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bcbio/bcbio-nextgen
bcbio/structural/shared.py
calc_paired_insert_stats_save
def calc_paired_insert_stats_save(in_bam, stat_file, nsample=1000000): """Calculate paired stats, saving to a file for re-runs. """ if utils.file_exists(stat_file): with open(stat_file) as in_handle: return yaml.safe_load(in_handle) else: stats = calc_paired_insert_stats(in_bam, nsample) with open(stat_file, "w") as out_handle: yaml.safe_dump(stats, out_handle, default_flow_style=False, allow_unicode=False) return stats
python
def calc_paired_insert_stats_save(in_bam, stat_file, nsample=1000000): """Calculate paired stats, saving to a file for re-runs. """ if utils.file_exists(stat_file): with open(stat_file) as in_handle: return yaml.safe_load(in_handle) else: stats = calc_paired_insert_stats(in_bam, nsample) with open(stat_file, "w") as out_handle: yaml.safe_dump(stats, out_handle, default_flow_style=False, allow_unicode=False) return stats
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Calculate paired stats, saving to a file for re-runs.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/structural/shared.py#L321-L331
train
219,000
bcbio/bcbio-nextgen
scripts/utils/upload_to_synapse.py
_accumulate_remotes
def _accumulate_remotes(synapse_parent_id, syn): """Retrieve references to all remote directories and files. """ remotes = {} s_base_folder = syn.get(synapse_parent_id) for (s_dirpath, s_dirpath_id), _, s_filenames in synapseutils.walk(syn, synapse_parent_id): remotes[s_dirpath] = s_dirpath_id if s_filenames: for s_filename, s_filename_id in s_filenames: remotes[os.path.join(s_dirpath, s_filename)] = s_filename_id return s_base_folder, remotes
python
def _accumulate_remotes(synapse_parent_id, syn): """Retrieve references to all remote directories and files. """ remotes = {} s_base_folder = syn.get(synapse_parent_id) for (s_dirpath, s_dirpath_id), _, s_filenames in synapseutils.walk(syn, synapse_parent_id): remotes[s_dirpath] = s_dirpath_id if s_filenames: for s_filename, s_filename_id in s_filenames: remotes[os.path.join(s_dirpath, s_filename)] = s_filename_id return s_base_folder, remotes
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Retrieve references to all remote directories and files.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/scripts/utils/upload_to_synapse.py#L38-L48
train
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bcbio/bcbio-nextgen
scripts/utils/upload_to_synapse.py
_remote_folder
def _remote_folder(dirpath, remotes, syn): """Retrieve the remote folder for files, creating if necessary. """ if dirpath in remotes: return remotes[dirpath], remotes else: parent_dir, cur_dir = os.path.split(dirpath) parent_folder, remotes = _remote_folder(parent_dir, remotes, syn) s_cur_dir = syn.store(synapseclient.Folder(cur_dir, parent=parent_folder)) remotes[dirpath] = s_cur_dir.id return s_cur_dir.id, remotes
python
def _remote_folder(dirpath, remotes, syn): """Retrieve the remote folder for files, creating if necessary. """ if dirpath in remotes: return remotes[dirpath], remotes else: parent_dir, cur_dir = os.path.split(dirpath) parent_folder, remotes = _remote_folder(parent_dir, remotes, syn) s_cur_dir = syn.store(synapseclient.Folder(cur_dir, parent=parent_folder)) remotes[dirpath] = s_cur_dir.id return s_cur_dir.id, remotes
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/scripts/utils/upload_to_synapse.py#L50-L60
train
219,002
bcbio/bcbio-nextgen
bcbio/structural/wham.py
run
def run(items, background=None): """Detect copy number variations from batched set of samples using WHAM. """ if not background: background = [] background_bams = [] paired = vcfutils.get_paired_bams([x["align_bam"] for x in items], items) if paired: inputs = [paired.tumor_data] if paired.normal_bam: background = [paired.normal_data] background_bams = [paired.normal_bam] else: assert not background inputs, background = shared.find_case_control(items) background_bams = [x["align_bam"] for x in background] orig_vcf = _run_wham(inputs, background_bams) out = [] for data in inputs: if "sv" not in data: data["sv"] = [] final_vcf = shared.finalize_sv(orig_vcf, data, items) data["sv"].append({"variantcaller": "wham", "vrn_file": final_vcf}) out.append(data) return out
