id int32 0 252k | repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
value | code stringlengths 51 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
|---|---|---|---|---|---|---|---|---|---|---|---|
237,800 | bcbio/bcbio-nextgen | bcbio/cwl/create.py | _write_expressiontool | def _write_expressiontool(step_dir, name, inputs, outputs, expression, parallel):
"""Create an ExpressionTool output for the given inputs
"""
out_file = os.path.join(step_dir, "%s.cwl" % name)
out = {"class": "ExpressionTool",
"cwlVersion": "v1.0",
"requirements": [{"class": "InlineJavascriptRequirement"}],
"inputs": [],
"outputs": [],
"expression": expression}
out = _add_inputs_to_tool(inputs, out, parallel)
out = _add_outputs_to_tool(outputs, out)
_tool_to_file(out, out_file)
return os.path.join("steps", os.path.basename(out_file)) | python | def _write_expressiontool(step_dir, name, inputs, outputs, expression, parallel):
out_file = os.path.join(step_dir, "%s.cwl" % name)
out = {"class": "ExpressionTool",
"cwlVersion": "v1.0",
"requirements": [{"class": "InlineJavascriptRequirement"}],
"inputs": [],
"outputs": [],
"expression": expression}
out = _add_inputs_to_tool(inputs, out, parallel)
out = _add_outputs_to_tool(outputs, out)
_tool_to_file(out, out_file)
return os.path.join("steps", os.path.basename(out_file)) | [
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237,801 | bcbio/bcbio-nextgen | bcbio/cwl/create.py | _clean_record | def _clean_record(rec):
"""Remove secondary files from record fields, which are currently not supported.
To be removed later when secondaryFiles added to records.
"""
if workflow.is_cwl_record(rec):
def _clean_fields(d):
if isinstance(d, dict):
if "fields" in d:
out = []
for f in d["fields"]:
f = utils.deepish_copy(f)
f.pop("secondaryFiles", None)
out.append(f)
d["fields"] = out
return d
else:
out = {}
for k, v in d.items():
out[k] = _clean_fields(v)
return out
else:
return d
return _clean_fields(rec)
else:
return rec | python | def _clean_record(rec):
if workflow.is_cwl_record(rec):
def _clean_fields(d):
if isinstance(d, dict):
if "fields" in d:
out = []
for f in d["fields"]:
f = utils.deepish_copy(f)
f.pop("secondaryFiles", None)
out.append(f)
d["fields"] = out
return d
else:
out = {}
for k, v in d.items():
out[k] = _clean_fields(v)
return out
else:
return d
return _clean_fields(rec)
else:
return rec | [
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237,802 | bcbio/bcbio-nextgen | bcbio/cwl/create.py | _get_record_fields | def _get_record_fields(d):
"""Get field names from a potentially nested record.
"""
if isinstance(d, dict):
if "fields" in d:
return d["fields"]
else:
for v in d.values():
fields = _get_record_fields(v)
if fields:
return fields | python | def _get_record_fields(d):
if isinstance(d, dict):
if "fields" in d:
return d["fields"]
else:
for v in d.values():
fields = _get_record_fields(v)
if fields:
return fields | [
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237,803 | bcbio/bcbio-nextgen | bcbio/cwl/create.py | _get_sentinel_val | def _get_sentinel_val(v):
"""Retrieve expected sentinel value for an output, expanding records.
"""
out = workflow.get_base_id(v["id"])
if workflow.is_cwl_record(v):
out += ":%s" % ";".join([x["name"] for x in _get_record_fields(v)])
return out | python | def _get_sentinel_val(v):
out = workflow.get_base_id(v["id"])
if workflow.is_cwl_record(v):
out += ":%s" % ";".join([x["name"] for x in _get_record_fields(v)])
return out | [
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237,804 | bcbio/bcbio-nextgen | bcbio/cwl/create.py | _place_input_binding | def _place_input_binding(inp_tool, inp_binding, parallel):
"""Check nesting of variables to determine where to place the input binding.
We want to allow having multiple files together (like fasta_indices), combined
with the itemSeparator, but also support having multiple samples where we pass
things independently.
"""
if (parallel in ["multi-combined", "multi-batch", "batch-split", "batch-parallel",
"batch-merge", "batch-single"] and
tz.get_in(["type", "type"], inp_tool) == "array"):
inp_tool["type"]["inputBinding"] = inp_binding
else:
inp_tool["inputBinding"] = inp_binding
return inp_tool | python | def _place_input_binding(inp_tool, inp_binding, parallel):
if (parallel in ["multi-combined", "multi-batch", "batch-split", "batch-parallel",
"batch-merge", "batch-single"] and
tz.get_in(["type", "type"], inp_tool) == "array"):
inp_tool["type"]["inputBinding"] = inp_binding
else:
inp_tool["inputBinding"] = inp_binding
return inp_tool | [
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237,805 | bcbio/bcbio-nextgen | bcbio/cwl/create.py | _place_secondary_files | def _place_secondary_files(inp_tool, inp_binding=None):
"""Put secondaryFiles at the level of the File item to ensure indexes get passed.
"""
def _is_file(val):
return (val == "File" or (isinstance(val, (list, tuple)) and
("File" in val or any(isinstance(x, dict) and _is_file(val)) for x in val)))
secondary_files = inp_tool.pop("secondaryFiles", None)
if secondary_files:
key = []
while (not _is_file(tz.get_in(key + ["type"], inp_tool))
and not _is_file(tz.get_in(key + ["items"], inp_tool))
and not _is_file(tz.get_in(key + ["items", "items"], inp_tool))):
key.append("type")
if tz.get_in(key, inp_tool):
inp_tool["secondaryFiles"] = secondary_files
elif inp_binding:
nested_inp_binding = copy.deepcopy(inp_binding)
nested_inp_binding["prefix"] = "ignore="
nested_inp_binding["secondaryFiles"] = secondary_files
inp_tool = tz.update_in(inp_tool, key, lambda x: nested_inp_binding)
return inp_tool | python | def _place_secondary_files(inp_tool, inp_binding=None):
def _is_file(val):
return (val == "File" or (isinstance(val, (list, tuple)) and
("File" in val or any(isinstance(x, dict) and _is_file(val)) for x in val)))
secondary_files = inp_tool.pop("secondaryFiles", None)
if secondary_files:
key = []
while (not _is_file(tz.get_in(key + ["type"], inp_tool))
and not _is_file(tz.get_in(key + ["items"], inp_tool))
and not _is_file(tz.get_in(key + ["items", "items"], inp_tool))):
key.append("type")
if tz.get_in(key, inp_tool):
inp_tool["secondaryFiles"] = secondary_files
elif inp_binding:
nested_inp_binding = copy.deepcopy(inp_binding)
nested_inp_binding["prefix"] = "ignore="
nested_inp_binding["secondaryFiles"] = secondary_files
inp_tool = tz.update_in(inp_tool, key, lambda x: nested_inp_binding)
return inp_tool | [
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237,806 | bcbio/bcbio-nextgen | bcbio/cwl/create.py | _do_scatter_var | def _do_scatter_var(v, parallel):
"""Logic for scattering a variable.
"""
# For batches, scatter records only at the top level (double nested)
if parallel.startswith("batch") and workflow.is_cwl_record(v):
return (tz.get_in(["type", "type"], v) == "array" and
tz.get_in(["type", "type", "type"], v) == "array")
# Otherwise, scatter arrays
else:
return (tz.get_in(["type", "type"], v) == "array") | python | def _do_scatter_var(v, parallel):
# For batches, scatter records only at the top level (double nested)
if parallel.startswith("batch") and workflow.is_cwl_record(v):
return (tz.get_in(["type", "type"], v) == "array" and
tz.get_in(["type", "type", "type"], v) == "array")
# Otherwise, scatter arrays
else:
return (tz.get_in(["type", "type"], v) == "array") | [
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237,807 | bcbio/bcbio-nextgen | bcbio/cwl/create.py | _step_template | def _step_template(name, run_file, inputs, outputs, parallel, step_parallelism, scatter=None):
"""Templating function for writing a step to avoid repeating namespaces.
"""
scatter_inputs = []
sinputs = []
for inp in inputs:
step_inp = {"id": workflow.get_base_id(inp["id"]), "source": inp["id"]}
if inp.get("wf_duplicate"):
step_inp["id"] += "_toolinput"
for attr in ["source", "valueFrom"]:
if attr in inp:
step_inp[attr] = inp[attr]
sinputs.append(step_inp)
# An initial parallel scatter and multiple chained parallel sample scatters
if (parallel == "multi-parallel" and
(not step_parallelism or
step_parallelism.get(workflow.get_step_prefix(inp["id"])) == "multi-parallel")):
scatter_inputs.append(step_inp["id"])
# scatter on inputs from previous processes that have been arrayed
elif (_is_scatter_parallel(parallel) and (_do_scatter_var(inp, parallel)
or (scatter and inp["id"] in scatter))):
scatter_inputs.append(step_inp["id"])
out = {"run": run_file,
"id": name,
"in": sinputs,
"out": [{"id": workflow.get_base_id(output["id"])} for output in outputs]}
if _is_scatter_parallel(parallel):
assert scatter_inputs, "Did not find items to scatter on: %s" % name
out.update({"scatterMethod": "dotproduct",
"scatter": scatter_inputs})
return out | python | def _step_template(name, run_file, inputs, outputs, parallel, step_parallelism, scatter=None):
scatter_inputs = []
sinputs = []
for inp in inputs:
step_inp = {"id": workflow.get_base_id(inp["id"]), "source": inp["id"]}
if inp.get("wf_duplicate"):
step_inp["id"] += "_toolinput"
for attr in ["source", "valueFrom"]:
if attr in inp:
step_inp[attr] = inp[attr]
sinputs.append(step_inp)
# An initial parallel scatter and multiple chained parallel sample scatters
if (parallel == "multi-parallel" and
(not step_parallelism or
step_parallelism.get(workflow.get_step_prefix(inp["id"])) == "multi-parallel")):
scatter_inputs.append(step_inp["id"])
# scatter on inputs from previous processes that have been arrayed
elif (_is_scatter_parallel(parallel) and (_do_scatter_var(inp, parallel)
or (scatter and inp["id"] in scatter))):
scatter_inputs.append(step_inp["id"])
out = {"run": run_file,
"id": name,
"in": sinputs,
"out": [{"id": workflow.get_base_id(output["id"])} for output in outputs]}
if _is_scatter_parallel(parallel):
assert scatter_inputs, "Did not find items to scatter on: %s" % name
out.update({"scatterMethod": "dotproduct",
"scatter": scatter_inputs})
return out | [
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237,808 | bcbio/bcbio-nextgen | bcbio/cwl/create.py | _get_cur_remotes | def _get_cur_remotes(path):
"""Retrieve remote references defined in the CWL.
"""
cur_remotes = set([])
if isinstance(path, (list, tuple)):
for v in path:
cur_remotes |= _get_cur_remotes(v)
elif isinstance(path, dict):
for v in path.values():
cur_remotes |= _get_cur_remotes(v)
elif path and isinstance(path, six.string_types):
if path.startswith(tuple(INTEGRATION_MAP.keys())):
cur_remotes.add(INTEGRATION_MAP.get(path.split(":")[0] + ":"))
return cur_remotes | python | def _get_cur_remotes(path):
cur_remotes = set([])
if isinstance(path, (list, tuple)):
for v in path:
cur_remotes |= _get_cur_remotes(v)
elif isinstance(path, dict):
for v in path.values():
cur_remotes |= _get_cur_remotes(v)
elif path and isinstance(path, six.string_types):
if path.startswith(tuple(INTEGRATION_MAP.keys())):
cur_remotes.add(INTEGRATION_MAP.get(path.split(":")[0] + ":"))
return cur_remotes | [
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237,809 | bcbio/bcbio-nextgen | bcbio/cwl/create.py | _flatten_samples | def _flatten_samples(samples, base_file, get_retriever):
"""Create a flattened JSON representation of data from the bcbio world map.
"""
flat_data = []
for data in samples:
data["reference"] = _indexes_to_secondary_files(data["reference"], data["genome_build"])
cur_flat = {}
for key_path in [["analysis"], ["description"], ["rgnames"], ["config", "algorithm"],
["metadata"], ["genome_build"], ["resources"],
["files"], ["reference"], ["genome_resources"], ["vrn_file"]]:
cur_key = "__".join(key_path)
for flat_key, flat_val in _to_cwldata(cur_key, tz.get_in(key_path, data), get_retriever):
cur_flat[flat_key] = flat_val
flat_data.append(cur_flat)
out = {}
for key in sorted(list(set(reduce(operator.add, [list(d.keys()) for d in flat_data])))):
# Periods in keys cause issues with WDL and some CWL implementations
clean_key = key.replace(".", "_")
out[clean_key] = []
for cur_flat in flat_data:
out[clean_key].append(cur_flat.get(key))
# special case for back-compatibility with fasta specifications -- yuck
if "reference__fasta__base" not in out and "reference__fasta" in out:
out["reference__fasta__base"] = out["reference__fasta"]
del out["reference__fasta"]
return _samplejson_to_inputs(out), out | python | def _flatten_samples(samples, base_file, get_retriever):
flat_data = []
for data in samples:
data["reference"] = _indexes_to_secondary_files(data["reference"], data["genome_build"])
cur_flat = {}
for key_path in [["analysis"], ["description"], ["rgnames"], ["config", "algorithm"],
["metadata"], ["genome_build"], ["resources"],
["files"], ["reference"], ["genome_resources"], ["vrn_file"]]:
cur_key = "__".join(key_path)
for flat_key, flat_val in _to_cwldata(cur_key, tz.get_in(key_path, data), get_retriever):
cur_flat[flat_key] = flat_val
flat_data.append(cur_flat)
out = {}
for key in sorted(list(set(reduce(operator.add, [list(d.keys()) for d in flat_data])))):
# Periods in keys cause issues with WDL and some CWL implementations
clean_key = key.replace(".", "_")
out[clean_key] = []
for cur_flat in flat_data:
out[clean_key].append(cur_flat.get(key))
# special case for back-compatibility with fasta specifications -- yuck
if "reference__fasta__base" not in out and "reference__fasta" in out:
out["reference__fasta__base"] = out["reference__fasta"]
del out["reference__fasta"]
return _samplejson_to_inputs(out), out | [
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] | 6a9348c0054ccd5baffd22f1bb7d0422f6978b20 | https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/cwl/create.py#L478-L503 |
237,810 | bcbio/bcbio-nextgen | bcbio/cwl/create.py | _indexes_to_secondary_files | def _indexes_to_secondary_files(gresources, genome_build):
"""Convert a list of genome indexes into a single file plus secondary files.
This ensures that all indices are staged together in a single directory.
"""
out = {}
for refname, val in gresources.items():
if isinstance(val, dict) and "indexes" in val:
# list of indexes -- aligners
if len(val.keys()) == 1:
indexes = sorted(val["indexes"])
if len(indexes) == 0:
if refname not in alignment.allow_noindices():
raise ValueError("Did not find indexes for %s: %s" % (refname, val))
elif len(indexes) == 1:
val = {"indexes": indexes[0]}
else:
val = {"indexes": {"base": indexes[0], "indexes": indexes[1:]}}
# directory plus indexes -- snpEff
elif "base" in val and os.path.isdir(val["base"]) and len(val["indexes"]) > 0:
indexes = val["indexes"]
val = {"base": indexes[0], "indexes": indexes[1:]}
elif isinstance(val, dict) and genome_build in val:
val = _indexes_to_secondary_files(val, genome_build)
out[refname] = val
return out | python | def _indexes_to_secondary_files(gresources, genome_build):
out = {}
for refname, val in gresources.items():
if isinstance(val, dict) and "indexes" in val:
# list of indexes -- aligners
if len(val.keys()) == 1:
indexes = sorted(val["indexes"])
if len(indexes) == 0:
if refname not in alignment.allow_noindices():
raise ValueError("Did not find indexes for %s: %s" % (refname, val))
elif len(indexes) == 1:
val = {"indexes": indexes[0]}
else:
val = {"indexes": {"base": indexes[0], "indexes": indexes[1:]}}
# directory plus indexes -- snpEff
elif "base" in val and os.path.isdir(val["base"]) and len(val["indexes"]) > 0:
indexes = val["indexes"]
val = {"base": indexes[0], "indexes": indexes[1:]}
elif isinstance(val, dict) and genome_build in val:
val = _indexes_to_secondary_files(val, genome_build)
out[refname] = val
return out | [
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237,811 | bcbio/bcbio-nextgen | bcbio/cwl/create.py | _add_suppl_info | def _add_suppl_info(inp, val):
"""Add supplementary information to inputs from file values.
"""
inp["type"] = _get_avro_type(val)
secondary = _get_secondary_files(val)
if secondary:
inp["secondaryFiles"] = secondary
return inp | python | def _add_suppl_info(inp, val):
inp["type"] = _get_avro_type(val)
secondary = _get_secondary_files(val)
if secondary:
inp["secondaryFiles"] = secondary
return inp | [
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237,812 | bcbio/bcbio-nextgen | bcbio/cwl/create.py | _get_secondary_files | def _get_secondary_files(val):
"""Retrieve associated secondary files.
Normalizes input values into definitions of available secondary files.
Requires indices to be present in all files, since declared CWL secondary
files are not optional. So if we have a mix of BAM (bai) and fastq (gbi) we
ignore the existing indices and will have to regenerate during processing.
"""
out = []
if isinstance(val, (tuple, list)):
s_counts = collections.defaultdict(int)
for x in val:
for s in _get_secondary_files(x):
s_counts[s] += 1
for s, count in s_counts.items():
if s and s not in out and count == len([x for x in val if x]):
out.append(s)
elif isinstance(val, dict) and (val.get("class") == "File" or "File" in val.get("class")):
if "secondaryFiles" in val:
for sf in [x["path"] for x in val["secondaryFiles"]]:
rext = _get_relative_ext(val["path"], sf)
if rext and rext not in out:
out.append(rext)
return out | python | def _get_secondary_files(val):
out = []
if isinstance(val, (tuple, list)):
s_counts = collections.defaultdict(int)
for x in val:
for s in _get_secondary_files(x):
s_counts[s] += 1
for s, count in s_counts.items():
if s and s not in out and count == len([x for x in val if x]):
out.append(s)
elif isinstance(val, dict) and (val.get("class") == "File" or "File" in val.get("class")):
if "secondaryFiles" in val:
for sf in [x["path"] for x in val["secondaryFiles"]]:
rext = _get_relative_ext(val["path"], sf)
if rext and rext not in out:
out.append(rext)
return out | [
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237,813 | bcbio/bcbio-nextgen | bcbio/cwl/create.py | _get_relative_ext | def _get_relative_ext(of, sf):
"""Retrieve relative extension given the original and secondary files.
"""
def half_finished_trim(orig, prefix):
return (os.path.basename(prefix).count(".") > 0 and
os.path.basename(orig).count(".") == os.path.basename(prefix).count("."))
# Handle remote files
if of.find(":") > 0:
of = os.path.basename(of.split(":")[-1])
if sf.find(":") > 0:
sf = os.path.basename(sf.split(":")[-1])
prefix = os.path.commonprefix([sf, of])
while prefix.endswith(".") or (half_finished_trim(sf, prefix) and half_finished_trim(of, prefix)):
prefix = prefix[:-1]
exts_to_remove = of.replace(prefix, "")
ext_to_add = sf.replace(prefix, "")
# Return extensions relative to original
if not exts_to_remove or exts_to_remove.startswith("."):
return str("^" * exts_to_remove.count(".") + ext_to_add)
else:
raise ValueError("No cross platform way to reference complex extension: %s %s" % (sf, of)) | python | def _get_relative_ext(of, sf):
def half_finished_trim(orig, prefix):
return (os.path.basename(prefix).count(".") > 0 and
os.path.basename(orig).count(".") == os.path.basename(prefix).count("."))
# Handle remote files
if of.find(":") > 0:
of = os.path.basename(of.split(":")[-1])
if sf.find(":") > 0:
sf = os.path.basename(sf.split(":")[-1])
prefix = os.path.commonprefix([sf, of])
while prefix.endswith(".") or (half_finished_trim(sf, prefix) and half_finished_trim(of, prefix)):
prefix = prefix[:-1]
exts_to_remove = of.replace(prefix, "")
ext_to_add = sf.replace(prefix, "")
# Return extensions relative to original
if not exts_to_remove or exts_to_remove.startswith("."):
return str("^" * exts_to_remove.count(".") + ext_to_add)
else:
raise ValueError("No cross platform way to reference complex extension: %s %s" % (sf, of)) | [
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237,814 | bcbio/bcbio-nextgen | bcbio/cwl/create.py | _get_avro_type | def _get_avro_type(val):
"""Infer avro type for the current input.
