_index,
+ negative_strand_mask=jax.device_put(
+ np.asarray([interval.negative_strand]), device
+ ),
+ strand_reindexing=jax.device_put(metadata.strand_reindexing, device),
+ )
+ predictions = _filter_predictions(
+ predictions, track_masks=jax.device_put(track_masks, device)
+ )
+ predictions = _upcast_single_batch_predictions(predictions)
+ return _construct_output_from_predictions(
+ predictions,
+ track_masks=track_masks,
+ metadata=metadata,
+ interval=interval,
+ )
+
+ 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 ontology_terms is not None:
+ ontology_terms = set(
+ ontology.from_curie(o) if isinstance(o, str) else o
+ for o in ontology_terms
+ )
+ reference_sequence, alternate_sequence = (
+ genome_io.extract_variant_sequences(
+ interval, variant, self._get_fasta_extractor(organism)
+ )
+ )
+ metadata = self._metadata[organism]
+ track_masks = metadata_lib.create_track_masks(
+ metadata,
+ requested_outputs=set(requested_outputs),
+ requested_ontologies=ontology_terms,
+ )
+ splice_sites = None
+ if splice_site_extractor := self._splice_site_extractors.get(organism):
+ splice_sites = splice_site_extractor.extract(interval)[np.newaxis]
+
+ with self._device_context as device, jax.transfer_guard('disallow'):
+ reference_sequence = jax.device_put(
+ np.asarray(self._one_hot_encoder.encode(reference_sequence))[
+ np.newaxis
+ ],
+ device,
+ )
+ alternate_sequence = jax.device_put(
+ np.asarray(self._one_hot_encoder.encode(alternate_sequence))[
+ np.newaxis
+ ],
+ device,
+ )
+ organism_indices = jax.device_put(
+ np.full((1,), convert_to_organism_index(organism), dtype=np.int32),
+ device,
+ )
+ reference_predictions, alt_predictions = self._predict_variant(
+ self._params,
+ self._state,
+ reference_sequence,
+ alternate_sequence,
+ jax.device_put(splice_sites, device),
+ organism_indices,
+ negative_strand_mask=jax.device_put(
+ np.asarray([interval.negative_strand]), device
+ ),
+ strand_reindexing=jax.device_put(metadata.strand_reindexing, device),
+ )
+ reference_predictions, alt_predictions = _filter_variant_predictions(
+ reference_predictions,
+ alt_predictions,
+ track_masks=jax.device_put(track_masks, device),
+ )
+ reference_predictions, alt_predictions = _upcast_single_batch_predictions(
+ (reference_predictions, alt_predictions)
+ )
+
+ return dna_output.VariantOutput(
+ reference=_construct_output_from_predictions(
+ reference_predictions,
+ track_masks=track_masks,
+ metadata=metadata,
+ interval=interval,
+ ),
+ alternate=_construct_output_from_predictions(
+ alt_predictions,
+ track_masks=track_masks,
+ metadata=metadata,
+ interval=interval,
+ ),
+ )
+
+ 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 not interval_scorers:
+ interval_scorers = list(
+ interval_scorers_lib.RECOMMENDED_INTERVAL_SCORERS.values()
+ )
+
+ sequence = self._get_fasta_extractor(organism).extract(interval)
+ sequence = np.array(self._one_hot_encoder.encode(sequence))[np.newaxis]
+ organism_indices = np.full(
+ (1,), convert_to_organism_index(organism), dtype=np.int32
+ )
+
+ requested_outputs = set(
+ scorer.requested_output for scorer in interval_scorers
+ )
+
+ track_metadata = self._metadata[organism]
+ track_masks = metadata_lib.create_track_masks(
+ track_metadata,
+ requested_outputs=requested_outputs,
+ requested_ontologies=None,
+ )
+
+ with self._device_context as device, jax.transfer_guard('disallow'):
+ predictions = self._predict(
+ self._params,
+ self._state,
+ jax.device_put(sequence, device),
+ jax.device_put(organism_indices, device),
+ negative_strand_mask=jax.device_put(
+ np.asarray([interval.negative_strand]), device
+ ),
+ strand_reindexing=jax.device_put(
+ track_metadata.strand_reindexing, device
+ ),
+ )
+ predictions = _filter_predictions(
+ predictions,
+ track_masks=jax.device_put(track_masks, device),
+ )
+ predictions = _upcast_single_batch_predictions(
+ predictions,
+ transfer_to_host=False,
+ )
+ output_metadata = self.output_metadata(organism)
+
+ results = []
+
+ for scorer_settings in interval_scorers:
+ scorer = self._interval_scorers[organism][
+ scorer_settings.base_interval_scorer
+ ]
+ masks, metadata = scorer.get_masks_and_metadata(
+ interval,
+ settings=scorer_settings,
+ track_metadata=output_metadata,
+ )
+ masks = jax.device_put(masks, device)
+ scores = scorer.score_interval(
+ predictions,
+ masks=masks,
+ settings=scorer_settings,
+ interval=interval,
+ )
+ result = scorer.finalize_interval(
+ jax.device_get(scores),
+ track_metadata=output_metadata,
+ mask_metadata=metadata,
+ settings=scorer_settings,
+ )
+ result.uns['interval'] = interval
+ result.uns['interval_scorer'] = scorer_settings
+ results.append(result)
+
+ return results
+
+ 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 not variant_scorers:
+ variant_scorers = variant_scorers_lib.get_recommended_scorers(
+ organism.to_proto()
+ )
+ for scorer in variant_scorers:
+ if scorer.base_variant_scorer not in self._variant_scorers[organism]:
+ raise ValueError(
+ f"Scorer '{scorer.base_variant_scorer}' is missing for"
+ f' {organism.name=}. This may be due to a missing variant scorer'
+ ' metadata.'
+ )
+ reference_sequence, alternate_sequence = (
+ genome_io.extract_variant_sequences(
+ interval, variant, self._get_fasta_extractor(organism)
+ )
+ )
+ reference_sequence = np.asarray(
+ self._one_hot_encoder.encode(reference_sequence)
+ )[np.newaxis]
+ alternate_sequence = np.asarray(
+ self._one_hot_encoder.encode(alternate_sequence)
+ )[np.newaxis]
+ organism_indices = np.full(
+ (1,), convert_to_organism_index(organism), dtype=np.int32
+ )
+ splice_sites = None
+ if splice_site_extractor := self._splice_site_extractors.get(organism):
+ splice_sites = splice_site_extractor.extract(interval)[np.newaxis]
+
+ requested_outputs = set(
+ scorer.requested_output for scorer in variant_scorers
+ )
+
+ track_metadata = self._metadata[organism]
+ track_masks = metadata_lib.create_track_masks(
+ track_metadata,
+ requested_outputs=requested_outputs,
+ requested_ontologies=None,
+ )
+
+ with self._device_context as device, jax.transfer_guard('disallow'):
+
+ reference_predictions, alternate_predictions = self._predict_variant(
+ self._params,
+ self._state,
+ jax.device_put(reference_sequence, device),
+ jax.device_put(alternate_sequence, device),
+ jax.device_put(splice_sites, device),
+ jax.device_put(organism_indices, device),
+ negative_strand_mask=jax.device_put(
+ np.asarray([interval.negative_strand]), device
+ ),
+ strand_reindexing=jax.device_put(
+ track_metadata.strand_reindexing, device
+ ),
+ )
+ reference_predictions, alternate_predictions = (
+ _filter_variant_predictions(
+ reference_predictions,
+ alternate_predictions,
+ track_masks=jax.device_put(track_masks, device),
+ )
+ )
+ reference_predictions, alternate_predictions = (
+ _upcast_single_batch_predictions(
+ (reference_predictions, alternate_predictions),
+ transfer_to_host=False,
+ )
+ )
+ output_metadata = self.output_metadata(organism)
+
+ results = []
+
+ for scorer_settings in variant_scorers:
+ scorer = self._variant_scorers[organism][
+ scorer_settings.base_variant_scorer
+ ]
+ masks, metadata = scorer.get_masks_and_metadata(
+ interval,
+ variant,
+ settings=scorer_settings,
+ track_metadata=output_metadata,
+ )
+ masks = jax.device_put(masks, device)
+ scores = scorer.score_variant(
+ reference_predictions,
+ alternate_predictions,
+ masks=masks,
+ settings=scorer_settings,
+ variant=variant,
+ interval=interval,
+ )
+ result = scorer.finalize_variant(
+ jax.device_get(scores),
+ track_metadata=output_metadata,
+ mask_metadata=metadata,
+ settings=scorer_settings,
+ )
+ result.uns['interval'] = interval
+ result.uns['variant'] = variant
+ result.uns['variant_scorer'] = scorer_settings
+ results.append(result)
+
+ return results
+
+ 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,
+ interval_variant: genome.Variant | None = None,
+ ) -> 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.
+ interval_variant: Optional variant to apply to the sequence. If provided,
+ the alternate allele is used for in-silico mutagenesis, otherwise the
+ unaltered reference sequence is used.
+
+ Returns:
+ List of variant scores for each variant in the ISM interval.
+ """
+ extractor = self._get_fasta_extractor(organism)
+ if interval_variant is not None:
+ _, sequence = genome_io.extract_variant_sequences(
+ interval, interval_variant, extractor
+ )
+ else:
+ sequence = extractor.extract(interval)
+
+ interval_slice = interval.intersect(ism_interval)
+ if interval_slice is None:
+ raise ValueError(f'{ism_interval=} not fully contained in {interval=}.')
+ start = interval_slice.start - interval.start
+ end = interval_slice.end - interval.start
+ ism_sequence = sequence[start:end]
+
+ variants = ism.ism_variants(ism_interval, ism_sequence)
+ return self.score_variants(
+ interval, variants, variant_scorers, organism=organism
+ )
+
+ def output_metadata(
+ self, organism: dna_model.Organism = dna_model.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.
+ """
+ return self._output_metadata_by_organism[organism]
+
+
+@typing.jaxtyped
+def _construct_output_from_predictions(
+ predictions: Mapping[
+ dna_output.OutputType,
+ PyTree[Float[Array, '...'] | Int32[Array, '...']],
+ ],
+ *,
+ track_masks: Mapping[dna_output.OutputType, Bool[np.ndarray, '_']],
+ metadata: AlphaGenomeOutputMetadata,
+ interval: genome.Interval | None = None,
+) -> dna_output.Output:
+ """Returns a dna_output.Output from model predictions with data on CPU."""
+
+ def _convert_to_track_data(
+ output_type: dna_output.OutputType,
+ ) -> track_data.TrackData | None:
+ track_metadata = metadata.get(output_type)
+ prediction = predictions.get(output_type)
+ if prediction is None or track_metadata is None:
+ # No tracks are predicted for this head.
+ return None
+ return track_data.TrackData(
+ values=jax.device_get(prediction),
+ resolution=metadata.resolution(output_type),
+ metadata=track_metadata[track_masks[output_type]],
+ interval=interval,
+ )
+
+ def _convert_to_junction_data() -> junction_data.JunctionData | None:
+ """Returns a splice junction prediction."""
+ output_type = dna_output.OutputType.SPLICE_JUNCTIONS
+ junction_metadata = metadata.get(output_type)
+ splice_junction_predictions = predictions.get(output_type)
+ if junction_metadata is None or splice_junction_predictions is None:
+ return None
+ splice_junctions = splice_junction_predictions['predictions']
+ splice_site_positions = splice_junction_predictions['splice_site_positions']
+
+ junction_predictions, strands, starts, ends = (
+ splice_junction.unstack_junction_predictions(
+ jax.device_get(splice_junctions),
+ jax.device_get(splice_site_positions),
+ interval,
+ )
+ )
+ chromosome = interval.chromosome if interval is not None else None
+ junctions = [
+ genome.Junction(chromosome, start, end, strand)
+ for start, end, strand in zip(starts, ends, strands)
+ if start < end
+ ]
+ return junction_data.JunctionData(
+ junctions=np.asarray(junctions),
+ values=junction_predictions,
+ metadata=junction_metadata[track_masks[output_type]],
+ interval=interval,
+ )
+
+ return dna_output.Output(
+ atac=_convert_to_track_data(dna_output.OutputType.ATAC),
+ dnase=_convert_to_track_data(dna_output.OutputType.DNASE),
+ procap=_convert_to_track_data(dna_output.OutputType.PROCAP),
+ cage=_convert_to_track_data(dna_output.OutputType.CAGE),
+ rna_seq=_convert_to_track_data(dna_output.OutputType.RNA_SEQ),
+ chip_histone=_convert_to_track_data(dna_output.OutputType.CHIP_HISTONE),
+ chip_tf=_convert_to_track_data(dna_output.OutputType.CHIP_TF),
+ contact_maps=_convert_to_track_data(dna_output.OutputType.CONTACT_MAPS),
+ splice_sites=_convert_to_track_data(dna_output.OutputType.SPLICE_SITES),
+ splice_site_usage=_convert_to_track_data(
+ dna_output.OutputType.SPLICE_SITE_USAGE
+ ),
+ splice_junctions=_convert_to_junction_data(),
+ )
+
+
+def convert_to_organism_index(
+ organism: dna_model.Organism,
+) -> int:
+ """Converts a dna_model.Organism to a organism index."""
+ match organism:
+ case dna_model.Organism.HOMO_SAPIENS:
+ return 0
+ case dna_model.Organism.MUS_MUSCULUS:
+ return 1
+ case _:
+ raise ValueError(f'Unsupported organism: {organism}')
+
+
+@dataclasses.dataclass(frozen=True, kw_only=True)
+class ModelSettings:
+ """Settings for the AlphaGenomeModel."""
+
+ # The maximum number of splice sites that are extracted from the splice site
+ # classification predictions.
+ num_splice_sites: int = 512
+ # The threshold to use for splice site prediction.
+ splice_site_threshold: float = 0.1
+
+
+@dataclasses.dataclass(frozen=True, kw_only=True)
+class OrganismSettings:
+ """Settings for a specific organism."""
+
+ # Optional output metadata. If None, we load the default AlphaGenome metadata
+ # for the organism.
+ metadata: AlphaGenomeOutputMetadata | None = None
+
+ # Optional paths to the reference genome and annotation data. If None,
+ # functions that accept intervals or variants will fail.
+ fasta_path: str | os.PathLike[str] | None = None
+
+ # Optional paths to the reference annotation data. If None, variant scorers
+ # that require a GTF will not be available.
+ gtf_feather_path: str | os.PathLike[str] | None = None
+
+ # Optional path to reference polyadenylation annotation data. If None,
+ # variant scorers that require a PAS GTF will not be available.
+ pas_feather_path: str | os.PathLike[str] | None = None
+
+ # Optional paths to the reference splice site data. If None, no reference
+ # splice sites will be used during variant predictions.
+ splice_site_starts_feather_path: str | os.PathLike[str] | None = None
+ splice_site_ends_feather_path: str | os.PathLike[str] | None = None
+
+
+def default_organism_settings() -> (
+ Mapping[dna_model.Organism, OrganismSettings]
+):
+ """Returns the default organism settings for the AlphaGenomeModel."""
+ return {
+ dna_model.Organism.HOMO_SAPIENS: OrganismSettings(
+ fasta_path=(
+ 'https://storage.googleapis.com/alphagenome/reference/gencode/'
+ 'hg38/GRCh38.p13.genome.fa'
+ ),
+ gtf_feather_path=(
+ 'https://storage.googleapis.com/alphagenome/reference/gencode/'
+ 'hg38/gencode.v46.annotation.gtf.gz.feather'
+ ),
+ pas_feather_path=(
+ 'https://storage.googleapis.com/alphagenome/reference/exon/hg38/'
+ 'polyadb_human_v3_exon3_contiguous_gtfv46.feather'
+ ),
+ splice_site_starts_feather_path=(
+ 'https://storage.googleapis.com/alphagenome/reference/gencode/'
+ 'hg38/gencode.v46.splice_sites_starts.feather'
+ ),
+ splice_site_ends_feather_path=(
+ 'https://storage.googleapis.com/alphagenome/reference/gencode/'
+ 'hg38/gencode.v46.splice_sites_ends.feather'
+ ),
+ ),
+ dna_model.Organism.MUS_MUSCULUS: OrganismSettings(
+ fasta_path=(
+ 'https://storage.googleapis.com/alphagenome/reference/gencode/'
+ 'mm10/GRCm38.p6.genome.fa'
+ ),
+ gtf_feather_path=(
+ 'https://storage.googleapis.com/alphagenome/reference/gencode/'
+ 'mm10/gencode.vM23.annotation.gtf.gz.feather'
+ ),
+ pas_feather_path=None,
+ splice_site_starts_feather_path=(
+ 'https://storage.googleapis.com/alphagenome/reference/gencode/'
+ 'mm10/gencode.vM23.splice_sites_starts.feather'
+ ),
+ splice_site_ends_feather_path=(
+ 'https://storage.googleapis.com/alphagenome/reference/gencode/'
+ 'mm10/gencode.vM23.splice_sites_ends.feather'
+ ),
+ ),
+ }
+
+
+@typing.jaxtyped
+def create_model(
+ metadata: Mapping[dna_model.Organism, AlphaGenomeOutputMetadata],
+ *,
+ num_splice_sites: int = model.DEFAULT_NUM_SPLICE_SITES,
+ splice_site_threshold: float = model.DEFAULT_SPLICE_SITE_THRESHOLD,
+) -> tuple[
+ Callable[
+ [chex.PRNGKey, Float[Array, 'B S 4'], Int32[Array, 'B']],
+ tuple[hk.Params, hk.State],
+ ],
+ ApplyFn,
+ JunctionsApplyFn,
+]:
+ """Helper to create AlphaGenome init and two apply functions."""
+
+ jmp_policy = jmp.get_policy('params=float32,compute=bfloat16,output=bfloat16')
+
+ @hk.transform_with_state
+ def _forward(
+ dna_sequence: Float[Array, 'B S 4'],
+ organism_index: Int32[Array, 'B'],
+ ):
+ """AlphaGenome default forward pass."""
+ with hk.mixed_precision.push_policy(model.AlphaGenome, jmp_policy):
+ return model.AlphaGenome(
+ metadata,
+ num_splice_sites=num_splice_sites,
+ splice_site_threshold=splice_site_threshold,
+ )(dna_sequence, organism_index)
+
+ def _apply_fn(
+ params: hk.Params,
+ state: hk.State,
+ dna_sequence: Float[Array, 'B S 4'],
+ organism_index: Int32[Array, 'B'],
+ ) -> PyTree[Shaped[Array, 'B ...']]:
+ """AlphaGenome default apply function."""
+ (predictions, _), _ = _forward.apply(
+ params, state, None, dna_sequence, organism_index
+ )
+ return predictions
+
+ def _junctions_apply_fn(
+ params: hk.Params,
+ state: hk.State,
+ trunk_embeddings: Float[Array, 'B S D'],
+ splice_site_positions: Int32[Array, 'B 4 K'],
+ organism_index: Int32[Array, 'B'],
+ ):
+ """AlphaGenome junctions apply function."""
+
+ @hk.transform_with_state
+ def _forward_junctions(
+ trunk_embeddings, splice_site_positions, organism_index
+ ):
+ with hk.mixed_precision.push_policy(model.AlphaGenome, jmp_policy):
+ return model.AlphaGenome(
+ metadata,
+ num_splice_sites=num_splice_sites,
+ splice_site_threshold=splice_site_threshold,
+ ).predict_junctions(
+ trunk_embeddings, splice_site_positions, organism_index
+ )
+
+ predictions, _ = _forward_junctions.apply(
+ params,
+ state,
+ None,
+ trunk_embeddings,
+ splice_site_positions,
+ organism_index,
+ )
+ return predictions
+
+ return _forward.init, _apply_fn, _junctions_apply_fn
+
+
+def create(
+ checkpoint_path: str | os.PathLike[str],
+ *,
+ organism_settings: (
+ Mapping[dna_model.Organism, OrganismSettings] | None
+ ) = None,
+ model_settings: ModelSettings = ModelSettings(),
+ device: jax.Device | None = None,
+) -> AlphaGenomeModel:
+ """Returns a AlphaGenomeModel from a checkpoint stored at the given path.
+
+ Args:
+ checkpoint_path: Path to the checkpoint to load.
+ organism_settings: Optional organism settings to use. If not set, will use
+ default organism settings.
+ model_settings: Settings for the model. If not set, will use default model
+ settings.
+ device: Optional device to use for model prediction. If None, the first
+ local device will be used.
+ """
+ if organism_settings is None:
+ organism_settings = default_organism_settings()
+
+ metadata = {}
+ fasta_extractors = {}
+ splice_site_extractors = {}
+ gtfs = {}
+ pas_gtfs = {}
+ validate_checkpoint = True
+
+ for organism, settings in organism_settings.items():
+ if settings.metadata is not None:
+ metadata[organism] = settings.metadata
+ validate_checkpoint = False
+ else:
+ metadata[organism] = metadata_lib.load(organism)
+
+ if settings.fasta_path is not None:
+ fasta_extractors[organism] = fasta.FastaExtractor(settings.fasta_path)
+ if settings.gtf_feather_path is not None:
+ gtfs[organism] = pd.read_feather(settings.gtf_feather_path)
+ if settings.pas_feather_path is not None:
+ pas_gtfs[organism] = pd.read_feather(settings.pas_feather_path)
+ if settings.splice_site_starts_feather_path is not None:
+ splice_site_extractors[organism] = (
+ splicing_io.SpliceSiteAnnotationExtractor(
+ junction_starts=pd.read_feather(
+ settings.splice_site_starts_feather_path
+ ),
+ junction_ends=pd.read_feather(
+ settings.splice_site_ends_feather_path
+ ),
+ )
+ )
+
+ init_fn, apply_fn, junctions_apply_fn = create_model(
+ metadata,
+ num_splice_sites=model_settings.num_splice_sites,
+ splice_site_threshold=model_settings.splice_site_threshold,
+ )
+
+ dna_sequence_shape = jax.ShapeDtypeStruct((1, 2048, 4), dtype=jnp.float32)
+ organism_index_shape = jax.ShapeDtypeStruct((1,), dtype=jnp.int32)
+ target_shapes = jax.eval_shape(
+ init_fn, jax.random.PRNGKey(0), dna_sequence_shape, organism_index_shape
+ )
+ checkpointer = ocp.StandardCheckpointer()
+ params, state = checkpointer.restore(
+ checkpoint_path,
+ target=target_shapes if validate_checkpoint else None,
+ strict=validate_checkpoint,
+ )
+
+ return AlphaGenomeModel(
+ params=params,
+ state=state,
+ apply_fn=apply_fn,
+ junctions_apply_fn=junctions_apply_fn,
+ metadata=metadata,
+ fasta_extractors=fasta_extractors,
+ splice_site_extractors=splice_site_extractors,
+ gtfs=gtfs,
+ pas_gtfs=pas_gtfs,
+ num_splice_sites=model_settings.num_splice_sites,
+ splice_site_threshold=model_settings.splice_site_threshold,
+ device=device,
+ )
+
+
+def create_from_kaggle(
+ model_version: str | ModelVersion,
+ *,
+ organism_settings: (
+ Mapping[dna_model.Organism, OrganismSettings] | None
+ ) = None,
+ device: jax.Device | None = None,
+) -> AlphaGenomeModel:
+ """Helper function to create a model from Kaggle.
+
+ Args:
+ model_version: The model version to use, e.g. all_folds.
+ organism_settings: Optional organism settings to use. If unset, will use
+ default organism settings.
+ device: Optional device to use for model prediction. If None, the first
+ local device will be used.
+
+ Returns:
+ AlphaGenomeModel created from the Kaggle checkpoint.
+ """
+ if kaggle_auth.get_username() is None:
+ kagglehub.login()
+
+ if isinstance(model_version, ModelVersion):
+ model_version = model_version.name
+
+ checkpoint_path = kagglehub.model_download(
+ f'google/alphagenome/jax/{model_version.lower()}'
+ )
+ return create(
+ checkpoint_path, organism_settings=organism_settings, device=device
+ )
+
+
+def create_from_huggingface(
+ model_version: str | ModelVersion,
+ *,
+ organism_settings: (
+ Mapping[dna_model.Organism, OrganismSettings] | None
+ ) = None,
+ device: jax.Device | None = None,
+) -> AlphaGenomeModel:
+ """Helper function to create a DNA model from HuggingFace.
+
+ Args:
+ model_version: The model version to use, e.g. all_folds.
+ organism_settings: Optional organism settings to use. If unset, will use
+ default organism settings.
+ device: Optional device to use for model prediction. If None, the first
+ local device will be used.
+
+ Returns:
+ AlphaGenomeModel created from the Hugging Face checkpoint.
+ """
+
+ try:
+ huggingface_hub.whoami()
+ except huggingface_hub.errors.LocalTokenNotFoundError:
+ huggingface_hub.login()
+
+ if isinstance(model_version, ModelVersion):
+ model_version = model_version.name
+
+ checkpoint_path = huggingface_hub.snapshot_download(
+ repo_id=f'google/alphagenome-{model_version.replace("_", "-").lower()}'
+ )
+ return create(
+ checkpoint_path, organism_settings=organism_settings, device=device
+ )
diff --git a/flax_model/alphagenome/model/dna_model_test.py b/flax_model/alphagenome/model/dna_model_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..d2f1b23a5c1548bc530ea18582d51836ca5a14de
--- /dev/null
+++ b/flax_model/alphagenome/model/dna_model_test.py
@@ -0,0 +1,872 @@
+# Copyright 2026 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 pathlib
+from unittest import mock
+
+from absl.testing import absltest
+from absl.testing import parameterized
+from flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome._sdk.data import ontology
+from flax_model.alphagenome._sdk.models import dna_output
+from flax_model.alphagenome._sdk.models import interval_scorers
+from flax_model.alphagenome._sdk.models import variant_scorers
+from flax_model.alphagenome.io import fasta
+from flax_model.alphagenome.io import splicing
+from flax_model.alphagenome.model import dna_model
+from flax_model.alphagenome.model.metadata import metadata
+import chex
+import huggingface_hub
+import jax
+import jax.numpy as jnp
+import kagglehub
+from kagglehub import auth as kaggle_auth
+import numpy as np
+import orbax.checkpoint as ocp
+import pandas as pd
+
+
+MOCK_SHAPES = ({}, {})
+
+
+def _create_mock_gtf() -> pd.DataFrame:
+ return pd.DataFrame({
+ 'Chromosome': 'chr1',
+ 'Start': [101, 101, 102, 103, 0, 0, 0, 80],
+ 'End': [200, 200, 200, 200, 108, 108, 40, 108],
+ 'Strand': ['+', '+', '+', '+', '-', '-', '-', '-'],
+ 'transcript_id': [None, 'T1', 'T2', 'T2', None, 'T4', 'T4', 'T4'],
+ 'gene_id': ['G1', 'G1', 'G1', 'G1', 'G2', 'G2', 'G2', 'G2'],
+ 'Feature': [
+ 'gene',
+ 'transcript',
+ 'transcript',
+ 'exon',
+ 'gene',
+ 'transcript',
+ 'exon',
+ 'exon',
+ ],
+ 'gene_type': 'protein_coding',
+ 'gene_name': 'GX_name',
+ 'transcript_type': 'protein_coding',
+ })
+
+
+def _create_mock_splice_sites() -> tuple[pd.DataFrame, pd.DataFrame]:
+ return pd.DataFrame(
+ {'Chromosome': 'chr1', 'Start': [40], 'Strand': ['-'], 'Tissue_0': [1]}
+ ), pd.DataFrame(
+ {'Chromosome': 'chr1', 'End': [80], 'Strand': ['-'], 'Tissue_0': [1]}
+ )
+
+
+def _create_polya_df_gtf() -> pd.DataFrame:
+ return pd.DataFrame({
+ 'End': [103, 104, 1, 81],
+ 'Start': [102, 103, 0, 80],
+ 'cutmode': [102, 103, 0, 80],
+ 'pas_strand': ['+', '+', '-', '-'],
+ 'pas_id': ['P1', 'P2', 'P3', 'P4'],
+ 'gene_id': ['G1', 'G1', 'G2', 'G2'],
+ 'pas_gene_id': ['G1', 'G1', 'G2', 'G2'],
+ 'Chromosome': 'chr1',
+ })
+
+
+def _get_test_fasta_path() -> str | os.PathLike[str]:
+ root_dir = pathlib.Path(__file__).parent.parent
+ return root_dir / 'io' / 'testdata' / 'example.fa'
+
+
+class DnaModelTest(parameterized.TestCase):
+
+ def setUp(self):
+ super().setUp()
+
+ self._num_tissues = 10
+ self._num_splice_sites = 100
+ self._metadata = metadata.AlphaGenomeOutputMetadata(
+ atac=pd.DataFrame({
+ 'name': ['atac1', 'atac1'],
+ 'strand': ['+', '-'],
+ 'ontology_curie': ['CL:0000001', 'CL:0000001'],
+ 'nonzero_mean': [1.0, 1.0],
+ }),
+ dnase=pd.DataFrame({
+ 'name': ['acc_1'],
+ 'strand': ['.'],
+ 'ontology_curie': ['UBERON:0000001'],
+ 'nonzero_mean': [1.0],
+ }),
+ contact_maps=pd.DataFrame({
+ 'name': ['hic_1', 'hic_2'],
+ 'ontology_curie': ['UBERON:0000001', 'UBERON:0000002'],
+ 'strand': '.',
+ }),
+ splice_sites=pd.DataFrame({
+ 'name': ['donor', 'acceptor', 'donor', 'acceptor', 'padding'],
+ 'strand': ['+', '+', '-', '-', '.'],
+ }),
+ splice_junctions=pd.DataFrame({
+ 'name': (
+ [f'tissue_{i}' for i in range(self._num_tissues)]
+ + ['Padding'] * 2
+ ),
+ 'ontology_curie': ['UBERON:0000001'] * (self._num_tissues + 2),
+ }),
+ chip_tf=pd.DataFrame({
+ 'name': ['chip_1', 'chip_2'],
+ 'strand': ['+', '-'],
+ 'ontology_curie': 'UBERON:0000001',
+ 'nonzero_mean': 1.0,
+ }),
+ )
+
+ def _apply_fn(params, state, dna_sequence, organism_index):
+ del params, state, organism_index
+ batch_size, sequence_length = dna_sequence.shape[0], dna_sequence.shape[1]
+ splice_site_positions = (
+ np.ones((batch_size, 4, self._num_splice_sites), dtype=np.int32) * -1
+ )
+ splice_site_positions[:, 0, 0] = 100 # pos donor
+ splice_site_positions[:, 1, 0] = 200 # pos acceptor
+ splice_site_positions[:, 2, 0] = 300 # neg donor
+ splice_site_positions[:, 3, 0] = 50 # neg acceptor
+ return {
+ 'atac': {
+ 'predictions_1bp': jnp.zeros(
+ (batch_size, sequence_length, len(self._metadata.atac)),
+ dtype=jnp.bfloat16,
+ )
+ },
+ 'dnase': {
+ 'predictions_1bp': jnp.zeros(
+ (batch_size, sequence_length, len(self._metadata.dnase))
+ )
+ },
+ 'contact_maps': {
+ 'predictions': jnp.zeros((
+ batch_size,
+ sequence_length // 2048,
+ sequence_length // 2048,
+ len(self._metadata.contact_maps),
+ ))
+ },
+ 'splice_sites_classification': {
+ 'predictions': jnp.ones(
+ (
+ batch_size,
+ sequence_length,
+ 5,
+ ),
+ dtype=jnp.bfloat16,
+ ),
+ },
+ 'splice_sites_junction': {
+ 'predictions': jnp.ones(
+ (
+ batch_size,
+ self._num_splice_sites,
+ self._num_splice_sites,
+ 2 * self._num_tissues + 4, # Add 4 to mimic padding.
+ ),
+ dtype=jnp.bfloat16,
+ ),
+ 'splice_site_positions': jnp.array(splice_site_positions),
+ },
+ 'embeddings_1bp': jnp.zeros(
+ (batch_size, sequence_length, 1536), dtype=jnp.bfloat16
+ ),
+ 'chip_tf': {
+ 'predictions_128bp': jnp.zeros(
+ (batch_size, sequence_length // 128, 2), dtype=jnp.bfloat16
+ )
+ },
+ }
+
+ self._mock_model = _apply_fn
+
+ def _apply_fn_junctions(
+ params, state, trunk_embeddings, splice_site_positions, organism_index
+ ):
+ del params, state, trunk_embeddings, organism_index
+ batch_size, _, num_splice_sites = splice_site_positions.shape
+ return {
+ 'predictions': jnp.ones(
+ (
+ batch_size,
+ num_splice_sites,
+ num_splice_sites,
+ 2 * self._num_tissues + 4, # Add 4 to mimic padding.
+ ),
+ dtype=jnp.bfloat16,
+ ),
+ 'splice_site_positions': jnp.array(splice_site_positions),
+ }
+
+ self._mock_model_junctions = _apply_fn_junctions
+
+ @parameterized.parameters([
+ dict(organism=dna_model.Organism.HOMO_SAPIENS, expected_index=0),
+ dict(organism=dna_model.Organism.MUS_MUSCULUS, expected_index=1),
+ ])
+ def test_convert_to_organism_index(self, organism, expected_index):
+ self.assertEqual(
+ dna_model.convert_to_organism_index(organism),
+ expected_index,
+ )
+
+ @parameterized.parameters([
+ dict(
+ requested_outputs=[
+ dna_output.OutputType.ATAC,
+ dna_output.OutputType.DNASE,
+ ],
+ requested_ontologies=None,
+ expected_shapes={
+ dna_output.OutputType.ATAC: (65_536, 2),
+ dna_output.OutputType.DNASE: (65_536, 1),
+ },
+ ),
+ dict(
+ requested_outputs=[
+ dna_output.OutputType.ATAC,
+ dna_output.OutputType.DNASE,
+ ],
+ requested_ontologies=[ontology.from_curie('CL:0000001')],
+ expected_shapes={
+ dna_output.OutputType.ATAC: (65_536, 2),
+ dna_output.OutputType.DNASE: (65_536, 0),
+ },
+ ),
+ ])
+ def test_predict_sequence(
+ self, requested_outputs, requested_ontologies, expected_shapes
+ ):
+ mock_fasta_extractor = mock.create_autospec(fasta.FastaExtractor)
+ mock_fasta_extractor.extract.side_effect = lambda x: 'A' * x.width
+ model = dna_model.AlphaGenomeModel(
+ params={},
+ state={},
+ apply_fn=self._mock_model,
+ junctions_apply_fn=self._mock_model_junctions,
+ metadata={dna_model.Organism.HOMO_SAPIENS: self._metadata},
+ fasta_extractors={
+ dna_model.Organism.HOMO_SAPIENS: mock_fasta_extractor
+ },
+ gtfs={dna_model.Organism.HOMO_SAPIENS: _create_mock_gtf()},
+ device=jax.local_devices()[0],
+ )
+ seq = 'ATGC' * int(2**14)
+ predictions = model.predict_sequence(
+ seq,
+ requested_outputs=requested_outputs,
+ ontology_terms=requested_ontologies,
+ )
+ for output_type, expected_shape in expected_shapes.items():
+ output = predictions.get(output_type)
+ self.assertIsNotNone(output)
+ chex.assert_shape(output.values, expected_shape)
+
+ @parameterized.parameters([
+ dict(
+ requested_outputs=[dna_output.OutputType.ATAC],
+ requested_ontologies=None,
+ expected_shapes={dna_output.OutputType.ATAC: (2048, 2)},
+ ),
+ dict(
+ requested_outputs=[dna_output.OutputType.DNASE],
+ requested_ontologies=[ontology.from_curie('UBERON:0000001')],
+ expected_shapes={dna_output.OutputType.DNASE: (2048, 1)},
+ ),
+ ])
+ def test_predict_interval(
+ self, requested_outputs, requested_ontologies, expected_shapes
+ ):
+ mock_fasta_extractor = mock.create_autospec(fasta.FastaExtractor)
+ mock_fasta_extractor.extract.side_effect = lambda x: 'A' * x.width
+ model = dna_model.AlphaGenomeModel(
+ params={},
+ state={},
+ apply_fn=self._mock_model,
+ junctions_apply_fn=self._mock_model_junctions,
+ metadata={dna_model.Organism.HOMO_SAPIENS: self._metadata},
+ fasta_extractors={
+ dna_model.Organism.HOMO_SAPIENS: mock_fasta_extractor
+ },
+ gtfs={dna_model.Organism.HOMO_SAPIENS: _create_mock_gtf()},
+ device=jax.local_devices()[0],
+ )
+ interval = genome.Interval.from_str('chr1:0-2048:.')
+ output = model.predict_interval(
+ interval,
+ requested_outputs=requested_outputs,
+ ontology_terms=requested_ontologies,
+ )
+ for output_type, expected_shape in expected_shapes.items():
+ output = output.get(output_type)
+ self.assertIsNotNone(output)
+ chex.assert_shape(output.values, expected_shape)
+
+ def test_predict_interval_with_splicing(self):
+ mock_fasta_extractor = mock.create_autospec(fasta.FastaExtractor)
+ mock_fasta_extractor.extract.side_effect = lambda x: 'A' * x.width
+ model = dna_model.AlphaGenomeModel(
+ params={},
+ state={},
+ apply_fn=self._mock_model,
+ junctions_apply_fn=self._mock_model_junctions,
+ metadata={dna_model.Organism.HOMO_SAPIENS: self._metadata},
+ fasta_extractors={
+ dna_model.Organism.HOMO_SAPIENS: mock_fasta_extractor
+ },
+ gtfs={dna_model.Organism.HOMO_SAPIENS: _create_mock_gtf()},
+ device=jax.local_devices()[0],
+ )
+ interval = genome.Interval.from_str('chr1:0-2048:.')
+ output = model.predict_interval(
+ interval,
+ requested_outputs=[
+ dna_output.OutputType.ATAC,
+ dna_output.OutputType.SPLICE_JUNCTIONS,
+ ],
+ ontology_terms=None,
+ )
+ atac_output = output.atac
+ self.assertIsNotNone(atac_output)
+ chex.assert_shape(atac_output.values, (interval.width, 2))
+
+ splice_junctions_output = output.splice_junctions
+ self.assertIsNotNone(splice_junctions_output)
+ self.assertLen(splice_junctions_output.junctions, 2)
+ chex.assert_shape(splice_junctions_output.values, (2, self._num_tissues))
+ self.assertEqual(
+ splice_junctions_output.junctions[0],
+ genome.Junction('chr1', 101, 200, '+'),
+ )
+ self.assertEqual(
+ splice_junctions_output.junctions[1],
+ genome.Junction('chr1', 51, 300, '-'),
+ )
+
+ def test_predict_interval_missing_fasta_raises_error(self):
+ model = dna_model.AlphaGenomeModel(
+ params={},
+ state={},
+ apply_fn=self._mock_model,
+ junctions_apply_fn=self._mock_model_junctions,
+ metadata={dna_model.Organism.HOMO_SAPIENS: self._metadata},
+ device=jax.local_devices()[0],
+ )
+ with self.assertRaisesRegex(
+ ValueError, "FastaExtractor not found for organism.name='HOMO_SAPIENS'"
+ ):
+ model.predict_interval(
+ genome.Interval.from_str('chr1:0-2048:.'),
+ requested_outputs=[dna_output.OutputType.ATAC],
+ ontology_terms=None,
+ )
+
+ @parameterized.parameters([
+ dict(
+ requested_outputs=[
+ dna_output.OutputType.ATAC,
+ dna_output.OutputType.DNASE,
+ ],
+ requested_ontologies=None,
+ expected_shapes={
+ dna_output.OutputType.ATAC: (2048, 2),
+ dna_output.OutputType.DNASE: (2048, 1),
+ },
+ ),
+ dict(
+ requested_outputs=[
+ dna_output.OutputType.ATAC,
+ dna_output.OutputType.DNASE,
+ ],
+ requested_ontologies=[ontology.from_curie('CL:0000001')],
+ expected_shapes={
+ dna_output.OutputType.ATAC: (2048, 2),
+ dna_output.OutputType.DNASE: (2048, 0),
+ },
+ ),
+ ])
+ def test_predict_variant(
+ self, requested_outputs, requested_ontologies, expected_shapes
+ ):
+ mock_fasta_extractor = mock.create_autospec(fasta.FastaExtractor)
+ mock_fasta_extractor.extract.side_effect = lambda x: 'A' * x.width
+
+ mock_splice_sites_extractor = mock.create_autospec(
+ splicing.SpliceSiteAnnotationExtractor, instance=True
+ )
+ mock_splice_sites_extractor.extract.side_effect = lambda x: np.zeros(
+ (x.width, 5), dtype=bool
+ )
+
+ model = dna_model.AlphaGenomeModel(
+ params={},
+ state={},
+ apply_fn=self._mock_model,
+ junctions_apply_fn=self._mock_model_junctions,
+ metadata={dna_model.Organism.HOMO_SAPIENS: self._metadata},
+ fasta_extractors={
+ dna_model.Organism.HOMO_SAPIENS: mock_fasta_extractor
+ },
+ gtfs={dna_model.Organism.HOMO_SAPIENS: _create_mock_gtf()},
+ device=jax.local_devices()[0],
+ splice_site_extractors={
+ dna_model.Organism.HOMO_SAPIENS: mock_splice_sites_extractor
+ },
+ )
+ interval = genome.Interval.from_str('chr1:0-2048:.')
+ predictions = model.predict_variant(
+ interval,
+ variant=genome.Variant.from_str('chr1:1024:A>C'),
+ requested_outputs=requested_outputs,
+ ontology_terms=requested_ontologies,
+ )
+ for output_type, expected_shape in expected_shapes.items():
+ output = predictions.reference.get(output_type)
+ self.assertIsNotNone(output)
+ chex.assert_shape(output.values, expected_shape)
+ for output_type, expected_shape in expected_shapes.items():
+ output = predictions.alternate.get(output_type)
+ self.assertIsNotNone(output)
+ chex.assert_shape(output.values, expected_shape)
+
+ def test_score_variant(self):
+ mock_fasta_extractor = mock.create_autospec(fasta.FastaExtractor)
+ mock_fasta_extractor.extract.side_effect = lambda x: 'A' * x.width
+
+ mock_splice_sites_extractor = mock.create_autospec(
+ splicing.SpliceSiteAnnotationExtractor, instance=True
+ )
+ mock_splice_sites_extractor.extract.side_effect = lambda x: np.zeros(
+ (x.width, 5), dtype=bool
+ )
+
+ model = dna_model.AlphaGenomeModel(
+ params={},
+ state={},
+ apply_fn=self._mock_model,
+ junctions_apply_fn=self._mock_model_junctions,
+ metadata={dna_model.Organism.HOMO_SAPIENS: self._metadata},
+ fasta_extractors={
+ dna_model.Organism.HOMO_SAPIENS: mock_fasta_extractor
+ },
+ gtfs={dna_model.Organism.HOMO_SAPIENS: _create_mock_gtf()},
+ pas_gtfs={dna_model.Organism.HOMO_SAPIENS: _create_polya_df_gtf()},
+ device=jax.local_devices()[0],
+ splice_site_extractors={
+ dna_model.Organism.HOMO_SAPIENS: mock_splice_sites_extractor
+ },
+ )
+ interval = genome.Interval.from_str('chr1:0-2048:.')
+ variant = genome.Variant.from_str('chr1:1024:A>C')
+ scorers = [
+ variant_scorers.CenterMaskScorer(
+ aggregation_type=variant_scorers.AggregationType.DIFF_MEAN,
+ requested_output=dna_output.OutputType.ATAC,
+ width=501,
+ ),
+ variant_scorers.CenterMaskScorer(
+ requested_output=dna_output.OutputType.CHIP_TF,
+ width=501,
+ aggregation_type=variant_scorers.AggregationType.DIFF_LOG2_SUM,
+ ),
+ variant_scorers.ContactMapScorer(),
+ variant_scorers.GeneMaskLFCScorer(
+ requested_output=dna_output.OutputType.ATAC
+ ),
+ variant_scorers.SpliceJunctionScorer(),
+ ]
+ output = model.score_variant(interval, variant, variant_scorers=scorers)
+ self.assertLen(output, len(scorers))
+ for result, scorer in zip(output, scorers):
+ self.assertEqual(result.uns['interval'], interval)
+ self.assertEqual(result.uns['variant'], variant)
+ self.assertEqual(result.uns['variant_scorer'], scorer)
+
+ df = variant_scorers.tidy_scores(output)
+ self.assertCountEqual(
+ [
+ 'variant_id',
+ 'scored_interval',
+ 'gene_id',
+ 'gene_name',
+ 'gene_type',
+ 'gene_strand',
+ 'junction_Start',
+ 'junction_End',
+ 'output_type',
+ 'variant_scorer',
+ 'track_name',
+ 'track_strand',
+ 'ontology_curie',
+ 'raw_score',
+ ],
+ df.columns,
+ )
+
+ def test_missing_gtf_raises_scorer_missing_error(self):
+ mock_fasta_extractor = mock.create_autospec(fasta.FastaExtractor)
+ mock_fasta_extractor.extract.side_effect = lambda x: 'A' * x.width
+ model = dna_model.AlphaGenomeModel(
+ params={},
+ state={},
+ apply_fn=self._mock_model,
+ junctions_apply_fn=self._mock_model_junctions,
+ metadata={dna_model.Organism.HOMO_SAPIENS: self._metadata},
+ fasta_extractors={
+ dna_model.Organism.HOMO_SAPIENS: mock_fasta_extractor
+ },
+ device=jax.local_devices()[0],
+ )
+ interval = genome.Interval.from_str('chr1:0-2048:.')
+ variant = genome.Variant.from_str('chr1:1024:A>C')
+ scorers = [
+ variant_scorers.GeneMaskLFCScorer(
+ requested_output=dna_output.OutputType.ATAC
+ )
+ ]
+ with self.assertRaisesRegex(
+ ValueError, "Scorer 'BaseVariantScorer.GENE_MASK_LFC' is missing"
+ ):
+ model.score_variant(interval, variant, variant_scorers=scorers)
+
+ def test_missing_pas_raises_scorer_missing_error(self):
+ model = dna_model.AlphaGenomeModel(
+ params={},
+ state={},
+ apply_fn=self._mock_model,
+ junctions_apply_fn=self._mock_model_junctions,
+ metadata={dna_model.Organism.HOMO_SAPIENS: self._metadata},
+ pas_gtfs={dna_model.Organism.HOMO_SAPIENS: _create_polya_df_gtf()},
+ device=jax.local_devices()[0],
+ )
+ interval = genome.Interval.from_str('chr1:0-2048:.')
+ variant = genome.Variant.from_str('chr1:1024:A>C')
+ scorers = [variant_scorers.PolyadenylationScorer()]
+ with self.assertRaisesRegex(
+ ValueError, "Scorer 'BaseVariantScorer.PA_QTL' is missing"
+ ):
+ model.score_variant(interval, variant, variant_scorers=scorers)
+
+ def test_score_interval(self):
+ mock_fasta_extractor = mock.create_autospec(fasta.FastaExtractor)
+ mock_fasta_extractor.extract.side_effect = lambda x: 'A' * x.width
+ model = dna_model.AlphaGenomeModel(
+ params={},
+ state={},
+ apply_fn=self._mock_model,
+ junctions_apply_fn=self._mock_model_junctions,
+ metadata={dna_model.Organism.HOMO_SAPIENS: self._metadata},
+ fasta_extractors={
+ dna_model.Organism.HOMO_SAPIENS: mock_fasta_extractor
+ },
+ gtfs={dna_model.Organism.HOMO_SAPIENS: _create_mock_gtf()},
+ pas_gtfs={dna_model.Organism.HOMO_SAPIENS: _create_polya_df_gtf()},
+ device=jax.local_devices()[0],
+ )
+ interval = genome.Interval.from_str('chr1:0-2048:.')
+ scorers = [
+ interval_scorers.GeneMaskScorer(
+ aggregation_type=interval_scorers.IntervalAggregationType.MEAN,
+ requested_output=dna_output.OutputType.ATAC,
+ width=501,
+ )
+ ]
+ output = model.score_interval(interval, interval_scorers=scorers)
+ self.assertLen(output, len(scorers))
+ for result, scorer in zip(output, scorers):
+ self.assertEqual(result.uns['interval'], interval)
+ self.assertEqual(result.uns['interval_scorer'], scorer)
+
+ @parameterized.parameters([
+ dict(
+ ism_interval=genome.Interval.from_str('chr1:10-11:.'),
+ interval_variant=None,
+ expected_variants=[
+ genome.Variant('chr1', 11, 'A', 'C'),
+ genome.Variant('chr1', 11, 'A', 'G'),
+ genome.Variant('chr1', 11, 'A', 'T'),
+ ],
+ ),
+ dict(
+ ism_interval=genome.Interval.from_str('chr1:10-11:.'),
+ interval_variant=genome.Variant.from_str('chr1:11:A>C'),
+ expected_variants=[
+ genome.Variant('chr1', 11, 'C', 'A'),
+ genome.Variant('chr1', 11, 'C', 'G'),
+ genome.Variant('chr1', 11, 'C', 'T'),
+ ],
+ ),
+ ])
+ def test_score_ism_variants(
+ self,
+ ism_interval: genome.Interval,
+ interval_variant: genome.Variant | None,
+ expected_variants: list[genome.Variant],
+ ):
+ mock_fasta_extractor = mock.create_autospec(fasta.FastaExtractor)
+ mock_fasta_extractor.extract.side_effect = lambda x: 'A' * x.width
+
+ mock_splice_sites_extractor = mock.create_autospec(
+ splicing.SpliceSiteAnnotationExtractor, instance=True
+ )
+ mock_splice_sites_extractor.extract.side_effect = lambda x: np.zeros(
+ (x.width, 5), dtype=bool
+ )
+
+ model = dna_model.AlphaGenomeModel(
+ params={},
+ state={},
+ apply_fn=self._mock_model,
+ junctions_apply_fn=self._mock_model_junctions,
+ metadata={dna_model.Organism.HOMO_SAPIENS: self._metadata},
+ fasta_extractors={
+ dna_model.Organism.HOMO_SAPIENS: mock_fasta_extractor
+ },
+ gtfs={dna_model.Organism.HOMO_SAPIENS: _create_mock_gtf()},
+ pas_gtfs={dna_model.Organism.HOMO_SAPIENS: _create_polya_df_gtf()},
+ device=jax.local_devices()[0],
+ splice_site_extractors={
+ dna_model.Organism.HOMO_SAPIENS: mock_splice_sites_extractor
+ },
+ )
+ interval = genome.Interval.from_str('chr1:0-2048:.')
+
+ scores = model.score_ism_variants(
+ interval,
+ ism_interval,
+ interval_variant=interval_variant,
+ variant_scorers=[
+ variant_scorers.CenterMaskScorer(
+ aggregation_type=variant_scorers.AggregationType.DIFF_MEAN,
+ requested_output=dna_output.OutputType.ATAC,
+ width=501,
+ )
+ ],
+ )
+ scores = sorted(scores, key=lambda x: str(x[0].uns['variant']))
+ self.assertLen(scores, len(expected_variants))
+ for expected, scores in zip(expected_variants, scores, strict=True):
+ self.assertEqual(expected, scores[0].uns['variant'])
+
+ @parameterized.parameters(
+ dict(model_metadata=None),
+ dict(
+ model_metadata=metadata.AlphaGenomeOutputMetadata(
+ atac=metadata.load(dna_model.Organism.HOMO_SAPIENS).atac,
+ dnase=metadata.load(dna_model.Organism.HOMO_SAPIENS).dnase,
+ )
+ ),
+ )
+ def test_create(
+ self, model_metadata: metadata.AlphaGenomeOutputMetadata | None
+ ):
+ init_fn, _, _ = dna_model.create_model(
+ {o: metadata.load(o) for o in dna_model.Organism}
+ )
+ params, state = jax.jit(init_fn)(
+ jax.random.PRNGKey(0),
+ jax.ShapeDtypeStruct((1, 2048, 4), dtype=jnp.float32),
+ jax.ShapeDtypeStruct((1,), dtype=jnp.int32),
+ )
+ checkpointer = ocp.StandardCheckpointer()
+ checkpoint_dir = os.path.join(self.create_tempdir().full_path, 'ckpt')
+ checkpointer.save(checkpoint_dir, (params, state))
+ gtf_path = os.path.join(self.create_tempdir().full_path, 'hg38.feather')
+ _create_mock_gtf().to_feather(gtf_path)
+ polya_gtf_path = os.path.join(
+ self.create_tempdir().full_path, 'polya_gtf.feather'
+ )
+ _create_polya_df_gtf().to_feather(polya_gtf_path)
+ splice_starts, splice_ends = _create_mock_splice_sites()
+ splice_starts_path = os.path.join(
+ self.create_tempdir().full_path, 'splice_starts.feather'
+ )
+ splice_ends_path = os.path.join(
+ self.create_tempdir().full_path, 'splice_ends.feather'
+ )
+ splice_starts.to_feather(splice_starts_path)
+ splice_ends.to_feather(splice_ends_path)
+ checkpointer.wait_until_finished()
+
+ model = dna_model.create(
+ checkpoint_dir,
+ organism_settings={
+ dna_model.Organism.HOMO_SAPIENS: dna_model.OrganismSettings(
+ fasta_path=_get_test_fasta_path(),
+ gtf_feather_path=gtf_path,
+ pas_feather_path=polya_gtf_path,
+ splice_site_starts_feather_path=splice_starts_path,
+ splice_site_ends_feather_path=splice_ends_path,
+ metadata=model_metadata,
+ )
+ },
+ device=jax.local_devices()[0],
+ )
+ self.assertIsInstance(model, dna_model.AlphaGenomeModel)
+
+ def test_default_organism_settings(self):
+ organism_settings = dna_model.default_organism_settings()
+ self.assertContainsSubset(
+ [
+ dna_model.Organism.HOMO_SAPIENS,
+ dna_model.Organism.MUS_MUSCULUS,
+ ],
+ organism_settings.keys(),
+ )
+
+ def test_create_model(self):
+ init, apply, apply_junctions = dna_model.create_model(
+ {dna_model.Organism.HOMO_SAPIENS: self._metadata}
+ )
+
+ dna_sequence_shape = jax.ShapeDtypeStruct((1, 2048, 4), dtype=jnp.float32)
+ organism_index_shape = jax.ShapeDtypeStruct((1,), dtype=jnp.int32)
+
+ params, state = jax.eval_shape(
+ init, jax.random.PRNGKey(0), dna_sequence_shape, organism_index_shape
+ )
+
+ predictions = jax.eval_shape(
+ apply, params, state, dna_sequence_shape, organism_index_shape
+ )
+ expected_predictions = {
+ dna_output.OutputType.ATAC: jax.ShapeDtypeStruct(
+ (1, 2048, 2), dtype=jnp.bfloat16
+ ),
+ dna_output.OutputType.DNASE: jax.ShapeDtypeStruct(
+ (1, 2048, 1), dtype=jnp.bfloat16
+ ),
+ dna_output.OutputType.CHIP_TF: jax.ShapeDtypeStruct(
+ (1, 16, 2), dtype=jnp.bfloat16
+ ),
+ dna_output.OutputType.SPLICE_SITES: jax.ShapeDtypeStruct(
+ (1, 2048, 5), dtype=jnp.bfloat16
+ ),
+ dna_output.OutputType.SPLICE_JUNCTIONS: {
+ 'predictions': jax.ShapeDtypeStruct(
+ (1, 512, 512, 24), dtype=jnp.bfloat16
+ ),
+ 'splice_site_positions': jax.ShapeDtypeStruct(
+ (1, 4, 512), dtype=jnp.int32
+ ),
+ },
+ dna_output.OutputType.CONTACT_MAPS: jax.ShapeDtypeStruct(
+ (1, 1, 1, 2), dtype=jnp.bfloat16
+ ),
+ }
+ chex.assert_trees_all_equal_shapes_and_dtypes(
+ dna_model.extract_predictions(predictions), expected_predictions
+ )
+
+ junction_predictions = jax.eval_shape(
+ apply_junctions,
+ params,
+ state,
+ predictions['embeddings_1bp'],
+ predictions['splice_sites_junction']['splice_site_positions'],
+ organism_index_shape,
+ )
+ expected_junction_predictions = {
+ 'predictions': jax.ShapeDtypeStruct(
+ (1, 512, 512, 24), dtype=jnp.bfloat16
+ ),
+ 'splice_junction_mask': jax.ShapeDtypeStruct(
+ (1, 512, 512, 24), dtype=jnp.bool
+ ),
+ 'splice_site_positions': jax.ShapeDtypeStruct(
+ (1, 4, 512), dtype=jnp.int32
+ ),
+ }
+ chex.assert_trees_all_equal_shapes_and_dtypes(
+ junction_predictions, expected_junction_predictions
+ )
+
+ @parameterized.parameters(('all_folds',), (dna_model.ModelVersion.ALL_FOLDS,))
+ @mock.patch.object(jax, 'eval_shape', return_value=MOCK_SHAPES, autospec=True)
+ def test_create_from_kaggle(self, version, mock_eval_shape):
+ del mock_eval_shape
+ checkpointer = ocp.StandardCheckpointer()
+ checkpoint_dir = os.path.join(self.create_tempdir().full_path, 'ckpt')
+ checkpointer.save(checkpoint_dir, ({}, {}))
+ checkpointer.wait_until_finished()
+ with (
+ mock.patch.object(kagglehub, 'model_download') as mock_model_download,
+ mock.patch.object(kaggle_auth, 'get_username') as mock_get_username,
+ mock.patch.object(kagglehub, 'login') as mock_login,
+ ):
+ mock_get_username.return_value = None
+ mock_model_download.return_value = checkpoint_dir
+ model = dna_model.create_from_kaggle(
+ version, organism_settings={}, device=jax.local_devices()[0]
+ )
+
+ self.assertIsInstance(model, dna_model.AlphaGenomeModel)
+ mock_get_username.assert_called_once()
+ mock_login.assert_called_once()
+ mock_model_download.assert_called_once_with(
+ 'google/alphagenome/jax/all_folds'
+ )
+
+ @parameterized.parameters(('fold_0',), (dna_model.ModelVersion.FOLD_0,))
+ @mock.patch.object(jax, 'eval_shape', return_value=MOCK_SHAPES, autospec=True)
+ def test_create_from_huggingface(self, version, mock_eval_shape):
+ del mock_eval_shape
+ checkpointer = ocp.StandardCheckpointer()
+ checkpoint_dir = os.path.join(self.create_tempdir().full_path, 'ckpt')
+ checkpointer.save(checkpoint_dir, ({}, {}))
+ checkpointer.wait_until_finished()
+ with (
+ mock.patch.object(
+ huggingface_hub, 'snapshot_download'
+ ) as mock_snapshot_download,
+ mock.patch.object(huggingface_hub, 'login') as mock_login,
+ mock.patch.object(huggingface_hub, 'whoami') as mock_whoami,
+ ):
+ mock_snapshot_download.return_value = checkpoint_dir
+ mock_whoami.side_effect = huggingface_hub.errors.LocalTokenNotFoundError()
+ model = dna_model.create_from_huggingface(
+ version, organism_settings={}, device=jax.local_devices()[0]
+ )
+
+ self.assertIsInstance(model, dna_model.AlphaGenomeModel)
+ mock_login.assert_called_once()
+ mock_whoami.assert_called_once()
+ mock_snapshot_download.assert_called_once_with(
+ repo_id='google/alphagenome-fold-0'
+ )
+
+ def test_default_create_with_cpu_raises_error(self):
+ with jax.default_device(jax.devices(backend='cpu')[0]):
+ with self.assertRaisesRegex(
+ ValueError, 'Cannot find any GPU or TPU devices'
+ ):
+ _ = dna_model.AlphaGenomeModel(
+ params={},
+ state={},
+ apply_fn=self._mock_model,
+ junctions_apply_fn=self._mock_model_junctions,
+ metadata={},
+ )
+
+
+if __name__ == '__main__':
+ absltest.main()
diff --git a/flax_model/alphagenome/model/embeddings.py b/flax_model/alphagenome/model/embeddings.py
new file mode 100644
index 0000000000000000000000000000000000000000..66cccd2559b5175ba074d1667b9768b5cbd4b624
--- /dev/null
+++ b/flax_model/alphagenome/model/embeddings.py
@@ -0,0 +1,102 @@
+# Copyright 2026 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.
+
+"""Embeddings for AlphaGenome."""
+
+from flax_model.alphagenome._sdk import typing
+from flax_model.alphagenome.model import layers
+import chex
+import haiku as hk
+import jax.numpy as jnp
+from jaxtyping import Array, Float, Int # pylint: disable=g-importing-member, g-multiple-import
+
+
+@typing.jaxtyped
+@chex.dataclass(frozen=True, kw_only=True)
+class Embeddings:
+ """AlphaGenome embeddings."""
+
+ embeddings_1bp: Float[Array, 'B S 1536'] | None = None
+ embeddings_128bp: Float[Array, 'B S//128 3072'] | None = None
+ embeddings_pair: Float[Array, 'B S//2048 S//2048 128'] | None = None
+
+ def get_sequence_embeddings(self, resolution: int) -> Float[Array, 'B S D']:
+ if resolution == 128:
+ return self.embeddings_128bp
+ elif resolution == 1:
+ return self.embeddings_1bp
+ else:
+ raise ValueError(f'Unsupported resolution: {resolution}')
+
+
+class OutputEmbedder(hk.Module):
+ """Generates output embeddings with organism-specific adjustments."""
+
+ def __init__(self, num_organisms: int, name: str | None = None):
+ """Initializes the OutputEmbedder module.
+
+ Args:
+ num_organisms: The number of organisms to embed. Typically 2 (human and
+ mouse).
+ name: The name of the module.
+ """
+ super().__init__(name=name)
+ self._num_organisms = num_organisms
+
+ @typing.jaxtyped
+ def __call__(
+ self,
+ x: Float[Array, 'B S D'],
+ organism_index: Int[Array, 'B'],
+ skip_x: Float[Array, 'B S_skip D_skip'] | None = None,
+ ) -> Float[Array, 'B S D_out']:
+ x = hk.Linear(2 * x.shape[-1])(x)
+ if skip_x is not None:
+ # Assumes skip_x needs to be upsampled to match x's sequence length
+ skip_x = hk.Linear(x.shape[-1], with_bias=False)(skip_x)
+ x += jnp.repeat(skip_x, x.shape[1] // skip_x.shape[1], axis=1)
+
+ x = layers.RMSBatchNorm()(x)
+ if self._num_organisms >= 1:
+ organism_embedding = hk.Embed(self._num_organisms, x.shape[-1])(
+ organism_index
+ )[:, None, :]
+ x += organism_embedding
+ return layers.gelu(x)
+
+
+class OutputPair(hk.Module):
+ """Generates output pairwise embeddings with organism-specific adjustments."""
+
+ def __init__(self, num_organisms: int, name: str | None = None):
+ """Initializes the OutputPair module.
+
+ Args:
+ num_organisms: The number of organisms to embed. Typically 2 (human and
+ mouse).
+ name: The name of the module.
+ """
+ super().__init__(name=name)
+ self._num_organisms = num_organisms
+
+ @typing.jaxtyped
+ def __call__(
+ self, x: Float[Array, 'B S S F'], organism_index: Int[Array, 'B']
+ ) -> Float[Array, 'B S S 128']:
+ x = (x + jnp.swapaxes(x, 1, 2)) / 2.0 # Symmetrize.
+ x = layers.LayerNorm(rms_norm=True)(x)
+ if self._num_organisms >= 1:
+ organism_embedding = hk.Embed(self._num_organisms, 128)(organism_index)
+ x += organism_embedding[:, None, None, :]
+ return layers.gelu(x)
diff --git a/flax_model/alphagenome/model/heads.py b/flax_model/alphagenome/model/heads.py
new file mode 100644
index 0000000000000000000000000000000000000000..5d16167885e1bea0bc181a8c5f27c426118af41d
--- /dev/null
+++ b/flax_model/alphagenome/model/heads.py
@@ -0,0 +1,1055 @@
+# Copyright 2026 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.
+
+"""Heads for AlphaGenome."""
+
+import abc
+from collections.abc import Mapping, Sequence
+import dataclasses
+import enum
+import functools
+import math
+from flax_model.alphagenome._sdk import typing
+from flax_model.alphagenome._sdk.data import junction_data
+from flax_model.alphagenome._sdk.data import track_data
+from flax_model.alphagenome._sdk.models import dna_model
+from flax_model.alphagenome._sdk.models import dna_output
+from flax_model.alphagenome.io import bundles
+from flax_model.alphagenome.model import attention
+from flax_model.alphagenome.model import embeddings as embeddings_module
+from flax_model.alphagenome.model import losses
+from flax_model.alphagenome.model import schemas
+from flax_model.alphagenome.model.metadata import metadata as metadata_lib
+import chex
+import haiku as hk
+import jax
+import jax.numpy as jnp
+from jaxtyping import Array, ArrayLike, Bool, Float, Int, PyTree, Shaped # pylint: disable=g-importing-member, g-multiple-import
+import numpy as np
+
+_SOFT_CLIP_VALUE = 10.0
+
+
+class HeadType(enum.Enum):
+ """Head types."""
+
+ GENOME_TRACKS = 'genome_tracks'
+ CONTACT_MAPS = 'contact_maps'
+ SPLICE_SITES_CLASSIFICATION = 'splice_sites_classification'
+ SPLICE_SITES_USAGE = 'splice_sites_usage'
+ SPLICE_SITES_JUNCTION = 'splice_sites_junction'
+
+
+class HeadName(enum.Enum):
+ """Output heads."""
+
+ ATAC = 'atac'
+ DNASE = 'dnase'
+ PROCAP = 'procap'
+ CAGE = 'cage'
+ RNA_SEQ = 'rna_seq'
+ CHIP_TF = 'chip_tf'
+ CHIP_HISTONE = 'chip_histone'
+ CONTACT_MAPS = 'contact_maps'
+ SPLICE_SITES_CLASSIFICATION = 'splice_sites_classification'
+ SPLICE_SITES_USAGE = 'splice_sites_usage'
+ SPLICE_SITES_JUNCTION = 'splice_sites_junction'
+
+
+@dataclasses.dataclass
+class HeadConfig:
+ type: HeadType
+ name: str
+ output_type: dna_output.OutputType
+
+
+@dataclasses.dataclass
+class GenomeTracksHeadConfig(HeadConfig):
+ resolutions: Sequence[int]
+ apply_squashing: bool
+ bundle: bundles.BundleName
+
+
+def create_head(
+ config: HeadConfig,
+ metadata: Mapping[
+ dna_model.Organism, metadata_lib.AlphaGenomeOutputMetadata
+ ],
+ *,
+ num_organisms: int | None = None,
+) -> 'Head':
+ match config.type:
+ case HeadType.GENOME_TRACKS:
+ assert isinstance(config, GenomeTracksHeadConfig)
+ return GenomeTracksHead(
+ name=config.name,
+ output_type=config.output_type,
+ metadata=metadata,
+ resolutions=config.resolutions,
+ apply_squashing=config.apply_squashing,
+ bundle=config.bundle,
+ num_organisms=num_organisms,
+ )
+ case HeadType.CONTACT_MAPS:
+ return ContactMapsHead(
+ name=config.name,
+ output_type=config.output_type,
+ metadata=metadata,
+ num_organisms=num_organisms,
+ )
+ case HeadType.SPLICE_SITES_CLASSIFICATION:
+ return SpliceSitesClassificationHead(
+ name=config.name,
+ output_type=config.output_type,
+ metadata=metadata,
+ num_organisms=num_organisms,
+ )
+ case HeadType.SPLICE_SITES_USAGE:
+ return SpliceSitesUsageHead(
+ name=config.name,
+ output_type=config.output_type,
+ metadata=metadata,
+ num_organisms=num_organisms,
+ )
+ case HeadType.SPLICE_SITES_JUNCTION:
+ return SpliceSitesJunctionHead(
+ name=config.name,
+ output_type=config.output_type,
+ metadata=metadata,
+ num_organisms=num_organisms,
+ )
+ case _:
+ raise ValueError(f'Unknown head type: {config.type}')
+
+
+def get_head_config(head_name: HeadName) -> HeadConfig:
+ """Returns a head for the given head name."""
+ match head_name:
+ case HeadName.ATAC:
+ return GenomeTracksHeadConfig(
+ type=HeadType.GENOME_TRACKS,
+ name=HeadName.ATAC.value,
+ output_type=dna_output.OutputType.ATAC,
+ resolutions=[1, 128],
+ apply_squashing=False,
+ bundle=bundles.BundleName.ATAC,
+ )
+ case HeadName.DNASE:
+ return GenomeTracksHeadConfig(
+ type=HeadType.GENOME_TRACKS,
+ name=HeadName.DNASE.value,
+ output_type=dna_output.OutputType.DNASE,
+ resolutions=[1, 128],
+ apply_squashing=False,
+ bundle=bundles.BundleName.DNASE,
+ )
+ case HeadName.PROCAP:
+ return GenomeTracksHeadConfig(
+ type=HeadType.GENOME_TRACKS,
+ name=HeadName.PROCAP.value,
+ output_type=dna_output.OutputType.PROCAP,
+ resolutions=[1, 128],
+ apply_squashing=False,
+ bundle=bundles.BundleName.PROCAP,
+ )
+ case HeadName.CAGE:
+ return GenomeTracksHeadConfig(
+ type=HeadType.GENOME_TRACKS,
+ name=HeadName.CAGE.value,
+ output_type=dna_output.OutputType.CAGE,
+ resolutions=[1, 128],
+ apply_squashing=False,
+ bundle=bundles.BundleName.CAGE,
+ )
+ case HeadName.RNA_SEQ:
+ return GenomeTracksHeadConfig(
+ type=HeadType.GENOME_TRACKS,
+ name=HeadName.RNA_SEQ.value,
+ output_type=dna_output.OutputType.RNA_SEQ,
+ resolutions=[1, 128],
+ apply_squashing=True,
+ bundle=bundles.BundleName.RNA_SEQ,
+ )
+ case HeadName.CHIP_TF:
+ return GenomeTracksHeadConfig(
+ type=HeadType.GENOME_TRACKS,
+ name=HeadName.CHIP_TF.value,
+ output_type=dna_output.OutputType.CHIP_TF,
+ resolutions=[128],
+ apply_squashing=False,
+ bundle=bundles.BundleName.CHIP_TF,
+ )
+ case HeadName.CHIP_HISTONE:
+ return GenomeTracksHeadConfig(
+ type=HeadType.GENOME_TRACKS,
+ name=HeadName.CHIP_HISTONE.value,
+ output_type=dna_output.OutputType.CHIP_HISTONE,
+ resolutions=[128],
+ apply_squashing=False,
+ bundle=bundles.BundleName.CHIP_HISTONE,
+ )
+ case HeadName.CONTACT_MAPS:
+ return HeadConfig(
+ type=HeadType.CONTACT_MAPS,
+ name=HeadName.CONTACT_MAPS.value,
+ output_type=dna_output.OutputType.CONTACT_MAPS,
+ )
+ case HeadName.SPLICE_SITES_CLASSIFICATION:
+ return HeadConfig(
+ type=HeadType.SPLICE_SITES_CLASSIFICATION,
+ name=HeadName.SPLICE_SITES_CLASSIFICATION.value,
+ output_type=dna_output.OutputType.SPLICE_SITES,
+ )
+ case HeadName.SPLICE_SITES_USAGE:
+ return HeadConfig(
+ type=HeadType.SPLICE_SITES_USAGE,
+ name=HeadName.SPLICE_SITES_USAGE.value,
+ output_type=dna_output.OutputType.SPLICE_SITE_USAGE,
+ )
+ case HeadName.SPLICE_SITES_JUNCTION:
+ return HeadConfig(
+ type=HeadType.SPLICE_SITES_JUNCTION,
+ name=HeadName.SPLICE_SITES_JUNCTION.value,
+ output_type=dna_output.OutputType.SPLICE_JUNCTIONS,
+ )
+ case _:
+ raise ValueError(f'Unknown head name: {head_name}')
+
+
+@typing.jaxtyped
+def _sum_pool(
+ x: Float[Array, 'B S C'], width: int
+) -> Float[Array, 'B S//{width} C']:
+ return x.reshape((x.shape[0], x.shape[1] // width, width, x.shape[2])).sum(
+ axis=-2, dtype=jnp.float32
+ )
+
+
+@typing.jaxtyped
+def _get_param_for_index(
+ params: Float[ArrayLike, 'P ...'], index: Int[Array, 'B']
+) -> Float[ArrayLike, 'B ...']:
+ """Returns a parameter for a specific index.
+
+ Embeds the params into the graph.
+
+ Args:
+ params: The parameters to embed.
+ index: The index to get the parameter for.
+ """
+ return jnp.asarray(params)[(index,)]
+
+
+class _MultiOrganismLinear(hk.Module):
+ """A linear layer with organism-specific weights and biases."""
+
+ def __init__(
+ self,
+ output_size: int,
+ num_organisms: int,
+ name: str | None = 'multi_organism_linear',
+ ):
+ super().__init__(name=name)
+ self._output_size = output_size
+ self._num_organisms = num_organisms
+
+ @typing.jaxtyped
+ def __call__(
+ self, x: Float[Array, 'B *S D'], organism_index: Int[Array, 'B']
+ ) -> Float[Array, 'B *S {self._output_size}']:
+ w_shape = (self._num_organisms, x.shape[-1], self._output_size)
+ stddev = 1.0 / np.sqrt(x.shape[-1])
+ w_init = hk.initializers.TruncatedNormal(stddev=stddev)
+ w = hk.get_parameter('w', w_shape, init=w_init).astype(x.dtype)
+ w = _get_param_for_index(w, organism_index)
+ b_shape = (self._num_organisms, self._output_size)
+ b = hk.get_parameter('b', b_shape, init=jnp.zeros).astype(x.dtype)
+ b = _get_param_for_index(b, organism_index)
+ num_inner_dims = len(x.shape) - 2
+ target_b_shape = (b.shape[0],) + (1,) * num_inner_dims + (b.shape[1],)
+ return jnp.einsum(
+ 'b...i,bij->b...j', x, w, preferred_element_type=jnp.float32
+ ) + b.reshape(target_b_shape)
+
+
+def predictions_scaling(
+ x: Float[ArrayLike, 'B S C'],
+ track_means: Float[ArrayLike, 'B C'],
+ resolution: int,
+ apply_squashing: bool,
+ soft_clip_value: float = _SOFT_CLIP_VALUE,
+) -> Float[ArrayLike, 'S C']:
+ """Scales predictions to experimental data scale.
+
+ Args:
+ x: Experimental target counts.
+ track_means: Mean values per track (broadcastable).
+ resolution: The bin resolution of the targets (e.g., 1 or 128).
+ apply_squashing: Whether to apply power law compression (for RNA-seq).
+ soft_clip_value: The value to soft clip the predictions to.
+
+ Returns:
+ Scaled predictions.
+ """
+ xnp = jnp if isinstance(x, jnp.ndarray) else np
+ x = xnp.where(
+ x > soft_clip_value,
+ (x + soft_clip_value) ** 2 / (4 * soft_clip_value),
+ x,
+ )
+ if apply_squashing:
+ x = xnp.power(x, 1.0 / 0.75)
+ x = x * (track_means[:, None] * resolution).astype(x.dtype)
+ return x
+
+
+@typing.jaxtyped
+def targets_scaling(
+ targets: Float[ArrayLike, 'B S C'],
+ track_means: Float[ArrayLike, 'B C'],
+ resolution: int,
+ apply_squashing: bool,
+ soft_clip_value: float = _SOFT_CLIP_VALUE,
+) -> Float[Array, 'B S C']:
+ """Scales experimental targets to the model prediction space.
+
+ Args:
+ targets: Experimental target counts.
+ track_means: Mean values per track.
+ resolution: The bin resolution of the targets (e.g., 1 or 128).
+ apply_squashing: Whether to apply power law compression (for RNA-seq).
+ soft_clip_value: The value to soft clip the targets to.
+
+ Returns:
+ Scaled targets ready for loss calculation.
+ """
+ xnp = jnp if isinstance(targets, jnp.ndarray) else np
+ targets = targets / (track_means[:, None] * resolution).astype(targets.dtype)
+
+ if apply_squashing:
+ targets = xnp.power(targets, 0.75)
+
+ # Where(targets > 10.0, 2 * Sqrt(x * 10.0) - 10.0, targets)
+ return xnp.where(
+ targets > soft_clip_value,
+ 2.0 * jnp.sqrt(targets * soft_clip_value) - soft_clip_value,
+ targets,
+ )
+
+
+class Head(metaclass=abc.ABCMeta):
+ """Abstract class for a model head."""
+
+ def __init__(
+ self,
+ *,
+ name: str,
+ output_type: dna_output.OutputType,
+ metadata: Mapping[
+ dna_model.Organism,
+ metadata_lib.AlphaGenomeOutputMetadata,
+ ],
+ num_organisms: int | None = None,
+ ):
+ """Initializes the Head class.
+
+ Args:
+ name: The name of the head.
+ output_type: The type of output to predict.
+ metadata: A dictionary of track metadata for each organism. The metadata
+ should be a ordered aligned with the organism index. E.g.,
+ organism_index=0 should correspond to the first organism in the metadata
+ dictionary.
+ num_organisms: Optional number of organisms. If not provided, the number
+ of organisms will be inferred from the metadata.
+ """
+
+ self._name = name
+ self._output_type = output_type
+ self._metadata = metadata
+ self._num_organisms = (
+ num_organisms if num_organisms is not None else len(metadata)
+ )
+ if not self._metadata:
+ raise ValueError('No metadata provided for any organism.')
+ self._num_tracks = self._get_num_tracks()
+
+ @property
+ def name(self) -> str:
+ return self._name
+
+ @property
+ def num_tracks(self) -> int:
+ """Returns the maximum number of tracks for the head across all organisms."""
+ return self._num_tracks
+
+ def _get_num_tracks(self) -> int:
+ """Returns the number of tracks for the head."""
+ num_tracks = []
+ for organism in self._metadata.keys():
+ if (track_metadata := self.get_metadata(organism)) is not None:
+ num_tracks.append(len(track_metadata))
+
+ if not num_tracks:
+ raise ValueError(
+ f'No metadata found for any organism for {self._output_type=}.'
+ )
+
+ if len(set(num_tracks)) > 1:
+ raise ValueError(
+ 'Number of tracks is not the same for all organisms. Please pad the'
+ ' metadata to have the same number of tracks for all organisms.'
+ )
+ return num_tracks[0]
+
+ @typing.jaxtyped
+ def get_multi_organism_track_mask(
+ self,
+ ) -> Bool[Array, '{len(self._metadata)} {self.num_tracks}']:
+ """Returns the track mask for all organisms."""