python
def run(items, background=None): """Detect copy number variations from batched set of samples using WHAM. """ if not background: background = [] background_bams = [] paired = vcfutils.get_paired_bams([x["align_bam"] for x in items], items) if paired: inputs = [paired.tumor_data] if paired.normal_bam: background = [paired.normal_data] background_bams = [paired.normal_bam] else: assert not background inputs, background = shared.find_case_control(items) background_bams = [x["align_bam"] for x in background] orig_vcf = _run_wham(inputs, background_bams) out = [] for data in inputs: if "sv" not in data: data["sv"] = [] final_vcf = shared.finalize_sv(orig_vcf, data, items) data["sv"].append({"variantcaller": "wham", "vrn_file": final_vcf}) out.append(data) return out
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/structural/wham.py#L19-L42
train
219,003
bcbio/bcbio-nextgen
bcbio/structural/wham.py
_run_wham
def _run_wham(inputs, background_bams): """Run WHAM on a defined set of inputs and targets. """ out_file = os.path.join(_sv_workdir(inputs[0]), "%s-wham.vcf.gz" % dd.get_sample_name(inputs[0])) if not utils.file_exists(out_file): with file_transaction(inputs[0], out_file) as tx_out_file: cores = dd.get_cores(inputs[0]) ref_file = dd.get_ref_file(inputs[0]) include_chroms = ",".join([c.name for c in ref.file_contigs(ref_file) if chromhacks.is_autosomal_or_x(c.name)]) all_bams = ",".join([x["align_bam"] for x in inputs] + background_bams) cmd = ("whamg -x {cores} -a {ref_file} -f {all_bams} -c {include_chroms} " "| bgzip -c > {tx_out_file}") do.run(cmd.format(**locals()), "WHAM SV caller: %s" % ", ".join(dd.get_sample_name(d) for d in inputs)) return vcfutils.bgzip_and_index(out_file, inputs[0]["config"])
python
def _run_wham(inputs, background_bams): """Run WHAM on a defined set of inputs and targets. """ out_file = os.path.join(_sv_workdir(inputs[0]), "%s-wham.vcf.gz" % dd.get_sample_name(inputs[0])) if not utils.file_exists(out_file): with file_transaction(inputs[0], out_file) as tx_out_file: cores = dd.get_cores(inputs[0]) ref_file = dd.get_ref_file(inputs[0]) include_chroms = ",".join([c.name for c in ref.file_contigs(ref_file) if chromhacks.is_autosomal_or_x(c.name)]) all_bams = ",".join([x["align_bam"] for x in inputs] + background_bams) cmd = ("whamg -x {cores} -a {ref_file} -f {all_bams} -c {include_chroms} " "| bgzip -c > {tx_out_file}") do.run(cmd.format(**locals()), "WHAM SV caller: %s" % ", ".join(dd.get_sample_name(d) for d in inputs)) return vcfutils.bgzip_and_index(out_file, inputs[0]["config"])
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/structural/wham.py#L48-L62
train
219,004
bcbio/bcbio-nextgen
bcbio/structural/wham.py
filter_by_background
def filter_by_background(in_vcf, full_vcf, background, data): """Filter SV calls also present in background samples. Skips filtering of inversions, which are not characterized differently between cases and controls in test datasets. """ Filter = collections.namedtuple('Filter', ['id', 'desc']) back_filter = Filter(id='InBackground', desc='Rejected due to presence in background sample') out_file = "%s-filter.vcf" % utils.splitext_plus(in_vcf)[0] if not utils.file_uptodate(out_file, in_vcf) and not utils.file_uptodate(out_file + ".vcf.gz", in_vcf): with file_transaction(data, out_file) as tx_out_file: with open(tx_out_file, "w") as out_handle: reader = vcf.VCFReader(filename=in_vcf) reader.filters["InBackground"] = back_filter full_reader = vcf.VCFReader(filename=full_vcf) writer = vcf.VCFWriter(out_handle, template=reader) for out_rec, rec in zip(reader, full_reader): rec_type = rec.genotype(dd.get_sample_name(data)).gt_type if rec_type == 0 or any(rec_type == rec.genotype(dd.get_sample_name(x)).gt_type for x in background): out_rec.add_filter("InBackground") writer.write_record(out_rec) return vcfutils.bgzip_and_index(out_file, data["config"])