"""
if isinstance(val, dict):
assert val.get("class") == "File" or "File" in val.get("class")
return "File"
elif isinstance(val, (tuple, list)):
types = []
for ctype in [_get_avro_type(v) for v in val]:
if isinstance(ctype, dict):
nested_types = [x["items"] for x in types if isinstance(x, dict)]
if ctype["items"] not in nested_types:
if isinstance(ctype["items"], (list, tuple)):
for t in ctype["items"]:
if t not in types:
types.append(t)
else:
if ctype not in types:
types.append(ctype)
elif isinstance(ctype, (list, tuple)):
for x in ctype:
if x not in types:
types.append(x)
elif ctype not in types:
types.append(ctype)
# handle empty types, allow null
if len(types) == 0:
types = ["null"]
# empty lists
if isinstance(val, (list, tuple)) and len(val) == 0:
types.append({"type": "array", "items": ["null"]})
types = _avoid_duplicate_arrays(types)
# Avoid empty null only arrays which confuse some runners
if len(types) == 1 and types[0] == "null":
types.append("string")
return {"type": "array", "items": (types[0] if len(types) == 1 else types)}
elif val is None:
return ["null"]
# encode booleans as string True/False and unencode on other side
elif isinstance(val, bool) or isinstance(val, six.string_types) and val.lower() in ["true", "false", "none"]:
return ["string", "null", "boolean"]
elif isinstance(val, int):
return "long"
elif isinstance(val, float):
return "double"
else:
return "string" | python | def _get_avro_type(val):
if isinstance(val, dict):
assert val.get("class") == "File" or "File" in val.get("class")
return "File"
elif isinstance(val, (tuple, list)):
types = []
for ctype in [_get_avro_type(v) for v in val]:
if isinstance(ctype, dict):
nested_types = [x["items"] for x in types if isinstance(x, dict)]
if ctype["items"] not in nested_types:
if isinstance(ctype["items"], (list, tuple)):
for t in ctype["items"]:
if t not in types:
types.append(t)
else:
if ctype not in types:
types.append(ctype)
elif isinstance(ctype, (list, tuple)):
for x in ctype:
if x not in types:
types.append(x)
elif ctype not in types:
types.append(ctype)
# handle empty types, allow null
if len(types) == 0:
types = ["null"]
# empty lists
if isinstance(val, (list, tuple)) and len(val) == 0:
types.append({"type": "array", "items": ["null"]})
types = _avoid_duplicate_arrays(types)
# Avoid empty null only arrays which confuse some runners
if len(types) == 1 and types[0] == "null":
types.append("string")
return {"type": "array", "items": (types[0] if len(types) == 1 else types)}
elif val is None:
return ["null"]
# encode booleans as string True/False and unencode on other side
elif isinstance(val, bool) or isinstance(val, six.string_types) and val.lower() in ["true", "false", "none"]:
return ["string", "null", "boolean"]
elif isinstance(val, int):
return "long"
elif isinstance(val, float):
return "double"
else:
return "string" | [
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237,815 | bcbio/bcbio-nextgen | bcbio/cwl/create.py | _avoid_duplicate_arrays | def _avoid_duplicate_arrays(types):
"""Collapse arrays when we have multiple types.
"""
arrays = [t for t in types if isinstance(t, dict) and t["type"] == "array"]
others = [t for t in types if not (isinstance(t, dict) and t["type"] == "array")]
if arrays:
items = set([])
for t in arrays:
if isinstance(t["items"], (list, tuple)):
items |= set(t["items"])
else:
items.add(t["items"])
if len(items) == 1:
items = items.pop()
else:
items = sorted(list(items))
arrays = [{"type": "array", "items": items}]
return others + arrays | python | def _avoid_duplicate_arrays(types):
arrays = [t for t in types if isinstance(t, dict) and t["type"] == "array"]
others = [t for t in types if not (isinstance(t, dict) and t["type"] == "array")]
if arrays:
items = set([])
for t in arrays:
if isinstance(t["items"], (list, tuple)):
items |= set(t["items"])
else:
items.add(t["items"])
if len(items) == 1:
items = items.pop()
else:
items = sorted(list(items))
arrays = [{"type": "array", "items": items}]
return others + arrays | [
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237,816 | bcbio/bcbio-nextgen | bcbio/cwl/create.py | _samplejson_to_inputs | def _samplejson_to_inputs(svals):
"""Convert sample output into inputs for CWL configuration files, with types.
"""
out = []
for key, val in svals.items():
out.append(_add_suppl_info({"id": "%s" % key}, val))
return out | python | def _samplejson_to_inputs(svals):
out = []
for key, val in svals.items():
out.append(_add_suppl_info({"id": "%s" % key}, val))
return out | [
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237,817 | bcbio/bcbio-nextgen | bcbio/cwl/create.py | _to_cwldata | def _to_cwldata(key, val, get_retriever):
"""Convert nested dictionary into CWL data, flatening and marking up files.
Moves file objects to the top level, enabling insertion in CWL inputs/outputs.
"""
out = []
if isinstance(val, dict):
if len(val) == 2 and "base" in val and "indexes" in val:
if len(val["indexes"]) > 0 and val["base"] == val["indexes"][0]:
out.append(("%s__indexes" % key, _item_to_cwldata(val["base"], get_retriever)))
else:
out.append((key, _to_cwlfile_with_indexes(val, get_retriever)))
# Dump shared nested keys like resources as a JSON string
elif key in workflow.ALWAYS_AVAILABLE or key in workflow.STRING_DICT:
out.append((key, _item_to_cwldata(json.dumps(val), get_retriever)))
elif key in workflow.FLAT_DICT:
flat = []
for k, vs in val.items():
if not isinstance(vs, (list, tuple)):
vs = [vs]
for v in vs:
flat.append("%s:%s" % (k, v))
out.append((key, _item_to_cwldata(flat, get_retriever)))
else:
remain_val = {}
for nkey, nval in val.items():
cur_nkey = "%s__%s" % (key, nkey)
cwl_nval = _item_to_cwldata(nval, get_retriever)
if isinstance(cwl_nval, dict):
out.extend(_to_cwldata(cur_nkey, nval, get_retriever))
elif key in workflow.ALWAYS_AVAILABLE:
remain_val[nkey] = nval
else:
out.append((cur_nkey, cwl_nval))
if remain_val:
out.append((key, json.dumps(remain_val, sort_keys=True, separators=(',', ':'))))
else:
out.append((key, _item_to_cwldata(val, get_retriever)))
return out | python | def _to_cwldata(key, val, get_retriever):
out = []
if isinstance(val, dict):
if len(val) == 2 and "base" in val and "indexes" in val:
if len(val["indexes"]) > 0 and val["base"] == val["indexes"][0]:
out.append(("%s__indexes" % key, _item_to_cwldata(val["base"], get_retriever)))
else:
out.append((key, _to_cwlfile_with_indexes(val, get_retriever)))
# Dump shared nested keys like resources as a JSON string
elif key in workflow.ALWAYS_AVAILABLE or key in workflow.STRING_DICT:
out.append((key, _item_to_cwldata(json.dumps(val), get_retriever)))
elif key in workflow.FLAT_DICT:
flat = []
for k, vs in val.items():
if not isinstance(vs, (list, tuple)):
vs = [vs]
for v in vs:
flat.append("%s:%s" % (k, v))
out.append((key, _item_to_cwldata(flat, get_retriever)))
else:
remain_val = {}
for nkey, nval in val.items():
cur_nkey = "%s__%s" % (key, nkey)
cwl_nval = _item_to_cwldata(nval, get_retriever)
if isinstance(cwl_nval, dict):
out.extend(_to_cwldata(cur_nkey, nval, get_retriever))
elif key in workflow.ALWAYS_AVAILABLE:
remain_val[nkey] = nval
else:
out.append((cur_nkey, cwl_nval))
if remain_val:
out.append((key, json.dumps(remain_val, sort_keys=True, separators=(',', ':'))))
else:
out.append((key, _item_to_cwldata(val, get_retriever)))
return out | [
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237,818 | bcbio/bcbio-nextgen | bcbio/cwl/create.py | _to_cwlfile_with_indexes | def _to_cwlfile_with_indexes(val, get_retriever):
"""Convert reads with ready to go indexes into the right CWL object.
Identifies the top level directory and creates a tarball, avoiding
trying to handle complex secondary setups which are not cross platform.
Skips doing this for reference files and standard setups like bwa, which
take up too much time and space to unpack multiple times.
"""
val["indexes"] = _index_blacklist(val["indexes"])
tval = {"base": _remove_remote_prefix(val["base"]),
"indexes": [_remove_remote_prefix(f) for f in val["indexes"]]}
# Standard named set of indices, like bwa
# Do not include snpEff, which we need to isolate inside a nested directory
# hisat2 indices do also not localize cleanly due to compilicated naming
cp_dir, cp_base = os.path.split(os.path.commonprefix([tval["base"]] + tval["indexes"]))
if (cp_base and cp_dir == os.path.dirname(tval["base"]) and
not ("/snpeff/" in cp_dir or "/hisat2" in cp_dir)):
return _item_to_cwldata(val["base"], get_retriever, val["indexes"])
else:
dirname = os.path.dirname(tval["base"])
assert all([x.startswith(dirname) for x in tval["indexes"]])
return {"class": "File", "path": directory_tarball(dirname)} | python | def _to_cwlfile_with_indexes(val, get_retriever):
val["indexes"] = _index_blacklist(val["indexes"])
tval = {"base": _remove_remote_prefix(val["base"]),
"indexes": [_remove_remote_prefix(f) for f in val["indexes"]]}
# Standard named set of indices, like bwa
# Do not include snpEff, which we need to isolate inside a nested directory
# hisat2 indices do also not localize cleanly due to compilicated naming
cp_dir, cp_base = os.path.split(os.path.commonprefix([tval["base"]] + tval["indexes"]))
if (cp_base and cp_dir == os.path.dirname(tval["base"]) and
not ("/snpeff/" in cp_dir or "/hisat2" in cp_dir)):
return _item_to_cwldata(val["base"], get_retriever, val["indexes"])
else:
dirname = os.path.dirname(tval["base"])
assert all([x.startswith(dirname) for x in tval["indexes"]])
return {"class": "File", "path": directory_tarball(dirname)} | [
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237,819 | bcbio/bcbio-nextgen | bcbio/cwl/create.py | _add_secondary_if_exists | def _add_secondary_if_exists(secondary, out, get_retriever):
"""Add secondary files only if present locally or remotely.
"""
secondary = [_file_local_or_remote(y, get_retriever) for y in secondary]
secondary = [z for z in secondary if z]
if secondary:
out["secondaryFiles"] = [{"class": "File", "path": f} for f in secondary]
return out | python | def _add_secondary_if_exists(secondary, out, get_retriever):
secondary = [_file_local_or_remote(y, get_retriever) for y in secondary]
secondary = [z for z in secondary if z]
if secondary:
out["secondaryFiles"] = [{"class": "File", "path": f} for f in secondary]
return out | [
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237,820 | bcbio/bcbio-nextgen | bcbio/cwl/create.py | _item_to_cwldata | def _item_to_cwldata(x, get_retriever, indexes=None):
""""Markup an item with CWL specific metadata.
"""
if isinstance(x, (list, tuple)):
return [_item_to_cwldata(subx, get_retriever) for subx in x]
elif (x and isinstance(x, six.string_types) and
(((os.path.isfile(x) or os.path.isdir(x)) and os.path.exists(x)) or
objectstore.is_remote(x))):
if _file_local_or_remote(x, get_retriever):
out = {"class": "File", "path": x}
if indexes:
out = _add_secondary_if_exists(indexes, out, get_retriever)
elif x.endswith(".bam"):
out = _add_secondary_if_exists([x + ".bai"], out, get_retriever)
elif x.endswith(".cram"):
out = _add_secondary_if_exists([x + ".crai"], out, get_retriever)
elif x.endswith((".vcf.gz", ".bed.gz")):
out = _add_secondary_if_exists([x + ".tbi"], out, get_retriever)
elif x.endswith(".fa"):
out = _add_secondary_if_exists([x + ".fai", os.path.splitext(x)[0] + ".dict"], out, get_retriever)
elif x.endswith(".fa.gz"):
out = _add_secondary_if_exists([x + ".fai", x + ".gzi", x.replace(".fa.gz", "") + ".dict"],
out, get_retriever)
elif x.endswith(".fq.gz") or x.endswith(".fastq.gz"):
out = _add_secondary_if_exists([x + ".gbi"], out, get_retriever)
elif x.endswith(".gtf"):
out = _add_secondary_if_exists([x + ".db"], out, get_retriever)
else:
out = {"class": "File", "path": directory_tarball(x)}
return out
elif isinstance(x, bool):
return str(x)
else:
return x | python | def _item_to_cwldata(x, get_retriever, indexes=None):
"if isinstance(x, (list, tuple)):
return [_item_to_cwldata(subx, get_retriever) for subx in x]
elif (x and isinstance(x, six.string_types) and
(((os.path.isfile(x) or os.path.isdir(x)) and os.path.exists(x)) or
objectstore.is_remote(x))):
if _file_local_or_remote(x, get_retriever):
out = {"class": "File", "path": x}
if indexes:
out = _add_secondary_if_exists(indexes, out, get_retriever)
elif x.endswith(".bam"):
out = _add_secondary_if_exists([x + ".bai"], out, get_retriever)
elif x.endswith(".cram"):
out = _add_secondary_if_exists([x + ".crai"], out, get_retriever)
elif x.endswith((".vcf.gz", ".bed.gz")):
out = _add_secondary_if_exists([x + ".tbi"], out, get_retriever)
elif x.endswith(".fa"):
out = _add_secondary_if_exists([x + ".fai", os.path.splitext(x)[0] + ".dict"], out, get_retriever)
elif x.endswith(".fa.gz"):
out = _add_secondary_if_exists([x + ".fai", x + ".gzi", x.replace(".fa.gz", "") + ".dict"],
out, get_retriever)
elif x.endswith(".fq.gz") or x.endswith(".fastq.gz"):
out = _add_secondary_if_exists([x + ".gbi"], out, get_retriever)
elif x.endswith(".gtf"):
out = _add_secondary_if_exists([x + ".db"], out, get_retriever)
else:
out = {"class": "File", "path": directory_tarball(x)}
return out
elif isinstance(x, bool):
return str(x)
else:
return x | [
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237,821 | bcbio/bcbio-nextgen | bcbio/cwl/create.py | _file_local_or_remote | def _file_local_or_remote(f, get_retriever):
"""Check for presence of a local or remote file.
"""
if os.path.exists(f):
return f
integration, config = get_retriever.integration_and_config(f)
if integration:
return integration.file_exists(f, config) | python | def _file_local_or_remote(f, get_retriever):
if os.path.exists(f):
return f
integration, config = get_retriever.integration_and_config(f)
if integration:
return integration.file_exists(f, config) | [
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237,822 | bcbio/bcbio-nextgen | bcbio/cwl/create.py | directory_tarball | def directory_tarball(dirname):
"""Create a tarball of a complex directory, avoiding complex secondaryFiles.
Complex secondary files do not work on multiple platforms and are not portable
to WDL, so for now we create a tarball that workers will unpack.
"""
assert os.path.isdir(dirname), dirname
base_dir, tarball_dir = os.path.split(dirname)
while not os.path.exists(os.path.join(base_dir, "seq")) and base_dir and base_dir != "/":
base_dir, extra_tarball = os.path.split(base_dir)
tarball_dir = os.path.join(extra_tarball, tarball_dir)
if base_dir == "/" and not os.path.exists(os.path.join(base_dir, "seq")):
raise ValueError("Did not find relative directory to create tarball for %s" % dirname)
tarball = os.path.join(base_dir, "%s-wf.tar.gz" % (tarball_dir.replace(os.path.sep, "--")))
if not utils.file_exists(tarball):
print("Preparing CWL input tarball: %s" % tarball)
with file_transaction({}, tarball) as tx_tarball:
with utils.chdir(base_dir):
with tarfile.open(tx_tarball, "w:gz") as tar:
tar.add(tarball_dir)
return tarball | python | def directory_tarball(dirname):
assert os.path.isdir(dirname), dirname
base_dir, tarball_dir = os.path.split(dirname)
while not os.path.exists(os.path.join(base_dir, "seq")) and base_dir and base_dir != "/":
base_dir, extra_tarball = os.path.split(base_dir)
tarball_dir = os.path.join(extra_tarball, tarball_dir)
if base_dir == "/" and not os.path.exists(os.path.join(base_dir, "seq")):
raise ValueError("Did not find relative directory to create tarball for %s" % dirname)
tarball = os.path.join(base_dir, "%s-wf.tar.gz" % (tarball_dir.replace(os.path.sep, "--")))
if not utils.file_exists(tarball):
print("Preparing CWL input tarball: %s" % tarball)
with file_transaction({}, tarball) as tx_tarball:
with utils.chdir(base_dir):
with tarfile.open(tx_tarball, "w:gz") as tar:
tar.add(tarball_dir)
return tarball | [
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237,823 | bcbio/bcbio-nextgen | bcbio/cwl/create.py | _calc_input_estimates | def _calc_input_estimates(keyvals, get_retriever):
"""Calculate estimations of input file sizes for disk usage approximation.
These are current dominated by fastq/BAM sizes, so estimate based on that.
"""
out = {}
for key, val in keyvals.items():
size = _calc_file_size(val, 0, get_retriever)
if size:
out[key] = size
return out | python | def _calc_input_estimates(keyvals, get_retriever):
out = {}
for key, val in keyvals.items():
size = _calc_file_size(val, 0, get_retriever)
if size:
out[key] = size
return out | [
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237,824 | bcbio/bcbio-nextgen | bcbio/cwl/create.py | _get_file_size | def _get_file_size(path, get_retriever):
"""Return file size in megabytes, including querying remote integrations
"""
integration, config = get_retriever.integration_and_config(path)
if integration:
return integration.file_size(path, config)
elif os.path.exists(path):
return os.path.getsize(path) / (1024.0 * 1024.0) | python | def _get_file_size(path, get_retriever):
integration, config = get_retriever.integration_and_config(path)
if integration:
return integration.file_size(path, config)
elif os.path.exists(path):
return os.path.getsize(path) / (1024.0 * 1024.0) | [
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237,825 | bcbio/bcbio-nextgen | bcbio/cwl/create.py | GetRetriever.integration_and_config | def integration_and_config(self, path):
"""Get a retriever and configuration for the given file path.
"""
if path.startswith(tuple(INTEGRATION_MAP.keys())):
key = INTEGRATION_MAP[path.split(":")[0] + ":"]
integration = self._integrations.get(key)
config = {}
for sample in self._samples:
config = tz.get_in(["config", key], sample)
if config:
break
return integration, config
return None, None | python | def integration_and_config(self, path):
if path.startswith(tuple(INTEGRATION_MAP.keys())):
key = INTEGRATION_MAP[path.split(":")[0] + ":"]
integration = self._integrations.get(key)
config = {}
for sample in self._samples:
config = tz.get_in(["config", key], sample)
if config:
break
return integration, config
return None, None | [
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237,826 | bcbio/bcbio-nextgen | bcbio/rnaseq/singlecellexperiment.py | make_scrnaseq_object | def make_scrnaseq_object(samples):
"""
load the initial se.rda object using sinclecell-experiment
"""
local_sitelib = R_sitelib()
counts_dir = os.path.dirname(dd.get_in_samples(samples, dd.get_combined_counts))
gtf_file = dd.get_in_samples(samples, dd.get_transcriptome_gtf)
if not gtf_file:
gtf_file = dd.get_in_samples(samples, dd.get_gtf_file)
rda_file = os.path.join(counts_dir, "se.rda")
if not file_exists(rda_file):
with file_transaction(rda_file) as tx_out_file:
rcode = "%s-run.R" % os.path.splitext(rda_file)[0]
rrna_file = "%s-rrna.txt" % os.path.splitext(rda_file)[0]
rrna_file = _find_rRNA_genes(gtf_file, rrna_file)
with open(rcode, "w") as out_handle:
out_handle.write(_script.format(**locals()))
rscript = Rscript_cmd()
try:
# do.run([rscript, "--no-environ", rcode],
# "SingleCellExperiment",
# log_error=False)
rda_file = rcode
except subprocess.CalledProcessError as msg:
logger.exception() | python | def make_scrnaseq_object(samples):
local_sitelib = R_sitelib()
counts_dir = os.path.dirname(dd.get_in_samples(samples, dd.get_combined_counts))
gtf_file = dd.get_in_samples(samples, dd.get_transcriptome_gtf)
if not gtf_file:
gtf_file = dd.get_in_samples(samples, dd.get_gtf_file)
rda_file = os.path.join(counts_dir, "se.rda")
if not file_exists(rda_file):
with file_transaction(rda_file) as tx_out_file:
rcode = "%s-run.R" % os.path.splitext(rda_file)[0]
rrna_file = "%s-rrna.txt" % os.path.splitext(rda_file)[0]
rrna_file = _find_rRNA_genes(gtf_file, rrna_file)
with open(rcode, "w") as out_handle:
out_handle.write(_script.format(**locals()))
rscript = Rscript_cmd()
try:
# do.run([rscript, "--no-environ", rcode],
# "SingleCellExperiment",
# log_error=False)
rda_file = rcode
except subprocess.CalledProcessError as msg:
logger.exception() | [
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237,827 | bcbio/bcbio-nextgen | bcbio/distributed/multi.py | runner | def runner(parallel, config):
"""Run functions, provided by string name, on multiple cores on the current machine.