+ track_masks = []
+ for organism in self._metadata.keys():
+ padding = self._metadata[organism].padding[self._output_type]
+ chex.assert_shape(padding, (self.num_tracks,))
+ track_masks.append(np.logical_not(padding))
+ return jnp.stack(track_masks).astype(bool)
+
+ def get_metadata(
+ self, organism: dna_model.Organism
+ ) -> track_data.TrackMetadata | junction_data.JunctionMetadata | None:
+ return self._metadata.get(organism, {}).get(self._output_type)
+
+ def __call__(
+ self,
+ embeddings: embeddings_module.Embeddings,
+ organism_index: Int[Array, 'B'],
+ **kwargs,
+ ) -> PyTree[Shaped[Array, 'B ...'] | None]:
+ """Calls the head's predict function as a module."""
+ return hk.to_module(self.predict)(self._name)(
+ embeddings, organism_index, **kwargs
+ )
+
+ @abc.abstractmethod
+ def predict(
+ self,
+ embeddings: embeddings_module.Embeddings,
+ organism_index: Int[Array, 'B'],
+ **kwargs,
+ ) -> PyTree[Shaped[Array, 'B ...'] | None]:
+ """Returns the predictions for the head."""
+
+ @abc.abstractmethod
+ def loss(
+ self,
+ predictions: PyTree[Shaped[Array, 'B ...']],
+ batch: schemas.DataBatch,
+ ) -> PyTree[Float[Array, '']]:
+ """Returns the loss for the head."""
+
+
+class GenomeTracksHead(Head):
+ """A model head that predicts at multiple resolutions.
+
+ This module takes embeddings at different resolutions and produces predictions
+ for a specified number of tracks. It uses organism-specific linear layers and
+ learnt scales to generate the predictions.
+ """
+
+ def __init__(
+ self,
+ *,
+ name: str,
+ output_type: dna_output.OutputType,
+ apply_squashing: bool,
+ resolutions: Sequence[int],
+ bundle: bundles.BundleName | None = None,
+ metadata: Mapping[
+ dna_model.Organism,
+ metadata_lib.AlphaGenomeOutputMetadata,
+ ],
+ num_organisms: int | None = None,
+ ):
+ """Initializes the BaseResolutionHead module.
+
+ Args:
+ name: The name of the head.
+ output_type: The type of output to predict.
+ apply_squashing: Whether to apply squashing to the predictions.
+ resolutions: The resolutions to predict.
+ bundle: The name of the dataset bundle associated with this head. This is
+ required to fetch the target tracks for loss computation. If None, the
+ `loss` method will not be functional.
+ metadata: A dictionary mapping each organism to its track metadata. The
+ order of organisms in the dictionary is important, as it must align with
+ the organism index provided in the data batches (e.g., organism_index=0
+ corresponds to the first organism in `metadata`). For each organism, the
+ metadata for `output_type` should contain the same number of rows
+ (tracks), padded if necessary. If the metadata includes a 'nonzero_mean'
+ column, these values are used to scale predictions and targets.
+ Otherwise, scaling is omitted. Note that squashing is only applied if
+ `apply_squashing` is True.
+ num_organisms: Optional number of organisms. If not provided, the number
+ of organisms will be inferred from the metadata.
+ """
+
+ super().__init__(
+ name=name,
+ output_type=output_type,
+ metadata=metadata,
+ num_organisms=num_organisms,
+ )
+ self._apply_squashing = apply_squashing
+ self._resolutions = sorted(resolutions)
+ self._bundle = bundle
+
+ def _get_track_means(organism: dna_model.Organism) -> Float[Array, 'C']:
+ metadata = self.get_metadata(organism)
+ if metadata is None or metadata.get('nonzero_mean') is None:
+ return jnp.ones((self.num_tracks,))
+ else:
+ return metadata['nonzero_mean'].values
+
+ self._track_means = jnp.stack(
+ [_get_track_means(organism) for organism in self._metadata.keys()]
+ )
+
+ @typing.jaxtyped
+ def unscale(
+ self,
+ x: Float[Array, 'B S C'],
+ organism_index: Int[Array, 'B'],
+ resolution: int,
+ ) -> Float[Array, 'B S C'] | None:
+ """Unscales predictions to experimental data scale.
+
+ Requires the column `nonzero_mean` to be present in the metadata to result
+ in valid scaling.
+
+ Args:
+ x: The predictions to unscale.
+ organism_index: The organism index.
+ resolution: The resolution of the predictions.
+ """
+ track_means = _get_param_for_index(self._track_means, organism_index)
+ return predictions_scaling(
+ x,
+ track_means=track_means,
+ resolution=resolution,
+ apply_squashing=self._apply_squashing,
+ )
+
+ @typing.jaxtyped
+ def scale(
+ self,
+ x: Float[Array, 'B S C'],
+ organism_index: Int[Array, 'B'],
+ resolution: int,
+ ) -> Float[Array, 'B S C']:
+ """Scales targets to model predictions scale."""
+ track_means = _get_param_for_index(self._track_means, organism_index)
+ return targets_scaling(
+ x,
+ track_means=track_means,
+ resolution=resolution,
+ apply_squashing=self._apply_squashing,
+ )
+
+ @hk.transparent
+ @typing.jaxtyped
+ def _predict(
+ self, x: Float[Array, 'B S D'], organism_index: Int[Array, 'B']
+ ) -> Float[Array, 'B S {self.num_tracks}']:
+ """Predicts genome tracks."""
+ x = _MultiOrganismLinear(self.num_tracks, self._num_organisms)(
+ x, organism_index
+ )
+ residual_scales = hk.get_parameter(
+ 'learnt_scale', (self._num_organisms, self.num_tracks), init=jnp.ones
+ ).astype(x.dtype)
+ residual_scale = _get_param_for_index(residual_scales, organism_index)
+ return jax.nn.softplus(x) * jax.nn.softplus(residual_scale[:, None, :])
+
+ def predict(
+ self,
+ embeddings: embeddings_module.Embeddings,
+ organism_index: Int[Array, 'B'],
+ **kwargs,
+ ) -> PyTree[Float[Array, 'B ...'] | None]:
+ predictions = {}
+ for resolution in self._resolutions:
+ with hk.name_scope(f'resolution_{resolution}'):
+ scaled_predictions = self._predict(
+ embeddings.get_sequence_embeddings(resolution), organism_index
+ )
+ predictions[f'scaled_predictions_{resolution}bp'] = scaled_predictions
+ predictions[f'predictions_{resolution}bp'] = self.unscale(
+ scaled_predictions, organism_index, resolution
+ )
+ return predictions
+
+ @typing.jaxtyped
+ def _compute_loss(
+ self,
+ *,
+ organism_index: Int[Array, 'B'],
+ predictions: Float[Array, 'B S C'],
+ targets: Float[Array, 'B S C'],
+ targets_mask: Bool[Array, 'B 1 C'] | None,
+ resolution: int,
+ ) -> PyTree[Float[Array, '']]:
+ """Computes the loss for the head at a given resolution."""
+ chex.assert_equal_shape([predictions, targets])
+ scaled_targets = self.scale(targets, organism_index, resolution)
+ all_losses = losses.multinomial_loss(
+ y_pred=predictions,
+ y_true=scaled_targets,
+ mask=targets_mask,
+ positional_weight=5.0,
+ multinomial_resolution=int(2**17) // resolution,
+ )
+ return all_losses
+
+ @typing.jaxtyped
+ def loss(
+ self,
+ predictions: PyTree[Float[Array, 'B ...']],
+ batch: schemas.DataBatch,
+ ) -> PyTree[Float[Array, '']]:
+ """Returns the loss for the head."""
+ if self._bundle is None:
+ raise ValueError('Bundle is required for loss computation.')
+
+ tracks, mask = batch.get_genome_tracks(self._bundle)
+
+ if mask.shape[-2] != 1:
+ raise ValueError(
+ 'We assume the mask to broadcast over the sequence length.'
+ )
+
+ bundle_resolution = self._bundle.get_resolution()
+ loss_sum, scalars = 0.0, {}
+
+ for resolution in self._resolutions:
+ predictions_for_resolution = predictions[
+ f'scaled_predictions_{resolution}bp'
+ ]
+ if resolution == bundle_resolution:
+ targets = tracks
+ else:
+ targets = _sum_pool(tracks, resolution)
+
+ all_losses = self._compute_loss(
+ organism_index=batch.get_organism_index(),
+ predictions=predictions_for_resolution,
+ targets=targets,
+ targets_mask=mask,
+ resolution=resolution,
+ )
+ for k, v in all_losses.items():
+ scalars[f'{k}_{resolution}bp'] = v
+ loss_sum += all_losses['loss']
+
+ scalars['loss'] = loss_sum
+ return scalars
+
+
+class ContactMapsHead(Head):
+ """A model head that predicts contact maps from pairwise embeddings."""
+
+ @typing.jaxtyped
+ @hk.transparent
+ def _predict(
+ self,
+ pair_embeddings: Float[Array, 'B S S D'],
+ organism_index: Int[Array, 'B'],
+ ) -> Float[Array, 'B S S {self.num_tracks}']:
+ """Predicts contact maps from pairwise embeddings."""
+ return _MultiOrganismLinear(self.num_tracks, self._num_organisms)(
+ pair_embeddings, organism_index
+ )
+
+ def predict(
+ self,
+ embeddings: embeddings_module.Embeddings,
+ organism_index: Int[Array, 'B'],
+ **kwargs,
+ ) -> PyTree[Float[Array, 'B ...'] | None]:
+ """Predicts contact maps from embeddings."""
+ return {
+ 'predictions': self._predict(embeddings.embeddings_pair, organism_index)
+ }
+
+ def _get_targets_mask(
+ self,
+ organism_index: Int[Array, 'B'],
+ ) -> Bool[Array, 'B 1 1 {self.num_tracks}']:
+ """Returns a mask for padding channels."""
+ track_mask = self.get_multi_organism_track_mask()[(organism_index,)]
+ return track_mask[:, None, None, :]
+
+ def loss(
+ self,
+ predictions: PyTree[Float[Array, 'B ...']],
+ batch: schemas.DataBatch,
+ ) -> PyTree[Float[Array, '']]:
+ """Returns the loss for the head."""
+ if (targets := batch.contact_maps) is None:
+ raise ValueError('contact_maps target not in batch.')
+
+ contact_predictions = predictions['predictions']
+ chex.assert_equal_shape([contact_predictions, targets])
+
+ # Mask out NaN targets (which happens when balancing a missing slice).
+ targets_mask = self._get_targets_mask(batch.get_organism_index())
+ targets_mask = jnp.where(jnp.isnan(targets), False, targets_mask)
+ targets = jnp.where(jnp.isnan(targets), 0.0, targets)
+
+ loss = losses.mse(
+ y_pred=contact_predictions, y_true=targets, mask=targets_mask
+ )
+ return {'loss': loss}
+
+
+class SpliceSitesClassificationHead(Head):
+ """A model head that predicts splice site classification."""
+
+ @typing.jaxtyped
+ @hk.transparent
+ def _predict_logits(
+ self, x: Float[Array, 'B S D'], organism_index: Int[Array, 'B']
+ ) -> Float[Array, 'B S {self.num_tracks}']:
+ """Splice site classification."""
+ return _MultiOrganismLinear(self.num_tracks, self._num_organisms)(
+ x, organism_index
+ )
+
+ def predict(
+ self,
+ embeddings: embeddings_module.Embeddings,
+ organism_index: Int[Array, 'B'],
+ **kwargs,
+ ) -> PyTree[Float[Array, 'B ...'] | None]:
+ """Predicts splice site classification from embeddings."""
+ embeddings_1bp = embeddings.get_sequence_embeddings(1)
+ logits = self._predict_logits(embeddings_1bp, organism_index)
+ probs = jax.nn.softmax(logits.astype(jnp.float32), axis=-1)
+ return {'logits': logits, 'predictions': probs}
+
+ def loss(
+ self,
+ predictions: PyTree[Float[Array, 'B ...']],
+ batch: schemas.DataBatch,
+ ) -> PyTree[Float[Array, '']]:
+ """Returns the loss for the head."""
+ if (splice_sites := batch.splice_sites) is None:
+ raise ValueError('splice_sites target not in batch.')
+ logits = predictions['logits']
+ chex.assert_equal_shape([splice_sites, logits])
+
+ classification_mask = jnp.any(splice_sites, axis=-1, keepdims=True)
+ loss = losses.cross_entropy_loss_from_logits(
+ y_pred_logits=logits,
+ # Label smoothing with FP32 machine precision (~1e-7) for 5 classes.
+ y_true=(1.0 - 1e-7) * splice_sites.astype(jnp.float32)
+ + 1e-7 / self.num_tracks,
+ mask=classification_mask,
+ axis=-1,
+ )
+ return {'loss': loss}
+
+
+class SpliceSitesUsageHead(Head):
+ """A model head that predicts splice site usage."""
+
+ @typing.jaxtyped
+ @hk.transparent
+ def _predict_logits(
+ self, x: Float[Array, 'B S D'], organism_index: Int[Array, 'B']
+ ) -> Float[Array, 'B S {self.num_tracks}']:
+ """Splice site usage."""
+ return _MultiOrganismLinear(self.num_tracks, self._num_organisms)(
+ x, organism_index
+ )
+
+ def predict(
+ self,
+ embeddings: embeddings_module.Embeddings,
+ organism_index: Int[Array, 'B'],
+ **kwargs,
+ ) -> PyTree[Float[Array, 'B ...'] | None]:
+ """Predicts splice site usage from embeddings."""
+ embeddings_1bp = embeddings.get_sequence_embeddings(1)
+ logits = self._predict_logits(embeddings_1bp, organism_index)
+ splice_site_usage = jax.nn.sigmoid(logits.astype(jnp.float32)).astype(
+ jnp.float16
+ )
+ return {'logits': logits, 'predictions': splice_site_usage}
+
+ @typing.jaxtyped
+ def _get_targets_mask(
+ self,
+ organism_index: Int[Array, 'B'],
+ ) -> Bool[Array, 'B 1 {self.num_tracks}']:
+ """Returns a mask for padding channels."""
+ track_mask = self.get_multi_organism_track_mask()[(organism_index,)]
+ return track_mask[:, None, :]
+
+ def loss(
+ self,
+ predictions: PyTree[Float[Array, 'B ...']],
+ batch: schemas.DataBatch,
+ ) -> PyTree[Float[Array, '']]:
+ """Returns the loss for the head."""
+ if (splice_site_usage := batch.splice_site_usage) is None:
+ raise ValueError('splice_site_usage target not in batch.')
+ logits = predictions['logits']
+ chex.assert_equal_shape([splice_site_usage, logits])
+ loss = losses.binary_crossentropy_from_logits(
+ y_pred=logits,
+ y_true=jnp.clip(splice_site_usage, 1e-7, 1.0 - 1e-7),
+ mask=self._get_targets_mask(batch.get_organism_index()),
+ )
+ return {'loss': loss}
+
+
+class SpliceSitesJunctionHead(Head):
+ """A model head that predicts splice site junctions."""
+
+ def __init__(
+ self,
+ *,
+ name: str,
+ output_type: dna_output.OutputType,
+ metadata: Mapping[
+ dna_model.Organism,
+ metadata_lib.AlphaGenomeOutputMetadata,
+ ],
+ num_organisms: int | None = None,
+ ):
+ """Initializes the SpliceSitesJunctionHead module."""
+ super().__init__(
+ name=name,
+ output_type=output_type,
+ metadata=metadata,
+ num_organisms=num_organisms,
+ )
+ self._hidden_dim = 768
+ self._max_position_encoding_distance = int(2**20)
+
+ def get_num_tissues(self, organism: dna_model.Organism) -> int:
+ """Returns the number of tissues for the given organism."""
+ metadata = self.get_metadata(organism)
+ if metadata is None:
+ raise ValueError(f'Metadata not found for organism {organism}.')
+ return len(metadata)
+
+ @property
+ def max_num_tissues(self) -> int:
+ """Returns the maximum number of tissues across all organisms from the metadata."""
+
+ return max(self.get_num_tissues(organism) for organism in self._metadata)
+
+ def _get_num_tracks(self) -> int:
+ """Returns the number of tracks.
+
+ Splice junctions metadata contains the tissues per organism, rather than the
+ tracks. The number of tracks is twice the number of tissues accounting for
+ the two strands.
+ """
+ return 2 * self.max_num_tissues
+
+ def get_multi_organism_track_mask(
+ self,
+ ) -> Bool[Array, '{self._num_organisms} {self.num_tracks}']:
+ """Returns the track mask for the head for human and mouse.
+
+ Splice junctions metadata contains the tissues per organism, rather than the
+ tracks.
+ """
+ track_masks = []
+ for organism in self._metadata:
+ num_tissues = self.get_num_tissues(organism)
+ tissue_mask = np.arange(self.max_num_tissues) < num_tissues
+ # Repeat the mask for the two strands.
+ track_mask = np.concatenate([tissue_mask, tissue_mask])
+ track_masks.append(track_mask)
+ return jnp.stack(track_masks).astype(bool)
+
+ @typing.jaxtyped
+ @hk.transparent
+ def _predict(
+ self,
+ x: Float[Array, 'B S D'],
+ splice_site_positions: Int[Array, 'B 4 P'],
+ organism_index: Int[Array, 'B'],
+ ) -> tuple[
+ Float[Array, 'B P P {self.num_tracks}'],
+ Bool[Array, 'B P P {self.num_tracks}'],
+ ]:
+ """Splice site junctions."""
+
+ chex.assert_shape(splice_site_positions, (None, 4, None))
+ pos_donor_idx = splice_site_positions[:, 0, :]
+ pos_accept_idx = splice_site_positions[:, 1, :]
+ neg_donor_idx = splice_site_positions[:, 2, :]
+ neg_accept_idx = splice_site_positions[:, 3, :]
+
+ def _index_embedding(embedding, indices):
+ return jax.vmap(functools.partial(jnp.take, axis=0))(embedding, indices)
+
+ shape = (self._num_organisms, 2, self.max_num_tissues, self._hidden_dim)
+
+ def _apply_rope(x, indices):
+ x = _index_embedding(x, indices).astype(jnp.float32)
+ params = hk.get_parameter(
+ 'embeddings',
+ (shape[0], math.prod(shape[1:])),
+ dtype=x.dtype,
+ init=jnp.zeros,
+ ).reshape(*shape)
+ params = _get_param_for_index(params, organism_index)
+ # scale and offset have shape [B, 1, num_tissues, C].
+ scale, offset = params[:, [0], :, :], params[:, [1], :, :]
+ x = scale * x[:, :, None, :] + offset # [B, num_indices, num_tissues, C]
+ return attention.apply_rope(
+ x, indices, self._max_position_encoding_distance
+ )
+
+ splice_site_logits = _MultiOrganismLinear(
+ self._hidden_dim, self._num_organisms
+ )(x, organism_index)
+
+ with hk.name_scope('pos_acceptor_logits'):
+ pos_accept_logits = _apply_rope(splice_site_logits, pos_accept_idx)
+ with hk.name_scope('pos_donor_logits'):
+ pos_donor_logits = _apply_rope(splice_site_logits, pos_donor_idx)
+ with hk.name_scope('neg_acceptor_logits'):
+ neg_accept_logits = _apply_rope(splice_site_logits, neg_accept_idx)
+ with hk.name_scope('neg_donor_logits'):
+ neg_donor_logits = _apply_rope(splice_site_logits, neg_donor_idx)
+ pos_counts = jax.nn.softplus(
+ jnp.einsum(
+ 'bdtc,batc->bdat',
+ pos_donor_logits,
+ pos_accept_logits,
+ # precision=jax.lax.DotAlgorithmPreset.BF16_BF16_F32,
+ precision=jax.lax.Precision.DEFAULT,
+ )
+ )
+ neg_counts = jax.nn.softplus(
+ jnp.einsum(
+ 'bdtc,batc->bdat',
+ neg_donor_logits,
+ neg_accept_logits,
+ # precision=jax.lax.DotAlgorithmPreset.BF16_BF16_F32,
+ precision=jax.lax.Precision.DEFAULT,
+ )
+ )
+ pos_mask = jnp.einsum('bd,ba->bda', pos_donor_idx >= 0, pos_accept_idx >= 0)
+ neg_mask = jnp.einsum('bd,ba->bda', neg_donor_idx >= 0, neg_accept_idx >= 0)
+ track_mask = self.get_multi_organism_track_mask()[(
+ organism_index,
+ )] # [B, 2 * num_tissues]
+ pos_mask = (
+ pos_mask[:, :, :, None]
+ * track_mask[:, None, None, : self.max_num_tissues]
+ )
+ neg_mask = (
+ neg_mask[:, :, :, None]
+ * track_mask[:, None, None, self.max_num_tissues :]
+ )
+ # Shape [B, D, A, 2 * num_tissues]
+ splice_junction_mask = jnp.concatenate([pos_mask, neg_mask], axis=-1)
+ pred_counts = jnp.concatenate((pos_counts, neg_counts), axis=-1)
+ pred_counts = jnp.where(splice_junction_mask, pred_counts, 0)
+ return pred_counts, splice_junction_mask
+
+ def predict(
+ self,
+ embeddings: embeddings_module.Embeddings,
+ organism_index: Int[Array, 'B'],
+ **kwargs,
+ ) -> PyTree[Shaped[Array, 'B ...'] | None]:
+ """Predicts splice site junctions from embeddings."""
+ if (splice_site_positions := kwargs.get('splice_site_positions')) is None:
+ raise ValueError(
+ 'splice_site_positions is required for junctions predictions.'
+ )
+ embeddings_1bp = embeddings.get_sequence_embeddings(1)
+ splice_site_junction, splice_junction_mask = self._predict(
+ embeddings_1bp, splice_site_positions, organism_index
+ )
+ return {
+ 'predictions': splice_site_junction,
+ 'splice_site_positions': splice_site_positions,
+ 'splice_junction_mask': splice_junction_mask,
+ }
+
+ def loss(
+ self,
+ predictions: PyTree[Shaped[Array, 'B ...']],
+ batch: schemas.DataBatch,
+ ) -> PyTree[Float[Array, '']]:
+ """Returns the loss for the head."""
+ if (count_target := batch.splice_junctions) is None:
+ raise ValueError('splice_junctions target not in batch.')
+
+ pred_pair = predictions['predictions']
+ pairs_mask = predictions['splice_junction_mask']
+ # Junctions shape is [batch, donors, acceptors, 2 * num_tissues].
+ chex.assert_equal_shape([pred_pair, count_target, pairs_mask])
+ chex.assert_rank(pred_pair, 4)
+
+ def _scale_junction_counts(counts):
+ return jnp.where(
+ counts > _SOFT_CLIP_VALUE,
+ 2.0 * jnp.sqrt(counts * _SOFT_CLIP_VALUE) - _SOFT_CLIP_VALUE,
+ counts,
+ )
+
+ accept_total_loss = losses.poisson_loss(
+ y_true=_scale_junction_counts(
+ count_target.sum(axis=-2, dtype=jnp.float32, where=pairs_mask)
+ ),
+ y_pred=pred_pair.sum(axis=-2, dtype=jnp.float32, where=pairs_mask),
+ mask=jnp.any(pairs_mask, axis=-2),
+ )
+ donor_total_loss = losses.poisson_loss(
+ y_true=_scale_junction_counts(
+ count_target.sum(axis=-3, dtype=jnp.float32, where=pairs_mask)
+ ),
+ y_pred=pred_pair.sum(axis=-3, dtype=jnp.float32, where=pairs_mask),
+ mask=jnp.any(pairs_mask, axis=-3),
+ )
+
+ # Ratios with cross entropy loss.
+ donor_ratios_loss = losses.cross_entropy_loss(
+ y_true=count_target,
+ y_pred=pred_pair,
+ mask=pairs_mask,
+ axis=-3,
+ )
+ acceptor_ratios_loss = losses.cross_entropy_loss(
+ y_true=count_target,
+ y_pred=pred_pair,
+ mask=pairs_mask,
+ axis=-2,
+ )
+ loss = (
+ donor_ratios_loss
+ + acceptor_ratios_loss
+ + 0.2 * (accept_total_loss + donor_total_loss)
+ )
+ return {'loss': loss}
diff --git a/flax_model/alphagenome/model/heads_test.py b/flax_model/alphagenome/model/heads_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..4543befd414cb7780f7dbe2be5d593e61cc5d2b7
--- /dev/null
+++ b/flax_model/alphagenome/model/heads_test.py
@@ -0,0 +1,677 @@
+# Copyright 2026 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 typing import Any
+
+from absl.testing import absltest
+from absl.testing import parameterized
+from flax_model.alphagenome._sdk import typing
+from flax_model.alphagenome._sdk.data import junction_data
+from flax_model.alphagenome._sdk.data import track_data
+from flax_model.alphagenome._sdk.models import dna_model
+from flax_model.alphagenome._sdk.models import dna_output
+from flax_model.alphagenome.io import bundles
+from flax_model.alphagenome.model import embeddings
+from flax_model.alphagenome.model import heads
+from flax_model.alphagenome.model import schemas
+from flax_model.alphagenome.model.metadata import metadata as metadata_lib
+import chex
+import haiku as hk
+import jax
+import jax.numpy as jnp
+import numpy as np
+import pandas as pd
+
+
+_EMBEDDING_DIM_PAIR = 128
+_EMBEDDING_DIM_1BP = 1536
+_EMBEDDING_DIM_128BP = 3072
+
+# Number of tracks for human and mouse.
+_MOCK_ATAC_NUM_TRACKS = (9, 7)
+_MOCK_CHIP_TF_NUM_TRACKS = (11, 10)
+_MOCK_CONTACT_MAPS_NUM_TRACKS = (8, 7)
+_MOCK_SPLICE_SITES_NUM_TRACKS = (5, 5)
+_MOCK_SPLICE_SITES_USAGE_NUM_TRACKS = (10, 9)
+_MOCK_SPLICE_SITES_JUNCTION_NUM_TISSUES = (15, 12) # Tissues, not tracks.
+
+
+_ORGANSIM_INDEX = {
+ dna_model.Organism.HOMO_SAPIENS: 0,
+ dna_model.Organism.MUS_MUSCULUS: 1,
+}
+
+
+def get_mock_output_metadata(
+ organism: dna_model.Organism,
+) -> metadata_lib.AlphaGenomeOutputMetadata:
+ """Returns mock output metadata for the given organisms."""
+
+ def _make_metadata_df(total_tracks, num_tracks) -> track_data.TrackMetadata:
+ df_tracks = pd.DataFrame({
+ 'name': [f'track_{i}' for i in range(num_tracks)],
+ 'nonzero_mean': [1.0] * num_tracks,
+ })
+ df_padding = pd.DataFrame({
+ 'name': ['padding'] * (total_tracks - num_tracks),
+ 'nonzero_mean': [0.0] * (total_tracks - num_tracks),
+ })
+ return pd.concat([df_tracks, df_padding], ignore_index=True)
+
+ def _make_junction_metadata_df(num_tissues) -> junction_data.JunctionMetadata:
+ return pd.DataFrame({
+ 'name': [f'tissue_{i}' for i in range(num_tissues)],
+ })
+
+ organism_idx = _ORGANSIM_INDEX[organism]
+ return metadata_lib.AlphaGenomeOutputMetadata(
+ atac=_make_metadata_df(
+ max(_MOCK_ATAC_NUM_TRACKS), _MOCK_ATAC_NUM_TRACKS[organism_idx]
+ ),
+ chip_tf=_make_metadata_df(
+ max(_MOCK_CHIP_TF_NUM_TRACKS),
+ _MOCK_CHIP_TF_NUM_TRACKS[organism_idx],
+ ),
+ contact_maps=_make_metadata_df(
+ max(_MOCK_CONTACT_MAPS_NUM_TRACKS),
+ _MOCK_CONTACT_MAPS_NUM_TRACKS[organism_idx],
+ ),
+ splice_sites=_make_metadata_df(
+ max(_MOCK_SPLICE_SITES_NUM_TRACKS),
+ _MOCK_SPLICE_SITES_NUM_TRACKS[organism_idx],
+ ),
+ splice_site_usage=_make_metadata_df(
+ max(_MOCK_SPLICE_SITES_USAGE_NUM_TRACKS),
+ _MOCK_SPLICE_SITES_USAGE_NUM_TRACKS[organism_idx],
+ ),
+ splice_junctions=_make_junction_metadata_df(
+ _MOCK_SPLICE_SITES_JUNCTION_NUM_TISSUES[organism_idx],
+ ),
+ )
+
+
+@typing.jaxtyped
+def get_mock_embeddings(
+ batch_size: int,
+ sequence_length: int,
+) -> embeddings.Embeddings:
+ """Returns mock embeddings for testing."""
+ return embeddings.Embeddings(
+ embeddings_pair=jnp.zeros(
+ (
+ batch_size,
+ sequence_length // 2048,
+ sequence_length // 2048,
+ _EMBEDDING_DIM_PAIR,
+ ),
+ dtype=jnp.bfloat16,
+ ),
+ embeddings_1bp=jnp.zeros(
+ (batch_size, sequence_length, _EMBEDDING_DIM_1BP), dtype=jnp.bfloat16
+ ),
+ embeddings_128bp=jnp.zeros(
+ (batch_size, sequence_length // 128, _EMBEDDING_DIM_128BP),
+ dtype=jnp.bfloat16,
+ ),
+ )
+
+
+@typing.jaxtyped
+def get_mock_batch(
+ batch_size: int,
+ sequence_length: int,
+ num_splice_sites: int,
+ organism_index: int = 0,
+) -> schemas.DataBatch:
+ """Returns mock target data for testing."""
+ return schemas.DataBatch(
+ organism_index=jnp.ones((batch_size,), dtype=jnp.int32) * organism_index,
+ atac=jnp.zeros(
+ (batch_size, sequence_length, max(_MOCK_ATAC_NUM_TRACKS)),
+ dtype=jnp.float32,
+ ),
+ atac_mask=jnp.ones(
+ (batch_size, 1, max(_MOCK_ATAC_NUM_TRACKS)),
+ dtype=bool,
+ ),
+ chip_tf=jnp.zeros(
+ (batch_size, sequence_length // 128, max(_MOCK_CHIP_TF_NUM_TRACKS)),
+ dtype=jnp.float32,
+ ),
+ chip_tf_mask=jnp.ones(
+ (batch_size, 1, max(_MOCK_CHIP_TF_NUM_TRACKS)),
+ dtype=bool,
+ ),
+ contact_maps=jnp.zeros(
+ (
+ batch_size,
+ sequence_length // 16 // 128,
+ sequence_length // 16 // 128,
+ max(_MOCK_CONTACT_MAPS_NUM_TRACKS),
+ ),
+ dtype=jnp.float32,
+ ),
+ splice_junctions=jnp.zeros(
+ (
+ batch_size,
+ num_splice_sites,
+ num_splice_sites,
+ max(_MOCK_SPLICE_SITES_JUNCTION_NUM_TISSUES) * 2,
+ ),
+ dtype=jnp.float32,
+ ),
+ splice_site_positions=jnp.zeros(
+ (batch_size, 4, num_splice_sites), dtype=jnp.int32
+ ),
+ splice_site_usage=jnp.zeros(
+ (
+ batch_size,
+ sequence_length,
+ max(_MOCK_SPLICE_SITES_USAGE_NUM_TRACKS),
+ ),
+ dtype=jnp.float32,
+ ),
+ splice_sites=jnp.ones(
+ (batch_size, sequence_length, max(_MOCK_SPLICE_SITES_NUM_TRACKS)),
+ dtype=bool,
+ ),
+ )
+
+
+class HeadsTest(parameterized.TestCase):
+
+ def setUp(self):
+ super().setUp()
+ self.batch_size = 1
+ # Set >= multinomial resolution of 2^17.
+ self.sequence_length = int(2**17)
+ self.num_splice_sites = 128
+
+ def test_head_num_tracks_with_default_metadata(self):
+ metadata = {
+ dna_model.Organism.HOMO_SAPIENS: metadata_lib.load(
+ dna_model.Organism.HOMO_SAPIENS
+ ),
+ dna_model.Organism.MUS_MUSCULUS: metadata_lib.load(
+ dna_model.Organism.MUS_MUSCULUS
+ ),
+ }
+ expected_num_tracks = {
+ heads.HeadName.ATAC: 256,
+ heads.HeadName.DNASE: 384,
+ heads.HeadName.PROCAP: 128,
+ heads.HeadName.CAGE: 640,
+ heads.HeadName.RNA_SEQ: 768,
+ heads.HeadName.CHIP_TF: 1664,
+ heads.HeadName.CHIP_HISTONE: 1152,
+ heads.HeadName.CONTACT_MAPS: 28,
+ heads.HeadName.SPLICE_SITES_CLASSIFICATION: 5,
+ heads.HeadName.SPLICE_SITES_USAGE: 734,
+ heads.HeadName.SPLICE_SITES_JUNCTION: 734,
+ }
+ for head_name in heads.HeadName:
+ with self.subTest(head_name.value):
+ config = heads.get_head_config(head_name)
+ head = heads.create_head(config, metadata)
+ self.assertEqual(head.num_tracks, expected_num_tracks[head_name])
+
+ def test_genome_tracks_head_no_metadata_raises_error(self):
+ metadata = {
+ dna_model.Organism.HOMO_SAPIENS: (
+ metadata_lib.AlphaGenomeOutputMetadata()
+ ),
+ }
+ with self.assertRaisesRegex(
+ ValueError, 'No metadata found for any organism'
+ ):
+ _ = heads.GenomeTracksHead(
+ name='test_head',
+ output_type=dna_output.OutputType.ATAC,
+ apply_squashing=True,
+ resolutions=[1],
+ bundle=bundles.BundleName.ATAC,
+ metadata=metadata,
+ )
+
+ def test_genome_tracks_head_inconsistent_num_tracks_raises_error(self):
+ def _make_metadata_df(num_tracks) -> track_data.TrackMetadata:
+ return pd.DataFrame({
+ 'name': [f'track_{i}' for i in range(num_tracks)],
+ 'nonzero_mean': [1.0] * num_tracks,
+ })
+
+ metadata = {
+ dna_model.Organism.HOMO_SAPIENS: metadata_lib.AlphaGenomeOutputMetadata(
+ atac=_make_metadata_df(10)
+ ),
+ dna_model.Organism.MUS_MUSCULUS: metadata_lib.AlphaGenomeOutputMetadata(
+ atac=_make_metadata_df(11)
+ ),
+ }
+ with self.assertRaisesRegex(
+ ValueError,
+ 'Number of tracks is not the same for all organisms',
+ ):
+ _ = heads.GenomeTracksHead(
+ name='test_head',
+ output_type=dna_output.OutputType.ATAC,
+ apply_squashing=True,
+ resolutions=[1],
+ bundle=bundles.BundleName.ATAC,
+ metadata=metadata,
+ )
+
+ def _test_head(
+ self,
+ head: heads.Head,
+ expected_params_shape: Any,
+ expected_output_shape: Any,
+ ):
+ @hk.transform
+ def forward(embeddings_input, batch):
+ kwargs = {}
+ if isinstance(head, heads.SpliceSitesJunctionHead):
+ kwargs['splice_site_positions'] = batch.splice_site_positions
+ output = head(embeddings_input, batch.organism_index, **kwargs)
+ loss = head.loss(output, batch)
+ return output, loss
+
+ rng = jax.random.PRNGKey(42)
+ embeddings_input = get_mock_embeddings(
+ self.batch_size, self.sequence_length
+ )
+ batch = get_mock_batch(
+ self.batch_size, self.sequence_length, self.num_splice_sites
+ )
+
+ params = forward.init(rng, embeddings_input, batch)
+ output, loss = forward.apply(params, rng, embeddings_input, batch)
+
+ to_shape = lambda t: jax.tree.map(lambda x: x.shape, t)
+ chex.assert_trees_all_equal(to_shape(params), expected_params_shape)
+ chex.assert_trees_all_equal(to_shape(output), expected_output_shape)
+ self.assertTrue(np.isfinite(loss['loss']).all())
+
+ @parameterized.named_parameters(
+ dict(
+ testcase_name='one_organism',
+ organisms=(dna_model.Organism.HOMO_SAPIENS,),
+ ),
+ dict(
+ testcase_name='two_organisms',
+ organisms=(
+ dna_model.Organism.HOMO_SAPIENS,
+ dna_model.Organism.MUS_MUSCULUS,
+ ),
+ ),
+ )
+ def test_genome_tracks_head(self, organisms: tuple[dna_model.Organism, ...]):
+ metadata = {
+ organism: get_mock_output_metadata(organism) for organism in organisms
+ }
+ head = heads.GenomeTracksHead(
+ name='test_head',
+ output_type=dna_output.OutputType.ATAC,
+ apply_squashing=True,
+ resolutions=[1, 128],
+ bundle=bundles.BundleName.ATAC,
+ metadata=metadata,
+ )
+ with self.subTest('num_organisms'):
+ self.assertLen(head.get_multi_organism_track_mask(), len(metadata))
+ with self.subTest('num_tracks'):
+ self.assertEqual(head.num_tracks, max(_MOCK_ATAC_NUM_TRACKS))
+ with self.subTest('track_mask'):
+ for i, organism in enumerate(organisms):
+ organism_idx = _ORGANSIM_INDEX[organism]
+ self.assertEqual(
+ np.sum(head.get_multi_organism_track_mask()[i]),
+ _MOCK_ATAC_NUM_TRACKS[organism_idx],
+ )
+
+ num_organisms = len(metadata)
+ num_tracks = head.num_tracks
+ expected_params_shape = {
+ 'test_head/resolution_1': {'learnt_scale': (num_organisms, num_tracks)},
+ 'test_head/resolution_1/multi_organism_linear': {
+ 'b': (num_organisms, num_tracks),
+ 'w': (num_organisms, _EMBEDDING_DIM_1BP, num_tracks),
+ },
+ 'test_head/resolution_128': {
+ 'learnt_scale': (num_organisms, num_tracks)
+ },
+ 'test_head/resolution_128/multi_organism_linear': {
+ 'b': (num_organisms, num_tracks),
+ 'w': (num_organisms, _EMBEDDING_DIM_128BP, num_tracks),
+ },
+ }
+ expected_output_shape = {
+ 'scaled_predictions_1bp': (
+ self.batch_size,
+ self.sequence_length,
+ num_tracks,
+ ),
+ 'predictions_1bp': (self.batch_size, self.sequence_length, num_tracks),
+ 'scaled_predictions_128bp': (
+ self.batch_size,
+ self.sequence_length // 128,
+ num_tracks,
+ ),
+ 'predictions_128bp': (
+ self.batch_size,
+ self.sequence_length // 128,
+ num_tracks,
+ ),
+ }
+ with self.subTest('params_shape'):
+ self._test_head(
+ head,
+ expected_params_shape,
+ expected_output_shape,
+ )
+
+ @parameterized.named_parameters(
+ dict(
+ testcase_name='one_organism',
+ organisms=(dna_model.Organism.HOMO_SAPIENS,),
+ ),
+ dict(
+ testcase_name='two_organisms',
+ organisms=(
+ dna_model.Organism.HOMO_SAPIENS,
+ dna_model.Organism.MUS_MUSCULUS,
+ ),
+ ),
+ )
+ def test_contact_maps_head(self, organisms: tuple[dna_model.Organism, ...]):
+ metadata = {
+ organism: get_mock_output_metadata(organism) for organism in organisms
+ }
+ config = heads.get_head_config(heads.HeadName.CONTACT_MAPS)
+ head = heads.create_head(config, metadata)
+
+ with self.subTest('num_organisms'):
+ self.assertLen(head.get_multi_organism_track_mask(), len(metadata))
+ with self.subTest('num_tracks'):
+ self.assertEqual(head.num_tracks, max(_MOCK_CONTACT_MAPS_NUM_TRACKS))
+ with self.subTest('track_mask'):
+ for i, organism in enumerate(organisms):
+ organism_idx = _ORGANSIM_INDEX[organism]
+ self.assertEqual(
+ np.sum(head.get_multi_organism_track_mask()[i]),
+ _MOCK_CONTACT_MAPS_NUM_TRACKS[organism_idx],
+ )
+
+ num_organisms = len(metadata)
+ num_tracks = head.num_tracks
+ prefix = config.name
+ expected_params_shape = {
+ f'{prefix}/multi_organism_linear': {
+ 'b': (num_organisms, num_tracks),
+ 'w': (num_organisms, _EMBEDDING_DIM_PAIR, num_tracks),
+ },
+ }
+ expected_output_shape = {
+ 'predictions': (
+ self.batch_size,
+ self.sequence_length // 2048,
+ self.sequence_length // 2048,
+ num_tracks,
+ ),
+ }
+ with self.subTest('params_shape'):
+ self._test_head(
+ head,
+ expected_params_shape,
+ expected_output_shape,
+ )
+
+ @parameterized.named_parameters(
+ dict(
+ testcase_name='one_organism',
+ organisms=(dna_model.Organism.HOMO_SAPIENS,),
+ ),
+ dict(
+ testcase_name='two_organisms',
+ organisms=(
+ dna_model.Organism.HOMO_SAPIENS,
+ dna_model.Organism.MUS_MUSCULUS,
+ ),
+ ),
+ )
+ def test_splice_sites_classification_head(
+ self, organisms: tuple[dna_model.Organism, ...]
+ ):
+ metadata = {
+ organism: get_mock_output_metadata(organism) for organism in organisms
+ }
+ config = heads.get_head_config(heads.HeadName.SPLICE_SITES_CLASSIFICATION)
+ head = heads.create_head(config, metadata)
+
+ with self.subTest('num_organisms'):
+ self.assertLen(head.get_multi_organism_track_mask(), len(metadata))
+ with self.subTest('num_tracks'):
+ self.assertEqual(head.num_tracks, max(_MOCK_SPLICE_SITES_NUM_TRACKS))
+ with self.subTest('track_mask'):
+ for i, organism in enumerate(organisms):
+ organism_idx = _ORGANSIM_INDEX[organism]
+ self.assertEqual(
+ np.sum(head.get_multi_organism_track_mask()[i]),
+ _MOCK_SPLICE_SITES_NUM_TRACKS[organism_idx],
+ )
+
+ num_organisms = len(metadata)
+ num_tracks = head.num_tracks
+ prefix = config.name
+ expected_params_shape = {
+ f'{prefix}/multi_organism_linear': {
+ 'b': (num_organisms, num_tracks),
+ 'w': (num_organisms, _EMBEDDING_DIM_1BP, num_tracks),
+ },
+ }
+ expected_output_shape = {
+ 'predictions': (
+ self.batch_size,
+ self.sequence_length,
+ num_tracks,
+ ),
+ 'logits': (
+ self.batch_size,
+ self.sequence_length,
+ num_tracks,
+ ),
+ }
+ self._test_head(
+ head,
+ expected_params_shape,
+ expected_output_shape,
+ )
+
+ @parameterized.named_parameters(
+ dict(
+ testcase_name='one_organism',
+ organisms=(dna_model.Organism.HOMO_SAPIENS,),
+ ),
+ dict(
+ testcase_name='two_organisms',
+ organisms=(
+ dna_model.Organism.HOMO_SAPIENS,
+ dna_model.Organism.MUS_MUSCULUS,
+ ),
+ ),
+ )
+ def test_splice_sites_usage_head(
+ self, organisms: tuple[dna_model.Organism, ...]
+ ):
+ metadata = {
+ organism: get_mock_output_metadata(organism) for organism in organisms
+ }
+ config = heads.get_head_config(heads.HeadName.SPLICE_SITES_USAGE)
+ head = heads.create_head(config, metadata)
+
+ with self.subTest('num_organisms'):
+ self.assertLen(head.get_multi_organism_track_mask(), len(metadata))
+ with self.subTest('num_tracks'):
+ self.assertEqual(
+ head.num_tracks, max(_MOCK_SPLICE_SITES_USAGE_NUM_TRACKS)
+ )
+ with self.subTest('track_mask'):
+ for i, organism in enumerate(organisms):
+ organism_idx = _ORGANSIM_INDEX[organism]
+ self.assertEqual(
+ np.sum(head.get_multi_organism_track_mask()[i]),
+ _MOCK_SPLICE_SITES_USAGE_NUM_TRACKS[organism_idx],
+ )
+
+ num_organisms = len(metadata)
+ num_tracks = head.num_tracks
+ prefix = config.name
+ expected_params_shape = {
+ f'{prefix}/multi_organism_linear': {
+ 'b': (num_organisms, num_tracks),
+ 'w': (num_organisms, _EMBEDDING_DIM_1BP, num_tracks),
+ },
+ }
+ expected_output_shape = {
+ 'predictions': (
+ self.batch_size,
+ self.sequence_length,
+ num_tracks,
+ ),
+ 'logits': (
+ self.batch_size,
+ self.sequence_length,
+ num_tracks,
+ ),
+ }
+ self._test_head(
+ head,
+ expected_params_shape,
+ expected_output_shape,
+ )
+
+ @parameterized.named_parameters(
+ dict(
+ testcase_name='one_organism',
+ organisms=(dna_model.Organism.HOMO_SAPIENS,),
+ ),
+ dict(
+ testcase_name='two_organisms',
+ organisms=(
+ dna_model.Organism.HOMO_SAPIENS,
+ dna_model.Organism.MUS_MUSCULUS,
+ ),
+ ),
+ )
+ def test_splice_sites_junction_head(
+ self, organisms: tuple[dna_model.Organism, ...]
+ ):
+ metadata = {
+ organism: get_mock_output_metadata(organism) for organism in organisms
+ }
+ config = heads.get_head_config(heads.HeadName.SPLICE_SITES_JUNCTION)
+ head = heads.create_head(config, metadata)
+ assert isinstance(head, heads.SpliceSitesJunctionHead)
+
+ with self.subTest('num_organisms'):
+ self.assertLen(head.get_multi_organism_track_mask(), len(metadata))
+ with self.subTest('num_tracks'):
+ self.assertEqual(
+ head.num_tracks, 2 * max(_MOCK_SPLICE_SITES_JUNCTION_NUM_TISSUES)
+ )
+ with self.subTest('track_mask'):
+ for i, organism in enumerate(organisms):
+ organism_idx = _ORGANSIM_INDEX[organism]
+ self.assertEqual(
+ np.sum(head.get_multi_organism_track_mask()[i]),
+ 2 * _MOCK_SPLICE_SITES_JUNCTION_NUM_TISSUES[organism_idx],
+ )
+
+ num_organisms = len(metadata)
+ num_tracks = head.num_tracks
+ num_tissues = head.max_num_tissues
+ hidden_dim = 768
+ prefix = config.name
+ expected_params_shape = {
+ f'{prefix}/pos_acceptor_logits': {
+ 'embeddings': (num_organisms, 2 * num_tissues * hidden_dim),
+ },
+ f'{prefix}/pos_donor_logits': {
+ 'embeddings': (num_organisms, 2 * num_tissues * hidden_dim),
+ },
+ f'{prefix}/neg_acceptor_logits': {
+ 'embeddings': (num_organisms, 2 * num_tissues * hidden_dim),
+ },
+ f'{prefix}/neg_donor_logits': {
+ 'embeddings': (num_organisms, 2 * num_tissues * hidden_dim),
+ },
+ f'{prefix}/multi_organism_linear': {
+ 'b': (num_organisms, hidden_dim),
+ 'w': (num_organisms, _EMBEDDING_DIM_1BP, hidden_dim),
+ },
+ }
+ expected_output_shape = {
+ 'predictions': (
+ self.batch_size,
+ self.num_splice_sites,
+ self.num_splice_sites,
+ num_tracks,
+ ),
+ 'splice_site_positions': (
+ self.batch_size,
+ 4,
+ self.num_splice_sites,
+ ),
+ 'splice_junction_mask': (
+ self.batch_size,
+ self.num_splice_sites,
+ self.num_splice_sites,
+ num_tracks,
+ ),
+ }
+ self._test_head(
+ head,
+ expected_params_shape,
+ expected_output_shape,
+ )
+
+ @parameterized.named_parameters(
+ dict(testcase_name='with_squashing', apply_squashing=True),
+ dict(testcase_name='without_squashing', apply_squashing=False),
+ )
+ def test_scaling_and_unscaling(self, apply_squashing: bool):
+ batch_size, sequence_length, num_tracks, resolution = 3, 2048, 10, 1
+ key = jax.random.PRNGKey(42)
+ key_data, key_means = jax.random.split(key)
+ track_means = jax.random.uniform(key_means, (batch_size, num_tracks)) + 0.01
+ input_data = jax.random.uniform(
+ key_data, (batch_size, sequence_length, num_tracks), minval=0, maxval=99
+ )
+ scaled_data = heads.targets_scaling(
+ input_data,
+ track_means=track_means,
+ resolution=resolution,
+ apply_squashing=apply_squashing,
+ )
+ unscaled_data = heads.predictions_scaling(
+ scaled_data,
+ track_means=track_means,
+ resolution=resolution,
+ apply_squashing=apply_squashing,
+ )
+ chex.assert_trees_all_close(input_data, unscaled_data, rtol=1e-5, atol=1e-5)
+
+
+if __name__ == '__main__':
+ absltest.main()
diff --git a/flax_model/alphagenome/model/interval_scoring/__init__.py b/flax_model/alphagenome/model/interval_scoring/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a14856a0caa4b98b6df7ed5a4868434153023a19
--- /dev/null
+++ b/flax_model/alphagenome/model/interval_scoring/__init__.py
@@ -0,0 +1,15 @@
+# Copyright 2026 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.
+
+"""AlphaGenome Interval Scoring."""
diff --git a/flax_model/alphagenome/model/interval_scoring/gene_mask.py b/flax_model/alphagenome/model/interval_scoring/gene_mask.py
new file mode 100644
index 0000000000000000000000000000000000000000..b932d34404bb5c2f2308a5a213645e68d5fd13bc
--- /dev/null
+++ b/flax_model/alphagenome/model/interval_scoring/gene_mask.py
@@ -0,0 +1,142 @@
+# Copyright 2026 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.
+
+"""Implementation of gene mask interval scoring."""
+
+from collections.abc import Mapping
+
+from flax_model.alphagenome._sdk import typing
+from flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome._sdk.data import track_data
+from flax_model.alphagenome._sdk.models import dna_output
+from flax_model.alphagenome._sdk.models import interval_scorers
+from flax_model.alphagenome.model.interval_scoring import interval_scoring
+from flax_model.alphagenome.model.variant_scoring import gene_mask_extractor as gene_mask_extractor_lib
+from flax_model.alphagenome.model.variant_scoring import variant_scoring
+import anndata
+import jax.numpy as jnp
+from jaxtyping import Array, Bool, Float32 # pylint: disable=g-multiple-import, g-importing-member
+import numpy as np
+import pandas as pd
+
+
+class GeneIntervalScorer(
+ interval_scoring.IntervalScorer[
+ Bool[np.ndarray | Array, 'M G'],
+ pd.DataFrame,
+ interval_scorers.GeneMaskScorer,
+ ]
+):
+ """Interval scorer that aggregates intervals across different genes."""
+
+ def __init__(
+ self, gene_mask_extractor: gene_mask_extractor_lib.GeneMaskExtractor
+ ):
+ """Initializes the GeneIntervalScorer.
+
+ Args:
+ gene_mask_extractor: Gene mask extractor to use.
+ """
+ self._gene_mask_extractor = gene_mask_extractor
+
+ def get_masks_and_metadata(
+ self,
+ interval: genome.Interval,
+ *,
+ settings: interval_scorers.GeneMaskScorer,
+ track_metadata: dna_output.OutputMetadata,
+ ) -> tuple[Bool[np.ndarray | Array, 'S G'], pd.DataFrame]:
+ """Get gene masks and metadata for the given interval.
+
+ Args:
+ interval: Genomic interval to extract gene masks for.
+ settings: The variant scorer settings.
+ track_metadata: Track metadata for the variant.
+
+ Returns:
+ Tuple of (gene variant masks, mask metadata). The mask metadata is the
+ part of the GTF pandas dataframe that was used to construct the gene
+ masks.
+ """
+ del track_metadata
+ if interval.negative_strand:
+ raise ValueError(
+ 'IntervalScorers do not support negative strands (negative strand'
+ ' predictions should already be reverse-complemented prior to'
+ ' scoring and thus masks should be generated on the positive strand).'
+ )
+ if settings.width is not None and settings.width > interval.width:
+ raise ValueError('Interval width must be >= the center mask width.')
+ resolution = variant_scoring.get_resolution(settings.requested_output)
+ target_interval = (
+ interval.resize(width=settings.width)
+ if settings.width is not None
+ else interval
+ )
+
+ gene_mask, metadata = self._gene_mask_extractor.extract(target_interval)
+ interval_padding = interval.width - target_interval.width
+ gene_mask = np.pad(
+ gene_mask,
+ ((interval_padding // 2, (interval_padding + 1) // 2), (0, 0)),
+ )
+ if resolution > 1:
+ gene_mask = gene_mask.reshape(
+ (gene_mask.shape[0] // resolution, resolution, -1)
+ ).max(axis=1)
+ return gene_mask, metadata
+
+ @typing.jaxtyped
+ def score_interval(
+ self,
+ predictions: Mapping[dna_output.OutputType, Float32[Array, 'S T']],
+ *,
+ masks: Bool[Array | np.ndarray, 'M G'],
+ settings: interval_scorers.GeneMaskScorer,
+ interval: genome.Interval | None = None,
+ ) -> interval_scoring.ScoreIntervalOutput:
+ """See base class."""
+ del interval
+ tracks = predictions[settings.requested_output]
+
+ match settings.aggregation_type:
+ case interval_scorers.IntervalAggregationType.MEAN:
+ output = jnp.einsum('lt,lg->gt', tracks, masks) / jnp.expand_dims(
+ masks.sum(axis=0), axis=-1
+ )
+ case interval_scorers.IntervalAggregationType.SUM:
+ output = jnp.einsum('lt,lg->gt', tracks, masks)
+ case _:
+ raise ValueError(
+ f'Unsupported aggregation type: {self._aggregation_type}.'
+ )
+
+ return {'score': output}
+
+ def finalize_interval(
+ self,
+ scores: interval_scoring.ScoreIntervalResult,
+ *,
+ track_metadata: dna_output.OutputMetadata,
+ mask_metadata: pd.DataFrame,
+ settings: interval_scorers.GeneMaskScorer,
+ ) -> anndata.AnnData:
+ """Returns summarized scores for the given scores and metadata."""
+ output_metadata = track_metadata.get(settings.requested_output)
+ assert isinstance(output_metadata, track_data.TrackMetadata)
+ return variant_scoring.create_anndata(
+ scores['score'],
+ obs=mask_metadata,
+ var=output_metadata,
+ )
diff --git a/flax_model/alphagenome/model/interval_scoring/gene_mask_test.py b/flax_model/alphagenome/model/interval_scoring/gene_mask_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd6589975c50fb9c6b56d443cc0e064c99c44baf
--- /dev/null
+++ b/flax_model/alphagenome/model/interval_scoring/gene_mask_test.py
@@ -0,0 +1,266 @@
+# Copyright 2026 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 flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome._sdk.models import dna_output
+from flax_model.alphagenome._sdk.models import interval_scorers
+from flax_model.alphagenome.model.interval_scoring import gene_mask
+from flax_model.alphagenome.model.variant_scoring import gene_mask_extractor
+from flax_model.alphagenome.model.variant_scoring import variant_scoring
+import anndata
+import jax.numpy as jnp
+import numpy as np
+import pandas as pd
+
+
+def _load_gtf():
+ return pd.DataFrame({
+ 'End': [306, 303, 305, 138, 15],
+ 'Start': [301, 302, 303, 0, 5],
+ 'Strand': ['+', '+', '+', '-', '-'],
+ 'transcript_id': ['', 'T1', 'T2', '', 'T3'],
+ 'gene_id': ['G1', 'G1', 'G1', 'G2', 'G2'],
+ 'Feature': [
+ 'gene',
+ 'transcript',
+ 'transcript',
+ 'gene',
+ 'transcript',
+ ],
+ # Fixed Fields
+ 'Chromosome': 'chr1',
+ 'Score': '.',
+ 'Frame': '.',
+ 'Source': 'ENSEMBL',
+ 'gene_type': 'protein_coding',
+ 'gene_name': 'GX_name',
+ 'transcript_type': 'protein_coding',
+ })
+
+
+class GeneIntervalScorerTest(parameterized.TestCase):
+
+ @parameterized.product(
+ aggregation_type=[
+ interval_scorers.IntervalAggregationType.SUM,
+ interval_scorers.IntervalAggregationType.MEAN,
+ ],
+ output_type=[dna_output.OutputType.RNA_SEQ, dna_output.OutputType.DNASE],
+ )
+ def test_gene_scorer_get_masks_and_metadata(
+ self, aggregation_type, output_type
+ ):
+ gtf = _load_gtf()
+
+ settings = interval_scorers.GeneMaskScorer(
+ requested_output=output_type,
+ aggregation_type=aggregation_type,
+ width=501,
+ )
+
+ interval = genome.Interval('chr1', 0, 2048)
+ interval_scorer = gene_mask.GeneIntervalScorer(
+ gene_mask_extractor.GeneMaskExtractor(
+ gtf=gtf,
+ gene_mask_type=gene_mask_extractor.GeneMaskType.EXONS,
+ )
+ )
+
+ track_metadata = dna_output.OutputMetadata(**{
+ output_type.name.lower(): pd.DataFrame({
+ 'name': np.arange(9).astype(str),
+ 'strand': '.',
+ })
+ })
+ masks, metadata = interval_scorer.get_masks_and_metadata(
+ interval, settings=settings, track_metadata=track_metadata
+ )
+ self.assertIsInstance(metadata, pd.DataFrame)
+ self.assertContainsSubset(
+ ['strand', 'gene_name', 'gene_id', 'gene_type'], metadata.columns
+ )
+
+ self.assertEqual(masks.shape[0], interval.width)
+
+ def test_gene_scorer_get_masks_and_metadata_negative_strand(self):
+ gtf = _load_gtf()
+ interval_scorer = gene_mask.GeneIntervalScorer(
+ gene_mask_extractor.GeneMaskExtractor(
+ gtf=gtf,
+ gene_mask_type=gene_mask_extractor.GeneMaskType.EXONS,
+ )
+ )
+ settings = interval_scorers.GeneMaskScorer(
+ requested_output=dna_output.OutputType.RNA_SEQ,
+ aggregation_type=interval_scorers.IntervalAggregationType.SUM,
+ width=501,
+ )
+ with self.assertRaisesRegex(
+ ValueError, 'IntervalScorers do not support negative strands'
+ ):
+ _ = interval_scorer.get_masks_and_metadata(
+ genome.Interval('chr1', 0, 2048, strand='-'),
+ settings=settings,
+ track_metadata=dna_output.OutputMetadata(),
+ )
+
+ @parameterized.product(
+ aggregation_type=[
+ interval_scorers.IntervalAggregationType.SUM,
+ interval_scorers.IntervalAggregationType.MEAN,
+ ],
+ output_type=[dna_output.OutputType.RNA_SEQ, dna_output.OutputType.DNASE],
+ )
+ def test_gene_scorer_score_interval(
+ self,
+ aggregation_type,
+ output_type,
+ ):
+ # Score a simple example for an interval of length 10, consisting of 100
+ # tracks, all of values 0..9 depending on the position. The mask
+ # includes positions 4-6.
+ tracks = jnp.arange(10, dtype=jnp.float32).repeat(100).reshape((10, 100))
+ mask = np.zeros((10, 3), dtype=bool)
+ mask[4:7] = True
+
+ gtf = _load_gtf()
+ settings = interval_scorers.GeneMaskScorer(
+ requested_output=output_type,
+ aggregation_type=aggregation_type,
+ width=501,
+ )
+ interval_scorer = gene_mask.GeneIntervalScorer(
+ gene_mask_extractor.GeneMaskExtractor(
+ gtf=gtf, gene_mask_type=gene_mask_extractor.GeneMaskType.BODY
+ ),
+ )
+ scores = interval_scorer.score_interval(
+ {output_type: tracks}, settings=settings, masks=mask
+ )
+
+ expected_num_tracks = 100
+ match aggregation_type:
+ case interval_scorers.IntervalAggregationType.SUM:
+ expected_score = 15.0
+ case interval_scorers.IntervalAggregationType.MEAN:
+ expected_score = 5.0
+ case _:
+ raise ValueError(f'Unknown aggregation type: {aggregation_type}')
+
+ np.testing.assert_almost_equal(
+ scores['score'][0],
+ np.full((expected_num_tracks,), expected_score, dtype=jnp.float32),
+ )
+
+ @parameterized.product(
+ center_mask_width=[501],
+ output_type=[
+ dna_output.OutputType.RNA_SEQ,
+ dna_output.OutputType.CHIP_TF,
+ ],
+ )
+ def test_gene_scorer_with_center_mask_width(
+ self,
+ center_mask_width,
+ output_type,
+ ):
+ gtf = _load_gtf()
+ interval = genome.Interval('chr1', 0, 1024)
+ interval_scorer = gene_mask.GeneIntervalScorer(
+ gene_mask_extractor.GeneMaskExtractor(
+ gtf=gtf,
+ gene_mask_type=gene_mask_extractor.GeneMaskType.BODY,
+ ),
+ )
+ settings = interval_scorers.GeneMaskScorer(
+ requested_output=output_type,
+ aggregation_type=interval_scorers.IntervalAggregationType.MEAN,
+ width=center_mask_width,
+ )
+ resolution = variant_scoring.get_resolution(output_type)
+ num_bins = interval.width // resolution
+ tracks = (
+ jnp.arange(num_bins, dtype=jnp.float32).repeat(4).reshape((num_bins, 4))
+ )
+
+ track_metadata = dna_output.OutputMetadata(**{
+ output_type.name.lower(): pd.DataFrame(
+ {'name': np.arange(4).astype(str), 'strand': '.'}
+ )
+ })
+ masks, metadata = interval_scorer.get_masks_and_metadata(
+ interval, settings=settings, track_metadata=track_metadata
+ )
+ scores = interval_scorer.score_interval(
+ {output_type: tracks}, settings=settings, masks=masks
+ )
+
+ expected_genes = ['G1']
+ expected_mask = np.zeros([num_bins, len(expected_genes)], dtype=bool)
+ expected_scores = np.full(
+ (len(expected_genes), 4), np.nan, dtype=jnp.float32
+ )
+
+ if resolution == 1:
+ expected_mask[301:306, 0] = True # G1
+ expected_scores[0] = 303.0
+ else:
+ expected_mask[2, 0] = True # G1
+ expected_scores[0] = 2.0
+ np.testing.assert_array_equal(masks, expected_mask)
+ np.testing.assert_almost_equal(scores['score'], expected_scores)
+ np.testing.assert_array_equal(metadata['gene_id'].values, expected_genes)
+
+ def test_gene_scorer_finalize(self):
+ interval = genome.Interval('chr1', 0, 2048)
+ track_metadata = dna_output.OutputMetadata(
+ rna_seq=pd.DataFrame({
+ 'name': np.arange(9).astype(str),
+ 'strand': '.',
+ })
+ )
+ expected_scores = np.full((1, 9), 15.0, dtype=jnp.float32)
+ scores = {'score': expected_scores}
+
+ gtf = _load_gtf()
+ interval_scorer = gene_mask.GeneIntervalScorer(
+ gene_mask_extractor.GeneMaskExtractor(
+ gtf=gtf, gene_mask_type=gene_mask_extractor.GeneMaskType.BODY
+ )
+ )
+
+ settings = interval_scorers.GeneMaskScorer(
+ requested_output=dna_output.OutputType.RNA_SEQ,
+ aggregation_type=interval_scorers.IntervalAggregationType.SUM,
+ width=2001,
+ )
+
+ _, mask_metadata = interval_scorer.get_masks_and_metadata(
+ interval, track_metadata=track_metadata, settings=settings
+ )
+ finalized_score = interval_scorer.finalize_interval(
+ scores,
+ track_metadata=track_metadata,
+ mask_metadata=mask_metadata,
+ settings=settings,
+ )
+
+ self.assertIsInstance(finalized_score, anndata.AnnData)
+ np.testing.assert_array_almost_equal(finalized_score.X, expected_scores)
+
+
+if __name__ == '__main__':
+ absltest.main()
diff --git a/flax_model/alphagenome/model/interval_scoring/interval_scoring.py b/flax_model/alphagenome/model/interval_scoring/interval_scoring.py
new file mode 100644
index 0000000000000000000000000000000000000000..a156e7adeb58da1903316305623db4cc1c0dbe17
--- /dev/null
+++ b/flax_model/alphagenome/model/interval_scoring/interval_scoring.py
@@ -0,0 +1,113 @@
+# Copyright 2026 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.
+
+"""Base class for interval scorers."""
+
+import abc
+from collections.abc import Mapping
+from typing import Generic, TypeVar
+
+from flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome._sdk.models import dna_output
+import anndata
+import jax
+from jaxtyping import Array, Float32 # pylint: disable=g-multiple-import, g-importing-member
+import numpy as np
+
+IntervalMaskT = TypeVar('IntervalMaskT')
+IntervalMetadataT = TypeVar('IntervalMetadataT')
+IntervalSettingsT = TypeVar('IntervalSettingsT')
+
+ScoreIntervalOutput = Mapping[str, jax.Array | np.ndarray]
+ScoreIntervalResult = Mapping[str, np.ndarray]
+
+
+class IntervalScorer(
+ Generic[IntervalMaskT, IntervalMetadataT, IntervalSettingsT],
+ metaclass=abc.ABCMeta,
+):
+ """Abstract class for interval scorers."""
+
+ @abc.abstractmethod
+ def get_masks_and_metadata(
+ self,
+ interval: genome.Interval,
+ *,
+ settings: IntervalSettingsT,
+ track_metadata: dna_output.OutputMetadata,
+ ) -> tuple[IntervalMaskT, IntervalMetadataT]:
+ """Returns masks and metadata for the given interval and metadata.
+
+ The generated masks and metadata will be passed to `score_interval` and
+ `finalize_interval` respectively.
+
+ Args:
+ interval: The interval to score.
+ settings: The interval scorer settings.
+ track_metadata: The model's track metadata.
+
+ Returns:
+ A tuple of (masks, metadata), where:
+ masks: The masks required to score the interval, such as gene or TSS or
+ strand masks. These will be passed into the jitted `score_interval`
+ function.
+ metadata: The metadata required to finalize the interval. These will
+ be passed into the `finalize_interval` function.
+
+ The formats/shapes of masks and metadata will vary across interval scorers
+ depending on their individual needs.
+ """
+
+ @abc.abstractmethod
+ def score_interval(
+ self,
+ predictions: Mapping[dna_output.OutputType, Float32[Array, 'S T']],
+ *,
+ masks: IntervalMaskT,
+ settings: IntervalSettingsT,
+ interval: genome.Interval | None = None,
+ ) -> ScoreIntervalOutput:
+ """Generates a score per track for the provided predictions.
+
+ Args:
+ predictions: Model predictions for the interval.
+ masks: The masks for scoring the interval.
+ settings: The interval scorer settings.
+ interval: The interval to score.
+
+ Returns:
+ Dictionary of scores to be passed to `finalize_interval`.
+ """
+
+ @abc.abstractmethod
+ def finalize_interval(
+ self,
+ scores: ScoreIntervalResult,
+ *,
+ track_metadata: dna_output.OutputMetadata,
+ mask_metadata: IntervalMetadataT,
+ settings: IntervalSettingsT,
+ ) -> anndata.AnnData:
+ """Returns finalized scores for the given scores and metadata.
+
+ Args:
+ scores: Dictionary of scores generated from `score_interval` function.
+ track_metadata: Metadata describing the tracks for each output_type.
+ mask_metadata: Metadata describing the masks.
+ settings: The interval scorer settings.
+
+ Returns:
+ An AnnData object containing the final interval outputs. The entries will
+ vary across scorers depending on their individual needs.
+ """
diff --git a/flax_model/alphagenome/model/layers.py b/flax_model/alphagenome/model/layers.py
new file mode 100644
index 0000000000000000000000000000000000000000..eaf5667be0a51ca276344d00463764ec70cfc2bc
--- /dev/null
+++ b/flax_model/alphagenome/model/layers.py
@@ -0,0 +1,123 @@
+# Copyright 2026 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.
+
+"""Common layers."""
+
+from flax_model.alphagenome._sdk import typing
+import haiku as hk
+import jax
+import jax.numpy as jnp
+from jaxtyping import Array, Float # pylint: disable=g-importing-member, g-multiple-import
+
+
+def gelu(x: jax.Array) -> jax.Array:
+ """Gaussian Error Linear Unit activation function."""
+ coef = jax.lax.convert_element_type(1.702, x.dtype)
+ return jax.nn.sigmoid(coef * x) * x
+
+
+@typing.jaxtyped
+def pool(
+ x: Float[Array, '... S D'], by: int = 2, reduce: str = 'max'
+) -> Float[Array, '... S/{by} D']:
+ """Applies pooling to the sequence dimension of the input.
+
+ Args:
+ x: The input sequence, where the second to last dimension is the sequence
+ dimension.
+ by: The pooling window size.
+ reduce: The pooling reduction method.
+
+ Returns:
+ The pooled sequence.
+ Raises:
+ NotImplementedError: If the reduce method is not supported.
+ """
+ if reduce == 'max':
+ return hk.MaxPool(window_shape=(by, 1), strides=(by, 1), padding='SAME')(x)
+ elif reduce in ['avg', 'mean']:
+ return hk.AvgPool(window_shape=(by, 1), strides=(by, 1), padding='SAME')(x)
+ else:
+ raise NotImplementedError(f'Reduce method={reduce} unknown.')
+
+
+class RMSBatchNorm(hk.Module):
+ r"""Root Mean Square Batch Normalization.
+
+ Normalization is applied to the last dimension of the input as
+ `x -> x * scale / sqrt(var + epsilon) + offset`.
+ The scale and offset are learned parameters. The variance is tracked
+ as an exponential moving average.
+
+ Variance is computed across the batch and sequence dimension.
+
+ Note: only support inference mode (i.e. no training).
+ """
+
+ def __call__(self, x: Float[Array, '... D']) -> Float[Array, '... D']:
+ param_shape = (1,) * (x.ndim - 1) + (x.shape[-1],)
+ variance = hk.get_state(
+ 'var_ema', param_shape, dtype=jnp.float32, init=jnp.ones
+ )
+ scale = hk.get_parameter(
+ 'scale', param_shape, dtype=x.dtype, init=jnp.ones
+ ).astype(x.dtype)
+ offset = hk.get_parameter(
+ 'offset', param_shape, dtype=x.dtype, init=jnp.zeros
+ )
+ inv = scale * jax.lax.rsqrt(variance + 1e-5).astype(x.dtype)
+ return x * inv + offset
+
+
+class LayerNorm(hk.Module):
+ """Layer Normalization."""
+
+ def __init__(
+ self, rms_norm: bool = False, axis: int = -1, name: str | None = None
+ ) -> None:
+ """Initializes the LayerNorm module.
+
+ Args:
+ rms_norm: If False, the input is centered before computing the
+ mean-squared for normalization. If True, the mean-squared is computed
+ directly on the uncentered input.
+ axis: The axis to apply the normalization to.
+ name: The name of the module.
+ """
+ super().__init__(name=name)
+ self._rms_norm = rms_norm
+ self._axis = axis
+
+ def __call__(self, x: Float[Array, '... D']) -> Float[Array, '... D']:
+ dtype = x.dtype
+ scale = hk.get_parameter(
+ 'scale', (x.shape[-1],), dtype, init=jnp.ones
+ ).astype(dtype)
+ offset = hk.get_parameter(
+ 'offset', (x.shape[-1],), dtype, init=jnp.zeros
+ ).astype(dtype)
+ scale = jax.lax.broadcast_to_rank(scale, x.ndim)
+ offset = jax.lax.broadcast_to_rank(offset, x.ndim)
+
+ if not self._rms_norm:
+ mean = jnp.mean(
+ x, axis=self._axis, dtype=jnp.float32, keepdims=True
+ ).astype(dtype)
+ x = x - mean
+
+ variance = jnp.mean(
+ jnp.square(x), axis=self._axis, dtype=jnp.float32, keepdims=True
+ )
+ inv = scale * jax.lax.rsqrt(variance + 1e-5).astype(dtype)
+ return inv * x + offset
diff --git a/flax_model/alphagenome/model/layers_test.py b/flax_model/alphagenome/model/layers_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..2104eabc66cf4217244e7d79d3a446059862f11a
--- /dev/null
+++ b/flax_model/alphagenome/model/layers_test.py
@@ -0,0 +1,71 @@
+# Copyright 2026 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 flax_model.alphagenome.model import layers
+import chex
+import haiku as hk
+import jax.numpy as jnp
+
+
+def _get_pool_layer(by: int):
+ """Helper function to create the pool layer."""
+
+ def pool_fn(x):
+ return layers.pool(x, by=by)
+
+ pool_mod = hk.transform(pool_fn)
+ return pool_mod
+
+
+class LayersTest(parameterized.TestCase):
+
+ def setUp(self):
+ super().setUp()
+ self._batch_size = 4
+ self._sequence_length = 16
+ self._num_channels = 8
+ self._by = 2
+
+ def test_pool_shape(self):
+ """Tests that the pool layer returns the expected shape."""
+ x = jnp.ones((self._batch_size, self._sequence_length, self._num_channels))
+ pool_mod = _get_pool_layer(by=self._by)
+ params = pool_mod.init(None, x)
+ out = pool_mod.apply(params, None, x)
+ chex.assert_shape(
+ out,
+ (
+ self._batch_size,
+ self._sequence_length // self._by,
+ self._num_channels,
+ ),
+ )
+
+ def test_pool_type_error(self):
+ """Tests that the pool layer raises a type error with invalid input."""
+ with self.subTest('wrong_dtype'):
+ x = jnp.ones(
+ (self._batch_size, self._sequence_length, self._num_channels),
+ dtype=jnp.int32, # Provide wrong type.
+ )
+ with self.assertRaises(TypeError):
+ pool_mod = _get_pool_layer(by=self._by)
+ params = pool_mod.init(None, x)
+ pool_mod.apply(params, None, x)
+
+
+if __name__ == '__main__':
+ absltest.main()
diff --git a/flax_model/alphagenome/model/losses.py b/flax_model/alphagenome/model/losses.py
new file mode 100644
index 0000000000000000000000000000000000000000..f1981e5793c111b45bb0d29cdd65220f93d54e2f
--- /dev/null
+++ b/flax_model/alphagenome/model/losses.py
@@ -0,0 +1,184 @@
+# Copyright 2026 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.
+
+"""Losses for AlphaGenome."""
+
+from flax_model.alphagenome._sdk import typing
+import chex
+from einshape import jax_einshape as einshape # pylint: disable=g-importing-member
+import jax
+import jax.numpy as jnp
+from jaxtyping import Array, Bool, Float, PyTree # pylint: disable=g-importing-member, g-multiple-import
+
+
+@typing.jaxtyped
+def _safe_masked_mean(
+ x: Float[Array, '*dims'],
+ mask: Bool[Array, '#*dims'] | None = None,
+) -> Float[Array, '']:
+ """Safe jnp.mean that handles completely masked arrays."""
+ if mask is None:
+ masked = x
+ mask = jnp.ones_like(x)
+ else:
+ # We need to broadcast mask to compute correct mean.
+ mask = jnp.broadcast_to(mask, x.shape)
+ masked = x * mask
+
+ return jnp.sum(jnp.asarray(masked), dtype=jnp.float32) / jnp.maximum(
+ 1.0, jnp.sum(mask, dtype=jnp.float32)
+ )
+
+
+@typing.jaxtyped
+def poisson_loss(
+ *,
+ y_true: Float[Array, '*dims'],
+ y_pred: Float[Array, '*dims'],
+ mask: Bool[Array, '#*dims'],
+) -> Float[Array, '']:
+ """Poisson loss."""
+ y_true = jnp.abs(y_true).astype(jnp.float32)
+ y_pred = y_pred.astype(jnp.float32)
+ y_pred_logits = jnp.log(y_pred + 1e-7)
+ # Substract the minimum value such that loss is zero at optimal prediction.
+ min_value = y_true - y_true * jnp.log(y_true + 1e-7)
+ loss = (y_pred - y_true * y_pred_logits) - min_value
+ return _safe_masked_mean(loss, mask)
+
+
+@typing.jaxtyped
+def multinomial_loss(
+ *,
+ y_true: Float[Array, '... S C'],
+ y_pred: Float[Array, '... S C'],
+ mask: Bool[Array, '... 1 C'],
+ multinomial_resolution: int,
+ positional_weight: float,
+) -> PyTree[Float[Array, '']]:
+ """Returns sum of multinomial losses and Poison loss on total count.
+
+ Args:
+ y_true: Target values.
+ y_pred: Model predictions.
+ mask: Array of bools.
+ multinomial_resolution: We split the input into sub-sequences and compute a
+ separate multinomial loss over each sub-sequence.
+ positional_weight: Weight of the positional loss.
+ """
+ chex.assert_equal_shape([y_true, y_pred])
+ if y_pred.shape[-2] % multinomial_resolution != 0:
+ raise ValueError(
+ f'{y_pred.shape[-2]=} must be divisible by {multinomial_resolution=}.'
+ )
+
+ num_segments = y_pred.shape[-2] // multinomial_resolution
+
+ # Remove the masked out bins from the totals sum.
+ y_true = jnp.maximum(y_true, 0) * mask
+ y_pred = y_pred * mask
+
+ # Split sequence into n sub-sequences of size multinomial_resolution.
+ y_true = einshape('...(ns)c->...nsc', y_true, n=num_segments)
+ y_pred = einshape('...(ns)c->...nsc', y_pred, n=num_segments)
+
+ total_pred = jnp.sum(y_pred, axis=-2, keepdims=True, dtype=jnp.float32)
+ total_true = jnp.sum(y_true, axis=-2, keepdims=True, dtype=jnp.float32)
+ mask = mask[..., None, :] # broadcast over segments.
+
+ loss_total_count = poisson_loss(
+ y_true=total_true,
+ y_pred=total_pred,
+ mask=mask,
+ )
+ # Magnitude of poisson loss is linear with the number of bins involved in the
+ # `total_count` prediction/target window. Normalization keeps overall loss
+ # magnitude invariant to input `multinomial_resolution`.
+ loss_total_count /= multinomial_resolution
+
+ prob_predictions = y_pred.astype(jnp.float32) / (total_pred + 1e-7)
+ loss_positional = -y_true * jnp.log(prob_predictions + 1e-7)
+ loss_positional = _safe_masked_mean(loss_positional, mask=mask)
+
+ return {
+ 'loss': loss_total_count + positional_weight * loss_positional,
+ 'loss_total': loss_total_count,
+ 'loss_positional': loss_positional,
+ 'max_sum_preds': jnp.max(total_pred),
+ 'max_preds': jnp.max(y_pred),
+ 'max_targets': jnp.max(y_true).astype(jnp.float32),
+ }
+
+
+def mse(
+ y_pred: Float[Array, '*dims'],
+ y_true: Float[Array, '*dims'],
+ mask: Bool[Array, '#*dims'],
+) -> Float[Array, '']:
+ """Mean squared error."""
+ return _safe_masked_mean(jnp.square(y_pred - y_true), mask)
+
+
+@typing.jaxtyped
+def cross_entropy_loss_from_logits(
+ *,
+ y_pred_logits: Float[Array, '*dims'],
+ y_true: Float[Array, '*dims'],
+ mask: Bool[Array, '#*dims'],
+ axis: int,
+) -> Float[Array, '']:
+ """Cross-entropy loss from logits."""