python
def filter_by_background(in_vcf, full_vcf, background, data): """Filter SV calls also present in background samples. Skips filtering of inversions, which are not characterized differently between cases and controls in test datasets. """ Filter = collections.namedtuple('Filter', ['id', 'desc']) back_filter = Filter(id='InBackground', desc='Rejected due to presence in background sample') out_file = "%s-filter.vcf" % utils.splitext_plus(in_vcf)[0] if not utils.file_uptodate(out_file, in_vcf) and not utils.file_uptodate(out_file + ".vcf.gz", in_vcf): with file_transaction(data, out_file) as tx_out_file: with open(tx_out_file, "w") as out_handle: reader = vcf.VCFReader(filename=in_vcf) reader.filters["InBackground"] = back_filter full_reader = vcf.VCFReader(filename=full_vcf) writer = vcf.VCFWriter(out_handle, template=reader) for out_rec, rec in zip(reader, full_reader): rec_type = rec.genotype(dd.get_sample_name(data)).gt_type if rec_type == 0 or any(rec_type == rec.genotype(dd.get_sample_name(x)).gt_type for x in background): out_rec.add_filter("InBackground") writer.write_record(out_rec) return vcfutils.bgzip_and_index(out_file, data["config"])
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Filter SV calls also present in background samples. Skips filtering of inversions, which are not characterized differently between cases and controls in test datasets.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/structural/wham.py#L64-L87
train
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bcbio/bcbio-nextgen
bcbio/variation/gatkjoint.py
run_region
def run_region(data, region, vrn_files, out_file): """Perform variant calling on gVCF inputs in a specific genomic region. """ broad_runner = broad.runner_from_config(data["config"]) if broad_runner.gatk_type() == "gatk4": genomics_db = _run_genomicsdb_import(vrn_files, region, out_file, data) return _run_genotype_gvcfs_genomicsdb(genomics_db, region, out_file, data) else: vrn_files = _batch_gvcfs(data, region, vrn_files, dd.get_ref_file(data), out_file) return _run_genotype_gvcfs_gatk3(data, region, vrn_files, dd.get_ref_file(data), out_file)
python
def run_region(data, region, vrn_files, out_file): """Perform variant calling on gVCF inputs in a specific genomic region. """ broad_runner = broad.runner_from_config(data["config"]) if broad_runner.gatk_type() == "gatk4": genomics_db = _run_genomicsdb_import(vrn_files, region, out_file, data) return _run_genotype_gvcfs_genomicsdb(genomics_db, region, out_file, data) else: vrn_files = _batch_gvcfs(data, region, vrn_files, dd.get_ref_file(data), out_file) return _run_genotype_gvcfs_gatk3(data, region, vrn_files, dd.get_ref_file(data), out_file)
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/gatkjoint.py#L18-L27
train
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bcbio/bcbio-nextgen
bcbio/variation/gatkjoint.py
_incomplete_genomicsdb
def _incomplete_genomicsdb(dbdir): """Check if a GenomicsDB output is incomplete and we should regenerate. Works around current inability to move GenomicsDB outputs and support transactional directories. """ for test_file in ["callset.json", "vidmap.json", "genomicsdb_array/genomicsdb_meta.json"]: if not os.path.exists(os.path.join(dbdir, test_file)): return True return False
python
def _incomplete_genomicsdb(dbdir): """Check if a GenomicsDB output is incomplete and we should regenerate. Works around current inability to move GenomicsDB outputs and support transactional directories. """ for test_file in ["callset.json", "vidmap.json", "genomicsdb_array/genomicsdb_meta.json"]: if not os.path.exists(os.path.join(dbdir, test_file)): return True return False
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/gatkjoint.py#L64-L73