"""
def run_parallel(fn_name, items):
items = [x for x in items if x is not None]
if len(items) == 0:
return []
items = diagnostics.track_parallel(items, fn_name)
fn, fn_name = (fn_name, fn_name.__name__) if callable(fn_name) else (get_fn(fn_name, parallel), fn_name)
logger.info("multiprocessing: %s" % fn_name)
if "wrapper" in parallel:
wrap_parallel = {k: v for k, v in parallel.items() if k in set(["fresources", "checkpointed"])}
items = [[fn_name] + parallel.get("wrapper_args", []) + [wrap_parallel] + list(x) for x in items]
return run_multicore(fn, items, config, parallel=parallel)
return run_parallel | python | def runner(parallel, config):
def run_parallel(fn_name, items):
items = [x for x in items if x is not None]
if len(items) == 0:
return []
items = diagnostics.track_parallel(items, fn_name)
fn, fn_name = (fn_name, fn_name.__name__) if callable(fn_name) else (get_fn(fn_name, parallel), fn_name)
logger.info("multiprocessing: %s" % fn_name)
if "wrapper" in parallel:
wrap_parallel = {k: v for k, v in parallel.items() if k in set(["fresources", "checkpointed"])}
items = [[fn_name] + parallel.get("wrapper_args", []) + [wrap_parallel] + list(x) for x in items]
return run_multicore(fn, items, config, parallel=parallel)
return run_parallel | [
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] | 6a9348c0054ccd5baffd22f1bb7d0422f6978b20 | https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/distributed/multi.py#L15-L29 |
237,828 | bcbio/bcbio-nextgen | bcbio/distributed/multi.py | zeromq_aware_logging | def zeromq_aware_logging(f):
"""Ensure multiprocessing logging uses ZeroMQ queues.
ZeroMQ and local stdout/stderr do not behave nicely when intertwined. This
ensures the local logging uses existing ZeroMQ logging queues.
"""
@functools.wraps(f)
def wrapper(*args, **kwargs):
config = None
for arg in args:
if config_utils.is_std_config_arg(arg):
config = arg
break
elif config_utils.is_nested_config_arg(arg):
config = arg["config"]
elif isinstance(arg, (list, tuple)) and config_utils.is_nested_config_arg(arg[0]):
config = arg[0]["config"]
break
assert config, "Could not find config dictionary in function arguments."
if config.get("parallel", {}).get("log_queue") and not config.get("parallel", {}).get("wrapper"):
handler = setup_local_logging(config, config["parallel"])
else:
handler = None
try:
out = f(*args, **kwargs)
finally:
if handler and hasattr(handler, "close"):
handler.close()
return out
return wrapper | python | def zeromq_aware_logging(f):
@functools.wraps(f)
def wrapper(*args, **kwargs):
config = None
for arg in args:
if config_utils.is_std_config_arg(arg):
config = arg
break
elif config_utils.is_nested_config_arg(arg):
config = arg["config"]
elif isinstance(arg, (list, tuple)) and config_utils.is_nested_config_arg(arg[0]):
config = arg[0]["config"]
break
assert config, "Could not find config dictionary in function arguments."
if config.get("parallel", {}).get("log_queue") and not config.get("parallel", {}).get("wrapper"):
handler = setup_local_logging(config, config["parallel"])
else:
handler = None
try:
out = f(*args, **kwargs)
finally:
if handler and hasattr(handler, "close"):
handler.close()
return out
return wrapper | [
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237,829 | bcbio/bcbio-nextgen | bcbio/distributed/multi.py | run_multicore | def run_multicore(fn, items, config, parallel=None):
"""Run the function using multiple cores on the given items to process.
"""
if len(items) == 0:
return []
if parallel is None or "num_jobs" not in parallel:
if parallel is None:
parallel = {"type": "local", "cores": config["algorithm"].get("num_cores", 1)}
sysinfo = system.get_info({}, parallel)
parallel = resources.calculate(parallel, items, sysinfo, config,
parallel.get("multiplier", 1),
max_multicore=int(parallel.get("max_multicore", sysinfo["cores"])))
items = [config_utils.add_cores_to_config(x, parallel["cores_per_job"]) for x in items]
if joblib is None:
raise ImportError("Need joblib for multiprocessing parallelization")
out = []
for data in joblib.Parallel(parallel["num_jobs"], batch_size=1, backend="multiprocessing")(joblib.delayed(fn)(*x) for x in items):
if data:
out.extend(data)
return out | python | def run_multicore(fn, items, config, parallel=None):
if len(items) == 0:
return []
if parallel is None or "num_jobs" not in parallel:
if parallel is None:
parallel = {"type": "local", "cores": config["algorithm"].get("num_cores", 1)}
sysinfo = system.get_info({}, parallel)
parallel = resources.calculate(parallel, items, sysinfo, config,
parallel.get("multiplier", 1),
max_multicore=int(parallel.get("max_multicore", sysinfo["cores"])))
items = [config_utils.add_cores_to_config(x, parallel["cores_per_job"]) for x in items]
if joblib is None:
raise ImportError("Need joblib for multiprocessing parallelization")
out = []
for data in joblib.Parallel(parallel["num_jobs"], batch_size=1, backend="multiprocessing")(joblib.delayed(fn)(*x) for x in items):
if data:
out.extend(data)
return out | [
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237,830 | bcbio/bcbio-nextgen | scripts/bcbio_fastq_umi_prep.py | _add_umis_with_fastp | def _add_umis_with_fastp(read_fq, umi_fq, out_fq, cores):
"""Add UMIs to reads from separate UMI file using fastp.
"""
with utils.open_gzipsafe(umi_fq) as in_handle:
in_handle.readline() # name
umi_size = len(in_handle.readline().strip())
cmd = ("fastp -Q -A -L -G -w 1 --in1 {read_fq} --in2 {umi_fq} "
"--umi --umi_prefix UMI --umi_loc read2 --umi_len {umi_size} "
"--out1 >(bgzip --threads {cores} -c > {out_fq}) --out2 /dev/null "
"-j /dev/null -h /dev/null")
do.run(cmd.format(**locals()), "Add UMIs to fastq file with fastp") | python | def _add_umis_with_fastp(read_fq, umi_fq, out_fq, cores):
with utils.open_gzipsafe(umi_fq) as in_handle:
in_handle.readline() # name
umi_size = len(in_handle.readline().strip())
cmd = ("fastp -Q -A -L -G -w 1 --in1 {read_fq} --in2 {umi_fq} "
"--umi --umi_prefix UMI --umi_loc read2 --umi_len {umi_size} "
"--out1 >(bgzip --threads {cores} -c > {out_fq}) --out2 /dev/null "
"-j /dev/null -h /dev/null")
do.run(cmd.format(**locals()), "Add UMIs to fastq file with fastp") | [
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237,831 | bcbio/bcbio-nextgen | scripts/bcbio_fastq_umi_prep.py | _find_umi | def _find_umi(files):
"""Find UMI file using different naming schemes.
R1/R2/R3 => R1/R3 with R2 UMI
R1/R2/I1 => R1/R2 with I1 UMI
"""
base = os.path.basename(_commonprefix(files))
def _file_ext(f):
exts = utils.splitext_plus(os.path.basename(f).replace(base, ""))[0].split("_")
exts = [x for x in exts if x]
return exts[0]
exts = dict([(_file_ext(f), f) for f in files])
if "I1" in exts:
return exts["R1"], exts["R2"], exts["I1"]
else:
assert "R3" in exts, exts
return exts["R1"], exts["R3"], exts["R2"] | python | def _find_umi(files):
base = os.path.basename(_commonprefix(files))
def _file_ext(f):
exts = utils.splitext_plus(os.path.basename(f).replace(base, ""))[0].split("_")
exts = [x for x in exts if x]
return exts[0]
exts = dict([(_file_ext(f), f) for f in files])
if "I1" in exts:
return exts["R1"], exts["R2"], exts["I1"]
else:
assert "R3" in exts, exts
return exts["R1"], exts["R3"], exts["R2"] | [
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237,832 | bcbio/bcbio-nextgen | scripts/bcbio_fastq_umi_prep.py | _commonprefix | def _commonprefix(files):
"""Retrieve a common prefix for files without extra _R1 _I1 extensions.
Allows alternative naming schemes (R1/R2/R3) (R1/R2/I1).
"""
out = os.path.commonprefix(files)
out = out.rstrip("_R")
out = out.rstrip("_I")
out = out.rstrip("_")
return out | python | def _commonprefix(files):
out = os.path.commonprefix(files)
out = out.rstrip("_R")
out = out.rstrip("_I")
out = out.rstrip("_")
return out | [
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237,833 | bcbio/bcbio-nextgen | bcbio/variation/vfilter.py | cutoff_w_expression | def cutoff_w_expression(vcf_file, expression, data, name="+", filterext="",
extra_cmd="", limit_regions="variant_regions"):
"""Perform cutoff-based soft filtering using bcftools expressions like %QUAL < 20 || DP < 4.
"""
base, ext = utils.splitext_plus(vcf_file)
out_file = "{base}-filter{filterext}{ext}".format(**locals())
if not utils.file_exists(out_file):
with file_transaction(data, out_file) as tx_out_file:
if vcfutils.vcf_has_variants(vcf_file):
bcftools = config_utils.get_program("bcftools", data["config"])
bgzip_cmd = "| bgzip -c" if out_file.endswith(".gz") else ""
intervals = ""
if limit_regions == "variant_regions":
variant_regions = dd.get_variant_regions(data)
if variant_regions:
intervals = "-T %s" % vcfutils.bgzip_and_index(variant_regions, data["config"])
cmd = ("{bcftools} filter -O v {intervals} --soft-filter '{name}' "
"-e '{expression}' -m '+' {vcf_file} {extra_cmd} {bgzip_cmd} > {tx_out_file}")
do.run(cmd.format(**locals()),
"Cutoff-based soft filtering %s with %s" % (vcf_file, expression), data)
else:
shutil.copy(vcf_file, out_file)
if out_file.endswith(".vcf.gz"):
out_file = vcfutils.bgzip_and_index(out_file, data["config"])
return out_file | python | def cutoff_w_expression(vcf_file, expression, data, name="+", filterext="",
extra_cmd="", limit_regions="variant_regions"):
base, ext = utils.splitext_plus(vcf_file)
out_file = "{base}-filter{filterext}{ext}".format(**locals())
if not utils.file_exists(out_file):
with file_transaction(data, out_file) as tx_out_file:
if vcfutils.vcf_has_variants(vcf_file):
bcftools = config_utils.get_program("bcftools", data["config"])
bgzip_cmd = "| bgzip -c" if out_file.endswith(".gz") else ""
intervals = ""
if limit_regions == "variant_regions":
variant_regions = dd.get_variant_regions(data)
if variant_regions:
intervals = "-T %s" % vcfutils.bgzip_and_index(variant_regions, data["config"])
cmd = ("{bcftools} filter -O v {intervals} --soft-filter '{name}' "
"-e '{expression}' -m '+' {vcf_file} {extra_cmd} {bgzip_cmd} > {tx_out_file}")
do.run(cmd.format(**locals()),
"Cutoff-based soft filtering %s with %s" % (vcf_file, expression), data)
else:
shutil.copy(vcf_file, out_file)
if out_file.endswith(".vcf.gz"):
out_file = vcfutils.bgzip_and_index(out_file, data["config"])
return out_file | [
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237,834 | bcbio/bcbio-nextgen | bcbio/variation/vfilter.py | _freebayes_custom | def _freebayes_custom(in_file, ref_file, data):
"""Custom FreeBayes filtering using bcbio.variation, tuned to human NA12878 results.
Experimental: for testing new methods.
"""
if vcfutils.get_paired_phenotype(data):
return None
config = data["config"]
bv_ver = programs.get_version("bcbio_variation", config=config)
if LooseVersion(bv_ver) < LooseVersion("0.1.1"):
return None
out_file = "%s-filter%s" % os.path.splitext(in_file)
if not utils.file_exists(out_file):
tmp_dir = utils.safe_makedir(os.path.join(os.path.dirname(in_file), "tmp"))
resources = config_utils.get_resources("bcbio_variation", config)
jvm_opts = resources.get("jvm_opts", ["-Xms750m", "-Xmx2g"])
java_args = ["-Djava.io.tmpdir=%s" % tmp_dir]
cmd = ["bcbio-variation"] + jvm_opts + java_args + \
["variant-filter", "freebayes", in_file, ref_file]
do.run(cmd, "Custom FreeBayes filtering using bcbio.variation")
return out_file | python | def _freebayes_custom(in_file, ref_file, data):
if vcfutils.get_paired_phenotype(data):
return None
config = data["config"]
bv_ver = programs.get_version("bcbio_variation", config=config)
if LooseVersion(bv_ver) < LooseVersion("0.1.1"):
return None
out_file = "%s-filter%s" % os.path.splitext(in_file)
if not utils.file_exists(out_file):
tmp_dir = utils.safe_makedir(os.path.join(os.path.dirname(in_file), "tmp"))
resources = config_utils.get_resources("bcbio_variation", config)
jvm_opts = resources.get("jvm_opts", ["-Xms750m", "-Xmx2g"])
java_args = ["-Djava.io.tmpdir=%s" % tmp_dir]
cmd = ["bcbio-variation"] + jvm_opts + java_args + \
["variant-filter", "freebayes", in_file, ref_file]
do.run(cmd, "Custom FreeBayes filtering using bcbio.variation")
return out_file | [
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237,835 | bcbio/bcbio-nextgen | bcbio/variation/vfilter.py | _freebayes_cutoff | def _freebayes_cutoff(in_file, data):
"""Perform filtering of FreeBayes results, flagging low confidence calls.
Filters using cutoffs on low depth based on Meynert et al's work modeling sensitivity
of homozygote and heterozygote calling on depth:
http://www.ncbi.nlm.nih.gov/pubmed/23773188
and high depth heterozygote SNP filtering based on Heng Li's work
evaluating variant calling artifacts:
http://arxiv.org/abs/1404.0929
Tuned based on NA12878 call comparisons to Genome in a Bottle reference genome.
"""
if not vcfutils.vcf_has_variants(in_file):
base, ext = utils.splitext_plus(in_file)
out_file = "{base}-filter{ext}".format(**locals())
if not utils.file_exists(out_file):
shutil.copy(in_file, out_file)
if out_file.endswith(".vcf.gz"):
out_file = vcfutils.bgzip_and_index(out_file, data["config"])
return out_file
depth_thresh, qual_thresh = None, None
if _do_high_depth_filter(data):
stats = _calc_vcf_stats(in_file)
if stats["avg_depth"] > 0:
depth_thresh = int(math.ceil(stats["avg_depth"] + 3 * math.pow(stats["avg_depth"], 0.5)))
qual_thresh = depth_thresh * 2.0 # Multiplier from default GATK QD cutoff filter
filters = ('(AF[0] <= 0.5 && (max(FORMAT/DP) < 4 || (max(FORMAT/DP) < 13 && %QUAL < 10))) || '
'(AF[0] > 0.5 && (max(FORMAT/DP) < 4 && %QUAL < 50))')
if depth_thresh:
filters += ' || (%QUAL < {qual_thresh} && max(FORMAT/DP) > {depth_thresh} && AF[0] <= 0.5)'.format(**locals())
return cutoff_w_expression(in_file, filters, data, name="FBQualDepth") | python | def _freebayes_cutoff(in_file, data):
if not vcfutils.vcf_has_variants(in_file):
base, ext = utils.splitext_plus(in_file)
out_file = "{base}-filter{ext}".format(**locals())
if not utils.file_exists(out_file):
shutil.copy(in_file, out_file)
if out_file.endswith(".vcf.gz"):
out_file = vcfutils.bgzip_and_index(out_file, data["config"])
return out_file
depth_thresh, qual_thresh = None, None
if _do_high_depth_filter(data):
stats = _calc_vcf_stats(in_file)
if stats["avg_depth"] > 0:
depth_thresh = int(math.ceil(stats["avg_depth"] + 3 * math.pow(stats["avg_depth"], 0.5)))
qual_thresh = depth_thresh * 2.0 # Multiplier from default GATK QD cutoff filter
filters = ('(AF[0] <= 0.5 && (max(FORMAT/DP) < 4 || (max(FORMAT/DP) < 13 && %QUAL < 10))) || '
'(AF[0] > 0.5 && (max(FORMAT/DP) < 4 && %QUAL < 50))')
if depth_thresh:
filters += ' || (%QUAL < {qual_thresh} && max(FORMAT/DP) > {depth_thresh} && AF[0] <= 0.5)'.format(**locals())
return cutoff_w_expression(in_file, filters, data, name="FBQualDepth") | [
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237,836 | bcbio/bcbio-nextgen | bcbio/variation/vfilter.py | _calc_vcf_stats | def _calc_vcf_stats(in_file):
"""Calculate statistics on VCF for filtering, saving to a file for quick re-runs.
"""
out_file = "%s-stats.yaml" % utils.splitext_plus(in_file)[0]
if not utils.file_exists(out_file):
stats = {"avg_depth": _average_called_depth(in_file)}
with open(out_file, "w") as out_handle:
yaml.safe_dump(stats, out_handle, default_flow_style=False, allow_unicode=False)
return stats
else:
with open(out_file) as in_handle:
stats = yaml.safe_load(in_handle)
return stats | python | def _calc_vcf_stats(in_file):
out_file = "%s-stats.yaml" % utils.splitext_plus(in_file)[0]
if not utils.file_exists(out_file):
stats = {"avg_depth": _average_called_depth(in_file)}
with open(out_file, "w") as out_handle:
yaml.safe_dump(stats, out_handle, default_flow_style=False, allow_unicode=False)
return stats
else:
with open(out_file) as in_handle:
stats = yaml.safe_load(in_handle)
return stats | [
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237,837 | bcbio/bcbio-nextgen | bcbio/variation/vfilter.py | _average_called_depth | def _average_called_depth(in_file):
"""Retrieve the average depth of called reads in the provided VCF.
"""
import cyvcf2
depths = []
for rec in cyvcf2.VCF(str(in_file)):
d = rec.INFO.get("DP")
if d is not None:
depths.append(int(d))
if len(depths) > 0:
return int(math.ceil(numpy.mean(depths)))
else:
return 0 | python | def _average_called_depth(in_file):
import cyvcf2
depths = []
for rec in cyvcf2.VCF(str(in_file)):
d = rec.INFO.get("DP")
if d is not None:
depths.append(int(d))
if len(depths) > 0:
return int(math.ceil(numpy.mean(depths)))
else:
return 0 | [
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237,838 | bcbio/bcbio-nextgen | bcbio/variation/vfilter.py | platypus | def platypus(in_file, data):
"""Filter Platypus calls, removing Q20 filter and replacing with depth and quality based filter.
Platypus uses its own VCF nomenclature: TC == DP, FR == AF
Platypus gVCF output appears to have an 0/1 index problem so the reference block
regions are 1 base outside regions of interest. We avoid limiting regions during
filtering when using it.
"""
filters = ('(FR[0] <= 0.5 && TC < 4 && %QUAL < 20) || '
'(TC < 13 && %QUAL < 10) || '
'(FR[0] > 0.5 && TC < 4 && %QUAL < 50)')
limit_regions = "variant_regions" if not vcfutils.is_gvcf_file(in_file) else None
return cutoff_w_expression(in_file, filters, data, name="PlatQualDepth",
extra_cmd="| sed 's/\\tQ20\\t/\\tPASS\\t/'", limit_regions=limit_regions) | python | def platypus(in_file, data):
filters = ('(FR[0] <= 0.5 && TC < 4 && %QUAL < 20) || '
'(TC < 13 && %QUAL < 10) || '
'(FR[0] > 0.5 && TC < 4 && %QUAL < 50)')
limit_regions = "variant_regions" if not vcfutils.is_gvcf_file(in_file) else None
return cutoff_w_expression(in_file, filters, data, name="PlatQualDepth",
extra_cmd="| sed 's/\\tQ20\\t/\\tPASS\\t/'", limit_regions=limit_regions) | [
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237,839 | bcbio/bcbio-nextgen | bcbio/variation/vfilter.py | gatk_snp_cutoff | def gatk_snp_cutoff(in_file, data):
"""Perform cutoff-based soft filtering on GATK SNPs using best-practice recommendations.
We have a more lenient mapping quality (MQ) filter compared to GATK defaults.
The recommended filter (MQ < 40) is too stringent, so we adjust to 30:
http://imgur.com/a/oHRVB
QD and FS are not calculated when generating gVCF output:
https://github.com/broadgsa/gatk-protected/blob/e91472ddc7d58ace52db0cab4d70a072a918d64c/protected/gatk-tools-protected/src/main/java/org/broadinstitute/gatk/tools/walkers/haplotypecaller/HaplotypeCaller.java#L300
The extra command removes escaped quotes in the VCF output which
pyVCF fails on.
Does not use the GATK best practice recommend SOR filter (SOR > 3.0) as it
has a negative impact on sensitivity relative to precision:
https://github.com/bcbio/bcbio_validations/tree/master/gatk4#na12878-hg38
"""
filters = ["MQRankSum < -12.5", "ReadPosRankSum < -8.0"]
# GATK Haplotype caller (v2.2) appears to have much larger HaplotypeScores
# resulting in excessive filtering, so avoid this metric
variantcaller = utils.get_in(data, ("config", "algorithm", "variantcaller"))
if variantcaller not in ["gatk-haplotype", "haplotyper"]:
filters.append("HaplotypeScore > 13.0")
# Additional filter metrics, unless using raw GATK HaplotypeCaller or Sentieon gVCFs
if not (vcfutils.is_gvcf_file(in_file) and variantcaller in ["gatk-haplotype", "haplotyper"]):
filters += ["QD < 2.0"]
filters += ["FS > 60.0"]
filters += _gatk_general()
filters += ["MQ < 30.0"]
return cutoff_w_expression(in_file, 'TYPE="snp" && (%s)' % " || ".join(filters), data, "GATKCutoffSNP", "SNP",
extra_cmd=r"""| sed 's/\\"//g'""") | python | def gatk_snp_cutoff(in_file, data):
filters = ["MQRankSum < -12.5", "ReadPosRankSum < -8.0"]
# GATK Haplotype caller (v2.2) appears to have much larger HaplotypeScores
# resulting in excessive filtering, so avoid this metric
variantcaller = utils.get_in(data, ("config", "algorithm", "variantcaller"))
if variantcaller not in ["gatk-haplotype", "haplotyper"]:
filters.append("HaplotypeScore > 13.0")
# Additional filter metrics, unless using raw GATK HaplotypeCaller or Sentieon gVCFs
if not (vcfutils.is_gvcf_file(in_file) and variantcaller in ["gatk-haplotype", "haplotyper"]):
filters += ["QD < 2.0"]
filters += ["FS > 60.0"]
filters += _gatk_general()
filters += ["MQ < 30.0"]
return cutoff_w_expression(in_file, 'TYPE="snp" && (%s)' % " || ".join(filters), data, "GATKCutoffSNP", "SNP",
extra_cmd=r"""| sed 's/\\"//g'""") | [
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http://imgur.com/a/oHRVB
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237,840 | bcbio/bcbio-nextgen | bcbio/bam/counts.py | random_regions | def random_regions(base, n, size):
"""Generate n random regions of 'size' in the provided base spread.