+ log_softmax_preds = jax.nn.log_softmax(
+ y_pred_logits.astype(jnp.float32), axis=axis
+ )
+ loss = -jnp.sum(y_true.astype(jnp.float32) * log_softmax_preds, axis=axis)
+ mask = jnp.any(mask, axis=axis)
+ return _safe_masked_mean(loss, mask)
+
+
+@typing.jaxtyped
+def binary_crossentropy_from_logits(
+ *,
+ y_pred: Float[Array, '*dims'],
+ y_true: Float[Array, '*dims'],
+ mask: Bool[Array, '#*dims'],
+) -> Float[Array, '']:
+ """Binary cross-entropy loss from sigmoid logits."""
+ loss = (
+ jnp.maximum(y_pred, 0)
+ - y_pred * y_true
+ + jnp.log1p(jnp.exp(-jnp.abs(y_pred)))
+ )
+ return _safe_masked_mean(loss, mask)
+
+
+@typing.jaxtyped
+def cross_entropy_loss(
+ *,
+ y_true: Float[Array, '*dims'],
+ y_pred: Float[Array, '*dims'],
+ mask: Bool[Array, '#*dims'],
+ axis: int,
+ eps: float = 1e-7,
+) -> Float[Array, '']:
+ """Cross entropy loss on counts."""
+ mask = jnp.broadcast_to(mask, y_true.shape)
+ chex.assert_equal_shape([y_true, y_pred, mask])
+ y_true = jnp.where(mask, y_true.astype(jnp.float32), 0)
+ p_true = y_true / jnp.maximum(y_true.sum(axis=axis, keepdims=True), eps)
+
+ log_normalizer = jnp.log((jnp.where(mask, y_pred, 0) + eps).sum(axis=axis))
+ log_likelihood = (p_true * jnp.log(y_pred + eps)).sum(axis=axis)
+ log_loss = log_normalizer - log_likelihood
+ return _safe_masked_mean(log_loss, mask.any(axis=axis))
diff --git a/flax_model/alphagenome/model/losses_test.py b/flax_model/alphagenome/model/losses_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..f411b524698ec39fc276805f94d6d60dc0bf483c
--- /dev/null
+++ b/flax_model/alphagenome/model/losses_test.py
@@ -0,0 +1,81 @@
+# Copyright 2026 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 flax_model.alphagenome.model import losses
+import jax.numpy as jnp
+import numpy as np
+
+
+class LossesTest(parameterized.TestCase):
+
+ def test_multinomial_loss_masking(self):
+ """Tests that masking correctly zeros out predictions and targets."""
+ y_true = jnp.array([[[10.0, 1.0, 3.0], [5.0, 2.0, 20.0]]])
+ y_pred = jnp.array([[[0.5, 2.5, 1.0], [2.5, 0.5, 1.0]]])
+ loss_full = losses.multinomial_loss(
+ y_true=y_true,
+ y_pred=y_pred,
+ mask=jnp.array([[[True, True, True]]]),
+ multinomial_resolution=1,
+ positional_weight=1.0,
+ )['loss']
+ loss_masked = losses.multinomial_loss(
+ y_true=y_true,
+ y_pred=y_pred,
+ mask=jnp.array([[[True, True, False]]]),
+ multinomial_resolution=1,
+ positional_weight=1.0,
+ )['loss']
+ y_true_zero = jnp.array([[[10.0, 1.0], [5.0, 2.0]]])
+ y_pred_zero = jnp.array([[[0.5, 2.5], [2.5, 0.5]]])
+ loss_truncated = losses.multinomial_loss(
+ y_true=y_true_zero,
+ y_pred=y_pred_zero,
+ mask=jnp.array([[[True, True]]]),
+ multinomial_resolution=1,
+ positional_weight=1.0,
+ )['loss']
+ np.testing.assert_almost_equal(loss_masked, loss_truncated, decimal=5)
+ np.testing.assert_array_less(loss_masked, loss_full)
+
+ def test_multinomial_loss_resolution_aggregation(self):
+ """Tests the resolution aggregation logic."""
+ # Seq length 4, all 1s.
+ y_true = jnp.ones((1, 4, 1))
+ y_pred = jnp.ones((1, 4, 1))
+ mask = jnp.ones((1, 1, 1), dtype=bool)
+ out_res1 = losses.multinomial_loss(
+ y_true=y_true,
+ y_pred=y_pred,
+ mask=mask,
+ multinomial_resolution=1,
+ positional_weight=1.0,
+ )
+ out_res4 = losses.multinomial_loss(
+ y_true=y_true,
+ y_pred=y_pred,
+ mask=mask,
+ multinomial_resolution=4,
+ positional_weight=1.0,
+ )
+ self.assertTrue(np.all(np.isfinite(out_res1['loss'])))
+ self.assertTrue(np.all(np.isfinite(out_res4['loss'])))
+ np.testing.assert_almost_equal(out_res1['max_sum_preds'], 1.0)
+ np.testing.assert_almost_equal(out_res4['max_sum_preds'], 4.0)
+
+
+if __name__ == '__main__':
+ absltest.main()
diff --git a/flax_model/alphagenome/model/metadata/OutputMetadataResponse_ORGANISM_HOMO_SAPIENS.textproto b/flax_model/alphagenome/model/metadata/OutputMetadataResponse_ORGANISM_HOMO_SAPIENS.textproto
new file mode 100644
index 0000000000000000000000000000000000000000..162450903f7f1db629ca136b2838f8c9c3b53566
--- /dev/null
+++ b/flax_model/alphagenome/model/metadata/OutputMetadataResponse_ORGANISM_HOMO_SAPIENS.textproto
@@ -0,0 +1,101442 @@
+output_metadata {
+ output_type: OUTPUT_TYPE_ATAC
+ tracks {
+ metadata {
+ name: "CL:0000084 ATAC-seq"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 84
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "T-cell"
+ stage: "adult"
+ }
+ assay: "ATAC-seq"
+ data_source: "encode"
+ endedness: ENDEDNESS_PAIRED
+ genetically_modified: false
+ nonzero_mean: 0.73974097
+ }
+ metadata {
+ name: "CL:0000100 ATAC-seq"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 100
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_IN_VITRO_DIFFERENTIATED_CELLS
+ name: "motor neuron"
+ stage: "adult"
+ }
+ assay: "ATAC-seq"
+ data_source: "encode"
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+ id: 77
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+ id: 136
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+ nonzero_mean: 41.716267
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+ id: 767
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+ id: 771
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+ id: 775
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+ nonzero_mean: 339.3872
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+ strand: STRAND_POSITIVE
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+ id: 784
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+ nonzero_mean: 103.16077
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+ strand: STRAND_POSITIVE
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+ id: 840
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+ nonzero_mean: 826.5434
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+ strand: STRAND_POSITIVE
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+ id: 895
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+ nonzero_mean: 91.958466
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+ strand: STRAND_POSITIVE
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+ id: 2092
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+ nonzero_mean: 112.50703
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+ strand: STRAND_POSITIVE
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+ id: 2166
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+ name: "epithelial cell of Malassez"
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+ nonzero_mean: 278.64233
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+ strand: STRAND_POSITIVE
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+ id: 2167
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+ id: 2397
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+ name: "CD14-positive, CD16-positive monocyte"
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+ nonzero_mean: 75.360245
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+ strand: STRAND_POSITIVE
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+ id: 2677
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+ nonzero_mean: 60.095337
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+ strand: STRAND_POSITIVE
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+ id: 12168
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+ nonzero_mean: 1110.8236
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+ name: "hCAGE CL:0000047"
+ strand: STRAND_POSITIVE
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+ id: 47
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+ name: "neuronal stem cell"
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+ nonzero_mean: 12.134685
+ }
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+ strand: STRAND_POSITIVE
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+ id: 62
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "osteoblast"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 30.618631
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 77
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "mesothelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 41.452156
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 97
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "mast cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 6.240642
+ }
+ metadata {
+ name: "hCAGE CL:0000134"
+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 134
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "mesenchymal cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 37.527195
+ }
+ metadata {
+ name: "hCAGE CL:0000138"
+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 138
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "chondrocyte"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 21.439882
+ }
+ metadata {
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 182
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "hepatocyte"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 34.04872
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 188
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "cell of skeletal muscle"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 24.676044
+ }
+ metadata {
+ name: "hCAGE CL:0000214"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 214
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "synovial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 14.118722
+ }
+ metadata {
+ name: "hCAGE CL:0000216"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 216
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "Sertoli cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 276.99112
+ }
+ metadata {
+ name: "hCAGE CL:0000235"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 235
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "macrophage"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 39.823517
+ }
+ metadata {
+ name: "hCAGE CL:0000307"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 307
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "tracheal epithelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 43.87181
+ }
+ metadata {
+ name: "hCAGE CL:0000312"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 312
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "keratinocyte"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 33.141487
+ }
+ metadata {
+ name: "hCAGE CL:0000322"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 322
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "pneumocyte"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 134.20897
+ }
+ metadata {
+ name: "hCAGE CL:0000388"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 388
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "tendon cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 55.28365
+ }
+ metadata {
+ name: "hCAGE CL:0000453"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 453
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "Langerhans cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 16.974895
+ }
+ metadata {
+ name: "hCAGE CL:0000515"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 515
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "skeletal muscle myoblast"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 33.867573
+ }
+ metadata {
+ name: "hCAGE CL:0000540"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 540
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "neuron"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 10.895456
+ }
+ metadata {
+ name: "hCAGE CL:0000558"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 558
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "reticulocyte"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 46.566463
+ }
+ metadata {
+ name: "hCAGE CL:0000569"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 569
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "cardiac mesenchymal cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 31.96952
+ }
+ metadata {
+ name: "hCAGE CL:0000575"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 575
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "corneal epithelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 100.49095
+ }
+ metadata {
+ name: "hCAGE CL:0000576"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 576
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "monocyte"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 15.82419
+ }
+ metadata {
+ name: "hCAGE CL:0000594"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 594
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "skeletal muscle satellite cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 25.070316
+ }
+ metadata {
+ name: "hCAGE CL:0000623"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 623
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "natural killer cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 17.459036
+ }
+ metadata {
+ name: "hCAGE CL:0000650"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 650
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "mesangial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 88.14716
+ }
+ metadata {
+ name: "hCAGE CL:0000708"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 708
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "leptomeningeal cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 47.901623
+ }
+ metadata {
+ name: "hCAGE CL:0000731"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 731
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "urothelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 67.40661
+ }
+ metadata {
+ name: "hCAGE CL:0000746"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 746
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "cardiac muscle cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 72.80143
+ }
+ metadata {
+ name: "hCAGE CL:0000767"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 767
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "basophil"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 62.97189
+ }
+ metadata {
+ name: "hCAGE CL:0000775"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 775
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "neutrophil"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 36.779
+ }
+ metadata {
+ name: "hCAGE CL:0000784"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 784
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "plasmacytoid dendritic cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 89.534355
+ }
+ metadata {
+ name: "hCAGE CL:0000798"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 798
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "gamma-delta T cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 66.087776
+ }
+ metadata {
+ name: "hCAGE CL:0000840"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 840
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "immature conventional dendritic cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 32.854946
+ }
+ metadata {
+ name: "hCAGE CL:0001016"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 1016
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "immature CD1a-positive Langerhans cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 27.767859
+ }
+ metadata {
+ name: "hCAGE CL:0002057"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2057
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "CD14-positive, CD16-negative classical monocyte"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 17.504896
+ }
+ metadata {
+ name: "hCAGE CL:0002138"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2138
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "endothelial cell of lymphatic vessel"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 57.869923
+ }
+ metadata {
+ name: "hCAGE CL:0002140"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2140
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "acinar cell of sebaceous gland"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 89.768456
+ }
+ metadata {
+ name: "hCAGE CL:0002144"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2144
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "capillary endothelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 66.49445
+ }
+ metadata {
+ name: "hCAGE CL:0002166"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2166
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "epithelial cell of Malassez"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 40.9922
+ }
+ metadata {
+ name: "hCAGE CL:0002167"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2167
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "olfactory epithelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 18.67061
+ }
+ metadata {
+ name: "hCAGE CL:0002188"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2188
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "glomerular endothelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 45.96765
+ }
+ metadata {
+ name: "hCAGE CL:0002224"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2224
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "lens epithelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 24.040731
+ }
+ metadata {
+ name: "hCAGE CL:0002231"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2231
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "epithelial cell of prostate"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 25.890713
+ }
+ metadata {
+ name: "hCAGE CL:0002252"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2252
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "epithelial cell of esophagus"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 180.21753
+ }
+ metadata {
+ name: "hCAGE CL:0002304"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2304
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "non-pigmented ciliary epithelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 22.0718
+ }
+ metadata {
+ name: "hCAGE CL:0002306"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2306
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "epithelial cell of proximal tubule"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 71.96796
+ }
+ metadata {
+ name: "hCAGE CL:0002327"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2327
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "mammary gland epithelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 26.254536
+ }
+ metadata {
+ name: "hCAGE CL:0002328"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2328
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "bronchial epithelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 36.567513
+ }
+ metadata {
+ name: "hCAGE CL:0002363"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2363
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "keratocyte"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 73.38771
+ }
+ metadata {
+ name: "hCAGE CL:0002367"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2367
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "trabecular meshwork cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 105.23827
+ }
+ metadata {
+ name: "hCAGE CL:0002368"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2368
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "respiratory epithelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 33.222343
+ }
+ metadata {
+ name: "hCAGE CL:0002372"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2372
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "myotube"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 42.713295
+ }
+ metadata {
+ name: "hCAGE CL:0002397"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2397
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "CD14-positive, CD16-positive monocyte"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 13.204062
+ }
+ metadata {
+ name: "hCAGE CL:0002453"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2453
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "oligodendrocyte precursor cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 151.70761
+ }
+ metadata {
+ name: "hCAGE CL:0002504"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2504
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "enteric smooth muscle cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 35.44853
+ }
+ metadata {
+ name: "hCAGE CL:0002521"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2521
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "subcutaneous fat cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 77.643105
+ }
+ metadata {
+ name: "hCAGE CL:0002536"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2536
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "amniotic epithelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 76.57481
+ }
+ metadata {
+ name: "hCAGE CL:0002537"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2537
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "amnion mesenchymal stem cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 89.0509
+ }
+ metadata {
+ name: "hCAGE CL:0002539"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2539
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "aortic smooth muscle cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 63.350178
+ }
+ metadata {
+ name: "hCAGE CL:0002540"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2540
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "mesenchymal stem cell of the bone marrow"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 27.279978
+ }
+ metadata {
+ name: "hCAGE CL:0002543"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2543
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
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+ }
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+ }
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+ strand: STRAND_POSITIVE
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+ id: 2544
+ }
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+ name: "aortic endothelial cell"
+ }
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+ }
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+ strand: STRAND_POSITIVE
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+ id: 2545
+ }
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+ name: "thoracic aorta endothelial cell"
+ }
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+ nonzero_mean: 122.50443
+ }
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+ strand: STRAND_POSITIVE
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+ id: 2547
+ }
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+ name: "fibroblast of the aortic adventitia"
+ }
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+ nonzero_mean: 37.14718
+ }
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+ strand: STRAND_POSITIVE
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+ id: 2548
+ }
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+ name: "cardiac fibroblast"
+ }
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+ nonzero_mean: 29.914381
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2549
+ }
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+ name: "fibroblast of choroid plexus"
+ }
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+ nonzero_mean: 71.33725
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2550
+ }
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+ name: "fibroblast of the conjuctiva"
+ }
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+ nonzero_mean: 21.548279
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2551
+ }
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+ name: "fibroblast of dermis"
+ }
+ assay: "hCAGE"
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+ nonzero_mean: 19.51736
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2552
+ }
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+ name: "fibroblast of gingiva"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 9.673295
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2553
+ }
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+ name: "lung fibroblast"
+ }
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+ nonzero_mean: 47.210205
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2554
+ }
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+ name: "fibroblast of lymphatic vessel"
+ }
+ assay: "hCAGE"
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+ nonzero_mean: 165.58525
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2555
+ }
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+ name: "fibroblast of mammary gland"
+ }
+ assay: "hCAGE"
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+ nonzero_mean: 183.8668
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2556
+ }
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+ name: "fibroblast of periodontium"
+ }
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+ nonzero_mean: 27.911106
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2557
+ }
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+ name: "fibroblast of pulmonary artery"
+ }
+ assay: "hCAGE"
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+ nonzero_mean: 62.941715
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2558
+ }
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+ name: "fibroblast of villous mesenchyme"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 87.756386
+ }
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+ name: "hCAGE CL:0002561"
+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2561
+ }
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+ name: "outer root sheath cell"
+ }
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+ nonzero_mean: 96.74042
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2563
+ }
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+ name: "intestinal epithelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 69.25311
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2564
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "nucleus pulposus cell of intervertebral disc"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 50.265064
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2565
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "iris pigment epithelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 41.941837
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2566
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "dark melanocyte"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 33.858326
+ }
+ metadata {
+ name: "hCAGE CL:0002567"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2567
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "light melanocyte"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 91.54638
+ }
+ metadata {
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2568
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "mesenchymal stem cell of Wharton\'s jelly"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 74.28569
+ }
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+ name: "hCAGE CL:0002569"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2569
+ }
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+ name: "mesenchymal stem cell of umbilical cord"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 57.374985
+ }
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+ name: "hCAGE CL:0002570"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2570
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "mesenchymal stem cell of adipose"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 36.56171
+ }
+ metadata {
+ name: "hCAGE CL:0002571"
+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2571
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "hepatic mesenchymal stem cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 18.641596
+ }
+ metadata {
+ name: "hCAGE CL:0002572"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2572
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "vertebral mesenchymal stem cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 157.35686
+ }
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+ name: "hCAGE CL:0002573"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2573
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "Schwann cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 67.37822
+ }
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+ name: "hCAGE CL:0002574"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2574
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "stromal cell of pancreas"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 54.611366
+ }
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+ name: "hCAGE CL:0002575"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2575
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "central nervous system pericyte"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 59.457504
+ }
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+ name: "hCAGE CL:0002576"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2576
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "perineural cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 47.03921
+ }
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+ name: "hCAGE CL:0002577"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2577
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "placental epithelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 76.00321
+ }
+ metadata {
+ name: "hCAGE CL:0002579"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2579
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "omentum preadipocyte"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 26.805828
+ }
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+ name: "hCAGE CL:0002580"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2580
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "preadipocyte of the breast"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 62.834743
+ }
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+ name: "hCAGE CL:0002581"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2581
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "perirenal preadipocyte"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 397.03735
+ }
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+ name: "hCAGE CL:0002582"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2582
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "visceral preadipocyte"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 23.033075
+ }
+ metadata {
+ name: "hCAGE CL:0002583"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2583
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "subcutaneous preadipocyte"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 33.979004
+ }
+ metadata {
+ name: "hCAGE CL:0002584"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2584
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "renal cortical epithelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 41.1263
+ }
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+ name: "hCAGE CL:0002586"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2586
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "retinal pigment epithelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 59.109314
+ }
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+ name: "hCAGE CL:0002588"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2588
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "smooth muscle cell of the umbilical vein"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 48.64505
+ }
+ metadata {
+ name: "hCAGE CL:0002589"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2589
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "smooth muscle cell of the brachiocephalic vasculature"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 26.219963
+ }
+ metadata {
+ name: "hCAGE CL:0002590"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2590
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "smooth muscle cell of the brain vasculature"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 54.1973
+ }
+ metadata {
+ name: "hCAGE CL:0002591"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2591
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "smooth muscle cell of the pulmonary artery"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 60.343357
+ }
+ metadata {
+ name: "hCAGE CL:0002592"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2592
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "smooth muscle cell of the coronary artery"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 75.50703
+ }
+ metadata {
+ name: "hCAGE CL:0002593"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2593
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "smooth muscle cell of the internal thoracic artery"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 30.011095
+ }
+ metadata {
+ name: "hCAGE CL:0002594"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2594
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "smooth muscle cell of the umbilical artery"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 48.85675
+ }
+ metadata {
+ name: "hCAGE CL:0002595"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2595
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "smooth muscle cell of the subclavian artery"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 84.72883
+ }
+ metadata {
+ name: "hCAGE CL:0002596"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2596
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "smooth muscle cell of the carotid artery"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 107.90396
+ }
+ metadata {
+ name: "hCAGE CL:0002597"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2597
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "smooth muscle cell of bladder"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 305.08017
+ }
+ metadata {
+ name: "hCAGE CL:0002598"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2598
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "bronchial smooth muscle cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 157.73636
+ }
+ metadata {
+ name: "hCAGE CL:0002599"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2599
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "smooth muscle cell of the esophagus"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 41.338352
+ }
+ metadata {
+ name: "hCAGE CL:0002600"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2600
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "smooth muscle cell of trachea"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 57.20531
+ }
+ metadata {
+ name: "hCAGE CL:0002601"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2601
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "uterine smooth muscle cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 74.29872
+ }
+ metadata {
+ name: "hCAGE CL:0002602"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2602
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "annulus pulposus cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 65.1128
+ }
+ metadata {
+ name: "hCAGE CL:0002603"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2603
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "astrocyte of the cerebellum"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 51.63285
+ }
+ metadata {
+ name: "hCAGE CL:0002605"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2605
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "astrocyte of the cerebral cortex"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 28.46219
+ }
+ metadata {
+ name: "hCAGE CL:0002615"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2615
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "adipocyte of omentum tissue"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 49.051792
+ }
+ metadata {
+ name: "hCAGE CL:0002616"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2616
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "perirenal adipocyte cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 215.66867
+ }
+ metadata {
+ name: "hCAGE CL:0002617"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2617
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "adipocyte of breast"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 44.72286
+ }
+ metadata {
+ name: "hCAGE CL:0002618"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2618
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "endothelial cell of umbilical vein"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 32.87194
+ }
+ metadata {
+ name: "hCAGE CL:0002620"
+ strand: STRAND_POSITIVE
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+ id: 2620
+ }
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+ }
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+ id: 2621
+ }
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+ }
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+ id: 2622
+ }
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+ }
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+ id: 2623
+ }
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+ }
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+ id: 2677
+ }
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+ }
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+ id: 1000280
+ }
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+ }
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+ id: 1000413
+ }
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+ }
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+ }
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+ id: 1000428
+ }
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+ }
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+ id: 1000487
+ }
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+ }
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+ id: 1000494
+ }
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+ }
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+ }
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+ id: 1086
+ }
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+ }
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+ }
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+ strand: STRAND_POSITIVE
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+ id: 1099
+ }
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+ }
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+ strand: STRAND_POSITIVE
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+ id: 1182
+ }
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+ }
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+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ }
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+ }
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+ strand: STRAND_POSITIVE
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+ id: 1253
+ }
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+ name: "THP-1"
+ }
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+ }
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+ strand: STRAND_POSITIVE
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+ id: 2059
+ }
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+ name: "HT1080"
+ }
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+ nonzero_mean: 65.99972
+ }
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+ strand: STRAND_POSITIVE
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+ id: 2067
+ }
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+ }
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+ nonzero_mean: 35.167183
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2074
+ }
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+ name: "PC-3"
+ }
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+ nonzero_mean: 47.836132
+ }
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+ strand: STRAND_POSITIVE
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+ id: 2101
+ }
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+ }
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+ nonzero_mean: 35.259254
+ }
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+ strand: STRAND_POSITIVE
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+ id: 2179
+ }
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+ name: "G401"
+ }
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+ nonzero_mean: 54.05591
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2285
+ }
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+ name: "NCI-H226"
+ }
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+ nonzero_mean: 86.691185
+ }
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+ strand: STRAND_POSITIVE
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+ id: 2324
+ }
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+ name: "Raji"
+ }
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+ nonzero_mean: 57.717403
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2784
+ }
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+ name: "GM12878"
+ }
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+ nonzero_mean: 21.34038
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2791
+ }
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+ name: "HeLa-S3"
+ }
+ assay: "hCAGE"
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+ nonzero_mean: 23.797667
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2793
+ }
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+ name: "HL-60"
+ }
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+ nonzero_mean: 79.936905
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2796
+ }
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+ name: "Jurkat"
+ }
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+ data_source: "fantom"
+ nonzero_mean: 64.35322
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2860
+ }
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+ name: "SK-N-MC"
+ }
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+ nonzero_mean: 180.76753
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 3044
+ }
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+ name: "NCI-H460"
+ }
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+ nonzero_mean: 78.241806
+ }
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+ strand: STRAND_POSITIVE
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+ id: 5441
+ }
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+ name: "DU 145"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 57.916393
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 2
+ }
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+ name: "uterine cervix"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 35.905205
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 7
+ }
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+ name: "pituitary gland"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 38.91668
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 14
+ }
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+ name: "zone of skin"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 90.94199
+ }
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+ name: "hCAGE UBERON:0000019"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 19
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "camera-type eye"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 73.059044
+ }
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+ name: "hCAGE UBERON:0000029"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 29
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "lymph node"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 132.45212
+ }
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+ name: "hCAGE UBERON:0000057"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 57
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "urethra"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 21.934435
+ }
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+ name: "hCAGE UBERON:0000178"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 178
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "blood"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 13.531625
+ }
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+ name: "hCAGE UBERON:0000305"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 305
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "amnion"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 44.147255
+ }
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+ name: "hCAGE UBERON:0000310"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 310
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "breast"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 203.00395
+ }
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+ name: "hCAGE UBERON:0000341"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 341
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "throat"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 36.270218
+ }
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+ name: "hCAGE UBERON:0000473"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 473
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "testis"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 19.597055
+ }
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+ name: "hCAGE UBERON:0000920"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 920
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "egg chorion"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 31.856163
+ }
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+ name: "hCAGE UBERON:0000941"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 941
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "cranial nerve II"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 43.4175
+ }
+ metadata {
+ name: "hCAGE UBERON:0000945"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 945
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "stomach"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 149.30127
+ }
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+ name: "hCAGE UBERON:0000947"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 947
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "aorta"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 51.70299
+ }
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+ name: "hCAGE UBERON:0000948"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 948
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "heart"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 21.734741
+ }
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+ name: "hCAGE UBERON:0000955"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 955
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "brain"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 28.432245
+ }
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+ name: "hCAGE UBERON:0000966"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 966
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "retina"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 32.835796
+ }
+ metadata {
+ name: "hCAGE UBERON:0000988"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 988
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "pons"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 28.06986
+ }
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+ name: "hCAGE UBERON:0000989"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 989
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "penis"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 55.62896
+ }
+ metadata {
+ name: "hCAGE UBERON:0000992"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 992
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "female gonad"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 46.649906
+ }
+ metadata {
+ name: "hCAGE UBERON:0000995"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 995
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "uterus"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 59.679214
+ }
+ metadata {
+ name: "hCAGE UBERON:0000996"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 996
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "vagina"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 192.70404
+ }
+ metadata {
+ name: "hCAGE UBERON:0000998"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 998
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "seminal vesicle"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 43.64366
+ }
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+ name: "hCAGE UBERON:0001000"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1000
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "vas deferens"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 68.61293
+ }
+ metadata {
+ name: "hCAGE UBERON:0001013"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1013
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "adipose tissue"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 21.103422
+ }
+ metadata {
+ name: "hCAGE UBERON:0001043"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1043
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "esophagus"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 51.765285
+ }
+ metadata {
+ name: "hCAGE UBERON:0001044"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1044
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "saliva-secreting gland"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 148.6288
+ }
+ metadata {
+ name: "hCAGE UBERON:0001052"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1052
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "rectum"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 130.85585
+ }
+ metadata {
+ name: "hCAGE UBERON:0001103"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1103
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "diaphragm"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 117.934105
+ }
+ metadata {
+ name: "hCAGE UBERON:0001134"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1134
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
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+ id: 1905
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+ id: 1987
+ }
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+ strand: STRAND_POSITIVE
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+ id: 2021
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+ id: 2022
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+ id: 2037
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+ id: 2038
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+ id: 2046
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+ id: 2048
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+ id: 2079
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+ strand: STRAND_POSITIVE
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+ id: 2084
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+ id: 2106
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+ }
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+ id: 2107
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+ id: 2110
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+ strand: STRAND_POSITIVE
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+ id: 2113
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+ id: 2114
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+ strand: STRAND_POSITIVE
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+ id: 2118
+ }
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+ name: "right ovary"
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+ }
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+ strand: STRAND_POSITIVE
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+ id: 2119
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+ }
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+ strand: STRAND_POSITIVE
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+ id: 2134
+ }
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+ }
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+ strand: STRAND_POSITIVE
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+ id: 2135
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+ name: "mitral valve"
+ }
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+ }
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+ strand: STRAND_POSITIVE
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+ id: 2146
+ }
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+ name: "pulmonary valve"
+ }
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+ }
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+ strand: STRAND_POSITIVE
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+ id: 2148
+ }
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+ name: "locus ceruleus"
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+ }
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+ strand: STRAND_POSITIVE
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+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 2171
+ }
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+ name: "lower lobe of right lung"
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+ data_source: "fantom"
+ nonzero_mean: 65.225464
+ }
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+ strand: STRAND_POSITIVE
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+ id: 2240
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "spinal cord"
+ }
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+ data_source: "fantom"
+ nonzero_mean: 15.438721
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 2331
+ }
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+ name: "umbilical cord"
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+ data_source: "fantom"
+ nonzero_mean: 234.7225
+ }
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+ strand: STRAND_POSITIVE
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+ id: 2336
+ }
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+ name: "corpus callosum"
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+ data_source: "fantom"
+ nonzero_mean: 31.147842
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 2360
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "meninx"
+ }
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+ data_source: "fantom"
+ nonzero_mean: 67.73757
+ }
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+ name: "hCAGE UBERON:0002363"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 2363
+ }
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+ name: "dura mater"
+ }
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+ data_source: "fantom"
+ nonzero_mean: 34.174473
+ }
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+ name: "hCAGE UBERON:0002367"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 2367
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "prostate gland"
+ }
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+ data_source: "fantom"
+ nonzero_mean: 47.348633
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 2369
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "adrenal gland"
+ }
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+ data_source: "fantom"
+ nonzero_mean: 829.89813
+ }
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+ name: "hCAGE UBERON:0002370"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 2370
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "thymus"
+ }
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+ id: 6322
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+ id: 6323
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+ id: 6659
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+ id: 7023
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+ id: 13777
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+ id: 784
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+ id: 840
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+ id: 895
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+ id: 2092
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+ id: 2166
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+ id: 2193
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+ id: 2397
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+ id: 2677
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+ strand: STRAND_NEGATIVE
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+ id: 12168
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+ strand: STRAND_NEGATIVE
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+ id: 47
+ }
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+ name: "neuronal stem cell"
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+ }
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+ strand: STRAND_NEGATIVE
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+ id: 62
+ }
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+ name: "osteoblast"
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+ }
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+ id: 77
+ }
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+ }
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+ id: 97
+ }
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+ name: "mast cell"
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+ }
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+ strand: STRAND_NEGATIVE
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+ id: 134
+ }
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+ name: "mesenchymal cell"
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+ nonzero_mean: 37.527195
+ }
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+ strand: STRAND_NEGATIVE
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+ id: 138
+ }
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+ name: "chondrocyte"
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+ nonzero_mean: 21.439882
+ }
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+ id: 182
+ }
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+ name: "hepatocyte"
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+ nonzero_mean: 34.04872
+ }
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+ strand: STRAND_NEGATIVE
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+ id: 188
+ }
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+ name: "cell of skeletal muscle"
+ }
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+ }
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+ strand: STRAND_NEGATIVE
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+ id: 214
+ }
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+ name: "synovial cell"
+ }
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+ }
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+ strand: STRAND_NEGATIVE
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+ id: 216
+ }
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+ name: "Sertoli cell"
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+ }
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+ strand: STRAND_NEGATIVE
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+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 235
+ }
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+ name: "macrophage"
+ }
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+ nonzero_mean: 39.823517
+ }
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+ strand: STRAND_NEGATIVE
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+ id: 307
+ }
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+ name: "tracheal epithelial cell"
+ }
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+ nonzero_mean: 43.87181
+ }
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+ strand: STRAND_NEGATIVE
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+ id: 312
+ }
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+ name: "keratinocyte"
+ }
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+ nonzero_mean: 33.141487
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 322
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "pneumocyte"
+ }
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+ nonzero_mean: 134.20897
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 388
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "tendon cell"
+ }
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+ data_source: "fantom"
+ nonzero_mean: 55.28365
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 453
+ }
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+ name: "Langerhans cell"
+ }
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+ data_source: "fantom"
+ nonzero_mean: 16.974895
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 515
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "skeletal muscle myoblast"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 33.867573
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 540
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "neuron"
+ }
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+ data_source: "fantom"
+ nonzero_mean: 10.895456
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 558
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "reticulocyte"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 46.566463
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 569
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "cardiac mesenchymal cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 31.96952
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 575
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "corneal epithelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 100.49095
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 576
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "monocyte"
+ }
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+ data_source: "fantom"
+ nonzero_mean: 15.82419
+ }
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+ name: "hCAGE CL:0000594"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 594
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "skeletal muscle satellite cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 25.070316
+ }
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+ name: "hCAGE CL:0000623"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 623
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "natural killer cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 17.459036
+ }
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+ strand: STRAND_NEGATIVE
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+ id: 650
+ }
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+ name: "mesangial cell"
+ }
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+ nonzero_mean: 88.14716
+ }
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+ strand: STRAND_NEGATIVE
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+ id: 708
+ }
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+ name: "leptomeningeal cell"
+ }
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+ nonzero_mean: 47.901623
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 731
+ }
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+ name: "urothelial cell"
+ }
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+ nonzero_mean: 67.40661
+ }
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+ strand: STRAND_NEGATIVE
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+ id: 746
+ }
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+ name: "cardiac muscle cell"
+ }
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+ nonzero_mean: 72.80143
+ }
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+ strand: STRAND_NEGATIVE
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+ id: 767
+ }
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+ name: "basophil"
+ }
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+ nonzero_mean: 62.97189
+ }
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+ strand: STRAND_NEGATIVE
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+ id: 775
+ }
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+ name: "neutrophil"
+ }
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+ nonzero_mean: 36.779
+ }
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+ strand: STRAND_NEGATIVE
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+ id: 784
+ }
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+ name: "plasmacytoid dendritic cell"
+ }
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+ nonzero_mean: 89.534355
+ }
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+ strand: STRAND_NEGATIVE
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+ id: 798
+ }
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+ name: "gamma-delta T cell"
+ }
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+ nonzero_mean: 66.087776
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 840
+ }
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+ name: "immature conventional dendritic cell"
+ }
+ assay: "hCAGE"
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+ nonzero_mean: 32.854946
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 1016
+ }
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+ name: "immature CD1a-positive Langerhans cell"
+ }
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+ nonzero_mean: 27.767859
+ }
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+ strand: STRAND_NEGATIVE
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+ id: 2057
+ }
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+ name: "CD14-positive, CD16-negative classical monocyte"
+ }
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+ nonzero_mean: 17.504896
+ }
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+ strand: STRAND_NEGATIVE
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+ id: 2138
+ }
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+ name: "endothelial cell of lymphatic vessel"
+ }
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+ nonzero_mean: 57.869923
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 2140
+ }
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+ name: "acinar cell of sebaceous gland"
+ }
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+ nonzero_mean: 89.768456
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 2144
+ }
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+ name: "capillary endothelial cell"
+ }
+ assay: "hCAGE"
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+ nonzero_mean: 66.49445
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 2166
+ }
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+ name: "epithelial cell of Malassez"
+ }
+ assay: "hCAGE"
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+ nonzero_mean: 40.9922
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 2167
+ }
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+ name: "olfactory epithelial cell"
+ }
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+ nonzero_mean: 18.67061
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 2188
+ }
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+ name: "glomerular endothelial cell"
+ }
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+ nonzero_mean: 45.96765
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 2224
+ }
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+ name: "lens epithelial cell"
+ }
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+ nonzero_mean: 24.040731
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 2231
+ }
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+ name: "epithelial cell of prostate"
+ }
+ assay: "hCAGE"
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+ nonzero_mean: 25.890713
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 2252
+ }
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+ name: "epithelial cell of esophagus"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 180.21753
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 2304
+ }
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+ name: "non-pigmented ciliary epithelial cell"
+ }
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+ nonzero_mean: 22.0718
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 2306
+ }
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+ name: "epithelial cell of proximal tubule"
+ }
+ assay: "hCAGE"
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+ nonzero_mean: 71.96796
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 2327
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "mammary gland epithelial cell"
+ }
+ assay: "hCAGE"
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+ nonzero_mean: 26.254536
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 2328
+ }
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+ name: "bronchial epithelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 36.567513
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 2363
+ }
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+ name: "keratocyte"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 73.38771
+ }
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+ name: "hCAGE CL:0002367"
+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 2367
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "trabecular meshwork cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 105.23827
+ }
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+ name: "hCAGE CL:0002368"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2368
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "respiratory epithelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 33.222343
+ }
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+ name: "hCAGE CL:0002372"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2372
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "myotube"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 42.713295
+ }
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+ name: "hCAGE CL:0002397"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2397
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "CD14-positive, CD16-positive monocyte"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 13.204062
+ }
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+ name: "hCAGE CL:0002453"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2453
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "oligodendrocyte precursor cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 151.70761
+ }
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+ name: "hCAGE CL:0002504"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2504
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "enteric smooth muscle cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 35.44853
+ }
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+ name: "hCAGE CL:0002521"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2521
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "subcutaneous fat cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 77.643105
+ }
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+ name: "hCAGE CL:0002536"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2536
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "amniotic epithelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 76.57481
+ }
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+ name: "hCAGE CL:0002537"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2537
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "amnion mesenchymal stem cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 89.0509
+ }
+ metadata {
+ name: "hCAGE CL:0002539"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2539
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "aortic smooth muscle cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 63.350178
+ }
+ metadata {
+ name: "hCAGE CL:0002540"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2540
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "mesenchymal stem cell of the bone marrow"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 27.279978
+ }
+ metadata {
+ name: "hCAGE CL:0002543"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2543
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "vein endothelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 67.78171
+ }
+ metadata {
+ name: "hCAGE CL:0002544"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2544
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "aortic endothelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 64.33608
+ }
+ metadata {
+ name: "hCAGE CL:0002545"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2545
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "thoracic aorta endothelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 122.50443
+ }
+ metadata {
+ name: "hCAGE CL:0002547"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2547
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "fibroblast of the aortic adventitia"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 37.14718
+ }
+ metadata {
+ name: "hCAGE CL:0002548"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2548
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "cardiac fibroblast"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 29.914381
+ }
+ metadata {
+ name: "hCAGE CL:0002549"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2549
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "fibroblast of choroid plexus"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 71.33725
+ }
+ metadata {
+ name: "hCAGE CL:0002550"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2550
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "fibroblast of the conjuctiva"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 21.548279
+ }
+ metadata {
+ name: "hCAGE CL:0002551"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2551
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "fibroblast of dermis"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 19.51736
+ }
+ metadata {
+ name: "hCAGE CL:0002552"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2552
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "fibroblast of gingiva"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 9.673295
+ }
+ metadata {
+ name: "hCAGE CL:0002553"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2553
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "lung fibroblast"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 47.210205
+ }
+ metadata {
+ name: "hCAGE CL:0002554"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2554
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "fibroblast of lymphatic vessel"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 165.58525
+ }
+ metadata {
+ name: "hCAGE CL:0002555"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2555
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "fibroblast of mammary gland"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 183.8668
+ }
+ metadata {
+ name: "hCAGE CL:0002556"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2556
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "fibroblast of periodontium"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 27.911106
+ }
+ metadata {
+ name: "hCAGE CL:0002557"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2557
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "fibroblast of pulmonary artery"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 62.941715
+ }
+ metadata {
+ name: "hCAGE CL:0002558"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2558
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "fibroblast of villous mesenchyme"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 87.756386
+ }
+ metadata {
+ name: "hCAGE CL:0002561"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2561
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "outer root sheath cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 96.74042
+ }
+ metadata {
+ name: "hCAGE CL:0002563"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2563
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "intestinal epithelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 69.25311
+ }
+ metadata {
+ name: "hCAGE CL:0002564"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2564
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "nucleus pulposus cell of intervertebral disc"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 50.265064
+ }
+ metadata {
+ name: "hCAGE CL:0002565"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2565
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "iris pigment epithelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 41.941837
+ }
+ metadata {
+ name: "hCAGE CL:0002566"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2566
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "dark melanocyte"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 33.858326
+ }
+ metadata {
+ name: "hCAGE CL:0002567"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2567
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "light melanocyte"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 91.54638
+ }
+ metadata {
+ name: "hCAGE CL:0002568"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2568
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "mesenchymal stem cell of Wharton\'s jelly"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 74.28569
+ }
+ metadata {
+ name: "hCAGE CL:0002569"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2569
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "mesenchymal stem cell of umbilical cord"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 57.374985
+ }
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+ id: 2581
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+ id: 2584
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+ id: 2586
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+ id: 2588
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+ id: 2589
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+ id: 2590
+ }
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+ id: 2591
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+ id: 2592
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+ id: 2593
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+ id: 2594
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+ id: 2595
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+ }
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+ id: 2596
+ }
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+ }
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+ id: 2597
+ }
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+ }
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+ id: 2598
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+ }
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+ id: 2599
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+ }
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+ strand: STRAND_NEGATIVE
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+ id: 2600
+ }
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+ }
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+ strand: STRAND_NEGATIVE
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+ id: 2601
+ }
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+ name: "uterine smooth muscle cell"
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+ nonzero_mean: 74.29872
+ }
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+ strand: STRAND_NEGATIVE
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+ id: 2602
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+ name: "annulus pulposus cell"
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+ nonzero_mean: 65.1128
+ }
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+ strand: STRAND_NEGATIVE
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+ id: 2603
+ }
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+ }
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+ strand: STRAND_NEGATIVE
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+ id: 2605
+ }
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+ }
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+ strand: STRAND_NEGATIVE
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+ id: 2615
+ }
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+ }
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+ nonzero_mean: 49.051792
+ }
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+ strand: STRAND_NEGATIVE
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+ id: 2616
+ }
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+ name: "perirenal adipocyte cell"
+ }
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+ nonzero_mean: 215.66867
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 2617
+ }
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+ name: "adipocyte of breast"
+ }
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+ nonzero_mean: 44.72286
+ }
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+ strand: STRAND_NEGATIVE
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+ id: 2618
+ }
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+ name: "endothelial cell of umbilical vein"
+ }
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+ nonzero_mean: 32.87194
+ }
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+ strand: STRAND_NEGATIVE
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+ id: 2620
+ }
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+ name: "skin fibroblast"
+ }
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+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 2621
+ }
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+ name: "gingival epithelial cell"
+ }
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+ nonzero_mean: 49.712242
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2622
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "prostate stromal cell"
+ }
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+ nonzero_mean: 32.768734
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2623
+ }
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+ name: "acinar cell of salivary gland"
+ }
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+ nonzero_mean: 34.92748
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2677
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "naive regulatory T cell"
+ }
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+ nonzero_mean: 61.67428
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 1000280
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "smooth muscle cell of colon"
+ }
+ assay: "hCAGE"
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+ nonzero_mean: 36.03932
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 1000413
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "endothelial cell of artery"
+ }
+ assay: "hCAGE"
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+ nonzero_mean: 60.488586
+ }
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+ name: "hCAGE CL:1000428"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 1000428
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "stem cell of epidermis"
+ }
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+ nonzero_mean: 120.64453
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 1000487
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "smooth muscle cell of prostate"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 38.21417
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 1000494
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "nephron tubule epithelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 40.15744
+ }
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+ name: "hCAGE EFO:0001086"
+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 1086
+ }
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+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "A549"
+ }
+ assay: "hCAGE"
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+ nonzero_mean: 98.96426
+ }
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+ name: "hCAGE EFO:0001099"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1099
+ }
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+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "Caco-2"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 88.74837
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 1182
+ }
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+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "HEK293"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 41.831764
+ }
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+ name: "hCAGE EFO:0001187"
+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 1187
+ }
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+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "HepG2"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 22.44794
+ }
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+ name: "hCAGE EFO:0001253"
+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 1253
+ }
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+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "THP-1"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 56.615913
+ }
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+ name: "hCAGE EFO:0002059"
+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 2059
+ }
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+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "HT1080"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 65.99972
+ }
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+ name: "hCAGE EFO:0002067"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 2067
+ }
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+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "K562"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 35.167183
+ }
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+ name: "hCAGE EFO:0002074"
+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 2074
+ }
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+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "PC-3"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 47.836132
+ }
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+ name: "hCAGE EFO:0002101"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 2101
+ }
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+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "A172"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 35.259254
+ }
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+ name: "hCAGE EFO:0002179"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 2179
+ }
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+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "G401"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 54.05591
+ }
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+ name: "hCAGE EFO:0002285"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 2285
+ }
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+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "NCI-H226"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 86.691185
+ }
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+ name: "hCAGE EFO:0002324"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 2324
+ }
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+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "Raji"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 57.717403
+ }
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+ name: "hCAGE EFO:0002784"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 2784
+ }
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+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "GM12878"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 21.34038
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+ id: 966
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+ id: 996
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+ id: 1000
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+ id: 1013
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+ id: 1103
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+ id: 1134
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+ strand: STRAND_NEGATIVE
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+ id: 1135
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+ id: 1154
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+ strand: STRAND_NEGATIVE
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+ id: 1155
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+ nonzero_mean: 35.344658
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+ id: 1255
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+ name: "urinary bladder"
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+ }
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+ strand: STRAND_NEGATIVE
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+ id: 1264
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+ name: "pancreas"
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+ nonzero_mean: 188.3638
+ }
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+ strand: STRAND_NEGATIVE
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+ id: 1301
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+ name: "epididymis"
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+ nonzero_mean: 41.035892
+ }
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+ strand: STRAND_NEGATIVE
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+ id: 1359
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+ name: "cerebrospinal fluid"
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+ nonzero_mean: 66.41243
+ }
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+ strand: STRAND_NEGATIVE
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+ id: 1389
+ }
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+ name: "soleus muscle"
+ }
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+ nonzero_mean: 52.88532
+ }
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+ strand: STRAND_NEGATIVE
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+ id: 1602
+ }
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+ name: "medial rectus extraocular muscle"
+ }
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+ nonzero_mean: 72.61691
+ }
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+ strand: STRAND_NEGATIVE
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+ id: 1603
+ }
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+ name: "lateral rectus extra-ocular muscle"
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+ nonzero_mean: 64.02964
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 1637
+ }
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+ name: "artery"
+ }
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+ nonzero_mean: 176.23216
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 1638
+ }
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+ name: "vein"
+ }
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+ data_source: "fantom"
+ nonzero_mean: 73.3497
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 1723
+ }
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+ name: "tongue"
+ }
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+ data_source: "fantom"
+ nonzero_mean: 45.90984
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1736
+ }
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+ name: "submandibular gland"
+ }
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+ data_source: "fantom"
+ nonzero_mean: 97.67344
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 1797
+ }
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+ name: "vitreous humor"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 51.01125
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1831
+ }
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+ name: "parotid gland"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 186.81967
+ }
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+ name: "hCAGE UBERON:0001870"
+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 1870
+ }
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+ name: "frontal cortex"
+ }
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+ data_source: "fantom"
+ nonzero_mean: 25.702576
+ }
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+ name: "hCAGE UBERON:0001871"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1871
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "temporal lobe"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 17.146074
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1872
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "parietal lobe"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 10.531946
+ }
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+ name: "hCAGE UBERON:0001873"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1873
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "caudate nucleus"
+ }
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+ data_source: "fantom"
+ nonzero_mean: 17.615751
+ }
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+ name: "hCAGE UBERON:0001874"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1874
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "putamen"
+ }
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+ data_source: "fantom"
+ nonzero_mean: 17.454985
+ }
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+ name: "hCAGE UBERON:0001875"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1875
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "globus pallidus"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 8.468857
+ }
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+ name: "hCAGE UBERON:0001876"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1876
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+ id: 6323
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+ id: 6659
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+ id: 7023
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+ id: 7190
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+ id: 8198
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+ id: 13777
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+ name: "skin of palm of manus"
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+ strand: STRAND_UNSTRANDED
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+ }
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+ nonzero_mean: 0.73138267
+ }
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+ }
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+ nonzero_mean: 0.8459455
+ }
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+ }
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+ nonzero_mean: 1.1765327
+ }
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+ }
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+ }
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+ nonzero_mean: 0.9373123
+ }
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+ nonzero_mean: 0.8258344
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+ nonzero_mean: 0.77114487
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+ id: 1187
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+ }
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+ strand: STRAND_UNSTRANDED
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+ }
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+ nonzero_mean: 0.7250581
+ }
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+ strand: STRAND_UNSTRANDED
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+ genetically_modified: true
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+ }
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+ strand: STRAND_UNSTRANDED
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+ }
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+ genetically_modified: true
+ nonzero_mean: 0.92572373
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+ id: 1187
+ }
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+ genetically_modified: true
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+ id: 1187
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+ nonzero_mean: 0.89792484
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+ strand: STRAND_UNSTRANDED
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+ nonzero_mean: 0.87965256
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+ genetically_modified: false
+ nonzero_mean: 0.7842143
+ }
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+ id: 1187
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+ genetically_modified: true
+ nonzero_mean: 0.71780527
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+ id: 1187
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+ nonzero_mean: 0.8266691
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
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+ nonzero_mean: 0.8733901
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.94052744
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.7163546
+ }
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+ id: 1187
+ }
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+ nonzero_mean: 0.79053193
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.8571573
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ genetically_modified: true
+ nonzero_mean: 0.8894299
+ }
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+ name: "EFO:0001187 TF ChIP-seq BATF2 genetically modified (insertion) using CRISPR targeting H. sapiens BATF2"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.88826275
+ }
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+ name: "EFO:0001187 TF ChIP-seq BAZ2A genetically modified (insertion) using CRISPR targeting H. sapiens BAZ2A"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.9506245
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+ name: "EFO:0001187 TF ChIP-seq BCL3 genetically modified (insertion) using CRISPR targeting H. sapiens BCL3"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.8422386
+ }
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+ name: "EFO:0001187 TF ChIP-seq BCL6 genetically modified (insertion) using CRISPR targeting H. sapiens BCL6"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.84560394
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: false
+ nonzero_mean: 0.8403932
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
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+ }
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+ genetically_modified: false
+ nonzero_mean: 0.9407266
+ }
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+ name: "EFO:0001187 TF ChIP-seq BORCS8-MEF2B genetically modified (insertion) using CRISPR targeting H. sapiens BORCS8"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ name: "HepG2"
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+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.98722553
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq BRCA1 genetically modified (insertion) using CRISPR targeting H. sapiens BRCA1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
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+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9496076
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq BRD4 genetically modified (insertion) using CRISPR targeting H. sapiens BRD4"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9229219
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq BRF2 genetically modified (insertion) using CRISPR targeting H. sapiens BRF2"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
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+ }
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+ transcription_factor_code: "BRF2"
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.82028997
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq CAMTA2 genetically modified (insertion) using CRISPR targeting H. sapiens CAMTA2"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ transcription_factor_code: "CAMTA2"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.8614745
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq CBFB genetically modified (insertion) using CRISPR targeting H. sapiens CBFB"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "CBFB"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.95478296
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq CBX1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
+ biosample {
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+ name: "HepG2"
+ stage: "child"
+ }
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+ transcription_factor_code: "CBX1"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 1.793231
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq CBX5 genetically modified (insertion) using CRISPR targeting H. sapiens CBX5"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "HepG2"
+ stage: "child"
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+ }
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
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+ genetically_modified: true
+ nonzero_mean: 0.86039954
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
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+ genetically_modified: true
+ nonzero_mean: 0.903864
+ }
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 1.180675
+ }
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
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+ genetically_modified: true
+ nonzero_mean: 1.12387
+ }
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
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+ genetically_modified: true
+ nonzero_mean: 0.8854897
+ }
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ data_source: "encode"
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+ genetically_modified: false
+ nonzero_mean: 1.0124357
+ }
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.80863154
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_PAIRED
+ genetically_modified: false
+ nonzero_mean: 0.7679792
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.6846129
+ }
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.73728406
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq CSRNP1 genetically modified (insertion) using CRISPR targeting H. sapiens CSRNP1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.7614041
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq CTCF"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 1.0367335
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq CTCF genetically modified (insertion) using CRISPR targeting H. sapiens CTCF"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.97801065
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq DLX6 genetically modified (insertion) using CRISPR targeting H. sapiens DLX6"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.82724357
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq DMAP1 genetically modified (insertion) using CRISPR targeting H. sapiens DMAP1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.94086397
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq DNMT1 genetically modified (insertion) using CRISPR targeting H. sapiens DNMT1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
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+ genetically_modified: true
+ nonzero_mean: 0.9599778
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq DNMT3B genetically modified (insertion) using CRISPR targeting H. sapiens DNMT3B"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9800147
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq DPF2 genetically modified (insertion) using CRISPR targeting H. sapiens DPF2"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 1.0381863
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq DR1 genetically modified (insertion) using CRISPR targeting H. sapiens DR1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.75567186
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq DRAP1 genetically modified (insertion) using CRISPR targeting H. sapiens DRAP1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.8447701
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq DZIP1 genetically modified (insertion) using CRISPR targeting H. sapiens DZIP1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.8786128
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq E2F1 genetically modified (insertion) using CRISPR targeting H. sapiens E2F1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.7706258
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq E2F4 genetically modified (insertion) using CRISPR targeting H. sapiens E2F4"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.766048
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq E2F5 genetically modified (insertion) using CRISPR targeting H. sapiens E2F5"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ name: "HepG2"
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 1.0544837
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq E2F8 genetically modified (insertion) using CRISPR targeting H. sapiens E2F8"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.79777426
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq EEA1 genetically modified (insertion) using CRISPR targeting H. sapiens EEA1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
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+ }
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+ transcription_factor_code: "EEA1"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.95805573
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq EED genetically modified (insertion) using CRISPR targeting H. sapiens EED"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ transcription_factor_code: "EED"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.856065
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq EGR1 genetically modified (insertion) using CRISPR targeting H. sapiens EGR1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ transcription_factor_code: "EGR1"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.820586
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq EHMT2"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.8669119
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ELF1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ transcription_factor_code: "ELF1"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 1.0358084
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ELF1 genetically modified (insertion) using CRISPR targeting H. sapiens ELF1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ transcription_factor_code: "ELF1"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.8516698
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ELF3 genetically modified (insertion) using CRISPR targeting H. sapiens ELF3"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ transcription_factor_code: "ELF3"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9217768
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ELF4 genetically modified (insertion) using CRISPR targeting H. sapiens ELF4"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ transcription_factor_code: "ELF4"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.86738145
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ELK1 genetically modified (insertion) using CRISPR targeting H. sapiens ELK1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "ELK1"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9412836
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq EP300"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "EP300"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.76096076
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq EP300 genetically modified (insertion) using CRISPR targeting H. sapiens EP300"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ transcription_factor_code: "EP300"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.78218985
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ERF genetically modified (insertion) using CRISPR targeting H. sapiens ERF"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "ERF"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.88129234
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ESRRA genetically modified (insertion) using CRISPR targeting H. sapiens ESRRA"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "ESRRA"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9067927
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ETS1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "ETS1"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.90084356
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ETV4"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "ETV4"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.8924593
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ETV4 genetically modified (insertion) using CRISPR targeting H. sapiens ETV4"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "ETV4"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9367424
+ }
+ metadata {
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+ id: 1187
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+ nonzero_mean: 0.9685952
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+ }
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+ nonzero_mean: 0.9896834
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+ nonzero_mean: 0.8385319
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+ nonzero_mean: 1.0402621
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+ nonzero_mean: 0.782456
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+ }
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+ genetically_modified: true
+ nonzero_mean: 0.86016226
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+ strand: STRAND_UNSTRANDED
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+ genetically_modified: true
+ nonzero_mean: 0.75615436
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+ strand: STRAND_UNSTRANDED
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+ nonzero_mean: 0.8882735
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+ strand: STRAND_UNSTRANDED
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+ nonzero_mean: 0.9278377
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+ strand: STRAND_UNSTRANDED
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+ nonzero_mean: 0.8943411
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+ strand: STRAND_UNSTRANDED
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+ nonzero_mean: 0.81353873
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+ strand: STRAND_UNSTRANDED
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+ genetically_modified: true
+ nonzero_mean: 0.86536175
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+ nonzero_mean: 1.1288439
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+ nonzero_mean: 1.1296805
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+ strand: STRAND_UNSTRANDED
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+ nonzero_mean: 0.7817408
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+ genetically_modified: true
+ nonzero_mean: 0.95274955
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+ genetically_modified: true
+ nonzero_mean: 0.76649916
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+ genetically_modified: false
+ nonzero_mean: 0.9864453
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
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+ genetically_modified: true
+ nonzero_mean: 0.869154
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+ genetically_modified: true
+ nonzero_mean: 0.80084455
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+ genetically_modified: true
+ nonzero_mean: 0.7213775
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+ name: "EFO:0001187 TF ChIP-seq GLI4 genetically modified (insertion) using CRISPR targeting H. sapiens GLI4"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
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+ genetically_modified: true
+ nonzero_mean: 0.7912455
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+ name: "EFO:0001187 TF ChIP-seq GLYR1 genetically modified (insertion) using CRISPR targeting H. sapiens GLYR1"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
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+ genetically_modified: true
+ nonzero_mean: 0.9311454
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+ name: "EFO:0001187 TF ChIP-seq GMEB1 genetically modified (insertion) using CRISPR targeting H. sapiens GMEB1"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
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+ nonzero_mean: 0.79631585
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
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+ genetically_modified: true
+ nonzero_mean: 1.0227953
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+ name: "EFO:0001187 TF ChIP-seq GPBP1L1 genetically modified (insertion) using CRISPR targeting H. sapiens GPBP1L1"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
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+ genetically_modified: true
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+ name: "EFO:0001187 TF ChIP-seq GZF1 genetically modified (insertion) using CRISPR targeting H. sapiens GZF1"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
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+ genetically_modified: true
+ nonzero_mean: 0.82583034
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+ name: "EFO:0001187 TF ChIP-seq HBP1 genetically modified (insertion) using CRISPR targeting H. sapiens HBP1"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 1.2320542
+ }
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+ id: 1187
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 1.038497
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq HDAC1"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.8245724
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+ name: "EFO:0001187 TF ChIP-seq HDAC1 genetically modified (insertion) using CRISPR targeting H. sapiens HDAC1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
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+ name: "HepG2"
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.7373652
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq HDAC2"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 1.0571245
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq HDAC2 genetically modified (insertion) using CRISPR targeting H. sapiens HDAC2"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ stage: "child"
+ }
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+ data_source: "encode"
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+ nonzero_mean: 0.8557186
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.93281776
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq HHEX genetically modified (insertion) using CRISPR targeting H. sapiens HHEX"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9259558
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq HIC2 genetically modified (insertion) using CRISPR targeting H. sapiens HIC2"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
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+ }
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+ data_source: "encode"
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+ genetically_modified: true
+ nonzero_mean: 0.85272926
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq HINFP genetically modified (insertion) using CRISPR targeting H. sapiens HINFP"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.98594093
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq HIVEP1 genetically modified (insertion) using CRISPR targeting H. sapiens HIVEP1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.92670894
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq HLF genetically modified (insertion) using CRISPR targeting H. sapiens HLF"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.7414058
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq HMG20A genetically modified (insertion) using CRISPR targeting H. sapiens HMG20A"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9154619
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq HMG20B genetically modified (insertion) using CRISPR targeting H. sapiens HMG20B"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.91216326
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq HMGA1 genetically modified (insertion) using CRISPR targeting H. sapiens HMGA1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.989146
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq HMGXB3 genetically modified (insertion) using CRISPR targeting H. sapiens HMGXB3"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.8381025
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq HMGXB4 genetically modified (insertion) using CRISPR targeting H. sapiens HMGXB4"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ transcription_factor_code: "HMGXB4"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.76312906
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq HNF1A"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.85052454
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.84007984
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.7699144
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq HNF4A"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ transcription_factor_code: "HNF4A"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 2.1114626
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq HNF4A genetically modified (insertion) using CRISPR targeting H. sapiens HNF4A"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ transcription_factor_code: "HNF4A"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.7774928
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
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+ }
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+ transcription_factor_code: "HNF4G"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.8917911
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq HNF4G genetically modified (insertion) using CRISPR targeting H. sapiens HNF4G"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9067246
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq HNRNPL"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ transcription_factor_code: "HNRNPL"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 1.1379462
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq HOMEZ genetically modified (insertion) using CRISPR targeting H. sapiens HOMEZ"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ transcription_factor_code: "HOMEZ"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 1.0409911
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq HOXA10 genetically modified (insertion) using CRISPR targeting H. sapiens HOXA10"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.77877605
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq HOXA3 genetically modified (insertion) using CRISPR targeting H. sapiens HOXA3"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.94684875
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq HOXA5 genetically modified (insertion) using CRISPR targeting H. sapiens HOXA5"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ assay: "TF ChIP-seq"
+ transcription_factor_code: "HOXA5"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.8952536
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq HOXA9 genetically modified (insertion) using CRISPR targeting H. sapiens HOXA9"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
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+ transcription_factor_code: "HOXA9"
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.84700376
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq HOXD1 genetically modified (insertion) using CRISPR targeting H. sapiens HOXD1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
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+ }
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+ transcription_factor_code: "HOXD1"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.94916594
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq IKZF5 genetically modified (insertion) using CRISPR targeting H. sapiens IKZF5"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
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+ }
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+ transcription_factor_code: "IKZF5"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.95365065
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq IRF2 genetically modified (insertion) using CRISPR targeting H. sapiens IRF2"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
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+ }
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+ transcription_factor_code: "IRF2"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9966672
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq IRF5 genetically modified (insertion) using CRISPR targeting H. sapiens IRF5"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ transcription_factor_code: "IRF5"
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.82358146
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq IRF9 genetically modified (insertion) using CRISPR targeting H. sapiens IRF9"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ transcription_factor_code: "IRF9"
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.86297023
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+ metadata {
+ name: "EFO:0001187 TF ChIP-seq IRX3 genetically modified (insertion) using CRISPR targeting H. sapiens IRX3"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9514657
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ISL2 genetically modified (insertion) using CRISPR targeting H. sapiens ISL2"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ transcription_factor_code: "ISL2"
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.77761805
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq JRK genetically modified (insertion) using CRISPR targeting H. sapiens JRK"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
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+ }
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+ transcription_factor_code: "JRK"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.780865
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq JUN"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
+ biosample {
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+ name: "HepG2"
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+ transcription_factor_code: "JUN"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.971917
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq JUN genetically modified (insertion) using CRISPR targeting H. sapiens JUN"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
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+ transcription_factor_code: "JUN"
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9014906
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq JUNB genetically modified (insertion) using CRISPR targeting H. sapiens JUNB"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
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+ }
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+ transcription_factor_code: "JUNB"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.7203684
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq JUND"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
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+ }
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+ transcription_factor_code: "JUND"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.7910396
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq JUND genetically modified (insertion) using CRISPR targeting H. sapiens JUND"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "HepG2"
+ stage: "child"
+ }
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+ transcription_factor_code: "JUND"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.8167651
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq KAT7 genetically modified (insertion) using CRISPR targeting H. sapiens KAT7"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "KAT7"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.93214506
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq KAT8 genetically modified (insertion) using CRISPR targeting H. sapiens KAT8"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "KAT8"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.8809492
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq KDM1A"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "KDM1A"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.76072824
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq KDM1A genetically modified (insertion) using CRISPR targeting H. sapiens KDM1A"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 1.1691986
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 1.291099
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ stage: "child"
+ }
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+ data_source: "encode"
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+ genetically_modified: true
+ nonzero_mean: 0.9718155
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq KDM4B genetically modified (insertion) using CRISPR targeting H. sapiens KDM4B"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 1.1509298
+ }
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.7970802
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.80475575
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq KDM6A genetically modified (insertion) using CRISPR targeting H. sapiens KDM6A"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 1.0480748
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq KLF11 genetically modified (insertion) using CRISPR targeting H. sapiens KLF11"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9424619
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq KLF12 genetically modified (insertion) using CRISPR targeting H. sapiens KLF12"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9071262
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq KLF16 genetically modified (insertion) using CRISPR targeting H. sapiens KLF16"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.88684815
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq KLF6 genetically modified (insertion) using CRISPR targeting H. sapiens KLF6"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 1.1416146
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq KLF9 genetically modified (insertion) using CRISPR targeting H. sapiens KLF9"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 1.1526613
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq KMT2A genetically modified (insertion) using CRISPR targeting H. sapiens KMT2A"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9038779
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq KMT2B genetically modified (insertion) using CRISPR targeting H. sapiens KMT2B"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.8935265
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq LBX2 genetically modified (insertion) using CRISPR targeting H. sapiens LBX2"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 1.0390533
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq LCOR genetically modified (insertion) using CRISPR targeting H. sapiens LCOR"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ transcription_factor_code: "LCOR"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.97888446
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq LCORL genetically modified (insertion) using CRISPR targeting H. sapiens LCORL"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.7449824
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq LIN54 genetically modified (insertion) using CRISPR targeting H. sapiens LIN54"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ transcription_factor_code: "LIN54"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.668508
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq MAFF"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ transcription_factor_code: "MAFF"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 1.0693045
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq MAFK"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "MAFK"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.75921667
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq MAX"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "MAX"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.7345673
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq MAX genetically modified (insertion) using CRISPR targeting H. sapiens MAX"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "MAX"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.7718338
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq MAZ"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
+ biosample {
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "MAZ"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.9855629
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq MAZ genetically modified (insertion) using CRISPR targeting H. sapiens MAZ"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "MAZ"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9352208
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq MBD1 genetically modified (insertion) using CRISPR targeting H. sapiens MBD1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "MBD1"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.7678639