train
219,007
bcbio/bcbio-nextgen
bcbio/variation/gatkjoint.py
_run_genotype_gvcfs_gatk3
def _run_genotype_gvcfs_gatk3(data, region, vrn_files, ref_file, out_file): """Performs genotyping of gVCFs into final VCF files. """ if not utils.file_exists(out_file): broad_runner = broad.runner_from_config(data["config"]) with file_transaction(data, out_file) as tx_out_file: assoc_files = tz.get_in(("genome_resources", "variation"), data, {}) if not assoc_files: assoc_files = {} params = ["-T", "GenotypeGVCFs", "-R", ref_file, "-o", tx_out_file, "-L", bamprep.region_to_gatk(region), "--max_alternate_alleles", "4"] for vrn_file in vrn_files: params += ["--variant", vrn_file] if assoc_files.get("dbsnp"): params += ["--dbsnp", assoc_files["dbsnp"]] broad_runner.new_resources("gatk-haplotype") cores = dd.get_cores(data) if cores > 1: # GATK performs poorly with memory usage when parallelizing # with a large number of cores but makes use of extra memory, # so we cap at 6 cores. # See issue #1565 for discussion # Recent GATK 3.x versions also have race conditions with multiple # threads, so limit to 1 and keep memory available # https://gatkforums.broadinstitute.org/wdl/discussion/8718/concurrentmodificationexception-in-gatk-3-7-genotypegvcfs # params += ["-nt", str(min(6, cores))] memscale = {"magnitude": 0.9 * cores, "direction": "increase"} else: memscale = None broad_runner.run_gatk(params, memscale=memscale, parallel_gc=True) return vcfutils.bgzip_and_index(out_file, data["config"])
python
def _run_genotype_gvcfs_gatk3(data, region, vrn_files, ref_file, out_file): """Performs genotyping of gVCFs into final VCF files. """ if not utils.file_exists(out_file): broad_runner = broad.runner_from_config(data["config"]) with file_transaction(data, out_file) as tx_out_file: assoc_files = tz.get_in(("genome_resources", "variation"), data, {}) if not assoc_files: assoc_files = {} params = ["-T", "GenotypeGVCFs", "-R", ref_file, "-o", tx_out_file, "-L", bamprep.region_to_gatk(region), "--max_alternate_alleles", "4"] for vrn_file in vrn_files: params += ["--variant", vrn_file] if assoc_files.get("dbsnp"): params += ["--dbsnp", assoc_files["dbsnp"]] broad_runner.new_resources("gatk-haplotype") cores = dd.get_cores(data) if cores > 1: # GATK performs poorly with memory usage when parallelizing # with a large number of cores but makes use of extra memory, # so we cap at 6 cores. # See issue #1565 for discussion # Recent GATK 3.x versions also have race conditions with multiple # threads, so limit to 1 and keep memory available # https://gatkforums.broadinstitute.org/wdl/discussion/8718/concurrentmodificationexception-in-gatk-3-7-genotypegvcfs # params += ["-nt", str(min(6, cores))] memscale = {"magnitude": 0.9 * cores, "direction": "increase"} else: memscale = None broad_runner.run_gatk(params, memscale=memscale, parallel_gc=True) return vcfutils.bgzip_and_index(out_file, data["config"])
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/gatkjoint.py#L102-L133
train
219,008
bcbio/bcbio-nextgen
bcbio/variation/gatkjoint.py
_batch_gvcfs
def _batch_gvcfs(data, region, vrn_files, ref_file, out_file=None): """Perform batching of gVCF files if above recommended input count. """ if out_file is None: out_file = vrn_files[0] # group to get below the maximum batch size, using 200 as the baseline max_batch = int(dd.get_joint_group_size(data)) if len(vrn_files) > max_batch: out = [] num_batches = int(math.ceil(float(len(vrn_files)) / max_batch)) for i, batch_vrn_files in enumerate(tz.partition_all(num_batches, vrn_files)): base, ext = utils.splitext_plus(out_file) batch_out_file = "%s-b%s%s" % (base, i, ext) out.append(run_combine_gvcfs(batch_vrn_files, region, ref_file, batch_out_file, data)) return _batch_gvcfs(data, region, out, ref_file) else: return vrn_files
python