"""
spread = size // 2
base_info = collections.defaultdict(list)
for space, start, end in base:
base_info[space].append(start + spread)
base_info[space].append(end - spread)
regions = []
for _ in range(n):
space = random.choice(base_info.keys())
pos = random.randint(min(base_info[space]), max(base_info[space]))
regions.append([space, pos-spread, pos+spread])
return regions | python | def random_regions(base, n, size):
spread = size // 2
base_info = collections.defaultdict(list)
for space, start, end in base:
base_info[space].append(start + spread)
base_info[space].append(end - spread)
regions = []
for _ in range(n):
space = random.choice(base_info.keys())
pos = random.randint(min(base_info[space]), max(base_info[space]))
regions.append([space, pos-spread, pos+spread])
return regions | [
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237,841 | bcbio/bcbio-nextgen | bcbio/bam/counts.py | NormalizedBam.all_regions | def all_regions(self):
"""Get a tuple of all chromosome, start and end regions.
"""
regions = []
for sq in self._bam.header["SQ"]:
regions.append((sq["SN"], 1, int(sq["LN"])))
return regions | python | def all_regions(self):
regions = []
for sq in self._bam.header["SQ"]:
regions.append((sq["SN"], 1, int(sq["LN"])))
return regions | [
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237,842 | bcbio/bcbio-nextgen | bcbio/bam/counts.py | NormalizedBam.read_count | def read_count(self, space, start, end):
"""Retrieve the normalized read count in the provided region.
"""
read_counts = 0
for read in self._bam.fetch(space, start, end):
read_counts += 1
return self._normalize(read_counts, self._total) | python | def read_count(self, space, start, end):
read_counts = 0
for read in self._bam.fetch(space, start, end):
read_counts += 1
return self._normalize(read_counts, self._total) | [
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237,843 | bcbio/bcbio-nextgen | bcbio/bam/counts.py | NormalizedBam.coverage_pileup | def coverage_pileup(self, space, start, end):
"""Retrieve pileup coverage across a specified region.
"""
return ((col.pos, self._normalize(col.n, self._total))
for col in self._bam.pileup(space, start, end)) | python | def coverage_pileup(self, space, start, end):
return ((col.pos, self._normalize(col.n, self._total))
for col in self._bam.pileup(space, start, end)) | [
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237,844 | bcbio/bcbio-nextgen | bcbio/heterogeneity/phylowgs.py | _prepare_summary | def _prepare_summary(evolve_file, ssm_file, cnv_file, work_dir, somatic_info):
"""Prepare a summary with gene-labelled heterogeneity from PhyloWGS predictions.
"""
out_file = os.path.join(work_dir, "%s-phylowgs.txt" % somatic_info.tumor_name)
if not utils.file_uptodate(out_file, evolve_file):
with file_transaction(somatic_info.tumor_data, out_file) as tx_out_file:
with open(tx_out_file, "w") as out_handle:
ssm_locs = _read_ssm_locs(ssm_file)
cnv_ssms = _read_cnv_ssms(cnv_file)
for i, (ids, tree) in enumerate(_evolve_reader(evolve_file)):
out_handle.write("* Tree %s\n" % (i + 1))
out_handle.write("\n" + "\n".join(tree) + "\n\n")
for nid, freq, gids in ids:
genes = _gids_to_genes(gids, ssm_locs, cnv_ssms, somatic_info.tumor_data)
out_handle.write("%s\t%s\t%s\n" % (nid, freq, ",".join(genes)))
out_handle.write("\n")
return out_file | python | def _prepare_summary(evolve_file, ssm_file, cnv_file, work_dir, somatic_info):
out_file = os.path.join(work_dir, "%s-phylowgs.txt" % somatic_info.tumor_name)
if not utils.file_uptodate(out_file, evolve_file):
with file_transaction(somatic_info.tumor_data, out_file) as tx_out_file:
with open(tx_out_file, "w") as out_handle:
ssm_locs = _read_ssm_locs(ssm_file)
cnv_ssms = _read_cnv_ssms(cnv_file)
for i, (ids, tree) in enumerate(_evolve_reader(evolve_file)):
out_handle.write("* Tree %s\n" % (i + 1))
out_handle.write("\n" + "\n".join(tree) + "\n\n")
for nid, freq, gids in ids:
genes = _gids_to_genes(gids, ssm_locs, cnv_ssms, somatic_info.tumor_data)
out_handle.write("%s\t%s\t%s\n" % (nid, freq, ",".join(genes)))
out_handle.write("\n")
return out_file | [
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237,845 | bcbio/bcbio-nextgen | bcbio/heterogeneity/phylowgs.py | _gids_to_genes | def _gids_to_genes(gids, ssm_locs, cnv_ssms, data):
"""Convert support ids for SNPs and SSMs into associated genes.
"""
locs = collections.defaultdict(set)
for gid in gids:
cur_locs = []
try:
cur_locs.append(ssm_locs[gid])
except KeyError:
for ssm_loc in cnv_ssms.get(gid, []):
cur_locs.append(ssm_locs[ssm_loc])
for chrom, pos in cur_locs:
locs[chrom].add(pos)
genes = set([])
with tx_tmpdir(data) as tmpdir:
chrom_prefix = "chr" if next(ref.file_contigs(dd.get_ref_file(data))).name.startswith("chr") else ""
loc_file = os.path.join(tmpdir, "battenberg_find_genes.bed")
with open(loc_file, "w") as out_handle:
for chrom in sorted(locs.keys()):
for loc in sorted(list(locs[chrom])):
out_handle.write("%s%s\t%s\t%s\n" % (chrom_prefix, chrom, loc - 1, loc))
ann_file = annotate.add_genes(loc_file, data, max_distance=10000)
for r in pybedtools.BedTool(ann_file):
for gene in r.name.split(","):
if gene != ".":
genes.add(gene)
return sorted(list(genes)) | python | def _gids_to_genes(gids, ssm_locs, cnv_ssms, data):
locs = collections.defaultdict(set)
for gid in gids:
cur_locs = []
try:
cur_locs.append(ssm_locs[gid])
except KeyError:
for ssm_loc in cnv_ssms.get(gid, []):
cur_locs.append(ssm_locs[ssm_loc])
for chrom, pos in cur_locs:
locs[chrom].add(pos)
genes = set([])
with tx_tmpdir(data) as tmpdir:
chrom_prefix = "chr" if next(ref.file_contigs(dd.get_ref_file(data))).name.startswith("chr") else ""
loc_file = os.path.join(tmpdir, "battenberg_find_genes.bed")
with open(loc_file, "w") as out_handle:
for chrom in sorted(locs.keys()):
for loc in sorted(list(locs[chrom])):
out_handle.write("%s%s\t%s\t%s\n" % (chrom_prefix, chrom, loc - 1, loc))
ann_file = annotate.add_genes(loc_file, data, max_distance=10000)
for r in pybedtools.BedTool(ann_file):
for gene in r.name.split(","):
if gene != ".":
genes.add(gene)
return sorted(list(genes)) | [
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237,846 | bcbio/bcbio-nextgen | bcbio/heterogeneity/phylowgs.py | _evolve_reader | def _evolve_reader(in_file):
"""Generate a list of region IDs and trees from a top_k_trees evolve.py file.
"""
cur_id_list = None
cur_tree = None
with open(in_file) as in_handle:
for line in in_handle:
if line.startswith("id,"):
if cur_id_list:
yield cur_id_list, cur_tree
cur_id_list = []
cur_tree = None
elif cur_tree is not None:
if line.strip() and not line.startswith("Number of non-empty"):
cur_tree.append(line.rstrip())
elif not line.strip() and cur_id_list and len(cur_id_list) > 0:
cur_tree = []
elif line.strip():
parts = []
for part in line.strip().split("\t"):
if part.endswith(","):
part = part[:-1]
parts.append(part)
if len(parts) > 4:
nid, freq, _, _, support = parts
cur_id_list.append((nid, freq, support.split("; ")))
if cur_id_list:
yield cur_id_list, cur_tree | python | def _evolve_reader(in_file):
cur_id_list = None
cur_tree = None
with open(in_file) as in_handle:
for line in in_handle:
if line.startswith("id,"):
if cur_id_list:
yield cur_id_list, cur_tree
cur_id_list = []
cur_tree = None
elif cur_tree is not None:
if line.strip() and not line.startswith("Number of non-empty"):
cur_tree.append(line.rstrip())
elif not line.strip() and cur_id_list and len(cur_id_list) > 0:
cur_tree = []
elif line.strip():
parts = []
for part in line.strip().split("\t"):
if part.endswith(","):
part = part[:-1]
parts.append(part)
if len(parts) > 4:
nid, freq, _, _, support = parts
cur_id_list.append((nid, freq, support.split("; ")))
if cur_id_list:
yield cur_id_list, cur_tree | [
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237,847 | bcbio/bcbio-nextgen | bcbio/heterogeneity/phylowgs.py | _read_cnv_ssms | def _read_cnv_ssms(in_file):
"""Map CNVs to associated SSMs
"""
out = {}
with open(in_file) as in_handle:
in_handle.readline() # header
for line in in_handle:
parts = line.strip().split()
if len(parts) > 3:
cnvid, _, _, ssms = parts
out[cnvid] = [x.split(",")[0] for x in ssms.split(";")]
return out | python | def _read_cnv_ssms(in_file):
out = {}
with open(in_file) as in_handle:
in_handle.readline() # header
for line in in_handle:
parts = line.strip().split()
if len(parts) > 3:
cnvid, _, _, ssms = parts
out[cnvid] = [x.split(",")[0] for x in ssms.split(";")]
return out | [
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237,848 | bcbio/bcbio-nextgen | bcbio/heterogeneity/phylowgs.py | _read_ssm_locs | def _read_ssm_locs(in_file):
"""Map SSMs to chromosomal locations.
"""
out = {}
with open(in_file) as in_handle:
in_handle.readline() # header
for line in in_handle:
sid, loc = line.split()[:2]
chrom, pos = loc.split("_")
out[sid] = (chrom, int(pos))
return out | python | def _read_ssm_locs(in_file):
out = {}
with open(in_file) as in_handle:
in_handle.readline() # header
for line in in_handle:
sid, loc = line.split()[:2]
chrom, pos = loc.split("_")
out[sid] = (chrom, int(pos))
return out | [
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237,849 | bcbio/bcbio-nextgen | bcbio/heterogeneity/phylowgs.py | _run_evolve | def _run_evolve(ssm_file, cnv_file, work_dir, data):
"""Run evolve.py to infer subclonal composition.
"""
exe = os.path.join(os.path.dirname(sys.executable), "evolve.py")
assert os.path.exists(exe), "Could not find evolve script for PhyloWGS runs."
out_dir = os.path.join(work_dir, "evolve")
out_file = os.path.join(out_dir, "top_k_trees")
if not utils.file_uptodate(out_file, cnv_file):
with file_transaction(data, out_dir) as tx_out_dir:
with utils.chdir(tx_out_dir):
cmd = [sys.executable, exe, "-r", "42", ssm_file, cnv_file]
do.run(cmd, "Run PhyloWGS evolution")
return out_file | python | def _run_evolve(ssm_file, cnv_file, work_dir, data):
exe = os.path.join(os.path.dirname(sys.executable), "evolve.py")
assert os.path.exists(exe), "Could not find evolve script for PhyloWGS runs."
out_dir = os.path.join(work_dir, "evolve")
out_file = os.path.join(out_dir, "top_k_trees")
if not utils.file_uptodate(out_file, cnv_file):
with file_transaction(data, out_dir) as tx_out_dir:
with utils.chdir(tx_out_dir):
cmd = [sys.executable, exe, "-r", "42", ssm_file, cnv_file]
do.run(cmd, "Run PhyloWGS evolution")
return out_file | [
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237,850 | bcbio/bcbio-nextgen | bcbio/heterogeneity/phylowgs.py | _prep_inputs | def _prep_inputs(vrn_info, cnv_info, somatic_info, work_dir, config):
"""Prepare inputs for running PhyloWGS from variant and CNV calls.
"""
exe = os.path.join(os.path.dirname(sys.executable), "create_phylowgs_inputs.py")
assert os.path.exists(exe), "Could not find input prep script for PhyloWGS runs."
ssm_file = os.path.join(work_dir, "ssm_data.txt")
cnv_file = os.path.join(work_dir, "cnv_data.txt")
if not utils.file_exists(ssm_file) or not utils.file_exists(cnv_file):
with file_transaction(somatic_info.tumor_data, ssm_file, cnv_file) as (tx_ssm_file, tx_cnv_file):
variant_type, input_vcf_file = _prep_vrn_file(vrn_info["vrn_file"], vrn_info["variantcaller"],
work_dir, somatic_info, cnv_info["ignore"], config)
input_cnv_file = _prep_cnv_file(cnv_info["subclones"], work_dir, somatic_info)
cmd = [sys.executable, exe,
"--sample-size", str(config["sample_size"]), "--tumor-sample", somatic_info.tumor_name,
"--battenberg", input_cnv_file, "--cellularity", _read_contam(cnv_info["contamination"]),
"--output-cnvs", tx_cnv_file, "--output-variants", tx_ssm_file,
"--variant-type", variant_type, input_vcf_file]
do.run(cmd, "Prepare PhyloWGS inputs.")
return ssm_file, cnv_file | python | def _prep_inputs(vrn_info, cnv_info, somatic_info, work_dir, config):
exe = os.path.join(os.path.dirname(sys.executable), "create_phylowgs_inputs.py")
assert os.path.exists(exe), "Could not find input prep script for PhyloWGS runs."
ssm_file = os.path.join(work_dir, "ssm_data.txt")
cnv_file = os.path.join(work_dir, "cnv_data.txt")
if not utils.file_exists(ssm_file) or not utils.file_exists(cnv_file):
with file_transaction(somatic_info.tumor_data, ssm_file, cnv_file) as (tx_ssm_file, tx_cnv_file):
variant_type, input_vcf_file = _prep_vrn_file(vrn_info["vrn_file"], vrn_info["variantcaller"],
work_dir, somatic_info, cnv_info["ignore"], config)
input_cnv_file = _prep_cnv_file(cnv_info["subclones"], work_dir, somatic_info)
cmd = [sys.executable, exe,
"--sample-size", str(config["sample_size"]), "--tumor-sample", somatic_info.tumor_name,
"--battenberg", input_cnv_file, "--cellularity", _read_contam(cnv_info["contamination"]),
"--output-cnvs", tx_cnv_file, "--output-variants", tx_ssm_file,
"--variant-type", variant_type, input_vcf_file]
do.run(cmd, "Prepare PhyloWGS inputs.")
return ssm_file, cnv_file | [
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237,851 | bcbio/bcbio-nextgen | bcbio/heterogeneity/phylowgs.py | _prep_cnv_file | def _prep_cnv_file(in_file, work_dir, somatic_info):
"""Prepare Battenberg CNV file for ingest by PhyloWGS.
The PhyloWGS preparation script does not handle 'chr' prefixed chromosomes (hg19 style)
correctly. This converts them over to GRCh37 (no 'chr') style to match preparation
work in _prep_vrn_file.
"""
out_file = os.path.join(work_dir, "%s-prep%s" % utils.splitext_plus(os.path.basename(in_file)))
if not utils.file_uptodate(out_file, in_file):
with file_transaction(somatic_info.tumor_data, out_file) as tx_out_file:
with open(in_file) as in_handle:
with open(tx_out_file, "w") as out_handle:
out_handle.write(in_handle.readline()) # header
for line in in_handle:
parts = line.split("\t")
parts[1] = _phylowgs_compatible_chroms(parts[1])
out_handle.write("\t".join(parts))
return out_file | python | def _prep_cnv_file(in_file, work_dir, somatic_info):
out_file = os.path.join(work_dir, "%s-prep%s" % utils.splitext_plus(os.path.basename(in_file)))
if not utils.file_uptodate(out_file, in_file):
with file_transaction(somatic_info.tumor_data, out_file) as tx_out_file:
with open(in_file) as in_handle:
with open(tx_out_file, "w") as out_handle:
out_handle.write(in_handle.readline()) # header
for line in in_handle:
parts = line.split("\t")
parts[1] = _phylowgs_compatible_chroms(parts[1])
out_handle.write("\t".join(parts))
return out_file | [
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237,852 | bcbio/bcbio-nextgen | bcbio/heterogeneity/phylowgs.py | _prep_vrn_file | def _prep_vrn_file(in_file, vcaller, work_dir, somatic_info, ignore_file, config):
"""Create a variant file to feed into the PhyloWGS prep script, limiting records.
Sorts by depth, adding top covered samples up to the sample_size supported
by PhyloWGS. The logic is that the higher depth samples will have better
resolution for frequency differences. More complex implementations could try
to subset based on a distribution of frequencies to best sample the potential
heterogeneity.
Handles MuTect and VarDict as inputs to PhyloWGS.
Fixes chromosome naming to use non chr-prefixed contigs, to match _prep_cnv_file.
"""
if vcaller.startswith("vardict"):
variant_type = "vardict"
elif vcaller == "mutect":
variant_type = "mutect-smchet"
else:
raise ValueError("Unexpected variant caller for PhyloWGS prep: %s" % vcaller)
out_file = os.path.join(work_dir, "%s-%s-prep.vcf" % (utils.splitext_plus(os.path.basename(in_file))[0],
vcaller))
if not utils.file_uptodate(out_file, in_file):
check_fn = _min_sample_pass(ignore_file)
with file_transaction(somatic_info.tumor_data, out_file) as tx_out_file:
tx_out_file_raw = "%s-raw%s" % utils.splitext_plus(tx_out_file)
# Filter inputs
with VariantFile(in_file) as bcf_in:
depths = [_sample_depth(rec, somatic_info.tumor_name) for rec in
filter(check_fn, bcf_in)]
depths.sort(reverse=True)
depth_thresh = depths[:config["sample_size"]][-1] if depths else 0
with VariantFile(in_file) as bcf_in:
with VariantFile(tx_out_file_raw, "w", header=bcf_in.header) as bcf_out:
for rec in bcf_in:
if (check_fn(rec) and
(depth_thresh < 5 or _sample_depth(rec, somatic_info.tumor_name) >= depth_thresh)):
bcf_out.write(rec)
# Fix potential chromosome issues
with open(tx_out_file_raw) as in_handle:
with open(tx_out_file, "w") as out_handle:
for line in in_handle:
if not line.startswith("#"):
parts = line.split("\t")
parts[0] = _phylowgs_compatible_chroms(parts[0])
line = "\t".join(parts)
out_handle.write(line)
return variant_type, out_file | python | def _prep_vrn_file(in_file, vcaller, work_dir, somatic_info, ignore_file, config):
if vcaller.startswith("vardict"):
variant_type = "vardict"
elif vcaller == "mutect":
variant_type = "mutect-smchet"
else:
raise ValueError("Unexpected variant caller for PhyloWGS prep: %s" % vcaller)
out_file = os.path.join(work_dir, "%s-%s-prep.vcf" % (utils.splitext_plus(os.path.basename(in_file))[0],
vcaller))
if not utils.file_uptodate(out_file, in_file):
check_fn = _min_sample_pass(ignore_file)
with file_transaction(somatic_info.tumor_data, out_file) as tx_out_file:
tx_out_file_raw = "%s-raw%s" % utils.splitext_plus(tx_out_file)
# Filter inputs
with VariantFile(in_file) as bcf_in:
depths = [_sample_depth(rec, somatic_info.tumor_name) for rec in
filter(check_fn, bcf_in)]
depths.sort(reverse=True)
depth_thresh = depths[:config["sample_size"]][-1] if depths else 0
with VariantFile(in_file) as bcf_in:
with VariantFile(tx_out_file_raw, "w", header=bcf_in.header) as bcf_out:
for rec in bcf_in:
if (check_fn(rec) and
(depth_thresh < 5 or _sample_depth(rec, somatic_info.tumor_name) >= depth_thresh)):
bcf_out.write(rec)
# Fix potential chromosome issues
with open(tx_out_file_raw) as in_handle:
with open(tx_out_file, "w") as out_handle:
for line in in_handle:
if not line.startswith("#"):
parts = line.split("\t")
parts[0] = _phylowgs_compatible_chroms(parts[0])
line = "\t".join(parts)
out_handle.write(line)
return variant_type, out_file | [
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Handles MuTect and VarDict as inputs to PhyloWGS.
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237,853 | bcbio/bcbio-nextgen | bcbio/srna/group.py | run_prepare | def run_prepare(*data):
"""
Run seqcluster prepare to merge all samples in one file
"""
out_dir = os.path.join(dd.get_work_dir(data[0][0]), "seqcluster", "prepare")
out_dir = os.path.abspath(safe_makedir(out_dir))
prepare_dir = os.path.join(out_dir, "prepare")
tools = dd.get_expression_caller(data[0][0])
if len(tools) == 0:
logger.info("You didn't specify any other expression caller tool."