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq MED1 genetically modified (insertion) using CRISPR targeting H. sapiens MED1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "MED1"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.75068367
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq MED13 genetically modified (insertion) using CRISPR targeting H. sapiens MED13"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
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+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "MED13"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9461348
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq MEF2A genetically modified (insertion) using CRISPR targeting H. sapiens MEF2A"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
+ biosample {
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "MEF2A"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9211392
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq MEF2D genetically modified (insertion) using CRISPR targeting H. sapiens MEF2D"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
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+ name: "HepG2"
+ stage: "child"
+ }
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+ transcription_factor_code: "MEF2D"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.77333605
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq MEIS1 genetically modified (insertion) using CRISPR targeting H. sapiens MEIS1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "MEIS1"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.8699822
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq MEIS2 genetically modified (insertion) using CRISPR targeting H. sapiens MEIS2"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "MEIS2"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9746243
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq MGA genetically modified (insertion) using CRISPR targeting H. sapiens MGA"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "MGA"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.6775914
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq MIER2 genetically modified (insertion) using CRISPR targeting H. sapiens MIER2"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "MIER2"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.88545775
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq MIER3 genetically modified (insertion) using CRISPR targeting H. sapiens MIER3"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "MIER3"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 1.0941529
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq MIXL1 genetically modified (insertion) using CRISPR targeting H. sapiens MIXL1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "MIXL1"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9499258
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq MLX genetically modified (insertion) using CRISPR targeting H. sapiens MLX"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "MLX"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.92768526
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq MNT"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "MNT"
+ data_source: "encode"
+ endedness: ENDEDNESS_PAIRED
+ genetically_modified: false
+ nonzero_mean: 0.78525686
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq MNX1 genetically modified (insertion) using CRISPR targeting H. sapiens MNX1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "MNX1"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.898628
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq MTA1 genetically modified (insertion) using CRISPR targeting H. sapiens MTA1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "MTA1"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.916018
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq MTERF2 genetically modified (insertion) using CRISPR targeting MTERF2"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "MTERF2"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.98426926
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq MTERF4 genetically modified (insertion) using CRISPR targeting H. sapiens MTERF4"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "MTERF4"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.83709306
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq MTF2 genetically modified (insertion) using CRISPR targeting H. sapiens MTF2"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ nonzero_mean: 0.8092882
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.8651671
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ nonzero_mean: 0.9163497
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ nonzero_mean: 0.85243005
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: false
+ nonzero_mean: 0.86856616
+ }
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+ id: 1187
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+ genetically_modified: false
+ nonzero_mean: 0.7688247
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.8381992
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.97542274
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.7448338
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.7920828
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ genetically_modified: true
+ nonzero_mean: 0.7196491
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ nonzero_mean: 0.82471246
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.7357526
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: false
+ nonzero_mean: 0.83722776
+ }
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+ name: "EFO:0001187 TF ChIP-seq NFAT5 genetically modified (insertion) using CRISPR targeting H. sapiens NFAT5"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.9975799
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.9190858
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.86119926
+ }
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+ genetically_modified: false
+ nonzero_mean: 0.80974877
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 1.0022056
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.9625779
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
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+ genetically_modified: true
+ nonzero_mean: 1.1339674
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+ id: 1187
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+ genetically_modified: true
+ nonzero_mean: 0.773661
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.8818619
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+ name: "EFO:0001187 TF ChIP-seq NFYC genetically modified (insertion) using CRISPR targeting H. sapiens NFYC"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.7825516
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq NKX3-1 genetically modified (insertion) using CRISPR targeting H. sapiens NKX3-1"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.8665351
+ }
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+ }
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+ genetically_modified: false
+ nonzero_mean: 1.142638
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ nonzero_mean: 0.7481791
+ }
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+ name: "EFO:0001187 TF ChIP-seq NR0B2 genetically modified (insertion) using CRISPR targeting H. sapiens NR0B2"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.90564334
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq NR2C2"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 1.3596267
+ }
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+ name: "EFO:0001187 TF ChIP-seq NR2C2 genetically modified (insertion) using CRISPR targeting H. sapiens NR2C2"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.77385134
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq NR2F1 genetically modified (insertion) using CRISPR targeting H. sapiens NR2F1"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ endedness: ENDEDNESS_PAIRED
+ genetically_modified: true
+ nonzero_mean: 0.7655856
+ }
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+ name: "EFO:0001187 TF ChIP-seq NR2F2"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.89913803
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq NR2F6"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.8041383
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq NR2F6 genetically modified (insertion) using CRISPR targeting H. sapiens NR2F6"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.97901917
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq NR5A1 genetically modified (insertion) using CRISPR targeting H. sapiens NR5A1"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ name: "HepG2"
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+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.7186427
+ }
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+ name: "EFO:0001187 TF ChIP-seq NRF1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.9646557
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq NRF1 genetically modified (insertion) using CRISPR targeting H. sapiens NRF1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.80055314
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq NRL genetically modified (insertion) using CRISPR targeting H. sapiens NRL"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9735867
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ONECUT1 genetically modified (insertion) using CRISPR targeting H. sapiens ONECUT1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.79045755
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ONECUT2 genetically modified (insertion) using CRISPR targeting H. sapiens ONECUT2"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
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+ name: "HepG2"
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+ }
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+ transcription_factor_code: "ONECUT2"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.90987086
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq PATZ1 genetically modified (insertion) using CRISPR targeting H. sapiens PATZ1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "PATZ1"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.935971
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq PAX8 genetically modified (insertion) using CRISPR targeting H. sapiens PAX8"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.80044174
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.88174915
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ genetically_modified: false
+ nonzero_mean: 1.2073888
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.7696751
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ genetically_modified: true
+ nonzero_mean: 0.6937997
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ genetically_modified: true
+ nonzero_mean: 1.8379198
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.87437797
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ genetically_modified: true
+ nonzero_mean: 0.7807845
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq POGK genetically modified (insertion) using CRISPR targeting H. sapiens POGK"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ genetically_modified: true
+ nonzero_mean: 0.82304454
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.7550853
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ genetically_modified: false
+ nonzero_mean: 1.2048081
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 1.0297232
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.9985802
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 1.7229251
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq POU2F1 genetically modified (insertion) using CRISPR targeting H. sapiens POU2F1"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.8800401
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq PPARG genetically modified (insertion) using CRISPR targeting H. sapiens PPARG"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.8973638
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq PRDM10 genetically modified (insertion) using CRISPR targeting H. sapiens PRDM10"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 1.0740415
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq PRDM15 genetically modified (insertion) using CRISPR targeting H. sapiens PRDM15"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.7786189
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq PRMT3 genetically modified (insertion) using CRISPR targeting H. sapiens PRMT3"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 1.0405605
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq PROX1 genetically modified (insertion) using CRISPR targeting H. sapiens PROX1"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ genetically_modified: true
+ nonzero_mean: 0.7121201
+ }
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+ name: "EFO:0001187 TF ChIP-seq RAD21"
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+ id: 1187
+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.7698555
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq RAD21 genetically modified (insertion) using CRISPR targeting H. sapiens RAD21"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ endedness: ENDEDNESS_PAIRED
+ genetically_modified: true
+ nonzero_mean: 0.79147804
+ }
+ metadata {
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+ id: 1187
+ }
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+ }
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+ genetically_modified: false
+ nonzero_mean: 0.80117005
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq RARA genetically modified (insertion) using CRISPR targeting H. sapiens RARA"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.9285251
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: false
+ nonzero_mean: 1.9982499
+ }
+ metadata {
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+ id: 1187
+ }
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+ genetically_modified: false
+ nonzero_mean: 1.1799737
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq RBPJ genetically modified (insertion) using CRISPR targeting H. sapiens RBPJ"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.8219014
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq RCOR2 genetically modified (insertion) using CRISPR targeting H. sapiens RCOR2"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.92265123
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq REL genetically modified (insertion) using CRISPR targeting H. sapiens REL"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 1.0532374
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq REPIN1 genetically modified (insertion) using CRISPR targeting H. sapiens REPIN1"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.92535293
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq RERE genetically modified (insertion) using CRISPR targeting H. sapiens RERE"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 1.0478591
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq REST"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.7586207
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq RFX1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_PAIRED
+ genetically_modified: false
+ nonzero_mean: 0.7884062
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq RFX3 genetically modified (insertion) using CRISPR targeting H. sapiens RFX3"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
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+ }
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+ transcription_factor_code: "RFX3"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.8227243
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq RFX5"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.9266943
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq RFXANK genetically modified (insertion) using CRISPR targeting H. sapiens RFXANK"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "RFXANK"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.94988954
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq RFXAP genetically modified (insertion) using CRISPR targeting H. sapiens RFXAP"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "RFXAP"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.7844979
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq RNF2"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "RNF2"
+ data_source: "encode"
+ endedness: ENDEDNESS_PAIRED
+ genetically_modified: false
+ nonzero_mean: 0.8168863
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq RREB1 genetically modified (insertion) using CRISPR targeting H. sapiens RREB1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ transcription_factor_code: "RREB1"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 1.0106076
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq RXRA"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
+ biosample {
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "RXRA"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 1.1971974
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq RXRA genetically modified (insertion) using CRISPR targeting H. sapiens RXRA"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "RXRA"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 1.1213042
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ data_source: "encode"
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+ genetically_modified: true
+ nonzero_mean: 0.861291
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 1.031803
+ }
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ nonzero_mean: 0.9423277
+ }
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ nonzero_mean: 0.9552118
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 1.1284693
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ nonzero_mean: 0.7817855
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: false
+ nonzero_mean: 1.0873052
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.95030004
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.9222258
+ }
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+ id: 1187
+ }
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+ genetically_modified: false
+ nonzero_mean: 0.80087006
+ }
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.76317513
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ nonzero_mean: 0.98637617
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 1.029334
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
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+ nonzero_mean: 0.90151906
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.7529114
+ }
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ genetically_modified: false
+ nonzero_mean: 0.90925246
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.74202925
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.86195225
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
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+ genetically_modified: true
+ nonzero_mean: 0.7249415
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
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+ genetically_modified: true
+ nonzero_mean: 0.7284869
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: false
+ nonzero_mean: 0.90332854
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.8848526
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq SOX5 genetically modified (insertion) using CRISPR targeting H. sapiens SOX5"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.8656462
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq SOX6 genetically modified (insertion) using CRISPR targeting H. sapiens SOX6"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.6767212
+ }
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+ name: "EFO:0001187 TF ChIP-seq SP1 genetically modified (insertion) using CRISPR targeting H. sapiens SP1"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.9969764
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
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+ nonzero_mean: 0.77177876
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ nonzero_mean: 0.743215
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 1.0091558
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq SPEN genetically modified (insertion) using CRISPR targeting H. sapiens SPEN"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9445015
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 1.1052982
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq SRF genetically modified (insertion) using CRISPR targeting H. sapiens SRF"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ transcription_factor_code: "SRF"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.77108306
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq SRSF1"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 1.1889074
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq SRY genetically modified (insertion) using CRISPR targeting H. sapiens SRY"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ transcription_factor_code: "SRY"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.81865746
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq SSRP1 genetically modified (insertion) using CRISPR targeting H. sapiens SSRP1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.8533491
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq STAG1 genetically modified (insertion) using CRISPR targeting H. sapiens STAG1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.8101588
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq STAT6 genetically modified (insertion) using CRISPR targeting H. sapiens STAT6"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ transcription_factor_code: "STAT6"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.77169865
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq SYNCRIP"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
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+ transcription_factor_code: "SYNCRIP"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 1.1821725
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq TAF1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 1.1410555
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq TAF1 genetically modified (insertion) using CRISPR targeting H. sapiens TAF1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "TAF1"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.8850846
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq TARDBP genetically modified (insertion) using CRISPR targeting H. sapiens TARDBP"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "TARDBP"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.74156994
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq TBL1XR1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "TBL1XR1"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.9321562
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq TBP"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
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+ name: "HepG2"
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+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ nonzero_mean: 0.7747897
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ genetically_modified: false
+ nonzero_mean: 0.797741
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ nonzero_mean: 0.8203737
+ }
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+ strand: STRAND_UNSTRANDED
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+ }
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+ nonzero_mean: 0.7893352
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: false
+ nonzero_mean: 0.87224954
+ }
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+ strand: STRAND_UNSTRANDED
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+ }
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+ genetically_modified: true
+ nonzero_mean: 0.7317715
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ genetically_modified: true
+ nonzero_mean: 0.9257048
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ genetically_modified: true
+ nonzero_mean: 1.0511607
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: false
+ nonzero_mean: 0.87596065
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ genetically_modified: true
+ nonzero_mean: 0.8421273
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq TFDP1 genetically modified (insertion) using CRISPR targeting H. sapiens TFDP1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9420638
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ genetically_modified: true
+ nonzero_mean: 0.9153624
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq TFE3 genetically modified (insertion) using CRISPR targeting H. sapiens TFE3"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9042627
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq TGIF2 genetically modified (insertion) using CRISPR targeting H. sapiens TGIF2"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ genetically_modified: true
+ nonzero_mean: 0.85958546
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq THAP11 genetically modified (insertion) using CRISPR targeting H. sapiens THAP11"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.91381633
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.88511854
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ genetically_modified: true
+ nonzero_mean: 0.7786097
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ genetically_modified: true
+ nonzero_mean: 0.95983195
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq THRA genetically modified (insertion) using CRISPR targeting H. sapiens THRA"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.9196424
+ }
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+ name: "EFO:0001187 TF ChIP-seq THRB genetically modified (insertion) using CRISPR targeting H. sapiens THRB"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.89207524
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq TIGD6 genetically modified (insertion) using CRISPR targeting H. sapiens TIGD6"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 1.0031519
+ }
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+ name: "EFO:0001187 TF ChIP-seq TMF1 genetically modified (insertion) using CRISPR targeting H. sapiens TMF1"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 1.0196383
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq TOPORS genetically modified (insertion) using CRISPR targeting H. sapiens TOPORS"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 1.0070366
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq TSC22D2 genetically modified (insertion) using CRISPR targeting H. sapiens TSC22D2"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ genetically_modified: true
+ nonzero_mean: 0.88274735
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq U2AF1"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 1.0612825
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq UBTF genetically modified (insertion) using CRISPR targeting H. sapiens UBTF"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.84334415
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq USF1"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 1.0987445
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq USF1 genetically modified (insertion) using CRISPR targeting H. sapiens USF1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.90084547
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.92077386
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq USF2 genetically modified (insertion) using CRISPR targeting H. sapiens USF2"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.7270067
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq WIZ genetically modified (insertion) using CRISPR targeting H. sapiens WIZ"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ transcription_factor_code: "WIZ"
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.8840749
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq XRCC5"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 1.051743
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq YEATS2 genetically modified (insertion) using CRISPR targeting H. sapiens YEATS2"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9096387
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq YEATS4 genetically modified (insertion) using CRISPR targeting H. sapiens YEATS4"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
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+ }
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+ transcription_factor_code: "YEATS4"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.80547017
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZBED4 genetically modified (insertion) using CRISPR targeting H. sapiens ZBED4"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ name: "HepG2"
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+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.89982814
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZBTB10 genetically modified (insertion) using CRISPR targeting H. sapiens ZBTB10"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9627752
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZBTB14 genetically modified (insertion) using CRISPR targeting H. sapiens ZBTB14"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.80318004
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZBTB2 genetically modified (insertion) using CRISPR targeting H. sapiens ZBTB2"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
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+ name: "HepG2"
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+ }
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+ transcription_factor_code: "ZBTB2"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9911439
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZBTB20 genetically modified (insertion) using CRISPR targeting H. sapiens ZBTB20"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "ZBTB20"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.89094865
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZBTB21 genetically modified (insertion) using CRISPR targeting H. sapiens ZBTB21"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "ZBTB21"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9285419
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZBTB25 genetically modified (insertion) using CRISPR targeting H. sapiens ZBTB25"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "HepG2"
+ stage: "child"
+ }
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+ nonzero_mean: 0.9135708
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ stage: "child"
+ }
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+ data_source: "encode"
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+ genetically_modified: true
+ nonzero_mean: 1.0108758
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ genetically_modified: true
+ nonzero_mean: 1.0485961
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
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+ genetically_modified: true
+ nonzero_mean: 1.1343882
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
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+ genetically_modified: true
+ nonzero_mean: 0.76407754
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ data_source: "encode"
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+ genetically_modified: true
+ nonzero_mean: 0.8237205
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ data_source: "encode"
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+ genetically_modified: true
+ nonzero_mean: 1.0577255
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.8480784
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9013466
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_PAIRED
+ genetically_modified: false
+ nonzero_mean: 0.8051753
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.7787505
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZBTB42 genetically modified (insertion) using CRISPR targeting H. sapiens ZBTB42"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.8204653
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZBTB43 genetically modified (insertion) using CRISPR targeting H. sapiens ZBTB43"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.8609236
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZBTB7A genetically modified (insertion) using CRISPR targeting H. sapiens ZBTB7A"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.77056336
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9123577
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZC3H13 genetically modified (insertion) using CRISPR targeting H. sapiens ZC3H13"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.82971364
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZC3H4 genetically modified (insertion) using CRISPR targeting H. sapiens ZC3H4"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 1.0672674
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.8621376
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZFHX3 genetically modified (insertion) using CRISPR targeting H. sapiens ZFHX3"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.81296855
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZFP1 genetically modified (insertion) using CRISPR targeting H. sapiens ZFP1"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 1.1859967
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZFP36L1 genetically modified (insertion) using CRISPR targeting H. sapiens ZFP36L1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.78046834
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZFP37 genetically modified (insertion) using CRISPR targeting H. sapiens ZFP37"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.97363466
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZFP64 genetically modified (insertion) using CRISPR targeting H. sapiens ZFP64"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9026479
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZFP82 genetically modified (insertion) using CRISPR targeting H. sapiens ZFP82"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.88678443
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZFP90 genetically modified (insertion) using CRISPR targeting H. sapiens ZFP90"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.7457436
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZFP91 genetically modified (insertion) using CRISPR targeting H. sapiens ZFP91"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ transcription_factor_code: "ZFP91"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.6807087
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZFX genetically modified (insertion) using CRISPR targeting H. sapiens ZFX"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
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+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "ZFX"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 1.0690839
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZFY genetically modified (insertion) using CRISPR targeting H. sapiens ZFY"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "ZFY"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.7878024
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZGPAT genetically modified (insertion) using CRISPR targeting H. sapiens ZGPAT"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ transcription_factor_code: "ZGPAT"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.7887438
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZHX1 genetically modified (insertion) using CRISPR targeting H. sapiens ZHX1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
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+ }
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+ transcription_factor_code: "ZHX1"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9614714
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZHX2"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "ZHX2"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.9897312
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZIK1 genetically modified (insertion) using CRISPR targeting H. sapiens ZIK1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ transcription_factor_code: "ZIK1"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 1.1059079
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZKSCAN1"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
+ biosample {
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "ZKSCAN1"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.8748328
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZKSCAN8 genetically modified (insertion) using CRISPR targeting H. sapiens ZKSCAN8"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "ZKSCAN8"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9579379
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZMAT3 genetically modified (insertion) using CRISPR targeting H. sapiens ZMAT3"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "ZMAT3"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.8773549
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZMYM2 genetically modified (insertion) using CRISPR targeting H. sapiens ZMYM2"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "ZMYM2"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.7612022
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZMYM3"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "ZMYM3"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.9067436
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZMYM3 genetically modified (insertion) using CRISPR targeting H. sapiens ZMYM3"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "ZMYM3"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.8465488
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZMYM4 genetically modified (insertion) using CRISPR targeting H. sapiens ZMYM4"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
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+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "ZMYM4"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.74258846
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZNF12 genetically modified (insertion) using CRISPR targeting H. sapiens ZNF12"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "ZNF12"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.93400633
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZNF121 genetically modified (insertion) using CRISPR targeting H. sapiens ZNF121"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1187
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "HepG2"
+ stage: "child"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "ZNF121"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.7899303
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZNF124 genetically modified (insertion) using CRISPR targeting H. sapiens ZNF124"
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+ name: "EFO:0001187 TF ChIP-seq ZNF33B genetically modified (insertion) using CRISPR targeting H. sapiens ZNF33B"
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+ name: "EFO:0001187 TF ChIP-seq ZNF34 genetically modified (insertion) using CRISPR targeting H. sapiens ZNF34"
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+ name: "EFO:0001187 TF ChIP-seq ZNF343 genetically modified (insertion) using CRISPR targeting H. sapiens ZNF343"
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+ id: 1187
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+ name: "EFO:0001187 TF ChIP-seq ZNF350 genetically modified (insertion) using CRISPR targeting H. sapiens ZNF350"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
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+ nonzero_mean: 0.96532506
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+ name: "EFO:0001187 TF ChIP-seq ZNF362 genetically modified (insertion) using CRISPR targeting H. sapiens ZNF362"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ nonzero_mean: 0.86067444
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZNF384"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ }
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+ name: "EFO:0001187 TF ChIP-seq ZNF619 genetically modified (insertion) using CRISPR targeting H. sapiens ZNF619"
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+ id: 1187
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+ nonzero_mean: 0.990168
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+ name: "EFO:0001187 TF ChIP-seq ZNF629 genetically modified (insertion) using CRISPR targeting H. sapiens ZNF629"
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+ }
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+ nonzero_mean: 0.8020602
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+ strand: STRAND_UNSTRANDED
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+ nonzero_mean: 0.8549058
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+ nonzero_mean: 0.87277454
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+ genetically_modified: true
+ nonzero_mean: 0.92786306
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+ strand: STRAND_UNSTRANDED
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+ nonzero_mean: 0.7928083
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+ strand: STRAND_UNSTRANDED
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+ nonzero_mean: 0.8465497
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+ nonzero_mean: 0.93146074
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+ nonzero_mean: 1.0018752
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+ nonzero_mean: 1.0056388
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+ nonzero_mean: 0.8330787
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+ nonzero_mean: 0.83035296
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+ nonzero_mean: 0.81185377
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+ name: "EFO:0001187 TF ChIP-seq ZNF780A genetically modified (insertion) using CRISPR targeting H. sapiens ZNF780A"
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+ nonzero_mean: 0.8805251
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+ name: "EFO:0001187 TF ChIP-seq ZNF782 genetically modified (insertion) using CRISPR targeting H. sapiens ZNF782"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
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+ genetically_modified: true
+ nonzero_mean: 0.8793778
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+ id: 1187
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+ nonzero_mean: 0.93710655
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+ strand: STRAND_UNSTRANDED
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+ id: 1187
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+ nonzero_mean: 0.8060355
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+ id: 1187
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+ nonzero_mean: 0.91449606
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+ name: "EFO:0001187 TF ChIP-seq ZNF790 genetically modified (insertion) using CRISPR targeting H. sapiens ZNF790"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
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+ genetically_modified: true
+ nonzero_mean: 0.94077784
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+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZNF792 genetically modified (insertion) using CRISPR targeting H. sapiens ZNF792"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
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+ nonzero_mean: 0.97873926
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZNF800 genetically modified (insertion) using CRISPR targeting H. sapiens ZNF800"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
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+ nonzero_mean: 0.8069674
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+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZNF816 genetically modified (insertion) using CRISPR targeting H. sapiens ZNF816"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
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+ genetically_modified: true
+ nonzero_mean: 0.797886
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+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZNF827 genetically modified (insertion) using CRISPR targeting H. sapiens ZNF827"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
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+ genetically_modified: true
+ nonzero_mean: 0.844661
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+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZNF83 genetically modified (insertion) using CRISPR targeting H. sapiens ZNF83"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
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+ genetically_modified: true
+ nonzero_mean: 0.991773
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+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZNF839 genetically modified (insertion) using CRISPR targeting H. sapiens ZNF839"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
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+ genetically_modified: true
+ nonzero_mean: 0.9501647
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZNF865 genetically modified (insertion) using CRISPR targeting H. sapiens ZNF865"
+ strand: STRAND_UNSTRANDED
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+ id: 1187
+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.82911205
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZNF878 genetically modified (insertion) using CRISPR targeting H. sapiens ZNF878"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.7349171
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZNF879 genetically modified (insertion) using CRISPR targeting H. sapiens ZNF879"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
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+ name: "HepG2"
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+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "ZNF879"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.91422164
+ }
+ metadata {
+ name: "EFO:0001187 TF ChIP-seq ZNF891 genetically modified (insertion) using CRISPR targeting H. sapiens ZNF891"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1187
+ }
+ biosample {
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+ name: "HepG2"
+ stage: "child"
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+ strand: STRAND_UNSTRANDED
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+ strand: STRAND_UNSTRANDED
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+ id: 9747
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+ nonzero_mean: 0.83110285
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+ strand: STRAND_UNSTRANDED
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+ id: 9747
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+ strand: STRAND_UNSTRANDED
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+ id: 9747
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+ endedness: ENDEDNESS_PAIRED
+ genetically_modified: true
+ nonzero_mean: 0.8117094
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+ strand: STRAND_UNSTRANDED
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+ id: 9747
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+ nonzero_mean: 0.95435697
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 9747
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+ genetically_modified: true
+ nonzero_mean: 0.8792568
+ }
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+ strand: STRAND_UNSTRANDED
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+ id: 9747
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+ }
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+ genetically_modified: true
+ nonzero_mean: 1.0199659
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+ strand: STRAND_UNSTRANDED
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+ id: 9747
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+ genetically_modified: true
+ nonzero_mean: 0.8360226
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+ strand: STRAND_UNSTRANDED
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+ id: 9747
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+ genetically_modified: true
+ nonzero_mean: 0.82790816
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+ name: "EFO:0009747 TF ChIP-seq HMGA2 genetically modified (insertion) using CRISPR targeting H. sapiens HMGA2"
+ strand: STRAND_UNSTRANDED
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+ id: 9747
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+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_PAIRED
+ genetically_modified: true
+ nonzero_mean: 0.8182102
+ }
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 9747
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+ stage: "adult"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.8219743
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 9747
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+ genetically_modified: true
+ nonzero_mean: 0.7492046
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+ strand: STRAND_UNSTRANDED
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+ id: 9747
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+ }
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.92763263
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 9747
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+ stage: "adult"
+ }
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.7811118
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 9747
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+ data_source: "encode"
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+ genetically_modified: true
+ nonzero_mean: 0.8625381
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+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 9747
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.8829251
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+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 9747
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+ genetically_modified: true
+ nonzero_mean: 0.81444037
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+ name: "EFO:0009747 TF ChIP-seq SP1 genetically modified (insertion) using CRISPR targeting H. sapiens SP1"
+ strand: STRAND_UNSTRANDED
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+ id: 9747
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+ genetically_modified: true
+ nonzero_mean: 0.8424248
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+ strand: STRAND_UNSTRANDED
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+ id: 9747
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+ genetically_modified: true
+ nonzero_mean: 0.8904773
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+ strand: STRAND_UNSTRANDED
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+ id: 9747
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9260721
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+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 9747
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+ endedness: ENDEDNESS_PAIRED
+ genetically_modified: true
+ nonzero_mean: 0.79829186
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+ metadata {
+ name: "EFO:0009747 TF ChIP-seq TP53 genetically modified (insertion) using CRISPR targeting H. sapiens TP53"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 9747
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.98063385
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+ metadata {
+ name: "EFO:0009747 TF ChIP-seq USF1 genetically modified (insertion) using CRISPR targeting H. sapiens USF1"
+ strand: STRAND_UNSTRANDED
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+ id: 9747
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+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.7855583
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+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 9747
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.8767126
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+ metadata {
+ name: "EFO:0009747 TF ChIP-seq ZBTB43 genetically modified (insertion) using CRISPR targeting H. sapiens ZBTB43"
+ strand: STRAND_UNSTRANDED
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+ id: 9747
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+ genetically_modified: true
+ nonzero_mean: 0.7904143
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+ metadata {
+ name: "EFO:0009747 TF ChIP-seq ZNF121 genetically modified (insertion) using CRISPR targeting H. sapiens ZNF121"
+ strand: STRAND_UNSTRANDED
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+ id: 9747
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 1.0113666
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+ metadata {
+ name: "EFO:0009747 TF ChIP-seq ZNF143 genetically modified (insertion) using CRISPR targeting H. sapiens ZNF143"
+ strand: STRAND_UNSTRANDED
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+ id: 9747
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.77276355
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+ metadata {
+ name: "EFO:0009747 TF ChIP-seq ZNF184 genetically modified (insertion) using CRISPR targeting H. sapiens ZNF184"
+ strand: STRAND_UNSTRANDED
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+ id: 9747
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9689387
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+ metadata {
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+ strand: STRAND_UNSTRANDED
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+ id: 9747
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.9380618
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+ strand: STRAND_UNSTRANDED
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+ id: 9747
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+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: true
+ nonzero_mean: 0.86809087
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+ metadata {
+ name: "EFO:0009747 TF ChIP-seq ZNF423 genetically modified (insertion) using CRISPR targeting H. sapiens ZNF423"
+ strand: STRAND_UNSTRANDED
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+ id: 9747
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+ endedness: ENDEDNESS_PAIRED
+ genetically_modified: true
+ nonzero_mean: 0.8296819
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+ metadata {
+ name: "EFO:0030018 TF ChIP-seq Cebpa"
+ strand: STRAND_UNSTRANDED
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+ id: 30018
+ }
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+ name: "BLaER1"
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+ transcription_factor_code: "Cebpa"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.93563324
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+ metadata {
+ name: "NTR:0000493 TF ChIP-seq CTCF"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 493
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "left ventricle myocardium inferior"
+ stage: "adult"
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+ assay: "TF ChIP-seq"
+ transcription_factor_code: "CTCF"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.8832361
+ }
+ metadata {
+ name: "NTR:0000512 TF ChIP-seq CTCF"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 512
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_IN_VITRO_DIFFERENTIATED_CELLS
+ name: "nephron progenitor cell"
+ stage: "embryonic"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "CTCF"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.837974
+ }
+ metadata {
+ name: "NTR:0001226 TF ChIP-seq CTCF"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 1226
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "parathyroid adenoma"
+ stage: "adult"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "CTCF"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.75042826
+ }
+ metadata {
+ name: "UBERON:0000317 TF ChIP-seq CTCF"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 317
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "colonic mucosa"
+ stage: "adult"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "CTCF"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 0.90573937
+ }
+ metadata {
+ name: "UBERON:0000473 TF ChIP-seq CTCF"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 473
+ }
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+ name: "testis"
+ stage: "adult"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "CTCF"
+ data_source: "encode"
+ endedness: ENDEDNESS_PAIRED
+ genetically_modified: false
+ nonzero_mean: 0.78738564
+ }
+ metadata {
+ name: "UBERON:0000945 TF ChIP-seq CTCF"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 945
+ }
+ biosample {
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+ name: "stomach"
+ stage: "adult"
+ }
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+ transcription_factor_code: "CTCF"
+ data_source: "encode"
+ endedness: ENDEDNESS_PAIRED
+ genetically_modified: false
+ nonzero_mean: 0.76264536
+ }
+ metadata {
+ name: "UBERON:0000945 TF ChIP-seq POLR2A"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
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+ id: 945
+ }
+ biosample {
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+ name: "stomach"
+ stage: "adult"
+ }
+ assay: "TF ChIP-seq"
+ transcription_factor_code: "POLR2A"
+ data_source: "encode"
+ endedness: ENDEDNESS_PAIRED
+ genetically_modified: false
+ nonzero_mean: 0.78645986
+ }
+ metadata {
+ name: "UBERON:0000945 TF ChIP-seq POLR2AphosphoS5"
+ strand: STRAND_UNSTRANDED
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+ data_source: "4dnucleome"
+ }
+ metadata {
+ name: "4dn:4DNFIXOWKAM6"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 7073
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "CyT49"
+ }
+ assay: "in situ Hi-C"
+ data_source: "4dnucleome"
+ }
+ metadata {
+ name: "4dn:4DNFIYGPDLKF"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 9747
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_IN_VITRO_DIFFERENTIATED_CELLS
+ name: "GM25256"
+ }
+ assay: "in situ Hi-C"
+ data_source: "4dnucleome"
+ }
+ }
+}
+output_metadata {
+ output_type: OUTPUT_TYPE_PROCAP
+ tracks {
+ metadata {
+ name: "ENCSR072YCM"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 2106
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "A673"
+ }
+ assay: "PRO-cap"
+ data_source: "encode"
+ genetically_modified: false
+ nonzero_mean: 13.308302
+ }
+ metadata {
+ name: "ENCSR182QNJ"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1099
+ }
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+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "Caco-2"
+ }
+ assay: "PRO-cap"
+ data_source: "encode"
+ genetically_modified: false
+ nonzero_mean: 14.002803
+ }
+ metadata {
+ name: "ENCSR740IPL"
+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2067
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "K562"
+ }
+ assay: "PRO-cap"
+ data_source: "encode"
+ genetically_modified: false
+ nonzero_mean: 15.765458
+ }
+ metadata {
+ name: "ENCSR797DEF"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 2819
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "Calu3"
+ }
+ assay: "PRO-cap"
+ data_source: "encode"
+ genetically_modified: false
+ nonzero_mean: 12.281321
+ }
+ metadata {
+ name: "ENCSR801ECP"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2618
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "endothelial cell of umbilical vein"
+ }
+ assay: "PRO-cap"
+ data_source: "encode"
+ genetically_modified: false
+ nonzero_mean: 13.973692
+ }
+ metadata {
+ name: "ENCSR860TYZ"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1200
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "MCF 10A"
+ }
+ assay: "PRO-cap"
+ data_source: "encode"
+ genetically_modified: false
+ nonzero_mean: 8.229502
+ }
+ metadata {
+ name: "ENCSR072YCM"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 2106
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "A673"
+ }
+ assay: "PRO-cap"
+ data_source: "encode"
+ genetically_modified: false
+ nonzero_mean: 13.308302
+ }
+ metadata {
+ name: "ENCSR182QNJ"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1099
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "Caco-2"
+ }
+ assay: "PRO-cap"
+ data_source: "encode"
+ genetically_modified: false
+ nonzero_mean: 14.002803
+ }
+ metadata {
+ name: "ENCSR740IPL"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 2067
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "K562"
+ }
+ assay: "PRO-cap"
+ data_source: "encode"
+ genetically_modified: false
+ nonzero_mean: 15.765458
+ }
+ metadata {
+ name: "ENCSR797DEF"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 2819
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "Calu3"
+ }
+ assay: "PRO-cap"
+ data_source: "encode"
+ genetically_modified: false
+ nonzero_mean: 12.281321
+ }
+ metadata {
+ name: "ENCSR801ECP"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2618
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+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "endothelial cell of umbilical vein"
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+ data_source: "encode"
+ genetically_modified: false
+ nonzero_mean: 13.973692
+ }
+ metadata {
+ name: "ENCSR860TYZ"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 1200
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "MCF 10A"
+ }
+ assay: "PRO-cap"
+ data_source: "encode"
+ genetically_modified: false
+ nonzero_mean: 8.229502
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
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+ strand: STRAND_UNSTRANDED
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diff --git a/flax_model/alphagenome/model/metadata/OutputMetadataResponse_ORGANISM_MUS_MUSCULUS.textproto b/flax_model/alphagenome/model/metadata/OutputMetadataResponse_ORGANISM_MUS_MUSCULUS.textproto
new file mode 100644
index 0000000000000000000000000000000000000000..191df891a5d004a741dce9502a07fb8a15c54c1f
--- /dev/null
+++ b/flax_model/alphagenome/model/metadata/OutputMetadataResponse_ORGANISM_MUS_MUSCULUS.textproto
@@ -0,0 +1,38015 @@
+output_metadata {
+ output_type: OUTPUT_TYPE_ATAC
+ tracks {
+ metadata {
+ name: "CL:0000553 ATAC-seq"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 553
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "megakaryocyte progenitor cell"
+ stage: "adult"
+ }
+ assay: "ATAC-seq"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 2.0059075
+ }
+ metadata {
+ name: "CL:0000557 ATAC-seq"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 557
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "granulocyte monocyte progenitor cell"
+ stage: "adult"
+ }
+ assay: "ATAC-seq"
+ data_source: "encode"
+ endedness: ENDEDNESS_SINGLE
+ genetically_modified: false
+ nonzero_mean: 1.8014821
+ }
+ metadata {
+ name: "CL:0000792 ATAC-seq"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 792
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "CD4-positive, CD25-positive, alpha-beta regulatory T cell"
+ stage: "adult"
+ }
+ assay: "ATAC-seq"
+ data_source: "encode"
+ endedness: ENDEDNESS_PAIRED
+ genetically_modified: false
+ nonzero_mean: 3.1498485
+ }
+ metadata {
+ name: "EFO:0005481 ATAC-seq"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_EFO
+ id: 5481
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_CELL_LINE
+ name: "Patski"
+ stage: "unknown"
+ }
+ assay: "ATAC-seq"
+ data_source: "encode"
+ endedness: ENDEDNESS_PAIRED
+ genetically_modified: false
+ nonzero_mean: 2.5373273
+ }
+ metadata {
+ name: "NTR:0004148 ATAC-seq"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_NTR
+ id: 4148
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "activated CD4-positive, CD25-positive, alpha-beta regulatory T cell"
+ stage: "adult"
+ }
+ assay: "ATAC-seq"
+ data_source: "encode"
+ endedness: ENDEDNESS_PAIRED
+ genetically_modified: false
+ nonzero_mean: 5.3025045
+ }
+ metadata {
+ name: "NTR:0004218 ATAC-seq"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_NTR
+ id: 4218
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "inflammation-experienced regulatory T-cells"
+ stage: "adult"
+ }
+ assay: "ATAC-seq"
+ data_source: "encode"
+ endedness: ENDEDNESS_PAIRED
+ genetically_modified: false
+ nonzero_mean: 3.5693285
+ }
+ metadata {
+ name: "UBERON:0000160 ATAC-seq"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 160
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "intestine"
+ stage: "embryonic"
+ }
+ assay: "ATAC-seq"
+ data_source: "encode"
+ endedness: ENDEDNESS_PAIRED
+ genetically_modified: false
+ nonzero_mean: 0.50628954
+ }
+ metadata {
+ name: "UBERON:0000945 ATAC-seq"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 945
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "stomach"
+ stage: "embryonic"
+ }
+ assay: "ATAC-seq"
+ data_source: "encode"
+ endedness: ENDEDNESS_PAIRED
+ genetically_modified: false
+ nonzero_mean: 0.76339036
+ }
+ metadata {
+ name: "UBERON:0000948 ATAC-seq"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 948
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "heart"
+ stage: "postnatal"
+ }
+ assay: "ATAC-seq"
+ data_source: "encode"
+ endedness: ENDEDNESS_PAIRED
+ genetically_modified: false
+ nonzero_mean: 1.6121877
+ }
+ metadata {
+ name: "UBERON:0001049 ATAC-seq"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1049
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "neural tube"
+ stage: "embryonic"
+ }
+ assay: "ATAC-seq"
+ data_source: "encode"
+ endedness: ENDEDNESS_PAIRED
+ genetically_modified: false
+ nonzero_mean: 0.6336626
+ }
+ metadata {
+ name: "UBERON:0001890 ATAC-seq"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1890
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "forebrain"
+ stage: "embryonic"
+ }
+ assay: "ATAC-seq"
+ data_source: "encode"
+ endedness: ENDEDNESS_PAIRED
+ genetically_modified: false
+ nonzero_mean: 0.9003088
+ }
+ metadata {
+ name: "UBERON:0001891 ATAC-seq"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1891
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "midbrain"
+ stage: "embryonic"
+ }
+ assay: "ATAC-seq"
+ data_source: "encode"
+ endedness: ENDEDNESS_PAIRED
+ genetically_modified: false
+ nonzero_mean: 0.39195514
+ }
+ metadata {
+ name: "UBERON:0002028 ATAC-seq"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 2028
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "hindbrain"
+ stage: "embryonic"
+ }
+ assay: "ATAC-seq"
+ data_source: "encode"
+ endedness: ENDEDNESS_PAIRED
+ genetically_modified: false
+ nonzero_mean: 0.9220704
+ }
+ metadata {
+ name: "UBERON:0002048 ATAC-seq"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 2048
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "lung"
+ stage: "embryonic"
+ }
+ assay: "ATAC-seq"
+ data_source: "encode"
+ endedness: ENDEDNESS_PAIRED
+ genetically_modified: false
+ nonzero_mean: 1.0387475
+ }
+ metadata {
+ name: "UBERON:0002101 ATAC-seq"
+ strand: STRAND_UNSTRANDED
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 2101
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "limb"
+ stage: "embryonic"
+ }
+ assay: "ATAC-seq"
+ data_source: "encode"
+ endedness: ENDEDNESS_PAIRED
+ genetically_modified: false
+ nonzero_mean: 0.7174459
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+ id: 2679
+ }
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+ id: 1846
+ }
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+ }
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+ id: 8345
+ }
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+ }
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+ id: 57
+ }
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+ }
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+ id: 120
+ }
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+ }
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+ id: 127
+ }
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+ }
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+ strand: STRAND_POSITIVE
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+ id: 129
+ }
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+ }
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+ id: 182
+ }
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+ }
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+ strand: STRAND_POSITIVE
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+ id: 556
+ }
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+ }
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+ strand: STRAND_POSITIVE
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+ id: 584
+ }
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+ }
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+ id: 708
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+ }
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+ id: 746
+ }
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+ }
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+ }
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+ strand: STRAND_POSITIVE
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+ id: 792
+ }
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+ }
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+ strand: STRAND_POSITIVE
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+ id: 815
+ }
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+ name: "regulatory T cell"
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+ }
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+ strand: STRAND_POSITIVE
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+ id: 895
+ }
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+ nonzero_mean: 14.649805
+ }
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+ strand: STRAND_POSITIVE
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+ id: 2027
+ }
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+ nonzero_mean: 34.760723
+ }
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+ strand: STRAND_POSITIVE
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+ id: 2476
+ }
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+ }
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+ strand: STRAND_POSITIVE
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+ id: 2518
+ }
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+ name: "kidney epithelial cell"
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+ }
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+ strand: STRAND_POSITIVE
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+ id: 2539
+ }
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+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2540
+ }
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+ name: "mesenchymal stem cell of the bone marrow"
+ }
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+ nonzero_mean: 92.87085
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2573
+ }
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+ name: "Schwann cell"
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+ }
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+ strand: STRAND_POSITIVE
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+ id: 2603
+ }
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+ }
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+ strand: STRAND_POSITIVE
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+ id: 2604
+ }
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+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2608
+ }
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+ name: "hippocampal neuron"
+ }
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+ nonzero_mean: 33.78767
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2609
+ }
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+ name: "neuron of cerebral cortex"
+ }
+ assay: "hCAGE"
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+ nonzero_mean: 27.179565
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2610
+ }
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+ name: "raphe nuclei neuron"
+ }
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+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2611
+ }
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+ name: "neuron of the dorsal spinal cord"
+ }
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+ nonzero_mean: 46.089535
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2612
+ }
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+ name: "neuron of the ventral spinal cord"
+ }
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+ nonzero_mean: 21.191984
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2613
+ }
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+ name: "striatum neuron"
+ }
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+ nonzero_mean: 28.230087
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2614
+ }
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+ name: "neuron of the substantia nigra"
+ }
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+ nonzero_mean: 19.907124
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2679
+ }
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+ name: "natural helper lymphocyte"
+ }
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+ nonzero_mean: 20.598215
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 7
+ }
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+ name: "pituitary gland"
+ }
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+ data_source: "fantom"
+ nonzero_mean: 13.597229
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 11
+ }
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+ name: "parasympathetic nervous system"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 35.16954
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 13
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "sympathetic nervous system"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 39.309265
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 14
+ }
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+ name: "zone of skin"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 14.241336
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 160
+ }
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+ name: "intestine"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 8.152917
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 305
+ }
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+ name: "amnion"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 73.25513
+ }
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+ name: "hCAGE UBERON:0000369"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 369
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "corpus striatum"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 35.717545
+ }
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+ name: "hCAGE UBERON:0000395"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 395
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "cochlear ganglion"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 3821.5642
+ }
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+ name: "hCAGE UBERON:0000411"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 411
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "visual cortex"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 4.5925612
+ }
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+ name: "hCAGE UBERON:0000468"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 468
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "multi-cellular organism"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 9.044567
+ }
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+ name: "hCAGE UBERON:0000473"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 473
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "testis"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 7.3344584
+ }
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+ name: "hCAGE UBERON:0000945"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 945
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "stomach"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 7.9631357
+ }
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+ name: "hCAGE UBERON:0000947"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 947
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "aorta"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 61.979507
+ }
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+ name: "hCAGE UBERON:0000948"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 948
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "heart"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 5.2578735
+ }
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+ name: "hCAGE UBERON:0000956"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 956
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "cerebral cortex"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 31.386848
+ }
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+ name: "hCAGE UBERON:0000981"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 981
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "femur"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 10.422715
+ }
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+ name: "hCAGE UBERON:0000991"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 991
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "gonad"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 69.94967
+ }
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+ name: "hCAGE UBERON:0000992"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 992
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "female gonad"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 26.524761
+ }
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+ name: "hCAGE UBERON:0000993"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 993
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "oviduct"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 53.848206
+ }
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+ name: "hCAGE UBERON:0000995"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 995
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "uterus"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 33.440983
+ }
+ metadata {
+ name: "hCAGE UBERON:0000996"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 996
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "vagina"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 74.360535
+ }
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+ name: "hCAGE UBERON:0001097"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1097
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "axillary lymph node"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 86.4815
+ }
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+ name: "hCAGE UBERON:0001153"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1153
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "caecum"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 397.71814
+ }
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+ name: "hCAGE UBERON:0001155"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1155
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "colon"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 71.374115
+ }
+ metadata {
+ name: "hCAGE UBERON:0001242"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1242
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "intestinal mucosa"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 95.13166
+ }
+ metadata {
+ name: "hCAGE UBERON:0001255"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1255
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "urinary bladder"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 75.30576
+ }
+ metadata {
+ name: "hCAGE UBERON:0001264"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1264
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "pancreas"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 17.015985
+ }
+ metadata {
+ name: "hCAGE UBERON:0001301"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1301
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "epididymis"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 77.781105
+ }
+ metadata {
+ name: "hCAGE UBERON:0001343"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1343
+ }
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+ }
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+ nonzero_mean: 23.197002
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 1374
+ }
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+ name: "biceps femoris"
+ }
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+ nonzero_mean: 80.4616
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 1723
+ }
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+ name: "tongue"
+ }
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+ data_source: "fantom"
+ nonzero_mean: 82.55242
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 1736
+ }
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+ name: "submandibular gland"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 83.95293
+ }
+ metadata {
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 1846
+ }
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+ name: "internal ear"
+ }
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+ nonzero_mean: 79.36323
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 1894
+ }
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+ name: "diencephalon"
+ }
+ assay: "hCAGE"
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+ nonzero_mean: 30.838457
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 1896
+ }
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+ name: "medulla oblongata"
+ }
+ assay: "hCAGE"
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+ nonzero_mean: 26.781517
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 1911
+ }
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+ name: "mammary gland"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 72.178
+ }
+ metadata {
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+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1954
+ }
+ biosample {
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+ name: "Ammon\'s horn"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 26.506804
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1962
+ }
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+ name: "gut-associated lymphoid tissue"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 77.35227
+ }
+ metadata {
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+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1987
+ }
+ biosample {
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+ name: "placenta"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 41.490517
+ }
+ metadata {
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+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 2005
+ }
+ biosample {
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+ name: "enteric nervous system"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 34.883015
+ }
+ metadata {
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2037
+ }
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+ name: "cerebellum"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 1.4653373
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2048
+ }
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+ name: "lung"
+ }
+ assay: "hCAGE"
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+ nonzero_mean: 6.0494323
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2102
+ }
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+ name: "forelimb"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 15.342677
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2106
+ }
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+ name: "spleen"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 13.448422
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2107
+ }
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+ name: "liver"
+ }
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+ data_source: "fantom"
+ nonzero_mean: 9.106997
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2108
+ }
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+ name: "small intestine"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 71.61957
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2113
+ }
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+ name: "kidney"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 6.424318
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2240
+ }
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+ name: "spinal cord"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 51.353176
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 2259
+ }
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+ name: "corpora quadrigemina"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 46.702587
+ }
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+ name: "hCAGE UBERON:0002367"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 2367
+ }
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+ name: "prostate gland"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 105.76006
+ }
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+ name: "hCAGE UBERON:0002369"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 2369
+ }
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+ name: "adrenal gland"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 14.322135
+ }
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+ name: "hCAGE UBERON:0002370"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 2370
+ }
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+ name: "thymus"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 5.4220276
+ }
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+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 2371
+ }
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+ name: "bone marrow"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 25.596548
+ }
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+ name: "hCAGE UBERON:0003199"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 3199
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "egg chamber"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 96.21967
+ }
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+ name: "hCAGE UBERON:0005366"
+ strand: STRAND_POSITIVE
+ ontology_term {
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+ id: 5366
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "olfactory lobe"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 69.50517
+ }
+ metadata {
+ name: "hCAGE UBERON:0007194"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 7194
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "vesicular gland"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 154.35504
+ }
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+ name: "hCAGE UBERON:0008345"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 8345
+ }
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+ type: BIOSAMPLE_TYPE_TISSUE
+ name: "ileal epithelium"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 85.81441
+ }
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+ name: "hCAGE UBERON:0010230"
+ strand: STRAND_POSITIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 10230
+ }
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+ name: "eyeball of camera-type eye"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 6.391803
+ }
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+ name: "LQhCAGE CL:0000355"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 355
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "multi-potent skeletal muscle stem cell"
+ }
+ assay: "LQhCAGE"
+ data_source: "fantom"
+ nonzero_mean: 49.478207
+ }
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+ name: "LQhCAGE CL:0000589"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 589
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "cochlear inner hair cell"
+ }
+ assay: "LQhCAGE"
+ data_source: "fantom"
+ nonzero_mean: 57.94036
+ }
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+ name: "LQhCAGE CL:0000682"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 682
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "M cell of gut"
+ }
+ assay: "LQhCAGE"
+ data_source: "fantom"
+ nonzero_mean: 62.229248
+ }
+ metadata {
+ name: "LQhCAGE CL:0002250"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2250
+ }
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+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "intestinal crypt stem cell"
+ }
+ assay: "LQhCAGE"
+ data_source: "fantom"
+ nonzero_mean: 46.34216
+ }
+ metadata {
+ name: "LQhCAGE CL:0002490"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2490
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "organ of Corti supporting cell"
+ }
+ assay: "LQhCAGE"
+ data_source: "fantom"
+ nonzero_mean: 53.684746
+ }
+ metadata {
+ name: "LQhCAGE CL:0002679"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2679
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "natural helper lymphocyte"
+ }
+ assay: "LQhCAGE"
+ data_source: "fantom"
+ nonzero_mean: 6.804822
+ }
+ metadata {
+ name: "LQhCAGE UBERON:0001846"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 1846
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "internal ear"
+ }
+ assay: "LQhCAGE"
+ data_source: "fantom"
+ nonzero_mean: 57.7682
+ }
+ metadata {
+ name: "LQhCAGE UBERON:0008345"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 8345
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "ileal epithelium"
+ }
+ assay: "LQhCAGE"
+ data_source: "fantom"
+ nonzero_mean: 113.6109
+ }
+ metadata {
+ name: "hCAGE CL:0000057"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 57
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "fibroblast"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 91.2073
+ }
+ metadata {
+ name: "hCAGE CL:0000120"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 120
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "granule cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 52.363674
+ }
+ metadata {
+ name: "hCAGE CL:0000127"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 127
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "astrocyte"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 50.04389
+ }
+ metadata {
+ name: "hCAGE CL:0000129"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 129
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "microglial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 69.32667
+ }
+ metadata {
+ name: "hCAGE CL:0000182"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 182
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "hepatocyte"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 22.757858
+ }
+ metadata {
+ name: "hCAGE CL:0000556"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 556
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "megakaryocyte"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 30.92711
+ }
+ metadata {
+ name: "hCAGE CL:0000584"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 584
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "enterocyte"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 79.27254
+ }
+ metadata {
+ name: "hCAGE CL:0000708"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 708
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "leptomeningeal cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 39.985085
+ }
+ metadata {
+ name: "hCAGE CL:0000746"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 746
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "cardiac muscle cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 45.310127
+ }
+ metadata {
+ name: "hCAGE CL:0000792"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 792
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "CD4-positive, CD25-positive, alpha-beta regulatory T cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 32.396492
+ }
+ metadata {
+ name: "hCAGE CL:0000815"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 815
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "regulatory T cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 22.00554
+ }
+ metadata {
+ name: "hCAGE CL:0000895"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 895
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "naive thymus-derived CD4-positive, alpha-beta T cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 14.649805
+ }
+ metadata {
+ name: "hCAGE CL:0002027"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2027
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "CD9-positive, CD41-positive megakaryocyte cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 34.760723
+ }
+ metadata {
+ name: "hCAGE CL:0002476"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2476
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "bone marrow macrophage"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 69.98207
+ }
+ metadata {
+ name: "hCAGE CL:0002518"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2518
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "kidney epithelial cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 72.04951
+ }
+ metadata {
+ name: "hCAGE CL:0002539"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2539
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "aortic smooth muscle cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 26.27274
+ }
+ metadata {
+ name: "hCAGE CL:0002540"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2540
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "mesenchymal stem cell of the bone marrow"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 92.87085
+ }
+ metadata {
+ name: "hCAGE CL:0002573"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2573
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "Schwann cell"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 29.621336
+ }
+ metadata {
+ name: "hCAGE CL:0002603"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2603
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "astrocyte of the cerebellum"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 90.208885
+ }
+ metadata {
+ name: "hCAGE CL:0002604"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2604
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "astrocyte of the hippocampus"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 62.070293
+ }
+ metadata {
+ name: "hCAGE CL:0002608"
+ strand: STRAND_NEGATIVE
+ ontology_term {
+ ontology_type: ONTOLOGY_TYPE_CL
+ id: 2608
+ }
+ biosample {
+ type: BIOSAMPLE_TYPE_PRIMARY_CELL
+ name: "hippocampal neuron"
+ }
+ assay: "hCAGE"
+ data_source: "fantom"
+ nonzero_mean: 33.78767
+ }
+ metadata {
+ name: "hCAGE CL:0002609"
+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 2609
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+ id: 1343
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+ id: 1374
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+ id: 1896
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+ id: 1911
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+ id: 1962
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+ id: 1987
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+ id: 2005
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+ id: 2037
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+ id: 2048
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+ id: 2102
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+ name: "forelimb"
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+ id: 2106
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+ name: "spleen"
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+ strand: STRAND_NEGATIVE
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+ id: 2107
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+ nonzero_mean: 9.106997
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+ id: 2108
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+ name: "small intestine"
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+ strand: STRAND_NEGATIVE
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+ id: 2113
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+ name: "kidney"
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+ strand: STRAND_NEGATIVE
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+ id: 2240
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+ name: "spinal cord"
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+ nonzero_mean: 51.353176
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+ strand: STRAND_NEGATIVE
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+ id: 2259
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+ name: "corpora quadrigemina"
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+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 2367
+ }
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+ name: "prostate gland"
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+ data_source: "fantom"
+ nonzero_mean: 105.76006
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+ strand: STRAND_NEGATIVE
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+ id: 2369
+ }
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+ name: "adrenal gland"
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+ nonzero_mean: 14.322135
+ }
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+ strand: STRAND_NEGATIVE
+ ontology_term {
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+ id: 2370
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+ name: "thymus"
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+ data_source: "fantom"
+ nonzero_mean: 5.4220276
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+ strand: STRAND_NEGATIVE
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+ ontology_type: ONTOLOGY_TYPE_UBERON
+ id: 2371
+ }
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+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ metadata {
+ name: "Padding"
+ strand: STRAND_UNSTRANDED
+ }
+ }
+}
diff --git a/flax_model/alphagenome/model/metadata/__init__.py b/flax_model/alphagenome/model/metadata/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ac91ecd062eee536567baa42db67767038a0cff
--- /dev/null
+++ b/flax_model/alphagenome/model/metadata/__init__.py
@@ -0,0 +1,15 @@
+# Copyright 2026 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.
+
+"""AlphaGenome Model Metadata."""
diff --git a/flax_model/alphagenome/model/metadata/metadata.py b/flax_model/alphagenome/model/metadata/metadata.py
new file mode 100644
index 0000000000000000000000000000000000000000..1112c5f1c80dd04fabd8648164610dfe24429c41
--- /dev/null
+++ b/flax_model/alphagenome/model/metadata/metadata.py
@@ -0,0 +1,159 @@
+# Copyright 2026 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.
+
+"""AlphaGenome output metadata."""
+
+from importlib import resources
+from collections.abc import Collection, Mapping
+import dataclasses
+import functools
+import pathlib
+
+from flax_model.alphagenome._sdk import typing
+from flax_model.alphagenome._sdk.data import ontology
+from flax_model.alphagenome._sdk.data import track_data
+from flax_model.alphagenome._sdk.models import dna_client
+from flax_model.alphagenome._sdk.models import dna_model
+from flax_model.alphagenome._sdk.models import dna_output
+from flax_model.alphagenome._sdk.protos import dna_model_service_pb2
+from google.protobuf import text_format
+from jaxtyping import Bool, Int32 # pylint: disable=g-importing-member, g-multiple-import
+import numpy as np
+
+_PATH_METADATA = 'model/metadata/'
+_PADDING_TRACK_NAME = 'padding'
+
+
+def _create_output_strand_reindexing(
+ metadata: track_data.TrackMetadata,
+ num_tracks: int,
+) -> Int32[np.ndarray, '_']:
+ """Creates the strand reindexing for a given output type's track metadata."""
+ strands = metadata['strand'].values
+ reindex_array = np.arange(num_tracks, dtype=np.int32)
+ positive_indices = np.flatnonzero(strands == '+')
+ negative_indices = np.flatnonzero(strands == '-')
+ reindex_array[positive_indices] = negative_indices
+ reindex_array[negative_indices] = positive_indices
+ return reindex_array
+
+
+@typing.jaxtyped
+@dataclasses.dataclass(frozen=True, kw_only=True)
+class AlphaGenomeOutputMetadata(dna_output.OutputMetadata):
+ """AlphaGenome output metadata."""
+
+ def resolution(self, output: dna_output.OutputType) -> int:
+ """Returns the resolution of the given output type."""
+ match output:
+ case dna_output.OutputType.ATAC:
+ return 1
+ case dna_output.OutputType.CAGE:
+ return 1
+ case dna_output.OutputType.DNASE:
+ return 1
+ case dna_output.OutputType.RNA_SEQ:
+ return 1
+ case dna_output.OutputType.CHIP_HISTONE:
+ return 128
+ case dna_output.OutputType.CHIP_TF:
+ return 128
+ case dna_output.OutputType.SPLICE_SITES:
+ return 1
+ case dna_output.OutputType.SPLICE_SITE_USAGE:
+ return 1
+ case dna_output.OutputType.SPLICE_JUNCTIONS:
+ return 1
+ case dna_output.OutputType.CONTACT_MAPS:
+ return 2048
+ case dna_output.OutputType.PROCAP:
+ return 1
+ case _:
+ raise ValueError(f'Unknown {output=}')
+
+ @functools.cached_property
+ def padding(self) -> Mapping[dna_output.OutputType, Bool[np.ndarray, '_']]:
+ """Returns mapping of output type to padding mask."""
+ padding = {}
+ for output_type in dna_output.OutputType:
+ if (metadata := self.get(output_type)) is not None:
+ padding[output_type] = (
+ metadata['name'].str.lower() == _PADDING_TRACK_NAME
+ ).values
+ return padding
+
+ @functools.cached_property
+ def strand_reindexing(
+ self,
+ ) -> Mapping[dna_output.OutputType, Int32[np.ndarray, '_']]:
+ """Return mapping of output type to negative strand reindexing."""
+ result = {}
+ for output_type in dna_output.OutputType:
+ if (metadata := self.get(output_type)) is not None:
+ num_tracks = len(metadata)
+ if output_type == dna_output.OutputType.SPLICE_JUNCTIONS:
+ # Splice junction metadata is strand agnostic, but predictions are
+ # structured as all positive followed by all negative strands.
+ result[output_type] = np.concatenate((
+ np.arange(num_tracks, num_tracks * 2),
+ np.arange(num_tracks),
+ ))
+ else:
+ result[output_type] = _create_output_strand_reindexing(
+ metadata, num_tracks
+ )
+ return result
+
+
+@typing.jaxtyped
+def create_track_masks(
+ metadata: AlphaGenomeOutputMetadata,
+ *,
+ requested_outputs: Collection[dna_output.OutputType],
+ requested_ontologies: Collection[ontology.OntologyTerm] | None,
+) -> Mapping[dna_output.OutputType, Bool[np.ndarray, '_']]:
+ """Creates track masks for the requested output types and ontologies."""
+ track_masks = {}
+ for output_type in requested_outputs:
+ if (output_metadata := metadata.get(output_type)) is None:
+ continue
+
+ if (
+ requested_ontologies is not None
+ # Splice sites are tissue agnostic.
+ and output_type != dna_output.OutputType.SPLICE_SITES
+ ):
+ ontology_curies = {o.ontology_curie for o in requested_ontologies}
+ mask = np.asarray(
+ [o in ontology_curies for o in output_metadata['ontology_curie']]
+ )
+ else:
+ mask = ~np.array(metadata.padding[output_type])
+
+ track_masks[output_type] = mask
+
+ return track_masks
+
+
+def load(organism: dna_model.Organism) -> AlphaGenomeOutputMetadata:
+ """Loads the metadata for a given organism."""
+ file_name = f'OutputMetadataResponse_ORGANISM_{organism.name}.textproto'
+ path = pathlib.Path(_PATH_METADATA, file_name)
+ content = resources.files('flax_model.alphagenome').joinpath(path).read_text()
+ metadata_response = text_format.Parse(
+ content, dna_model_service_pb2.MetadataResponse()
+ )
+ return AlphaGenomeOutputMetadata(
+ **vars(dna_client.construct_output_metadata(iter((metadata_response,))))
+ )
diff --git a/flax_model/alphagenome/model/metadata/metadata_test.py b/flax_model/alphagenome/model/metadata/metadata_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e1bf26ef80de5c939ef7e22aac335b26dccfdfb
--- /dev/null
+++ b/flax_model/alphagenome/model/metadata/metadata_test.py
@@ -0,0 +1,215 @@
+# Copyright 2026 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 Mapping, Sequence
+from absl.testing import absltest
+from absl.testing import parameterized
+from flax_model.alphagenome._sdk.data import ontology
+from flax_model.alphagenome._sdk.models import dna_model
+from flax_model.alphagenome._sdk.models import dna_output
+from flax_model.alphagenome.model.metadata import metadata as metadata_lib
+import chex
+import jax
+import numpy as np
+import pandas as pd
+
+
+class MetadataTest(parameterized.TestCase):
+
+ @parameterized.parameters([
+ dict(organism=dna_model.Organism.HOMO_SAPIENS),
+ dict(organism=dna_model.Organism.MUS_MUSCULUS),
+ ])
+ def test_load(self, organism: dna_model.Organism):
+ metadata = metadata_lib.load(organism)
+ self.assertIsInstance(metadata, dna_output.OutputMetadata)
+ reindexing = metadata.strand_reindexing
+ self.assertIsInstance(reindexing, dict)
+ self.assertCountEqual(dna_output.OutputType, reindexing.keys())
+
+ def test_organism_shapes_match(self):
+ metadata = tuple(metadata_lib.load(o) for o in dna_model.Organism)
+ for output_type in dna_output.OutputType:
+ with self.subTest(f'{output_type=}'):
+ track_metadata = tuple(m.get(output_type).values for m in metadata)
+ # Only assert the first track dimension matches, as fields may differ.
+ chex.assert_equal_shape_prefix(track_metadata, 1)
+
+ @parameterized.named_parameters([
+ dict(
+ testcase_name='Normal',
+ output_type=dna_output.OutputType.ATAC,
+ names=[f'track_{i}' for i in range(6)],
+ strands=['+', '+', '+', '-', '-', '-'],
+ expected=[3, 4, 5, 0, 1, 2],
+ ),
+ dict(
+ testcase_name='Unstranded',
+ output_type=dna_output.OutputType.RNA_SEQ,
+ names=[f'track_{i}' for i in range(6)],
+ strands=['.', '.', '.', '.', '.', '.'],
+ expected=[0, 1, 2, 3, 4, 5],
+ ),
+ dict(
+ testcase_name='Interleaved',
+ output_type=dna_output.OutputType.RNA_SEQ,
+ names=[f'track_{i}' for i in range(6)],
+ strands=['+', '-', '+', '-', '+', '-'],
+ expected=[1, 0, 3, 2, 5, 4],
+ ),
+ dict(
+ testcase_name='Padding',
+ output_type=dna_output.OutputType.DNASE,
+ names=[f'track_{i}' for i in range(6)] + ['Padding', 'padding'],
+ strands=['+', '-', '+', '-', '+', '-', '.', '.'],
+ expected=[1, 0, 3, 2, 5, 4, 6, 7],
+ ),
+ dict(
+ testcase_name='SpliceJunctions',
+ output_type=dna_output.OutputType.SPLICE_JUNCTIONS,
+ names=[f'track_{i}' for i in range(3)],
+ strands=['.'] * 3,
+ expected=[3, 4, 5, 0, 1, 2],
+ ),
+ dict(
+ testcase_name='SpliceJunctionPadding',
+ output_type=dna_output.OutputType.SPLICE_JUNCTIONS,
+ names=[f'track_{i}' for i in range(3)] + ['PADDING'],
+ strands=['.'] * 4,
+ expected=[4, 5, 6, 7, 0, 1, 2, 3],
+ ),
+ ])
+ def test_strand_reindexing(
+ self,
+ output_type: dna_output.OutputType,
+ names: Sequence[str],
+ strands: Sequence[str],
+ expected: Sequence[int],
+ ):
+ metadata = metadata_lib.AlphaGenomeOutputMetadata(
+ **{
+ output_type.name.lower(): pd.DataFrame(
+ {'name': names, 'strand': strands}
+ ),
+ },
+ )
+ result = metadata.strand_reindexing.get(output_type)
+ result = result.tolist() if result is not None else None
+ self.assertEqual(result, expected)
+
+ @parameterized.named_parameters(
+ (
+ 'Normal',
+ dna_output.OutputType.ATAC,
+ [f'track_{i}' for i in range(6)],
+ ['.'] * 6,
+ [False] * 6,
+ ),
+ (
+ 'Padding',
+ dna_output.OutputType.DNASE,
+ [f'track_{i}' for i in range(3)] + ['Padding', 'Padding'],
+ ['.'] * 3 + ['.', '.'],
+ [False] * 3 + [True, True],
+ ),
+ )
+ def test_padding(
+ self,
+ output_type: dna_output.OutputType,
+ names: Sequence[str],
+ strands: Sequence[str],
+ expected: Sequence[int],
+ ):
+ metadata = metadata_lib.AlphaGenomeOutputMetadata(
+ **{
+ output_type.name.lower(): pd.DataFrame(
+ {'name': names, 'strand': strands}
+ )
+ },
+ )
+ np.testing.assert_array_equal(metadata.padding.get(output_type), expected)
+
+ @parameterized.named_parameters(
+ (
+ 'SingleOutput',
+ [dna_output.OutputType.ATAC],
+ None,
+ {dna_output.OutputType.ATAC: [True, True, True]},
+ ),
+ (
+ 'SingleOntology',
+ [dna_output.OutputType.ATAC],
+ [ontology.from_curie('CL:0000084')],
+ {dna_output.OutputType.ATAC: [True, True, False]},
+ ),
+ (
+ 'MultipleOutputsMissingOntology',
+ [dna_output.OutputType.ATAC, dna_output.OutputType.DNASE],
+ [ontology.from_curie('CL:0000001')],
+ {
+ dna_output.OutputType.ATAC: [False, False, False],
+ dna_output.OutputType.DNASE: [True, True, True, False, False],
+ },
+ ),
+ (
+ 'SpliceSites',
+ [dna_output.OutputType.SPLICE_SITES],
+ [ontology.from_curie('CL:0000001')],
+ {
+ dna_output.OutputType.SPLICE_SITES: [True, True, True],
+ },
+ ),
+ (
+ 'AllOutputs',
+ list(dna_output.OutputType),
+ None,
+ {
+ dna_output.OutputType.ATAC: [True, True, True],
+ dna_output.OutputType.DNASE: [True, True, True, False, False],
+ dna_output.OutputType.SPLICE_SITES: [True, True, True],
+ },
+ ),
+ )
+ def test_create_track_masks(
+ self,
+ requested_outputs: Sequence[dna_output.OutputType],
+ requested_ontologies: Sequence[str] | None,
+ expected: Mapping[dna_output.OutputType, ...],
+ ):
+ example_metadata = metadata_lib.AlphaGenomeOutputMetadata(
+ atac=pd.DataFrame({
+ 'name': ['track_1', 'track_2', 'track_3'],
+ 'strand': ['+', '-', '.'],
+ 'ontology_curie': ['CL:0000084', 'CL:0000084', 'UBERONE:0000001'],
+ }),
+ dnase=pd.DataFrame({
+ 'name': [f'track_{i}' for i in range(3)] + ['Padding', 'Padding'],
+ 'strand': ['+'] * 3 + ['.', '.'],
+ 'ontology_curie': ['CL:0000001'] * 3 + ['', ''],
+ }),
+ splice_sites=pd.DataFrame({
+ 'name': [f'track_{i}' for i in range(3)],
+ 'strand': ['.'] * 3,
+ }),
+ )
+ track_masks = metadata_lib.create_track_masks(
+ example_metadata,
+ requested_outputs=requested_outputs,
+ requested_ontologies=requested_ontologies,
+ )
+ jax.tree.map(np.testing.assert_array_equal, track_masks, expected)
+
+
+if __name__ == '__main__':
+ absltest.main()
diff --git a/flax_model/alphagenome/model/model.py b/flax_model/alphagenome/model/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..36bf968730421a556d8f2ac28f07d30e50aa6265
--- /dev/null
+++ b/flax_model/alphagenome/model/model.py
@@ -0,0 +1,282 @@
+# Copyright 2026 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.
+
+"""AlphaGenome model."""
+
+from collections.abc import Mapping
+
+from flax_model.alphagenome._sdk import typing
+from flax_model.alphagenome._sdk.models import dna_model
+from flax_model.alphagenome.model import attention
+from flax_model.alphagenome.model import convolutions
+from flax_model.alphagenome.model import embeddings as embeddings_module
+from flax_model.alphagenome.model import heads as heads_module
+from flax_model.alphagenome.model import layers
+from flax_model.alphagenome.model import schemas
+from flax_model.alphagenome.model import splicing
+from flax_model.alphagenome.model.metadata import metadata as metadata_lib
+import haiku as hk
+import jax
+from jaxtyping import Array, Float, Int, PyTree, Shaped # pylint: disable=g-importing-member, g-multiple-import
+
+
+DEFAULT_NUM_SPLICE_SITES = 512
+DEFAULT_SPLICE_SITE_THRESHOLD = 0.1
+
+
+class SequenceEncoder(hk.Module):
+ """Encodes a sequence of DNA into embeddings."""
+
+ @typing.jaxtyped
+ def __call__(
+ self, dna_sequence: Float[Array, 'B S 4']
+ ) -> tuple[Float[Array, 'B S//128 D'], dict[str, Array]]:
+ intermediates = {}
+ x = convolutions.DnaEmbedder()(dna_sequence)
+ intermediates['bin_size_1'] = x
+ x = layers.pool(x)
+ for block_idx, bin_size in enumerate([2, 4, 8, 16, 32, 64]):
+ x = convolutions.DownResBlock(f'downres_block_{block_idx}')(x)
+ intermediates[f'bin_size_{bin_size}'] = x
+ x = layers.pool(x)
+ return x, intermediates
+
+
+class SequenceDecoder(hk.Module):
+ """Decodes a sequence of embeddings."""
+
+ @typing.jaxtyped
+ def __call__(
+ self, x: Float[Array, 'B S D'], intermediates: dict[str, Array]
+ ) -> Float[Array, 'B S_final D_final']:
+ for bin_size in [64, 32, 16, 8, 4, 2, 1]:
+ x = convolutions.UpResBlock()(x, intermediates[f'bin_size_{bin_size}'])
+ return x
+
+
+class TransformerTower(hk.Module):
+ """Transformer tower with interleaved pairwise updates."""
+
+ @typing.jaxtyped
+ def __call__(
+ self, x: Float[Array, 'B S C']
+ ) -> tuple[Float[Array, 'B S C'], Float[Array, 'B S//16 S//16 F'] | None]:
+ pair_x = None
+ for i in range(9):
+ if i % 2 == 0:
+ pair_x = attention.PairUpdateBlock()(x, pair_x)
+ mha_bias = attention.AttentionBiasBlock()(pair_x)
+ x += attention.MHABlock()(x, mha_bias)
+ x += attention.MLPBlock()(x)
+ return x, pair_x
+
+
+class AlphaGenome(hk.Module):
+ """Main AlphaGenome model.
+
+ The model architecture consists of a sequence encoder, a transformer tower,
+ and a sequence decoder. The output of the decoder is used to generate
+ embeddings at 1bp resolution, while the output of the transformer tower
+ is used to generate embeddings at 128bp resolution and pair embeddings.
+ These embeddings are then passed to various heads to make predictions.
+ """
+
+ def __init__(
+ self,
+ output_metadata: Mapping[
+ dna_model.Organism, metadata_lib.AlphaGenomeOutputMetadata
+ ],
+ *,
+ num_splice_sites: int = DEFAULT_NUM_SPLICE_SITES,
+ splice_site_threshold: float = DEFAULT_SPLICE_SITE_THRESHOLD,
+ freeze_trunk_embeddings: bool = False,
+ num_organisms: int = 2,
+ name: str | None = None,
+ ):
+ """Initializes the AlphaGenome model.
+
+ Args:
+ output_metadata: Metadata for the output tracks for each organism.
+ num_splice_sites: The maximum number of splice sites that are extracted
+ from the splice site classification predictions.
+ splice_site_threshold: The threshold to use for splice site prediction.
+ freeze_trunk_embeddings: Whether to stop the gradient to the embeddings.
+ This is useful for training only the heads in fine-tuning.
+ num_organisms: The number of organisms. This is used to initialize the
+ organism embedding layer. Default is 2, for human and mouse. Leave at 2
+ to load pre-trained weights.
+ name: The name of the module.
+ """
+
+ super().__init__(name=name or 'alphagenome')
+ self._output_metadata = output_metadata
+ self._num_splice_sites = num_splice_sites
+ self._splice_site_threshold = splice_site_threshold
+ self._freeze_trunk_embeddings = freeze_trunk_embeddings
+ self._num_organisms = num_organisms
+ self._heads: dict[heads_module.HeadName, heads_module.Head] = {}
+ for head in heads_module.HeadName:
+ output_type = heads_module.get_head_config(head).output_type
+ organisms_with_metadata = [
+ organism
+ for organism, metadata in output_metadata.items()
+ if metadata.get(output_type) is not None
+ ]
+ if not organisms_with_metadata:
+ # None of the organisms have metadata for this output type. Skip.
+ continue
+ missing_organisms = set(self._output_metadata.keys()) - set(
+ organisms_with_metadata
+ )
+ if missing_organisms:
+ raise ValueError(
+ f'No metadata found for output type "{output_type.name}" for the'
+ f' following organisms: {missing_organisms}. We expect the same set'
+ ' of output types for all organisms. Use padding to account for'
+ ' missing tracks.'
+ )
+ self._heads[head] = heads_module.create_head(
+ heads_module.get_head_config(head),
+ self._output_metadata,
+ num_organisms=num_organisms,
+ )
+
+ @hk.name_like('__call__')
+ def predict_junctions(
+ self,
+ trunk_embeddings: Float[Array, 'B S D'],
+ splice_site_positions: Int[Array, 'B 4 K'],
+ organism_index: Int[Array, 'B'],
+ ) -> PyTree[Float[Array, 'B ...'] | None]:
+ """Predicts splice site junctions from embeddings and splice site positions.
+
+ Args:
+ trunk_embeddings: The trunk embeddings to use for predictions.
+ splice_site_positions: The splice site positions. Format: [batch, 4,
+ num_splice_sites] with order: [donor_pos_idx, accept_pos_idx,
+ donor_neg_idx, accept_neg_idx]
+ organism_index: The organism index.
+
+ Returns:
+ The predictions for splice site junctions.
+ """
+ junction_head = self._heads.get(heads_module.HeadName.SPLICE_SITES_JUNCTION)
+ if junction_head is None:
+ raise ValueError('Junction head is not supported by this model.')
+ with hk.name_scope('head'):
+ return junction_head(
+ embeddings_module.Embeddings(embeddings_1bp=trunk_embeddings),
+ organism_index,
+ splice_site_positions=splice_site_positions,
+ )
+
+ @typing.jaxtyped
+ def __call__(
+ self,
+ dna_sequence: Float[Array, 'B S 4'],
+ organism_index: Int[Array, 'B'],
+ ) -> tuple[PyTree[Shaped[Array, 'B ...']], embeddings_module.Embeddings]:
+ """Encodes a sequence of DNA and makes predictions for various heads.
+
+ Args:
+ dna_sequence: The sequence of DNA to encode.
+ organism_index: The organism index.
+
+ Returns:
+ A tuple of (predictions, embeddings), where predictions is a dictionary
+ of predictions for various heads.
+ """
+ trunk, intermediates = SequenceEncoder()(dna_sequence)
+ if self._num_organisms >= 1:
+ organism_embedding_trunk = hk.Embed(self._num_organisms, trunk.shape[-1])(
+ organism_index
+ )
+ trunk += organism_embedding_trunk[:, None, :]
+ trunk, pair_activations = TransformerTower()(trunk)
+
+ x = SequenceDecoder()(trunk, intermediates)
+
+ embeddings_128bp = embeddings_module.OutputEmbedder(self._num_organisms)(
+ trunk, organism_index
+ )
+ embeddings_1bp = embeddings_module.OutputEmbedder(self._num_organisms)(
+ x, organism_index, embeddings_128bp
+ )
+ embeddings_pair = embeddings_module.OutputPair(self._num_organisms)(
+ pair_activations, organism_index
+ )
+
+ embeddings = embeddings_module.Embeddings(
+ embeddings_1bp=embeddings_1bp,
+ embeddings_128bp=embeddings_128bp,
+ embeddings_pair=embeddings_pair,
+ )
+ if self._freeze_trunk_embeddings:
+ embeddings = jax.lax.stop_gradient(embeddings)
+ predictions = {
+ 'embeddings_1bp': embeddings_1bp,
+ }
+ with hk.name_scope('head'):
+ for head_name, head_fn in self._heads.items():
+ if head_name == heads_module.HeadName.SPLICE_SITES_JUNCTION:
+ # This head is handled separately (see below).
+ continue
+ predictions[head_name.value] = head_fn(
+ embeddings,
+ organism_index,
+ )
+
+ # Handle the splice junction head separately. It requires splice site
+ # positions as input, which are derived from the splice site
+ # classification predictions.
+ if (
+ junction_head := heads_module.HeadName.SPLICE_SITES_JUNCTION
+ ) in self._heads:
+ if heads_module.HeadName.SPLICE_SITES_CLASSIFICATION not in self._heads:
+ raise ValueError(
+ 'SPLICE_SITES_CLASSIFICATION head is required for junctions'
+ ' predictions.'
+ )
+ splice_sites_probabilities = predictions[
+ heads_module.HeadName.SPLICE_SITES_CLASSIFICATION.value
+ ]['predictions']
+ splice_site_positions = splicing.generate_splice_site_positions(
+ splice_sites_probabilities,
+ alt=None,
+ splice_sites=None,
+ k=self._num_splice_sites,
+ pad_to_length=self._num_splice_sites,
+ threshold=self._splice_site_threshold,
+ )
+ predictions[junction_head.value] = self.predict_junctions(
+ embeddings.embeddings_1bp, splice_site_positions, organism_index
+ )
+ return predictions, embeddings
+
+ @typing.jaxtyped
+ def loss(
+ self, batch: schemas.DataBatch
+ ) -> tuple[
+ Float[Array, ''], PyTree[Float[Array, '']], PyTree[Shaped[Array, 'B ...']]
+ ]:
+ """Returns the loss for the model."""
+ predictions, _ = self(batch.dna_sequence, batch.get_organism_index())
+ total_loss, all_scalars = 0.0, {}
+ for head_name, head_fn in self._heads.items():
+ scalars = head_fn.loss(predictions[head_name.value], batch)
+ all_scalars.update(
+ {f'{head_name.value}_{k}': v for k, v in scalars.items()}
+ )
+ total_loss += scalars['loss']
+ return total_loss, all_scalars, predictions
diff --git a/flax_model/alphagenome/model/model_test.py b/flax_model/alphagenome/model/model_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..815623c0de2aaaba7551a8ec39551ddcb332cdef
--- /dev/null
+++ b/flax_model/alphagenome/model/model_test.py
@@ -0,0 +1,491 @@
+# Copyright 2026 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 flax_model.alphagenome._sdk.data import junction_data
+from flax_model.alphagenome._sdk.data import track_data
+from flax_model.alphagenome._sdk.models import dna_model
+from flax_model.alphagenome.model import embeddings as embeddings_lib
+from flax_model.alphagenome.model import model as model_lib
+from flax_model.alphagenome.model import schemas
+from flax_model.alphagenome.model.metadata import metadata as metadata_lib
+import chex
+import haiku as hk
+import jax
+import jax.numpy as jnp
+from jaxtyping import Array, Float, Int # pylint: disable=g-importing-member, g-multiple-import
+import numpy as np
+import pandas as pd
+
+
+def _mock_track_metadata(
+ num_tracks: int, num_padding: int = 0
+) -> track_data.TrackMetadata:
+ return pd.DataFrame({
+ 'name': (
+ [f'track_{i}' for i in range(num_tracks)] + ['padding'] * num_padding
+ ),
+ 'nonzero_mean': [1.0] * num_tracks + [0.0] * num_padding,
+ })
+
+
+def _mock_junction_metadata(
+ num_tissues: int, num_padding: int = 0
+) -> junction_data.JunctionMetadata:
+ return pd.DataFrame({
+ 'tissue': (
+ [f'tissue_{i}' for i in range(num_tissues)]
+ + ['padding'] * num_padding
+ ),
+ 'name': (
+ [f'tissue_{i}' for i in range(num_tissues)]
+ + ['padding'] * num_padding
+ ),
+ })
+
+
+class ModelTest(parameterized.TestCase):
+
+ def test_create_model_metadata_consistency(self):
+ """Tests that the model raises an error if the metadata is inconsistent."""
+ output_metadata_missing = {
+ dna_model.Organism.HOMO_SAPIENS: metadata_lib.AlphaGenomeOutputMetadata(
+ atac=_mock_track_metadata(num_tracks=13)
+ ),
+ dna_model.Organism.MUS_MUSCULUS: (
+ metadata_lib.AlphaGenomeOutputMetadata()
+ ),
+ }
+
+ @hk.transform_with_state
+ def forward(dna_sequence, organism_index):
+ return model_lib.AlphaGenome(output_metadata_missing)(
+ dna_sequence, organism_index
+ )
+
+ with self.assertRaisesRegex(
+ ValueError,
+ r'No metadata found for output type "ATAC" for the following organisms:'
+ r'.*MUS_MUSCULUS.*',
+ ):
+ jax.eval_shape(
+ forward.init,
+ jax.random.key(0),
+ jax.ShapeDtypeStruct((1, 2048, 4), jnp.float32),
+ jax.ShapeDtypeStruct((1,), jnp.int32),
+ )
+
+ @parameterized.named_parameters(
+ dict(
+ testcase_name='no_organisms',
+ organisms=(),
+ ),
+ dict(
+ testcase_name='one_organism',
+ organisms=(dna_model.Organism.HOMO_SAPIENS,),
+ ),
+ dict(
+ testcase_name='two_organisms',
+ organisms=(
+ dna_model.Organism.HOMO_SAPIENS,
+ dna_model.Organism.MUS_MUSCULUS,
+ ),
+ ),
+ )
+ def test_create_headless_model(
+ self, organisms: tuple[dna_model.Organism, ...]
+ ):
+ """Tests that the model can be created without any heads."""
+ output_metadata = {
+ organism: metadata_lib.AlphaGenomeOutputMetadata()
+ for organism in organisms
+ }
+
+ @hk.transform_with_state
+ def forward(dna_sequence, organism_index):
+ return model_lib.AlphaGenome(output_metadata)(
+ dna_sequence, organism_index
+ )
+
+ params_shape, state_shape = jax.eval_shape(
+ forward.init,
+ jax.random.key(0),
+ seq_input := jax.ShapeDtypeStruct((1, 2048, 4), jnp.float32),
+ organism_index := jax.ShapeDtypeStruct((1,), jnp.int32),
+ )
+ (predictions_shape, embeddings_shape), _ = jax.eval_shape(
+ forward.apply,
+ params_shape,
+ state_shape,
+ jax.random.key(0),
+ seq_input,
+ organism_index,
+ )
+ # Predictions are only the embeddings in this case.
+ chex.assert_trees_all_equal_shapes(
+ predictions_shape,
+ {'embeddings_1bp': jnp.zeros((1, 2048, 1536))},
+ )
+ chex.assert_trees_all_equal_shapes(
+ embeddings_shape,
+ embeddings_lib.Embeddings(
+ embeddings_1bp=jnp.zeros((1, 2048, 1536)),
+ embeddings_128bp=jnp.zeros((1, 16, 3072), jnp.float32),
+ embeddings_pair=jnp.zeros((1, 1, 1, 128), jnp.float32),
+ ),
+ )
+
+ def test_create_model(self):
+ num_tracks = 13
+ output_metadata = {
+ dna_model.Organism.HOMO_SAPIENS: metadata_lib.AlphaGenomeOutputMetadata(
+ atac=_mock_track_metadata(num_tracks=num_tracks)
+ ),
+ dna_model.Organism.MUS_MUSCULUS: metadata_lib.AlphaGenomeOutputMetadata(
+ atac=_mock_track_metadata(num_tracks=7, num_padding=num_tracks - 7)
+ ),
+ }
+
+ @hk.transform_with_state
+ def forward(
+ dna_sequence: Float[Array, 'B S 4'], organism_index: Int[Array, 'B']
+ ):
+ return model_lib.AlphaGenome(output_metadata)(
+ dna_sequence, organism_index
+ )
+
+ seq_length = int(2**14)
+ key = jax.random.PRNGKey(42)
+ dna_sequence = jnp.zeros((1, seq_length, 4))
+ organism_index = jnp.zeros((1,), dtype=jnp.int32)
+
+ params, state = forward.init(key, dna_sequence, organism_index)
+
+ (predictions, embeddings), _ = forward.apply(
+ params, state, key, dna_sequence, organism_index
+ )
+ self.assertEqual(embeddings.embeddings_1bp.shape, (1, seq_length, 1536))
+ self.assertEqual(
+ embeddings.embeddings_128bp.shape, (1, seq_length // 128, 3072)
+ )
+ self.assertEqual(
+ embeddings.embeddings_pair.shape,
+ (1, seq_length // 128 // 16, seq_length // 128 // 16, 128),
+ )
+ self.assertEqual(
+ predictions['atac']['scaled_predictions_1bp'].shape,
+ (1, seq_length, num_tracks),
+ )
+ self.assertEqual(
+ predictions['atac']['scaled_predictions_128bp'].shape,
+ (1, seq_length // 128, num_tracks),
+ )
+
+ def test_predict_junctions_with_same_params(self):
+ seq_length = 2048
+ num_tissues = 15
+ num_splice_sites = 17
+ output_metadata = {
+ dna_model.Organism.HOMO_SAPIENS: metadata_lib.AlphaGenomeOutputMetadata(
+ splice_sites=_mock_track_metadata(5),
+ splice_junctions=_mock_junction_metadata(num_tissues),
+ ),
+ }
+
+ @hk.transform_with_state
+ def forward(
+ dna_sequence: Float[Array, 'B S 4'], organism_index: Int[Array, 'B']
+ ):
+ return model_lib.AlphaGenome(
+ output_metadata, num_splice_sites=num_splice_sites
+ )(dna_sequence, organism_index)
+
+ key = jax.random.PRNGKey(42)
+ dna_sequence = jax.ShapeDtypeStruct((1, seq_length, 4), jnp.float32)
+ organism_index = jax.ShapeDtypeStruct((1,), jnp.int32)
+
+ params, state = jax.eval_shape(
+ forward.init, key, dna_sequence, organism_index
+ )
+
+ (predictions, embeddings), _ = jax.eval_shape(
+ forward.apply, params, state, key, dna_sequence, organism_index
+ )
+
+ @hk.transform_with_state
+ def predict_junctions_fn(embeddings, splice_site_positions, organism_index):
+ return model_lib.AlphaGenome(
+ output_metadata, num_splice_sites=num_splice_sites
+ ).predict_junctions(embeddings, splice_site_positions, organism_index)
+
+ splice_site_positions = predictions['splice_sites_junction'][
+ 'splice_site_positions'
+ ]
+ self.assertEqual(splice_site_positions.shape, (1, 4, num_splice_sites))
+ junction_predictions, _ = jax.eval_shape(
+ predict_junctions_fn.apply,
+ params,
+ state,
+ key,
+ embeddings.embeddings_1bp,
+ splice_site_positions,
+ organism_index,
+ )
+ self.assertIsNotNone(junction_predictions)
+ self.assertEqual(
+ junction_predictions['predictions'].shape,
+ (1, num_splice_sites, num_splice_sites, num_tissues * 2),
+ )
+ self.assertEqual(
+ junction_predictions['splice_junction_mask'].shape,
+ (1, num_splice_sites, num_splice_sites, num_tissues * 2),
+ )
+
+ def test_loss_and_grads(self):
+ seq_length = 131072 # Must be larger or equal to multinomial_resolution.
+ batch_size = 1
+ num_splice_sites = 17
+ num_tissues_junctions = 1
+ output_metadata = {
+ dna_model.Organism.HOMO_SAPIENS: metadata_lib.AlphaGenomeOutputMetadata(
+ atac=_mock_track_metadata(num_tracks=1),
+ contact_maps=_mock_track_metadata(num_tracks=1),
+ splice_sites=_mock_track_metadata(num_tracks=5),
+ splice_site_usage=_mock_track_metadata(num_tracks=4),
+ splice_junctions=_mock_junction_metadata(
+ num_tissues=num_tissues_junctions
+ ),
+ )
+ }
+
+ batch = schemas.DataBatch(
+ dna_sequence=jnp.zeros((batch_size, seq_length, 4), dtype=jnp.float32),
+ organism_index=jnp.zeros((batch_size,), dtype=jnp.int32),
+ atac=jnp.zeros((batch_size, seq_length, 1), dtype=jnp.float32),
+ atac_mask=jnp.ones((batch_size, 1, 1), dtype=bool),
+ contact_maps=jnp.zeros(
+ (batch_size, seq_length // 2048, seq_length // 2048, 1),
+ dtype=jnp.float32,
+ ),
+ splice_sites=jnp.zeros((batch_size, seq_length, 5), dtype=bool),
+ splice_site_usage=jnp.zeros(
+ (batch_size, seq_length, 4), dtype=jnp.float32
+ ),
+ splice_junctions=jnp.zeros(
+ (
+ batch_size,
+ num_splice_sites,
+ num_splice_sites,
+ num_tissues_junctions * 2,
+ ),
+ dtype=jnp.float32,
+ ),
+ )
+
+ @hk.transform_with_state
+ def forward(batch):
+ return model_lib.AlphaGenome(
+ output_metadata, num_splice_sites=num_splice_sites
+ ).loss(batch)
+
+ key = jax.random.PRNGKey(42)
+ params, state = jax.eval_shape(forward.init, key, batch)
+ (loss, scalars, predictions), _ = jax.eval_shape(
+ forward.apply, params, state, key, batch
+ )
+ chex.assert_shape(loss, ())
+ chex.assert_tree_shape(scalars, ())
+ chex.assert_tree_shape_prefix(predictions, (batch_size,))
+ self.assertContainsSubset(
+ [
+ 'atac_loss',
+ 'contact_maps_loss',
+ 'splice_sites_classification_loss',
+ 'splice_sites_usage_loss',
+ 'splice_sites_junction_loss',
+ ],
+ scalars.keys(),
+ )
+
+ def grads_fn(params, state, batch):
+ def _loss_fn(params):
+ (loss, scalars, predictions), new_state = forward.apply(
+ params, state, None, batch
+ )
+ return loss, (scalars, predictions, new_state)
+
+ (loss, (scalars, predictions, new_state)), grads = jax.value_and_grad(
+ _loss_fn, has_aux=True
+ )(params)
+ return loss, scalars, new_state, predictions, grads
+
+ loss2, scalars2, state2, predictions2, grads = jax.eval_shape(
+ grads_fn, params, state, batch
+ )
+ chex.assert_trees_all_equal_shapes(loss, loss2)
+ chex.assert_trees_all_equal_shapes(scalars, scalars2)
+ chex.assert_trees_all_equal_shapes(state, state2)
+ chex.assert_trees_all_equal_shapes(predictions, predictions2)
+ chex.assert_trees_all_equal_shapes(params, grads)
+
+ def test_finetuning_weights_merging(self):
+ """Tests that the weights can be merged for fine-tuning.