def _batch_gvcfs(data, region, vrn_files, ref_file, out_file=None): """Perform batching of gVCF files if above recommended input count. """ if out_file is None: out_file = vrn_files[0] # group to get below the maximum batch size, using 200 as the baseline max_batch = int(dd.get_joint_group_size(data)) if len(vrn_files) > max_batch: out = [] num_batches = int(math.ceil(float(len(vrn_files)) / max_batch)) for i, batch_vrn_files in enumerate(tz.partition_all(num_batches, vrn_files)): base, ext = utils.splitext_plus(out_file) batch_out_file = "%s-b%s%s" % (base, i, ext) out.append(run_combine_gvcfs(batch_vrn_files, region, ref_file, batch_out_file, data)) return _batch_gvcfs(data, region, out, ref_file) else: return vrn_files
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Perform batching of gVCF files if above recommended input count.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/gatkjoint.py#L137-L153
train
219,009
bcbio/bcbio-nextgen
bcbio/qc/preseq.py
_get_preseq_params
def _get_preseq_params(data, preseq_cmd, read_count): """ Get parameters through resources. If "step" or "extrap" limit are not provided, then calculate optimal values based on read count. """ defaults = { 'seg_len': 100000, # maximum segment length when merging paired end bam reads 'steps': 300, # number of points on the plot 'extrap_fraction': 3, # extrapolate up to X times read_count 'extrap': None, # extrapolate up to X reads 'step': None, # step size (number of reads between points on the plot) 'options': '', } params = {} main_opts = [("-e", "-extrap"), ("-l", "-seg_len"), ("-s", "-step")] other_opts = config_utils.get_resources("preseq", data["config"]).get("options", []) if isinstance(other_opts, str): other_opts = [other_opts] for sht, lng in main_opts: if sht in other_opts: i = other_opts.index(sht) elif lng in other_opts: i = other_opts.index(lng) else: i = None if i is not None: params[lng[1:]] = other_opts[i + 1] other_opts = other_opts[:i] + other_opts[i + 2:] params['options'] = ' '.join(other_opts) for k, v in config_utils.get_resources("preseq", data["config"]).items(): if k != 'options': params[k] = v params['steps'] = params.get('steps', defaults['steps']) if preseq_cmd == 'c_curve': params['extrap_fraction'] = 1 else: if params.get('step') is None: if params.get('extrap') is None: unrounded__extrap = read_count * params.get('extrap_fraction', defaults['extrap_fraction']) unrounded__step = unrounded__extrap // params['steps'] if params.get('extrap_fraction') is not None: # extrap_fraction explicitly provided params['extrap'] = unrounded__extrap params['step'] = unrounded__step else: power_of_10 = 10 ** math.floor(math.log(unrounded__step, 10)) rounded__step = int(math.floor(unrounded__step // power_of_10) * power_of_10) rounded__extrap = int(rounded__step) * params['steps'] params['step'] = rounded__step params['extrap'] = rounded__extrap else: params['step'] = params['extrap'] // params['steps'] elif params.get('extrap') is None: params['extrap'] = params['step'] * params['steps'] params['step'] = params.get('step', defaults['step']) params['extrap'] = params.get('extrap', defaults['extrap']) params['seg_len'] = params.get('seg_len', defaults['seg_len']) logger.info("Preseq: running {steps} steps of size {step}, extap limit {extrap}".format(**params)) return params
python
def _get_preseq_params(data, preseq_cmd, read_count): """ Get parameters through resources. If "step" or "extrap" limit are not provided, then calculate optimal values based on read count. """ defaults = { 'seg_len': 100000, # maximum segment length when merging paired end bam reads 'steps': 300, # number of points on the plot 'extrap_fraction': 3, # extrapolate up to X times read_count 'extrap': None, # extrapolate up to X reads 'step': None, # step size (number of reads between points on the plot) 'options': '', } params = {} main_opts = [("-e", "-extrap"), ("-l", "-seg_len"), ("-s", "-step")] other_opts = config_utils.get_resources("preseq", data["config"]).get("options", []) if isinstance(other_opts, str): other_opts = [other_opts] for