"You can add to the YAML file:"
"expression_caller:[trna, seqcluster, mirdeep2]")
fn = []
for sample in data:
name = sample[0]["rgnames"]['sample']
fn.append("%s\t%s" % (sample[0]['collapse'], name))
args = namedtuple('args', 'debug print_debug minc minl maxl out')
args = args(False, False, 2, 17, 40, out_dir)
ma_out = op.join(out_dir, "seqs.ma")
seq_out = op.join(out_dir, "seqs.fastq")
min_shared = max(int(len(fn) / 10.0), 1)
if not file_exists(ma_out):
seq_l, sample_l = prepare._read_fastq_files(fn, args)
with file_transaction(ma_out) as ma_tx:
with open(ma_tx, 'w') as ma_handle:
with open(seq_out, 'w') as seq_handle:
logger.info("Prepare seqs.fastq with -minl 17 -maxl 40 -minc 2 --min_shared 0.1")
prepare._create_matrix_uniq_seq(sample_l, seq_l, ma_handle, seq_handle, min_shared)
for sample in data:
sample[0]["seqcluster_prepare_ma"] = ma_out
sample[0]["seqcluster_prepare_fastq"] = seq_out
return data | python | def run_prepare(*data):
out_dir = os.path.join(dd.get_work_dir(data[0][0]), "seqcluster", "prepare")
out_dir = os.path.abspath(safe_makedir(out_dir))
prepare_dir = os.path.join(out_dir, "prepare")
tools = dd.get_expression_caller(data[0][0])
if len(tools) == 0:
logger.info("You didn't specify any other expression caller tool."
"You can add to the YAML file:"
"expression_caller:[trna, seqcluster, mirdeep2]")
fn = []
for sample in data:
name = sample[0]["rgnames"]['sample']
fn.append("%s\t%s" % (sample[0]['collapse'], name))
args = namedtuple('args', 'debug print_debug minc minl maxl out')
args = args(False, False, 2, 17, 40, out_dir)
ma_out = op.join(out_dir, "seqs.ma")
seq_out = op.join(out_dir, "seqs.fastq")
min_shared = max(int(len(fn) / 10.0), 1)
if not file_exists(ma_out):
seq_l, sample_l = prepare._read_fastq_files(fn, args)
with file_transaction(ma_out) as ma_tx:
with open(ma_tx, 'w') as ma_handle:
with open(seq_out, 'w') as seq_handle:
logger.info("Prepare seqs.fastq with -minl 17 -maxl 40 -minc 2 --min_shared 0.1")
prepare._create_matrix_uniq_seq(sample_l, seq_l, ma_handle, seq_handle, min_shared)
for sample in data:
sample[0]["seqcluster_prepare_ma"] = ma_out
sample[0]["seqcluster_prepare_fastq"] = seq_out
return data | [
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237,854 | bcbio/bcbio-nextgen | bcbio/srna/group.py | run_align | def run_align(*data):
"""
Prepare data to run alignment step, only once for each project
"""
work_dir = dd.get_work_dir(data[0][0])
out_dir = op.join(work_dir, "seqcluster", "prepare")
seq_out = op.join(out_dir, "seqs.fastq")
bam_dir = op.join(work_dir, "align")
new_bam_file = op.join(bam_dir, "seqs.bam")
tools = dd.get_expression_caller(data[0][0])
if not file_exists(new_bam_file):
sample = process_alignment(data[0][0], [seq_out, None])
bam_file = dd.get_work_bam(sample[0][0])
shutil.move(bam_file, new_bam_file)
shutil.move(bam_file + ".bai", new_bam_file + ".bai")
shutil.rmtree(op.join(bam_dir, sample[0][0]["rgnames"]['sample']))
for sample in data:
# sample[0]["align_bam"] = sample[0]["clean_fastq"]
sample[0]["cluster_bam"] = new_bam_file
if "mirdeep2" in tools:
novel_db = mirdeep.run(data)
return data | python | def run_align(*data):
work_dir = dd.get_work_dir(data[0][0])
out_dir = op.join(work_dir, "seqcluster", "prepare")
seq_out = op.join(out_dir, "seqs.fastq")
bam_dir = op.join(work_dir, "align")
new_bam_file = op.join(bam_dir, "seqs.bam")
tools = dd.get_expression_caller(data[0][0])
if not file_exists(new_bam_file):
sample = process_alignment(data[0][0], [seq_out, None])
bam_file = dd.get_work_bam(sample[0][0])
shutil.move(bam_file, new_bam_file)
shutil.move(bam_file + ".bai", new_bam_file + ".bai")
shutil.rmtree(op.join(bam_dir, sample[0][0]["rgnames"]['sample']))
for sample in data:
# sample[0]["align_bam"] = sample[0]["clean_fastq"]
sample[0]["cluster_bam"] = new_bam_file
if "mirdeep2" in tools:
novel_db = mirdeep.run(data)
return data | [
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237,855 | bcbio/bcbio-nextgen | bcbio/srna/group.py | run_cluster | def run_cluster(*data):
"""
Run seqcluster cluster to detect smallRNA clusters
"""
sample = data[0][0]
tools = dd.get_expression_caller(data[0][0])
work_dir = dd.get_work_dir(sample)
out_dir = op.join(work_dir, "seqcluster", "cluster")
out_dir = op.abspath(safe_makedir(out_dir))
prepare_dir = op.join(work_dir, "seqcluster", "prepare")
bam_file = data[0][0]["cluster_bam"]
if "seqcluster" in tools:
gtf_file = dd.get_transcriptome_gtf(sample) if dd.get_transcriptome_gtf(sample) else dd.get_srna_gtf_file(sample)
sample["seqcluster"] = _cluster(bam_file, data[0][0]["seqcluster_prepare_ma"],
out_dir, dd.get_ref_file(sample),
gtf_file)
sample["report"] = _report(sample, dd.get_ref_file(sample))
if "mirge" in tools:
sample["mirge"] = mirge.run(data)
out_mirna = _make_isomir_counts(data, out_dir=op.join(work_dir, "mirbase"))
if out_mirna:
sample = dd.set_mirna_counts(sample, out_mirna[0])
sample = dd.set_isomir_counts(sample, out_mirna[1])
out_novel = _make_isomir_counts(data, "seqbuster_novel", op.join(work_dir, "mirdeep2"), "_novel")
if out_novel:
sample = dd.set_novel_mirna_counts(sample, out_novel[0])
sample = dd.set_novel_isomir_counts(sample, out_novel[1])
data[0][0] = sample
data = spikein.combine_spikein(data)
return data | python | def run_cluster(*data):
sample = data[0][0]
tools = dd.get_expression_caller(data[0][0])
work_dir = dd.get_work_dir(sample)
out_dir = op.join(work_dir, "seqcluster", "cluster")
out_dir = op.abspath(safe_makedir(out_dir))
prepare_dir = op.join(work_dir, "seqcluster", "prepare")
bam_file = data[0][0]["cluster_bam"]
if "seqcluster" in tools:
gtf_file = dd.get_transcriptome_gtf(sample) if dd.get_transcriptome_gtf(sample) else dd.get_srna_gtf_file(sample)
sample["seqcluster"] = _cluster(bam_file, data[0][0]["seqcluster_prepare_ma"],
out_dir, dd.get_ref_file(sample),
gtf_file)
sample["report"] = _report(sample, dd.get_ref_file(sample))
if "mirge" in tools:
sample["mirge"] = mirge.run(data)
out_mirna = _make_isomir_counts(data, out_dir=op.join(work_dir, "mirbase"))
if out_mirna:
sample = dd.set_mirna_counts(sample, out_mirna[0])
sample = dd.set_isomir_counts(sample, out_mirna[1])
out_novel = _make_isomir_counts(data, "seqbuster_novel", op.join(work_dir, "mirdeep2"), "_novel")
if out_novel:
sample = dd.set_novel_mirna_counts(sample, out_novel[0])
sample = dd.set_novel_isomir_counts(sample, out_novel[1])
data[0][0] = sample
data = spikein.combine_spikein(data)
return data | [
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237,856 | bcbio/bcbio-nextgen | bcbio/srna/group.py | _cluster | def _cluster(bam_file, ma_file, out_dir, reference, annotation_file=None):
"""
Connect to seqcluster to run cluster with python directly
"""
seqcluster = op.join(get_bcbio_bin(), "seqcluster")
# cl = ["cluster", "-o", out_dir, "-m", ma_file, "-a", bam_file, "-r", reference]
if annotation_file:
annotation_file = "-g " + annotation_file
else:
annotation_file = ""
if not file_exists(op.join(out_dir, "counts.tsv")):
cmd = ("{seqcluster} cluster -o {out_dir} -m {ma_file} -a {bam_file} -r {reference} {annotation_file}")
do.run(cmd.format(**locals()), "Running seqcluster.")
counts = op.join(out_dir, "counts.tsv")
stats = op.join(out_dir, "read_stats.tsv")
json = op.join(out_dir, "seqcluster.json")
return {'out_dir': out_dir, 'count_file': counts, 'stat_file': stats, 'json': json} | python | def _cluster(bam_file, ma_file, out_dir, reference, annotation_file=None):
seqcluster = op.join(get_bcbio_bin(), "seqcluster")
# cl = ["cluster", "-o", out_dir, "-m", ma_file, "-a", bam_file, "-r", reference]
if annotation_file:
annotation_file = "-g " + annotation_file
else:
annotation_file = ""
if not file_exists(op.join(out_dir, "counts.tsv")):
cmd = ("{seqcluster} cluster -o {out_dir} -m {ma_file} -a {bam_file} -r {reference} {annotation_file}")
do.run(cmd.format(**locals()), "Running seqcluster.")
counts = op.join(out_dir, "counts.tsv")
stats = op.join(out_dir, "read_stats.tsv")
json = op.join(out_dir, "seqcluster.json")
return {'out_dir': out_dir, 'count_file': counts, 'stat_file': stats, 'json': json} | [
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237,857 | bcbio/bcbio-nextgen | bcbio/srna/group.py | _report | def _report(data, reference):
"""
Run report of seqcluster to get browser options for results
"""
seqcluster = op.join(get_bcbio_bin(), "seqcluster")
work_dir = dd.get_work_dir(data)
out_dir = safe_makedir(os.path.join(work_dir, "seqcluster", "report"))
out_file = op.join(out_dir, "seqcluster.db")
json = op.join(work_dir, "seqcluster", "cluster", "seqcluster.json")
cmd = ("{seqcluster} report -o {out_dir} -r {reference} -j {json}")
if not file_exists(out_file):
do.run(cmd.format(**locals()), "Run report on clusters")
return out_file | python | def _report(data, reference):
seqcluster = op.join(get_bcbio_bin(), "seqcluster")
work_dir = dd.get_work_dir(data)
out_dir = safe_makedir(os.path.join(work_dir, "seqcluster", "report"))
out_file = op.join(out_dir, "seqcluster.db")
json = op.join(work_dir, "seqcluster", "cluster", "seqcluster.json")
cmd = ("{seqcluster} report -o {out_dir} -r {reference} -j {json}")
if not file_exists(out_file):
do.run(cmd.format(**locals()), "Run report on clusters")
return out_file | [
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237,858 | bcbio/bcbio-nextgen | bcbio/srna/group.py | report | def report(data):
"""Create a Rmd report for small RNAseq analysis"""
work_dir = dd.get_work_dir(data[0][0])
out_dir = op.join(work_dir, "report")
safe_makedir(out_dir)
summary_file = op.join(out_dir, "summary.csv")
with file_transaction(summary_file) as out_tx:
with open(out_tx, 'w') as out_handle:
out_handle.write("sample_id,%s\n" % _guess_header(data[0][0]))
for sample in data:
info = sample[0]
group = _guess_group(info)
files = info["seqbuster"] if "seqbuster" in info else "None"
out_handle.write(",".join([dd.get_sample_name(info),
group]) + "\n")
_modify_report(work_dir, out_dir)
return summary_file | python | def report(data):
work_dir = dd.get_work_dir(data[0][0])
out_dir = op.join(work_dir, "report")
safe_makedir(out_dir)
summary_file = op.join(out_dir, "summary.csv")
with file_transaction(summary_file) as out_tx:
with open(out_tx, 'w') as out_handle:
out_handle.write("sample_id,%s\n" % _guess_header(data[0][0]))
for sample in data:
info = sample[0]
group = _guess_group(info)
files = info["seqbuster"] if "seqbuster" in info else "None"
out_handle.write(",".join([dd.get_sample_name(info),
group]) + "\n")
_modify_report(work_dir, out_dir)
return summary_file | [
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237,859 | bcbio/bcbio-nextgen | bcbio/srna/group.py | _modify_report | def _modify_report(summary_path, out_dir):
"""Read Rmd template and dump with project path."""
summary_path = op.abspath(summary_path)
template = op.normpath(op.join(op.dirname(op.realpath(template_seqcluster.__file__)), "report.rmd"))
content = open(template).read()
out_content = string.Template(content).safe_substitute({'path_abs': summary_path})
out_file = op.join(out_dir, "srna_report.rmd")
with open(out_file, 'w') as out_handle:
out_handle.write(out_content)
return out_file | python | def _modify_report(summary_path, out_dir):
summary_path = op.abspath(summary_path)
template = op.normpath(op.join(op.dirname(op.realpath(template_seqcluster.__file__)), "report.rmd"))
content = open(template).read()
out_content = string.Template(content).safe_substitute({'path_abs': summary_path})
out_file = op.join(out_dir, "srna_report.rmd")
with open(out_file, 'w') as out_handle:
out_handle.write(out_content)
return out_file | [
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237,860 | bcbio/bcbio-nextgen | bcbio/srna/group.py | _make_isomir_counts | def _make_isomir_counts(data, srna_type="seqbuster", out_dir=None, stem=""):
"""
Parse miraligner files to create count matrix.
"""
work_dir = dd.get_work_dir(data[0][0])
if not out_dir:
out_dir = op.join(work_dir, "mirbase")
out_novel_isomir = append_stem(op.join(out_dir, "counts.tsv"), stem)
out_novel_mirna = append_stem(op.join(out_dir, "counts_mirna.tsv"), stem)
logger.debug("Create %s count data at %s." % (srna_type, out_dir))
if file_exists(out_novel_mirna):
return [out_novel_mirna, out_novel_isomir]
out_dts = []
for sample in data:
if sample[0].get(srna_type):
miraligner_fn = sample[0][srna_type]
reads = _read_miraligner(miraligner_fn)
if reads:
out_file, dt, dt_pre = _tab_output(reads, miraligner_fn + ".back", dd.get_sample_name(sample[0]))
out_dts.append(dt)
else:
logger.debug("WARNING::%s has NOT miRNA annotated for %s. Check if fasta files is small or species value." % (dd.get_sample_name(sample[0]), srna_type))
if out_dts:
out_files = _create_counts(out_dts, out_dir)
out_files = [move_safe(out_files[0], out_novel_isomir), move_safe(out_files[1], out_novel_mirna)]
return out_files
else:
logger.debug("WARNING::any samples have miRNA annotated for %s. Check if fasta files is small or species value." % srna_type) | python | def _make_isomir_counts(data, srna_type="seqbuster", out_dir=None, stem=""):
work_dir = dd.get_work_dir(data[0][0])
if not out_dir:
out_dir = op.join(work_dir, "mirbase")
out_novel_isomir = append_stem(op.join(out_dir, "counts.tsv"), stem)
out_novel_mirna = append_stem(op.join(out_dir, "counts_mirna.tsv"), stem)
logger.debug("Create %s count data at %s." % (srna_type, out_dir))
if file_exists(out_novel_mirna):
return [out_novel_mirna, out_novel_isomir]
out_dts = []
for sample in data:
if sample[0].get(srna_type):
miraligner_fn = sample[0][srna_type]
reads = _read_miraligner(miraligner_fn)
if reads:
out_file, dt, dt_pre = _tab_output(reads, miraligner_fn + ".back", dd.get_sample_name(sample[0]))
out_dts.append(dt)
else:
logger.debug("WARNING::%s has NOT miRNA annotated for %s. Check if fasta files is small or species value." % (dd.get_sample_name(sample[0]), srna_type))
if out_dts:
out_files = _create_counts(out_dts, out_dir)
out_files = [move_safe(out_files[0], out_novel_isomir), move_safe(out_files[1], out_novel_mirna)]
return out_files
else:
logger.debug("WARNING::any samples have miRNA annotated for %s. Check if fasta files is small or species value." % srna_type) | [
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237,861 | bcbio/bcbio-nextgen | bcbio/bam/coverage.py | _split_regions | def _split_regions(chrom, start, end):
"""Split regions longer than 100kb into smaller sections.
"""
window_size = 1e5
if end - start < window_size * 5:
return [(chrom, start, end)]
else:
out = []
for r in pybedtools.BedTool().window_maker(w=window_size,
b=pybedtools.BedTool("%s\t%s\t%s" % (chrom, start, end),
from_string=True)):
out.append((r.chrom, r.start, r.end))
return out | python | def _split_regions(chrom, start, end):
window_size = 1e5
if end - start < window_size * 5:
return [(chrom, start, end)]
else:
out = []
for r in pybedtools.BedTool().window_maker(w=window_size,
b=pybedtools.BedTool("%s\t%s\t%s" % (chrom, start, end),
from_string=True)):
out.append((r.chrom, r.start, r.end))
return out | [
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237,862 | bcbio/bcbio-nextgen | bcbio/bam/coverage.py | plot_multiple_regions_coverage | def plot_multiple_regions_coverage(samples, out_file, data, region_bed=None, stem_bed=None):
"""
given a list of bcbio samples and a bed file or BedTool of regions,
makes a plot of the coverage in the regions for the set of samples
if given a bed file or BedTool of locations in stem_bed with a label,
plots lollipops at those locations
"""
mpl.use('Agg', force=True)
PAD = 100
if file_exists(out_file):
return out_file
in_bams = [dd.get_align_bam(x) for x in samples]
samplenames = [dd.get_sample_name(x) for x in samples]
if isinstance(region_bed, six.string_types):
region_bed = pybedtools.BedTool(region_bed)
if isinstance(stem_bed, six.string_types):
stem_bed = pybedtools.BedTool(stem_bed)
if stem_bed is not None: # tabix indexed bedtools eval to false
stem_bed = stem_bed.tabix()
plt.clf()
plt.cla()
with file_transaction(out_file) as tx_out_file:
with backend_pdf.PdfPages(tx_out_file) as pdf_out:
sns.despine()
for line in region_bed:
for chrom, start, end in _split_regions(line.chrom, max(line.start - PAD, 0),
line.end + PAD):
df = _combine_regional_coverage(in_bams, samplenames, chrom,
start, end, os.path.dirname(tx_out_file), data)
plot = sns.tsplot(df, time="position", unit="chrom",
value="coverage", condition="sample")
if stem_bed is not None: # tabix indexed bedtools eval to false
interval = pybedtools.Interval(chrom, start, end)
_add_stems_to_plot(interval, stem_bed, samples, plot)
plt.title("{chrom}:{start}-{end}".format(**locals()))
pdf_out.savefig(plot.get_figure())
plt.close()
return out_file | python | def plot_multiple_regions_coverage(samples, out_file, data, region_bed=None, stem_bed=None):
mpl.use('Agg', force=True)
PAD = 100
if file_exists(out_file):
return out_file
in_bams = [dd.get_align_bam(x) for x in samples]
samplenames = [dd.get_sample_name(x) for x in samples]
if isinstance(region_bed, six.string_types):
region_bed = pybedtools.BedTool(region_bed)
if isinstance(stem_bed, six.string_types):
stem_bed = pybedtools.BedTool(stem_bed)
if stem_bed is not None: # tabix indexed bedtools eval to false
stem_bed = stem_bed.tabix()
plt.clf()
plt.cla()
with file_transaction(out_file) as tx_out_file:
with backend_pdf.PdfPages(tx_out_file) as pdf_out:
sns.despine()
for line in region_bed:
for chrom, start, end in _split_regions(line.chrom, max(line.start - PAD, 0),
line.end + PAD):
df = _combine_regional_coverage(in_bams, samplenames, chrom,
start, end, os.path.dirname(tx_out_file), data)
plot = sns.tsplot(df, time="position", unit="chrom",
value="coverage", condition="sample")
if stem_bed is not None: # tabix indexed bedtools eval to false
interval = pybedtools.Interval(chrom, start, end)
_add_stems_to_plot(interval, stem_bed, samples, plot)
plt.title("{chrom}:{start}-{end}".format(**locals()))
pdf_out.savefig(plot.get_figure())
plt.close()
return out_file | [
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237,863 | bcbio/bcbio-nextgen | bcbio/variation/mutect.py | _config_params | def _config_params(base_config, assoc_files, region, out_file, items):
"""Add parameters based on configuration variables, associated files and genomic regions.