+
+ We test this by creating a base model and a fine-tuning model, where the
+ fine-tuning model has fewer heads and tracks than the base model. We then
+ merge the weights of the base model and the fine-tuning model, and check
+ that the loss can be computed with the merged weights.
+ """
+ seq_length = 131072
+ batch_size = 1
+ key = jax.random.key(0)
+
+ # Setup base model.
+ base_metadata = {
+ dna_model.Organism.HOMO_SAPIENS: metadata_lib.AlphaGenomeOutputMetadata(
+ atac=_mock_track_metadata(num_tracks=(atac_tracks_base := 5)),
+ rna_seq=_mock_track_metadata(num_tracks=(rna_seq_tracks_base := 3)),
+ ),
+ }
+
+ @hk.transform_with_state
+ def base_forward(batch: schemas.DataBatch):
+ return model_lib.AlphaGenome(base_metadata).loss(batch)
+
+ base_batch = schemas.DataBatch(
+ dna_sequence=np.zeros((batch_size, seq_length, 4), dtype=np.float32),
+ organism_index=np.zeros((batch_size,), dtype=np.int32),
+ atac=jnp.zeros(
+ (batch_size, seq_length, atac_tracks_base), dtype=np.float32
+ ),
+ atac_mask=np.ones((batch_size, 1, atac_tracks_base), dtype=bool),
+ rna_seq=np.zeros(
+ (batch_size, seq_length, rna_seq_tracks_base), dtype=np.float32
+ ),
+ rna_seq_mask=np.ones((batch_size, 1, rna_seq_tracks_base), dtype=bool),
+ )
+ base_params, base_state = jax.eval_shape(base_forward.init, key, base_batch)
+
+ # 2. Setup fine-tuning model.
+ ft_metadata = {
+ dna_model.Organism.HOMO_SAPIENS: metadata_lib.AlphaGenomeOutputMetadata(
+ atac=_mock_track_metadata(num_tracks=(atac_tracks_ft := 7)),
+ ),
+ }
+
+ @hk.transform_with_state
+ def ft_forward(batch: schemas.DataBatch):
+ return model_lib.AlphaGenome(ft_metadata).loss(batch)
+
+ ft_batch = schemas.DataBatch(
+ dna_sequence=np.zeros((batch_size, seq_length, 4), dtype=np.float32),
+ organism_index=np.zeros((batch_size,), dtype=np.int32),
+ atac=jnp.zeros(
+ (batch_size, seq_length, atac_tracks_ft), dtype=np.float32
+ ),
+ atac_mask=np.ones((batch_size, 1, atac_tracks_ft), dtype=bool),
+ )
+ ft_params, ft_state = jax.eval_shape(ft_forward.init, key, ft_batch)
+
+ # 3. Merge weights: base model weights + fine-tuning model head weights.
+ def merge(base_weights, ft_weights):
+ ft_head_weights = hk.data_structures.filter(
+ lambda module_name, name, v: 'head' in module_name, ft_weights
+ )
+ return hk.data_structures.merge(base_weights, ft_head_weights)
+
+ merged_params = merge(base_params, ft_params)
+ merged_state = merge(base_state, ft_state)
+
+ # 4. Check that we can compute loss with merged weights
+ (loss, scalars, predictions), _ = jax.eval_shape(
+ ft_forward.apply, merged_params, merged_state, key, ft_batch
+ )
+ chex.assert_shape(loss, ())
+ self.assertContainsSubset(['atac_loss'], scalars.keys())
+ self.assertNotIn('rna_seq_loss', scalars.keys())
+ chex.assert_tree_shape_prefix(predictions, (batch_size,))
+ self.assertEqual(
+ predictions['atac']['scaled_predictions_1bp'].shape,
+ (batch_size, seq_length, atac_tracks_ft),
+ )
+
+ def test_freeze_trunk_embeddings(self):
+ if jax.local_devices()[0].platform.lower() not in {'gpu', 'tpu'}:
+ self.skipTest('This test requires accelerator devices.')
+
+ seq_length, batch_size, key = 131072, 1, jax.random.key(0)
+
+ metadata = {
+ dna_model.Organism.HOMO_SAPIENS: metadata_lib.AlphaGenomeOutputMetadata(
+ atac=_mock_track_metadata(num_tracks=(atac_tracks_base := 5)),
+ rna_seq=_mock_track_metadata(num_tracks=(rna_seq_tracks_base := 3)),
+ ),
+ }
+
+ @hk.transform_with_state
+ def forward(batch: schemas.DataBatch):
+ return model_lib.AlphaGenome(metadata, freeze_trunk_embeddings=True).loss(
+ batch
+ )
+
+ @jax.jit
+ def grads_fn(params, state, batch):
+ def loss_fn(params, state, batch):
+ (loss, _, _), _ = forward.apply(params, state, None, batch)
+ return loss
+
+ grads_fn = jax.grad(loss_fn)
+ return grads_fn(params, state, batch)
+
+ batch = schemas.DataBatch(
+ dna_sequence=np.zeros((batch_size, seq_length, 4), dtype=np.float32),
+ organism_index=np.zeros((batch_size,), dtype=np.int32),
+ atac=jnp.zeros(
+ (batch_size, seq_length, atac_tracks_base), dtype=np.float32
+ ),
+ atac_mask=np.ones((batch_size, 1, atac_tracks_base), dtype=bool),
+ rna_seq=np.zeros(
+ (batch_size, seq_length, rna_seq_tracks_base), dtype=np.float32
+ ),
+ rna_seq_mask=np.ones((batch_size, 1, rna_seq_tracks_base), dtype=bool),
+ )
+ batch = jax.device_put(batch, jax.local_devices()[0])
+
+ params, state = jax.jit(forward.init)(key, batch)
+ grads = jax.jit(grads_fn)(params, state, batch)
+ chex.assert_trees_all_equal_shapes(params, grads)
+ grads_trunk = hk.data_structures.filter(
+ lambda module_name, name, v: 'head' not in module_name, grads
+ )
+ grads_head = hk.data_structures.filter(
+ lambda module_name, name, v: 'head' in module_name, grads
+ )
+ # Gradients in the trunk should be zero.
+ chex.assert_trees_all_equal(
+ grads_trunk,
+ jax.tree.map(np.zeros_like, grads_trunk),
+ )
+ # Gradients in the head should be non-zero.
+ head_grad_sq_norm = jax.tree_util.tree_reduce(
+ lambda acc, x: acc + jnp.sum(jnp.square(x)),
+ grads_head,
+ initializer=0.0,
+ )
+ self.assertGreater(head_grad_sq_norm, 0.0)
+
+
+if __name__ == '__main__':
+ absltest.main()
diff --git a/flax_model/alphagenome/model/one_hot_encoder.py b/flax_model/alphagenome/model/one_hot_encoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..a21e7fbd47fe3faa37ab3f8e706b5d28f939204b
--- /dev/null
+++ b/flax_model/alphagenome/model/one_hot_encoder.py
@@ -0,0 +1,54 @@
+# Copyright 2026 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 one-hot encoder for DNA sequences."""
+
+import numpy as np
+import numpy.typing as np_typing
+
+
+class DNAOneHotEncoder:
+ """A one-hot encoder for DNA sequences.
+
+ A -> [1, 0, 0, 0]
+ C -> [0, 1, 0, 0]
+ G -> [0, 0, 1, 0]
+ T -> [0, 0, 0, 1]
+
+ all other characters are encoded as zeros [0, 0, 0, 0].
+ """
+
+ def __init__(self, dtype: np_typing.DTypeLike = np.float32):
+ self._lookup_table = np.zeros((256, 4), dtype=dtype)
+ self._lookup_table[ord('A')] = [1, 0, 0, 0]
+ self._lookup_table[ord('C')] = [0, 1, 0, 0]
+ self._lookup_table[ord('G')] = [0, 0, 1, 0]
+ self._lookup_table[ord('T')] = [0, 0, 0, 1]
+ self._lookup_table[ord('a')] = self._lookup_table[ord('A')]
+ self._lookup_table[ord('c')] = self._lookup_table[ord('C')]
+ self._lookup_table[ord('g')] = self._lookup_table[ord('G')]
+ self._lookup_table[ord('t')] = self._lookup_table[ord('T')]
+
+ def encode(self, seq: str) -> np.ndarray:
+ """One-hot encodes a DNA sequence string.
+
+ Args:
+ seq: The DNA sequence string (e.g., "AGCTNacgt").
+
+ Returns:
+ A 2D numpy array of shape (sequence_length, 4) containing the
+ one-hot encoded representation of the sequence.
+ """
+ byte_values = np.frombuffer(seq.encode('latin1'), dtype=np.uint8)
+ return self._lookup_table[byte_values]
diff --git a/flax_model/alphagenome/model/one_hot_encoder_test.py b/flax_model/alphagenome/model/one_hot_encoder_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0e58f104df3bfb6bea387ae65a18614a2ff882d
--- /dev/null
+++ b/flax_model/alphagenome/model/one_hot_encoder_test.py
@@ -0,0 +1,44 @@
+# Copyright 2026 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 flax_model.alphagenome.model import one_hot_encoder
+import numpy as np
+
+
+class OneHotEncoderTest(parameterized.TestCase):
+
+ @parameterized.product(
+ sequence=['AGCTN', 'agctn'],
+ output_type=[np.int8, np.float32],
+ )
+ def test_encode(self, sequence: str, output_type: np.dtype):
+ expected = np.array(
+ [
+ [1, 0, 0, 0],
+ [0, 0, 1, 0],
+ [0, 1, 0, 0],
+ [0, 0, 0, 1],
+ [0, 0, 0, 0],
+ ],
+ dtype=output_type,
+ )
+ encoder = one_hot_encoder.DNAOneHotEncoder(dtype=output_type)
+ encoded = encoder.encode(sequence)
+ np.testing.assert_array_equal(encoded, expected)
+
+
+if __name__ == '__main__':
+ absltest.main()
diff --git a/flax_model/alphagenome/model/schemas.py b/flax_model/alphagenome/model/schemas.py
new file mode 100644
index 0000000000000000000000000000000000000000..e106adf8ce6ca8c15c4ac9df3d566504ec3c0a00
--- /dev/null
+++ b/flax_model/alphagenome/model/schemas.py
@@ -0,0 +1,85 @@
+# Copyright 2026 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.
+
+"""Common schemas for the model."""
+
+from flax_model.alphagenome._sdk import typing
+from flax_model.alphagenome.io import bundles
+import chex
+from jaxtyping import ArrayLike, Bool, Float, Int # pylint: disable=g-importing-member, g-multiple-import
+
+
+@typing.jaxtyped
+@chex.dataclass(frozen=True)
+class DataBatch:
+ """Input batch for the model."""
+
+ dna_sequence: Float[ArrayLike, 'B S_DNA 4'] | None = None
+ organism_index: Int[ArrayLike, 'B'] | None = None
+ atac: Float[ArrayLike, 'B S C_ATAC'] | None = None
+ atac_mask: Bool[ArrayLike, 'B #S C_ATAC'] | None = None
+ dnase: Float[ArrayLike, 'B S C_DNASE'] | None = None
+ dnase_mask: Bool[ArrayLike, 'B #S C_DNASE'] | None = None
+ procap: Float[ArrayLike, 'B S C_PROCAP'] | None = None
+ procap_mask: Bool[ArrayLike, 'B #S C_PROCAP'] | None = None
+ chip_histone: Float[ArrayLike, 'B S//128 C_CHIP_HISTONE'] | None = None
+ chip_histone_mask: Bool[ArrayLike, 'B #S//128 C_CHIP_HISTONE'] | None = None
+ chip_tf: Float[ArrayLike, 'B S//128 C_CHIP_TF'] | None = None
+ chip_tf_mask: Bool[ArrayLike, 'B #S//128 C_CHIP_TF'] | None = None
+ rna_seq: Float[ArrayLike, 'B S C_RNA_SEQ'] | None = None
+ rna_seq_mask: Bool[ArrayLike, 'B #S C_RNA_SEQ'] | None = None
+ rna_seq_strand: Int[ArrayLike, 'B 1 C_RNA_SEQ'] | None = None
+ cage: Float[ArrayLike, 'B S C_CAGE'] | None = None
+ cage_mask: Bool[ArrayLike, 'B #S C_CAGE'] | None = None
+ contact_maps: Float[ArrayLike, 'B S//2048 S//2048 C_CONTACT_MAPS'] | None = (
+ None
+ )
+ splice_junctions: Float[ArrayLike, 'B P P C_SPLICE_JUNCTIONS'] | None = None
+ splice_site_positions: Int[ArrayLike, 'B 4 P'] | None = None
+ splice_site_usage: Float[ArrayLike, 'B S C_SPLICE_SITE_USAGE'] | None = None
+ splice_sites: Bool[ArrayLike, 'B S C_SPLICE_SITES'] | None = None
+
+ def get_organism_index(self) -> Int[ArrayLike, 'B']:
+ """Returns the organism index data."""
+ if self.organism_index is None:
+ raise ValueError('Organism index data is not present in the batch.')
+ return self.organism_index
+
+ def get_genome_tracks(
+ self, bundle: bundles.BundleName
+ ) -> tuple[Float[ArrayLike, 'B S C'], Bool[ArrayLike, 'B #S C']]:
+ """Returns the genome tracks data for the given bundle if present."""
+ match bundle:
+ case bundles.BundleName.ATAC:
+ data, mask = self.atac, self.atac_mask
+ case bundles.BundleName.DNASE:
+ data, mask = self.dnase, self.dnase_mask
+ case bundles.BundleName.PROCAP:
+ data, mask = self.procap, self.procap_mask
+ case bundles.BundleName.CAGE:
+ data, mask = self.cage, self.cage_mask
+ case bundles.BundleName.RNA_SEQ:
+ data, mask = self.rna_seq, self.rna_seq_mask
+ case bundles.BundleName.CHIP_TF:
+ data, mask = self.chip_tf, self.chip_tf_mask
+ case bundles.BundleName.CHIP_HISTONE:
+ data, mask = self.chip_histone, self.chip_histone_mask
+ case _:
+ raise ValueError(
+ f'Unknown bundle name: {bundle!r}. Is it a genome tracks bundle?'
+ )
+
+ if data is None or mask is None:
+ raise ValueError(f'{bundle.name!r} data is not present in the batch.')
+ return data, mask
diff --git a/flax_model/alphagenome/model/splicing.py b/flax_model/alphagenome/model/splicing.py
new file mode 100644
index 0000000000000000000000000000000000000000..33a63a4b8704d0e777962396f3176f6b76f1b52b
--- /dev/null
+++ b/flax_model/alphagenome/model/splicing.py
@@ -0,0 +1,77 @@
+# Copyright 2026 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.
+
+"""Splicing utilities."""
+
+from flax_model.alphagenome._sdk import typing
+from flax_model.alphagenome.model.variant_scoring import splice_junction
+import jax
+import jax.numpy as jnp
+from jaxtyping import Array, Bool, Float, Int32 # pylint: disable=g-importing-member, g-multiple-import
+
+
+@typing.jaxtyped
+def _top_k_splice_sites(
+ x: Float[Array, 'B S 5'],
+ *,
+ k: int,
+ pad_to_length: int,
+ threshold: float,
+) -> Int32[Array, 'B 4 K']:
+ """Returns the top k splice sites from the predictions.
+
+ Args:
+ x: Array of shape [batch, sequence_length, 5] containing splice site
+ predictions (donor +ve, acceptor +ve, donor -ve, acceptor -ve, other).
+ k: Number of top splice sites to return.
+ pad_to_length: Pad the output to this length.
+ threshold: Threshold to filter out low confidence splice sites.
+ """
+ batch_size = x.shape[0]
+ values, positions = jax.lax.approx_max_k(x[..., :4], k, reduction_dimension=1)
+ if threshold > 0:
+ positions = jnp.where(values < threshold, jnp.inf, positions)
+ positions = jnp.sort(positions, axis=1, descending=False)
+ if threshold > 0:
+ positions = jnp.where(positions == jnp.inf, -1, positions)
+ # positions shape [batch, 4 (+ve and -ve donors and acceptors), k].
+ positions = positions.swapaxes(1, 2).astype(jnp.int32)
+ if positions.shape[2] < pad_to_length:
+ padding_shape = (batch_size, 4, pad_to_length - positions.shape[2])
+ padding = jnp.full(
+ padding_shape, splice_junction.PAD_VALUE, dtype=jnp.int32
+ )
+ positions = jnp.concatenate([positions, padding], axis=2)
+ return positions
+
+
+@typing.jaxtyped
+def generate_splice_site_positions(
+ ref: Float[Array, 'B S 5'],
+ alt: Float[Array, 'B S 5'] | None,
+ splice_sites: Bool[Array, 'B S 5'] | None,
+ *,
+ k: int,
+ pad_to_length: int,
+ threshold: float,
+) -> Int32[Array, 'B 4 K']:
+ """Returns the top k splice sites from predictions and (true) splice sites."""
+
+ if alt is not None:
+ ref = jnp.maximum(ref, alt)
+ if splice_sites is not None:
+ ref = jnp.maximum(ref, splice_sites)
+ return _top_k_splice_sites(
+ ref, k=k, pad_to_length=pad_to_length, threshold=threshold
+ )
diff --git a/flax_model/alphagenome/model/splicing_test.py b/flax_model/alphagenome/model/splicing_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..f4b1f8aae39cc3367d51abb20b5ac13ed6da7909
--- /dev/null
+++ b/flax_model/alphagenome/model/splicing_test.py
@@ -0,0 +1,113 @@
+# Copyright 2026 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 flax_model.alphagenome.model import splicing
+import jax.numpy as jnp
+import jaxtyping
+import numpy as np
+
+
+class SplicingTest(parameterized.TestCase):
+
+ def setUp(self):
+ super().setUp()
+ # Disable jaxtyped decorator for testing as parameterized
+ # test_generate_splice_site_positions test doesn't follow the jaxtyped
+ # annotations.
+ jaxtyping.config.update('jaxtyping_disable', True)
+
+ def test_generate_splice_site_positions_no_alt_no_mask(self):
+ # ref probabilities not normalized but it doesn't matter for this test.
+ ref = jnp.array([[
+ [0.09, 0.2, 0.9, 0.4, 0.0],
+ [0.5, 0.6, 0.7, 0.05, 0.0],
+ [0.05, 0.8, 0.6, 0.6, 0.0],
+ ]])
+ k = 2
+ pad_to_length = 5
+ threshold = 0.1
+
+ result = splicing.generate_splice_site_positions(
+ ref,
+ alt=None,
+ splice_sites=None,
+ k=k,
+ pad_to_length=pad_to_length,
+ threshold=threshold,
+ )
+ expected = jnp.array([[
+ [1, -1, -1, -1, -1], # donor +ve
+ [1, 2, -1, -1, -1], # acceptor +ve
+ [0, 1, -1, -1, -1], # donor -ve
+ [0, 2, -1, -1, -1], # acceptor -ve
+ ]])
+ np.testing.assert_array_equal(result, expected)
+
+ @parameterized.parameters(
+ dict(
+ ref=np.array([[[0.1], [0.2], [0.9], [0.8], [0.0]]]),
+ alt=None,
+ splice_site_mask=None,
+ probability_threshold=0.0,
+ expected=np.array([[[2, 3]]]),
+ ),
+ dict(
+ ref=np.array([[[0.1], [0.2], [0.9], [0.8], [0.0]]]),
+ alt=np.array([[[0.9], [0.1], [0.1], [0.1], [0.0]]]),
+ splice_site_mask=None,
+ probability_threshold=0.0,
+ expected=np.array([[[0, 2]]]),
+ ),
+ dict(
+ ref=np.array([[[0.0], [0.2], [0.9], [0.8], [0.0]]]),
+ alt=None,
+ splice_site_mask=np.array([[[0.05], [0.1], [0.1], [0.1], [0.9]]]),
+ probability_threshold=0.1,
+ expected=np.array([[[2, 4]]]),
+ ),
+ dict(
+ ref=np.array([[[0.1], [0.2], [0.9], [0.8], [0.0]]]),
+ alt=None,
+ splice_site_mask=None,
+ probability_threshold=0.5,
+ expected=np.array([[[2, 3]]]),
+ ),
+ dict(
+ ref=np.array([[[0.1], [0.2], [0.3], [0.4], [0.0]]]),
+ alt=None,
+ splice_site_mask=None,
+ probability_threshold=0.5,
+ expected=np.array([[[-1, -1]]]),
+ ),
+ )
+ def test_generate_splice_site_positions(
+ self, ref, alt, splice_site_mask, probability_threshold, expected
+ ):
+ actual = splicing.generate_splice_site_positions(
+ jnp.array(ref),
+ alt=jnp.array(alt) if alt is not None else None,
+ splice_sites=jnp.array(splice_site_mask)
+ if splice_site_mask is not None
+ else None,
+ k=2,
+ pad_to_length=1,
+ threshold=probability_threshold,
+ )
+ np.testing.assert_array_equal(actual, expected)
+
+
+if __name__ == '__main__':
+ absltest.main()
diff --git a/flax_model/alphagenome/model/variant_scoring/__init__.py b/flax_model/alphagenome/model/variant_scoring/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f67172b865c647acebdffa999ab64826f2d93080
--- /dev/null
+++ b/flax_model/alphagenome/model/variant_scoring/__init__.py
@@ -0,0 +1,15 @@
+# Copyright 2026 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.
+
+"""AlphaGenome Variant Scoring."""
diff --git a/flax_model/alphagenome/model/variant_scoring/center_mask.py b/flax_model/alphagenome/model/variant_scoring/center_mask.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca6ce4dd58a4a5f519a0f58338456e59f2c24154
--- /dev/null
+++ b/flax_model/alphagenome/model/variant_scoring/center_mask.py
@@ -0,0 +1,176 @@
+# Copyright 2026 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.
+
+"""Implementation of center mask variant scoring."""
+
+import functools
+import math
+
+from flax_model.alphagenome._sdk import typing
+from flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome._sdk.data import track_data
+from flax_model.alphagenome._sdk.models import dna_output
+from flax_model.alphagenome._sdk.models import variant_scorers
+from flax_model.alphagenome.model.variant_scoring import variant_scoring
+import anndata
+import jax
+import jax.numpy as jnp
+from jaxtyping import Array, Bool, Float32 # pylint: disable=g-multiple-import, g-importing-member
+import numpy as np
+
+
+def create_center_mask(
+ interval: genome.Interval,
+ variant: genome.Variant,
+ *,
+ width: int | None,
+ resolution: int,
+) -> Bool[np.ndarray, 'S 1']:
+ """Creates a mask centered on a variant for a given interval."""
+ if width is None:
+ if interval.start <= variant.start < interval.end:
+ mask = np.ones([interval.width // resolution, 1], dtype=bool)
+ else:
+ mask = np.zeros([interval.width // resolution, 1], dtype=bool)
+ else:
+ target_resolution_width = math.ceil(width / resolution)
+
+ # Determine the position of the variant in the specified resolution.
+ base_resolution_center = variant.position - interval.start
+ target_resolution_center = base_resolution_center // resolution
+
+ # Compute start and end indices of the variant-centered mask.
+ target_resolution_start = max(
+ target_resolution_center - target_resolution_width // 2, 0
+ )
+ target_resolution_end = min(
+ (target_resolution_center - target_resolution_width // 2)
+ + target_resolution_width,
+ interval.width // resolution,
+ )
+
+ # If the variant is not within the interval, we return an empty mask.
+ # Otherwise, we build the mask using our target_resolution_start/end,
+ # taking into account the resolution.
+ mask = np.zeros([interval.width // resolution, 1], dtype=bool)
+ if interval.start <= variant.start < interval.end:
+ mask[target_resolution_start:target_resolution_end] = 1
+
+ return mask
+
+
+@functools.partial(jax.jit, static_argnames=['aggregation_type'])
+@typing.jaxtyped
+def _apply_aggregation(
+ ref: Float32[Array, 'S T'],
+ alt: Float32[Array, 'S T'],
+ masks: Bool[Array, 'S 1'],
+ aggregation_type: variant_scorers.AggregationType,
+):
+ match aggregation_type:
+ case variant_scorers.AggregationType.DIFF_MEAN:
+ return alt.mean(axis=0, where=masks) - ref.mean(axis=0, where=masks)
+ case variant_scorers.AggregationType.ACTIVE_MEAN:
+ return jnp.maximum(
+ alt.mean(axis=0, where=masks),
+ ref.mean(axis=0, where=masks),
+ )
+ case variant_scorers.AggregationType.DIFF_SUM:
+ return alt.sum(axis=0, where=masks) - ref.sum(axis=0, where=masks)
+ case variant_scorers.AggregationType.ACTIVE_SUM:
+ return jnp.maximum(
+ alt.sum(axis=0, where=masks),
+ ref.sum(axis=0, where=masks),
+ )
+ case variant_scorers.AggregationType.L2_DIFF:
+ return jnp.sqrt(jnp.sum((alt - ref) ** 2, axis=0, where=masks))
+ case variant_scorers.AggregationType.L2_DIFF_LOG1P:
+ return jnp.sqrt(
+ jnp.sum(
+ (jnp.log1p(alt) - jnp.log1p(ref)) ** 2,
+ axis=0,
+ where=masks,
+ )
+ )
+ case variant_scorers.AggregationType.DIFF_SUM_LOG2:
+ return jnp.sum(jnp.log2(alt + 1), axis=0, where=masks) - jnp.sum(
+ jnp.log2(ref + 1), axis=0, where=masks
+ )
+ case variant_scorers.AggregationType.DIFF_LOG2_SUM:
+ return jnp.log2(1 + jnp.sum(alt, axis=0, where=masks)) - jnp.log2(
+ 1 + jnp.sum(ref, axis=0, where=masks)
+ )
+ case _:
+ raise ValueError(f'Unknown bin aggregation type: {aggregation_type}.')
+
+
+class CenterMaskVariantScorer(variant_scoring.VariantScorer):
+ """Variant scorer that aggregates ALT - REF in a window around the variant."""
+
+ def get_masks_and_metadata(
+ self,
+ interval: genome.Interval,
+ variant: genome.Variant,
+ *,
+ settings: variant_scorers.CenterMaskScorer,
+ track_metadata: dna_output.OutputMetadata,
+ ) -> tuple[np.ndarray, None]:
+ """See base class."""
+ del track_metadata
+
+ resolution = variant_scoring.get_resolution(settings.requested_output)
+ mask = create_center_mask(
+ interval, variant, width=settings.width, resolution=resolution
+ )
+
+ return mask, None
+
+ @typing.jaxtyped
+ def score_variant(
+ self,
+ ref: variant_scoring.ScoreVariantInput,
+ alt: variant_scoring.ScoreVariantInput,
+ *,
+ masks: Bool[Array, '_ 1'],
+ settings: variant_scorers.CenterMaskScorer,
+ variant: genome.Variant | None = None,
+ interval: genome.Interval | None = None,
+ ) -> variant_scoring.ScoreVariantOutput:
+ """See base class."""
+ del variant, interval # Unused.
+ alt = alt[settings.requested_output]
+ ref = ref[settings.requested_output]
+
+ output = _apply_aggregation(ref, alt, masks, settings.aggregation_type)
+ return {'score': output}
+
+ def finalize_variant(
+ self,
+ scores: variant_scoring.ScoreVariantResult,
+ *,
+ track_metadata: dna_output.OutputMetadata,
+ mask_metadata: None,
+ settings: variant_scorers.CenterMaskScorer,
+ ) -> anndata.AnnData:
+ """See base class."""
+ del mask_metadata # Unused.
+ output_metadata = track_metadata.get(settings.requested_output)
+ assert isinstance(output_metadata, track_data.TrackMetadata)
+
+ num_tracks = len(output_metadata)
+ return variant_scoring.create_anndata(
+ scores['score'][np.newaxis, :num_tracks],
+ obs=None,
+ var=output_metadata,
+ )
diff --git a/flax_model/alphagenome/model/variant_scoring/center_mask_test.py b/flax_model/alphagenome/model/variant_scoring/center_mask_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..c64062e9898eef5a80dce0d5d103b5008c5f8000
--- /dev/null
+++ b/flax_model/alphagenome/model/variant_scoring/center_mask_test.py
@@ -0,0 +1,206 @@
+# Copyright 2026 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 flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome._sdk.data import track_data
+from flax_model.alphagenome._sdk.models import dna_output
+from flax_model.alphagenome._sdk.models import variant_scorers
+from flax_model.alphagenome.model.variant_scoring import center_mask
+import anndata
+import chex
+import jax
+import jax.numpy as jnp
+import numpy as np
+
+
+class CenterMaskVariantScorerTest(parameterized.TestCase):
+
+ @parameterized.parameters([
+ dict(
+ width=None,
+ output_type=dna_output.OutputType.ATAC,
+ variant=genome.Variant('chr1', 10, '', ''),
+ expected_start=0,
+ expected_end=2048,
+ ),
+ dict(
+ width=None,
+ output_type=dna_output.OutputType.ATAC,
+ variant=genome.Variant('chr1', 2049, '', ''),
+ expected_start=-1,
+ expected_end=-1,
+ ),
+ dict(
+ width=501,
+ output_type=dna_output.OutputType.RNA_SEQ,
+ variant=genome.Variant('chr1', 1, '', ''),
+ expected_start=0,
+ expected_end=252,
+ ),
+ dict(
+ width=501,
+ output_type=dna_output.OutputType.RNA_SEQ,
+ variant=genome.Variant('chr1', 2048, '', ''),
+ expected_start=1798,
+ expected_end=2048,
+ ),
+ ])
+ def test_masks_and_metadata(
+ self,
+ width: int | None,
+ output_type: dna_output.OutputType,
+ variant: genome.Variant,
+ expected_start: int,
+ expected_end: int,
+ ):
+ settings = variant_scorers.CenterMaskScorer(
+ requested_output=output_type,
+ aggregation_type=variant_scorers.AggregationType.DIFF_MEAN,
+ width=width,
+ )
+ interval = genome.Interval('chr1', 0, 2048)
+ (
+ masks,
+ metadata,
+ ) = center_mask.CenterMaskVariantScorer().get_masks_and_metadata(
+ interval,
+ variant,
+ settings=settings,
+ track_metadata=dna_output.OutputMetadata(),
+ )
+ self.assertIsNone(metadata)
+
+ chex.assert_shape(masks, (2048, 1))
+ if expected_start >= 0:
+ self.assertEqual(masks.argmax(axis=0).item(), expected_start)
+ if expected_end >= 0:
+ self.assertEqual(
+ masks.shape[0] - masks[::-1].argmax(axis=0).item(), expected_end
+ )
+
+ expected_mask = np.zeros_like(masks)
+ if expected_start != expected_end:
+ expected_mask[expected_start:expected_end] = True
+ np.testing.assert_array_equal(masks, expected_mask)
+
+ @parameterized.product(
+ [
+ dict(
+ # (0 + 0 + 0) / 3 - (4 + 5 + 6) / 3 = -5
+ aggregation_type=variant_scorers.AggregationType.DIFF_MEAN,
+ expected_score=-5.0,
+ ),
+ dict(
+ # max((0 + 0 + 0) / 3, (4 + 5 + 6) / 3) = 5
+ aggregation_type=variant_scorers.AggregationType.ACTIVE_MEAN,
+ expected_score=5.0,
+ ),
+ dict(
+ # (0 + 0 + 0) - (4 + 5 + 6) = -15
+ aggregation_type=variant_scorers.AggregationType.DIFF_SUM,
+ expected_score=-15.0,
+ ),
+ dict(
+ # max((0 + 0 + 0), (4 + 5 + 6)) = 15
+ aggregation_type=variant_scorers.AggregationType.ACTIVE_SUM,
+ expected_score=15.0,
+ ),
+ dict(
+ # sqrt((0 - 4)**2 + (0 - 5)**2 + (0 - 6)**2) =~ 8.77
+ aggregation_type=variant_scorers.AggregationType.L2_DIFF,
+ expected_score=8.774964332580566,
+ ),
+ dict(
+ # sqrt((log1p(0) - log1p(4))**2 + (log1p(0) - log1p(5))**2 +
+ # (log1p(0) - log1p(6))**2) =~ 3.096
+ aggregation_type=variant_scorers.AggregationType.L2_DIFF_LOG1P,
+ expected_score=3.096329875472752,
+ ),
+ dict(
+ # log2(0 + 1) * 3 - log2(4 + 1) - log2(5 + 1) - log2(6 + 1)
+ # =~ -7.71
+ aggregation_type=variant_scorers.AggregationType.DIFF_SUM_LOG2,
+ expected_score=-7.714245517666122,
+ ),
+ dict(
+ # log2( 1 + (0 + 0 + 0)) - log2( 1 + (4 + 5 + 6)) = -4.0
+ aggregation_type=variant_scorers.AggregationType.DIFF_LOG2_SUM,
+ expected_score=-4.0,
+ ),
+ ],
+ transfer_guard=['disallow', 'allow'],
+ )
+ def test_score_variant(
+ self,
+ aggregation_type: variant_scorers.AggregationType,
+ expected_score: float,
+ transfer_guard: str,
+ ):
+ settings = variant_scorers.CenterMaskScorer(
+ requested_output=dna_output.OutputType.ATAC,
+ aggregation_type=aggregation_type,
+ width=None,
+ )
+
+ ref = jnp.arange(10, dtype=jnp.float32).repeat(100).reshape((10, 100))
+ alt = jnp.zeros((10, 100), dtype=jnp.float32)
+ mask = np.zeros((10, 1), dtype=bool)
+ mask[4:7] = True
+ mask = jnp.array(mask)
+
+ scorer = center_mask.CenterMaskVariantScorer()
+
+ with jax.transfer_guard(transfer_guard):
+ scores = scorer.score_variant(
+ {dna_output.OutputType.ATAC: ref},
+ {dna_output.OutputType.ATAC: alt},
+ masks=jax.device_put(mask),
+ settings=settings,
+ )['score']
+
+ np.testing.assert_almost_equal(
+ scores, np.full_like(scores, expected_score), decimal=6
+ )
+
+ def test_finalize_variant(self):
+ scorer = center_mask.CenterMaskVariantScorer()
+ scores = {'score': np.ones((10,), dtype=np.float32)}
+ expected_scores = np.ones((8,), dtype=np.float32)
+ track_metadata = dna_output.OutputMetadata(
+ atac=track_data.TrackMetadata(
+ dict(
+ name=np.arange(8).astype(str),
+ strand='.',
+ )
+ )
+ )
+ finalized_score = scorer.finalize_variant(
+ scores,
+ track_metadata=track_metadata,
+ mask_metadata=None,
+ settings=variant_scorers.CenterMaskScorer(
+ requested_output=dna_output.OutputType.ATAC,
+ width=None,
+ aggregation_type=variant_scorers.AggregationType.DIFF_MEAN,
+ ),
+ )
+ self.assertIsInstance(finalized_score, anndata.AnnData)
+ np.testing.assert_array_equal(finalized_score.X[0], expected_scores)
+ self.assertEqual(finalized_score.n_vars, expected_scores.shape[-1])
+
+
+if __name__ == '__main__':
+ absltest.main()
diff --git a/flax_model/alphagenome/model/variant_scoring/contact_map.py b/flax_model/alphagenome/model/variant_scoring/contact_map.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff189f9338f0c51c62307f4581e5a04566a9b6ee
--- /dev/null
+++ b/flax_model/alphagenome/model/variant_scoring/contact_map.py
@@ -0,0 +1,130 @@
+# Copyright 2026 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.
+
+"""Implementation of contact map variant scorer."""
+
+from flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome._sdk.data import track_data
+from flax_model.alphagenome._sdk.models import dna_output
+from flax_model.alphagenome._sdk.models import variant_scorers
+from flax_model.alphagenome.model.variant_scoring import center_mask
+from flax_model.alphagenome.model.variant_scoring import variant_scoring
+import anndata
+import jax
+import jax.numpy as jnp
+from jaxtyping import Array, Bool # pylint: disable=g-multiple-import, g-importing-member
+import numpy as np
+
+
+class ContactMapScorer(variant_scoring.VariantScorer):
+ """Implements the contact map variant scoring strategy from Zhou 2022 (Orca).
+
+ Designed for single nucleotide variant (SNV) scoring, where the expected
+ effect is local to the variant position.
+
+ Not compatible with indels or structural variants that involve changing more
+ than the number of nucleotides in a 2Kb window.
+
+ Citation: As described in the Zhou manuscript:
+ https://doi.org/10.1038/s41588-022-01065-4:
+
+ "The disruption impact on local genome interactions is measured by 1-Mb
+ structural impact score, which is the average absolute log fold change of
+ interactions between the disruption position and all other positions in the
+ 1-Mb window"
+
+ The Orca scoring strategy is open sourced here (line 120):
+ https://github.com/jzhoulab/orca_manuscript/blob/main/virtual_screen/local_interaction_screen.py
+ """
+
+ def get_masks_and_metadata(
+ self,
+ interval: genome.Interval,
+ variant: genome.Variant,
+ *,
+ settings: variant_scorers.ContactMapScorer,
+ track_metadata: dna_output.OutputMetadata,
+ ) -> tuple[np.ndarray, None]:
+ """See base class."""
+ del track_metadata # Unused.
+
+ resolution = variant_scoring.get_resolution(settings.requested_output)
+ mask = center_mask.create_center_mask(
+ interval, variant, width=resolution, resolution=resolution
+ )
+
+ if mask.sum() > 1:
+ raise ValueError(
+ 'The ContactMapScorer only accepts input variants that affect one bin'
+ ' position. However, there is more than one position affected by the'
+ ' variant at this bin resolution. This could indicate a malformed'
+ ' center mask. Please check `create_center_mask` logic. Debugging'
+ f' details: {variant=}, {interval=}, bin width={resolution},'
+ f' {mask.sum()=}.'
+ )
+ elif mask.sum() == 0:
+ raise ValueError(
+ 'The variant does not affect any positions at this bin resolution.'
+ f' Debugging details: {variant=}, {interval=}, bin'
+ f' width={resolution}, {mask.sum()=}.'
+ )
+ return mask, None
+
+ def score_variant(
+ self,
+ ref: variant_scoring.ScoreVariantInput,
+ alt: variant_scoring.ScoreVariantInput,
+ *,
+ masks: Bool[Array, '_ 1'],
+ settings: variant_scorers.ContactMapScorer,
+ variant: genome.Variant | None = None,
+ interval: genome.Interval | None = None,
+ ) -> variant_scoring.ScoreVariantOutput:
+ del variant, interval # Unused.
+ ref = ref[settings.requested_output]
+ alt = alt[settings.requested_output]
+
+ # Mean absolute difference, reduced over contact map rows.
+ # Ref, alt shape: [H, W, C]
+ # Temps shape: [W, C]
+ abs_diff = jnp.abs(alt - ref).mean(axis=0)
+
+ # JAX dynamic slicing does not work with transfer_guard.
+ with jax.transfer_guard('allow'):
+ # Use center mask to select the variant row.
+ # Right now, assumes there is a single value.
+ # Output shape: [1, C]
+ output = abs_diff[jnp.argmax(masks), :]
+
+ return {'score': output}
+
+ def finalize_variant(
+ self,
+ scores: variant_scoring.ScoreVariantResult,
+ *,
+ track_metadata: dna_output.OutputMetadata,
+ mask_metadata: None,
+ settings: variant_scorers.ContactMapScorer,
+ ) -> anndata.AnnData:
+ """See base class."""
+ del mask_metadata # Unused.
+ output_metadata = track_metadata.get(settings.requested_output)
+ assert isinstance(output_metadata, track_data.TrackMetadata)
+
+ num_tracks = len(output_metadata)
+ return variant_scoring.create_anndata(
+ scores['score'][np.newaxis, :num_tracks],
+ obs=None,
+ var=track_metadata.get(settings.requested_output),
+ )
diff --git a/flax_model/alphagenome/model/variant_scoring/contact_map_test.py b/flax_model/alphagenome/model/variant_scoring/contact_map_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..8ba5f7388a10646f1a21644faf240ff7220d29e5
--- /dev/null
+++ b/flax_model/alphagenome/model/variant_scoring/contact_map_test.py
@@ -0,0 +1,174 @@
+# Copyright 2026 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 flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome._sdk.data import track_data
+from flax_model.alphagenome._sdk.models import dna_output
+from flax_model.alphagenome._sdk.models import variant_scorers
+from flax_model.alphagenome.model.variant_scoring import center_mask
+from flax_model.alphagenome.model.variant_scoring import contact_map
+import anndata
+import jax
+import jax.numpy as jnp
+import numpy as np
+
+
+def _interval_variant_pairs(interval_width: int, bin_width: int):
+ return {
+ 'interval': genome.Interval('chr1', 0, interval_width),
+ 'variant': genome.Variant(
+ chromosome='chr1',
+ position=interval_width // 2,
+ reference_bases='A',
+ alternate_bases='C',
+ ),
+ 'variant_wrong_chr': genome.Variant(
+ chromosome='chrX',
+ position=interval_width,
+ reference_bases='A',
+ alternate_bases='C',
+ ),
+ 'variant_indel': genome.Variant(
+ chromosome='chr1',
+ position=interval_width // 2,
+ reference_bases='A',
+ alternate_bases='C' * bin_width * 10,
+ ),
+ }
+
+
+class ContactMapVariantScorerTest(parameterized.TestCase):
+
+ @parameterized.named_parameters([
+ dict(
+ testcase_name='snv',
+ interval=genome.Interval('chr1', 0, 196608),
+ variant=genome.Variant('chr1', 196608 // 2, 'A', 'C'),
+ ),
+ dict(
+ testcase_name='indel_variant',
+ interval=genome.Interval('chr1', 0, 196608),
+ variant=genome.Variant('chr1', 196608 // 2, 'A', 'C' * 2048 * 10),
+ ),
+ ])
+ def test_masks_and_metadata(
+ self, interval: genome.Interval, variant: genome.Variant
+ ):
+ scorer = contact_map.ContactMapScorer()
+
+ mask, mask_metadata = scorer.get_masks_and_metadata(
+ interval,
+ variant,
+ settings=variant_scorers.ContactMapScorer(),
+ track_metadata=dna_output.OutputMetadata(),
+ )
+ self.assertIsNone(mask_metadata)
+ self.assertEqual(mask.sum(), 1)
+
+ def test_no_variant_overlap_errors(self):
+ scorer = contact_map.ContactMapScorer()
+ with self.assertRaisesRegex(
+ ValueError, 'The variant does not affect any positions'
+ ):
+ scorer.get_masks_and_metadata(
+ genome.Interval('chr1', 0, 196608),
+ genome.Variant('chrX', 196608, 'A', 'C'),
+ settings=variant_scorers.ContactMapScorer(),
+ track_metadata=dna_output.OutputMetadata(),
+ )
+
+ def test_multiple_bins_raises_error(self):
+ scorer = contact_map.ContactMapScorer()
+ mock_mask = np.array([False, True, True]) # More than one True.
+
+ # Mock a call to create_center_mask to return a badly formed center mask.
+ with mock.patch.object(
+ center_mask, 'create_center_mask', autospec=True, return_value=mock_mask
+ ):
+ with self.assertRaisesRegex(
+ ValueError, 'only accepts input variants that affect one bin'
+ ):
+ scorer.get_masks_and_metadata(
+ genome.Interval('chr1', 0, 1644),
+ genome.Variant('chr1', 822, 'A', 'C'),
+ settings=variant_scorers.ContactMapScorer(),
+ track_metadata=dna_output.OutputMetadata(),
+ )
+
+ @parameterized.product(transfer_guard=['disallow', 'allow'])
+ def test_score_variant(self, transfer_guard: str):
+ symmetric_diff = jnp.array(
+ [[1, 2, 1], [2, -3, 4], [1, 4, 5]], dtype=jnp.float32
+ )
+
+ # Rescaling the second channel to make things slightly different.
+ second_channel_scale = 0.5
+
+ # Concatenate on channel dimension to represent two tissues.
+ alt_contact_map = jnp.concat(
+ [
+ symmetric_diff[:, :, jnp.newaxis],
+ symmetric_diff[:, :, jnp.newaxis] * second_channel_scale,
+ ],
+ axis=-1,
+ )
+
+ # Ref alt.
+ ref = {dna_output.OutputType.CONTACT_MAPS: jnp.zeros_like(alt_contact_map)}
+ alt = {dna_output.OutputType.CONTACT_MAPS: alt_contact_map}
+
+ # Simple center mask.
+ mask = np.array([False, True, False])[:, None]
+
+ # Score center column of both channels of the diff.
+ variant_scores = (
+ jnp.array([1, second_channel_scale]) # Channel scales.
+ * jnp.abs(symmetric_diff[:, 1]).mean() # Mean abs diff.
+ )
+ scorer = contact_map.ContactMapScorer()
+ with jax.transfer_guard(transfer_guard):
+ scores = scorer.score_variant(
+ ref, alt, masks=mask, settings=variant_scorers.ContactMapScorer()
+ )
+ np.testing.assert_array_equal(scores['score'], variant_scores)
+
+ def test_finalize_variant(self):
+ scorer = contact_map.ContactMapScorer()
+ scores = {'score': np.ones((10,), dtype=np.float32)}
+ expected_scores = np.ones((8,), dtype=np.float32)
+ track_metadata = dna_output.OutputMetadata(
+ contact_maps=track_data.TrackMetadata(
+ dict(
+ name=np.arange(8).astype(str),
+ strand='.',
+ )
+ )
+ )
+ finalized_score = scorer.finalize_variant(
+ scores,
+ track_metadata=track_metadata,
+ mask_metadata=None,
+ settings=variant_scorers.ContactMapScorer(),
+ )
+ self.assertIsInstance(finalized_score, anndata.AnnData)
+ np.testing.assert_array_equal(finalized_score.X[0], expected_scores)
+ self.assertEqual(finalized_score.n_vars, expected_scores.shape[-1])
+
+
+if __name__ == '__main__':
+ absltest.main()
diff --git a/flax_model/alphagenome/model/variant_scoring/gene_mask.py b/flax_model/alphagenome/model/variant_scoring/gene_mask.py
new file mode 100644
index 0000000000000000000000000000000000000000..9bbc0b755ffcfc82449e5e1a51a863666188c306
--- /dev/null
+++ b/flax_model/alphagenome/model/variant_scoring/gene_mask.py
@@ -0,0 +1,175 @@
+# Copyright 2026 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.
+
+"""Implementation of gene mask variant scoring."""
+
+import functools
+
+from flax_model.alphagenome._sdk import typing
+from flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome._sdk.data import track_data
+from flax_model.alphagenome._sdk.models import dna_output
+from flax_model.alphagenome._sdk.models import variant_scorers
+from flax_model.alphagenome.model.variant_scoring import gene_mask_extractor as gene_mask_extractor_lib
+from flax_model.alphagenome.model.variant_scoring import variant_scoring
+import anndata
+import jax
+import jax.numpy as jnp
+from jaxtyping import Array, Bool, Float32 # pylint: disable=g-multiple-import, g-importing-member
+import numpy as np
+import pandas as pd
+
+
+_VariantScorerSettings = (
+ variant_scorers.GeneMaskLFCScorer
+ | variant_scorers.GeneMaskActiveScorer
+ | variant_scorers.GeneMaskSplicingScorer
+)
+
+
+@functools.partial(jax.jit, static_argnames=['settings'])
+@typing.jaxtyped
+def _score_gene_variant(
+ ref, alt, gene_mask, *, settings: _VariantScorerSettings
+) -> Float32[Array, 'G T']:
+ """Returns a function to score a variant given the settings."""
+
+ match settings.base_variant_scorer:
+ case variant_scorers.BaseVariantScorer.GENE_MASK_LFC:
+ # Scores are the log fold change between the mean prediction of REF and
+ # ALT within each gene mask.
+ gene_mask_sum = gene_mask.sum(axis=0)[:, jnp.newaxis]
+ ref_mean = jnp.einsum('lt,lg->gt', ref, gene_mask) / gene_mask_sum
+ alt_mean = jnp.einsum('lt,lg->gt', alt, gene_mask) / gene_mask_sum
+ return jnp.log(alt_mean + 1e-3) - jnp.log(ref_mean + 1e-3)
+ case variant_scorers.BaseVariantScorer.GENE_MASK_ACTIVE:
+ # Scores are the maximum of the mean prediction for REF and ALT within
+ # each gene mask.
+ gene_mask_sum = gene_mask.sum(axis=0)[:, jnp.newaxis]
+ ref_score = jnp.einsum('lt,lg->gt', ref, gene_mask) / gene_mask_sum
+ alt_score = jnp.einsum('lt,lg->gt', alt, gene_mask) / gene_mask_sum
+ return jnp.maximum(alt_score, ref_score)
+ case variant_scorers.BaseVariantScorer.GENE_MASK_SPLICING:
+ # Scores are the maximum of the absolute difference between REF and ALT
+ # within each gene mask.
+ # Even for relatively small number of genes the naive implementation
+ # runs out of memory. We use a map here to reduce the memory footprint.
+ return jax.lax.map(
+ lambda mask: jnp.max(jnp.abs(alt - ref) * mask[:, None], axis=0),
+ jnp.matrix_transpose(gene_mask),
+ ) # shape: (G, T)
+ case _:
+ raise ValueError(
+ f'Unsupported base variant scorer: {settings.base_variant_scorer}.'
+ )
+
+
+class GeneVariantScorer(
+ variant_scoring.VariantScorer[
+ Bool[np.ndarray | Array, 'S G'],
+ pd.DataFrame,
+ _VariantScorerSettings,
+ ]
+):
+ """Variant scorer that computes scores for different genes."""
+
+ def __init__(
+ self,
+ gene_mask_extractor: gene_mask_extractor_lib.GeneMaskExtractor,
+ ):
+ """Initializes the GeneVariantScorer.
+
+ Args:
+ gene_mask_extractor: Gene mask extractor to use.
+ """
+ self._gene_mask_extractor = gene_mask_extractor
+
+ def get_masks_and_metadata(
+ self,
+ interval: genome.Interval,
+ variant: genome.Variant,
+ *,
+ settings: _VariantScorerSettings,
+ track_metadata: dna_output.OutputMetadata,
+ ) -> tuple[Bool[np.ndarray | Array, 'S G'], pd.DataFrame]:
+ """Get gene masks and metadata for the given interval and variant.
+
+ Note that the gene mask returned for the REF allele is just the normal
+ gene mask extracted from the GTF file from the interval. However, the gene
+ mask can be different for the ALT allele in the case of indels. We handle
+ this by extracting the indel alignment masks, that will be used to align the
+ ALT to the REF predictions, so that the same gene mask can be applied to
+ both.
+
+ Args:
+ interval: Genomic interval to extract gene masks for.
+ variant: Variant that may alter the gene mask in the case of the ALT
+ allele.
+ settings: The variant scorer settings.
+ track_metadata: Track metadata for the variant.
+
+ Returns:
+ Tuple of (gene variant masks, mask metadata). The mask metadata is the
+ part of the GTF pandas dataframe that was used to construct the gene
+ masks.
+ """
+ del track_metadata
+ if variant_scoring.get_resolution(settings.requested_output) != 1:
+ raise ValueError(
+ 'Only resolution = 1 is supported for gene variant scoring.'
+ )
+ gene_mask, metadata = self._gene_mask_extractor.extract(interval, variant)
+ return (gene_mask, metadata)
+
+ @typing.jaxtyped
+ def score_variant(
+ self,
+ ref: variant_scoring.ScoreVariantInput,
+ alt: variant_scoring.ScoreVariantInput,
+ *,
+ masks: Bool[Array, 'S G'],
+ settings: _VariantScorerSettings,
+ variant: genome.Variant | None = None,
+ interval: genome.Interval | None = None,
+ ) -> variant_scoring.ScoreVariantOutput:
+ alt = alt[settings.requested_output]
+ ref = ref[settings.requested_output]
+ gene_mask = masks
+ alt = variant_scoring.align_alternate(alt, variant, interval)
+
+ output = _score_gene_variant(ref, alt, gene_mask, settings=settings)
+ return {'score': output}
+
+ def finalize_variant(
+ self,
+ scores: variant_scoring.ScoreVariantResult,
+ *,
+ track_metadata: dna_output.OutputMetadata,
+ mask_metadata: pd.DataFrame,
+ settings: _VariantScorerSettings,
+ ) -> anndata.AnnData:
+ """Returns summarized scores for the given scores and metadata."""
+ output_metadata = track_metadata.get(settings.requested_output)
+ assert isinstance(output_metadata, track_data.TrackMetadata)
+ strand_mask = (
+ np.asarray(mask_metadata['strand'].values)[:, None]
+ == output_metadata['strand'].values[None]
+ ) | (output_metadata['strand'].values[None] == '.')
+
+ scores = np.where(strand_mask, scores['score'], np.nan)
+ return variant_scoring.create_anndata(
+ scores,
+ obs=mask_metadata,
+ var=output_metadata,
+ )
diff --git a/flax_model/alphagenome/model/variant_scoring/gene_mask_extractor.py b/flax_model/alphagenome/model/variant_scoring/gene_mask_extractor.py
new file mode 100644
index 0000000000000000000000000000000000000000..9d2528c9c0b5174c98c216875b44c480a6b4e595
--- /dev/null
+++ b/flax_model/alphagenome/model/variant_scoring/gene_mask_extractor.py
@@ -0,0 +1,504 @@
+# Copyright 2026 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.
+
+"""Gene mask extractor."""
+
+import abc
+import collections
+import dataclasses
+import enum
+from typing import Sequence
+
+from flax_model.alphagenome._sdk import typing
+from flax_model.alphagenome._sdk.data import gene_annotation
+from flax_model.alphagenome._sdk.data import genome
+from jaxtyping import Array, Bool # pylint: disable=g-multiple-import, g-importing-member
+import numpy as np
+import pandas as pd
+
+
+class GeneMaskType(enum.Enum):
+ """Type of gene mask."""
+
+ BODY = enum.auto()
+ EXONS = enum.auto()
+
+
+class GeneQueryType(enum.Enum):
+ """Type of gene query."""
+
+ VARIANT_OVERLAPPING = enum.auto()
+ INTERVAL_CONTAINED = enum.auto()
+
+
+class GeneMaskExtractor:
+ """Mask extractor yielding tuple (mask, metadata)."""
+
+ def __init__(
+ self,
+ gtf: pd.DataFrame,
+ gene_mask_type: GeneMaskType,
+ *,
+ gene_query_type: GeneQueryType = GeneQueryType.INTERVAL_CONTAINED,
+ filter_protein_coding: bool = False,
+ cache_size: int = 2,
+ ):
+ self._gene_mask_type = gene_mask_type
+ self._gene_query_type = gene_query_type
+ self._filter_protein_coding = filter_protein_coding
+ self._lru_cache = collections.OrderedDict()
+ self._cache_size = cache_size
+ if self._filter_protein_coding:
+ # Use protein-coding annotations at gene level for gene body masks,
+ # and at transcript level for exon/TSS masks.
+ gtf = gene_annotation.filter_protein_coding(
+ gtf, include_gene_entries=(gene_mask_type == GeneMaskType.BODY)
+ )
+
+ match self._gene_mask_type:
+ case GeneMaskType.BODY:
+ self._mask_extractor = _GeneBodyAnnotationExtractor(
+ gtf,
+ gene_query_type=self._gene_query_type,
+ )
+ case GeneMaskType.EXONS:
+ if self._gene_query_type != GeneQueryType.INTERVAL_CONTAINED:
+ raise ValueError(
+ 'Exon masks only support INTERVAL_CONTAINED query type.'
+ )
+ self._mask_extractor = _GeneExonAnnotationExtractor(gtf)
+ case _:
+ raise ValueError(f'Unknown gene mask type: {self._gene_mask_type}')
+
+ @typing.jaxtyped
+ def extract(
+ self,
+ interval: genome.Interval,
+ variant: genome.Variant | None = None,
+ transcript_ids: Sequence[str] | None = None,
+ ) -> tuple[Bool[np.ndarray, 'S G'], pd.DataFrame]:
+ """Extracts gene masks and metadata for a given interval."""
+ key = hash((str(interval), str(variant), *(transcript_ids or [])))
+ if key in self._lru_cache:
+ mask, metadata = self._lru_cache[key]
+ self._lru_cache.move_to_end(key)
+ return mask, metadata.copy()
+ else:
+ mask, annotations = self._mask_extractor.extract(
+ interval=interval, variant=variant, transcript_ids=transcript_ids
+ )
+ mask.setflags(write=False)
+ self._lru_cache[key] = (mask, annotations.get_metadata())
+ while len(self._lru_cache) > self._cache_size:
+ self._lru_cache.popitem(last=False)
+ return mask, annotations.get_metadata()
+
+
+@dataclasses.dataclass(frozen=True)
+class _GeneAnnotation:
+ """Gene annotation."""
+
+ gene_id: Sequence[str]
+ gene_name: Sequence[str]
+ gene_type: Sequence[str]
+ strand: Sequence[str]
+ interval_start: Sequence[int]
+ chromosome: Sequence[str]
+ start: Sequence[int]
+ end: Sequence[int]
+
+ def get_metadata(self) -> pd.DataFrame:
+ """Returns the metadata as a DataFrame."""
+ return pd.DataFrame({
+ 'gene_id': self.gene_id,
+ 'strand': self.strand,
+ 'gene_name': self.gene_name,
+ 'gene_type': self.gene_type,
+ 'interval_start': self.interval_start,
+ 'Chromosome': self.chromosome,
+ 'Start': self.start,
+ 'End': self.end,
+ }).reset_index(drop=True)
+
+
+class _GeneAnnotationExtractor(abc.ABC):
+ """Base class for gene annotation extractors."""
+
+ @property
+ def bin_size(self) -> int:
+ return 1
+
+ @abc.abstractmethod
+ def extract(
+ self,
+ *,
+ interval: genome.Interval,
+ variant: genome.Variant | None = None,
+ transcript_ids: Sequence[str] | None = None,
+ ) -> tuple[Bool[np.ndarray, 'S G'], _GeneAnnotation]:
+ """Returns a list of gene annotations for the given interval or variant."""
+ raise NotImplementedError()
+
+
+class _GeneBodyAnnotationExtractor(_GeneAnnotationExtractor):
+ """Extracts gene annotations for a given interval."""
+
+ _COLUMNS = [
+ 'Chromosome',
+ 'Start',
+ 'End',
+ 'Strand',
+ 'gene_id',
+ 'gene_name',
+ 'gene_type',
+ ]
+
+ def __init__(
+ self,
+ gtf: pd.DataFrame,
+ gene_query_type: GeneQueryType,
+ ):
+ """Init.
+
+ Args:
+ gtf: GTF DataFrame.
+ gene_query_type: Type of gene query.
+ """
+ self._gene_mask_extractor = _GeneMaskExtractor(gtf)
+ self._gene_query_type = gene_query_type
+ self._df_gene_gtf = gtf[gtf.Feature == 'gene'][self._COLUMNS]
+ self._df_start_end = {
+ chromosome: (dfc, dfc['Start'].values, dfc['End'].values)
+ for chromosome, dfc in self._df_gene_gtf.groupby(
+ 'Chromosome', observed=False
+ )
+ }
+ self._df_empty = self._df_gene_gtf.iloc[:0]
+
+ def _extract_interval_contained(
+ self, interval: genome.Interval
+ ) -> pd.DataFrame:
+ """Extracts genes contained by the given interval."""
+ if interval.chromosome not in self._df_start_end:
+ return self._df_empty
+ else:
+ dfc, start, end = self._df_start_end[interval.chromosome]
+ return dfc[(start >= interval.start) & (end <= interval.end)]
+
+ def _extract_variant_overlapping(
+ self, variant: genome.Variant
+ ) -> pd.DataFrame:
+ """Extracts genes overlapping the given variant."""
+ if variant.chromosome not in self._df_start_end:
+ return self._df_empty
+ else:
+ dfc, start, end = self._df_start_end[variant.chromosome]
+ variant_end = max(
+ variant.end, variant.start + len(variant.alternate_bases)
+ )
+ return dfc[(end > variant.start) & (start < variant_end)]
+
+ def extract(
+ self,
+ *,
+ interval: genome.Interval,
+ variant: genome.Variant | None = None,
+ transcript_ids: Sequence[str] | None = None,
+ ) -> tuple[Bool[np.ndarray, 'S G'], _GeneAnnotation]:
+ """Returns a list of gene annotations for the given interval.
+
+ Args:
+ interval: Interval to extract at.
+ variant: Variant to extract at.
+ transcript_ids: Not supported and should not be provided.
+ """
+ if transcript_ids is not None:
+ raise ValueError('transcript_ids not supported for gene body extractor.')
+
+ match self._gene_query_type:
+ case GeneQueryType.VARIANT_OVERLAPPING:
+ if variant is None:
+ raise ValueError('No variant provided for VARIANT_OVERLAPPING query.')
+ gene_subset = self._extract_variant_overlapping(variant)
+ case GeneQueryType.INTERVAL_CONTAINED:
+ gene_subset = self._extract_interval_contained(interval)
+ case _:
+ raise ValueError(f'Unknown gene query type: {self._gene_query_type}')
+
+ mask = np.empty((interval.width, len(gene_subset)), dtype=bool)
+
+ for i, row in enumerate(gene_subset.itertuples()):
+ mask[:, i] = self._gene_mask_extractor.extract(interval, row.gene_id)
+
+ annotations = _GeneAnnotation(
+ gene_id=gene_subset.gene_id,
+ gene_name=gene_subset.gene_name,
+ gene_type=gene_subset.gene_type,
+ strand=gene_subset.Strand,
+ interval_start=[interval.start] * len(gene_subset),
+ chromosome=gene_subset.Chromosome,
+ start=gene_subset.Start,
+ end=gene_subset.End,
+ )
+ return mask, annotations
+
+
+class _GeneMaskExtractor:
+ """Generates binary masks for genes."""
+
+ def __init__(self, gtf: pd.DataFrame):
+ """Init.
+
+ Args:
+ gtf: GTF DataFrame.
+ """
+ self._gtf = gtf
+ self._genes_by_gene_id = gtf[gtf.Feature == 'gene'][
+ ['Chromosome', 'Start', 'End', 'Strand', 'gene_id']
+ ].groupby('gene_id', sort=False)
+
+ def extract(
+ self,
+ interval: genome.Interval,
+ gene_id: str,
+ ) -> Bool[Array | np.ndarray, 'S']:
+ """Extracts gene masks for a specific gene.
+
+ Args:
+ interval: Interval to extract at.
+ gene_id: Gene ID to extract mask for.
+
+ Returns:
+ Boolean mask of shape (interval.width, 2).
+ """
+ if gene_id not in self._gtf['gene_id'].values:
+ raise ValueError(f'Gene ID {gene_id} not found in GTF.')
+
+ genes = self._genes_by_gene_id.get_group(gene_id)
+ intervals = [
+ genome.Interval(chr, start, end, strand)
+ for chr, start, end, strand in zip(
+ genes.Chromosome, genes.Start, genes.End, genes.Strand
+ )
+ ]
+ mask = np.zeros((interval.width,), dtype=bool)
+ for gene_interval in intervals:
+ if interval.overlaps(gene_interval):
+ relative_start = max(gene_interval.start - interval.start, 0)
+ relative_end = min(gene_interval.end - interval.start, interval.width)
+ mask[relative_start:relative_end] = True
+ return mask
+
+
+class _GeneExonAnnotationExtractor(_GeneAnnotationExtractor):
+ """Generates binary masks of overlapping exons."""
+
+ _GENE_COLUMNS = [
+ 'Chromosome',
+ 'Start',
+ 'End',
+ 'Strand',
+ 'gene_id',
+ 'gene_name',
+ 'gene_type',
+ ]
+
+ def __init__(self, gtf: pd.DataFrame):
+ """Init.
+
+ Args:
+ gtf: GTF DataFrame.
+ """
+ self._exon_mask_extractor = _ExonMaskExtractor(gtf)
+
+ # We use transcript's TSS to determine which transcripts to annotate.
+ self._tss = _PositionExtractor(
+ gene_annotation.extract_tss(gtf), position_column='Start'
+ )
+ self._gtf = gtf
+ self._gene_df = gtf[gtf.Feature == 'gene'][self._GENE_COLUMNS].set_index(
+ 'gene_id'
+ )
+
+ def extract(
+ self,
+ *,
+ interval: genome.Interval,
+ variant: genome.Variant | None = None,
+ transcript_ids: Sequence[str] | None = None,
+ ) -> tuple[Bool[np.ndarray, 'S G'], _GeneAnnotation]:
+ """Extracts exon masks.
+
+ Args:
+ interval: Interval to extract at.
+ variant: Variant to extract at.
+ transcript_ids: Optional list of transcript ids to extract.
+
+ Returns:
+ A list of gene annotations for the given interval.
+ """
+ if transcript_ids is not None:
+ # Contains start/end positions for the transcripts.
+ transcript_subset = self._gtf[
+ self._gtf.transcript_id.isin(transcript_ids)
+ ]
+ else:
+ # Contains TSS positions for the transcripts.
+ transcript_subset = self._tss.extract(interval)
+
+ # Mask for each gene is the OR of the masks for all its exons.
+ gene_masks = {}
+
+ for row in transcript_subset.itertuples():
+ transcript_id = row.transcript_id
+ gene_id = row.gene_id
+
+ exon_mask = self._exon_mask_extractor.extract(interval, transcript_id)
+ if (gene_mask := gene_masks.get(gene_id)) is not None:
+ gene_mask |= exon_mask
+ else:
+ gene_masks[gene_id] = exon_mask
+
+ unique_gene_ids = list(transcript_subset['gene_id'].unique())
+ gene_metadata = self._gene_df.loc[unique_gene_ids]
+ mask = np.empty((interval.width, len(unique_gene_ids)), dtype=bool)
+ for i, gene_id in enumerate(unique_gene_ids):
+ mask[:, i] = gene_masks[gene_id]
+
+ annotations = _GeneAnnotation(
+ gene_id=unique_gene_ids,
+ gene_name=gene_metadata.gene_name,
+ gene_type=gene_metadata.gene_type,
+ strand=gene_metadata.Strand,
+ interval_start=[interval.start] * len(unique_gene_ids),
+ chromosome=gene_metadata.Chromosome,
+ start=gene_metadata.Start,
+ end=gene_metadata.End,
+ )
+ return mask, annotations
+
+
+class _PositionExtractor:
+ """Extractor focused on single position, rather than an interval.
+
+ This extractor can be up to 20x faster than PyRangesExtractor.
+
+ Interval is considered semi-open [start, end). Rows where
+ - chromosome == interval.chromosome
+ - position >= interval.start
+ - position < interval.end
+ are returned.
+
+ Note: This code doesn't consider the stand information.
+ """
+
+ def __init__(
+ self,
+ df: pd.DataFrame,
+ position_column: str,
+ chromosome_column: str = 'Chromosome',
+ ):
+ """Init.
+
+ Args:
+ df: dataframe to query with `position_column` and `chromosome_column`.
+ position_column: Which column in df to use as 0-based position.
+ chromosome_column: Which column in df to use as chromosome.
+ """
+ self._df_position = {
+ chromosome: (dfc, dfc[position_column].values)
+ for chromosome, dfc in df.groupby(chromosome_column, observed=False)
+ }
+
+ self._df_empty = df.iloc[:0]
+
+ def extract(self, interval: genome.Interval) -> pd.DataFrame:
+ if interval.chromosome not in self._df_position:
+ return self._df_empty
+ else:
+ dfc, position = self._df_position[interval.chromosome]
+ return dfc[(position >= interval.start) & (position < interval.end)]
+
+
+class _ExonMaskExtractor:
+ """Generates binary masks for exons."""
+
+ def __init__(self, gtf: pd.DataFrame):
+ """Init.
+
+ Args:
+ gtf: GTF DataFrame.
+ """
+ self._exon_extractor = _ExonExtractor(gtf)
+
+ def extract(
+ self,
+ interval: genome.Interval,
+ transcript_id: str,
+ ) -> Bool[Array | np.ndarray, 'S']:
+ """Extracts exon masks for a single transcript.
+
+ Args:
+ interval: Interval to extract at.
+ transcript_id: The transcript to extract exons for.
+
+ Returns:
+ Boolean mask of shape (interval.width, 2).
+ """
+ exons = self._exon_extractor.extract(transcript_id)
+ mask = np.zeros((interval.width,), dtype=bool)
+ for exon in exons:
+ if interval.overlaps(exon):
+ relative_start = max(exon.start - interval.start, 0)
+ relative_end = min(exon.end - interval.start, interval.width)
+ mask[relative_start:relative_end] = True
+ return mask
+
+
+class _ExonExtractor:
+ """Extracts exons for a single transcript."""
+
+ def __init__(self, gtf: pd.DataFrame):
+ """Init.
+
+ Args:
+ gtf: GTF DataFrame.
+ """
+ self._gtf = gtf
+ self._exons_by_transcript_id = gtf[gtf.Feature == 'exon'][
+ ['Chromosome', 'Start', 'End', 'Strand', 'transcript_id']
+ ].groupby('transcript_id', sort=False)
+
+ def extract(
+ self,
+ transcript_id: str,
+ ) -> list[genome.Interval]:
+ """Extracts exons as List[genome.Interval] for a single transcript.
+
+ Args:
+ transcript_id: The transcript to extract exons for.
+
+ Returns:
+ List of exon intervals for the transcript.
+ """
+ try:
+ exons = self._exons_by_transcript_id.get_group(transcript_id)
+ return [
+ genome.Interval(chr, start, end, strand)
+ for chr, start, end, strand in zip(
+ exons.Chromosome, exons.Start, exons.End, exons.Strand
+ )
+ ]
+ except KeyError:
+ return []
diff --git a/flax_model/alphagenome/model/variant_scoring/gene_mask_extractor_test.py b/flax_model/alphagenome/model/variant_scoring/gene_mask_extractor_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..7a0e11eb1244af22899c7a53f356e19b3f4d779c
--- /dev/null
+++ b/flax_model/alphagenome/model/variant_scoring/gene_mask_extractor_test.py
@@ -0,0 +1,301 @@
+# Copyright 2026 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 flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome.model.variant_scoring import gene_mask_extractor
+import chex
+import numpy as np
+import pandas as pd
+
+
+def _get_dummy_gtf() -> pd.DataFrame:
+ """Returns a dummy GTF DataFrame for testing."""
+ data = [
+ ['gene', 'chr1', 10, 100, '+', 'gene1', 'name1', 'type1', ''],
+ ['transcript', 'chr1', 10, 100, '+', 'gene1', 'name1', 'type1', 't1'],
+ ['exon', 'chr1', 10, 50, '+', 'gene1', 'name1', 'type1', 't1'],
+ ['exon', 'chr1', 70, 100, '+', 'gene1', 'name1', 'type1', 't1'],
+ ['gene', 'chr1', 120, 150, '-', 'gene2', 'name2', 'type2', ''],
+ ['transcript', 'chr1', 120, 150, '-', 'gene2', 'name2', 'type2', 't2'],
+ ['exon', 'chr1', 120, 150, '-', 'gene2', 'name2', 'type2', 't2'],
+ ]
+ return pd.DataFrame(
+ data,
+ columns=[
+ 'Feature',
+ 'Chromosome',
+ 'Start',
+ 'End',
+ 'Strand',
+ 'gene_id',
+ 'gene_name',
+ 'gene_type',
+ 'transcript_id',
+ ],
+ )
+
+
+class GeneMaskExtractorTest(parameterized.TestCase):
+
+ def setUp(self):
+ super().setUp()
+ self._gtf = _get_dummy_gtf()
+
+ @parameterized.named_parameters([
+ # gene_mask_type=BODY, gene_query_type=INTERVAL_CONTAINED
+ dict(
+ testcase_name='body_interval_two_genes',
+ gene_mask_type=gene_mask_extractor.GeneMaskType.BODY,
+ gene_query_type=gene_mask_extractor.GeneQueryType.INTERVAL_CONTAINED,
+ variant=None,
+ interval=genome.Interval('chr1', 0, 200),
+ expected_num_genes=2,
+ expected_masks_segments=[(0, [(10, 100)]), (1, [(120, 150)])],
+ expected_gene_ids=['gene1', 'gene2'],
+ ),
+ dict(
+ testcase_name='body_interval_two_genes_shifted_interval',
+ gene_mask_type=gene_mask_extractor.GeneMaskType.BODY,
+ gene_query_type=gene_mask_extractor.GeneQueryType.INTERVAL_CONTAINED,
+ variant=None,
+ interval=genome.Interval('chr1', 5, 200),
+ expected_num_genes=2,
+ expected_masks_segments=[(0, [(5, 95)]), (1, [(115, 145)])],
+ expected_gene_ids=['gene1', 'gene2'],
+ ),
+ dict(
+ testcase_name=(
+ 'body_interval_two_genes_shifted_interval_outside_of_gene_range'
+ ),
+ gene_mask_type=gene_mask_extractor.GeneMaskType.BODY,
+ gene_query_type=gene_mask_extractor.GeneQueryType.INTERVAL_CONTAINED,
+ variant=None,
+ interval=genome.Interval('chr1', 15, 200),
+ expected_num_genes=1,
+ expected_masks_segments=[(0, [(105, 135)])],
+ expected_gene_ids=['gene2'],
+ ),
+ dict(
+ testcase_name='body_interval_one_gene',
+ gene_mask_type=gene_mask_extractor.GeneMaskType.BODY,
+ gene_query_type=gene_mask_extractor.GeneQueryType.INTERVAL_CONTAINED,
+ variant=None,
+ interval=genome.Interval('chr1', 0, 110),
+ expected_num_genes=1,
+ expected_masks_segments=[(0, [(10, 100)])],
+ expected_gene_ids=['gene1'],
+ ),
+ dict(
+ testcase_name='body_interval_no_genes',
+ gene_mask_type=gene_mask_extractor.GeneMaskType.BODY,
+ gene_query_type=gene_mask_extractor.GeneQueryType.INTERVAL_CONTAINED,
+ variant=None,
+ interval=genome.Interval('chr1', 0, 10),
+ expected_num_genes=0,
+ expected_masks_segments=[],
+ expected_gene_ids=[],
+ ),
+ dict(
+ testcase_name='body_interval_no_genes_in_region',
+ gene_mask_type=gene_mask_extractor.GeneMaskType.BODY,
+ gene_query_type=gene_mask_extractor.GeneQueryType.INTERVAL_CONTAINED,
+ variant=None,
+ interval=genome.Interval('chr1', 300, 400),
+ expected_num_genes=0,
+ expected_masks_segments=[],
+ expected_gene_ids=[],
+ ),
+ dict(
+ testcase_name='body_interval_wrong_chromosome',
+ gene_mask_type=gene_mask_extractor.GeneMaskType.BODY,
+ gene_query_type=gene_mask_extractor.GeneQueryType.INTERVAL_CONTAINED,
+ variant=None,
+ interval=genome.Interval('chr2', 0, 200),
+ expected_num_genes=0,
+ expected_masks_segments=[],
+ expected_gene_ids=[],
+ ),
+ # gene_mask_type=BODY, gene_query_type=VARIANT_OVERLAPPING
+ dict(
+ testcase_name='body_variant_variant_in_gene1',
+ gene_mask_type=gene_mask_extractor.GeneMaskType.BODY,
+ gene_query_type=gene_mask_extractor.GeneQueryType.VARIANT_OVERLAPPING,
+ interval=genome.Interval('chr1', 0, 200),
+ variant=genome.Variant('chr1', 55, 'A', 'G'),
+ expected_num_genes=1,
+ expected_masks_segments=[(0, [(10, 100)])],
+ expected_gene_ids=['gene1'],
+ ),
+ dict(
+ testcase_name='body_variant_variant_in_gene2',
+ gene_mask_type=gene_mask_extractor.GeneMaskType.BODY,
+ gene_query_type=gene_mask_extractor.GeneQueryType.VARIANT_OVERLAPPING,
+ interval=genome.Interval('chr1', 0, 200),
+ variant=genome.Variant('chr1', 130, 'A', 'G'),
+ expected_num_genes=1,
+ expected_masks_segments=[(0, [(120, 150)])],
+ expected_gene_ids=['gene2'],
+ ),
+ dict(
+ testcase_name='body_variant_variant_between_genes',
+ gene_mask_type=gene_mask_extractor.GeneMaskType.BODY,
+ gene_query_type=gene_mask_extractor.GeneQueryType.VARIANT_OVERLAPPING,
+ interval=genome.Interval('chr1', 0, 200),
+ variant=genome.Variant('chr1', 110, 'A', 'G'),
+ expected_num_genes=0,
+ expected_masks_segments=[],
+ expected_gene_ids=[],
+ ),
+ dict(
+ testcase_name='body_variant_variant_overlapping_two_genes',
+ gene_mask_type=gene_mask_extractor.GeneMaskType.BODY,
+ gene_query_type=gene_mask_extractor.GeneQueryType.VARIANT_OVERLAPPING,
+ interval=genome.Interval('chr1', 0, 200),
+ variant=genome.Variant('chr1', 99, 'A' * 30, 'G'),
+ expected_num_genes=2,
+ expected_masks_segments=[(0, [(10, 100)]), (1, [(120, 150)])],
+ expected_gene_ids=['gene1', 'gene2'],
+ ),
+ dict(
+ testcase_name='body_variant_variant_in_gene1_shifted_interval',
+ gene_mask_type=gene_mask_extractor.GeneMaskType.BODY,
+ gene_query_type=gene_mask_extractor.GeneQueryType.VARIANT_OVERLAPPING,
+ interval=genome.Interval('chr1', 10, 110),
+ variant=genome.Variant('chr1', 55, 'A', 'G'),
+ expected_num_genes=1,
+ expected_masks_segments=[(0, [(0, 90)])],
+ expected_gene_ids=['gene1'],
+ ),
+ # gene_mask_type=EXONS, gene_query_type=INTERVAL_CONTAINED
+ dict(
+ testcase_name='exons_interval_two_genes',
+ gene_mask_type=gene_mask_extractor.GeneMaskType.EXONS,
+ gene_query_type=gene_mask_extractor.GeneQueryType.INTERVAL_CONTAINED,
+ variant=None,
+ interval=genome.Interval('chr1', 0, 200),
+ expected_num_genes=2,
+ expected_masks_segments=[
+ (0, [(10, 50), (70, 100)]),
+ (1, [(120, 150)]),
+ ],
+ expected_gene_ids=['gene1', 'gene2'],
+ ),
+ dict(
+ testcase_name='exons_interval_two_genes_shifted_interval',
+ gene_mask_type=gene_mask_extractor.GeneMaskType.EXONS,
+ gene_query_type=gene_mask_extractor.GeneQueryType.INTERVAL_CONTAINED,
+ variant=None,
+ interval=genome.Interval('chr1', 5, 200),
+ expected_num_genes=2,
+ expected_masks_segments=[(0, [(5, 45), (65, 95)]), (1, [(115, 145)])],
+ expected_gene_ids=['gene1', 'gene2'],
+ ),
+ dict(
+ testcase_name=(
+ 'exons_interval_two_genes_shifted_interval_outside_of_gene_range'
+ ),
+ gene_mask_type=gene_mask_extractor.GeneMaskType.EXONS,
+ gene_query_type=gene_mask_extractor.GeneQueryType.INTERVAL_CONTAINED,
+ variant=None,
+ interval=genome.Interval('chr1', 15, 200),
+ expected_num_genes=1,
+ expected_masks_segments=[(0, [(105, 135)])],
+ expected_gene_ids=['gene2'],
+ ),
+ dict(
+ testcase_name='exons_interval_one_gene',
+ gene_mask_type=gene_mask_extractor.GeneMaskType.EXONS,
+ gene_query_type=gene_mask_extractor.GeneQueryType.INTERVAL_CONTAINED,
+ variant=None,
+ interval=genome.Interval('chr1', 0, 110),
+ expected_num_genes=1,
+ expected_masks_segments=[(0, [(10, 50), (70, 100)])],
+ expected_gene_ids=['gene1'],
+ ),
+ dict(
+ testcase_name='exons_interval_no_genes',
+ gene_mask_type=gene_mask_extractor.GeneMaskType.EXONS,
+ gene_query_type=gene_mask_extractor.GeneQueryType.INTERVAL_CONTAINED,
+ variant=None,
+ interval=genome.Interval('chr1', 0, 10),
+ expected_num_genes=0,
+ expected_masks_segments=[],
+ expected_gene_ids=[],
+ ),
+ dict(
+ testcase_name='exons_interval_no_genes_in_region',
+ gene_mask_type=gene_mask_extractor.GeneMaskType.EXONS,
+ gene_query_type=gene_mask_extractor.GeneQueryType.INTERVAL_CONTAINED,
+ variant=None,
+ interval=genome.Interval('chr1', 300, 400),
+ expected_num_genes=0,
+ expected_masks_segments=[],
+ expected_gene_ids=[],
+ ),
+ dict(
+ testcase_name='exons_interval_wrong_chromosome',
+ gene_mask_type=gene_mask_extractor.GeneMaskType.EXONS,
+ gene_query_type=gene_mask_extractor.GeneQueryType.INTERVAL_CONTAINED,
+ variant=None,
+ interval=genome.Interval('chr2', 0, 200),
+ expected_num_genes=0,
+ expected_masks_segments=[],
+ expected_gene_ids=[],
+ ),
+ ])
+ def test_gene_mask_extractor(
+ self,
+ gene_mask_type,
+ gene_query_type,
+ interval,
+ variant,
+ expected_num_genes,
+ expected_masks_segments,
+ expected_gene_ids,
+ ):
+ extractor = gene_mask_extractor.GeneMaskExtractor(
+ self._gtf,
+ gene_mask_type=gene_mask_type,
+ gene_query_type=gene_query_type,
+ )
+ mask, metadata = extractor.extract(interval, variant)
+
+ chex.assert_shape(mask, (interval.width, expected_num_genes))
+ self.assertLen(metadata, expected_num_genes)
+ if expected_num_genes > 0:
+ self.assertListEqual(metadata.gene_id.tolist(), expected_gene_ids)
+
+ for i, segments in expected_masks_segments:
+ expected_mask = np.zeros(interval.width, dtype=bool)
+ for start, end in segments:
+ expected_mask[start:end] = True
+ np.testing.assert_array_equal(mask[:, i], expected_mask)
+
+ def test_gene_mask_extractor_with_wrong_gene_id_raises_error(self):
+ extractor = gene_mask_extractor._GeneMaskExtractor(self._gtf) # pylint: disable=protected-access
+ interval = genome.Interval('chr1', 0, 200)
+ with self.assertRaisesRegex(ValueError, 'Gene ID wrong_gene_id not found'):
+ extractor.extract(interval, gene_id='wrong_gene_id')
+
+ def test_exon_extractor_transcript_with_no_exons_returns_empty(self):
+ extractor = gene_mask_extractor._ExonExtractor(self._gtf) # pylint: disable=protected-access
+ exons = extractor.extract(transcript_id='wrong_transcript_id')
+ self.assertEmpty(exons)
+
+
+if __name__ == '__main__':
+ absltest.main()
diff --git a/flax_model/alphagenome/model/variant_scoring/gene_mask_test.py b/flax_model/alphagenome/model/variant_scoring/gene_mask_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..bc5e721f90bf5715e7ba816cf8c0414c0893097e
--- /dev/null
+++ b/flax_model/alphagenome/model/variant_scoring/gene_mask_test.py
@@ -0,0 +1,386 @@
+# Copyright 2026 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 flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome._sdk.models import dna_output
+from flax_model.alphagenome._sdk.models import variant_scorers
+from flax_model.alphagenome.model.variant_scoring import gene_mask
+from flax_model.alphagenome.model.variant_scoring import gene_mask_extractor as gene_mask_extractor_lib
+import anndata
+import jax
+import jax.numpy as jnp
+import numpy as np
+import pandas as pd
+
+
+def _get_mock_gtf():
+ # Two genes (G1, G2).