"""
params = []
dbsnp = assoc_files.get("dbsnp")
if dbsnp:
params += ["--dbsnp", dbsnp]
cosmic = assoc_files.get("cosmic")
if cosmic:
params += ["--cosmic", cosmic]
variant_regions = bedutils.population_variant_regions(items)
region = subset_variant_regions(variant_regions, region, out_file, items)
if region:
params += ["-L", bamprep.region_to_gatk(region), "--interval_set_rule",
"INTERSECTION"]
# set low frequency calling parameter if adjusted
# to set other MuTect parameters on contamination, pass options to resources for mutect
# --fraction_contamination --minimum_normal_allele_fraction
min_af = tz.get_in(["algorithm", "min_allele_fraction"], base_config)
if min_af:
params += ["--minimum_mutation_cell_fraction", "%.2f" % (min_af / 100.0)]
resources = config_utils.get_resources("mutect", base_config)
if resources.get("options") is not None:
params += [str(x) for x in resources.get("options", [])]
# Output quality scores
if "--enable_qscore_output" not in params:
params.append("--enable_qscore_output")
# drf not currently supported in MuTect to turn off duplicateread filter
# params += gatk.standard_cl_params(items)
return params | python | def _config_params(base_config, assoc_files, region, out_file, items):
params = []
dbsnp = assoc_files.get("dbsnp")
if dbsnp:
params += ["--dbsnp", dbsnp]
cosmic = assoc_files.get("cosmic")
if cosmic:
params += ["--cosmic", cosmic]
variant_regions = bedutils.population_variant_regions(items)
region = subset_variant_regions(variant_regions, region, out_file, items)
if region:
params += ["-L", bamprep.region_to_gatk(region), "--interval_set_rule",
"INTERSECTION"]
# set low frequency calling parameter if adjusted
# to set other MuTect parameters on contamination, pass options to resources for mutect
# --fraction_contamination --minimum_normal_allele_fraction
min_af = tz.get_in(["algorithm", "min_allele_fraction"], base_config)
if min_af:
params += ["--minimum_mutation_cell_fraction", "%.2f" % (min_af / 100.0)]
resources = config_utils.get_resources("mutect", base_config)
if resources.get("options") is not None:
params += [str(x) for x in resources.get("options", [])]
# Output quality scores
if "--enable_qscore_output" not in params:
params.append("--enable_qscore_output")
# drf not currently supported in MuTect to turn off duplicateread filter
# params += gatk.standard_cl_params(items)
return params | [
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237,864 | bcbio/bcbio-nextgen | bcbio/variation/mutect.py | _mutect_call_prep | def _mutect_call_prep(align_bams, items, ref_file, assoc_files,
region=None, out_file=None):
"""Preparation work for MuTect.
"""
base_config = items[0]["config"]
broad_runner = broad.runner_from_path("picard", base_config)
broad_runner.run_fn("picard_index_ref", ref_file)
broad_runner = broad.runner_from_config(base_config, "mutect")
_check_mutect_version(broad_runner)
for x in align_bams:
bam.index(x, base_config)
paired = vcfutils.get_paired_bams(align_bams, items)
if not paired:
raise ValueError("Specified MuTect calling but 'tumor' phenotype not present in batch\n"
"https://bcbio-nextgen.readthedocs.org/en/latest/contents/"
"pipelines.html#cancer-variant-calling\n"
"for samples: %s" % ", " .join([dd.get_sample_name(x) for x in items]))
params = ["-R", ref_file, "-T", "MuTect", "-U", "ALLOW_N_CIGAR_READS"]
params += ["--read_filter", "NotPrimaryAlignment"]
params += ["-I:tumor", paired.tumor_bam]
params += ["--tumor_sample_name", paired.tumor_name]
if paired.normal_bam is not None:
params += ["-I:normal", paired.normal_bam]
params += ["--normal_sample_name", paired.normal_name]
if paired.normal_panel is not None:
params += ["--normal_panel", paired.normal_panel]
params += _config_params(base_config, assoc_files, region, out_file, items)
return broad_runner, params | python | def _mutect_call_prep(align_bams, items, ref_file, assoc_files,
region=None, out_file=None):
base_config = items[0]["config"]
broad_runner = broad.runner_from_path("picard", base_config)
broad_runner.run_fn("picard_index_ref", ref_file)
broad_runner = broad.runner_from_config(base_config, "mutect")
_check_mutect_version(broad_runner)
for x in align_bams:
bam.index(x, base_config)
paired = vcfutils.get_paired_bams(align_bams, items)
if not paired:
raise ValueError("Specified MuTect calling but 'tumor' phenotype not present in batch\n"
"https://bcbio-nextgen.readthedocs.org/en/latest/contents/"
"pipelines.html#cancer-variant-calling\n"
"for samples: %s" % ", " .join([dd.get_sample_name(x) for x in items]))
params = ["-R", ref_file, "-T", "MuTect", "-U", "ALLOW_N_CIGAR_READS"]
params += ["--read_filter", "NotPrimaryAlignment"]
params += ["-I:tumor", paired.tumor_bam]
params += ["--tumor_sample_name", paired.tumor_name]
if paired.normal_bam is not None:
params += ["-I:normal", paired.normal_bam]
params += ["--normal_sample_name", paired.normal_name]
if paired.normal_panel is not None:
params += ["--normal_panel", paired.normal_panel]
params += _config_params(base_config, assoc_files, region, out_file, items)
return broad_runner, params | [
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237,865 | bcbio/bcbio-nextgen | bcbio/variation/mutect.py | _SID_call_prep | def _SID_call_prep(align_bams, items, ref_file, assoc_files, region=None, out_file=None):
"""Preparation work for SomaticIndelDetector.
"""
base_config = items[0]["config"]
for x in align_bams:
bam.index(x, base_config)
params = ["-R", ref_file, "-T", "SomaticIndelDetector", "-U", "ALLOW_N_CIGAR_READS"]
# Limit per base read start count to between 200-10000, i.e. from any base
# can no more 10000 new reads begin.
# Further, limit maxNumberOfReads accordingly, otherwise SID discards
# windows for high coverage panels.
paired = vcfutils.get_paired_bams(align_bams, items)
params += ["--read_filter", "NotPrimaryAlignment"]
params += ["-I:tumor", paired.tumor_bam]
min_af = float(get_in(paired.tumor_config, ("algorithm", "min_allele_fraction"), 10)) / 100.0
if paired.normal_bam is not None:
params += ["-I:normal", paired.normal_bam]
# notice there must be at least 4 reads of coverage in normal
params += ["--filter_expressions", "T_COV<6||N_COV<4||T_INDEL_F<%s||T_INDEL_CF<0.7" % min_af]
else:
params += ["--unpaired"]
params += ["--filter_expressions", "COV<6||INDEL_F<%s||INDEL_CF<0.7" % min_af]
if region:
params += ["-L", bamprep.region_to_gatk(region), "--interval_set_rule",
"INTERSECTION"]
return params | python | def _SID_call_prep(align_bams, items, ref_file, assoc_files, region=None, out_file=None):
base_config = items[0]["config"]
for x in align_bams:
bam.index(x, base_config)
params = ["-R", ref_file, "-T", "SomaticIndelDetector", "-U", "ALLOW_N_CIGAR_READS"]
# Limit per base read start count to between 200-10000, i.e. from any base
# can no more 10000 new reads begin.
# Further, limit maxNumberOfReads accordingly, otherwise SID discards
# windows for high coverage panels.
paired = vcfutils.get_paired_bams(align_bams, items)
params += ["--read_filter", "NotPrimaryAlignment"]
params += ["-I:tumor", paired.tumor_bam]
min_af = float(get_in(paired.tumor_config, ("algorithm", "min_allele_fraction"), 10)) / 100.0
if paired.normal_bam is not None:
params += ["-I:normal", paired.normal_bam]
# notice there must be at least 4 reads of coverage in normal
params += ["--filter_expressions", "T_COV<6||N_COV<4||T_INDEL_F<%s||T_INDEL_CF<0.7" % min_af]
else:
params += ["--unpaired"]
params += ["--filter_expressions", "COV<6||INDEL_F<%s||INDEL_CF<0.7" % min_af]
if region:
params += ["-L", bamprep.region_to_gatk(region), "--interval_set_rule",
"INTERSECTION"]
return params | [
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237,866 | bcbio/bcbio-nextgen | bcbio/variation/mutect.py | _fix_mutect_output | def _fix_mutect_output(orig_file, config, out_file, is_paired):
"""Adjust MuTect output to match other callers.
- Rename allelic fraction field in mutect output from FA to FREQ to standarize with other tools
- Remove extra 'none' samples introduced when calling tumor-only samples
"""
out_file_noc = out_file.replace(".vcf.gz", ".vcf")
none_index = -1
with file_transaction(config, out_file_noc) as tx_out_file:
with open_gzipsafe(orig_file) as in_handle:
with open(tx_out_file, 'w') as out_handle:
for line in in_handle:
if not is_paired and line.startswith("#CHROM"):
parts = line.rstrip().split("\t")
none_index = parts.index("none")
del parts[none_index]
line = "\t".join(parts) + "\n"
elif line.startswith("##FORMAT=<ID=FA"):
line = line.replace("=FA", "=FREQ")
elif not line.startswith("#"):
if none_index > 0:
parts = line.rstrip().split("\t")
del parts[none_index]
line = "\t".join(parts) + "\n"
line = line.replace("FA", "FREQ")
out_handle.write(line)
return bgzip_and_index(out_file_noc, config) | python | def _fix_mutect_output(orig_file, config, out_file, is_paired):
out_file_noc = out_file.replace(".vcf.gz", ".vcf")
none_index = -1
with file_transaction(config, out_file_noc) as tx_out_file:
with open_gzipsafe(orig_file) as in_handle:
with open(tx_out_file, 'w') as out_handle:
for line in in_handle:
if not is_paired and line.startswith("#CHROM"):
parts = line.rstrip().split("\t")
none_index = parts.index("none")
del parts[none_index]
line = "\t".join(parts) + "\n"
elif line.startswith("##FORMAT=<ID=FA"):
line = line.replace("=FA", "=FREQ")
elif not line.startswith("#"):
if none_index > 0:
parts = line.rstrip().split("\t")
del parts[none_index]
line = "\t".join(parts) + "\n"
line = line.replace("FA", "FREQ")
out_handle.write(line)
return bgzip_and_index(out_file_noc, config) | [
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- Remove extra 'none' samples introduced when calling tumor-only samples | [
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237,867 | bcbio/bcbio-nextgen | bcbio/variation/population.py | prep_gemini_db | def prep_gemini_db(fnames, call_info, samples, extras):
"""Prepare a gemini database from VCF inputs prepared with snpEff.
"""
data = samples[0]
name, caller, is_batch = call_info
build_type = _get_build_type(fnames, samples, caller)
out_dir = utils.safe_makedir(os.path.join(data["dirs"]["work"], "gemini"))
gemini_vcf = get_multisample_vcf(fnames, name, caller, data)
# If we're building a gemini database, normalize the inputs
if build_type:
passonly = all("gemini_allvariants" not in dd.get_tools_on(d) for d in samples)
gemini_vcf = normalize.normalize(gemini_vcf, data, passonly=passonly)
decomposed = True
else:
decomposed = False
ann_vcf = run_vcfanno(gemini_vcf, data, decomposed)
gemini_db = os.path.join(out_dir, "%s-%s.db" % (name, caller))
if ann_vcf and build_type and not utils.file_exists(gemini_db):
ped_file = create_ped_file(samples + extras, gemini_vcf)
# Original approach for hg19/GRCh37
if vcfanno.is_human(data, builds=["37"]) and "gemini_orig" in build_type:
gemini_db = create_gemini_db_orig(gemini_vcf, data, gemini_db, ped_file)
else:
gemini_db = create_gemini_db(ann_vcf, data, gemini_db, ped_file)
# only pass along gemini_vcf_downstream if uniquely created here
if os.path.islink(gemini_vcf):
gemini_vcf = None
return [[(name, caller), {"db": gemini_db if utils.file_exists(gemini_db) else None,
"vcf": ann_vcf or gemini_vcf,
"decomposed": decomposed}]] | python | def prep_gemini_db(fnames, call_info, samples, extras):
data = samples[0]
name, caller, is_batch = call_info
build_type = _get_build_type(fnames, samples, caller)
out_dir = utils.safe_makedir(os.path.join(data["dirs"]["work"], "gemini"))
gemini_vcf = get_multisample_vcf(fnames, name, caller, data)
# If we're building a gemini database, normalize the inputs
if build_type:
passonly = all("gemini_allvariants" not in dd.get_tools_on(d) for d in samples)
gemini_vcf = normalize.normalize(gemini_vcf, data, passonly=passonly)
decomposed = True
else:
decomposed = False
ann_vcf = run_vcfanno(gemini_vcf, data, decomposed)
gemini_db = os.path.join(out_dir, "%s-%s.db" % (name, caller))
if ann_vcf and build_type and not utils.file_exists(gemini_db):
ped_file = create_ped_file(samples + extras, gemini_vcf)
# Original approach for hg19/GRCh37
if vcfanno.is_human(data, builds=["37"]) and "gemini_orig" in build_type:
gemini_db = create_gemini_db_orig(gemini_vcf, data, gemini_db, ped_file)
else:
gemini_db = create_gemini_db(ann_vcf, data, gemini_db, ped_file)
# only pass along gemini_vcf_downstream if uniquely created here
if os.path.islink(gemini_vcf):
gemini_vcf = None
return [[(name, caller), {"db": gemini_db if utils.file_exists(gemini_db) else None,
"vcf": ann_vcf or gemini_vcf,
"decomposed": decomposed}]] | [
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237,868 | bcbio/bcbio-nextgen | bcbio/variation/population.py | _back_compatible_gemini | def _back_compatible_gemini(conf_files, data):
"""Provide old install directory for configuration with GEMINI supplied tidy VCFs.
Handles new style (bcbio installed) and old style (GEMINI installed)
configuration and data locations.
"""
if vcfanno.is_human(data, builds=["37"]):
for f in conf_files:
if f and os.path.basename(f) == "gemini.conf" and os.path.exists(f):
with open(f) as in_handle:
for line in in_handle:
if line.startswith("file"):
fname = line.strip().split("=")[-1].replace('"', '').strip()
if fname.find(".tidy.") > 0:
return install.get_gemini_dir(data)
return None | python | def _back_compatible_gemini(conf_files, data):
if vcfanno.is_human(data, builds=["37"]):
for f in conf_files:
if f and os.path.basename(f) == "gemini.conf" and os.path.exists(f):
with open(f) as in_handle:
for line in in_handle:
if line.startswith("file"):
fname = line.strip().split("=")[-1].replace('"', '').strip()
if fname.find(".tidy.") > 0:
return install.get_gemini_dir(data)
return None | [
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Handles new style (bcbio installed) and old style (GEMINI installed)
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237,869 | bcbio/bcbio-nextgen | bcbio/variation/population.py | run_vcfanno | def run_vcfanno(vcf_file, data, decomposed=False):
"""Run vcfanno, providing annotations from external databases if needed.
Puts together lua and conf files from multiple inputs by file names.
"""
conf_files = dd.get_vcfanno(data)
if conf_files:
with_basepaths = collections.defaultdict(list)
gemini_basepath = _back_compatible_gemini(conf_files, data)
for f in conf_files:
name = os.path.splitext(os.path.basename(f))[0]
if f.endswith(".lua"):
conf_file = None
lua_file = f
else:
conf_file = f
lua_file = "%s.lua" % utils.splitext_plus(conf_file)[0]
if lua_file and not os.path.exists(lua_file):
lua_file = None
data_basepath = gemini_basepath if name == "gemini" else None
if conf_file and os.path.exists(conf_file):
with_basepaths[(data_basepath, name)].append(conf_file)
if lua_file and os.path.exists(lua_file):
with_basepaths[(data_basepath, name)].append(lua_file)
conf_files = with_basepaths.items()
out_file = None
if conf_files:
VcfannoIn = collections.namedtuple("VcfannoIn", ["conf", "lua"])
bp_files = collections.defaultdict(list)
for (data_basepath, name), anno_files in conf_files:
anno_files = list(set(anno_files))
if len(anno_files) == 1:
cur = VcfannoIn(anno_files[0], None)
elif len(anno_files) == 2:
lua_files = [x for x in anno_files if x.endswith(".lua")]
assert len(lua_files) == 1, anno_files
lua_file = lua_files[0]
anno_files.remove(lua_file)
cur = VcfannoIn(anno_files[0], lua_file)
else:
raise ValueError("Unexpected annotation group %s" % anno_files)
bp_files[data_basepath].append(cur)
for data_basepath, anno_files in bp_files.items():
ann_file = vcfanno.run(vcf_file, [x.conf for x in anno_files],
[x.lua for x in anno_files], data,
basepath=data_basepath,
decomposed=decomposed)
if ann_file:
out_file = ann_file
vcf_file = ann_file
return out_file | python | def run_vcfanno(vcf_file, data, decomposed=False):
conf_files = dd.get_vcfanno(data)
if conf_files:
with_basepaths = collections.defaultdict(list)
gemini_basepath = _back_compatible_gemini(conf_files, data)
for f in conf_files:
name = os.path.splitext(os.path.basename(f))[0]
if f.endswith(".lua"):
conf_file = None
lua_file = f
else:
conf_file = f
lua_file = "%s.lua" % utils.splitext_plus(conf_file)[0]
if lua_file and not os.path.exists(lua_file):
lua_file = None
data_basepath = gemini_basepath if name == "gemini" else None
if conf_file and os.path.exists(conf_file):
with_basepaths[(data_basepath, name)].append(conf_file)
if lua_file and os.path.exists(lua_file):
with_basepaths[(data_basepath, name)].append(lua_file)
conf_files = with_basepaths.items()
out_file = None
if conf_files:
VcfannoIn = collections.namedtuple("VcfannoIn", ["conf", "lua"])
bp_files = collections.defaultdict(list)
for (data_basepath, name), anno_files in conf_files:
anno_files = list(set(anno_files))
if len(anno_files) == 1:
cur = VcfannoIn(anno_files[0], None)
elif len(anno_files) == 2:
lua_files = [x for x in anno_files if x.endswith(".lua")]
assert len(lua_files) == 1, anno_files
lua_file = lua_files[0]
anno_files.remove(lua_file)
cur = VcfannoIn(anno_files[0], lua_file)
else:
raise ValueError("Unexpected annotation group %s" % anno_files)
bp_files[data_basepath].append(cur)
for data_basepath, anno_files in bp_files.items():
ann_file = vcfanno.run(vcf_file, [x.conf for x in anno_files],
[x.lua for x in anno_files], data,
basepath=data_basepath,
decomposed=decomposed)
if ann_file:
out_file = ann_file
vcf_file = ann_file
return out_file | [
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237,870 | bcbio/bcbio-nextgen | bcbio/variation/population.py | get_ped_info | def get_ped_info(data, samples):
"""Retrieve all PED info from metadata
"""
family_id = tz.get_in(["metadata", "family_id"], data, None)
if not family_id:
family_id = _find_shared_batch(samples)
return {
"gender": {"male": 1, "female": 2, "unknown": 0}.get(get_gender(data)),
"individual_id": dd.get_sample_name(data),
"family_id": family_id,
"maternal_id": tz.get_in(["metadata", "maternal_id"], data, -9),
"paternal_id": tz.get_in(["metadata", "paternal_id"], data, -9),
"affected": get_affected_status(data),
"ethnicity": tz.get_in(["metadata", "ethnicity"], data, -9)
} | python | def get_ped_info(data, samples):
family_id = tz.get_in(["metadata", "family_id"], data, None)
if not family_id:
family_id = _find_shared_batch(samples)
return {
"gender": {"male": 1, "female": 2, "unknown": 0}.get(get_gender(data)),
"individual_id": dd.get_sample_name(data),
"family_id": family_id,
"maternal_id": tz.get_in(["metadata", "maternal_id"], data, -9),
"paternal_id": tz.get_in(["metadata", "paternal_id"], data, -9),
"affected": get_affected_status(data),
"ethnicity": tz.get_in(["metadata", "ethnicity"], data, -9)
} | [
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237,871 | bcbio/bcbio-nextgen | bcbio/variation/population.py | create_ped_file | def create_ped_file(samples, base_vcf, out_dir=None):
"""Create a GEMINI-compatible PED file, including gender, family and phenotype information.
Checks for a specified `ped` file in metadata, and will use sample information from this file
before reconstituting from metadata information.
"""
out_file = "%s.ped" % utils.splitext_plus(base_vcf)[0]
if out_dir:
out_file = os.path.join(out_dir, os.path.basename(out_file))
sample_ped_lines = {}
header = ["#Family_ID", "Individual_ID", "Paternal_ID", "Maternal_ID", "Sex", "Phenotype", "Ethnicity"]
for md_ped in list(set([x for x in [tz.get_in(["metadata", "ped"], data)
for data in samples] if x is not None])):
with open(md_ped) as in_handle:
reader = csv.reader(in_handle, dialect="excel-tab")
for parts in reader:
if parts[0].startswith("#") and len(parts) > len(header):
header = header + parts[len(header):]
else:
sample_ped_lines[parts[1]] = parts
if not utils.file_exists(out_file):
with file_transaction(samples[0], out_file) as tx_out_file:
with open(tx_out_file, "w") as out_handle:
want_samples = set(vcfutils.get_samples(base_vcf))
writer = csv.writer(out_handle, dialect="excel-tab")
writer.writerow(header)
for data in samples:
ped_info = get_ped_info(data, samples)
sname = ped_info["individual_id"]
if sname in want_samples:
want_samples.remove(sname)
if sname in sample_ped_lines:
writer.writerow(sample_ped_lines[sname])
else:
writer.writerow([ped_info["family_id"], ped_info["individual_id"],
ped_info["paternal_id"], ped_info["maternal_id"],
ped_info["gender"], ped_info["affected"],
ped_info["ethnicity"]])
return out_file | python | def create_ped_file(samples, base_vcf, out_dir=None):
out_file = "%s.ped" % utils.splitext_plus(base_vcf)[0]
if out_dir:
out_file = os.path.join(out_dir, os.path.basename(out_file))
sample_ped_lines = {}
header = ["#Family_ID", "Individual_ID", "Paternal_ID", "Maternal_ID", "Sex", "Phenotype", "Ethnicity"]
for md_ped in list(set([x for x in [tz.get_in(["metadata", "ped"], data)
for data in samples] if x is not None])):
with open(md_ped) as in_handle:
reader = csv.reader(in_handle, dialect="excel-tab")
for parts in reader:
if parts[0].startswith("#") and len(parts) > len(header):
header = header + parts[len(header):]
else:
sample_ped_lines[parts[1]] = parts
if not utils.file_exists(out_file):
with file_transaction(samples[0], out_file) as tx_out_file:
with open(tx_out_file, "w") as out_handle:
want_samples = set(vcfutils.get_samples(base_vcf))
writer = csv.writer(out_handle, dialect="excel-tab")
writer.writerow(header)
for data in samples:
ped_info = get_ped_info(data, samples)
sname = ped_info["individual_id"]
if sname in want_samples:
want_samples.remove(sname)
if sname in sample_ped_lines:
writer.writerow(sample_ped_lines[sname])
else:
writer.writerow([ped_info["family_id"], ped_info["individual_id"],
ped_info["paternal_id"], ped_info["maternal_id"],
ped_info["gender"], ped_info["affected"],
ped_info["ethnicity"]])
return out_file | [
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237,872 | bcbio/bcbio-nextgen | bcbio/variation/population.py | _is_small_vcf | def _is_small_vcf(vcf_file):
"""Check for small VCFs which we want to analyze quicker.
"""
count = 0
small_thresh = 250
with utils.open_gzipsafe(vcf_file) as in_handle:
for line in in_handle:
if not line.startswith("#"):
count += 1
if count > small_thresh:
return False
return True | python | def _is_small_vcf(vcf_file):
count = 0
small_thresh = 250
with utils.open_gzipsafe(vcf_file) as in_handle:
for line in in_handle:
if not line.startswith("#"):
count += 1
if count > small_thresh:
return False
return True | [
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237,873 | bcbio/bcbio-nextgen | bcbio/variation/population.py | get_multisample_vcf | def get_multisample_vcf(fnames, name, caller, data):
"""Retrieve a multiple sample VCF file in a standard location.
Handles inputs with multiple repeated input files from batches.
"""
unique_fnames = []
for f in fnames:
if f not in unique_fnames:
unique_fnames.append(f)
out_dir = utils.safe_makedir(os.path.join(data["dirs"]["work"], "gemini"))
if len(unique_fnames) > 1:
gemini_vcf = os.path.join(out_dir, "%s-%s.vcf.gz" % (name, caller))
vrn_file_batch = None
for variant in data.get("variants", []):
if variant["variantcaller"] == caller and variant.get("vrn_file_batch"):
vrn_file_batch = variant["vrn_file_batch"]
if vrn_file_batch:
utils.symlink_plus(vrn_file_batch, gemini_vcf)
return gemini_vcf
else:
return vcfutils.merge_variant_files(unique_fnames, gemini_vcf, dd.get_ref_file(data),
data["config"])
else:
gemini_vcf = os.path.join(out_dir, "%s-%s%s" % (name, caller, utils.splitext_plus(unique_fnames[0])[1]))
utils.symlink_plus(unique_fnames[0], gemini_vcf)
return gemini_vcf | python | def get_multisample_vcf(fnames, name, caller, data):
unique_fnames = []
for f in fnames:
if f not in unique_fnames:
unique_fnames.append(f)
out_dir = utils.safe_makedir(os.path.join(data["dirs"]["work"], "gemini"))
if len(unique_fnames) > 1:
gemini_vcf = os.path.join(out_dir, "%s-%s.vcf.gz" % (name, caller))
vrn_file_batch = None
for variant in data.get("variants", []):
if variant["variantcaller"] == caller and variant.get("vrn_file_batch"):
vrn_file_batch = variant["vrn_file_batch"]
if vrn_file_batch:
utils.symlink_plus(vrn_file_batch, gemini_vcf)
return gemini_vcf
else:
return vcfutils.merge_variant_files(unique_fnames, gemini_vcf, dd.get_ref_file(data),
data["config"])
else:
gemini_vcf = os.path.join(out_dir, "%s-%s%s" % (name, caller, utils.splitext_plus(unique_fnames[0])[1]))
utils.symlink_plus(unique_fnames[0], gemini_vcf)
return gemini_vcf | [
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237,874 | bcbio/bcbio-nextgen | bcbio/variation/population.py | get_gemini_files | def get_gemini_files(data):
"""Enumerate available gemini data files in a standard installation.
"""
try:
from gemini import annotations, config
except ImportError:
return {}
return {"base": config.read_gemini_config()["annotation_dir"],
"files": annotations.get_anno_files().values()} | python | def get_gemini_files(data):
try:
from gemini import annotations, config
except ImportError:
return {}
return {"base": config.read_gemini_config()["annotation_dir"],
"files": annotations.get_anno_files().values()} | [
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237,875 | bcbio/bcbio-nextgen | bcbio/variation/population.py | _group_by_batches | def _group_by_batches(samples, check_fn):
"""Group data items into batches, providing details to retrieve results.
"""
batch_groups = collections.defaultdict(list)
singles = []
out_retrieve = []
extras = []
for data in [x[0] for x in samples]:
if check_fn(data):
batch = tz.get_in(["metadata", "batch"], data)
name = str(dd.get_sample_name(data))
if batch:
out_retrieve.append((str(batch), data))
else:
out_retrieve.append((name, data))
for vrn in data["variants"]:
if vrn.get("population", True):
if batch:
batch_groups[(str(batch), vrn["variantcaller"])].append((vrn["vrn_file"], data))
else:
singles.append((name, vrn["variantcaller"], data, vrn["vrn_file"]))
else:
extras.append(data)
return batch_groups, singles, out_retrieve, extras | python | def _group_by_batches(samples, check_fn):
batch_groups = collections.defaultdict(list)
singles = []
out_retrieve = []
extras = []
for data in [x[0] for x in samples]:
if check_fn(data):
batch = tz.get_in(["metadata", "batch"], data)
name = str(dd.get_sample_name(data))
if batch:
out_retrieve.append((str(batch), data))
else:
out_retrieve.append((name, data))
for vrn in data["variants"]:
if vrn.get("population", True):
if batch:
batch_groups[(str(batch), vrn["variantcaller"])].append((vrn["vrn_file"], data))
else:
singles.append((name, vrn["variantcaller"], data, vrn["vrn_file"]))
else:
extras.append(data)
return batch_groups, singles, out_retrieve, extras | [
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237,876 | bcbio/bcbio-nextgen | bcbio/variation/population.py | prep_db_parallel | def prep_db_parallel(samples, parallel_fn):
"""Prepares gemini databases in parallel, handling jointly called populations.
"""
batch_groups, singles, out_retrieve, extras = _group_by_batches(samples, _has_variant_calls)
to_process = []
has_batches = False
for (name, caller), info in batch_groups.items():
fnames = [x[0] for x in info]
to_process.append([fnames, (str(name), caller, True), [x[1] for x in info], extras])
has_batches = True
for name, caller, data, fname in singles:
to_process.append([[fname], (str(name), caller, False), [data], extras])
output = parallel_fn("prep_gemini_db", to_process)
out_fetch = {}
for batch_id, out_file in output:
out_fetch[tuple(batch_id)] = out_file
out = []
for batch_name, data in out_retrieve:
out_variants = []
for vrn in data["variants"]:
use_population = vrn.pop("population", True)
if use_population:
vrn["population"] = out_fetch[(batch_name, vrn["variantcaller"])]
out_variants.append(vrn)
data["variants"] = out_variants
out.append([data])
for x in extras:
out.append([x])
return out | python | def prep_db_parallel(samples, parallel_fn):
batch_groups, singles, out_retrieve, extras = _group_by_batches(samples, _has_variant_calls)
to_process = []
has_batches = False
for (name, caller), info in batch_groups.items():
fnames = [x[0] for x in info]
to_process.append([fnames, (str(name), caller, True), [x[1] for x in info], extras])
has_batches = True
for name, caller, data, fname in singles:
to_process.append([[fname], (str(name), caller, False), [data], extras])
output = parallel_fn("prep_gemini_db", to_process)
out_fetch = {}
for batch_id, out_file in output:
out_fetch[tuple(batch_id)] = out_file
out = []
for batch_name, data in out_retrieve:
out_variants = []
for vrn in data["variants"]:
use_population = vrn.pop("population", True)
if use_population:
vrn["population"] = out_fetch[(batch_name, vrn["variantcaller"])]
out_variants.append(vrn)
data["variants"] = out_variants
out.append([data])
for x in extras:
out.append([x])
return out | [
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237,877 | bcbio/bcbio-nextgen | bcbio/workflow/stormseq.py | _get_s3_files | def _get_s3_files(local_dir, file_info, params):
"""Retrieve s3 files to local directory, handling STORMSeq inputs.
"""
assert len(file_info) == 1
files = file_info.values()[0]
fnames = []
for k in ["1", "2"]:
if files[k] not in fnames:
fnames.append(files[k])
out = []
for fname in fnames:
bucket, key = fname.replace("s3://", "").split("/", 1)
if params["access_key_id"] == "TEST":
out.append(os.path.join(local_dir, os.path.basename(key)))
else:
out.append(s3.get_file(local_dir, bucket, key, params))
return out | python | def _get_s3_files(local_dir, file_info, params):
assert len(file_info) == 1
files = file_info.values()[0]
fnames = []
for k in ["1", "2"]:
if files[k] not in fnames:
fnames.append(files[k])
out = []
for fname in fnames:
bucket, key = fname.replace("s3://", "").split("/", 1)
if params["access_key_id"] == "TEST":
out.append(os.path.join(local_dir, os.path.basename(key)))
else:
out.append(s3.get_file(local_dir, bucket, key, params))
return out | [
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237,878 | bcbio/bcbio-nextgen | bcbio/pipeline/fastq.py | _gzip_fastq | def _gzip_fastq(in_file, out_dir=None):
"""
gzip a fastq file if it is not already gzipped, handling conversion
from bzip to gzipped files
"""
if fastq.is_fastq(in_file) and not objectstore.is_remote(in_file):
if utils.is_bzipped(in_file):
return _bzip_gzip(in_file, out_dir)
elif not utils.is_gzipped(in_file):
if out_dir:
gzipped_file = os.path.join(out_dir, os.path.basename(in_file) + ".gz")
else:
gzipped_file = in_file + ".gz"
if file_exists(gzipped_file):
return gzipped_file
message = "gzipping {in_file} to {gzipped_file}.".format(
in_file=in_file, gzipped_file=gzipped_file)
with file_transaction(gzipped_file) as tx_gzipped_file:
do.run("gzip -c {in_file} > {tx_gzipped_file}".format(**locals()),
message)
return gzipped_file
return in_file | python | def _gzip_fastq(in_file, out_dir=None):
if fastq.is_fastq(in_file) and not objectstore.is_remote(in_file):
if utils.is_bzipped(in_file):
return _bzip_gzip(in_file, out_dir)
elif not utils.is_gzipped(in_file):
if out_dir:
gzipped_file = os.path.join(out_dir, os.path.basename(in_file) + ".gz")
else:
gzipped_file = in_file + ".gz"
if file_exists(gzipped_file):
return gzipped_file
message = "gzipping {in_file} to {gzipped_file}.".format(
in_file=in_file, gzipped_file=gzipped_file)
with file_transaction(gzipped_file) as tx_gzipped_file:
do.run("gzip -c {in_file} > {tx_gzipped_file}".format(**locals()),
message)
return gzipped_file
return in_file | [
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237,879 | bcbio/bcbio-nextgen | bcbio/pipeline/fastq.py | _pipeline_needs_fastq | def _pipeline_needs_fastq(config, data):
"""Determine if the pipeline can proceed with a BAM file, or needs fastq conversion.
"""
aligner = config["algorithm"].get("aligner")
support_bam = aligner in alignment.metadata.get("support_bam", [])
return aligner and not support_bam | python | def _pipeline_needs_fastq(config, data):
aligner = config["algorithm"].get("aligner")
support_bam = aligner in alignment.metadata.get("support_bam", [])
return aligner and not support_bam | [
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237,880 | bcbio/bcbio-nextgen | bcbio/pipeline/fastq.py | convert_bam_to_fastq | def convert_bam_to_fastq(in_file, work_dir, data, dirs, config):
"""Convert BAM input file into FASTQ files.
"""
return alignprep.prep_fastq_inputs([in_file], data) | python | def convert_bam_to_fastq(in_file, work_dir, data, dirs, config):
return alignprep.prep_fastq_inputs([in_file], data) | [
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237,881 | bcbio/bcbio-nextgen | bcbio/pipeline/fastq.py | merge | def merge(files, out_file, config):
"""merge smartly fastq files. It recognizes paired fastq files."""
pair1 = [fastq_file[0] for fastq_file in files]
if len(files[0]) > 1:
path = splitext_plus(out_file)
pair1_out_file = path[0] + "_R1" + path[1]
pair2 = [fastq_file[1] for fastq_file in files]
pair2_out_file = path[0] + "_R2" + path[1]
_merge_list_fastqs(pair1, pair1_out_file, config)
_merge_list_fastqs(pair2, pair2_out_file, config)
return [pair1_out_file, pair2_out_file]
else:
return _merge_list_fastqs(pair1, out_file, config) | python | def merge(files, out_file, config):
pair1 = [fastq_file[0] for fastq_file in files]
if len(files[0]) > 1:
path = splitext_plus(out_file)
pair1_out_file = path[0] + "_R1" + path[1]
pair2 = [fastq_file[1] for fastq_file in files]
pair2_out_file = path[0] + "_R2" + path[1]
_merge_list_fastqs(pair1, pair1_out_file, config)
_merge_list_fastqs(pair2, pair2_out_file, config)
return [pair1_out_file, pair2_out_file]
else:
return _merge_list_fastqs(pair1, out_file, config) | [
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237,882 | bcbio/bcbio-nextgen | bcbio/pipeline/fastq.py | _merge_list_fastqs | def _merge_list_fastqs(files, out_file, config):
"""merge list of fastq files into one"""
if not all(map(fastq.is_fastq, files)):
raise ValueError("Not all of the files to merge are fastq files: %s " % (files))
assert all(map(utils.file_exists, files)), ("Not all of the files to merge "
"exist: %s" % (files))
if not file_exists(out_file):
files = [_gzip_fastq(fn) for fn in files]
if len(files) == 1:
if "remove_source" in config and config["remove_source"]:
shutil.move(files[0], out_file)
else:
os.symlink(files[0], out_file)
return out_file
with file_transaction(out_file) as file_txt_out:
files_str = " ".join(list(files))
cmd = "cat {files_str} > {file_txt_out}".format(**locals())
do.run(cmd, "merge fastq files %s" % files)
return out_file | python | def _merge_list_fastqs(files, out_file, config):
if not all(map(fastq.is_fastq, files)):
raise ValueError("Not all of the files to merge are fastq files: %s " % (files))
assert all(map(utils.file_exists, files)), ("Not all of the files to merge "
"exist: %s" % (files))
if not file_exists(out_file):
files = [_gzip_fastq(fn) for fn in files]
if len(files) == 1:
if "remove_source" in config and config["remove_source"]:
shutil.move(files[0], out_file)
else:
os.symlink(files[0], out_file)
return out_file
with file_transaction(out_file) as file_txt_out:
files_str = " ".join(list(files))
cmd = "cat {files_str} > {file_txt_out}".format(**locals())
do.run(cmd, "merge fastq files %s" % files)
return out_file | [
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237,883 | bcbio/bcbio-nextgen | bcbio/bed/__init__.py | decomment | def decomment(bed_file, out_file):
"""
clean a BED file
"""
if file_exists(out_file):
return out_file
with utils.open_gzipsafe(bed_file) as in_handle, open(out_file, "w") as out_handle:
for line in in_handle:
if line.startswith("#") or line.startswith("browser") or line.startswith("track"):
continue
else:
out_handle.write(line)
return out_file | python | def decomment(bed_file, out_file):
if file_exists(out_file):
return out_file
with utils.open_gzipsafe(bed_file) as in_handle, open(out_file, "w") as out_handle:
for line in in_handle:
if line.startswith("#") or line.startswith("browser") or line.startswith("track"):
continue
else:
out_handle.write(line)
return out_file | [
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237,884 | bcbio/bcbio-nextgen | bcbio/bed/__init__.py | concat | def concat(bed_files, catted=None):
"""
recursively concat a set of BED files, returning a
sorted bedtools object of the result
"""
bed_files = [x for x in bed_files if x]
if len(bed_files) == 0:
if catted:
# move to a .bed extension for downstream tools if not already
sorted_bed = catted.sort()
if not sorted_bed.fn.endswith(".bed"):
return sorted_bed.moveto(sorted_bed.fn + ".bed")
else:
return sorted_bed
else:
return catted
if not catted:
bed_files = list(bed_files)
catted = bt.BedTool(bed_files.pop())
else:
catted = catted.cat(bed_files.pop(), postmerge=False,
force_truncate=False)
return concat(bed_files, catted) | python | def concat(bed_files, catted=None):
bed_files = [x for x in bed_files if x]
if len(bed_files) == 0:
if catted:
# move to a .bed extension for downstream tools if not already
sorted_bed = catted.sort()
if not sorted_bed.fn.endswith(".bed"):
return sorted_bed.moveto(sorted_bed.fn + ".bed")
else:
return sorted_bed
else:
return catted
if not catted:
bed_files = list(bed_files)
catted = bt.BedTool(bed_files.pop())
else:
catted = catted.cat(bed_files.pop(), postmerge=False,
force_truncate=False)
return concat(bed_files, catted) | [
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237,885 | bcbio/bcbio-nextgen | bcbio/bed/__init__.py | merge | def merge(bedfiles):
"""
given a BED file or list of BED files merge them an return a bedtools object
"""
if isinstance(bedfiles, list):
catted = concat(bedfiles)
else:
catted = concat([bedfiles])
if catted:
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else:
return catted | python | def merge(bedfiles):
if isinstance(bedfiles, list):
catted = concat(bedfiles)
else:
catted = concat([bedfiles])
if catted:
return concat(bedfiles).sort().merge()
else:
return catted | [
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237,886 | bcbio/bcbio-nextgen | bcbio/structural/hydra.py | select_unaligned_read_pairs | def select_unaligned_read_pairs(in_bam, extra, out_dir, config):
"""Retrieve unaligned read pairs from input alignment BAM, as two fastq files.
"""
runner = broad.runner_from_config(config)
base, ext = os.path.splitext(os.path.basename(in_bam))
nomap_bam = os.path.join(out_dir, "{}-{}{}".format(base, extra, ext))
if not utils.file_exists(nomap_bam):
with file_transaction(nomap_bam) as tx_out:
runner.run("FilterSamReads", [("INPUT", in_bam),
("OUTPUT", tx_out),
("EXCLUDE_ALIGNED", "true"),
("WRITE_READS_FILES", "false"),
("SORT_ORDER", "queryname")])
has_reads = False
with pysam.Samfile(nomap_bam, "rb") as in_pysam:
for read in in_pysam:
if read.is_paired:
has_reads = True
break
if has_reads:
out_fq1, out_fq2 = ["{}-{}.fq".format(os.path.splitext(nomap_bam)[0], i) for i in [1, 2]]
runner.run_fn("picard_bam_to_fastq", nomap_bam, out_fq1, out_fq2)
return out_fq1, out_fq2
else:
return None, None | python | def select_unaligned_read_pairs(in_bam, extra, out_dir, config):
runner = broad.runner_from_config(config)
base, ext = os.path.splitext(os.path.basename(in_bam))
nomap_bam = os.path.join(out_dir, "{}-{}{}".format(base, extra, ext))
if not utils.file_exists(nomap_bam):
with file_transaction(nomap_bam) as tx_out:
runner.run("FilterSamReads", [("INPUT", in_bam),
("OUTPUT", tx_out),
("EXCLUDE_ALIGNED", "true"),
("WRITE_READS_FILES", "false"),
("SORT_ORDER", "queryname")])
has_reads = False
with pysam.Samfile(nomap_bam, "rb") as in_pysam:
for read in in_pysam:
if read.is_paired:
has_reads = True
break
if has_reads:
out_fq1, out_fq2 = ["{}-{}.fq".format(os.path.splitext(nomap_bam)[0], i) for i in [1, 2]]
runner.run_fn("picard_bam_to_fastq", nomap_bam, out_fq1, out_fq2)
return out_fq1, out_fq2
else:
return None, None | [
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237,887 | bcbio/bcbio-nextgen | bcbio/structural/hydra.py | remove_nopairs | def remove_nopairs(in_bam, out_dir, config):
"""Remove any reads without both pairs present in the file.