+ # G1 has 3 transcripts (T1, T2, T3). G2 has 1 transcript (T4). The T4
+ # transcript is further annotated as consisting of 2 exons.
+ gtf = pd.DataFrame({
+ 'End': [200, 200, 200, 200, 108, 108, 40, 108],
+ 'Start': [101, 101, 102, 103, 0, 0, 0, 80],
+ 'Strand': ['+', '+', '+', '+', '-', '-', '-', '-'],
+ 'transcript_id': ['', 'T1', 'T2', 'T3', '', 'T4', 'T4', 'T4'],
+ 'transcript_type': [
+ '',
+ 'protein_coding',
+ 'protein_coding',
+ 'protein_coding',
+ '',
+ 'protein_coding',
+ 'protein_coding',
+ 'protein_coding',
+ ],
+ 'gene_id': ['G1', 'G1', 'G1', 'G1', 'G2', 'G2', 'G2', 'G2'],
+ 'gene_name': [
+ 'gene_1_name',
+ 'gene_2_name',
+ 'gene_1_name',
+ 'gene_1_name',
+ 'gene_2_name',
+ 'gene_2_name',
+ 'gene_2_name',
+ 'gene_2_name',
+ ],
+ 'Feature': [
+ 'gene',
+ 'transcript',
+ 'transcript',
+ 'transcript',
+ 'gene',
+ 'transcript',
+ 'exon',
+ 'exon',
+ ],
+ })
+ gtf['Chromosome'] = 'chr1'
+ gtf['Score'] = '.'
+ gtf['Frame'] = '.'
+ gtf['Source'] = 'ENSEMBL'
+ gtf['gene_type'] = 'protein_coding'
+ return gtf
+
+
+def _get_mock_rnaseq_track_metadata() -> pd.DataFrame:
+ return pd.DataFrame({
+ 'name': [
+ 'CL:0000047 polyA plus RNA-seq',
+ 'CL:0000062 total RNA-seq',
+ 'CL:0000084 polyA plus RNA-seq',
+ 'CL:0000084 total RNA-seq',
+ 'CL:0000115 total RNA-seq',
+ 'CL:0000127 total RNA-seq',
+ (
+ 'EFO:0000572 gtex Cells_EBV-transformed_lymphocytes polyA plus'
+ ' RNA-seq'
+ ),
+ 'EFO:0002009 gtex Cells_Cultured_fibroblasts polyA plus RNA-seq',
+ 'UBERON:0000007 gtex Pituitary polyA plus RNA-seq',
+ 'UBERON:0000458 gtex Cervix_Endocervix polyA plus RNA-seq',
+ ],
+ 'strand': ['+', '+', '+', '+', '+', '+', '.', '.', '.', '.'],
+ 'gtex_tissue': [
+ '',
+ '',
+ '',
+ '',
+ '',
+ '',
+ 'Cells_EBV-transformed_lymphocytes',
+ 'Cells_Cultured_fibroblasts',
+ 'Pituitary',
+ 'Cervix_Endocervix',
+ ],
+ 'padding': [False] * 10,
+ })
+
+
+class GeneMaskVariantScorerTest(parameterized.TestCase):
+
+ def test_get_masks_and_metadata_body(self):
+ gene_mask_extractor = gene_mask_extractor_lib.GeneMaskExtractor(
+ gtf=_get_mock_gtf(),
+ gene_mask_type=gene_mask_extractor_lib.GeneMaskType.BODY,
+ )
+ gene_variant_scorer = gene_mask.GeneVariantScorer(
+ gene_mask_extractor=gene_mask_extractor,
+ )
+ interval = genome.Interval('chr1', 0, 256)
+ variant = genome.Variant('chr1', 10, 'C', 'T')
+ settings = variant_scorers.GeneMaskLFCScorer(
+ requested_output=dna_output.OutputType.RNA_SEQ,
+ )
+ track_metadata = dna_output.OutputMetadata(
+ rna_seq=_get_mock_rnaseq_track_metadata()
+ )
+ gene_masks, metadata = gene_variant_scorer.get_masks_and_metadata(
+ interval, variant, settings=settings, track_metadata=track_metadata
+ )
+ self.assertIsInstance(metadata, pd.DataFrame)
+ self.assertLen(metadata, 2)
+ self.assertSequenceEqual(list(metadata.gene_id), ['G1', 'G2'])
+
+ self.assertEqual(gene_masks.shape, (256, 2))
+ expected_g1_mask = np.zeros(256, dtype=bool)
+ expected_g1_mask[101:200] = True
+ expected_g2_mask = np.zeros(256, dtype=bool)
+ expected_g2_mask[0:108] = True
+ np.testing.assert_array_equal(gene_masks[:, 0], expected_g1_mask)
+ np.testing.assert_array_equal(gene_masks[:, 1], expected_g2_mask)
+
+ @parameterized.product(
+ [
+ dict(
+ settings=variant_scorers.GeneMaskLFCScorer(
+ requested_output=dna_output.OutputType.RNA_SEQ,
+ ),
+ # ref values for gene: 4, 5, 6. mean = 5.
+ # alt values for gene: 1, 1, 1. mean = 1.
+ # expected_score = log(1 + 1e-3) - log(5 + 1e-3) = -1.6086375
+ expected_score=-1.6086375,
+ ),
+ dict(
+ settings=variant_scorers.GeneMaskActiveScorer(
+ requested_output=dna_output.OutputType.RNA_SEQ,
+ ),
+ # ref values for gene: 4, 5, 6. mean = 5.
+ # alt values for gene: 1, 1, 1. mean = 1.
+ # expected_score = max(1, 5) = 5.
+ expected_score=5.0,
+ ),
+ ],
+ transfer_guard=['disallow', 'allow'],
+ )
+ def test_score_variant(
+ self, settings, expected_score: float, transfer_guard: str
+ ):
+ gtf = pd.DataFrame({
+ 'End': [7],
+ 'Start': [4],
+ 'Strand': ['+'],
+ 'gene_id': ['G1'],
+ 'gene_name': ['gene_1_name'],
+ 'Feature': ['gene'],
+ 'Chromosome': ['chr1'],
+ 'Score': ['.'],
+ 'Frame': ['.'],
+ 'Source': ['ENSEMBL'],
+ 'gene_type': ['protein_coding'],
+ 'transcript_id': [''],
+ 'transcript_type': [''],
+ })
+ gene_mask_extractor = gene_mask_extractor_lib.GeneMaskExtractor(
+ gtf=gtf,
+ gene_mask_type=gene_mask_extractor_lib.GeneMaskType.BODY,
+ )
+ gene_variant_scorer = gene_mask.GeneVariantScorer(
+ gene_mask_extractor=gene_mask_extractor,
+ )
+ interval = genome.Interval('chr1', 0, 11)
+ variant = genome.Variant('chr1', 1, 'C', 'T')
+ track_metadata = dna_output.OutputMetadata(
+ rna_seq=_get_mock_rnaseq_track_metadata()
+ )
+ num_tracks = len(track_metadata.rna_seq)
+ gene_masks, _ = gene_variant_scorer.get_masks_and_metadata(
+ interval, variant, settings=settings, track_metadata=track_metadata
+ )
+ expected_g1_mask = np.zeros((11, 1), dtype=bool)
+ expected_g1_mask[4:7, 0] = True
+ np.testing.assert_array_equal(gene_masks, expected_g1_mask)
+
+ ref = (
+ jnp.arange(11, dtype=jnp.float32)
+ .reshape(-1, 1)
+ .repeat(num_tracks, axis=1)
+ )
+ alt = jnp.ones((11, num_tracks), dtype=jnp.float32)
+ with jax.transfer_guard(transfer_guard):
+ scores = gene_variant_scorer.score_variant(
+ ref={settings.requested_output: ref},
+ alt={settings.requested_output: alt},
+ masks=jax.device_put(gene_masks),
+ settings=settings,
+ variant=variant,
+ interval=interval,
+ )
+ np.testing.assert_almost_equal(
+ scores['score'],
+ np.ones((1, num_tracks), dtype=jnp.float32) * expected_score,
+ decimal=5,
+ )
+
+ @parameterized.product(
+ variant=[
+ genome.Variant('chr1', 5, 'A', 'AT'),
+ genome.Variant('chr1', 4, 'AA', 'AAT'),
+ genome.Variant('chr1', 3, 'AAA', 'AAAT'),
+ ],
+ transfer_guard=['disallow', 'allow'],
+ )
+ def test_gene_variant_scorer_splicing_insertion(
+ self, variant: genome.Variant, transfer_guard: str
+ ):
+ # Score an insertion that adds a predicted splicing donor at position 5.
+ # In addition, there is a predicted splicing acceptor at position 7 in the
+ # REF (position 8 in the ALT due to the insertion), which should be neutral.
+ gtf = pd.DataFrame({
+ 'Chromosome': ['chr1'],
+ 'Start': [0],
+ 'End': [20],
+ 'Strand': ['+'],
+ 'Feature': ['gene'],
+ 'gene_id': ['G1'],
+ 'gene_name': ['Gene1'],
+ 'gene_type': ['protein_coding'],
+ 'transcript_id': [''],
+ 'transcript_type': [''],
+ })
+ variant_scorer = gene_mask.GeneVariantScorer(
+ gene_mask_extractor=gene_mask_extractor_lib.GeneMaskExtractor(
+ gtf=gtf,
+ gene_mask_type=gene_mask_extractor_lib.GeneMaskType.BODY,
+ gene_query_type=(
+ gene_mask_extractor_lib.GeneQueryType.VARIANT_OVERLAPPING
+ ),
+ ),
+ )
+ track_metadata = dna_output.OutputMetadata()
+ settings = variant_scorers.GeneMaskSplicingScorer(
+ requested_output=dna_output.OutputType.SPLICE_SITES,
+ width=None,
+ )
+ interval = variant.reference_interval.resize(9)
+ masks, _ = variant_scorer.get_masks_and_metadata(
+ interval, variant, settings=settings, track_metadata=track_metadata
+ )
+
+ alt = jnp.zeros((9, 4), dtype=jnp.float32)
+ alt = alt.at[4 - interval.start, 0].set(1)
+ alt = alt.at[7 - interval.start, 2].set(1)
+ ref = jnp.zeros((9, 4), dtype=jnp.float32)
+ ref = ref.at[6 - interval.start, 2].set(1)
+
+ with jax.transfer_guard(transfer_guard):
+ scores = variant_scorer.score_variant(
+ {dna_output.OutputType.SPLICE_SITES: ref},
+ {dna_output.OutputType.SPLICE_SITES: alt},
+ masks=jax.device_put(masks),
+ settings=settings,
+ variant=variant,
+ interval=interval,
+ )
+ # A new splicing donor was inserted, so the 0th track should be 1.0. A
+ # splicing acceptor was kept intact, so the 1st track should remain 0.0.
+ np.testing.assert_array_equal(
+ scores['score'][0], np.array([1, 0, 0, 0], dtype=jnp.float32)
+ )
+
+ @parameterized.parameters(
+ genome.Variant('chr1', 5, 'AT', 'A'),
+ genome.Variant('chr1', 4, 'AAT', 'AA'),
+ genome.Variant('chr1', 3, 'AAAT', 'AAA'),
+ )
+ def test_gene_variant_scorer_splicing_deletion(self, variant):
+ # Score a deletion that removes a predicted splicing donor at position 5.
+ # In addition, there is a predicted splicing acceptor at position 4,
+ # which should be neutral.
+ gtf = pd.DataFrame({
+ 'Chromosome': ['chr1'],
+ 'Start': [0],
+ 'End': [20],
+ 'Strand': ['+'],
+ 'Feature': ['gene'],
+ 'gene_id': ['G1'],
+ 'gene_name': ['Gene1'],
+ 'gene_type': ['protein_coding'],
+ 'transcript_id': [''],
+ 'transcript_type': [''],
+ })
+ variant_scorer = gene_mask.GeneVariantScorer(
+ gene_mask_extractor=gene_mask_extractor_lib.GeneMaskExtractor(
+ gtf=gtf,
+ gene_mask_type=gene_mask_extractor_lib.GeneMaskType.BODY,
+ gene_query_type=(
+ gene_mask_extractor_lib.GeneQueryType.VARIANT_OVERLAPPING
+ ),
+ ),
+ )
+ track_metadata = dna_output.OutputMetadata()
+ settings = variant_scorers.GeneMaskSplicingScorer(
+ requested_output=dna_output.OutputType.SPLICE_SITES,
+ width=None,
+ )
+ interval = variant.reference_interval.resize(9)
+ masks, _ = variant_scorer.get_masks_and_metadata(
+ interval, variant, settings=settings, track_metadata=track_metadata
+ )
+
+ alt = jnp.zeros((9, 4), dtype=jnp.float32)
+ alt = alt.at[4 - interval.start, 2].set(1)
+ ref = jnp.zeros((9, 4), dtype=jnp.float32)
+ ref = ref.at[5 - interval.start, 0].set(1)
+ ref = ref.at[4 - interval.start, 2].set(1)
+
+ scores = variant_scorer.score_variant(
+ {dna_output.OutputType.SPLICE_SITES: ref},
+ {dna_output.OutputType.SPLICE_SITES: alt},
+ masks=jax.device_put(masks),
+ settings=settings,
+ variant=variant,
+ interval=interval,
+ )
+ # A splicing donor was deleted, so the 0th track should be 1.0. A
+ # splicing acceptor was kept intact, so the other tracks should remain 0.0.
+ np.testing.assert_array_equal(
+ scores['score'][0], np.array([1, 0, 0, 0], dtype=jnp.float32)
+ )
+
+ def test_finalize_variant(self):
+ gene_mask_extractor = gene_mask_extractor_lib.GeneMaskExtractor(
+ gtf=_get_mock_gtf(),
+ gene_mask_type=gene_mask_extractor_lib.GeneMaskType.BODY,
+ )
+ gene_variant_scorer = gene_mask.GeneVariantScorer(
+ gene_mask_extractor=gene_mask_extractor,
+ )
+ interval = genome.Interval('chr1', 0, 256)
+ variant = genome.Variant('chr1', 10, 'C', 'T')
+ settings = variant_scorers.GeneMaskLFCScorer(
+ requested_output=dna_output.OutputType.RNA_SEQ,
+ )
+ df_track_metadata = _get_mock_rnaseq_track_metadata()
+ track_metadata = dna_output.OutputMetadata(rna_seq=df_track_metadata)
+ _, mask_metadata = gene_variant_scorer.get_masks_and_metadata(
+ interval, variant, settings=settings, track_metadata=track_metadata
+ )
+ self.assertLen(mask_metadata, 2) # Two genes.
+ scores = {'score': np.ones((2, len(df_track_metadata)), dtype=np.float32)}
+ finalized_variant = gene_variant_scorer.finalize_variant(
+ scores,
+ track_metadata=track_metadata,
+ mask_metadata=mask_metadata,
+ settings=settings,
+ )
+ self.assertIsInstance(finalized_variant, anndata.AnnData)
+ self.assertLen(finalized_variant.obs, 2)
+ self.assertLen(finalized_variant.var, len(df_track_metadata))
+
+
+if __name__ == '__main__':
+ absltest.main()
diff --git a/flax_model/alphagenome/model/variant_scoring/polyadenylation.py b/flax_model/alphagenome/model/variant_scoring/polyadenylation.py
new file mode 100644
index 0000000000000000000000000000000000000000..c4f56f81cafd3b8da497c2dae815adc361a71902
--- /dev/null
+++ b/flax_model/alphagenome/model/variant_scoring/polyadenylation.py
@@ -0,0 +1,227 @@
+# Copyright 2026 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.
+
+"""Implements a variant scorer for polyadenylation."""
+
+from flax_model.alphagenome._sdk import typing
+from flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome._sdk.models import dna_output
+from flax_model.alphagenome._sdk.models import variant_scorers
+from flax_model.alphagenome.model.variant_scoring import gene_mask_extractor
+from flax_model.alphagenome.model.variant_scoring import variant_scoring
+import anndata
+import chex
+import jax
+import jax.numpy as jnp
+from jaxtyping import Array, Bool, Float32 # pylint: disable=g-multiple-import, g-importing-member
+import numpy as np
+import pandas as pd
+
+MAX_GENES = 22
+MAX_PAS = 136
+
+_PAS_MASK_WIDTH = 400
+
+
+@typing.jaxtyped
+@chex.dataclass(frozen=True)
+class PolyadenylationVariantMasks:
+ pas_mask: Bool[Array | np.ndarray, 'S G P']
+ gene_mask: Bool[Array | np.ndarray, 'G']
+
+
+@jax.jit
+@typing.jaxtyped
+def _aggregate_maximum_ratio_coverage_fc(
+ ref: Float32[Array, 'S T'],
+ alt: Float32[Array, 'S T'],
+ gene_pas_mask: Bool[Array, 'S G P'],
+) -> Float32[Array, 'G T']:
+ """Implements the Borzoi statistic for paQTL variant scoring."""
+
+ ref_aggregation = jnp.einsum('pc,pga->gac', ref, gene_pas_mask)
+ alt_aggregation = jnp.einsum('pc,pga->gac', alt, gene_pas_mask)
+ covr_ratio = alt_aggregation / ref_aggregation
+ covr_ratio = jnp.nan_to_num(covr_ratio, posinf=0, neginf=0, nan=0)
+ # (gene, pas, tracks)
+
+ # Get proximal vs distal counts for all possible polyadenylation site
+ # split versions.
+ k_interval = jnp.arange(MAX_PAS) # All PAS for the interval.
+ # Create mask for potential proximal pas site splits across the interval.
+ # Each PAS is added to the mask in sequential order for each gene, which
+ # ensures that aggregating the proximal counts take the first k PAS sites
+ # for each gene.
+ proximal_sites = k_interval[None] <= k_interval[:, None]
+
+ # Get total number of pas sites per gene
+ k_total = gene_pas_mask.max(axis=0).sum(axis=-1)[:, None] # (gene, 1)
+ # Get number of pas sites included in the proximal split per gene.
+ k_gene = gene_pas_mask.max(axis=0).cumsum(axis=-1) # (gene, k)
+ k_scaling = ((k_total - k_gene) / k_gene).T[:, :, None] # (k, gene, 1)
+
+ proximal_counts = jnp.einsum('gac,ka->kgc', covr_ratio, proximal_sites)
+ distal_counts = jnp.einsum('gac,ka->kgc', covr_ratio, ~proximal_sites)
+
+ scores = jnp.abs(jnp.log2(k_scaling * proximal_counts / distal_counts))
+ # We are converting nan to num to keep all the padding cases at 0.
+ scores = jnp.nan_to_num(scores, posinf=0, neginf=0, nan=0)
+ # [k, genes, tracks]
+ return scores.max(axis=0)
+
+
+class PolyadenylationVariantScorer(variant_scoring.VariantScorer):
+ """Variant scorer for polyadenylation."""
+
+ def __init__(
+ self,
+ gtf: pd.DataFrame,
+ pas_gtf: pd.DataFrame,
+ ):
+ self._gene_mask_extractor = gene_mask_extractor.GeneMaskExtractor(
+ gtf,
+ gene_mask_extractor.GeneMaskType.BODY,
+ gene_query_type=gene_mask_extractor.GeneQueryType.VARIANT_OVERLAPPING,
+ )
+ if 'gene_id_nopatch' not in pas_gtf:
+ pas_gtf['gene_id_nopatch'] = pas_gtf['gene_id'].str.split(
+ '.', expand=True
+ )[0]
+ self._pas_per_gene = {
+ gene_id_gtf: df
+ for gene_id_gtf, df in pas_gtf.groupby('gene_id_nopatch')
+ }
+
+ def get_masks_and_metadata(
+ self,
+ interval: genome.Interval,
+ variant: genome.Variant,
+ *,
+ settings: variant_scorers.PolyadenylationScorer,
+ track_metadata: dna_output.OutputMetadata,
+ ) -> tuple[PolyadenylationVariantMasks, pd.DataFrame]:
+ """See base class."""
+ del settings, track_metadata
+ _, gene_metadata = self._gene_mask_extractor.extract(interval, variant)
+ if len(gene_metadata) > MAX_GENES:
+ raise ValueError(
+ f'Too many genes found for interval {interval}: {len(gene_metadata)}'
+ )
+ gene_metadata_rows = []
+ gene_padding_mask = np.zeros(MAX_GENES, dtype=bool)
+ pas_mask = np.zeros(
+ (interval.width, MAX_GENES, MAX_PAS),
+ dtype=bool,
+ )
+ has_gene_id_nopatch = 'gene_id_nopatch' in gene_metadata.columns
+ for gene_index, gene_row in gene_metadata.iterrows():
+ gene_id = (
+ gene_row['gene_id_nopatch']
+ if has_gene_id_nopatch
+ else gene_row['gene_id'].split('.')[0]
+ )
+ if gene_id not in self._pas_per_gene:
+ continue
+ gene_pas = self._pas_per_gene[gene_id]
+ gene_pas = gene_pas[gene_pas['pas_strand'] == gene_row['strand']]
+ if (
+ gene_pas.shape[0] == 0
+ # Check at least 80% of a gene's PAS sites fall within the interval.
+ or np.mean((gene_pas['Start'] >= interval.start).values) < 0.8
+ or np.mean((gene_pas['End'] < interval.end).values) < 0.8
+ ):
+ # No PAS sites in interval for the gene.
+ continue
+
+ # Only look at PAS sites that fall in the interval.
+ gene_pas = gene_pas[
+ (gene_pas['Start'] >= interval.start)
+ & (gene_pas['End'] < interval.end)
+ ]
+
+ if gene_pas.shape[0] == 1:
+ # Only one PAS site in interval for the gene.
+ continue
+ else:
+ pas_interval_start = gene_pas['Start'] - interval.start
+ gene_pas = gene_pas.sort_values(by='Start')
+ # Get PAS metadata for gene.
+ gene_row_metadata = gene_row.to_dict()
+ dist = np.abs(gene_pas['Start'] - variant.position)
+ gene_row_metadata['num_pas'] = len(gene_pas)
+ gene_row_metadata['min_pas_var_distance'] = dist.min()
+ gene_padding_mask[gene_index] = True
+ gene_metadata_rows.append(gene_row_metadata)
+
+ for (pas_index, pas_row), p_interval_start in zip(
+ gene_pas.reset_index(drop=True).iterrows(),
+ pas_interval_start,
+ strict=True,
+ ):
+ # Defaults`to only doing upstream coverage of PAS site.
+ if pas_row.pas_strand == '+':
+ bin_end = p_interval_start + 1
+ bin_start = bin_end - _PAS_MASK_WIDTH
+ else:
+ bin_start = p_interval_start
+ bin_end = bin_start + _PAS_MASK_WIDTH
+ bin_start = max(min(bin_start, interval.width), 0)
+ bin_end = max(min(bin_end, interval.width), 0)
+ pas_mask[bin_start:bin_end, gene_index, pas_index] = True
+
+ return (
+ PolyadenylationVariantMasks(
+ pas_mask=pas_mask, gene_mask=gene_padding_mask
+ ),
+ pd.DataFrame(gene_metadata_rows),
+ )
+
+ def score_variant(
+ self,
+ ref: variant_scoring.ScoreVariantInput,
+ alt: variant_scoring.ScoreVariantInput,
+ *,
+ masks: PolyadenylationVariantMasks,
+ settings: variant_scorers.PolyadenylationScorer,
+ variant: genome.Variant | None = None,
+ interval: genome.Interval | None = None,
+ ) -> variant_scoring.ScoreVariantOutput:
+ """See base class."""
+ ref = ref[settings.requested_output]
+ alt = alt[settings.requested_output]
+
+ alt = variant_scoring.align_alternate(alt, variant, interval)
+ return {
+ 'scores': _aggregate_maximum_ratio_coverage_fc(
+ ref, alt, jnp.asarray(masks.pas_mask)
+ ),
+ 'gene_mask': masks.gene_mask,
+ }
+
+ def finalize_variant(
+ self,
+ scores: variant_scoring.ScoreVariantResult,
+ *,
+ track_metadata: dna_output.OutputMetadata,
+ mask_metadata: pd.DataFrame,
+ settings: variant_scorers.PolyadenylationScorer,
+ ) -> anndata.AnnData:
+ """See base class."""
+
+ track_metadata = track_metadata.get(settings.requested_output)
+ return variant_scoring.create_anndata(
+ scores['scores'][scores['gene_mask']],
+ obs=mask_metadata,
+ var=track_metadata,
+ )
diff --git a/flax_model/alphagenome/model/variant_scoring/polyadenylation_test.py b/flax_model/alphagenome/model/variant_scoring/polyadenylation_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..24bd0544a9d94d42cfc5d9976ee0ed218d73af22
--- /dev/null
+++ b/flax_model/alphagenome/model/variant_scoring/polyadenylation_test.py
@@ -0,0 +1,292 @@
+# Copyright 2026 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 flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome._sdk.models import dna_output
+from flax_model.alphagenome._sdk.models import variant_scorers
+from flax_model.alphagenome.model.variant_scoring import polyadenylation
+import anndata
+import chex
+import jax
+import jax.numpy as jnp
+import numpy as np
+import pandas as pd
+
+
+def _load_pas_scores():
+ common = dict(
+ Chromosome='chr1',
+ )
+ # PAS sites for 3 genes. G1 PAS sites fall in the middle of the interval, G2
+ # PAS sites fall at the beginning of the interval with 80% of them in the
+ # interval, and G3 PAS sites fall outside of the interval.
+ apa_gtf = pd.DataFrame({
+ 'Start': [
+ 1575,
+ 1585,
+ 1605,
+ 980,
+ 1000,
+ 1010,
+ 1020,
+ 1045,
+ 3120,
+ 3150,
+ 3180,
+ 1100,
+ ],
+ 'pas_strand': [
+ '+',
+ '+',
+ '+',
+ '-',
+ '-',
+ '-',
+ '-',
+ '-',
+ '+',
+ '+',
+ '+',
+ '+',
+ ],
+ 'pas_gene_id': [
+ 'G1',
+ 'G1',
+ 'G1',
+ 'G2',
+ 'G2',
+ 'G2',
+ 'G2',
+ 'G2',
+ 'G3',
+ 'G3',
+ 'G3',
+ 'G4',
+ ],
+ })
+ apa_gtf['End'] = apa_gtf['Start'] + 1
+ apa_gtf['cutmode'] = apa_gtf['Start']
+ apa_gtf['pas_id'] = apa_gtf.index
+ apa_gtf['gene_id'] = apa_gtf['pas_gene_id']
+
+ for k, v in common.items():
+ apa_gtf[k] = v
+ return apa_gtf
+
+
+def _load_gtf():
+ """Loads a GTF PAS scoring test."""
+ common = dict(
+ Chromosome='chr1',
+ Score='.',
+ Frame='.',
+ Source='ENSEMBL',
+ gene_type='protein_coding',
+ Feature='gene',
+ )
+ # Relative to variant at position 1700 in interval (chr1, 1000, 3048)
+ # Three genes (G1, G2, G3).
+ # One gene is at the beginning of the query interval, middle, and end.
+ # Only uses gene elements.
+ gtf = pd.DataFrame({
+ 'Start': [1500, 950, 1600, 1050],
+ 'End': [1800, 1800, 3100, 1800],
+ 'Strand': ['+', '-', '+', '+'],
+ 'gene_id': ['G1', 'G2', 'G3', 'G4'],
+ 'gene_name': [
+ 'gene_1_name',
+ 'gene_2_name',
+ 'gene_3_name',
+ 'gene_4_name',
+ ],
+ })
+ for k, v in common.items():
+ gtf[k] = v
+ return gtf
+
+
+def _load_pas_scoring_inputs() -> (
+ tuple[genome.Variant, genome.Interval, pd.DataFrame, pd.DataFrame]
+):
+ """Loads PAS scoring test specific gtf, PAS gtf, interval, and variant."""
+ interval = genome.Interval('chr1', 1000, 3048)
+ variant = genome.Variant('chr1', 1700, 'C', 'T', name='var_0')
+
+ return variant, interval, _load_gtf(), _load_pas_scores()
+
+
+class PolyadenylationTest(parameterized.TestCase):
+
+ def test_masks_and_metadata(self):
+ variant, interval, gtf, pas_gtf = _load_pas_scoring_inputs()
+
+ scorer = polyadenylation.PolyadenylationVariantScorer(gtf, pas_gtf)
+ settings = variant_scorers.PolyadenylationScorer()
+ masks, metadata = scorer.get_masks_and_metadata(
+ interval,
+ variant,
+ settings=settings,
+ track_metadata=dna_output.OutputMetadata(),
+ )
+ self.assertSameElements(
+ [
+ 'Chromosome',
+ 'Start',
+ 'End',
+ 'interval_start',
+ 'strand',
+ 'gene_name',
+ 'gene_id',
+ 'gene_type',
+ 'num_pas',
+ 'min_pas_var_distance',
+ ],
+ metadata.columns,
+ )
+ # Check there is only metadata for the 2 genes with >1 PAS in the interval.
+ self.assertLen(metadata, 2)
+
+ chex.assert_shape(masks.gene_mask, (polyadenylation.MAX_GENES,))
+ self.assertEqual(masks.gene_mask.sum(), 2)
+
+ chex.assert_shape(
+ masks.pas_mask,
+ (interval.width, polyadenylation.MAX_GENES, polyadenylation.MAX_PAS),
+ )
+
+ @parameterized.product(transfer_guard=['disallow', 'allow'])
+ def test_pas_variant_scorer_score_variant(self, transfer_guard: str):
+ # Use mask for seq len 15, 5 genes, MAX_PAS sites with PAS mask width == 1
+ # so aggregated across sequence length == actual value at that point in the
+ # track.
+ interval = genome.Interval('chr1', 1000, 1015)
+ variant = genome.Variant('chr1', 1005, 'C', 'T', name='var_0')
+ gene_pas_mask = np.zeros(
+ (interval.width, 6, polyadenylation.MAX_PAS), dtype=bool
+ )
+ # G1 has one PAS sites
+ gene_pas_mask[0, 0, 0] = True
+ # G2 has 2 PAS sites
+ for i in range(1, 3):
+ gene_pas_mask[i, 1, i] = True
+ # G3 has 3 PAS sites
+ for i in range(3, 6):
+ gene_pas_mask[i, 2, i] = True
+ # G4 has 4 PAS sites
+ for i in range(6, 10):
+ gene_pas_mask[i, 3, i] = True
+ # G5 has 4 PAS
+ for i in range(10, 14):
+ gene_pas_mask[i, 4, i] = True
+
+ # Set up tracks such that MAX COVR RATIO is equal for all tracks
+ # Score should be zero because only 1 PAS site
+ g1_values = jnp.array([10], jnp.float32).repeat(100).reshape(1, 100)
+ # Score should be 0.5, only one coverage ratio fc to take the max over
+ # because only 2 pas sites to make proximal-distal split
+ g2_values = jnp.array([2, 4], jnp.float32).repeat(100).reshape(2, 100)
+ # Score should be 5, argmax k = 2
+ g3_values = jnp.array([2, 48, 5], jnp.float32).repeat(100).reshape(3, 100)
+ # Score should be 2, argmax k = 3
+ g4_values = (
+ jnp.array([0, 5, 55, 10], jnp.float32).repeat(100).reshape(4, 100)
+ )
+ # Score should be 10, argmax k = 2
+ g5_values = (
+ jnp.array([10, 90, 5, 5, 0], jnp.float32).repeat(100).reshape(5, 100)
+ )
+
+ # Max ratio coverage uses alt_agg/ref_agg, so set alt tracks to the
+ # covr_ratios needed for test case answers and ref tracks to ones.
+ alt = jnp.concatenate(
+ [g1_values, g2_values, g3_values, g4_values, g5_values],
+ axis=0,
+ )
+ ref = jnp.ones_like(alt)
+ masks = polyadenylation.PolyadenylationVariantMasks(
+ pas_mask=gene_pas_mask,
+ gene_mask=np.array([True] * 5 + [False]),
+ )
+ _, _, gtf, pas_gtf = _load_pas_scoring_inputs()
+ variant_scorer = polyadenylation.PolyadenylationVariantScorer(
+ gtf=gtf, pas_gtf=pas_gtf
+ )
+ with jax.transfer_guard(transfer_guard):
+ scores = variant_scorer.score_variant(
+ {dna_output.OutputType.RNA_SEQ: ref},
+ {dna_output.OutputType.RNA_SEQ: alt},
+ masks=jax.device_put(masks),
+ settings=variant_scorers.PolyadenylationScorer(),
+ interval=interval,
+ variant=variant,
+ )
+ np.testing.assert_array_equal(
+ scores['scores'][0, :], jnp.zeros(100, dtype=jnp.float32)
+ )
+ np.testing.assert_array_equal(
+ scores['scores'][1, :],
+ jnp.abs(jnp.log2(np.ones(100, dtype=jnp.float32) * 0.5)),
+ )
+ np.testing.assert_array_equal(
+ scores['scores'][2, :],
+ jnp.abs(jnp.log2(np.ones(100, dtype=jnp.float32) * (4 / 53))),
+ )
+ np.testing.assert_array_equal(
+ scores['scores'][3, :],
+ jnp.abs(jnp.log2(np.ones(100, dtype=jnp.float32) * (5 / 65))),
+ )
+ np.testing.assert_array_equal(
+ scores['scores'][4, :],
+ jnp.abs(jnp.log2(np.ones(100, dtype=jnp.float32) * 10)),
+ )
+
+ def test_finalize_variant(self):
+ variant, interval, gtf, pas_gtf = _load_pas_scoring_inputs()
+ settings = variant_scorers.PolyadenylationScorer()
+ variant_scorer = polyadenylation.PolyadenylationVariantScorer(
+ gtf=gtf, pas_gtf=pas_gtf
+ )
+ track_metadata = pd.DataFrame(
+ {'name': np.arange(10).astype(str), 'Strand': '.', 'padding': False}
+ )
+ # Scenario of 3 genes in the interval (max possible=4) and 10 output tracks.
+ expected_scores = np.ones((4, 10), dtype=jnp.float32)
+ scores = {
+ 'scores': expected_scores,
+ 'gene_mask': np.array([True, False, True, False]),
+ }
+
+ _, mask_metadata = variant_scorer.get_masks_and_metadata(
+ interval,
+ variant,
+ settings=settings,
+ track_metadata=dna_output.OutputMetadata(),
+ )
+ finalized_variant = variant_scorer.finalize_variant(
+ scores,
+ settings=settings,
+ track_metadata=dna_output.OutputMetadata(rna_seq=track_metadata),
+ mask_metadata=mask_metadata,
+ )
+
+ self.assertIsInstance(finalized_variant, anndata.AnnData)
+ self.assertTrue(finalized_variant.X.flags['C_CONTIGUOUS'])
+
+ self.assertLen(finalized_variant.var, expected_scores.shape[-1])
+
+
+if __name__ == '__main__':
+ absltest.main()
diff --git a/flax_model/alphagenome/model/variant_scoring/splice_junction.py b/flax_model/alphagenome/model/variant_scoring/splice_junction.py
new file mode 100644
index 0000000000000000000000000000000000000000..35849e4d24a5abd9fc847edd63099ab07035244b
--- /dev/null
+++ b/flax_model/alphagenome/model/variant_scoring/splice_junction.py
@@ -0,0 +1,306 @@
+# Copyright 2026 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.
+
+"""Implementation of splice junction variant scoring."""
+
+from flax_model.alphagenome._sdk import typing
+from flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome._sdk.data import junction_data
+from flax_model.alphagenome._sdk.models import dna_output
+from flax_model.alphagenome._sdk.models import variant_scorers
+from flax_model.alphagenome.model.variant_scoring import gene_mask_extractor
+from flax_model.alphagenome.model.variant_scoring import variant_scoring
+import anndata
+import einshape
+import jax
+import jax.numpy as jnp
+from jaxtyping import Float, Int32, Shaped # pylint: disable=g-multiple-import, g-importing-member
+import numpy as np
+import pandas as pd
+import pyranges
+
+MAX_SPLICE_SITES = 256
+PAD_VALUE = -1
+
+
+def _create_empty(mask_metadata: pd.DataFrame, track_metadata: pd.DataFrame):
+ """Create empty AnnData object for splice junction scoring."""
+ junction_columns = [
+ 'junction_Start',
+ 'junction_End',
+ ]
+ return variant_scoring.create_anndata(
+ np.zeros((0, len(track_metadata['name'])), dtype=np.float32),
+ obs=pd.DataFrame(columns=list(mask_metadata.columns) + junction_columns),
+ var=pd.DataFrame({
+ 'strand': '.',
+ 'name': track_metadata['name'],
+ 'gtex_tissue': track_metadata.get('gtex_tissue'),
+ 'ontology_curie': track_metadata.get('ontology_curie'),
+ 'biosample_type': track_metadata.get('biosample_type'),
+ 'biosample_name': track_metadata.get('biosample_name'),
+ 'biosample_life_stage': track_metadata.get('biosample_life_stage'),
+ 'data_source': track_metadata.get('data_source'),
+ 'Assay title': track_metadata.get('Assay title'),
+ }),
+ )
+
+
+def _create(
+ junction_scores: pd.DataFrame,
+ mask_metadata: pd.DataFrame,
+ track_metadata: pd.DataFrame,
+) -> anndata.AnnData:
+ """Converts a dataframe of junction scores to an AnnData object."""
+ if mask_metadata.empty or junction_scores.empty:
+ raise ValueError('Both junction_scores and mask_metadata must be non-empty')
+
+ junction_scores = junction_scores[
+ junction_scores['gene_id'].isin(mask_metadata['gene_id'])
+ ]
+
+ gene_max_scores = []
+ track_names = track_metadata['name']
+ for gene_id in junction_scores['gene_id'].unique():
+ gene_junction_scores = junction_scores[
+ junction_scores.gene_id == gene_id
+ ].reset_index(drop=True)
+ gene_junction_scores = gene_junction_scores.iloc[
+ gene_junction_scores[track_names].values.argmax(0)
+ ]
+ gene_max_scores.append(gene_junction_scores)
+ junction_scores = pd.concat(gene_max_scores)
+ score_values = junction_scores[track_names].values
+
+ # Merge junction information with mask metadata.
+ junctions_all_genes = junction_scores[['gene_id', 'Start', 'End']]
+ junctions_all_genes.columns = ['gene_id', 'junction_Start', 'junction_End']
+ mask_metadata = junctions_all_genes.merge(
+ mask_metadata, on='gene_id', sort=False
+ )
+ # Create the final track metadata.
+ track_metadata = pd.DataFrame({
+ 'strand': '.', # We already matched prediction by strand.
+ 'name': track_names,
+ 'gtex_tissue': track_metadata['gtex_tissue'],
+ 'ontology_curie': track_metadata.get('ontology_curie'),
+ 'biosample_type': track_metadata.get('biosample_type'),
+ 'biosample_name': track_metadata.get('biosample_name'),
+ 'biosample_life_stage': track_metadata.get('biosample_life_stage'),
+ 'data_source': track_metadata.get('data_source'),
+ 'Assay title': track_metadata.get('Assay title'),
+ })
+ ann_data = variant_scoring.create_anndata(
+ score_values,
+ obs=mask_metadata,
+ var=track_metadata,
+ )
+ # Remove duplicated junctions. Per gene, we report junctions that has maximum
+ # score in any tissue. For the reported junctions, we return their predictions
+ # in all tissues.
+ return ann_data[~ann_data.obs.duplicated()].copy()
+
+
+@typing.jaxtyped
+def unstack_junction_predictions(
+ splice_junction_prediction: Float[np.ndarray, 'D D _'],
+ splice_site_positions: Int32[np.ndarray, '4 D'],
+ interval: genome.Interval | None = None,
+) -> tuple[
+ Float[np.ndarray, 'num_junctions num_tracks'],
+ Shaped[np.ndarray, 'num_junctions'],
+ Int32[np.ndarray, 'num_junctions'],
+ Int32[np.ndarray, 'num_junctions'],
+]:
+ """Unstack splice junction predictions to long format."""
+ # Unpack splice junction predictions.
+ splice_junction_prediction = einshape.numpy_einshape(
+ 'da(st)->dast', splice_junction_prediction, s=2
+ )
+ # Convert splice site positions.
+ remove_padding_fn = lambda x: x[x != PAD_VALUE]
+ pos_donors = remove_padding_fn(splice_site_positions[0])
+ pos_acceptors = remove_padding_fn(splice_site_positions[1])
+ neg_donors = remove_padding_fn(splice_site_positions[2])
+ neg_acceptors = remove_padding_fn(splice_site_positions[3])
+ junction_pred_pos = splice_junction_prediction[
+ : len(pos_donors), : len(pos_acceptors), 0
+ ]
+ junction_pred_pos = einshape.numpy_einshape('dat->(da)t', junction_pred_pos)
+ num_pos_donors = len(pos_donors)
+ pos_donors = np.repeat(pos_donors, len(pos_acceptors))
+ pos_acceptors = np.tile(pos_acceptors, num_pos_donors)
+ junction_pred_neg = splice_junction_prediction[
+ : len(neg_donors), : len(neg_acceptors), 1
+ ]
+ junction_pred_neg = einshape.numpy_einshape('dat->(da)t', junction_pred_neg)
+ num_neg_donors = len(neg_donors)
+ neg_donors = np.repeat(neg_donors, len(neg_acceptors))
+ neg_acceptors = np.tile(neg_acceptors, num_neg_donors)
+ # Combine into a single output.
+ junction_predictions = np.concatenate(
+ [junction_pred_pos, junction_pred_neg], axis=0
+ )
+ # Junction start and end positions.
+ starts = np.concatenate([pos_donors, neg_acceptors]) + 1
+ starts += interval.start if interval is not None else 0
+ ends = np.concatenate([pos_acceptors, neg_donors])
+ ends += interval.start if interval is not None else 0
+ strands = np.array(['+'] * len(pos_donors) + ['-'] * len(neg_donors))
+ filter_mask = (starts < ends) & (starts > 0)
+
+ return (
+ junction_predictions[filter_mask],
+ strands[filter_mask],
+ starts[filter_mask],
+ ends[filter_mask],
+ )
+
+
+def junction_predictions_to_dataframe(
+ splice_junction_prediction: Float[np.ndarray, 'D D _'],
+ splice_site_positions: Int32[np.ndarray, 'T_mul_4 D'],
+ metadata: junction_data.JunctionMetadata,
+ interval: genome.Interval,
+) -> pd.DataFrame:
+ """Convert splice junction predictions to a dataframe."""
+ junction_predictions, strands, starts, ends = unstack_junction_predictions(
+ splice_junction_prediction, splice_site_positions, interval
+ )
+ junctions = pd.DataFrame({
+ 'Chromosome': interval.chromosome,
+ 'Start': starts,
+ 'End': ends,
+ 'Strand': strands,
+ })
+ predictions = pd.DataFrame(junction_predictions, columns=metadata['name'])
+ return pd.concat([junctions, predictions], axis=1)
+
+
+class SpliceJunctionVariantScorer(variant_scoring.VariantScorer):
+ """Implements the SpliceJunction variant scoring strategy.
+
+ Scores variants by the maximum of absolute delta pair counts of junctions
+ within the input interval. Junctions are annotated by overlapping with the
+ gtf gene intervals.
+ """
+
+ def __init__(self, gtf: pd.DataFrame):
+ self._gene_mask_extractor = gene_mask_extractor.GeneMaskExtractor(
+ gtf,
+ gene_mask_extractor.GeneMaskType.BODY,
+ gene_query_type=gene_mask_extractor.GeneQueryType.VARIANT_OVERLAPPING,
+ filter_protein_coding=True,
+ )
+
+ def get_masks_and_metadata(
+ self,
+ interval: genome.Interval,
+ variant: genome.Variant,
+ *,
+ settings: variant_scorers.SpliceJunctionScorer,
+ track_metadata: dna_output.OutputMetadata,
+ ) -> tuple[None, pd.DataFrame]:
+ """See base class."""
+ del settings, track_metadata
+ _, metadata = self._gene_mask_extractor.extract(interval, variant)
+ metadata['interval'] = interval
+ return None, metadata
+
+ def score_variant(
+ self,
+ ref: variant_scoring.ScoreVariantInput,
+ alt: variant_scoring.ScoreVariantInput,
+ *,
+ masks: None,
+ settings: variant_scorers.SpliceJunctionScorer,
+ variant: genome.Variant | None = None,
+ interval: genome.Interval | None = None,
+ ) -> variant_scoring.ScoreVariantOutput:
+ """See base class."""
+ del variant, interval, masks
+ ref_junctions = ref[settings.requested_output]['predictions']
+ alt_junctions = alt[settings.requested_output]['predictions']
+
+ splice_site_positions = ref[settings.requested_output][
+ 'splice_site_positions'
+ ]
+
+ # JAX dynamic slicing does not work with transfer_guard.
+ with jax.transfer_guard('allow'):
+ # Ignore splice sites beyond the max_splice_sites specified. This works
+ # because padding splice sites are always at the end of the array.
+ ref_junctions = ref_junctions[:MAX_SPLICE_SITES, :MAX_SPLICE_SITES]
+ alt_junctions = alt_junctions[:MAX_SPLICE_SITES, :MAX_SPLICE_SITES]
+ splice_site_positions = splice_site_positions[:, :MAX_SPLICE_SITES]
+
+ @jax.jit
+ def _apply_log_offset(x):
+ return jnp.log(x + 1e-7)
+
+ ref_junctions = _apply_log_offset(ref_junctions)
+ alt_junctions = _apply_log_offset(alt_junctions)
+
+ return {
+ 'delta_counts': (alt_junctions - ref_junctions).astype(jnp.float16),
+ 'splice_site_positions': splice_site_positions,
+ }
+
+ def finalize_variant(
+ self,
+ scores: variant_scoring.ScoreVariantResult,
+ *,
+ track_metadata: dna_output.OutputMetadata,
+ mask_metadata: pd.DataFrame,
+ settings: variant_scorers.SpliceJunctionScorer,
+ ) -> anndata.AnnData:
+ """See base class."""
+ track_metadata = track_metadata.get(settings.requested_output)
+
+ if mask_metadata.empty:
+ return _create_empty(mask_metadata, track_metadata)
+
+ delta_counts = scores['delta_counts']
+
+ interval = mask_metadata['interval'].values[0]
+ mask_metadata = mask_metadata.drop(columns=['interval'])
+
+ delta_counts = junction_predictions_to_dataframe(
+ np.abs(delta_counts, dtype=np.float32),
+ scores['splice_site_positions'],
+ metadata=track_metadata,
+ interval=interval,
+ )
+ if delta_counts.empty:
+ return _create_empty(mask_metadata, track_metadata)
+
+ junction_scores = (
+ pyranges.PyRanges(delta_counts)
+ .join(
+ pyranges.PyRanges(
+ mask_metadata.rename(columns={'strand': 'Strand'})
+ ),
+ strandedness='same',
+ )
+ .df
+ )
+
+ if not junction_scores.empty:
+ junction_scores = junction_scores[
+ (junction_scores['Start'] > junction_scores['Start_b'])
+ & (junction_scores['End'] < junction_scores['End_b'])
+ ]
+ return _create(junction_scores, mask_metadata, track_metadata)
+ else:
+ return _create_empty(mask_metadata, track_metadata)
diff --git a/flax_model/alphagenome/model/variant_scoring/splice_junction_test.py b/flax_model/alphagenome/model/variant_scoring/splice_junction_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..7aaf103d09c95f0234f52817245878557e72d7f1
--- /dev/null
+++ b/flax_model/alphagenome/model/variant_scoring/splice_junction_test.py
@@ -0,0 +1,248 @@
+# Copyright 2026 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 flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome._sdk.models import dna_output
+from flax_model.alphagenome._sdk.models import variant_scorers
+from flax_model.alphagenome.model.variant_scoring import splice_junction
+import anndata
+import chex
+import jax
+import jax.numpy as jnp
+import numpy as np
+import pandas as pd
+
+
+def _create_fake_gtf():
+ # Two genes (G1, G2).
+ # G1 has 3 transcripts (T1, T2, T3). G2 has 1 transcript (T4). The T4
+ # transcript is further annotated as consisting of 2 exons.
+ gtf = pd.DataFrame({
+ 'End': [200, 200, 200, 200, 108, 108, 40, 108],
+ 'Start': [101, 101, 102, 103, 0, 0, 0, 80],
+ 'Strand': ['+', '+', '+', '+', '-', '-', '-', '-'],
+ 'transcript_id': ['', 'T1', 'T2', 'T3', '', 'T4', 'T4', 'T4'],
+ 'transcript_type': [
+ '',
+ 'protein_coding',
+ 'protein_coding',
+ 'protein_coding',
+ '',
+ 'protein_coding',
+ 'protein_coding',
+ 'protein_coding',
+ ],
+ 'gene_id': ['G1', 'G1', 'G1', 'G1', 'G2', 'G2', 'G2', 'G2'],
+ 'gene_name': [
+ 'gene_1_name',
+ 'gene_2_name',
+ 'gene_1_name',
+ 'gene_1_name',
+ 'gene_2_name',
+ 'gene_2_name',
+ 'gene_2_name',
+ 'gene_2_name',
+ ],
+ 'Feature': [
+ 'gene',
+ 'transcript',
+ 'transcript',
+ 'transcript',
+ 'gene',
+ 'transcript',
+ 'exon',
+ 'exon',
+ ],
+ })
+ gtf['Chromosome'] = 'chr1'
+ gtf['Score'] = '.'
+ gtf['Frame'] = '.'
+ gtf['Source'] = 'ENSEMBL'
+ gtf['gene_type'] = 'protein_coding'
+ return gtf
+
+
+def _junction_track_metadata() -> pd.DataFrame:
+ return pd.DataFrame({
+ 'name': [
+ 'Brain_Cerebellum',
+ 'Adipose_Subcutaneous',
+ 'UBERON:0036149',
+ ],
+ 'other': ['foo', 'foo', 'foo'],
+ 'gtex_tissue': [
+ 'Brain_Cerebellum',
+ 'Adipose_Subcutaneous',
+ '',
+ ],
+ })
+
+
+class SpliceJunctionVariantScorerTest(parameterized.TestCase):
+
+ def test_masks_and_metadata(
+ self,
+ ):
+ settings = variant_scorers.SpliceJunctionScorer()
+ interval = genome.Interval('chr1', 0, 2048)
+ variant = genome.Variant('chr1', 105, 'C', 'T', name='var_0')
+ scorer = splice_junction.SpliceJunctionVariantScorer(_create_fake_gtf())
+ (
+ masks,
+ metadata,
+ ) = scorer.get_masks_and_metadata(
+ interval,
+ variant,
+ settings=settings,
+ track_metadata=dna_output.OutputMetadata(),
+ )
+ self.assertIsNone(masks)
+ self.assertIsInstance(metadata, pd.DataFrame)
+ self.assertContainsSubset(
+ ['gene_id', 'strand', 'Chromosome', 'Start', 'End', 'interval'],
+ metadata.columns,
+ )
+ # We expect to retrieve metadata for 2 genes overlapping the variant.
+ self.assertSequenceEqual(list(metadata.gene_id), ['G1', 'G2'])
+
+ @parameterized.product(transfer_guard=['disallow', 'allow'])
+ def test_score_variant(self, transfer_guard: str):
+ scorer = splice_junction.SpliceJunctionVariantScorer(_create_fake_gtf())
+
+ interval = genome.Interval('chr1', 0, 2048)
+ variant = genome.Variant('chr1', 105, 'C', 'T')
+
+ max_splice_sites = splice_junction.MAX_SPLICE_SITES
+ num_splice_sites = max_splice_sites + 1
+ num_tracks = 5
+ splice_site_positions = (
+ jnp.arange(num_splice_sites * 4, dtype=jnp.int32).reshape(
+ (4, num_splice_sites)
+ )
+ + 1
+ )
+ reference_predictions = jnp.arange(
+ num_splice_sites * num_splice_sites * num_tracks, dtype=np.float32
+ ).reshape((num_splice_sites, num_splice_sites, num_tracks))
+ alternative_predictions = jnp.zeros_like(reference_predictions)
+ with jax.transfer_guard(transfer_guard):
+ scores = scorer.score_variant(
+ ref={
+ dna_output.OutputType.SPLICE_JUNCTIONS: {
+ 'predictions': reference_predictions,
+ 'splice_site_positions': splice_site_positions,
+ }
+ },
+ alt={
+ dna_output.OutputType.SPLICE_JUNCTIONS: {
+ 'predictions': alternative_predictions,
+ 'splice_site_positions': splice_site_positions,
+ }
+ },
+ masks=None,
+ settings=variant_scorers.SpliceJunctionScorer(),
+ interval=interval,
+ variant=variant,
+ )
+
+ np.testing.assert_array_equal(
+ scores['splice_site_positions'],
+ splice_site_positions[:, :max_splice_sites],
+ )
+ chex.assert_shape(
+ scores['delta_counts'], (max_splice_sites, max_splice_sites, num_tracks)
+ )
+
+ @parameterized.product(return_empty_anndata=[True, False])
+ def test_finalize_variant(self, return_empty_anndata: bool):
+ settings = variant_scorers.SpliceJunctionScorer()
+ chromosome = 'chr2' if return_empty_anndata else 'chr1'
+ interval = genome.Interval(chromosome, 0, 2048)
+ variant = genome.Variant(chromosome, 10, 'C', 'T', name='var_0')
+ gtf = _create_fake_gtf()
+ # Create mock track metadata.
+ track_metadata = _junction_track_metadata()
+ scorer = splice_junction.SpliceJunctionVariantScorer(gtf)
+ _, mask_metadata = scorer.get_masks_and_metadata(
+ interval,
+ variant,
+ track_metadata=dna_output.OutputMetadata(
+ splice_junctions=track_metadata
+ ),
+ settings=settings,
+ )
+ if return_empty_anndata:
+ self.assertEmpty(mask_metadata)
+ else:
+ self.assertLen(mask_metadata, 1) # Only 1 gene overlapping the variant.
+
+ # Create mock predictions.
+ num_tracks = len(track_metadata) * 2 # 2 strands per track.
+ max_splice_sites = 5
+ predictions = (
+ np.arange(max_splice_sites * max_splice_sites * num_tracks)
+ .reshape((max_splice_sites, max_splice_sites, num_tracks))
+ .astype(np.float32)
+ )
+ splice_site_positions = (
+ np.arange(4 * max_splice_sites, dtype=np.int32).reshape(
+ (4, max_splice_sites)
+ )
+ ) + 1
+ # Make sure donor are upstream of acceptor.
+ splice_site_positions[1] = splice_site_positions[1] + 1
+ splice_site_positions[2] = splice_site_positions[2] + 20
+ splice_site_positions[3] = splice_site_positions[3] + 10
+ scores = {
+ 'delta_counts': predictions,
+ 'splice_site_positions': splice_site_positions,
+ }
+ results = scorer.finalize_variant(
+ scores,
+ track_metadata=dna_output.OutputMetadata(
+ splice_junctions=track_metadata
+ ),
+ mask_metadata=mask_metadata,
+ settings=settings,
+ )
+
+ self.assertIsInstance(results, anndata.AnnData)
+ for column in [
+ 'junction_Start',
+ 'junction_End',
+ 'gene_id',
+ 'strand',
+ 'Chromosome',
+ ]:
+ self.assertIn(column, results.obs.columns)
+
+ names = {
+ 'Brain_Cerebellum',
+ 'Adipose_Subcutaneous',
+ 'UBERON:0036149',
+ }
+ self.assertEqual(set(results.var['name'].tolist()), names)
+
+ if return_empty_anndata:
+ self.assertEqual(results.shape, (0, 3))
+ else:
+ # We have 1 gene x 3 tissues, and the two junctions have the same
+ # junction that has the maximum score.
+ self.assertEqual(results.shape, (1, 3))
+
+
+if __name__ == '__main__':
+ absltest.main()
diff --git a/flax_model/alphagenome/model/variant_scoring/variant_scoring.py b/flax_model/alphagenome/model/variant_scoring/variant_scoring.py
new file mode 100644
index 0000000000000000000000000000000000000000..444703a1c7ac4f5da030fa70e140048f8c1c2894
--- /dev/null
+++ b/flax_model/alphagenome/model/variant_scoring/variant_scoring.py
@@ -0,0 +1,259 @@
+# Copyright 2026 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.
+
+"""Base class for variant scorers."""
+
+import abc
+from collections.abc import Mapping
+import functools
+from typing import Generic, TypeVar
+
+from flax_model.alphagenome._sdk import typing
+from flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome._sdk.models import dna_output
+import anndata
+import jax
+import jax.numpy as jnp
+from jaxtyping import Array, Float32, Int32, PyTree # pylint: disable=g-multiple-import, g-importing-member
+import numpy as np
+import pandas as pd
+
+
+VariantMaskT = TypeVar('VariantMaskT')
+VariantMetadataT = TypeVar('VariantMetadataT')
+VariantSettingsT = TypeVar('VariantSettingsT')
+
+ScoreVariantOutput = Mapping[str, jax.Array | np.ndarray]
+ScoreVariantResult = Mapping[str, np.ndarray]
+
+ScoreVariantInput = Mapping[
+ dna_output.OutputType, PyTree[Float32[Array, '...'] | Int32[Array, '...']]
+]
+
+
+@typing.jaxtyped
+def create_anndata(
+ scores: Float32[np.ndarray, 'G T'],
+ *,
+ obs: pd.DataFrame | None,
+ var: pd.DataFrame,
+) -> anndata.AnnData:
+ """Helper function for creating AnnData objects."""
+ var = var.copy()
+ # We explicitly cast the dataframe indices to str to avoid
+ # ImplicitModificationWarning being logged over and over again.
+ var.index = var.index.map(str)
+
+ if obs is not None:
+ obs = obs.copy()
+ obs.index = obs.index.map(str).astype(str)
+ return anndata.AnnData(np.ascontiguousarray(scores), obs=obs, var=var)
+
+
+def get_resolution(output_type: dna_output.OutputType):
+ match output_type:
+ case dna_output.OutputType.ATAC:
+ return 1
+ case dna_output.OutputType.CAGE:
+ return 1
+ case dna_output.OutputType.DNASE:
+ return 1
+ case dna_output.OutputType.RNA_SEQ:
+ return 1
+ case dna_output.OutputType.CHIP_HISTONE:
+ return 128
+ case dna_output.OutputType.CHIP_TF:
+ return 128
+ case dna_output.OutputType.SPLICE_SITES:
+ return 1
+ case dna_output.OutputType.SPLICE_SITE_USAGE:
+ return 1
+ case dna_output.OutputType.SPLICE_JUNCTIONS:
+ return 1
+ case dna_output.OutputType.CONTACT_MAPS:
+ return 2048
+ case dna_output.OutputType.PROCAP:
+ return 1
+ case _:
+ raise ValueError(f'Unknown output type: {output_type}.')
+
+
+class VariantScorer(
+ Generic[VariantMaskT, VariantMetadataT, VariantSettingsT],
+ metaclass=abc.ABCMeta,
+):
+ """Abstract class for variant scorers."""
+
+ @abc.abstractmethod
+ def get_masks_and_metadata(
+ self,
+ interval: genome.Interval,
+ variant: genome.Variant,
+ *,
+ settings: VariantSettingsT,
+ track_metadata: dna_output.OutputMetadata,
+ ) -> tuple[VariantMaskT, VariantMetadataT]:
+ """Returns masks and metadata for the given interval, variant and metadata.
+
+ The generated masks and metadata will be passed to `score_variant` and
+ `finalize_variant` respectively.
+
+ Args:
+ interval: The interval to score.
+ variant: The variant to extract the masks/metadata for.
+ settings: The variant scorer settings.
+ track_metadata: The track metadata required to finalize the variant. These
+ will be passed into the `finalize_variants` function.
+
+ Returns:
+ A tuple of (masks, metadata), where:
+ masks: The masks required to score the variant, such as gene or TSS or
+ strand masks. These will be passed into the jitted `score_variants`
+ function.
+ metadata: The metadata required to finalize the variant. These will
+ be passed into the `finalize_variants` function.
+
+ The formats/shapes of masks and metadata will vary across variant scorers
+ depending on their individual needs.
+ """
+
+ @abc.abstractmethod
+ def score_variant(
+ self,
+ ref: ScoreVariantInput,
+ alt: ScoreVariantInput,
+ *,
+ masks: VariantMaskT,
+ settings: VariantSettingsT,
+ variant: genome.Variant | None = None,
+ interval: genome.Interval | None = None,
+ ) -> ScoreVariantOutput:
+ """Generates a score per track for the provided ref/alt predictions.
+
+ Args:
+ ref: Reference predictions.
+ alt: Alternative predictions.
+ masks: The masks for scoring the variant.
+ settings: The variant scorer settings.
+ variant: The variant to score.
+ interval: The interval to score.
+
+ Returns:
+ Dictionary of scores to be passed to `finalize_variant`.
+ """
+
+ @abc.abstractmethod
+ def finalize_variant(
+ self,
+ scores: ScoreVariantResult,
+ *,
+ track_metadata: dna_output.OutputMetadata,
+ mask_metadata: VariantMetadataT,
+ settings: VariantSettingsT,
+ ) -> anndata.AnnData:
+ """Returns finalized scores for the given scores and metadata.
+
+ Args:
+ scores: Dictionary of scores generated from `score_variant` function.
+ track_metadata: Metadata describing the tracks for each output_type.
+ mask_metadata: Metadata describing the masks.
+ settings: The variant scorer settings.
+
+ Returns:
+ A VariantOutputType object containing the final variant outputs. The
+ entries will vary across scorers depending on their individual needs.
+ """
+
+
+@typing.jaxtyped
+def align_alternate(
+ alt: Float32[Array | np.ndarray, 'S T'],
+ variant: genome.Variant,
+ interval: genome.Interval,
+) -> Float32[Array, 'S T']:
+ """Aligns ALT predictions to match the REF allele's sequence length.
+
+ This function adjusts the `alt` prediction array to account for indels
+ (insertions or deletions) present in the `variant`.
+
+ For insertions, the function summarizes the inserted region by taking the
+ maximum value across the alternate bases and pads the end with zeros to
+ maintain the original sequence length.
+
+ For deletions, zero signal is inserted at the locations corresponding to the
+ deleted bases in the reference.
+
+ Args:
+ alt: The ALT allele predictions, shape [sequence_length, num_tracks].
+ variant: The variant containing the indel information.
+ interval: The genomic interval.
+
+ Returns:
+ The aligned ALT predictions, shape [sequence_length, num_tracks].
+ """
+
+ insertion_length = len(variant.alternate_bases) - len(variant.reference_bases)
+ deletion_length = -insertion_length
+ variant_start_in_vector = variant.start - interval.start
+ # We assume that variants are left-aligned, and that insertions/deletions
+ # for multi-change variants occur at the end of the variant.
+ # We only need to align that insertion/deletion portion.
+ variant_start_in_vector += (
+ min(len(variant.reference_bases), len(variant.alternate_bases)) - 1
+ )
+ original_length = alt.shape[0]
+
+ # Summarize potential insertions by computing the maximum score across
+ # alternate bases.
+
+ @functools.partial(jax.jit, static_argnames=['insertion_length'])
+ def _apply(alt, insertion_length: int):
+ if insertion_length > 0:
+ pool_alt_past_ref = jnp.max(
+ alt[
+ variant_start_in_vector : variant_start_in_vector
+ + insertion_length
+ + 1
+ ],
+ axis=0,
+ keepdims=True,
+ )
+ alt = jnp.concatenate(
+ [
+ alt[:variant_start_in_vector],
+ pool_alt_past_ref,
+ alt[(variant_start_in_vector + insertion_length + 1) :],
+ jnp.zeros((insertion_length, alt.shape[1])),
+ ],
+ axis=0,
+ )
+ # Truncate to the original sequence length in case the alt insertion
+ # spills over the original sequence length. This happens only for
+ # insertions longer than half the interval.
+ alt = alt[:original_length]
+ elif deletion_length > 0:
+ # Handle potential deletions by inserting zero signal at deletion
+ # locations.
+ alt = jnp.concatenate(
+ [
+ alt[: (variant_start_in_vector + 1)],
+ jnp.zeros((deletion_length, alt.shape[1])),
+ alt[(variant_start_in_vector + 1) :],
+ ],
+ axis=0,
+ )
+ alt = alt[:original_length]
+ return alt
+
+ return _apply(alt, insertion_length)
diff --git a/flax_model/alphagenome/model/variant_scoring/variant_scoring_test.py b/flax_model/alphagenome/model/variant_scoring/variant_scoring_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1fa959b0f36e2ad37ea4c4ebfb8093a966cf453
--- /dev/null
+++ b/flax_model/alphagenome/model/variant_scoring/variant_scoring_test.py
@@ -0,0 +1,76 @@
+# Copyright 2026 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 flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome.model.variant_scoring import variant_scoring
+import chex
+import numpy as np
+
+
+class AlignAlternateTest(parameterized.TestCase):
+
+ @parameterized.named_parameters(
+ dict(
+ testcase_name='snp',
+ variant=genome.Variant('chr1', 2, 'C', 'T'),
+ expected_aligned_alt=np.arange(10, dtype=np.float32).reshape(-1, 1),
+ ),
+ dict(
+ testcase_name='insertion',
+ variant=genome.Variant('chr1', 2, 'C', 'TGA'),
+ expected_aligned_alt=np.array(
+ [0, 3, 4, 5, 6, 7, 8, 9, 0, 0], dtype=np.float32
+ ).reshape(-1, 1),
+ ),
+ dict(
+ testcase_name='multi_insertion',
+ variant=genome.Variant('chr1', 2, 'CC', 'CCTGA'),
+ expected_aligned_alt=np.array(
+ [0, 1, 5, 6, 7, 8, 9, 0, 0, 0], dtype=np.float32
+ ).reshape(-1, 1),
+ ),
+ dict(
+ testcase_name='multi_insertion_long_insertion',
+ variant=genome.Variant('chr1', 5, 'A', 'AAAAAAA'),
+ expected_aligned_alt=np.array(
+ [0, 1, 2, 3, 9, 0, 0, 0, 0, 0], dtype=np.float32
+ ).reshape(-1, 1),
+ ),
+ dict(
+ testcase_name='deletion',
+ variant=genome.Variant('chr1', 2, 'CGA', 'C'),
+ expected_aligned_alt=np.array(
+ [0, 1, 0, 0, 2, 3, 4, 5, 6, 7], dtype=np.float32
+ ).reshape(-1, 1),
+ ),
+ dict(
+ testcase_name='multi_deletion',
+ variant=genome.Variant('chr1', 2, 'CCCGA', 'CCC'),
+ expected_aligned_alt=np.array(
+ [0, 1, 2, 3, 0, 0, 4, 5, 6, 7], dtype=np.float32
+ ).reshape(-1, 1),
+ ),
+ )
+ def test_align_alt(self, variant, expected_aligned_alt):
+ interval = genome.Interval('chr1', 0, 10)
+ alt = np.arange(10, dtype=np.float32).reshape(-1, 1)
+ aligned_alt = variant_scoring.align_alternate(alt, variant, interval)
+ chex.assert_shape(aligned_alt, (10, 1))
+ np.testing.assert_array_equal(aligned_alt, expected_aligned_alt)
+
+
+if __name__ == '__main__':
+ absltest.main()
diff --git a/flax_model/alphagenome/package_config.py b/flax_model/alphagenome/package_config.py
new file mode 100644
index 0000000000000000000000000000000000000000..baeaadce647c3f4d2c5ef5dca2ee48b64e15d508
--- /dev/null
+++ b/flax_model/alphagenome/package_config.py
@@ -0,0 +1,25 @@
+#!/usr/bin/env python3
+"""
+AlphaGenome package config.
+
+Expose runtime metadata files through the normal packaging pipeline so
+install scripts do not need to copy files into site-packages manually.
+"""
+
+ALPHAGENOME_PACKAGE_DATA = {
+ "flax_model.alphagenome.model.metadata": [
+ "*.textproto",
+ ],
+}
+
+ALPHAGENOME_MANIFEST_RULES = [
+ "recursive-include flax_model/alphagenome/model/metadata *.textproto",
+]
+
+
+def get_package_data():
+ return ALPHAGENOME_PACKAGE_DATA
+
+
+def get_manifest_rules():
+ return ALPHAGENOME_MANIFEST_RULES
diff --git a/scripts/inference.sh b/scripts/inference.sh
new file mode 100644
index 0000000000000000000000000000000000000000..49bf6da3c67453f33b9f0463f1c32bb263c85cff
--- /dev/null
+++ b/scripts/inference.sh
@@ -0,0 +1,28 @@
+#!/usr/bin/env bash
+
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+PROJECT_DIR="$(cd "${SCRIPT_DIR}/.." && pwd)"
+
+if [[ -n "${ONESCIENCE_DATASETS_DIR:-}" ]]; then
+ DATA_ROOT_DIR="${ONESCIENCE_DATASETS_DIR}/AlphaGenome"
+else
+ DATA_ROOT_DIR="${PROJECT_DIR}/data"
+fi
+
+if [[ -n "${ONESCIENCE_MODELS_DIR:-}" ]]; then
+ MODEL_ROOT_DIR="${ONESCIENCE_MODELS_DIR}/AlphaGenome"
+else
+ MODEL_ROOT_DIR="${PROJECT_DIR}/weight"
+fi
+
+export PYTHONPATH="${PROJECT_DIR}:${PYTHONPATH:-}"
+
+python "${SCRIPT_DIR}/run_inference.py" \
+ --fasta_path "${DATA_ROOT_DIR}/reference/HOMO_SAPIENS/GRCh38.p13.genome.fa" \
+ --model_dir "${MODEL_ROOT_DIR}/alphagenome-all-folds" \
+ --chromosome chr19 \
+ --start 10587331 \
+ --end 11635907 \
+ --output_dir "${PROJECT_DIR}/outputs"
diff --git a/scripts/run_finetuning.py b/scripts/run_finetuning.py
new file mode 100644
index 0000000000000000000000000000000000000000..ed06cc5532ba6664cb9fcceac7b58e75c2e56c70
--- /dev/null
+++ b/scripts/run_finetuning.py
@@ -0,0 +1,214 @@
+# Copyright 2026 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.
+
+"""AlphaGenome finetuning example script.
+
+This script demonstrates how to finetune the AlphaGenome model on custom genomic data, suitable for the following scenarios:
+ - ATAC-seq/ChIP-seq signal prediction for new cell types or tissues
+ - Adaptation to specific experimental data
+ - Transfer learning to new species
+
+Data requirements:
+ - Reference genome FASTA file
+ - BigWig signal track files pointed to by file_path in metadata
+ - Training regions CSV file (columns: chromosome, start, end)
+
+Usage:
+ python run_finetuning.py \
+ --fasta_path /path/to/GRCh38.fa \
+ --regions_csv /path/to/regions.csv \
+ --output_dir ./finetuned_model \
+ --num_steps 1000 \
+ --batch_size 2
+"""
+
+import pathlib
+import sys
+
+_PROJECT_ROOT = pathlib.Path(__file__).resolve().parents[1]
+_SRC_DIR = _PROJECT_ROOT / "src"
+if str(_SRC_DIR) not in sys.path:
+ sys.path.insert(0, str(_SRC_DIR))
+
+from absl import app
+from absl import flags
+from absl import logging
+
+from alphagenome._sdk.data import fold_intervals
+from alphagenome._sdk.models import dna_model as dna_model_types
+import jax
+import optax
+import orbax.checkpoint as ocp
+
+from alphagenome.finetuning.finetune import (
+ get_dataset_iterator,
+ get_forward_fn,
+ get_train_step,
+)
+from alphagenome.evals.track_prediction import load_model as load_model_from_kaggle
+from alphagenome.model.metadata import metadata as metadata_lib
+
+FLAGS = flags.FLAGS
+
+flags.DEFINE_string(
+ 'fasta_path',
+ None,
+ 'Reference genome FASTA path.',
+ required=True,
+)
+flags.DEFINE_string(
+ 'regions_csv',
+ None,
+ 'Training regions CSV path with chromosome,start,end columns.',
+ required=True,
+)
+flags.DEFINE_list(
+ 'bigwig_paths',
+ None,
+ 'Deprecated compatibility flag. BigWig paths are read from metadata '
+ 'file_path columns.',
+)
+flags.DEFINE_string(
+ 'model_dir',
+ None,
+ 'Pretrained checkpoint directory. If unset, Kaggle Hub is used.',
+)
+flags.DEFINE_string(
+ 'output_dir',
+ './finetuned_model',
+ 'Directory to save finetuned checkpoints.',
+)
+flags.DEFINE_integer('num_steps', 1000, 'Number of training steps.')
+flags.DEFINE_integer('batch_size', 2, 'Training batch size.')
+flags.DEFINE_float('learning_rate', 1e-5, 'Initial learning rate.')
+flags.DEFINE_integer('log_every', 50, 'Log interval in steps.')
+flags.DEFINE_integer('save_every', 200, 'Checkpoint interval in steps.')
+flags.DEFINE_enum(
+ 'model_version', 'FOLD_0',
+ ['FOLD_0', 'FOLD_1', 'FOLD_2', 'FOLD_3', 'FOLD_4'],
+ 'Pretrained model version.',
+)
+flags.DEFINE_enum(
+ 'organism', 'HOMO_SAPIENS',
+ ['HOMO_SAPIENS', 'MUS_MUSCULUS'],
+ 'Target organism.',
+)
+
+
+def _resolve_local_model_dir(path: str) -> pathlib.Path:
+ model_dir = pathlib.Path(path).expanduser()
+ if not model_dir.is_dir():
+ raise FileNotFoundError(
+ f'Pretrained checkpoint directory does not exist: {model_dir}'
+ )
+ return model_dir
+
+
+def _load_pretrained_state(model_version: dna_model_types.ModelVersion):
+ if FLAGS.model_dir:
+ checkpoint_path = _resolve_local_model_dir(FLAGS.model_dir)
+ logging.info('Loading pretrained model from local checkpoint: %s',
+ checkpoint_path)
+ return ocp.StandardCheckpointer().restore(str(checkpoint_path))
+
+ logging.info('Loading pretrained model from Kaggle Hub: %s',
+ model_version.name)
+ params, state, _ = load_model_from_kaggle(model_version)
+ return params, state
+
+
+def main(_):
+ output_dir = pathlib.Path(FLAGS.output_dir)
+ output_dir.mkdir(parents=True, exist_ok=True)
+
+ model_version = dna_model_types.ModelVersion[FLAGS.model_version]
+ organism = dna_model_types.Organism[FLAGS.organism]
+
+ logging.info('JAX devices: %s', jax.devices())
+ logging.info('Finetuning config: lr=%.2e, steps=%d, batch_size=%d',
+ FLAGS.learning_rate, FLAGS.num_steps, FLAGS.batch_size)
+
+ # Load pretrained model parameters, the training forward function reconstructs the loss from the finetuning module.
+ params, state = _load_pretrained_state(model_version)
+
+ # Load output metadata.
+ output_metadata = metadata_lib.load(organism)
+
+ # Build the optimizer, using warmup + cosine decay to balance stability and convergence.
+ schedule = optax.warmup_cosine_decay_schedule(
+ init_value=0.0,
+ peak_value=FLAGS.learning_rate,
+ warmup_steps=100,
+ decay_steps=FLAGS.num_steps,
+ )
+ optimizer = optax.chain(
+ optax.clip_by_global_norm(1.0),
+ optax.adam(learning_rate=schedule),
+ )
+ opt_state = optimizer.init(params)
+
+ # Build finetuning training steps.
+ forward = get_forward_fn({organism: output_metadata})
+ train_step = get_train_step(
+ predict_fn=forward.apply,
+ optimizer=optimizer,
+ )
+
+ # Build dataset iterator.
+ logging.info('Building finetuning dataset iterator...')
+ dataset_iter = get_dataset_iterator(
+ batch_size=FLAGS.batch_size,
+ sequence_length=1_048_576,
+ output_metadata=output_metadata,
+ model_version=model_version,
+ subset=fold_intervals.Subset.TRAIN,
+ organism=organism,
+ fasta_path=FLAGS.fasta_path,
+ example_regions_path=FLAGS.regions_csv,
+ )
+
+ # Configure checkpoint manager.
+ checkpointer = ocp.CheckpointManager(
+ output_dir / 'checkpoints',
+ options=ocp.CheckpointManagerOptions(max_to_keep=3),
+ )
+
+ # Training loop.
+ logging.info('Starting finetuning training...')
+ for step, batch in enumerate(dataset_iter):
+ if step >= FLAGS.num_steps:
+ break
+
+ params, state, opt_state, metrics = train_step(
+ params, state, opt_state, batch
+ )
+
+ if step % FLAGS.log_every == 0:
+ loss = float(metrics.get('loss', float('nan')))
+ logging.info('Step %d/%d | loss=%.4f', step, FLAGS.num_steps, loss)
+
+ if step % FLAGS.save_every == 0 and step > 0:
+ checkpointer.save(step, args=ocp.args.StandardSave({'params': params, 'state': state}))
+ logging.info('Checkpoint saved (step=%d)', step)
+
+ # Save final model.
+ checkpointer.save(
+ FLAGS.num_steps,
+ args=ocp.args.StandardSave({'params': params, 'state': state}),
+ )
+ logging.info('Finetuning complete, final model saved to %s', output_dir / 'checkpoints')
+
+
+if __name__ == '__main__':
+ app.run(main)
\ No newline at end of file
diff --git a/scripts/run_inference.py b/scripts/run_inference.py
new file mode 100644
index 0000000000000000000000000000000000000000..8470a19129f7a5f6a1765bc6ee2af0b469946ba7
--- /dev/null
+++ b/scripts/run_inference.py
@@ -0,0 +1,164 @@
+# Copyright 2026 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.
+
+"""AlphaGenome inference example script.
+
+This script demonstrates how to use AlphaGenome for multimodal prediction on genomic intervals, including:
+ - Gene expression (RNA-seq, CAGE)
+ - Chromatin accessibility (ATAC-seq, DNase-seq)
+ - Transcription factor binding (ChIP-seq)
+ - Hi-C contact maps
+ - Splice sites
+
+Usage:
+ # Automatically download the model using Kaggle Hub (default mode)
+ python run_inference.py
+
+ # Specify a local reference genome
+ python run_inference.py \
+ --fasta_path /path/to/GRCh38.fa \
+ --chromosome chr19 \
+ --start 10587331 \
+ --end 11635907 \
+ --output_dir ./outputs
+"""
+
+import pathlib
+import sys
+
+_PROJECT_ROOT = pathlib.Path(__file__).resolve().parents[1]
+_SRC_DIR = _PROJECT_ROOT / "src"
+if str(_SRC_DIR) not in sys.path:
+ sys.path.insert(0, str(_SRC_DIR))
+
+from absl import app
+from absl import flags
+from absl import logging
+
+from alphagenome._sdk.data import genome
+from alphagenome.model import dna_model as dna_model_types
+import numpy as np
+
+from alphagenome.model.dna_model import (
+ create,
+ create_from_kaggle,
+ OrganismSettings,
+)
+
+FLAGS = flags.FLAGS
+
+flags.DEFINE_string(
+ 'fasta_path',
+ None,
+ 'Reference genome FASTA path. A .fai index is required.',
+)
+flags.DEFINE_string(
+ 'model_dir',
+ None,
+ 'Local AlphaGenome checkpoint directory. If unset, Kaggle Hub is used.',
+)
+flags.DEFINE_string('chromosome', 'chr1', 'Chromosome name, for example chr1.')
+flags.DEFINE_integer('start', 1_000_000, 'Interval start, 0-based.')
+flags.DEFINE_integer('end', 2_048_576, 'Interval end, exclusive.')
+flags.DEFINE_string('output_dir', './outputs', 'Output directory.')
+flags.DEFINE_enum(
+ 'organism', 'HOMO_SAPIENS',
+ ['HOMO_SAPIENS', 'MUS_MUSCULUS'],
+ 'Target organism.',
+)
+flags.DEFINE_enum(
+ 'model_version', 'FOLD_0',
+ ['FOLD_0', 'FOLD_1', 'FOLD_2', 'FOLD_3', 'FOLD_4', 'all_folds'],
+ 'Model version, fold, or all_folds ensemble.',
+)
+
+
+def main(_):
+ output_dir = pathlib.Path(FLAGS.output_dir)
+ output_dir.mkdir(parents=True, exist_ok=True)
+
+ organism = dna_model_types.Organism[FLAGS.organism]
+ model_version = FLAGS.model_version
+ interval = genome.Interval(
+ chromosome=FLAGS.chromosome,
+ start=FLAGS.start,
+ end=FLAGS.end,
+ )
+
+ logging.info('Loading model weights...')
+ if FLAGS.model_dir:
+ organism_settings = None
+ if FLAGS.fasta_path:
+ organism_settings = {
+ organism: OrganismSettings(
+ fasta_path=FLAGS.fasta_path,
+ ),
+ }
+ alphagenome_model = create(
+ checkpoint_path=FLAGS.model_dir,
+ organism_settings=organism_settings,
+ )
+ else:
+ alphagenome_model = create_from_kaggle(model_version)
+
+ logging.info('Running inference: %s', interval)
+ if FLAGS.fasta_path or not FLAGS.model_dir:
+ predictions = alphagenome_model.predict_interval(
+ interval,
+ organism=organism,
+ requested_outputs={
+ dna_model_types.OutputType.ATAC,
+ dna_model_types.OutputType.DNASE,
+ dna_model_types.OutputType.CAGE,
+ dna_model_types.OutputType.RNA_SEQ,
+ dna_model_types.OutputType.CHIP_TF,
+ dna_model_types.OutputType.CHIP_HISTONE,
+ },
+ ontology_terms=None,
+ )
+ else:
+ logging.warning(
+ 'No fasta_path was provided; using a random sequence for demo.'
+ )
+ rng = np.random.default_rng(42)
+ bases = np.array(['A', 'C', 'G', 'T'])
+ seq_len = interval.end - interval.start
+ dna_sequence = ''.join(rng.choice(bases, size=seq_len))
+ predictions = alphagenome_model.predict_sequence(
+ dna_sequence,
+ organism=organism,
+ requested_outputs={
+ dna_model_types.OutputType.ATAC,
+ dna_model_types.OutputType.DNASE,
+ dna_model_types.OutputType.RNA_SEQ,
+ },
+ ontology_terms=None,
+ interval=interval,
+ )
+
+ logging.info('Saving predictions to %s', output_dir)
+ for attr_name in ['atac', 'dnase', 'cage', 'rna_seq', 'chip_tf',
+ 'chip_histone', 'contact_maps', 'procap']:
+ output = getattr(predictions, attr_name, None)
+ if output is not None:
+ output_path = output_dir / f'{attr_name}.npy'
+ np.save(output_path, np.array(output.values))
+ logging.info(' saved %s: shape=%s', attr_name,
+ np.array(output.values).shape)
+
+ logging.info('Inference finished.')
+
+
+if __name__ == '__main__':
+ app.run(main)
\ No newline at end of file
diff --git a/scripts/run_track.sh b/scripts/run_track.sh
new file mode 100644
index 0000000000000000000000000000000000000000..50091a40c7f7ffc688591305a61a18939a2848ad
--- /dev/null
+++ b/scripts/run_track.sh
@@ -0,0 +1,28 @@
+#!/usr/bin/env bash
+
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+PROJECT_DIR="$(cd "${SCRIPT_DIR}/.." && pwd)"
+
+if [[ -n "${ONESCIENCE_DATASETS_DIR:-}" ]]; then
+ DATA_ROOT_DIR="${ONESCIENCE_DATASETS_DIR}/AlphaGenome"
+else
+ DATA_ROOT_DIR="${PROJECT_DIR}/data"
+fi
+
+if [[ -n "${ONESCIENCE_MODELS_DIR:-}" ]]; then
+ MODEL_ROOT_DIR="${ONESCIENCE_MODELS_DIR}/AlphaGenome"
+else
+ MODEL_ROOT_DIR="${PROJECT_DIR}/weight"
+fi
+
+export PYTHONPATH="${PROJECT_DIR}:${PYTHONPATH:-}"
+
+mkdir -p "${PROJECT_DIR}/outputs_track"
+python "${SCRIPT_DIR}/run_track_prediction_eval.py" \
+ --model_dir "${MODEL_ROOT_DIR}/alphagenome-all-folds" \
+ --model_version ALL_FOLDS \
+ --data_dir "${DATA_ROOT_DIR}/v1/train" \
+ --output_path "${PROJECT_DIR}/outputs_track/eval_results.csv"
+
diff --git a/scripts/run_track_prediction_eval.py b/scripts/run_track_prediction_eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..a161247a263e3184fa6e0e2cedb580355af57b01
--- /dev/null
+++ b/scripts/run_track_prediction_eval.py
@@ -0,0 +1,337 @@
+# Copyright 2026 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.
+
+"""Runs AlphaGenome track prediction evaluation.
+
+By default this script refuses to download model weights. Pass --model_dir to
+load a local Orbax checkpoint, or explicitly pass --allow_download=true to use
+the original Kaggle Hub path.
+"""
+
+import functools
+import pathlib
+import pprint
+import sys
+
+_PROJECT_ROOT = pathlib.Path(__file__).resolve().parents[1]
+if str(_PROJECT_ROOT) not in sys.path:
+ sys.path.insert(0, str(_PROJECT_ROOT))
+
+from absl import app
+from absl import flags
+from absl import logging
+from flax_model.alphagenome._sdk.data import fold_intervals
+from flax_model.alphagenome._sdk.models import dna_output
+from flax_model.alphagenome._sdk.models import dna_model as dna_model_types
+from flax_model.alphagenome.evals.track_prediction import (
+ load_model as load_model_from_kaggle,
+)
+from flax_model.alphagenome.evals import regression_metrics
+from flax_model.alphagenome.io.bundles import BundleName
+from flax_model.alphagenome.io.dataset import get_numpy_dataset_iterator
+from flax_model.alphagenome.model import dna_model as research_dna_model
+from flax_model.alphagenome.model.metadata import metadata as metadata_lib
+import jax
+import jax.numpy as jnp
+from jax import sharding
+from jax.experimental import mesh_utils
+import orbax.checkpoint as ocp
+import pandas as pd
+import tensorflow as tf
+
+
+FLAGS = flags.FLAGS
+PS = sharding.PartitionSpec
+
+flags.DEFINE_string(
+ "model_version",
+ "FOLD_0",
+ "Evaluation fold/model version. Supported values are "
+ + ",".join(version.name for version in dna_model_types.ModelVersion)
+ + ".",
+)
+flags.DEFINE_enum(
+ "organism",
+ "HOMO_SAPIENS",
+ ["HOMO_SAPIENS", "MUS_MUSCULUS"],
+ "Organism to evaluate.",
+)
+flags.DEFINE_string(
+ "output_path",
+ "./track_prediction_results.csv",
+ "Output CSV path.",
+)
+flags.DEFINE_string(
+ "model_dir",
+ None,
+ "Local AlphaGenome Orbax checkpoint directory. When set, model weights are "
+ "loaded from this directory and no online model download is attempted.",
+)
+flags.DEFINE_bool(
+ "allow_download",
+ False,
+ "Allow Kaggle Hub model download only when --model_dir is not provided.",
+)
+flags.DEFINE_string(
+ "data_dir",
+ None,
+ "Optional AlphaGenome TFRecord root. If unset, the dataset loader uses its "
+ "built-in default path.",
+)
+flags.DEFINE_list(
+ "bundles",
+ None,
+ "Comma-separated bundle names. Defaults to all supported evaluation "
+ "bundles: ATAC,CAGE,CHIP_HISTONE,CHIP_TF,DNASE,PROCAP,RNA_SEQ.",
+)
+
+_DEFAULT_EVAL_BUNDLES = [
+ BundleName.ATAC,
+ BundleName.CAGE,
+ BundleName.CHIP_HISTONE,
+ BundleName.CHIP_TF,
+ BundleName.DNASE,
+ BundleName.PROCAP,
+ BundleName.RNA_SEQ,
+]
+
+
+def _parse_model_version(value: str) -> dna_model_types.ModelVersion:
+ normalized = value.replace("-", "_").upper()
+ if normalized.startswith("ALPHAGENOME_"):
+ normalized = normalized.removeprefix("ALPHAGENOME_")
+ try:
+ return dna_model_types.ModelVersion[normalized]
+ except KeyError as exc:
+ valid = ", ".join(version.name for version in dna_model_types.ModelVersion)
+ raise ValueError(
+ f"Unsupported --model_version={value!r}. Valid values: {valid}."
+ ) from exc
+
+
+def _resolve_local_model_dir(path: str) -> pathlib.Path:
+ model_dir = pathlib.Path(path).expanduser()
+ if not model_dir.is_dir():
+ raise FileNotFoundError(
+ f"Local AlphaGenome model directory does not exist: {model_dir}"
+ )
+ if not (model_dir / "_CHECKPOINT_METADATA").exists():
+ raise FileNotFoundError(
+ "The model directory does not look like an Orbax checkpoint. "
+ f"Missing: {model_dir / '_CHECKPOINT_METADATA'}"
+ )
+ return model_dir
+
+
+def load_model_from_local_checkpoint(
+ checkpoint_path: str,
+ organism: dna_model_types.Organism,
+):
+ """Loads eval params/state/predict_fn like run_inference.py does."""
+ checkpoint_path = _resolve_local_model_dir(checkpoint_path)
+ logging.info("Loading AlphaGenome model from local checkpoint: %s", checkpoint_path)
+ metadata = {organism: metadata_lib.load(organism)}
+ init_fn, apply_fn, _ = research_dna_model.create_model(metadata)
+ dna_sequence_shape = jax.ShapeDtypeStruct((1, 2048, 4), dtype=jnp.float32)
+ organism_index_shape = jax.ShapeDtypeStruct((1,), dtype=jnp.int32)
+ target_shapes = jax.eval_shape(
+ init_fn,
+ jax.random.PRNGKey(0),
+ dna_sequence_shape,
+ organism_index_shape,
+ )
+ params, state = ocp.StandardCheckpointer().restore(
+ str(checkpoint_path),
+ target=target_shapes,
+ strict=True,
+ )
+
+ @jax.jit
+ def predict(params, state, dna_sequence, organism_index):
+ predictions = apply_fn(params, state, dna_sequence, organism_index)
+ return research_dna_model.extract_predictions(predictions)
+
+ return params, state, predict
+
+
+def _load_model(
+ model_version: dna_model_types.ModelVersion,
+ organism: dna_model_types.Organism,
+):
+ if FLAGS.model_dir:
+ return load_model_from_local_checkpoint(FLAGS.model_dir, organism)
+ if FLAGS.allow_download:
+ logging.warning(
+ "No --model_dir was provided; downloading model weights from "
+ "Kaggle Hub because --allow_download=true."
+ )
+ return load_model_from_kaggle(model_version)
+ raise ValueError(
+ "No --model_dir was provided. Refusing to download model weights. "
+ "Pass --model_dir /path/to/alphagenome-all-folds, or explicitly pass "
+ "--allow_download=true to use Kaggle Hub."
+ )
+
+
+def _resolve_data_dir(path: str | None) -> str | None:
+ if not path:
+ return None
+ if path.startswith("gs://"):
+ return path
+ data_dir = pathlib.Path(path).expanduser()
+ if not data_dir.is_dir():
+ raise FileNotFoundError(
+ f"Local AlphaGenome dataset directory does not exist: {data_dir}"
+ )
+ return str(data_dir)
+
+
+def _mesh_context(mesh):
+ if hasattr(jax, "set_mesh"):
+ return jax.set_mesh(mesh)
+ return mesh
+
+
+def create_eval_step(predict_fn, bundles):
+ """Returns a JAX-version-compatible eval step."""
+
+ @jax.jit
+ def eval_step(params, state, batch):
+ predictions = predict_fn(
+ params,
+ state,
+ batch.dna_sequence,
+ batch.organism_index,
+ )
+ metrics_step = {}
+ for bundle in bundles:
+ targets_true, mask = batch.get_genome_tracks(bundle)
+ targets_pred = predictions[dna_output.OutputType[bundle.name]]
+ targets_pred = regression_metrics.crop_sequence_length(
+ targets_pred,
+ target_length=targets_true.shape[-2],
+ )
+ metrics_step[bundle.name] = regression_metrics.update_regression_metrics(
+ targets_true,
+ targets_pred,
+ mask,
+ )
+ return metrics_step
+
+ return eval_step
+
+
+def evaluate(params, state, predict_fn, bundles, dataset_iterator):
+ """Evaluates the model without relying on the library's jit decorator style."""
+ devices = mesh_utils.create_device_mesh((jax.local_device_count(),))
+ mesh = jax.sharding.Mesh(devices, axis_names=("data",))
+ sharding_rep = sharding.NamedSharding(mesh, PS())
+ sharding_data = sharding.NamedSharding(mesh, PS("data"))
+
+ params = jax.device_put(params, sharding_rep)
+ state = jax.device_put(state, sharding_rep)
+
+ eval_step = create_eval_step(predict_fn, bundles)
+ metrics = {
+ bundle.name: regression_metrics.initialize_regression_metrics()
+ for bundle in bundles
+ }
+ num_elements = 0
+
+ for i, (batch, _) in enumerate(dataset_iterator):
+ num_elements += batch.dna_sequence.shape[0]
+ if i % 5 == 1:
+ finalized = pprint.pformat(
+ regression_metrics.finalize_regression_metrics(metrics)
+ )
+ logging.info("step %d: %s", i, finalized)
+
+ with _mesh_context(mesh):
+ batch = jax.device_put(batch, sharding_data)
+ step_metrics = eval_step(params, state, batch)
+
+ step_metrics = jax.device_get(step_metrics)
+ metrics = regression_metrics.reduce_regression_metrics(
+ metrics,
+ step_metrics,
+ )
+
+ logging.info("num_elements: %d", num_elements)
+ return regression_metrics.finalize_regression_metrics(metrics)
+
+
+def main(_):
+ # TensorFlow is only used for data loading; keep GPUs available to JAX.
+ tf.config.set_visible_devices([], "GPU")
+
+ model_version = _parse_model_version(FLAGS.model_version)
+ organism = dna_model_types.Organism[FLAGS.organism]
+ data_dir = _resolve_data_dir(FLAGS.data_dir)
+
+ logging.info("JAX devices: %s", jax.devices())
+ logging.info(
+ "Evaluation config: model_version=%s, organism=%s, model_dir=%s, "
+ "data_dir=%s",
+ model_version.name,
+ FLAGS.organism,
+ FLAGS.model_dir or "",
+ data_dir or "",
+ )
+
+ if FLAGS.bundles:
+ eval_bundles = [BundleName[bundle.strip()] for bundle in FLAGS.bundles]
+ else:
+ eval_bundles = _DEFAULT_EVAL_BUNDLES
+ logging.info("Evaluation bundles: %s", [bundle.name for bundle in eval_bundles])
+
+ params, state, predict_fn = _load_model(model_version, organism)
+
+ dataset_iterator = get_numpy_dataset_iterator(
+ batch_size=jax.local_device_count(),
+ organism=organism,
+ model_version=model_version,
+ subset=fold_intervals.Subset.VALID,
+ bundles=eval_bundles,
+ path=data_dir,
+ )
+
+ logging.info("Starting evaluation.")
+ results = evaluate(
+ params=params,
+ state=state,
+ predict_fn=predict_fn,
+ bundles=eval_bundles,
+ dataset_iterator=dataset_iterator,
+ )
+
+ rows = []
+ for bundle_name, metrics in results.items():
+ for metric_name, value in metrics.items():
+ logging.info("%s / %s: %.4f", bundle_name, metric_name, value)
+ rows.append({
+ "bundle": bundle_name,
+ "metric": metric_name,
+ "value": float(value),
+ "model_version": model_version.name,
+ "organism": FLAGS.organism,
+ })
+
+ output_path = pathlib.Path(FLAGS.output_path)
+ output_path.parent.mkdir(parents=True, exist_ok=True)
+ pd.DataFrame(rows).to_csv(output_path, index=False)
+ logging.info("Saved results to: %s", output_path)
+
+
+if __name__ == "__main__":
+ app.run(main)
diff --git a/scripts/run_variant.sh b/scripts/run_variant.sh
new file mode 100644
index 0000000000000000000000000000000000000000..3b4d2a1841efa392287ea2fa3e25bce9dfcbaf4a
--- /dev/null
+++ b/scripts/run_variant.sh
@@ -0,0 +1,25 @@
+#!/usr/bin/env bash
+
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+PROJECT_DIR="$(cd "${SCRIPT_DIR}/.." && pwd)"
+
+if [[ -n "${ONESCIENCE_DATASETS_DIR:-}" ]]; then
+ DATA_ROOT_DIR="${ONESCIENCE_DATASETS_DIR}/AlphaGenome"
+else
+ DATA_ROOT_DIR="${PROJECT_DIR}/data"
+fi
+
+if [[ -n "${ONESCIENCE_MODELS_DIR:-}" ]]; then
+ MODEL_ROOT_DIR="${ONESCIENCE_MODELS_DIR}/AlphaGenome"
+else
+ MODEL_ROOT_DIR="${PROJECT_DIR}/weight"
+fi
+
+export PYTHONPATH="${PROJECT_DIR}:${PYTHONPATH:-}"
+
+python "${SCRIPT_DIR}/run_variant_scoring.py" \
+ --fasta_path "${DATA_ROOT_DIR}/reference/HOMO_SAPIENS/GRCh38.p13.genome.fa" \
+ --model_dir "${MODEL_ROOT_DIR}/alphagenome-all-folds" \
+ --output_dir "${PROJECT_DIR}/outputs_variant"
diff --git a/scripts/run_variant_scoring.py b/scripts/run_variant_scoring.py
new file mode 100644
index 0000000000000000000000000000000000000000..29d013a20f931f64dd7a16f0a99430128c3ec011
--- /dev/null
+++ b/scripts/run_variant_scoring.py
@@ -0,0 +1,254 @@
+# Copyright 2026 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.
+
+"""Example script for AlphaGenome variant scoring.
+
+This version avoids failing when the model was created without annotation-backed
+variant scorers such as GENE_MASK_LFC. It keeps the recommended scorers that
+are actually available in the current model instance and skips the rest.
+"""
+
+from collections.abc import Sequence
+import pathlib
+import sys
+
+_PROJECT_ROOT = pathlib.Path(__file__).resolve().parents[1]
+if str(_PROJECT_ROOT) not in sys.path:
+ sys.path.insert(0, str(_PROJECT_ROOT))
+
+from absl import app
+from absl import flags
+from absl import logging
+from flax_model.alphagenome._sdk.data import genome
+from flax_model.alphagenome._sdk.models import dna_model as dna_model_types
+from flax_model.alphagenome._sdk.models import variant_scorers as variant_scorers_lib
+import pandas as pd
+from flax_model.alphagenome.model.dna_model import (
+ OrganismSettings,
+ create,
+ create_from_kaggle,
+)
+
+FLAGS = flags.FLAGS
+
+flags.DEFINE_string(
+ "vcf_path",
+ None,
+ "Path to a VCF file. If unset, built-in demo variants are used.",
+)
+flags.DEFINE_string(
+ "fasta_path",
+ None,
+ "Path to the reference genome FASTA file. Required when --model_dir is set.",
+)
+flags.DEFINE_string(
+ "model_dir",
+ None,
+ "Local AlphaGenome checkpoint directory. If unset, Kaggle Hub is used.",
+)
+flags.DEFINE_string("output_dir", "./outputs", "Directory for CSV outputs.")
+flags.DEFINE_enum(
+ "organism",
+ "HOMO_SAPIENS",
+ ["HOMO_SAPIENS", "MUS_MUSCULUS"],
+ "Target organism.",
+)
+flags.DEFINE_enum(
+ "model_version",
+ "all_folds",
+ ["FOLD_0", "FOLD_1", "FOLD_2", "FOLD_3", "FOLD_4", "all_folds"],
+ "Model version to download from Kaggle.",
+)
+
+
+DEMO_VARIANTS = [
+ ("chr22:36201698:A>C", "eQTL with SuSiE PIP > 0.9 in GTEx Colon"),
+ ("chr3:120280774:G>T", "caQTL in GM12878 (DNase)"),
+ ("chr21:46126238:G>C", "Splice junction variant in COL6A2"),
+]
+
+
+def load_demo_variants() -> list[tuple[genome.Variant, str]]:
+ """Builds the built-in demo variants."""
+ return [
+ (genome.Variant.from_str(variant_str), description)
+ for variant_str, description in DEMO_VARIANTS
+ ]
+
+
+def load_variants_from_vcf(vcf_path: str) -> list[tuple[genome.Variant, str]]:
+ """Loads variants from a VCF file."""
+ variants_df = pd.read_csv(
+ vcf_path,
+ sep="\t",
+ comment="#",
+ names=["CHROM", "POS", "ID", "REF", "ALT", "QUAL", "FILTER", "INFO"],
+ )
+ variants_with_desc = []
+ for _, row in variants_df.iterrows():
+ variant = genome.Variant(
+ chromosome=row["CHROM"],
+ position=int(row["POS"]),
+ reference_bases=row["REF"],
+ alternate_bases=str(row["ALT"]).split(",")[0],
+ )
+ description = row["ID"] if pd.notna(row["ID"]) else "unknown"
+ variants_with_desc.append((variant, str(description)))
+ return variants_with_desc
+
+
+def resolve_variant_scorers(
+ alphagenome_model,
+ organism: dna_model_types.Organism,
+) -> tuple[
+ Sequence[variant_scorers_lib.VariantScorerTypes],
+ list[str],
+]:
+ """Returns the recommended scorers supported by the current model."""
+ recommended_scorers = list(
+ variant_scorers_lib.get_recommended_scorers(organism.to_proto())
+ )
+ available_scorer_map = getattr(alphagenome_model, "_variant_scorers", {}).get(
+ organism, {}
+ )
+
+ if not available_scorer_map:
+ logging.warning(
+ "Unable to inspect model variant scorers. Falling back to the full "
+ "recommended scorer list."
+ )
+ return recommended_scorers, []
+
+ available_base_scorers = set(available_scorer_map)
+ selected_scorers = [
+ scorer
+ for scorer in recommended_scorers
+ if scorer.base_variant_scorer in available_base_scorers
+ ]
+ skipped_scorers = [
+ scorer.base_variant_scorer.name
+ for scorer in recommended_scorers
+ if scorer.base_variant_scorer not in available_base_scorers
+ ]
+
+ if not selected_scorers:
+ available_names = sorted(
+ base_scorer.name for base_scorer in available_base_scorers
+ )
+ raise ValueError(
+ "No compatible recommended variant scorers are available for "
+ f"{organism.name}. Available scorers: {available_names}."
+ )
+
+ return selected_scorers, skipped_scorers
+
+
+def load_alphagenome_model(organism: dna_model_types.Organism):
+ """Loads AlphaGenome from a local checkpoint when provided."""
+ organism_settings = None
+ if FLAGS.fasta_path:
+ organism_settings = {
+ organism: OrganismSettings(
+ fasta_path=FLAGS.fasta_path,
+ ),
+ }
+
+ if FLAGS.model_dir:
+ if not FLAGS.fasta_path:
+ raise ValueError("--fasta_path is required when using --model_dir.")
+ return create(
+ checkpoint_path=FLAGS.model_dir,
+ organism_settings=organism_settings,
+ )
+
+ return create_from_kaggle(
+ FLAGS.model_version,
+ organism_settings=organism_settings,
+ )
+
+
+def main(_):
+ output_dir = pathlib.Path(FLAGS.output_dir)
+ output_dir.mkdir(parents=True, exist_ok=True)
+
+ organism = dna_model_types.Organism[FLAGS.organism]
+
+ logging.info("Loading AlphaGenome model...")
+ alphagenome_model = load_alphagenome_model(organism)
+
+ variant_scorers, skipped_scorers = resolve_variant_scorers(
+ alphagenome_model, organism
+ )
+ logging.info(
+ "Using variant scorers: %s",
+ ", ".join(scorer.base_variant_scorer.name for scorer in variant_scorers),
+ )
+ if skipped_scorers:
+ logging.warning(
+ "Skipping unavailable recommended scorers: %s. This usually means the "
+ "model was loaded without the required annotation resources.",
+ ", ".join(skipped_scorers),
+ )
+
+ if FLAGS.vcf_path:
+ logging.info("Loading variants from VCF: %s", FLAGS.vcf_path)
+ variants_with_desc = load_variants_from_vcf(FLAGS.vcf_path)
+ else:
+ logging.info("Using built-in demo variants.")
+ variants_with_desc = load_demo_variants()
+
+ logging.info("Scoring %d variants...", len(variants_with_desc))
+ all_results = []
+
+ for variant, description in variants_with_desc:
+ logging.info("Processing variant: %s (%s)", variant, description)
+
+ interval = variant.reference_interval.resize(2**20)
+ scores = alphagenome_model.score_variant(
+ interval=interval,
+ variant=variant,
+ variant_scorers=variant_scorers,
+ organism=organism,
+ )
+
+ all_results.append(
+ {
+ "variant": str(variant),
+ "description": description,
+ "num_score_tables": len(scores),
+ "used_variant_scorers": ",".join(
+ scorer.base_variant_scorer.name for scorer in variant_scorers
+ ),
+ "skipped_variant_scorers": ",".join(skipped_scorers),
+ }
+ )
+
+ for i, adata in enumerate(scores):
+ scorer_name = str(adata.uns.get("variant_scorer", f"scorer_{i}"))
+ scorer_label = scorer_name.replace(" ", "_").replace("/", "_")
+ save_path = output_dir / (
+ f"variant_{variant.chromosome}_{variant.position}_{scorer_label}.csv"
+ )
+ adata.to_df().to_csv(save_path)
+ logging.info(
+ "Saved score table %d: %s (shape=%s)", i, save_path.name, adata.shape
+ )
+
+ summary_path = output_dir / "variant_scoring_summary.csv"
+ pd.DataFrame(all_results).to_csv(summary_path, index=False)
+ logging.info("Saved summary to %s", summary_path)
+
+
+if __name__ == "__main__":
+ app.run(main)
diff --git a/weight/README.md b/weight/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..df65117bb5effa1bea7067ce7b9d773ac5a35e9a
--- /dev/null
+++ b/weight/README.md
@@ -0,0 +1,13 @@
+# AlphaGenome Weights
+
+Place local AlphaGenome Orbax checkpoints here when not using the shared OneScience model directory.
+
+Expected local layout:
+
+```text
+weight/
+└── alphagenome-all-folds/
+ ├── _CHECKPOINT_METADATA
+ ├── _METADATA
+ └── ...
+```
diff --git a/weight/alphagenome-all-folds/README.md b/weight/alphagenome-all-folds/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..b7aa388be0a821de28823536ce8d59dc2a347a30
--- /dev/null
+++ b/weight/alphagenome-all-folds/README.md
@@ -0,0 +1,334 @@
+---
+license: other
+license_name: alphagenome
+license_link: https://deepmind.google.com/science/alphagenome/model-terms
+extra_gated_heading: Access AlphaGenome on Hugging Face
+extra_gated_prompt: >-
+ AlphaGenome is provided for non-commercial use only and is subject to the
+ [Model Terms of Use](https://deepmind.google.com/science/alphagenome/model-terms).
+ To accept terms, please login, complete the required fields and click Accept.
+ Requests are processed immediately.
+extra_gated_button_content: Accept and continue
+extra_gated_fields:
+ Organization, university, or other affiliation(s): text
+language:
+- en
+tags:
+- biology
+---
+
+## Description
+
+AlphaGenome is a unified DNA sequence model designed to advance regulatory
+variant-effect prediction and shed light on genome function. It analyzes DNA
+sequences of up to 1 million base pairs to deliver predictions at single
+base-pair resolution across diverse modalities, including gene expression,
+splicing patterns, chromatin features, and contact maps.
+
+The core architecture features a U-Net-style design that combines an encoder for
+downsampling, transformers with inter-device communication to capture long-range
+interactions, and a decoder for upsampling. These components feed into
+task-specific output heads that generate predictions at their respective
+assay-specific resolutions.
+
+By achieving state-of-the-art performance across diverse genomic benchmarks,
+AlphaGenome provides a robust framework for understanding the molecular function
+of DNA sequences and interpreting non-coding variation.
+
+## Inputs and outputs
+
+* **Input:** Up to 1 Mb (2\*\*20) one-hot encoded DNA sequence, and an
+ organism type index (representing human or mouse).
+* **Output:** 11 diverse modalities, including:
+ * RNA expression (RNA-Seq, CAGE-seq and PRO-cap).
+ * Chromatin accessibility (DNase-seq and ATAC-seq).
+ * Histone modifications.
+ * Transcription factor binding.
+ * Chromatin contact maps.
+ * Splice sites and their usage.
+ * Splice junction coordinates and strength.
+
+## Citation
+
+```
+@article{alphagenome,
+ title={Advancing regulatory variant effect prediction with {AlphaGenome}},
+ 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},
+ journal={Nature},
+ volume={649},
+ number={8099},
+ year={2026},
+ doi={10.1038/s41586-025-10014-0},
+ publisher={Nature Publishing Group UK London}
+}
+```
+
+## Installation
+
+To install the accompanying code necessary to run the model, please run the
+following:
+
+```shell
+$ pip install git+https://github.com/google-deepmind/alphagenome_research.git
+```
+
+## Usage
+
+In addition to the model, we provide a DNA model class that wraps the core model
+and provides a more intuitive set of functions for creating predictions, scoring
+variants, performing in silico mutagenesis (ISM) and more.
+
+Here's an example of making a variant prediction:
+
+```python
+from alphagenome.data import genome
+from alphagenome.visualization import plot_components
+from alphagenome_research.model import dna_model
+import matplotlib.pyplot as plt
+
+model = dna_model.create_from_huggingface('all_folds')
+
+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_model.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()
+```
+
+## Model Data
+
+AlphaGenome was trained to predict read coverage for a wide range of different
+functional genomics assays – including RNA-seq, DNase-seq, CAGE, ChIP-seq
+– directly from human and mouse reference genome sequences. The training
+data was sourced from large public consortia including
+[ENCODE](http://encodeproject.org/), [GTEx](https://www.gtexportal.org/),
+[4D Nucleome](https://4dnucleome.org/) and
+[FANTOM5](https://fantom.gsc.riken.jp/5/), encompassing experimental
+measurements of key regulatory modalities across hundreds of cell types and
+tissues. For more information, please refer to the
+[AlphaGenome manuscript](https://www.biorxiv.org/content/10.1101/2025.06.25.661532v2).
+
+Along with the release of the weights and model inference code, we are releasing
+the complete training, validation and test datasets used for AlphaGenome. The
+data is stored in compressed TFRecord format and can be loaded as follows:
+
+```python
+from alphagenome.data import fold_intervals
+from alphagenome_research.io import dataset
+from alphagenome_research.model import dna_model
+
+ds_iter = dataset.create_dataset(
+ organism=dna_model.Organism.HOMO_SAPIENS,
+ fold_split=dna_model.ModelVersion.ALL_FOLDS,
+ subset=fold_intervals.Subset.TRAIN,
+).as_numpy_iterator()
+element = next(ds_iter)
+```
+
+Each element in the dataset is a tuple, where each item corresponds to a
+specific data bundle. The example below illustrates this structure, showing the
+ATAC and DNase bundles alongside the input DNA sequence, masks, and interval
+metadata:
+
+```python
+({"atac": "bfloat16[1052672,256]",
+ "atac_mask": "bool[1,256]",
+ "dna_sequence": "float32[1052672,4]",
+ "interval/chromosome": b"chr7",
+ "interval/end": "int64[]",
+ "interval/start": "int64[]"},
+ {"dna_sequence": "float32[1052672,4]",
+ "dnase": "bfloat16[1052672,384]",
+ "dnase_mask": "bool[1,384]",
+ "interval/chromosome": b"chr7",
+ "interval/end": "int64[]",
+ "interval/start": "int64[]"},
+...
+)
+```
+
+**Notes:**
+
+* **Interval extension**: To support data augmentation via sequence shifting
+ during training, the 1 Mb intervals were extended by 4,096 base pairs (2,048
+ bp on each side).
+* **GTEx data exclusion**: The released dataset does not contain GTEx tissue
+ data due to licensing restrictions, though the corresponding column headers
+ remain in the bundle’s metadata. However, users can easily extend the
+ dataset with GTEx or other external sources using the provided interval
+ metadata (chromosome, start, end).
+
+## Implementation Information
+
+### Hardware
+
+AlphaGenome training involved a two-stage process: pre-training and
+distillation. Pre-training was carried out on 256
+[Tensor Processing Units (TPUv3)](https://docs.cloud.google.com/tpu/docs/v3)
+using sequence parallelism across groups of 4 interconnected chips. We leverage
+TPU pods (large clusters of TPUs) to provide a scalable solution for training,
+to enable large batch sizes which can lead to better model quality. For
+distillation, AlphaGenome was trained on 64 NVIDIA H100 GPUs without sequence
+parallelism. The evaluation of all models was carried out on NVIDIA H100 GPUs
+without sequence parallelism.
+
+### Software
+
+Training was done using [JAX](https://github.com/google/jax) and
+[JAXline](https://github.com/google-deepmind/jaxline).
+
+JAX allows researchers to take advantage of the latest generation of hardware,
+including TPUs, for faster and more efficient training of large models.
+
+JAXline is a distributed JAX training and evaluation framework. It is designed
+to be forked, covering only the most general aspects of experiment boilerplate.
+
+## Evaluation
+
+AlphaGenome demonstrates state-of-the-art performance across a diverse set of
+genomic prediction tasks. The model was evaluated using two primary approaches:
+
+1. **Genome Track Prediction**: Assessing the ability to predict functional
+ genomic signals (read coverage) on previously unseen DNA sequences (held-out
+ test intervals).
+2. **Variant Effect Prediction (VEP):** Assessing the ability to predict the
+ molecular consequences of genetic variants (e.g., single nucleotide
+ variants) by comparing predictions for reference and alternative alleles
+ against ground-truth datasets (e.g., experimental QTL effect sizes, readouts
+ from reporter assays).
+
+Key Highlights:
+
+* **Broad SOTA Performance:** AlphaGenome matched or outperformed the best
+ available external models on **22 out of 24** genome track prediction
+ evaluations.
+* **Variant Interpretation:** For variant effect prediction, AlphaGenome
+ matched or exceeded top-performing external models on **25 out of 26**
+ evaluations.
+* **Multimodal Capability:** Unlike specialized models, AlphaGenome jointly
+ predicts all assessed modalities – including splicing, expression,
+ accessibility, and 3D contact maps – within a single framework.
+* **Single-Pass Efficiency:** The distilled student model achieves this
+ performance and broad coverage with a single inference pass, eliminating the
+ need for complex model ensembling.
+
+The tables below detail the performance metrics for specific modalities and
+tasks.
+
+## Benchmark Results
+
+The following table focuses on the accuracy of the pre-trained, non-distilled
+model in predicting genomic tracks on unseen sequences:
+
+Modality | Evaluation | Metric | Resolution | Value | Baseline Model | Relative Improvement (%)
+:-------------------- | :------------------------------- | :---------- | :--------- | :---- | :------------- | :-----------------------
+**Splicing** | Splice site classification | auPRC | 1 bp | 0.79 | DeltaSplice | 1.0
+ | Splice site usage | Pearson r | 1 bp | 0.86 | DeltaSplice | 6.7
+**RNA expression** | RNA-seq coverage | Pearson r | 1 bp | 0.59 | Borzoi | 28.2
+ | RNA-seq coverage | Pearson r | 32 bp | 0.78 | Borzoi | 4.6
+ | RNA-seq gene expr. LFC | Pearson r | Gene | 0.57 | Borzoi | 14.7
+ | CAGE coverage | Pearson r | 32 bp | 0.74 | Borzoi | 4.4
+ | CAGE coverage | Pearson r | 128 bp | 0.71 | Enformer | \-0.3
+ | Alternative PA | Spearman r | Gene | 0.87 | Borzoi | 13.1
+**DNA accessibility** | DNase-seq coverage | Profile JSD | 1 bp | 0.51 | ChromBPNet | 6.4
+ | DNase-seq coverage | Pearson r | 32 bp | 0.86 | Borzoi | 4.7
+ | DNase-seq coverage | Pearson r | 128 bp | 0.87 | Enformer | 2.4
+ | ATAC-seq coverage | Profile JSD | 1 bp | 0.46 | ChromBPNet | 1.6
+ | ATAC-seq coverage | Pearson r | 32 bp | 0.57 | Borzoi | 3.4
+ | ATAC-seq coverage | Pearson r | 128 bp | 0.72 | Enformer | 2.7
+**Histone mods** | Histone ChIP-seq coverage | Pearson r | 32 bp | 0.69 | Borzoi | 3.1
+ | Histone ChIP-seq coverage | Pearson r | 128 bp | 0.71 | Enformer | 2.4
+**TF binding** | TF ChIP-seq coverage | Pearson r | 32 bp | 0.55 | Borzoi | 5.0
+ | TF ChIP-seq coverage | Pearson r | 128 bp | 0.58 | Enformer | 1.4
+**DNA contact maps** | Orca Contact maps | Pearson r | 4000 bp | 0.79 | Orca | 6.3
+ | Orca Contact maps cell type diff | Pearson r | 4000 bp | 0.42 | Orca | 42.3
+
+And the next table details the performance of the distilled model in predicting
+the functional effects of genetic variants:
+
+Modality | Evaluation | Type | Metric | Value | Baseline Model | Relative Improvement (%)
+:-------------------- | :---------------------------- | :-------- | :--------- | :---- | :------------- | :-----------------------
+**Splicing** | ClinVar splice site region | Causality | auPRC | 0.57 | Pangolin | 3.7
+ | ClinVar noncoding | \- | auPRC | 0.66 | Pangolin | 2.9
+ | ClinVar missense | \- | auPRC | 0.18 | DeltaSplice | 13.7
+ | Splicing outlier (zero-shot) | \- | auPRC | 0.22 | Pangolin | 59.1
+ | Splicing outlier (supervised) | \- | auPRC | 0.28 | AbSplice | 13.0
+ | sQTL | Causality | auPRC | 0.76 | Pangolin | 13.9
+ | MFASS | \- | auPRC | 0.51 | Pangolin | \-5.7
+**RNA expression** | eQTL | Direction | Spearman r | 0.49 | Borzoi | 25.5
+ | eQTL (zero-shot) | Causality | auROC | 0.71 | Borzoi | 5.4
+ | eQTL (supervised) | Causality | auROC | 0.80 | Borzoi | 15.6
+ | ENCODE E2G (zero-shot) | \- | auPRC | 0.75 | Borzoi | 13.0
+ | paQTL | \- | auPRC | 0.63 | Borzoi | 7.3
+**DNA accessibility** | CAGI5 MPRA | Causality | Pearson r | 0.65 | Borzoi | 6.3
+ | ds/caQTL | Direction | Pearson r | 0.70 | ChromBPNet | 7.7
+ | ds/caQTL | Causality | auPRC | 0.52 | Borzoi | 18.0
+**TF binding** | bQTL | Direction | Pearson r | 0.55 | Borzoi | 2.8
+ | bQTL | Causality | auPRC | 0.50 | Borzoi | 6.0
+
+## Usage and Limitations
+
+### License
+
+Unless required by applicable law or agreed to in writing, all software and
+materials distributed here (under the
+[Apache 2.0 License](https://github.com/google-deepmind/alphagenome_research/blob/main/README.md)
+with respect to model code and
+[non-commercial terms](https://deepmind.google.com/science/alphagenome/model-terms)
+for the model parameters) 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.
+
+### Intended usage
+
+* Non-Commercial use only: The model parameters are restricted to
+ non-commercial use by non-commercial organizations (e.g., universities,
+ non-profits, research institutes, and journalism). It must not be used for
+ any commercial activities or on behalf of commercial entities.
+* Model derivatives: If you fine-tune or modify this model, the resulting
+ model is classified as a "Derivative". All derivatives are subject to these
+ exact same terms, meaning strictly no commercial use is permitted for
+ fine-tuned versions.
+* Distillation: Training a new model using the outputs or predictions of
+ AlphaGenome is also restricted; resulting models must be governed by the
+ AlphaGenome Model Parameters Terms of Use
+
+### Limitations
+
+* Like other sequence-based models, accurately capturing the influence of very
+ distant regulatory elements, like those over 100,000 DNA letters away, is
+ still an ongoing challenge. Another priority for future work is further
+ increasing the model’s ability to capture cell- and tissue-specific
+ patterns. We haven't designed or validated AlphaGenome for personal genome
+ prediction, a known challenge for AI models. Instead, we focused more on
+ characterising the performance on individual genetic variants. And while
+ AlphaGenome can predict molecular outcomes, it doesn't give the full picture
+ of how genetic variations lead to complex traits or diseases. These often
+ involve broader biological processes, like developmental and environmental
+ factors, that are beyond the direct scope of our model.
diff --git a/weight/alphagenome-all-folds/_CHECKPOINT_METADATA b/weight/alphagenome-all-folds/_CHECKPOINT_METADATA
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\ No newline at end of file
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
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+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": [
+ {
+ "file_id": "1yQPDjB3OAjAWzYxjzgrqu4fBC77SX6JW",
+ "timestamp": 1768497944747
+ }
+ ],
+ "last_runtime": {},
+ "toc_visible": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Quick start\n",
+ "\n",
+ "Welcome to the quick start guide for running the open-source AlphaGenome model.\n",
+ "\n",
+ "The goal of this tutorial is to quickly get you up to speed with running the\n",
+ "model and making some predictions.\n",
+ "\n",
+ "We **strongly** recommend you use our hosted\n",
+ "[AlphaGenome model](https://deepmind.google.com/science/alphagenome), unless you\n",
+ "need to do something which isn't supported (e.g. fine-tuning, running offline or\n",
+ "large scale inference). This is because it doesn't require any specialist\n",
+ "hardware, so is quicker and easier to use."
+ ],
+ "metadata": {
+ "id": "fThla-YxL5EJ"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Prerequisites\n",
+ "\n",
+ "- Familiarity with the [AlphaGenome API](https://www.alphagenomedocs.com/).\n",
+ " See the API\n",
+ " [quick start guide](https://colab.research.google.com/github/google-deepmind/alphagenome/blob/main/colabs/quick_start.ipynb)\n",
+ " to get started.\n",
+ "- Machine with H100 NVIDIA GPU, equivalent TPU or newer. You will need to have\n",
+ " installed JAX with the correct drivers for your system, please follow\n",
+ " [this guide](https://docs.jax.dev/en/latest/installation.html) on how to do\n",
+ " this. For this tutorial, the easiest option is to use\n",
+ " [Colab Pro](https://colab.research.google.com/signup)."
+ ],
+ "metadata": {
+ "id": "7ecqRixOFBUB"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "cellView": "form",
+ "id": "bs74JX_gLlXA",
+ "executionInfo": {
+ "status": "ok",
+ "timestamp": 1768586096416,
+ "user_tz": 0,
+ "elapsed": 266,
+ "user": {
+ "displayName": "",
+ "userId": ""
+ }
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# @title Install the AlphaGenome model source code.\n",
+ "\n",
+ "# @markdown Run this cell to install the AlphaGenome model code.\n",
+ "from IPython.display import clear_output\n",
+ "! gh auth status > /dev/null || GH_PROMPT_DISABLED=1 gh auth login -w\n",
+ "# Clone and pip install alphagenome_research\n",
+ "! rm -rf ./alphagenome_research \\\n",
+ " && gh repo clone https://github.com/google-deepmind/alphagenome_research\n",
+ "! pip install ./alphagenome_research\n",
+ "clear_output()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## **Imports**"
+ ],
+ "metadata": {
+ "id": "f5oVDefsND-h"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import os\n",
+ "\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.visualization import plot_components\n",
+ "from alphagenome_research.model import dna_model\n",
+ "import matplotlib\n",
+ "import numpy as np\n",
+ "import pandas as pd"
+ ],
+ "metadata": {
+ "id": "YOTYj8gGNDSG",
+ "executionInfo": {
+ "status": "ok",
+ "timestamp": 1768586122939,
+ "user_tz": 0,
+ "elapsed": 26190,
+ "user": {
+ "displayName": "",
+ "userId": ""
+ }
+ }
+ },
+ "execution_count": 2,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Optional: XLA flags for determinism\n",
+ "\n",
+ "These aren't strictly necessary, but it helps improve the model's deterministic\n",
+ "outputs by disabling certain XLA optimizations that are stochastic. See the\n",
+ "[XLA determinism](https://openxla.org/xla/determinism) documentation for more\n",
+ "details.\n",
+ "\n",
+ "These flags must be run before executing any model code."
+ ],
+ "metadata": {
+ "id": "u_dJYSLhoqFR"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Flags to improve determinism.\n",
+ "os.environ['XLA_FLAGS'] = ' '.join([\n",
+ " '--xla_gpu_deterministic_ops',\n",
+ " '--xla_gpu_enable_scatter_determinism_expander=True',\n",
+ " '--xla_gpu_enable_triton_gemm=False',\n",
+ "])\n",
+ "# Increase GPU and CPU memory to reduce out of memory errors.\n",
+ "os.environ['XLA_PYTHON_CLIENT_MEM_FRACTION'] = '0.9'"
+ ],
+ "metadata": {
+ "id": "yQIaCQcap3hB",
+ "executionInfo": {
+ "status": "ok",
+ "timestamp": 1768586123248,
+ "user_tz": 0,
+ "elapsed": 2,
+ "user": {
+ "displayName": "",
+ "userId": ""
+ }
+ }
+ },
+ "execution_count": 3,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Loading the DNA Model\n",
+ "\n",
+ "For this tutorial, we will load the model such that we can interact with it in\n",
+ "the same way as the hosted\n",
+ "[AlphaGenome API](https://deepmind.google.com/science/alphagenome). This wraps\n",
+ "the core model and provides a more intuitive set of functions for creating\n",
+ "predictions, scoring variants, performing in-silico-mutagenesis (ISM) and more.\n",
+ "\n",
+ "It also lets us use the plotting code to easily visualize predictions."
+ ],
+ "metadata": {
+ "id": "MXn4TJQANHJQ"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "First, we need to download the pre-trained weights, which can be obtained from\n",
+ "either [Kaggle](https://www.kaggle.com/models/google/alphagenome) or\n",
+ "[Hugging Face](https://huggingface.co/collections/google/alphagenome).\n",
+ "\n",
+ "The easiest way to do this is to use the following helper functions:\n",
+ "\n",
+ "- `dna_model.create_from_kaggle('all_folds')` for Kaggle, or\n",
+ "- `dna_model.create_from_huggingface('all_folds')` for Hugging Face.\n",
+ "\n",
+ "These functions will authenticate, download the weights and configure the DNA\n",
+ "model wrapper appropriately."
+ ],
+ "metadata": {
+ "id": "T31LgxZ5ELIm"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "model = dna_model.create_from_huggingface('all_folds')\n",
+ "clear_output()"
+ ],
+ "metadata": {
+ "id": "I-70P4knNMWp",
+ "executionInfo": {
+ "status": "ok",
+ "timestamp": 1768586152938,
+ "user_tz": 0,
+ "elapsed": 29430,
+ "user": {
+ "displayName": "",
+ "userId": ""
+ }
+ }
+ },
+ "execution_count": 4,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "You should now have a model instance that you can interact with.\n",
+ "\n",
+ "For example, we can see how many human tracks the model has been trained on by\n",
+ "running the following:"
+ ],
+ "metadata": {
+ "id": "-vgO0gjgFpXT"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "print('Number of human tracks per output type\\n')\n",
+ "metadata = model.output_metadata(dna_model.Organism.HOMO_SAPIENS)\n",
+ "\n",
+ "total = 0\n",
+ "for output_type in dna_model.OutputType:\n",
+ " num_tracks = len(metadata.get(output_type))\n",
+ " # Splice junctions are strand agnostic, so return 2 * num_tracks.\n",
+ " if output_type == dna_model.OutputType.SPLICE_JUNCTIONS:\n",
+ " num_tracks *= 2\n",
+ " total += num_tracks\n",
+ " print(f'{output_type.name:<20} {num_tracks}')\n",
+ "print(f'\\n{\"Total:\":<20} {total}')"
+ ],
+ "metadata": {
+ "id": "887gKFCHFwxh",
+ "executionInfo": {
+ "status": "ok",
+ "timestamp": 1768586153228,
+ "user_tz": 0,
+ "elapsed": 55,
+ "user": {
+ "displayName": "",
+ "userId": ""
+ }
+ },
+ "outputId": "c0884b0a-d3df-4f25-bd06-2c18fbb0a5d4"
+ },
+ "execution_count": 5,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Number of human tracks per output type\n",
+ "\n",
+ "ATAC 167\n",
+ "CAGE 546\n",
+ "DNASE 305\n",
+ "RNA_SEQ 667\n",
+ "CHIP_HISTONE 1116\n",
+ "CHIP_TF 1617\n",
+ "SPLICE_SITES 4\n",
+ "SPLICE_SITE_USAGE 734\n",
+ "SPLICE_JUNCTIONS 734\n",
+ "CONTACT_MAPS 28\n",
+ "PROCAP 12\n",
+ "\n",
+ "Total: 5930\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Reproducing paper figures\n",
+ "\n",
+ "For the rest of this tutorial, we will reproduce several figures from the\n",
+ "AlphaGenome manuscript using the open-source model and visualization library to\n",
+ "plot the results."
+ ],
+ "metadata": {
+ "id": "lKy6yycB_jZC"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "We first load up a GTF file containing gene and transcript locations as\n",
+ "annotated by GENCODE (more information on GTF format\n",
+ "[here](https://www.gencodegenes.org/pages/data_format.html)):"
+ ],
+ "metadata": {
+ "id": "UadG364DGTOq"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "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",
+ "# MANE Select defines one curated transcript per gene.\n",
+ "gtf_transcripts = gene_annotation.filter_protein_coding(gtf)\n",
+ "gtf_transcripts = gene_annotation.filter_to_mane_select_transcript(\n",
+ " gtf_transcripts\n",
+ ")\n",
+ "transcript_extractor = transcript_utils.TranscriptExtractor(gtf_transcripts)"
+ ],
+ "metadata": {
+ "id": "SSBR56ryEuNo",
+ "executionInfo": {
+ "status": "ok",
+ "timestamp": 1768586157896,
+ "user_tz": 0,
+ "elapsed": 4435,
+ "user": {
+ "displayName": "",
+ "userId": ""
+ }
+ }
+ },
+ "execution_count": 6,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Figure 2a\n",
+ "\n",
+ "This figure presents several AlphaGenome track predictions within a 1 Mb region\n",
+ "of human chromosome 19 (0-based coordinates: 10587331-11635907) in the HepG2\n",
+ "cell line."
+ ],
+ "metadata": {
+ "id": "dr6yH0mANMtc"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "def _get_colors_hex(cmap_name, n):\n",
+ " cmap = matplotlib.colormaps[cmap_name]\n",
+ " return [matplotlib.colors.rgb2hex(cmap(i)) for i in np.linspace(0, 1, n)][1:]\n",
+ "\n",
+ "\n",
+ "interval = genome.Interval('chr19', 1_058_7331, 1_163_5907, strand='+')\n",
+ "predictions = model.predict_interval(\n",
+ " interval,\n",
+ " requested_outputs={\n",
+ " dna_model.OutputType.RNA_SEQ,\n",
+ " dna_model.OutputType.DNASE,\n",
+ " dna_model.OutputType.ATAC,\n",
+ " dna_model.OutputType.CHIP_TF,\n",
+ " dna_model.OutputType.CHIP_HISTONE,\n",
+ " dna_model.OutputType.CONTACT_MAPS,\n",
+ " },\n",
+ " ontology_terms=['EFO:0001187'],\n",
+ ")\n",
+ "colors_hex = _get_colors_hex('viridis', 10)\n",
+ "\n",
+ "output_rna_seq = predictions.rna_seq.filter_tracks(\n",
+ " (predictions.rna_seq.metadata['Assay title'] == 'total RNA-seq').values\n",
+ ")\n",
+ "\n",
+ "output_chip_histone = predictions.chip_histone.filter_tracks(\n",
+ " (predictions.chip_histone.metadata['histone_mark'] == 'H3K27ac').values\n",
+ ")\n",
+ "\n",
+ "output_chip_tf = predictions.chip_tf.filter_tracks(\n",
+ " (\n",
+ " (predictions.chip_tf.metadata['transcription_factor'] == 'CTCF')\n",
+ " & (predictions.chip_tf.metadata['genetically_modified'].isnull())\n",
+ " ).values\n",
+ ")\n",
+ "\n",
+ "_ = plot_components.plot(\n",
+ " [\n",
+ " plot_components.TranscriptAnnotation(\n",
+ " transcript_extractor.extract(interval)\n",
+ " ),\n",
+ " plot_components.Tracks(\n",
+ " output_rna_seq,\n",
+ " ylabel_template='RNA-seq ({strand})',\n",
+ " track_colors=colors_hex[0:2],\n",
+ " shared_y_scale=True,\n",
+ " ),\n",
+ " plot_components.Tracks(\n",
+ " predictions.atac,\n",
+ " ylabel_template='ATAC',\n",
+ " track_colors=colors_hex[3],\n",
+ " shared_y_scale=True,\n",
+ " ),\n",
+ " plot_components.Tracks(\n",
+ " predictions.dnase,\n",
+ " ylabel_template='DNase',\n",
+ " track_colors=colors_hex[4],\n",
+ " shared_y_scale=True,\n",
+ " ),\n",
+ " plot_components.Tracks(\n",
+ " output_chip_histone,\n",
+ " ylabel_template='H3K27ac',\n",
+ " filled=True,\n",
+ " track_colors=colors_hex[5],\n",
+ " shared_y_scale=True,\n",
+ " ),\n",
+ " plot_components.Tracks(\n",
+ " output_chip_tf,\n",
+ " ylabel_template='CTCF',\n",
+ " filled=True,\n",
+ " track_colors=colors_hex[6],\n",
+ " shared_y_scale=True,\n",
+ " ),\n",
+ " plot_components.ContactMaps(\n",
+ " predictions.contact_maps,\n",
+ " ylabel_template='Contact\\nmaps',\n",
+ " ),\n",
+ " ],\n",
+ " interval=interval,\n",
+ " fig_width=14,\n",
+ " xlabel='{}:{}-{} (1 Mb)'.format(\n",
+ " interval.chromosome, interval.start, interval.end\n",
+ " ),\n",
+ ")"
+ ],
+ "metadata": {
+ "colab": {
+ "height": 963
+ },
+ "id": "W-MjjfVjNy2H",
+ "executionInfo": {
+ "status": "ok",
+ "timestamp": 1768586181252,
+ "user_tz": 0,
+ "elapsed": 23105,
+ "user": {
+ "displayName": "",
+ "userId": ""
+ }
+ },
+ "outputId": "7f17f85a-5ac5-4d9f-8cb9-632064864085"
+ },
+ "execution_count": 7,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 946,
+ "width": 882
+ }
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Figure 3c\n",
+ "\n",
+ "This figure shows an example of a variant causing alternative splice junction\n",
+ "formation in the *COL6A2* gene by creating a new splicing donor and disrupting\n",
+ "the extant one. Predicted tracks are from the Aorta tissue."
+ ],
+ "metadata": {
+ "id": "jbof-Pc-cufW"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "variant = genome.Variant.from_str('chr21:46126238:G>C')\n",
+ "interval = variant.reference_interval.resize(2**20)\n",
+ "\n",
+ "predictions = model.predict_variant(\n",
+ " interval,\n",
+ " variant,\n",
+ " requested_outputs=[\n",
+ " dna_model.OutputType.SPLICE_JUNCTIONS,\n",
+ " dna_model.OutputType.SPLICE_SITE_USAGE,\n",
+ " dna_model.OutputType.RNA_SEQ,\n",
+ " ],\n",
+ " ontology_terms=['UBERON:0001496'],\n",
+ ")\n",
+ "\n",
+ "ref_alt_colors = {'REF': 'skyblue', 'ALT': 'red'}\n",
+ "plot_interval = genome.Interval.from_str('chr21:46125238-46126738')\n",
+ "rng = np.random.default_rng(seed=42)\n",
+ "\n",
+ "ref_splice_sites = predictions.reference.splice_site_usage\n",
+ "ref_splice_sites = ref_splice_sites.filter_tracks(\n",
+ " (ref_splice_sites.metadata['Assay title'] == 'polyA plus RNA-seq').values\n",
+ ")\n",
+ "\n",
+ "alt_splice_sites = predictions.alternate.splice_site_usage\n",
+ "alt_splice_sites = alt_splice_sites.filter_tracks(\n",
+ " (alt_splice_sites.metadata['Assay title'] == 'polyA plus RNA-seq').values\n",
+ ")\n",
+ "\n",
+ "_ = plot_components.plot(\n",
+ " [\n",
+ " plot_components.TranscriptAnnotation(\n",
+ " transcript_extractor.extract(interval)\n",
+ " ),\n",
+ " plot_components.Sashimi(\n",
+ " predictions.reference.splice_junctions.filter_by_tissue( # pytype: disable=attribute-error\n",
+ " 'Artery_Aorta'\n",
+ " ).filter_to_strand( # pytype: disable=attribute-error\n",
+ " '+'\n",
+ " ),\n",
+ " ylabel_template='Splice\\njunctions',\n",
+ " normalize_values=False,\n",
+ " rng=rng,\n",
+ " ),\n",
+ " plot_components.Sashimi(\n",
+ " predictions.alternate.splice_junctions.filter_by_tissue( # pytype: disable=attribute-error\n",
+ " 'Artery_Aorta'\n",
+ " ).filter_to_strand( # pytype: disable=attribute-error\n",
+ " '+'\n",
+ " ),\n",
+ " ylabel_template='',\n",
+ " normalize_values=False,\n",
+ " rng=rng,\n",
+ " ),\n",
+ " plot_components.OverlaidTracks(\n",
+ " tdata={\n",
+ " 'REF': ref_splice_sites.filter_to_positive_strand(),\n",
+ " 'ALT': alt_splice_sites.filter_to_positive_strand(),\n",
+ " },\n",
+ " colors=ref_alt_colors,\n",
+ " ylabel_template='Splice site\\nusage',\n",
+ " ),\n",
+ " plot_components.OverlaidTracks(\n",
+ " tdata={\n",
+ " 'REF': predictions.reference.rna_seq.filter_to_unstranded(), # pytype: disable=attribute-error\n",
+ " 'ALT': predictions.alternate.rna_seq.filter_to_unstranded(), # pytype: disable=attribute-error\n",
+ " },\n",
+ " colors=ref_alt_colors,\n",
+ " ylabel_template='RNA-seq\\n(predicted)',\n",
+ " ),\n",
+ " ],\n",
+ " annotations=[plot_components.VariantAnnotation([variant])],\n",
+ " interval=plot_interval,\n",
+ " fig_width=14,\n",
+ " xlabel='{}:{}-{} (1.5 kb)'.format(\n",
+ " plot_interval.chromosome, plot_interval.start, plot_interval.end\n",
+ " ),\n",
+ ")"
+ ],
+ "metadata": {
+ "colab": {
+ "height": 387
+ },
+ "id": "aLr0CSjVyG0d",
+ "executionInfo": {
+ "status": "ok",
+ "timestamp": 1768586200231,
+ "user_tz": 0,
+ "elapsed": 18662,
+ "user": {
+ "displayName": "",
+ "userId": ""
+ }
+ },
+ "outputId": "dbb6e017-61f9-4e40-fd41-ee9b3e68016c"
+ },
+ "execution_count": 8,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 370,
+ "width": 875
+ }
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Figure 4b\n",
+ "\n",
+ "Example predictions for a known eQTL (chr22:36201698:A>C) with SuSiE PIP > 0.9\n",
+ "in GTEx Colon (Sigmoid) tissue."
+ ],
+ "metadata": {
+ "id": "fH5fjLbU10lL"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "variant = genome.Variant.from_str('chr22:36201698:A>C')\n",
+ "interval = variant.reference_interval.resize(2**20)\n",
+ "\n",
+ "predictions = model.predict_variant(\n",
+ " interval,\n",
+ " variant,\n",
+ " requested_outputs=[dna_model.OutputType.RNA_SEQ],\n",
+ " ontology_terms=['UBERON:0001159'],\n",
+ ")\n",
+ "\n",
+ "ref_alt_colors = {'REF': 'skyblue', 'ALT': 'red'}\n",
+ "plot_interval = genome.Interval.from_str('chr22:36184123-36209840')\n",
+ "\n",
+ "_ = plot_components.plot(\n",
+ " [\n",
+ " plot_components.TranscriptAnnotation(\n",
+ " transcript_extractor.extract(interval)\n",
+ " ),\n",
+ " plot_components.OverlaidTracks(\n",
+ " tdata={\n",
+ " 'REF': predictions.reference.rna_seq.filter_to_unstranded(),\n",
+ " 'ALT': predictions.alternate.rna_seq.filter_to_unstranded(),\n",
+ " },\n",
+ " colors=ref_alt_colors,\n",
+ " ylabel_template='RNA-seq (predicted)',\n",
+ " ),\n",
+ " ],\n",
+ " annotations=[plot_components.VariantAnnotation([variant])],\n",
+ " interval=plot_interval,\n",
+ " fig_width=10,\n",
+ " xlabel='{}:{}-{} (25 kb)'.format(\n",
+ " plot_interval.chromosome, plot_interval.start, plot_interval.end\n",
+ " ),\n",
+ ")"
+ ],
+ "metadata": {
+ "colab": {
+ "height": 224
+ },
+ "id": "tS7MfLP8145d",
+ "executionInfo": {
+ "status": "ok",
+ "timestamp": 1768586201594,
+ "user_tz": 0,
+ "elapsed": 1027,
+ "user": {
+ "displayName": "",
+ "userId": ""
+ }
+ },
+ "outputId": "378abae8-6df9-4054-fdc1-a2fbb85399d5"
+ },
+ "execution_count": 9,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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J1KtSPT/L4gVv8fGRdyYrbRmvd2ek9CF//vz5LsxW8O1Zvny5O6xf4bYCUt3mRx55xH0Z0KHsBQUFVhm0bvnss8+KXeaF6upJG0phmto8KFAvTwuLylDe+0yhl1oBeMH6jiptfW+99ZYLs/WYVd9Zr12AHpultS8oz+30rluhfqiOHTta165d3fPHC2oV4qotgh5jem6EW0b0uIrkseHdfgW+CvBD6Tmt8FjtIcoadby8t6UsasGg/R8uZN+Rx5TarDzzzDOul7W+oJfnB4PS6McStdRQ+wr13L7pppuKzaPXMV2nfoy58sor3euLejOqfUi4AFzr8/piAwBqlieffNK91+h1XW0XAmlsBPWOD/3hNdyPpBrwTe/T+kxdWejRCwAAgIgo8Bo+fLirhFR46QVIgaFmKFU0KMwIHGlYA1YpEHr44YfD9kNT1aJO4UKjkuiDtkS6jHqUqjepwhZVI5eXgqhvv/3WVeNpoCv1O/UqM1TRF9gfVTRgm/qXhlYaKmBUb1FVZO4I9f9VnzhVlGjwMfUcVXik+0n3mypTFa6HeuKJJ9w2jR8/3t2vChN3hkj2v0I5VUGrGnr16tUuqFyxYoUL0Xr06FHu64x0farMVE9khZN67GofBQZx+vKmMO/oo4/eocemfhgQXU84eszoBwSdVP2pfs0KP9WTV2GzRgUP5IWooaOEq/p32rRpLtBX1f0ee+zh9r9+AMjLy3N9fcNV62pQGP0QoUrcspT3tpRGfaLV61AVuoGDsFXGY0pfukO/eCto1Q9Y2r8VoRHaVQmv7dbrVrgfVEQVz3PmzPFfj56Tev3T/aDHZWhvRr0maPA/9dkGANQcGRkZ7rVfP7zqM0I4OnJDr/ul8XqwV2Y1r1NpTSAAAACwS7vyyitdH7HHHnuszHm9/q3t2rVz/TtD7bbbbr7GjRuH7X154oknlmu7nn76abdcr169wva7DaXepqeddppbZvjw4WHnmTt3rjuV5D//+Y9b3ju1aNHCN27cuGLzPf/88+5y9eht3ry578MPP3S9Yf/880/fOeec4y7r2rVrsd6ugSLp0StZWVm+Sy+9NGi7dDr11FN9c+bMKTa/piUnJ/suv/xy/7RI+u5WZN7y7n+vT3Hg7UhMTPQ9/PDDQb1pyyPS9c2bN89/n8XHx7v+x6tWrXL9lTW/pmm/qSfsjjw2jzjiCHfZhAkTwi571llnucvfeuutYstce+21QfMuXLjQ9RPWZc2aNSu2ruXLl/sOOuigoNufkJDgu+GGG1w/4lAvv/yym+edd96J6HFYkdtSWn9ezat1VtZjSn2lH3jgAd+vv/7qS09P961du9Y9Hnr37u1/DKufcHl79Or21qtXz9eyZUvfrFmzSu3R+9prr5XYA1K9HMPZfffd3eUl9ZkGAOz8Hr3Lly8v9lmrpFNJ1E9fYzvUqVPHt3Hjxkrddlo3AAAAICLTp09352X1cQ3tXRqun2vbtm1d5WQ4avUQKVXDXnPNNa73rar5wvW7DaV2E6piVD/NktoV7L777u5UkgceeMBVeaqy8qeffnLVutovqtILpOpR71zVvurlpspZVTW++uqrrqWDqhy17TtCozar36xaaqinp/5W5af+r6refv36ud7KHrUAUAWh2kx4PVR3lkj2v1cBq32sqmlV495yyy128803ux6wgX1NdZtVeRt40rSKri/wPvvnP//pRs5u3ry5e4xdf/31rp+x+uWWVMWzI4/NQF4VcWALEV2nKkLVQ1jtFrQvVamr55nX0zn0+fbzzz+7Q0I1GrgeC+np6fbXX3/ZXXfd5fa/HhvqLe1RmxVt92mnnRZR25SK3paS6LEr5amwLesxpaMP/vOf/9hee+3lKqFVhaXHg6rZ1TNXPRW9qvxIqY2HqrrbtGnjXht79uxZ6vzh+gpre3Xor+6jcBo1auTO9VwGANQMaWlp7rx3797u/a20U0n0GVC92/U+q6M3KhNBLwAAACKyadMmd65B2CJV0odXhRsl9acNN2BZOArzNGBRs2bNXGDj9SgtjUI7hWQaNEqHWevQuh2hQ8v1QV+HgytoVV82r11DYFil6wk91F+hl9dOITCErQgFXer1psMA1QdZAZFaDOj///3vf10grUHhPBosSuGSBtoKbQFQlSqy/xWQKhi//fbbXTipQ901OFjg40CDWgWewgW9ka4vMGBUMB/Km1bafRbJY9P7ohgYsoa2IgicT9R24ccff7Tzzz/fhbXqHaw2Imrd4Q3oouv0qDWDvkRqsDUFmWrpoftb4eSNN95oV111letTq/vEc8EFF7h+g+F6bpekIrelJF6vQ4XpkdiR57R+dFErGSlvi4TvvvvO/WCiPsL64aos+rEglEJ5PU+9/RNK4byE9n8EAFSfunXruh8Q1Y6noi2v1DLIa5NU2Qh6AQAAUK7Q9u+//67SPRZJ1Z+q91RxqPBEAWe3bt3KXObaa691A6GpL+bYsWMrPeD0Kka1PR5vuzR4VmDv3tBQ0Qt0KsobcC1cz2NvmgJCj6qQta2DBg3yDzynk6obRRWO+rsyq0wqY/971eSBvY418nVoBY2mVXR9qnJWACjhbn9Z91mkj01vuiq6w/EGCgvte6v7SH1/1RNblcg6VxWrBjmU/fbbzz+v+vWqeln9bsP9gFLSY0MDsTVt2jTosaFwWVS1rr81SNuO3pZwvKDaq+yt6seUbqeox255aEA3VYPrxxLtm7IGVlRf6FCqGtft9B5voXSZfhTzKnsBADXDdddd596D9eOoVwgRSEet6f00HB1do/69OsqkMgdh8zAYGwAAACKiyrWZM2e6QKW0tgZV7a233rJzzz3XVRZPmjSpzEpeBX8a5V4VikcccYR9/PHHVVIh5wXgCmY8GuRLh4nr0GsFPaFVfV441759+x26bh3+J6rcVKgcSNMkMTHRP037QdsVSpW/77zzjtvOY4891lJSUmxHVeb+D7ePd0RJ61MrDlXl6v4JHXSwtPusPI9NXYdC03HjxrkB4UIHVlNoqnYMkVSqi1fRO3To0GKPi5IO/Q/32ND2a6CZcGGtql7VJqJPnz6ukr0qbouCdoWv3gBvVf2Y8lrSRLqfPaocVvsG7W8dgqsv/Bqkr6THpkL/0AHX9GVfVdeB+9Kj4FmPT7WEiOTHLwDAjtH7vndE0KpVq/xHb6hFkuhzk35cFAW8+pFU70M6SkhHde22226uwleD9Or9Uj8ChhvQ1BuErSqqeZ1K7fgLAACAXZYG79JAVA0bNgw7uNdff/1VbKCu8847r9QBikoa5Kgko0aN8sXGxvo6dOjgW7JkSUSDNF100UVuvUcddVTEgxqFG4xNg51NnTo17Pzff/+9G5RJ2xa6nAav0vWfe+65QQM+zZ492w3qpX26YMGCHRqMTbct3HVoIDwNJKLLNFhVWSp7MLaK7P9vvvkm7KB6a9as8e29995uXS+88EKZ69mR9WmQLU3fa6+9ggZJ0f/33HPPsI/T8j42dd90797drevjjz/2T9f9pwH0NP3+++8v9hjUKXQf33bbbW7+Y445ptj8DRo0cJe9+OKLQZfptniDfT377LNlbm9pj8OK3JbSnHLKKW6ZcM+Lijymvv3227CDrb3++uu+mJgYNzCfHs+RCH2d0m0/++yz3bSTTz652GPNe63r0qVL0MB32u7999/fXfbqq68Wu56JEye6y/71r39FtF0AgB0zYsSIUgdW0wDDocaMGePee5s2beoGOdXAu/vtt597rww3qK/eB/TZryoGYfNQ0QsAAICIqD+oKhcuu+wyV4Gm/rJdunRxhxer0leVpKpirCpatyoodIi0DtXWIdOhdKi9BpLyqAerKh1V7adKRA2iFkrTAw9DFx3qLoEDaehQ/QEDBrhq5n322cf1OVXlow6/mzhxopvn4YcfLlbtrAG/vvrqK1ft9+uvv7p2Caqk1ABd6kP66KOPWufOnYOW0TZ/++237v869F7UY1WH6YuuQz1WPQ8++KBNmzbNXYcqTFRhKbre33//3VWh6FDzHaVt8ipHVf3rVXp61S4S2DahIvtflZqqpNG+VnWM+phqgDD1X9V9oHn1OIhURdZ3+OGHu/616oGrQyuPO+44f4sM3QdaRpWvO/LY1HZoPt1Xp556qjtp+3Sf6fmk7VVrgkDa1xrAS1WsqihWFemECRPcfayWDbr/Q6tONYCbqoouvvhiNzifnrs6pPSTTz5xj0NV6l944YW2IypyW0pzyimnuOfHF198Uey5UZHHlKpudd/oEFk9b/W8Uy9t9VlWBa76WFe0ql63XRW9ycnJbrtOPvlkV+kb2itYrymqDte+UZ9oVSEvXLjQjjnmmGKVvjJ+/Hj/vgAAVL07igZ0LQ8d/aRTpNT+aUfbdZWpSuJjAAAA7LKmTZvmKte86oWWLVv6hgwZ4nvvvfeqtKLXu7w81Ra6/rKWCbeN3mWBVKl31113+Q477DBf69atfUlJSa4qo1OnTr5zzjnHN3369BL32bZt21ylSLdu3Vz1YP369d16Pv/887Dzl7Xd2n+hFi1a5Lv00kt9HTt2dNeh7evcubPvyiuv9C1fvtwXibKqdCO5D3Z0/7/22mvu8aXK2NTUVP9jTBUzb7/9tqvoLI8dWZ8qLfv16+eW033dq1cv31NPPeWqOMu7X8JVAomq41X12rhxY3e/qfLz9ttv92VkZIStQj7rrLN87du3d9ujKnJVDj3++OO+7OzsEm/HlClTfCeddJKvRYsWroJct2efffZxVbaRVrlHUllenttSGt0WVUX17du32GUVeUw98MADvsMPP9zXpk0bt9/03NDzZNiwYb5Zs2aVa9tKep3S40jPNV02ePBg/232XutUXa19p/tO+0aPxzvuuKNYhbao+ljb2rNnz3JtGwAAMdoFVRslAwAAAAAQufvvv99Vw2swm3A9bHdlqt7XQG+vv/66nX322dW9OQCAKELQCwAAAACoUdReoVu3bm5AQwWftYXqsDTYnVpCqLUEA7EBAMojtlxzAwAAAABQxdTzVhWt++67r23btq3W7G/1k1Y174svvkjICwAoNyp6AQAAAAAAACDKUdELAAAAAAAAAFGOoBcAAAAAAAAAohxBLwAAAAAAAABEOYJeAAAAAAAAAIhyBL0AAAAAAAAAEOUIegEAAAAAAAAgyhH0AgAAAAAAAECUI+gFAAAAAAAAgChH0AsAAAAAAAAAUY6gFwAAAAAAAACiHEEvAAAAAAAAAEQ5gl4AAAAAAAAAiHIEvQAAAAAAAABg0e3/AStx6nYLMungAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 207,
+ "width": 701
+ }
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Figure 5e\n",
+ "\n",
+ "Example AlphaGenome predictions for selected caQTLs. Shown are ALT-REF\n",
+ "differences in a predicted DNase track (GM12878)."