"""
runner = broad.runner_from_config(config)
out_bam = os.path.join(out_dir, "{}-safepair{}".format(*os.path.splitext(os.path.basename(in_bam))))
if not utils.file_exists(out_bam):
read_counts = collections.defaultdict(int)
with pysam.Samfile(in_bam, "rb") as in_pysam:
for read in in_pysam:
if read.is_paired:
read_counts[read.qname] += 1
with pysam.Samfile(in_bam, "rb") as in_pysam:
with file_transaction(out_bam) as tx_out_bam:
with pysam.Samfile(tx_out_bam, "wb", template=in_pysam) as out_pysam:
for read in in_pysam:
if read_counts[read.qname] == 2:
out_pysam.write(read)
return runner.run_fn("picard_sort", out_bam, "queryname") | python | def remove_nopairs(in_bam, out_dir, config):
runner = broad.runner_from_config(config)
out_bam = os.path.join(out_dir, "{}-safepair{}".format(*os.path.splitext(os.path.basename(in_bam))))
if not utils.file_exists(out_bam):
read_counts = collections.defaultdict(int)
with pysam.Samfile(in_bam, "rb") as in_pysam:
for read in in_pysam:
if read.is_paired:
read_counts[read.qname] += 1
with pysam.Samfile(in_bam, "rb") as in_pysam:
with file_transaction(out_bam) as tx_out_bam:
with pysam.Samfile(tx_out_bam, "wb", template=in_pysam) as out_pysam:
for read in in_pysam:
if read_counts[read.qname] == 2:
out_pysam.write(read)
return runner.run_fn("picard_sort", out_bam, "queryname") | [
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237,888 | bcbio/bcbio-nextgen | bcbio/structural/hydra.py | tiered_alignment | def tiered_alignment(in_bam, tier_num, multi_mappers, extra_args,
genome_build, pair_stats,
work_dir, dirs, config):
"""Perform the alignment of non-mapped reads from previous tier.
"""
nomap_fq1, nomap_fq2 = select_unaligned_read_pairs(in_bam, "tier{}".format(tier_num),
work_dir, config)
if nomap_fq1 is not None:
base_name = "{}-tier{}out".format(os.path.splitext(os.path.basename(in_bam))[0],
tier_num)
config = copy.deepcopy(config)
dirs = copy.deepcopy(dirs)
config["algorithm"]["bam_sort"] = "queryname"
config["algorithm"]["multiple_mappers"] = multi_mappers
config["algorithm"]["extra_align_args"] = ["-i", int(pair_stats["mean"]),
int(pair_stats["std"])] + extra_args
out_bam, ref_file = align_to_sort_bam(nomap_fq1, nomap_fq2,
lane.rg_names(base_name, base_name, config),
genome_build, "novoalign",
dirs, config,
dir_ext=os.path.join("hydra", os.path.split(nomap_fq1)[0]))
return out_bam
else:
return None | python | def tiered_alignment(in_bam, tier_num, multi_mappers, extra_args,
genome_build, pair_stats,
work_dir, dirs, config):
nomap_fq1, nomap_fq2 = select_unaligned_read_pairs(in_bam, "tier{}".format(tier_num),
work_dir, config)
if nomap_fq1 is not None:
base_name = "{}-tier{}out".format(os.path.splitext(os.path.basename(in_bam))[0],
tier_num)
config = copy.deepcopy(config)
dirs = copy.deepcopy(dirs)
config["algorithm"]["bam_sort"] = "queryname"
config["algorithm"]["multiple_mappers"] = multi_mappers
config["algorithm"]["extra_align_args"] = ["-i", int(pair_stats["mean"]),
int(pair_stats["std"])] + extra_args
out_bam, ref_file = align_to_sort_bam(nomap_fq1, nomap_fq2,
lane.rg_names(base_name, base_name, config),
genome_build, "novoalign",
dirs, config,
dir_ext=os.path.join("hydra", os.path.split(nomap_fq1)[0]))
return out_bam
else:
return None | [
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237,889 | bcbio/bcbio-nextgen | bcbio/structural/hydra.py | convert_bam_to_bed | def convert_bam_to_bed(in_bam, out_file):
"""Convert BAM to bed file using BEDTools.
"""
with file_transaction(out_file) as tx_out_file:
with open(tx_out_file, "w") as out_handle:
subprocess.check_call(["bamToBed", "-i", in_bam, "-tag", "NM"],
stdout=out_handle)
return out_file | python | def convert_bam_to_bed(in_bam, out_file):
with file_transaction(out_file) as tx_out_file:
with open(tx_out_file, "w") as out_handle:
subprocess.check_call(["bamToBed", "-i", in_bam, "-tag", "NM"],
stdout=out_handle)
return out_file | [
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237,890 | bcbio/bcbio-nextgen | bcbio/structural/hydra.py | hydra_breakpoints | def hydra_breakpoints(in_bam, pair_stats):
"""Detect structural variation breakpoints with hydra.
"""
in_bed = convert_bam_to_bed(in_bam)
if os.path.getsize(in_bed) > 0:
pair_bed = pair_discordants(in_bed, pair_stats)
dedup_bed = dedup_discordants(pair_bed)
return run_hydra(dedup_bed, pair_stats)
else:
return None | python | def hydra_breakpoints(in_bam, pair_stats):
in_bed = convert_bam_to_bed(in_bam)
if os.path.getsize(in_bed) > 0:
pair_bed = pair_discordants(in_bed, pair_stats)
dedup_bed = dedup_discordants(pair_bed)
return run_hydra(dedup_bed, pair_stats)
else:
return None | [
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237,891 | bcbio/bcbio-nextgen | bcbio/structural/hydra.py | detect_sv | def detect_sv(align_bam, genome_build, dirs, config):
"""Detect structural variation from discordant aligned pairs.
"""
work_dir = utils.safe_makedir(os.path.join(dirs["work"], "structural"))
pair_stats = shared.calc_paired_insert_stats(align_bam)
fix_bam = remove_nopairs(align_bam, work_dir, config)
tier2_align = tiered_alignment(fix_bam, "2", True, [],
genome_build, pair_stats,
work_dir, dirs, config)
if tier2_align:
tier3_align = tiered_alignment(tier2_align, "3", "Ex 1100", ["-t", "300"],
genome_build, pair_stats,
work_dir, dirs, config)
if tier3_align:
hydra_bps = hydra_breakpoints(tier3_align, pair_stats) | python | def detect_sv(align_bam, genome_build, dirs, config):
work_dir = utils.safe_makedir(os.path.join(dirs["work"], "structural"))
pair_stats = shared.calc_paired_insert_stats(align_bam)
fix_bam = remove_nopairs(align_bam, work_dir, config)
tier2_align = tiered_alignment(fix_bam, "2", True, [],
genome_build, pair_stats,
work_dir, dirs, config)
if tier2_align:
tier3_align = tiered_alignment(tier2_align, "3", "Ex 1100", ["-t", "300"],
genome_build, pair_stats,
work_dir, dirs, config)
if tier3_align:
hydra_bps = hydra_breakpoints(tier3_align, pair_stats) | [
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237,892 | bcbio/bcbio-nextgen | bcbio/log/logbook_zmqpush.py | inject | def inject(**params):
"""
A Logbook processor to inject arbitrary information into log records.
Simply pass in keyword arguments and use as a context manager:
>>> with inject(identifier=str(uuid.uuid4())).applicationbound():
... logger.debug('Something happened')
"""
def callback(log_record):
log_record.extra.update(params)
return logbook.Processor(callback) | python | def inject(**params):
def callback(log_record):
log_record.extra.update(params)
return logbook.Processor(callback) | [
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237,893 | bcbio/bcbio-nextgen | bcbio/log/logbook_zmqpush.py | ZeroMQPullSubscriber.recv | def recv(self, timeout=None):
"""Overwrite standard recv for timeout calls to catch interrupt errors.
"""
if timeout:
try:
testsock = self._zmq.select([self.socket], [], [], timeout)[0]
except zmq.ZMQError as e:
if e.errno == errno.EINTR:
testsock = None
else:
raise
if not testsock:
return
rv = self.socket.recv(self._zmq.NOBLOCK)
return LogRecord.from_dict(json.loads(rv))
else:
return super(ZeroMQPullSubscriber, self).recv(timeout) | python | def recv(self, timeout=None):
if timeout:
try:
testsock = self._zmq.select([self.socket], [], [], timeout)[0]
except zmq.ZMQError as e:
if e.errno == errno.EINTR:
testsock = None
else:
raise
if not testsock:
return
rv = self.socket.recv(self._zmq.NOBLOCK)
return LogRecord.from_dict(json.loads(rv))
else:
return super(ZeroMQPullSubscriber, self).recv(timeout) | [
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237,894 | bcbio/bcbio-nextgen | bcbio/variation/pisces.py | run | def run(align_bams, items, ref_file, assoc_files, region=None, out_file=None):
"""Run tumor only pisces calling
Handles bgzipping output file and fixing VCF sample naming to match BAM sample.
"""
paired = vcfutils.get_paired_bams(align_bams, items)
assert paired and not paired.normal_bam, ("Pisces supports tumor-only variant calling: %s" %
(",".join([dd.get_sample_name(d) for d in items])))
vrs = bedutils.population_variant_regions(items)
target = shared.subset_variant_regions(vrs, region,
out_file, items=items, do_merge=True)
min_af = float(dd.get_min_allele_fraction(paired.tumor_data)) / 100.0
if not utils.file_exists(out_file):
base_out_name = utils.splitext_plus(os.path.basename(paired.tumor_bam))[0]
raw_file = "%s.vcf" % utils.splitext_plus(out_file)[0]
with file_transaction(paired.tumor_data, raw_file) as tx_out_file:
ref_dir = _prep_genome(os.path.dirname(tx_out_file), paired.tumor_data)
out_dir = os.path.dirname(tx_out_file)
cores = dd.get_num_cores(paired.tumor_data)
emit_min_af = min_af / 10.0
cmd = ("pisces --bampaths {paired.tumor_bam} --genomepaths {ref_dir} --intervalpaths {target} "
"--maxthreads {cores} --minvf {emit_min_af} --vffilter {min_af} "
"--ploidy somatic --gvcf false -o {out_dir}")
# Recommended filtering for low frequency indels
# https://github.com/bcbio/bcbio-nextgen/commit/49d0cbb1f6dcbea629c63749e2f9813bd06dcee3#commitcomment-29765373
cmd += " -RMxNFilter 5,9,0.35"
# For low frequency UMI tagged variants, set higher variant thresholds
# https://github.com/Illumina/Pisces/issues/14#issuecomment-399756862
if min_af < (1.0 / 100.0):
cmd += " --minbasecallquality 30"
do.run(cmd.format(**locals()), "Pisces tumor-only somatic calling")
shutil.move(os.path.join(out_dir, "%s.vcf" % base_out_name),
tx_out_file)
vcfutils.bgzip_and_index(raw_file, paired.tumor_data["config"],
prep_cmd="sed 's#%s.bam#%s#' | %s" %
(base_out_name, dd.get_sample_name(paired.tumor_data),
vcfutils.add_contig_to_header_cl(dd.get_ref_file(paired.tumor_data), out_file)))
return vcfutils.bgzip_and_index(out_file, paired.tumor_data["config"]) | python | def run(align_bams, items, ref_file, assoc_files, region=None, out_file=None):
paired = vcfutils.get_paired_bams(align_bams, items)
assert paired and not paired.normal_bam, ("Pisces supports tumor-only variant calling: %s" %
(",".join([dd.get_sample_name(d) for d in items])))
vrs = bedutils.population_variant_regions(items)
target = shared.subset_variant_regions(vrs, region,
out_file, items=items, do_merge=True)
min_af = float(dd.get_min_allele_fraction(paired.tumor_data)) / 100.0
if not utils.file_exists(out_file):
base_out_name = utils.splitext_plus(os.path.basename(paired.tumor_bam))[0]
raw_file = "%s.vcf" % utils.splitext_plus(out_file)[0]
with file_transaction(paired.tumor_data, raw_file) as tx_out_file:
ref_dir = _prep_genome(os.path.dirname(tx_out_file), paired.tumor_data)
out_dir = os.path.dirname(tx_out_file)
cores = dd.get_num_cores(paired.tumor_data)
emit_min_af = min_af / 10.0
cmd = ("pisces --bampaths {paired.tumor_bam} --genomepaths {ref_dir} --intervalpaths {target} "
"--maxthreads {cores} --minvf {emit_min_af} --vffilter {min_af} "
"--ploidy somatic --gvcf false -o {out_dir}")
# Recommended filtering for low frequency indels
# https://github.com/bcbio/bcbio-nextgen/commit/49d0cbb1f6dcbea629c63749e2f9813bd06dcee3#commitcomment-29765373
cmd += " -RMxNFilter 5,9,0.35"
# For low frequency UMI tagged variants, set higher variant thresholds
# https://github.com/Illumina/Pisces/issues/14#issuecomment-399756862
if min_af < (1.0 / 100.0):
cmd += " --minbasecallquality 30"
do.run(cmd.format(**locals()), "Pisces tumor-only somatic calling")
shutil.move(os.path.join(out_dir, "%s.vcf" % base_out_name),
tx_out_file)
vcfutils.bgzip_and_index(raw_file, paired.tumor_data["config"],
prep_cmd="sed 's#%s.bam#%s#' | %s" %
(base_out_name, dd.get_sample_name(paired.tumor_data),
vcfutils.add_contig_to_header_cl(dd.get_ref_file(paired.tumor_data), out_file)))
return vcfutils.bgzip_and_index(out_file, paired.tumor_data["config"]) | [
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237,895 | bcbio/bcbio-nextgen | bcbio/variation/pisces.py | _prep_genome | def _prep_genome(out_dir, data):
"""Create prepped reference directory for pisces.
Requires a custom GenomeSize.xml file present.
"""
genome_name = utils.splitext_plus(os.path.basename(dd.get_ref_file(data)))[0]
out_dir = utils.safe_makedir(os.path.join(out_dir, genome_name))
ref_file = dd.get_ref_file(data)
utils.symlink_plus(ref_file, os.path.join(out_dir, os.path.basename(ref_file)))
with open(os.path.join(out_dir, "GenomeSize.xml"), "w") as out_handle:
out_handle.write('<sequenceSizes genomeName="%s">' % genome_name)
for c in pysam.AlignmentFile("%s.dict" % utils.splitext_plus(ref_file)[0]).header["SQ"]:
cur_ploidy = ploidy.get_ploidy([data], region=[c["SN"]])
out_handle.write('<chromosome fileName="%s" contigName="%s" totalBases="%s" knownBases="%s" '
'isCircular="false" ploidy="%s" md5="%s"/>' %
(os.path.basename(ref_file), c["SN"], c["LN"], c["LN"], cur_ploidy, c["M5"]))
out_handle.write('</sequenceSizes>')
return out_dir | python | def _prep_genome(out_dir, data):
genome_name = utils.splitext_plus(os.path.basename(dd.get_ref_file(data)))[0]
out_dir = utils.safe_makedir(os.path.join(out_dir, genome_name))
ref_file = dd.get_ref_file(data)
utils.symlink_plus(ref_file, os.path.join(out_dir, os.path.basename(ref_file)))
with open(os.path.join(out_dir, "GenomeSize.xml"), "w") as out_handle:
out_handle.write('<sequenceSizes genomeName="%s">' % genome_name)
for c in pysam.AlignmentFile("%s.dict" % utils.splitext_plus(ref_file)[0]).header["SQ"]:
cur_ploidy = ploidy.get_ploidy([data], region=[c["SN"]])
out_handle.write('<chromosome fileName="%s" contigName="%s" totalBases="%s" knownBases="%s" '
'isCircular="false" ploidy="%s" md5="%s"/>' %
(os.path.basename(ref_file), c["SN"], c["LN"], c["LN"], cur_ploidy, c["M5"]))
out_handle.write('</sequenceSizes>')
return out_dir | [
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237,896 | bcbio/bcbio-nextgen | bcbio/variation/deepvariant.py | run | def run(align_bams, items, ref_file, assoc_files, region, out_file):
"""Return DeepVariant calling on germline samples.
region can be a single region or list of multiple regions for multicore calling.
"""
assert not vcfutils.is_paired_analysis(align_bams, items), \
("DeepVariant currently only supports germline calling: %s" %
(", ".join([dd.get_sample_name(d) for d in items])))
assert len(items) == 1, \
("DeepVariant currently only supports single sample calling: %s" %
(", ".join([dd.get_sample_name(d) for d in items])))
out_file = _run_germline(align_bams[0], items[0], ref_file,
region, out_file)
return vcfutils.bgzip_and_index(out_file, items[0]["config"]) | python | def run(align_bams, items, ref_file, assoc_files, region, out_file):
assert not vcfutils.is_paired_analysis(align_bams, items), \
("DeepVariant currently only supports germline calling: %s" %
(", ".join([dd.get_sample_name(d) for d in items])))
assert len(items) == 1, \
("DeepVariant currently only supports single sample calling: %s" %
(", ".join([dd.get_sample_name(d) for d in items])))
out_file = _run_germline(align_bams[0], items[0], ref_file,
region, out_file)
return vcfutils.bgzip_and_index(out_file, items[0]["config"]) | [
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237,897 | bcbio/bcbio-nextgen | bcbio/variation/deepvariant.py | _run_germline | def _run_germline(bam_file, data, ref_file, region, out_file):
"""Single sample germline variant calling.
"""
work_dir = utils.safe_makedir("%s-work" % utils.splitext_plus(out_file)[0])
region_bed = strelka2.get_region_bed(region, [data], out_file, want_gzip=False)
example_dir = _make_examples(bam_file, data, ref_file, region_bed, out_file, work_dir)
if _has_candidate_variants(example_dir):
tfrecord_file = _call_variants(example_dir, region_bed, data, out_file)
return _postprocess_variants(tfrecord_file, data, ref_file, out_file)
else:
return vcfutils.write_empty_vcf(out_file, data["config"], [dd.get_sample_name(data)]) | python | def _run_germline(bam_file, data, ref_file, region, out_file):
work_dir = utils.safe_makedir("%s-work" % utils.splitext_plus(out_file)[0])
region_bed = strelka2.get_region_bed(region, [data], out_file, want_gzip=False)
example_dir = _make_examples(bam_file, data, ref_file, region_bed, out_file, work_dir)
if _has_candidate_variants(example_dir):
tfrecord_file = _call_variants(example_dir, region_bed, data, out_file)
return _postprocess_variants(tfrecord_file, data, ref_file, out_file)
else:
return vcfutils.write_empty_vcf(out_file, data["config"], [dd.get_sample_name(data)]) | [
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237,898 | bcbio/bcbio-nextgen | bcbio/variation/deepvariant.py | _make_examples | def _make_examples(bam_file, data, ref_file, region_bed, out_file, work_dir):
"""Create example pileup images to feed into variant calling.
"""
log_dir = utils.safe_makedir(os.path.join(work_dir, "log"))
example_dir = utils.safe_makedir(os.path.join(work_dir, "examples"))
if len(glob.glob(os.path.join(example_dir, "%s.tfrecord*.gz" % dd.get_sample_name(data)))) == 0:
with tx_tmpdir(data) as tx_example_dir:
cmd = ["dv_make_examples.py", "--cores", dd.get_num_cores(data), "--ref", ref_file,
"--reads", bam_file, "--regions", region_bed, "--logdir", log_dir,
"--examples", tx_example_dir, "--sample", dd.get_sample_name(data)]
do.run(cmd, "DeepVariant make_examples %s" % dd.get_sample_name(data))
for fname in glob.glob(os.path.join(tx_example_dir, "%s.tfrecord*.gz" % dd.get_sample_name(data))):
utils.copy_plus(fname, os.path.join(example_dir, os.path.basename(fname)))
return example_dir | python | def _make_examples(bam_file, data, ref_file, region_bed, out_file, work_dir):
log_dir = utils.safe_makedir(os.path.join(work_dir, "log"))
example_dir = utils.safe_makedir(os.path.join(work_dir, "examples"))
if len(glob.glob(os.path.join(example_dir, "%s.tfrecord*.gz" % dd.get_sample_name(data)))) == 0:
with tx_tmpdir(data) as tx_example_dir:
cmd = ["dv_make_examples.py", "--cores", dd.get_num_cores(data), "--ref", ref_file,
"--reads", bam_file, "--regions", region_bed, "--logdir", log_dir,
"--examples", tx_example_dir, "--sample", dd.get_sample_name(data)]
do.run(cmd, "DeepVariant make_examples %s" % dd.get_sample_name(data))
for fname in glob.glob(os.path.join(tx_example_dir, "%s.tfrecord*.gz" % dd.get_sample_name(data))):
utils.copy_plus(fname, os.path.join(example_dir, os.path.basename(fname)))
return example_dir | [
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237,899 | bcbio/bcbio-nextgen | bcbio/variation/deepvariant.py | _call_variants | def _call_variants(example_dir, region_bed, data, out_file):
"""Call variants from prepared pileup examples, creating tensorflow record file.
"""
tf_out_file = "%s-tfrecord.gz" % utils.splitext_plus(out_file)[0]
if not utils.file_exists(tf_out_file):
with file_transaction(data, tf_out_file) as tx_out_file:
model = "wes" if strelka2.coverage_interval_from_bed(region_bed) == "targeted" else "wgs"
cmd = ["dv_call_variants.py", "--cores", dd.get_num_cores(data),
"--outfile", tx_out_file, "--examples", example_dir,
"--sample", dd.get_sample_name(data), "--model", model]
do.run(cmd, "DeepVariant call_variants %s" % dd.get_sample_name(data))
return tf_out_file | python | def _call_variants(example_dir, region_bed, data, out_file):
tf_out_file = "%s-tfrecord.gz" % utils.splitext_plus(out_file)[0]
if not utils.file_exists(tf_out_file):
with file_transaction(data, tf_out_file) as tx_out_file:
model = "wes" if strelka2.coverage_interval_from_bed(region_bed) == "targeted" else "wgs"
cmd = ["dv_call_variants.py", "--cores", dd.get_num_cores(data),
"--outfile", tx_out_file, "--examples", example_dir,
"--sample", dd.get_sample_name(data), "--model", model]
do.run(cmd, "DeepVariant call_variants %s" % dd.get_sample_name(data))
return tf_out_file | [
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