+ ],
+ "metadata": {
+ "id": "gOsXnT7j8OjZ"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "variant = genome.Variant.from_str('chr3:120280774:G>T')\n",
+ "interval = variant.reference_interval.resize(2**20)\n",
+ "\n",
+ "predictions = model.predict_variant(\n",
+ " interval,\n",
+ " variant,\n",
+ " requested_outputs=[dna_model.OutputType.DNASE],\n",
+ " ontology_terms=['EFO:0002784'],\n",
+ ")\n",
+ "\n",
+ "plot_interval = genome.Interval.from_str('chr3:120280518-120281030')\n",
+ "\n",
+ "figure = plot_components.plot(\n",
+ " [\n",
+ " plot_components.Tracks(\n",
+ " predictions.alternate.dnase - predictions.reference.dnase,\n",
+ " ylabel_template='',\n",
+ " track_colors='red',\n",
+ " filled=True,\n",
+ " ),\n",
+ " ],\n",
+ " annotations=[plot_components.VariantAnnotation([variant])],\n",
+ " interval=plot_interval,\n",
+ " fig_width=10,\n",
+ " xlabel='{}:{}-{} (512bp)'.format(\n",
+ " plot_interval.chromosome, plot_interval.start, plot_interval.end\n",
+ " ),\n",
+ ")\n",
+ "figure.tight_layout()\n",
+ "\n",
+ "variant = genome.Variant.from_str('chr12:103880767:G>A')\n",
+ "interval = variant.reference_interval.resize(2**20)\n",
+ "\n",
+ "predictions = model.predict_variant(\n",
+ " interval,\n",
+ " variant,\n",
+ " requested_outputs=[dna_model.OutputType.DNASE],\n",
+ " ontology_terms=['EFO:0002784'],\n",
+ ")\n",
+ "\n",
+ "plot_interval = interval.resize(512)\n",
+ "\n",
+ "figure = plot_components.plot(\n",
+ " [\n",
+ " plot_components.Tracks(\n",
+ " predictions.alternate.dnase - predictions.reference.dnase,\n",
+ " ylabel_template='',\n",
+ " track_colors='blue',\n",
+ " filled=True,\n",
+ " ),\n",
+ " ],\n",
+ " annotations=[plot_components.VariantAnnotation([variant])],\n",
+ " interval=plot_interval,\n",
+ " fig_width=10,\n",
+ " xlabel='{}:{}-{} (512bp)'.format(\n",
+ " plot_interval.chromosome, plot_interval.start, plot_interval.end\n",
+ " ),\n",
+ ")\n",
+ "figure.tight_layout()"
+ ],
+ "metadata": {
+ "colab": {
+ "height": 306
+ },
+ "id": "kDi0hH-v8dWM",
+ "executionInfo": {
+ "status": "ok",
+ "timestamp": 1768586203433,
+ "user_tz": 0,
+ "elapsed": 1564,
+ "user": {
+ "displayName": "",
+ "userId": ""
+ }
+ },
+ "outputId": "5792f341-f28c-4192-bc7e-ede097ae3f64"
+ },
+ "execution_count": 10,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 144,
+ "width": 712
+ }
+ }
+ },
+ {
+ "output_type": "display_data",
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "image/png": {
+ "height": 145,
+ "width": 712
+ }
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Figure 6b\n",
+ "\n",
+ "Detailed ALT-REF predictions for an oncogenic insertion (chr1:47239296:C>ACG)\n",
+ "characterized in Mansour *et al.* 2014."
+ ],
+ "metadata": {
+ "id": "0aXw7OlH9yqF"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "variant = genome.Variant.from_str('chr1:47239296:C>ACG')\n",
+ "interval = variant.reference_interval.resize(2**17)\n",
+ "prediction = model.predict_variant(\n",
+ " interval,\n",
+ " variant,\n",
+ " ontology_terms=['CL:0001059'],\n",
+ " requested_outputs={\n",
+ " dna_model.OutputType.RNA_SEQ,\n",
+ " dna_model.OutputType.CHIP_HISTONE,\n",
+ " dna_model.OutputType.DNASE,\n",
+ " },\n",
+ ")\n",
+ "\n",
+ "tal1_interval = genome.Interval.from_str('chr1:47209255-47242023:-')\n",
+ "\n",
+ "chip_alt_ref = (\n",
+ " prediction.alternate.chip_histone.filter_to_nonpositive_strand()\n",
+ " - prediction.reference.chip_histone.filter_to_nonpositive_strand()\n",
+ ")\n",
+ "\n",
+ "\n",
+ "def _get_colors_hex(cmap_name, n):\n",
+ " cmap = matplotlib.colormaps[cmap_name]\n",
+ " return [matplotlib.colors.rgb2hex(cmap(i)) for i in np.linspace(0, 1, n)][1:]\n",
+ "\n",
+ "\n",
+ "colors_hex = _get_colors_hex('viridis', 9)\n",
+ "\n",
+ "_ = plot_components.plot(\n",
+ " [\n",
+ " plot_components.TranscriptAnnotation(\n",
+ " transcript_extractor.extract(interval)\n",
+ " ),\n",
+ " # RNA-seq tracks.\n",
+ " plot_components.Tracks(\n",
+ " tdata=(\n",
+ " prediction.alternate.rna_seq.filter_to_nonpositive_strand()\n",
+ " - prediction.reference.rna_seq.filter_to_nonpositive_strand()\n",
+ " ),\n",
+ " ylabel_template='RNA-seq',\n",
+ " shared_y_scale=True,\n",
+ " filled=True,\n",
+ " global_ylims=(0, 1.5),\n",
+ " ),\n",
+ " # DNase tracks.\n",
+ " plot_components.Tracks(\n",
+ " tdata=(\n",
+ " prediction.alternate.dnase.filter_to_nonpositive_strand()\n",
+ " - prediction.reference.dnase.filter_to_nonpositive_strand()\n",
+ " ),\n",
+ " ylabel_template='DNase',\n",
+ " shared_y_scale=True,\n",
+ " filled=True,\n",
+ " global_ylims=(-4, 10),\n",
+ " ),\n",
+ " # ChIP histone.\n",
+ " plot_components.Tracks(\n",
+ " chip_alt_ref.filter_tracks(np.eye(1, 6, 0, dtype=bool)[0]),\n",
+ " ylabel_template='H3K27ac',\n",
+ " shared_y_scale=True,\n",
+ " global_ylims=(0, 1400),\n",
+ " filled=True,\n",
+ " track_colors=colors_hex[1],\n",
+ " ),\n",
+ " plot_components.Tracks(\n",
+ " chip_alt_ref.filter_tracks(np.eye(1, 6, 1, dtype=bool)[0]),\n",
+ " ylabel_template='H3K36me3',\n",
+ " shared_y_scale=True,\n",
+ " filled=True,\n",
+ " global_ylims=(-200, 0),\n",
+ " track_colors=colors_hex[2],\n",
+ " ),\n",
+ " plot_components.Tracks(\n",
+ " chip_alt_ref.filter_tracks(np.eye(1, 6, 2, dtype=bool)[0]),\n",
+ " ylabel_template='H3K4me1',\n",
+ " shared_y_scale=True,\n",
+ " filled=True,\n",
+ " global_ylims=(0, 250),\n",
+ " track_colors=colors_hex[3],\n",
+ " ),\n",
+ " plot_components.Tracks(\n",
+ " chip_alt_ref.filter_tracks(np.eye(1, 6, 3, dtype=bool)[0]),\n",
+ " ylabel_template='H3K4me3',\n",
+ " shared_y_scale=True,\n",
+ " filled=True,\n",
+ " global_ylims=(0, 1000),\n",
+ " track_colors=colors_hex[4],\n",
+ " ),\n",
+ " plot_components.Tracks(\n",
+ " chip_alt_ref.filter_tracks(np.eye(1, 6, 4, dtype=bool)[0]),\n",
+ " ylabel_template='H3K9me3',\n",
+ " shared_y_scale=True,\n",
+ " filled=True,\n",
+ " global_ylims=(0, 900),\n",
+ " track_colors=colors_hex[5],\n",
+ " ),\n",
+ " plot_components.Tracks(\n",
+ " chip_alt_ref.filter_tracks(np.eye(1, 6, 5, dtype=bool)[0]),\n",
+ " ylabel_template='H3K27me3',\n",
+ " shared_y_scale=True,\n",
+ " filled=True,\n",
+ " global_ylims=(-30, 5),\n",
+ " track_colors=colors_hex[6],\n",
+ " ),\n",
+ " ],\n",
+ " annotations=[plot_components.VariantAnnotation([variant])],\n",
+ " interval=tal1_interval,\n",
+ " fig_width=16,\n",
+ " title=(\n",
+ " 'Predicted ALT-REF differences in CD34+ Common Myeloid Progenitors'\n",
+ " ' (CMPs)'\n",
+ " ),\n",
+ ")"
+ ],
+ "metadata": {
+ "colab": {
+ "height": 620
+ },
+ "id": "8PwlkfYR9xiK",
+ "executionInfo": {
+ "status": "ok",
+ "timestamp": 1768586221442,
+ "user_tz": 0,
+ "elapsed": 17766,
+ "user": {
+ "displayName": "",
+ "userId": ""
+ }
+ },
+ "outputId": "b9ac1d40-c530-4cc3-ca6c-35e3728184a0"
+ },
+ "execution_count": 11,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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1blzPZAstDi3lAKByBv6/e8uV+bIF5/ze/mT0NTJ6wh8SHH76vMfpYIFXbxhoHq/cXJzljRGD5I7P/6CkA1AMWgq0hkf1Cy5X39/HfFefPJuJEygJAhMAO6GBB31bN86tme3p5mrrJgEAADukmQd83N3MBMu4pktrWbz9gCzbdZBdCgAAYENDilnGIaeUA4DK56HLLzED9QriVd1Vvhxzndw2forJKpjj/kE95MHLLzmvNGGnxvXktj6d5ecVm63ebqCiu/XSTsVaTj9n3ZrVl9mb9lq9Tah8skPHANhM63q15Nv7h8tXY66Tmy/tKJ2b1CMoAQAAoBzpj+pXbxwgNQj2AAAAsGkA7qAOLYq9vJ9nddNJCaDy6BgUKHde1rXIZbRE4Ff3Xife1V3F0cFBxt44UB66oud5QQk5HrmypzQqoNwhgP/0aN5AmgcGFHuXdGvagN2HUiFjAmDDtMGPDuklV3VqZWpkAQAAwHYCvDzklRsGyFM/zeFlACqxas5OcnHzhtKvbRPzm2zauh2yfFeIZGZl2bppAFDlXdKiofh6XjiFdA7thGxcy0+2Hwmv8vsOqAyqV3ORd26+wgQpXUiT2v7y93N3SEJyqjQqohRiznrfGDHYlIDgjA/lRYNmAn29JOxMbKXKlpCjW9P6VmsLKjcCE4AScipjEIGnWzW5Z0B3ubV3J1PnCgAAAPZh8EUt5KrOB2XOZtIRApWJZkPp06aJ9G/X1HR6ubtWy72vd+vGEhoZI7+v2SZ/rt8pcUkpNm0rAFiS1llPz8isMDv1yhKUccgRVNOXwASgknhuaF9pUILMBhpcrlNxaJbiW/t0kl9WbClDC4Hi69mykXx211BZvH2/fL98o+wJjbDb3Vff30f6tGlc4sfU9vGUkzHxVmsXKid6RYESquFRXZ4f3l8iYhLMRau45GSJT0qV2OQU839iSqq50KWp5LzdXM3c8+xcU8zpjyw/T3f2OwAAgB169YaBcuBEpOw7fsrWTQFgId8+cIO0rFuzyItqT13TRx4YfInM3rxHJi3baIIVAKCi69O6sSzdeVAqAldnJxNAVlKNaxU9UhpAxaDnY9f3aGfVbTx6ZS9ZsfuQHD0dbdXtAGp4j3YmQPCKTi3l8o4tZMOBY+Z3xup9R+xuB91yacdiZSo5N2tR16b1GdiBEiMwAShFCp4RPS9ivwEAAFRC7q4uMv7Oa2XkJ79JVEKSrZsDwAK/33Q0bXE//zdd0kF83N3kacq6AKjg9FjWr02TChOYoCM1dWBPSWkpBwAVnw7m045Oa8ou6TBI7pwwlZIOsCodoNq3TZPc//W93aN5QzPpIIi5m/dKSMQZORwRZQKi0zOLzm7k4uQktWt4WiV42sO1mlzXvW2pHqvlHMg4iZIiMAEAAAAA8qjr5y3j7rhaxnz1p2Rc4AIBAPtWz89bXEtYQk/LPWgJv4xMqhADqLhaBAZImwa1paK4slOrUj2uuMFnAOzbkFKUcimNLk3qy4D2zWTxjgNiL4Zf3E7S0jNk4bb9kpyWXq7b1g7vVvVqSvuGdeREdFyFCWazd9d0aSMuzk4F3qeZ3PJmc0vLyDABBxqkcPhUlETExEuAt4fU9fU21ybq+nqJv6eHODo6yFM/zTbvE0u6rkfbUgUGqm7NGli0LagaCEwAAAAAgAIi/1+4rp+89edSm+0bZ0dHaVizRvYFidyLEt4S6Otlyos99eNsCQ4/bbP2ARVBaUbSeld3kw4NA2XL4eNWaRMAlIcWdQOkSW0/0+mknR72zNejuvRuXbLa1jkaBPiY7DiZWQSTARVV88AAaVYnoNy2d+MlHewqMOG+gT0k0Ndbnh92mSzYFix/bdgl24+EW2VbGnw7qENz6RhU1wQjaFBCNefsbkItUX39hz9L2JlYq2y7Krm+R/EzEOj3tP5mKc7vlueG9pM1+45IfHKqWIJ+f956aadSP76Bv4/U8vE0wRSWoBnsUtLSCRCv5AhMAAAAAIACaPkuTbM4de2Ockm3nDNyomXdADPXzoSci0QF+ea+4XLnF1PlUMQZq7cPqKhKm+L70tZBBCYAqNCa1wkwnR0aoLDr2EmxZw9fcYm4lTC7TQ49V9La9NSMByqu8sqWkOPi5g2lYUANuzhuXNQo0AQlKK/qrnLDxe3NdPBEpHww8x9Zve+IxbbVtLafvH3z5dK2QZ0C73d3rSZvjbxc7vqCUhdl0TEoUJrU9hdr0CCAx6+61GIDKPq3a2q+Q0tLS1RYspzDU9f0kQHtmsnCbcFmndusFKAD2yIwAQAAAAAK8cKwy+TgyTOyOSSsbD+8nBxzsx3kz37gbUYZ1PbxLHFNVX8vd5l4/3AZPeEPq9SatNQISE0jvT/8tMVGUQAloQE+pdGrZZB8Nm8NOxtAhdXibJro1vVq2XVgQrM6/nJ9j/ZlWoeWc7CHDkYApXNFx/INTNCU+Dde3F7GzV4ptnZ5xxYF3t60jr98OOoquXX8FAk5eabMo+JH9+siD15+yQVLnHVtWl9u79tZfvpnc5m2WZVd372dVdd/48UdZObGPRbJqnFbn9JnS8hhqcAEvb4xtGsb8x69+dKOZjoWGS3ztwTLzI27TZkLVA4EJgAAAABAIbQu5EejrpabP/1NwqPiSrSfnBwdpXfrIPPjuk+bxkVmPygtDWj41gQnTDU1Qe2Fq7OTuaB112XdzMgfFRWfZDJQ7As/JcHHT8m6/ccIVoDVNa5Vutrj2pHn5+kuZ+ITLd4mALA2jXVsena0Zuv6tex6h2taag3gLIugWr6yYs8hi7UJQPlmDCjLiO3SGtqtrYyft1rSMjLFlrSsQmE83Vzl0zuvlZs/+a3Uqfs1cOvNkYNN6YbieuTKXrJyz2Ey85WCezWXQoNNLBlYM/bGgXLTR79KRmbp379t6teSzo3rlbk93Zo1EEu4vU/n8wJnGvjXkDEDu5sAivu/mS6bD1FqrzIo21nf2VQdFxrZExQUZJY5fPhwvts/+OADGTJkiLnf09NTvL29pX379vLkk09KaGjoeevRx+t6dPmCbNiwQWrWrClOTk7y1Vdf5d7+3HPPyYABA6RBgwZSvXp18fPzk06dOsnrr78ukZGRhbY7KytLfvzxR+nXr595jD62cePGctNNN0lwcLDYkxUrVsjtt98u7dq1E39/f3FzczNtvfbaa2XJkiW2bh4AAABQYWnk/vg7r73g6JK8P/CfH9ZPlo4dI5/dNVQGdmhulaCEHPX8fEzmBH9Pd7E1/WV4bdfWMvv5O+WxIZfmBiUoX8/qcnGLhnJH3y7y9s1XyI8P3WRKWAD2WMpBL/hd2qqRxdsDAOVBL+RrnWbVpp79Bib0a9vEnBvY6lgPwPauLOcyDnl/mwy+yLodyBfSKaiu1KnhdcHAgv/dNsT8zioJXV47c/948tYSBSUoLa2jJR+cHEu6Vej7WUtiWFuLwAC5o2/nMgcClDRrY0E0A6SWmCgLT7dqctMlHQq9v3o1F/ns7qHS6mw2KFRsNs2Y8PXXX5uAhL59+0rt2rUlLS1NtmzZIh9//LF89913snz5chNAUBzz58+XG264QdLT02Xq1Kly/fXX596n6+vcubMMGjRIatWqJQkJCbJu3Tp57bXX5JtvvjF/a9BCXsnJyXLjjTfK7NmzpWXLlnLLLbeIl5eXHD9+XFauXGkCE1q0sO0XV15Lly41U48ePaR///7i4eEhR48elZkzZ8qsWbPk5ZdfljfffNPWzQQAAAAqpFb1asn3D9wgszftlcysrAKX8fVwM6MjmtUJKPf26QUrDU7QeqDRicliCz2aN5Cnru5T7JGZOjJq3Kir5L5vpktGZsH7FCgLP8/qUsOjeqkfr+UcNE0qAFQ02mGRo3lggOlcsrfvWs2S8PQ1fSx2HgSg4tESA9YeXV6UET07WCQFfWkV97n3ad3YZDHQDA/FUc/PW94ceblJsV9a7RvWkbv7d5dvFq8v9Tqqout6WLeMQ173D75YFmwLlrAzsSV+bC1vD7ncQoE5GtxQ1nIOI3pelG9QQ0G8q7vJl/deJ6M//0OOUL6pQrNpYMLOnTvNyP5zTZw4Ue6991556aWXZO7cuRdczy+//CJ33XWXuLu7m+X79Ml/UhsbG1vgdnT977zzjrz77rvyxRdf5LvvqaeeMkEJL7zwgrz11lvi6Jg/uYQGUdiT559/3gRanCssLMwEZejzfPDBByUwMLDc26b7Svfv1q1bTeDJ7t27zW36Ot9zzz1FPlYzVkyYMME8RjNhaKDK008/LVdffXWByyclJcl7770nU6ZMkSNHjpgsHJrxQrNjtG7d2krPEAAAAFVBh0aBZrJX2vHw9X3Xy91fTit1qs/S6NCwjjxw+SXSq2WjEo+46NG8oTw7tJ+8+9cyq7UPVVdQGUfQ9jTvac2maLEmAUC5ByZoxict6xAcftqu9v6tl3aSRhYKKCBjAlAxdW/WQAK8PGy2fc0k0KyOvxw4UXhWbWvRc0zNrFdc9wzoJnvCImTR9v1FLnd993byzNA+pgxEWd0/qIf8szvElOPDhTWt7Wd+G5cXzSLw8vAB8sDEv0r82JG9OpqylZZSlsCEas5Oclvv4g1Q1+PFN/cNl1Gf/y4nY+JLtT1UglIOZVFQsIDSUglq//6iD7Jq3LhxMmrUKFPCQTMZnBuUUJrtHDx40JSC6Natm7z99tvnBSUoF5fsdGg5Ro8ebS7CHTp0SD7//HNp06aN2a6WndCgAC0LoTSbQ/fu3U1GA83e8PDDD5vsDAXZu3evWa9mc3B1dTVZJTRzw759+4r9HOvVqyc9e/aUzMxMCQkJEVvQDBWPP/64/PDDD3LixAmpU6d4B2cNQNDnHx4eLmPGjJHbbrtNduzYIddcc43Zx+dKSUkxWTHeeOMNE5Dw2GOPycCBA+Wvv/6Srl27yvr1RPcBAACgcmtTv7b89fQoeW5oXxnQvpnUsGKphM6N68rX914vvzw6Ui5tlV2+rzRu7nWRuYAGWFrjWmXr8NJsC+0blN/FRQCwlBbnpDrW8wN74utRXe4d1N2iGXIuNNISgP0Z0rmVTbevv1+KSh9vTZ0a15PaJUh/r219a+RgE0hRED/P7PKDr48YZJGgBKUd11rSQTPc4MKu79HeIqURSkJ/h1/ZsWTlUDRg8cZL2lu0Hd2a5c9IXxLDurWVAO/iByjV9fM2wQllyYyHKpwxoTBaekB16FD4l4J29D/zzDMmMEFLLSxYsEAaNWpkke1MnjzZdOTfcccdJtuCLnfs2DHx9/c3ZRKaNWtWZGe6lqDQzvPBgwebUgqamSE1NVX8/PxMZoNhw4ZJ7969ZdGiRSYbQEZGhnz55ZfnlabQchSaWUDXpdsMDQ2V6dOny5w5c2TZsmUmE8KFREREmA55DWzQ/WQLOZksOnbsaDI2aGYHzWBQlDVr1pjXtmnTpvLvv/+Kr2/2BSV9zbt06ZKbNUEDP3J89NFHsnr1alPS4/fff88NKBkxYoTZ55pVQwMbCgo0AQAAACqLOr5eclufzmbKzMySkIhI2XgwTDYeDJWNIaESGZdY5pFNOnqma9P6Frnwout4afhlpp1bD4eXeX1Ajia1Cr5wW9KLfduPnmCnAqhQNItSXlpmaca/u8RePHzFJSYls6XouUTjmr4cr4EKxMXJUQa0b2rrZsg1XVvLx3NWSVJq+WbILk0afXfXaib4YOQnv0lsUkru7f3bNZWxNw40wQmW1rJuTXlw8CXFLiNRHjzdqpn+wYQU+8lqrsEb13SxTcbuZ4f2lVX7DktcnvdEUa7t2trinfoN/H2klo+nRJQwi4GWcxndr0uJt9ektp98NeY6ufvLqXb1PkA5ByYUVEYgR3R0dJGP/fbbb02ne3x8vOk4Xrx4sQky0JT8BUlPTzdZErSEQ48ePUxHvQYNXMiHH35othETEyMbN26UVatWmaAEDRbISzvClS6nHeORkZH5TnQfeOABGT9+vCktcK5NmzbJ9u3bTaaCnP2iQQUffPCB6aDX+3NKCugIfy1N8P3335uOes2goKKiouTmm282y69YscJkX8ixa9cu85y1BMLmzZvP274+Ly1BoftI96kGRmhwxWef/Z+9+wCPqkzbOH6nhyQkhBJaKKFXkSKgdLAAFlCxi4pt7a51d3XXsq7furt27L0jWBFRBOnSCb23BEgoCem9f9f7YiIhCaTMZFL+v+s610lmzjlz5kzqvPf7PFPVtGn197o1vL29NW7cuArtYypWGCbUURhKMEwQ4e6779YzzzyjDz/8sCjgYH4RFe7z3//+t1j4YMKECTYIYipqLF68WKNGjXLQMwMAAABqNnd3N3Vq0dQuVw/pY/9uNm9iRccn6XBCig4lJOtQfHLROjWr7BYQHUIa6+bRA9S/Q+V7lZbF29NTL910sa5+6QtKMqLGVEwwhnRrrzfmrnTI+QBAdWjg7anQxkHFbuvR+vh7jjUlNGFmlTqjfQ9BMqD2GNY9zKEBpcoy1QXG9+uqb1ZuqbbHNNnu884oe/LrqbRp2kj/m3yh7nrvO/l6eepvl47SJQN6OHWmvhk4/uK39TpWxYC7ozxw4VAN6NhGD3w8S/uOxqsmGNWzo4IDXDOD31QbeGLSGD32xRzl5OWfdvvytk2oCPP1V5l2DiagY76mK6Nnm+aaevME3fHud8rOzSu6vUlDP3Vt2UxdWzW1FaRaNw6s9koWqKZgwulmwJ8umHBimX3TQuGLL74oszJBdHS0DSWYCgSmUkJQUPE/tk8VTDh69GjR52PHjrXtBUwbiJOrDBhPPPGEbQVg9jMD4qtXr9af/vQnvfHGG3af0sIY//jHP4pCCUajRo10ySWX2EH0hx56qCiUYJgqBmY2vznO9u3bi4IJn3zyiQ1zFLaEOFHPnj1tW4OXX35Z27ZtK3G/CSac+Fo0bNjQPvbkyZNVmyxYsKDoNTqZCTmYYILZpvC5mvYbBw4cUJcuXRQWFlbqPiaYYPYhmAAAAID6yvxDHuTna5eaVtbZ9It8ZcoluvG16co64Y0FwJU9x80bXqYlSmJ66S0YAaCm6dyiqQ0mnsi8MW/ek/+906xLPXrJCKeUBXdEGA1A9RnX1zXVnUtj2jlUZzDBBL2bBZa/jcPJzunaTj/85SZbOcAZVRJKa+lw+eDeentexVtlm0HiqlbsO1H31iH2XDzc3TXt/mv0xPR5+mXjLrna5YNc25pw7Jld1aVlUz0141etjzx0ytB1h+ZVrypXmsoEE8zEhyo9Zqc2ev/OSdq4/7A6NW9iK3xUpC0EXMNhfwWamTdlLadrsbBy5Uq73bFjxzR37lx7mynXb9oZlMaEAkylgfj4eFs5wVQeKI8jR47YxzFr0xJh37599jgnVx4wrRUM03bgu+++U69evRQQEGDbOHz99dd2Nr5pG2DaM5xswICS30itWrUqek4nKwwxmOoGhVasWGHXGzdutKGFk5ddu47/oDVhhpPdcccd9jlmZGTY4MKUKVPsNTK31xZpaWk2fGKuuXkNTta5c2e7LrwOxs6dO+3aBBNKU9o+AAAAAGoWMwj89FXnu/o0UAf4eHqoVXBglY9j3vQ8u2vF2kYCQE1q42D4+Xg5JKxVVSN7dtDgLm2dcuz2zQgmALWF+Zk0okcH1RQmsN27bYsa3cbhZG2bNqqWUEKhSTYMUPFZ549cMsKhv3/+dulI+/d5YWuL/00ebx+jMufmKC0aNXTa77aKMIGDj+6+Uo9fNlp+3l6lbnPD8NO3h69KSKAiTEiimwMqOp3ZvpVuHNHfHo9QQj2rmOAIph3DeeedZysmdOvWzQ6o79+/Xw0aFC+BYlocmJnvZha8aVVgKhJ8//33JbYrS/PmzXXppZeqX79+diDbPM6WLX8k4gpbB5jZ+icfs0+fPnZGvpmhb4IB5vMTlVa9wdPT87T35eT80QelsHXEu+++e8rnYdpSlMXX19dWZ3jllVdscOPtt9+21R8mTZqkms600DDKqoRRePuJLUIqs8+plBYiMcxrfmLVCwAAAACOdWG/bgps4KN5m3Zr7d4oHYw7/rc+UNGS3ifPGK6sYd3a6+f1x8PwAFAbgwmFs0xdWfLa9JF++OLhTjt+TQheACif0b06qkEZA6eucsXZZ2jzgSPV8rPw3N6Va+PgSmbwfWTPjpq/eU+59zFVx8xzTU7P1P99t7DK53DJgO7qG/ZHtfLCioA3jOin7qEheviT2YpPrf52E5cO7FkUlnA18/+PaeE4okeYnvlmgZZujyi6r1OLJjrbiQGKNk2CFBIUoJikssctT3TL6LOcdi6o2WrGd8tJTPuDs88+W7Gxsdq6dWuZ28ybN0/Dhw+3VRbGjx9/yoH60phKDqYVgnkMU62hUNeuXYseozSFwQVTlcAZCgfRTcWEU1WiuPHGG8t1PBPgMBYtWlTpczKtLMwP+fIu119/vZytIn1hzPWq6D4AAAAAXNdz9p9Xna+fHrtZvz5xm/5z3ThdcXZvBh3gkpLeVEwAUJuYtg2lMYM2rtS/Q2u1c2JVgzZNg+yAH4Cab1zfbqppxp7ZRQ0b+Dj9cQZ0DK21s7qvOueMCm1/yVk95OPlqUsG9JC/j3eVB9wfuHDYKdsIzHjwOnVoXr0hNdOayFSTqGlaBgfq9Vsm6LnrxinY//jka1NVwJnjY+bYAzqElmvbM9q11IAOxUMmqD9qVMWEE5lS/idWFCiNKfVv2j1MnDjRhhPOP/98/fzzz2XOmi/NoUPH+614eHgU3TZmzBhNnTq1WBWFQqb6wO7du4sG651h8ODB+uabb7R06VKdcUbFfthX9lqeTseOHW0VhvIqbF9RGYWvX2EVhJOVVh3hdPskJyeX2OdUwsPDK1RJAQAAAIBzNA8K0Ph+3eximB6lB44l6FBCig7FJ+tQQvIf64RkZeceb82H+s2RM2ebNvRXj9AQbYuKcdgxAcBZTI/p0vRwQLnkqjjvjONtVp3F29NTrRsHUmkJqOHMLHpnztquLFPBYcKAHvps6XqnPs4FfZz7s9CZBndua9vmRMYmlGv7wgF7f19vTRzYQ58v3VDpx24WGHDaQIf5v/HdP12um16fUW2/C0xbDlMloCYyQQFTjXBot/badzSuRLUJZzirU6h+Wr/jtNvdOvosJhHXYy4LJpgWDXl5eerQoWQvIdN2YM2aNWrTpo169z512si0WjDtHK644grNmjXLhgpMSKFx4+NvQuzYscNWPmjRoniPoPz8fP3jH/9QTEyMzjnnnKIqCIUVBsx5/fLLL7Yqg2kvUeiZZ56xg98jRowocUxHmTJlip599lk9/fTTtq3FwIEDS5z7kiVLNHLkyKLbFi9erGHDhsn9pJIxpuWEOZZx4YUXVvqc5s+fr+ri7++v1q1b20DF4cOH1bJly2L3FwZDTBuOk6tc7Nq1q9RjlrYPAAAAgNqnSUM/u/QNK3lfTm6elmyP0Kzw7Vq8bZ9y8/JdcYqoATo4uKT30G5hBBMA1HhmcCTIr/SJRY7o41wVY6qhdLkJpdECCqjZzu/TRV4nTBKtSUyFNmcGE0xVlzG9a28wwQx0m6oJ/5m5uFyVIU4MCl895MxKBxNM1YXGAX7l/j343h2TdOPrM3QkMUXOdv2wvqrpzN8F1RFKKKxccSr9wlrpynP6aFSvjtVyPqiZXBZMWL9+vS677DIbCjCDxc2bN1dcXJxWrlypzZs322oIn376abFKBmXx8fGxFQauu+46ffXVV3bA/tdff1VISIitqPDII4/Ylg9m1n+TJk109OhRO5C/b98+Gy549913ix3P29tbH3/8sa3AYEIKl156qW37YMISJhDQrFkzvfPOO067NuYcv/76a/u4pnqCCVv07NnThg4OHDigFStW2GuVmZlZtM+ECRNsAGPQoEE20JGbm2tDCeb5m4/vvffeYgGLmm706NH29Tfnb4IaJzJVMQq3KWRe27Zt29pgQkREhMLCwk67DwAAAIC6xcvTww58mCUxLUNzNuzSD2u3VUu/WNQsYQ4u4zq0Wzu98+sqhx4TAKqrWoJhSpS3bdpIB44lVvuF79u+VbXMKG0fEmwDigBqrnF9j08wrIk6NG+ic3t30q+b9zhtNrkJWNdmpj3DKz8tU2ZO7im3u+Kk9gam0sKQbu21bEdkhR+zRaOGcq9AB4JWjQP13h3HKyccS0mXs/Rp11K92jpn8nJtZf7OCAn0V0xyWtFtppqRaedxcf/uatO0kUvPD/U8mNCvXz898MADtl3B7NmzFR8fb1sFmEoFDz30kO6//347wF5eXl5emjZtmq2g8Mknn9iKBmaW/7nnnqvbb79dy5Yt08aNG5WYmGhn5JswxOTJk3XfffcVVVc40dChQ7V27VpbtWDhwoV2PxOeMMcylRZCQ8vXK6WyTBhh06ZNev75523lBnOdTGDCtEgwg+uXX355se3NeZpKESbYYSpHmGoU5nxNm4tbb71VF1xwgWqTO+64wwYTTLUH8xwKK1pERkbq9ddft2GUEwMLJq1n9nnsscf06KOPavr06UXVI2bOnGmvX48ePezXBQAAAIC6r5F/A109pI9d9h2N1y8bdykpPaPUbbNy8rR0e4SOJqVW+3nC8UzrVEf3Me/dtqUd1EvJyHLocQGguoIJRvfWIS4JJpzr5DYOJw58AXAMU9UgJ8+xLdJaBQeqXzXN3K6sv0wYqWU79ysjO8cpZf9ru8AGvrY9wDerSrZBP7FdR2lVcq4demaFgwmmDVCAr3eFz9P8L/DuHZdryutfKTH9jwm+jnT98JpfLaG6mXG6a4aeqWm/bdCw7mE2kNA3rBVtG1CMW0FBQUHxmwDHe+6552xbDWPDhg02JGKqZXTu3LkoCGICFCcyAZUXX3zRhkAmTZqk7OxsGzgw1SKmTp2qe+65p9j2WVlZNrSxfPlyDRgwwIY7TIUJU0XDhDoWLFhgK0pURf/+/e06PDy8SscBAAAAULPk5edrzZ4ozQrfpnmb9jjlzUhUDzMrZ87jtzj8uA998qPmbjzeJhAAaqJ/XztWF/XvXub97y9Yo5dn/6bqNvfvt6hlcKDTH2ft3ihNeeMrpz8OUB/86bxB+mDBGuU4sDXa7ecO0r3jzlFN9/GicD0/a4lDj+nh7qYFT95e7pYENdmO6Bhd8eLnZd5/w4h+euSSkhNE8/MLdPF/Pip3QM60cJj1lxvVctXvVbDHrq3wuW6PitHNb36l1MxsOVLzoAD9/PjNNbYtCVCTHZ9SDjiZaclg2mOYxYQSDBMgKLztt99K/lP0wgsv6KOPPrLtNkzrDFMJw7S0MBUhTg4lGKaKgmnh8cQTT9gKFy+99JLmzZtnKy6YNhxVDSUAAAAAqLs83N01uEtbPXvNWC166nY9e80FGtS5jZ19j9rlxH62jjS0W/GWgQBQ03Rp1ey0FROqW682zasllFDYygGAYwzvHqbx/bo59HJeMqDs4FRNcu2wM9X5NBVoKurCft3rRCjB6NY6xLYxKMukk9o4FHJ3d7PV7MrrwYuGVfn3R/fQEL11+2Xy8/aSI5nnQSgBqGWtHFC/LFq0qFL73XjjjXYpL9PKw7S1MAsAAAAAVIafj7ctO2mWuJR07Y9N0KGE5D+W+GQdTkhRdHyyw0vcouYGE4Z0beeU4wKAI3h6uCvsNK0MXBFMMGW4q0uTAD819PVRSiZtd4Cq8HR3V9dWzTR5eD/NXLPNIRfzzPYtHd5qy1nMgPMTl4/R5NemO+R4phXBAxcNVV1y1ZA+2rj/cInbB3QMPeXf4hPP6qmpPy8/ZXU6c71MWHx0r44OOVcTonjt1ol66OMflZBWemu/ijCVHMoKXwA4PYIJAAAAAACUoUlDP7v0U8l+uCkZWZq2bIM+WbxOSU7qXYqK69DcOcGEkKAA+yb9zkOxTjk+AFRFh5DG8vI8dUnp4IAGahnc0Ibr6mIwwfS2DgsJ1qYDR6rtMYG6qHOrpnbw1fzdM7BTG63ec7DKxzSB39rkzLBWunxQL32zakuVj3XvuCFq2tBfdcn5Z3TW/2YuLjHQf8VpBuwbNvCxlTOmL99U6v3dWjXTizdepDZNGzn0fM/qGKpf/n6Lvl21RR8tCteRxMr/Hry4f3c18m/g0PMD6hNaOQAAAAAAUAnmjTXTK/eXx2/Rny8cqmDeoKoRzKCUs5x7RienHRsAqqK8Zcers2qCGdR09ODS6dDOAXBMC5ZCk4f3rfLxvD09dMGZXVTbOOLve/Mz96pzzlBdY4Irlw3qVey2Rn6+GtP79H8rXzPkzFJvN0GQT++72mm/Nxp4e+m6YX3109+m6J9Xna/2lazgcd2w0s8fQPkQTAAAAAAAoAr8fb11y+izNOfxW/TwJcNthQXUvVYOhTOkAKAm6lLeYEJoSJ2sllAdvwOA+qJXmxZFHw/v3kFtqzhQPKpnRwU28FVtY2bFP3jRsCod47HLRsnDvW4Ow11xdm+5uf3x+SVn9bCBhdPp2KKJBnVuU/S5j6eHnrnqfD115XnyLcf+VWWqC106sKe+f/QGvXDDhRUK7A3u3FadWpTv9y2A0tHKAQAAAAAAB/Dz8dKNI/rrqnP6aP7mPUouo71DYnqmZizfqGMp6Vx3BzMztRoHOC8Y0rpxkC0Fu2ZvlNMeAwAqo0urZuXarkfoHzOhne08F1SZqewMWAB/6HlCxQR3dzc7y/zf3y2s9CUyA9a11YSzeuj7NVsVvi+6wvteNrCXzmzfSnWV+bt4ePcwLd4WYT+fdJo2DieaMnKAbRHSpkkj27rBVNipbiYwcn6fLjZE99XKzfrv94uUlZt3yn0cUUEEqO8IJgAAAAAA4EBmps+F/bqdcpspowbYHqfvL1ijmKRUrn8tmil78YAeBBNQLqZPcnp2jg4cS+SKoca0cuhRTa0cOjZvrA7Nm6i60coBqPrfsR1P+t6deFYPTf15mVIzsyt8PFNJ7Jwu7Wrty+Lm5qa/Xz5ak174XHn5+eXeL7CBj+6/cIjqOhNaMcGEs7u0rdDf4UO6tdfsv05Rs6CAaqmScLrX+Mqzz7Ahkkc+na19R+NL3a5d00Ya2i2s2s8PqGvqZg0ZAAAAAABqMPMG3LVDz9TPj03RPyaNUavgQFefUp1QHcGE88/o7PI3UFHzmbLVH99zlb59eLLuPH+wvDx4Cw7OE+Tnq5BA/3Jt2zTQX83KuW1VnOuCNg6GKTnv5eHhkscG6oJurZvJ86TfWX4+3hWaDX8iE9Y9+Xi1jSndf+OIfhXa5/4Lhzq1ildNcXaXdpp2/zX63+QLK7xvm6aNatTf1KYl0rT7r7WVLkpzzdAzbQURAFVTu38jAAAAAABQi3l7etoZOj/+7Sb988rzyj3jFaULC3F+CW9/X2+d64Ly5Kg9Jg/vp5duusi2dzG9lu+64Gx998gNdjYhaoYBHUPtz1wTDKsLvb/NYIqZ8Vlew6phxqcpje2q36uje3V0yWPDtWr74HdN0atNi1JvN4Faj0oMyl4yoPa2cTjRn84brE4tmpS7Fcblg0of3K6LerVtYQNydYH52+3pq87Tf64bJ38fr6LbA3y9NfGsni49N6CuqDlxJAAAAAAA6ikzu/PSQb3sEp+arnX7orV2X5TW7o3WrsOxKihw9RnWDh2aO79iQuGb7D+G76iWx0Lt4e7mpr9OHGln1J2sXbNgvX37Zfplwy7994fFik1Oc8k51mdmJv3FA7rron7dFdok6I/bmzTSAx/PqlSJ8pqiS6uKhdomj+irb1dvceq1NmEJVzEzu3/ZuMtljw/XuG5oXzub+cOFa3kJqsAMqpemZXCgzu3duULfW6alUddWzerMgPWXf75WP4Zv10eLwhUZm1Dqdia68ffLRteJ0Ft9Nr5fNxu4MK0dtkXF6Iqzz7DhZABVRzABAAAAAIAaxJR9NSWwC8tgJ6VnFgUVwvdFa3tUjPJJKrislYMxsFMbNQ8K0NGk1Gp5vLqqdeNA9Q1rVSdCHn7eXvrfDRdqePeyZ6KbGe1j+3a1fZXfnb9aXy7bqIzsnGo9z7rKDHyZ78vS+Hh5aET3DurTvmWpVQUGd2mrz+69Wne//72i45NVG3Vu0bTCZcmHdQ/T0u0RTquWUJEKDo5mvhbaNAnSwbgkl50Dqpf5crt6SB/7eyUuJU0/rN3OS1BJZjC2LJNH9KtQMOGSs+pGtYRCpgrS5YN769KBvbRw614bgtm4/3CxbSad3fuU1xC1hwnZmb8Pth48qt7teE0BRyGYAAAAAABADWZKo47q1dEuRmpmltZHHNLavceDCubNstz8fNV33p4eatU4sFoey8yCu6h/d72/YE21PF5dbnlwfp/OWrB5r9Jr8QB9SFCA3rh1YrlnhTZs4KMHLxqmKSMH6NMl6/TFb+uVllV7n7+rjerZUc9dN87OZq2sji2a6LP7rtZ9H/ygzQeOqLbp3a5lhfcx/dKdF0xwbbsbM2veDB6+PPs3l54Hqs/IHh2LKqE8deV5ik/N0G87InkJKsiUqzdVZMrSp11L9QtrpXURh057LNP2YXzfbnXyNTA/Y8b07mSX9RHR+mDhWi3auk9NAvx0//ihrj49OJCXp4fODGvFNQUciGACAAAAAAC1SICvj53pahYjPStHG/eboMLxqgqb9x9RTl6e6htTKr86y+aadg4EEyovsIGPLh3YU34+3vrTeYP0Ui0cQGzg7aVrhvTRTSMHKDigQYX3N/vcN36IbhzZX58vXW9DCtXZTqBzy6Ya0CFUvds21/5jiTbsZAbls3PzalW45aGLhznke79pQ3+9f+ckPT5tjuZt2iNHf62M7tVRv27arSwHX9/LBvaqVNsEU1Wge+sQbY+Ocej5tAoOVI/Q0kvBV6cJZ/XQ1J+XK4/gXr1w7QktdEx7rBduuEi3vfW1NtXCoJEr9QxtbgfdT+Wlmy7WY9N+0bLTBD+GdmuvJg39VNf1DWutqWGtdSg+Wd5eHjZQDAAoG8EEAAAAAABqMTNL+Owu7exiZObkavP+w3aQce2+aG2MPOTwgbCaKCwkuFofr0PzxurdtkWtnF1dE1x1Th8bSjCuH95X36zaogPHElVbZpReO7SvJg/vq0b+FQ8knMwMYtx1wdl2kP2L3zbYkEJCWoYcrXtoiA0iDOgYame8lnbuWebnx4EjNuRkfoZs2n9YGdm5qmnc3dz014kjdc0Jg5GOChA8P/kiPT9riQ2KVLW0vBn8nzCgh51Va77e32vRRK/8tMxh59syuKEemTC8kufnZkMxf/38ZznSuWd0cmkbhxODJqN7dXB4yAQ1T8fmjTWoc5sSfxu9futE3fDaDEXExKu2VN8JaxasVXsOuuwcerZpXq6WY2/cMlGfLA7Xyz8tKzP8YwKc9Ul1Ve0CgNqOYAIAAAAAAHWIr5enzurUxi5Gdm6ubfdgKyrsjdL6yEN1sq99WEjjan9M86Y7wYSK8/Jw1zVD+xR97u3pqUcnjNA9789UTWZaMEwe1lfXDuvrlBmR5vimesStY87SzkOx9ns2fN/xli1J6ZmVOqYZHz7/jC66/bxB5ZpVb/pnm+CCWXSeVFBQoLjUdDsT9FBC8u/rFB1OSFZMUqqt+mBmyJsBmZbBgWptPg4OVJNAPyWmZSg6PlmHE1JO2DdZGyIPVakyhJ+3l/53w4Ua/nvVGEczs4UfuWS4jialaO7G3ZWqBjJl1ABd1K+7WgQ3LHafCQLMXrdDe47EOeRcn7nqfFtFp7JMKxXT7uBIYooc5bzenVVTTBp8BsGEesD8TC4tDGPCV2/ffqmuf/VLxSSnqSYzLQBMxRZfT09d8t+PXfZ3Wq+2Lcr9c/KmUQPUr0NrPfrZT/Zn/ck/B0f27OCkswQA1GYEEwAAAAAAqMPMoK8pM2uW284daNs87Ig2g55R+mzJuhr/Zn15dXBBMGFc3676z8xFys0rfbYgSndhv+5qFhhQ7DYzyDykW/vTloZ2BU8Pd90+ZqAmj+hXpUHg8jJtCUwpfLPcMKKf8vMLtOdo3PEqKHuPBxXiU9NPW1HAfH3efu5AdWjepNLnYgb7zMxzs5zRrmWF9jWvsVnObF/8dhNQ+MvnP2lD5OFKzSh+/ZYJ6tY6RM5knvczV12gA7GJ2nEottz7mWDHG7ddavuwl8aUmH9y0rma/Np0h5SuH9S5bZWOYc7HVCx5/oclcoRxZ3ZVn/YV+zpxpsGd26p148ASg6aoO0ygy4SAymICU+Z78rpXptXY6lEm6PbuHZerfbPjlZ/uHXeO/jtzsUvOpVc5KiacyPxemPHgdXr6q1+LBbnM7x/z9ycAACervuaLAAAAAADA5cxAlGlBYGb0fnj3lWrRqPiM3trKFRUTzGDCyB7MCKyoG0f2K3Ug+NFLRthB+ZoktEmQPrv3Kt15wdnVEkooa2aqqXZgBqJfvPEiLXrqds189Eb9Y9IYO/gTEuhftK2Hu5ut5DHzLzfquevGVSmU4CymusKHd12p28YMVHkL/pvJ0GbQ+4v7r3F6KOHEUvCvTLlEweVs12Gu/Ys3XFhmKKHQmWGtdOXZZ1Tp3No1baQ/XzhUjnD5oF62PUlV3TxqgP2aqwltHE783rlsUC9XnwacyHz9mu/VU+naqpn+eumoGvk6+Pt46+3bL1PnEyramJ/1PUKr5+fciRoHNKjU34SBDXz1/OQL7e8kH08P+3N94lk9nXKOAIDaj9gaAAAAAAD1VNumjfThXVfolje/tiXWa7N2v880rG5mEPjXzfQwL6+h3dqrU4vSWwp0aN5Y1w07U58sXlfp18MMqtw0sr+8PD1K3Jedk6vvVm/VrsPHynUsMxD+xBVjXBZIKIsZ+DXXyixmgNu0W4iKS9K26Bg7mNWmSSPVdKYKxX3jh+isTqH62+dzbLuIsio/XNivm24dYyo/NHZJiMKEQW5965sy+6gXevrK8zS8nEGl+y8cogVb9uhYyqkrX5R1Tf519QVq4H3qwdjyMl/fkwb31keLwiu1vwlk/P3yMfYYNZEZIH3jlxXKyy9w9anAwcz3wtVD/mgLdLoAw+rdB/Xzhp015nUw38Nv3Xapep5UpcAE9J684lxd8/I05RcUlKtqgXl+T86YV6Xz6dmmRaWDRWY/8/vonC7tdCwlrdwtIQAA9Q/BBAAAAAAA6jEzI9yEE25+86taW+7a9LQ/3YxJZxnavb2dZRifmnHKAIiZHW56RpsAiF3izTrltCX56xoTGjiVO84brB/Dd1Tquozu1VH/vOp8W8miLNcO7atF2/bp7XkrtS0qpsxy/H+bONLOtK5Js7/LYs6xTdNGdqltzu7STl8/fL0e/+IXLd+1v9jA3CUDuttAgvn+caUBHUP12KUj9cw3C8rc5oELh2pCBWYImxnGf5k4Uo98+lOFz+fGkf1t1QVHum5oX326ZP1pwxelzfY2wY1zurZTTWXaf4zo0UELtux19anAwczr2rpxULl/Tpqg2ZaDR3QwLsnlr4W3p4em3nxJmd/LppXPdcPM9+Wpg3rdW4fozdsm2p8pcSnpevXnZdXWxqGsvynNAgBAWQgmAAAAAABQz5lZwR/ddaVueetrHTiWqNomLMQ11RIKW2OM79tNny1dX+rAw62jz9LNo8+yg92lMWGFeZt26515q7S/CtfezFo2AxT9O4TqzPYt9fa8VdpxKFY1iTm/gZ3anLZf+P3jh1Ro5qeXh7seuWSEnTl7uiCBKe1uAgyjenbQ0h2RenvuSm06cKTo/k4tmuh/k8eXWdUBjte0ob/evO1SfbhorT5bsk4je3bULaPPqlGDW1ee00c7Dx3TjBWbStx3/fC+tjVORV3Qp4tmrtmm33ZElnsf8/V59wVny9FaBDfUuL5dbCiovFoGN9Trt0wsVoK+prp8cG+CCXWQqbBT0eog/5t8oa6f+qVy8yoWwnEkE7x66aaLNahz21Nud8/Ys+3fB0cSU8r8eWDaQJhQgnHrmLPs33Dfr9nqsmACAACn41Zg6r0BKJf+/Y/P7AgPr1x5OwAAAACoyY4mperWN79WZGyCapPrh/W1s49dZXtUjK586fNit5lyxo9dNqrcLSbMTOU5G3bpnV9Xad/R+NNu7+nubss/m9ncZunbvpX8T+gTn5Seqdvf/qbMqgCuYPrPm7L8p5OfX6Cnv/pVs9dtV1Zu3im3bd8s2AYJurWuXD9u87bYil0H7KxUcyzTXsBRJfJRt+Tk5unWt7/Run3RRbeN69tVz107zgZeKiM6PkkT/vuJsnJyyzWY+fl9V5co++4oO6JjdMWLxX+OlcWcw2s3T1DTQH/VBubn69hnPyhzgBe1jxmU//bhyZWqamMCUP+ZuViu8u9rx+qi/t3Lte2irXt17wc/lLjdVJL56O4r1CwwoMTPqTve/U6r9xys8HktfOp2GxQDapw5v4f/xq519ZkAcACCCUAFEEwAAAAAUNfFJqfafurlGRyvKUwvZlf2NzeD25Ne+Ey7Dh9Ts0B/PTphhJ0NXZkBEzOAZmZImooHe47EFau+0Ltti+NBhA6h6tO+5WkH0JMzMnXHO99p8wkVASorJNBf/75unL5ZuUVzNuwsV9/rE7Vo1FA/PTbFVpgor+zcXG05cFTh+6K1dm+U1kceshUmCplS/49fNlp+Pn8EMgBnMqXSr3nlCx1OSNHZXdraigFenuX/mi7NhwvX6sUfl552u7vOH6w7nVAt4US3vfWNVu4+cMrWDWZA9aGLh9W6AM+bc1fqjV9WuPo04CBPTBqjK84+o9K/s+/78Act2rqv2l8PU93nhhH9KrTPAx/N0q+b9xRrX2VCCS2DA0vd3gQTJ0+droiY+Ar9jp73j1srdF5AtSGYANQpBBOACiCYAAAAAKC+DL49P2uxflpX8QHo6hYW0ljT7r+mWLUAVzADAGaGoqkIYMpFV5WpGrB42z5bvcIEEsxSVjuIU0nJyNKd736njfsPV/pcTCji47uvVK+2LYqe63vzV2v2uh3Kyy/f18fDlwzXjSOOVyGsrJy8PFudYkPkYbVv1kjDe3So0vGAylY5WLItQhPO6uGQUIz5ur7m5WnaeULrFXc3N4UEBahVcEM7+Gi+/686p488Pdyd+qIt2xFpZ1ufyJzL2V3b6ZL+3TWqV8daF0goZKolXPCv953yO80M6pqfk7WxFVJtFNjAR/P+cZv8fCr/tWgG702gsDqraJiWLw9eNKxS1awm/OcjpWXl2JDgR3dfqTZNG51yn4NxibrulS+VkJZRrsc4t3cn214CqJEIJgB1CsEEoAIIJgAAAACoT8wgy7u/rtas8O12Jn9N07CBjw0llLddQn2Vlpmtu97/vlgJ+or4v2vH6uJSyk4fPJao9+av0cy120759dHIv4F+fmyKQwIbQF10LDlNq/YctBVXzGzo5o0CKlRdxFHMTPJrX5mmLQeP2lL5Ewb00Ph+3WxIoi645/3vtXhbhMOP+9otExTaOMheu/QTqro4S/fQEPl5e9lqMvXRzaMG6IFKDPCfbEPEId30xoxyB+yqwlT4+dfVF1SqklJhS4dvVm3RAxcOVYfmTcq1z4bIQ7rlza+VfZqWSMb944fo1jEDK3VugNMRTADqFIIJQAUQTAAAAABQH0XFJen9BWv0/Zqtys2rGQEFM4v3jVsnaki39q4+lVohPSvHDsqt2RtVof1uGtlfD108/JTbxCSlan9sQpn3m+BIXRnYBOq61MwsxadmqE2ToEoPotZUZnD33g9+cOgxzzujk1688fhM84Vb9ur+D3+Qs4e5P73nKnVo0Vg3vf6Vdh8+pvrEtDN667ZLK1VBqDSfLlmn//2wWM4sDjW0W3u9evMlLgkbma/5Rz79SZk5uafc7t0/Xa7BXdpW23kBFUIwAahTCCYAFUAwAQAAAEB9djghWR8sXGtL+OeUMQMvKzfXqW/wF3r44uG6cWTVWgPUNxnZOXrok9laur18M4aHdG2n12+dKA9355aPB4DqYIJ1t7z5ldZFHHLI8fx9vDTzLzep+QnBq3d+XaWpPy+Xs5h2Qc9dN64oFDZ56nQdSkhWfdClZVN9ePcVCmzg6/CKJaZCVHR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JtMUN+H3nV6+RFTm/bff+5e8TI3Pi/yGR/of2en2NjjrZWHi/mUXZX4lBx8vsuH/0KqiRW7VClmdf7ZEZnFoaWj8b+flEuGydVQ058sm+c6TRUe2ydWJofg7Qdm2ZFZ9IesVHXluCfCAcGf+AFNZ8J9tLnnVbyGxG7O2SHHySDAQNrHyddZU4pGlA7g/DK5hgFbtMjb7BBG06ey/WME1W5aeyvvCfUt2QI4OZfl85Num51t+lsPo7+TLrCq/6+yGY4DkEE4BeIJgAAADg/Zoc9WbWXU7VV/1el/Y5nR3/9y5n8LhyuzcXPW5KTg/krGsNHsQFzpOYgOYwQrB9VLcDI86DMtrzFXAFbXWiJcytFtuA7tBtxf+WTUWPdHmdcL/JMj3mFonwm9zt+moaCmVN3q1eWS61s9Yb5XW7JdcLW9KE2lMlNfzHEmIfZWaa1TQWmaUGT2obi6WqIVtKal03W3440AoJ02P+OGBVEjqiwbpl2b8xAwrR/jNMqeaOgka9kVmxVNbl3yH1TeUt51gk2D7CtHLS1zZt5xRgi5O6xhLz3KkxzyHn86lIfKwBMtPMzgzr9X3vLXtX1ubfLgNJQ4tz4v8pdmuQy9dNq53hK9xvksxPeqG15ZlTRd1e065tT9l/pbox12PbN9RoeOg4sz8tklO1TNbk3ip1TSUufbz09VNf6wZSWulr8n3BPQN6n/D+YEKQfYTMjvu7qdjSEw1N1ZJb9bX5LKhBquqG3OZTY77UNOQPipYPxyT+W6IDZrQ7b3vJ8yZQ6S0IJngOwQSgFwgmAAAADA+NTTWyPPsaya/RUrB9o72mdTDSarHLQNLyzMW1G3t9Ow0LVNTvkbzqlabCQXelXWMDj5QRwadKYtBxYrMG9mlbtxQ9KVuKH+vTbYG2jkp4TGID53hkp2wtflo2Fz160Pk2a7AcEnm1jDHl3dsPlHTF4WiSbSXPyOYi/dvwnllJBwq0JciJI940s5yXpJ8/KA7CumpG/7iwi0xYq7sBdP3dM8o/NrN6S+uaD7qjY8nBp8jhsX/u1d+Su2g4oLDme4kPnOey93gdmNB1BtmTTIsDmzVABsqWoidkS/HjA3JfSUEnyqy4u8XH4uu2yldarr20brsMpNiAuVJZv08qGzIG9H6x33FJz5vWSJ1pbKqV3WVvmvdsVw6we6sj4x+ShKCj271Grc69WQpq1vV73alhP5IpUde57XWgu+873+bfJXvK/zvg9w3vCybod+LUsAtlUuRVLpuI4HA0mgBia1jBGVxozJWq+iwpqt0g7hYfeIzMTXiwg21zyDc510t21efiDQgmeA7BBKAXCCYAAAAMHw1NVabcZ1++/E+KuEomRvzCo7M6+0NnbWv/Sw0p6FJnc6go/+kmjKC9Jl1RflkPbqzK/b1kVn7qgq3GcBXjf7gcnfSkR7fhwJDNiODTZGrUb/s1k7qgeq2syr1ZahrzxRvNjX/ItLZQ3xf8zSXl7D3Fx+Ivo0LOMoMtIb4pfW7Jk1GxSNLLF0llQ7rLt1FL/R8WfbMZVG87214rOehMfJ297ucTuf9kizJLi1hkT9k7sqvs9R61GbFZAmVU6EJJDb1AguzJHV4nr3q1rMq9SeqbSnu07QmBx8rs+HsHPOg3XOh78br8O2Vv+f/cej9JQSfJEXF/dXu4pKhmvXyeedmAVY/Smd86s1QHj77IvJQe9h6qpjIj9rYeXbe+qVJ2lrwkO0qelwZHpQzG95NGR02vb2e3hrSputI/WulpftKLB32P0eDPluInTLWovvx9aRUGDWxqyzdP0ipzn2X8ZMADTPCuYIJ+NpkS9VsJ8R01oPe7OvdWSa/4wI33YJUTR7wuob6pHV5a11gmSzN+LFUNmTLUEUzwHIIJQC8QTAAAABhe9Iv3V1m/6PFMVp01MSPmNhkVepZ404BFVUOWGRAKsMW6JQDyReZlUlq3w+XrxvBwbNKzEuU/zdObYUqI51R+JRMirnDZQXcdQCmu2dBh+Xb9d2nttiFZaSA5+GQ5Im5/KeXaxhJZvG+h1DeVyWCnr/Mh9hQJ9Wsus68HbrWkv69PqMtec3eXvSHfFfxVXCXS71A5OvEJ8bH693kdjY46yaj42AzodfSeqBUwNJiREnK22H1Cul1fZX2WrMy5UUrqum5nERswW46Mf1B8rH593nb0bKBuefa1kle9wi27K8x3ohyb9MyAVYLQMM33BX+XRke1W+8n0JYoxyU91xpCK6z5Tr7KulKaHHVuvV/sZ7eGyoKR7/Q6MKvvO9tLnpW00lc6fbx0sF8va3TUDsgu11nKh8f9RXIqvzRVk3oSUosPPFomR/7afEb/Nv/PklW5tN/boa+5CUHHdHp5fvUa85knt2pZjz6DaBUY3UYNyA2W0HZW5Wdm5jeGbjBBW2bZfULFxxIgNqu/2CwB4mMNNH+z+nfQm6CO1eIrcVr5piFTyrr5Tqptlg6NusFjldrqmypkafqP3RJiVaNCzpaZsV23eCqu3SxfZFwmTVIvQxnBBM8hmAD0AsEEAACA4aemoUi+yvqZlNfv7vbAqPZKjfPQQYqhTAeotPQypXXRW3pAfm7CQ8N2x1U15Mqu0tdld9lbPZ597mk60HPSiP+Kvy2q3fk7S16W9YX3ymA1OfI3khB4tAT7pgxI+em9Ze/K2vw7+93OI9g+0oR3XFHlxhmcKKxZJztLX5bsyi9N6fSxYRdJQtCxYrXYet026duCv8q+8nc7vDzK/zCZl/CvAW1rMJzpYMeXmVe4fBaxrzVCjk9+SQLtCTLQny2+zf+Tqf7kDvq577ikZyXEd3S78zMrlsjK3N8NWMWG4e6w6FtkTNh5fb69VrrIqvxcbBb/dtVi/H0iTShXL99QeJ9kVCwWdxoTer5Mi/59a0URDQvtKfufbC1+ssPKSVrFTKsPRAdMb/f6vLf83ZZQTlWfK4DMT3qpRwECDXdkVS6R9PKPpKBmbYeBNa0iNzLk9EHRhufAtllLMi7otn0dBmcwQUOL8xIe6/R5qp8vMiuXyJ6y/3b43HTSgOno0B/IiODTWwOmzZUDV7epHJhlzve1hpuATUro2b3+vONqRTWbTIUeV4eTfSx+smDkuz2ajKCvMxrsGsoIJngOwQSgFwgmAAAADE/N5Xkvby1Z6OcTLWG+Olt2rIS1zJjVU39mow53OvtKW2cMxdnf8Jzjk1+RcL+Jw/4haGiqlvSKD81s9u5CVJ6mVWVSQn9w0Pk6CPNp+vlSUb9HBpukoBPliLi/D/hMT61QsDr3/8TRx3CCDggfl/ycBNtHyGDlrBDxfcG94pCG1vPDfSeZKg89qb4A137e+TzjUqluzHHJ+ixiM49jdMAM8YTmgdp3ZH3BfS4t26+za49KeLzdoPBQClp5i+ZB9BcGZNA7t2qFqWRTWe/6WcpTIq+TceGXdPgeowOsaWWvy7biZ0wAUauPHBL1GzO7u7P3pMr6DFmdd6sU1Xzf622ZE3+/JAYd1+vbVTXkmPBGRvkiqXdUyFitoBN67oAE+fpKWyetzrvZ05uBXgYTbJYgOXHEGz0Ou5XX7ZU95f81gc+6pmJTYUErd40OPUci/KZ0+9lO/5603ZaGgXwH0WeS7cXPysaiB126zvHhl8uUqKt7dN3syq9kRc41MpQRTPAcgglALxBMAAAAGL7qGkvNQQntH+6qmadoL630dfneheXL4d10sFj7zqP9IJyWYtf+zwU161y6a4JsI8RmDZLSuq19Xke0/0w5OvGpTg8CayuM5YPsIKcO7p808k0zg9ZT5aZX5fyh1+Vyddab7mutaDAUaAn8lTm/k5rGAgmxj5Fjkp7mvdaDlaJ0MD+9YlG3Ja27Mz3mVhkdeq4MhuoyWuY+t+prl6xPK2QlBy/o8jrrC/4hO0tfcsn9oSMWOS7peYn0nzJgu6exqda0f9hW8oxL2nVYxS4zY/8kI0JO6VFFk/K6XS0DqdZur9/kaDDbuqXo8R6HfjX0cHzyy4Om3YK7ORyN8kn6uVJRv9fTmzIMWPtVAaptMKGzgGt3NABbUrvNVLmxW4NkqNOqH8uyf+WyqkC+1jA5eeR7PQ6Earu593cfO6QnFRBM8Jzu38XcQN/cunuDS0lJMdfZs6d9Uv7ee++V0047zVweHBwsoaGhMnXqVLn++uslIyPjoPXo7XU9ev2OrFq1SmJiYsTHx0cef/xxc15hYaE8/fTT8oMf/EDGjh0rAQEBEhYWJkcddZT8+9//lqamg19EL7vsstbfq7PTCSecIIPJl19+KRdffLFMmTJFoqKixN/fX0aPHi1nnXWWLFmyxNObBwAAAAwqvj5hZmYcoQT3GRP6QzO7qq+zMhMCj5URwaeaspz6b3gzq0yK/KWnN2LQ0WMPcYFz5ajEJ0xZWldJCfmBnDDiVZmf/LxMCL+iT4eTdIbx9Jg/dnk8KC7wKIkNmCuDyfSY//NYKEElBs2XOQn3m/3Xc1Y5PO6vQyaU4GzbcHzyqzIt+iY5Jukp3ms9yN8WKRMiLpcTR7wuJyS/Yf7mA22JvV7P6NAfDopQggq0xcnc+IdkVuyfxW4N69e6pkb9tttQQvP1rpfEoMF1LNib6PvSQIYSlI/VTyZFXiknJr8usQFH9rut0VGJj/UolNB8/WCJ9D+0R6EEpaXmJ0b8TI5L0qo5o3p0m0mRvxg2oQSllTYmhF/u6c3wegG2eDks+iaXrEsrhYwKObtPt9XWLPqa4Q2hBKWvBTNj7zItJlxhYsTPe1WlSvdjxAC/BsN7eKRigvMNrqu71iDB3r17Zffu3e1CBRoU0EDCtGnTJC4uTurr6+Xbb7+VL774woQUPv/8c5k+fXq7YIIOto8aNeqgkMOiRYvkvPPOk4aGBnn55ZflnHPOMedrQOGXv/ylJCQkyPz582XkyJGSm5srb7/9tpSWlsq5554rb7zxRrs36nfeeUe+++67Dn+XF154QXbt2mVCFTfeeKMMFnfccYc89dRTMnv2bElOTpagoCDZt2+fvPvuu1JRUSG33nqr3HXXXZ7ezEGFigkAAACAe+lsFi25nFu1XCobNHze1VdWi8QEHG7KcerseWdvUOd6tHSnzvYsrdsp5XVp0uCo7tNsFN2O6gbXlLUePCxmwDMm4AjZUvSEKW3qqp7bh0T+RiobMk053+rG3B7NxE8OOdmUSs2oWNSj+xkZfIbMiuP7anczAb/Nv9uUr+0rPdg5I/Y281xpq6D6W1mTd2tr392emBz5K3PQsztldWmyJP38PrcvUJF+h3bYWqe2sahX/aT1teWIuHtkMMirWiUrcq6Txh68jungfmrYBQOyXRge9BhuUe1600s+s/IT87fUlWj/GXJU4uNmIGiw0VnvNY2FnVzqkIamKqltLJTaxmLze+p1dan/1uDXmNDzezx4q2X4P953ltQ05rv0dxjuNFyyYOR/PRpg0r8JfT/RHvQ6Y7mgeq00OKq6vZ2fT5RE+E2WKVHXSajvmAFr97Sh8H7TNqczYb7jTThsOAUTnN8XFu87u1efZ9A7Y0IvkGnRfzCfG7XtWF8rJljER3xP32mCZnBtS4X4wGNkTvx9JtDUG5uL/iVbi58asg8HFRM8Z8gFE2pqaszM/gPpAPsvfvELOfXUU+XDDz/sNpjw4osvyuWXXy6BgYFmIP6YY45pvWzp0qVSWVkpp59+ulit+1OIOTk5csQRR0h6erq8+eabJqDQnZKSEklMTJTGxkbJzMyU6OhoGSw625e6nTNmzJCCggJThUIDGmhGMAEAAAAYOHogVQcpneECPQCsZWwDbHGSHHyKmbEYYIsdkG2pbyw321Jat8Nshy5Larf2aJBwMNG+rCmhZ0tq2IUSZE8259U0FMq6/Dslp+qrfq1bQw5akth5wFBDHYU135py4BkVn5jeyG0PzOugr1a4iPA7xBwn0LLDq3L/IFmVS7u8Hz0wuWDkO63bj87pY6B9ztNKX+31btLqBTNjbhd/W3SnZaW/L/i77Ct/r9t1aRWEuQkP9HiQ8rv8e2RX2Wu93mZ9dkyL/p2khv2ow0v1OfZd/l96FNbQKgknjnhzULXuKaz+Ttbk3dbpIKfFYpPx4ZeaWbKAu+jfUX71akmv+EiyKpZKg6Oy3eWBtgSZn/yiRyuNDCabix6XrcVPuHSdFrGa98CK+n0yHM2IuUNSQhfKYBvgLq7dJHlVK01YQdu/6WMU6jtWwnzHNS/9xnr070IHMNfl39FhsGh23D8kKXh4VvjYVfqmfFdwt6c3w2vNS3hM4gLnmNDXZxk/kfL63X0KJmjlHr/Tex4uHU7083ha6St9rlg1L+FfYrMG9Pq2+dVr5Kus7kPHgxXBBM8ZcsGEzmglg/DwcFNRYceOHV0GE+677z753e9+ZwbctWqCtoLoqb/85S9yyy23yG9+8xt5+OGHu72+Xueaa66RCy+8UF555ZWD2j8899xzpprCBx98IP/617/Mz/Hx8SZkcfPNN5t9pdUZtNrCpk2bTFWD888/X/7xj390GCrYunWr3HPPPaYVQ15entkn2kLi9ttvlwkTJvT499Q2FloF4uuvv5Z58+b1+HbejmACAAAAAKfqhnxZl3eH5FYv99hOifKfbgb39WCyBjmcwQkT5qjdafrmau/PIFuyGawdFXqWKQd8IP1+vrvsTdlQeJ80Omp7tQ1aYv6QyKtlbNiPOy0xrAfsc6u+keLaDWYmrVa60BK6HV1vRfZ1Xe5TLQ2ufcvRM/rYbip6yPSa7gkfi58pQa5l2HsyczKz4lNZl/9nEzyxWYMlzHesGYDRlirOn9tWM+kJnZn8afp53c7KPvB5qNUNDqzu0NH+0P7gm4se6fJ6s+Puk6Tg43t8/8BwpBUBcqq+NiEFXVrFJsckPSPhfj0/BuntqhpyZdHe0/rVX/1AkyN/LePDL5N95e/LluInvLCqU+f4DNA/+r66Lu8uya76vPU8fb8+wVRL8EjXb49rdNTJx3vPlJrGPE9vitfRQPQZoz9rDaZqyFvDCY2Oml6tZ8HmLAmyjxDLKWvctKVDm1YC+jzzEimt296r2+ln9GMS/93rz+mt9+uok/d3H9vrx3OwIJjgOV7TdPO995oT+oceemiXXz41kKDBBB2k//jjj01goTfs9uYXUZutZ7tOKzkoDRp0Rts7aAuKM888UxYsWGAqOGj4oa6uTiIjI+Wmm26Ss88+W44++mj55JNP5NFHHzUVGB577LF269GQhbaj0PYWui4NaWjFA21BocGHzz77zFRC6I4GGlauXCl+fn69CjMAAAAAwHASYIuRuQmPyK6y12VD4T+lyVE3IPcb5jvBhBG0YkSgfX+FOz8fkSB7kiQGHdfugFFNQ56ZwdpREMBJB6DHhP3QBAZW590iJbWbe7QtejD78Ni7JcxvXJfX0wOSCUFHm1N315sd/w9Znv0bKahZ18HlvjKhB+0A0P6xPSTyGrFa/LqctRvuN6nleXWKeW73VFLwiRIfeJTUNpVKgE+sS8pAa5WC45Nfka3FT8qesndMuKa7lhNzEx40/be7o9s3MeIKCbTFy9q8O7Rw+0HXGRF8GqEEoAe0ZYq+BuipvqlSHI4G8fUJY9+1oVWEEgKPaTcQ3N9A4oTwn5r3dK2ANCLkNNld+qZsLX7aZW2ZBiv93bUkPPpOKzbMif+nqRy0vuAfpvLXpIgrh20oQflYfE21Ia0wBdeKD5rXrlpWqG+qCRdrW4eesltDmr/H8OB0ysfqJ0fGP2D2a0ffnzqi+3RewqN9DiWY+7X4SpT/DMnzYEgfQ5NHKyboLP7OPPDAA6YKQmcVE55++mkz6F5RUSEbNmyQTz/9VEaMGGEG7seNG3dQxYSkpCSZP3++aeEwe/ZsM1AfFRXVq+1uaGiQ6dOny8aNG00I4OSTT+7y+itWrJC5c+fK+PHjZdu2bQdd7qyYoOGIZcuWmW10tn/QUEF1dbVpNfHll1/KpEmTzGW1tbVmG9LS0kxLidjY5rKlxcXFMmbMGPHx8THXnzx5cuv9aKUF/Z11O9atO/iFac2aNfL++++b30/3qQYjysrKTLWHX/7yl73aR96OigkAAAAAOqIle9fk3ioldVvcGkiYFftnUwrYnbRqwZbiJ2VHyQvS1En1BKvYTQWGyVG/NgelXE0HuL7OukqKaze2O39s2EVyaPSNLr+/4WJb8X9M9QSnYPuo1jBCiG/vJm4MFC1VvqXocdMSRPu/H0hn0M1LeESC7SN7ve68qlXyTc717UrR+/lEy0kj3mRwFYDL5FQtM4G7/tKqNCcmv94ulOikZdJ3lr4k24qflUZHlXibAJ94mZ/8kvjbaBHiKlX12VLZkGFCqcOdVh37eN8ZvarUhO4dHvsXGRFy6kHna9WOPeVv92gX6nefkctaxhGpmNBtC7ddZW/KxsIHumw3qIHeY5Oedcln/+3Fz8nGogfEVSZF/NJUAnS2xdF2EQe2jHIVKiYM04oJd955Z59vq8EEndXvdPjhh8vLL79sBvQ7kpmZaUIJWoFAKyWEhfU+vauVCzSUcNppp3UbSlBPPvmkWf78513P5vjjH//YGkpQ2n7hrLPOkv/85z9yww03tIYSlFYxuOCCC+SOO+6QLVu2tAYTnn/+eRNoeOSRR9qFEtQhhxxitkHDHps3bz7ocg0mtH0sQkJCzH1ffPHF3f6OAAAAAACdATRGjkt+zvSSbi6Z31HJZosE20eIjyVQSuu29mq3xQbMldnxfxe7Ncjtu1tnNh0S+WuZHPFLcXRSetqi/3VRgaG/9PfUwWbtW6rtKZSPJUDGh1/utvscDiZE/FQCbHGmlK7OcA73neiSCgfupIGDw+P+YsqWbyp6VHKqvmy9LNLvUDky4QFTYaEvYgOPMAdmdcCwujHXnDcj5lZCCQBcKi7gSAm0JUlVQ2a/1qOvTx2FEpTNGigTI34uCYHHyrLsq72qLL22GDoy4X5CCS6mz6XOnk/Djc0aIOPCLpaNRQ96elO8hkV8JC7wqA4vmxb9eymu3SSldQdP5m0rMegEU8VKpPMJzmizzy1WSQ0731Qy+zb/T5JXvX/81Em/h+p3LFcFkmMCjxBxUZ5Hq2OMDfuR2H1CTHWNseE/liZHg6nkl1u1QtJKX5G6plLX3BmGbzChq2INWiVh7969nV7+zTffmGVhYaGpAqCtD3Q2+2uvvSannHLKQdePiYmR5ORk+fbbb+WSSy6R119/3Qzy99RDDz1kWkBMnDhRXnjhhW6vr9Ue9D58fX1NZYSuzJo166DzEhMT283Qb8sZYtDqBm2rM6jvv//ehBYOtH17c38ZDTMcGEy46qqrzKmmpsZUqHj88cfNPtIqDvozAAAAAKBnA/pToq42B4O2FD1mBu61d6eewnzHSojvGHPgU78L96b9w6iQs2V6zP+1K4U6UAe3LOK50r5aDnxewmPyZdYVUlG/V8aG/ZhBCRcYGaIHeIeeML/xpl1DYc13sq34GfHziZLDov9gSsn3b71j5bjk5+W7gnskwm+SJAQd67JtBgDn++no0HPbVazprZHBZ0hy8Mk9eq3U17Tl2VdLWUuwbzBKDbtQ/H1iZHPRv7pt1zMz9k4J95s4YNuG4Wl02A9lW8mzUs/Ap0vEBMwSX5+QTlsPzI6/11Sbc4jDBEz9faJMqxHnSduKafuWwR6gHYyC7InmO5S2bNlQ8M/WigMWscmR8fdJhP8hLruvcN8J4msNc0lgQCvjaSihLavFZlq16WlM2AWysfBB2Vv+Tr/vC8M4mOAK2o7hpJNOMhUTNDSgA+oaaAgICGh3PW2JsHTpUjn11FNNqwKtSPDOO+8cdL2OPProo3LttdeaAf0lS5aYqgvd0eoMVVVVcuGFF0p0dHSX1+2oeoPNZuv2svr6+tbzNKChnnrqqS7vS1tfdMbf399UZ3jwwQdNy4gnnnhCTjzxRDnvvPO6XCcAAAAAYL/ogOlydFJzBb2O6AG21LALTOnc7to/TI78lUwI/9mwPSjnb4uSY5P+IwXVayQhaL6nNwe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