sht, lng in main_opts: if sht in other_opts: i = other_opts.index(sht) elif lng in other_opts: i = other_opts.index(lng) else: i = None if i is not None: params[lng[1:]] = other_opts[i + 1] other_opts = other_opts[:i] + other_opts[i + 2:] params['options'] = ' '.join(other_opts) for k, v in config_utils.get_resources("preseq", data["config"]).items(): if k != 'options': params[k] = v params['steps'] = params.get('steps', defaults['steps']) if preseq_cmd == 'c_curve': params['extrap_fraction'] = 1 else: if params.get('step') is None: if params.get('extrap') is None: unrounded__extrap = read_count * params.get('extrap_fraction', defaults['extrap_fraction']) unrounded__step = unrounded__extrap // params['steps'] if params.get('extrap_fraction') is not None: # extrap_fraction explicitly provided params['extrap'] = unrounded__extrap params['step'] = unrounded__step else: power_of_10 = 10 ** math.floor(math.log(unrounded__step, 10)) rounded__step = int(math.floor(unrounded__step // power_of_10) * power_of_10) rounded__extrap = int(rounded__step) * params['steps'] params['step'] = rounded__step params['extrap'] = rounded__extrap else: params['step'] = params['extrap'] // params['steps'] elif params.get('extrap') is None: params['extrap'] = params['step'] * params['steps'] params['step'] = params.get('step', defaults['step']) params['extrap'] = params.get('extrap', defaults['extrap']) params['seg_len'] = params.get('seg_len', defaults['seg_len']) logger.info("Preseq: running {steps} steps of size {step}, extap limit {extrap}".format(**params)) return params
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/qc/preseq.py#L51-L114
train
219,010
bcbio/bcbio-nextgen
bcbio/variation/germline.py
split_somatic
def split_somatic(items): """Split somatic batches, adding a germline target. Enables separate germline calling of samples using shared alignments. """ items = [_clean_flat_variantcaller(x) for x in items] somatic_groups, somatic, non_somatic = vcfutils.somatic_batches(items) # extract germline samples to run from normals in tumor/normal pairs germline_added = set([]) germline = [] for somatic_group in somatic_groups: paired = vcfutils.get_paired(somatic_group) if paired and paired.normal_data: cur = utils.deepish_copy(paired.normal_data) vc = dd.get_variantcaller(cur) if isinstance(vc, dict) and "germline" in vc: if cur["description"] not in germline_added: germline_added.add(cur["description"]) cur["rgnames"]["sample"] = cur["description"] cur["metadata"]["batch"] = "%s-germline" % cur["description"] cur["metadata"]["phenotype"] = "germline" cur = remove_align_qc_tools(cur) cur["config"]["algorithm"]["variantcaller"] = vc["germline"] germline.append(cur) # Fix variantcalling specification for only somatic targets somatic_out = [] for data in somatic: vc = dd.get_variantcaller(data) if isinstance(vc, dict) and "somatic" in vc: data["config"]["algorithm"]["variantcaller"] = vc["somatic"] somatic_out.append(data) return non_somatic + somatic_out + germline
python
def split_somatic(items): """Split somatic batches, adding a germline target. Enables separate germline calling of samples using shared alignments. """ items = [_clean_flat_variantcaller(x) for x in items] somatic_groups, somatic, non_somatic = vcfutils.somatic_batches(items) # extract germline samples to run from normals in tumor/normal pairs germline_added = set([]) germline = [] for somatic_group in somatic_groups: paired = vcfutils.get_paired(somatic_group) if paired and paired.normal_data: cur = utils.deepish_copy(paired.normal_data) vc = dd.get_variantcaller(cur) if isinstance(vc, dict) and "germline" in vc: if cur["description"] not in germline_added: germline_added.add(cur["description"]) cur["rgnames"]["sample"] = cur["description"] cur["metadata"]["batch"] = "%s-germline" % cur["description"] cur["metadata"]["phenotype"] = "germline" cur = remove_align_qc_tools(cur) cur["config"]["algorithm"]["variantcaller"] = vc["germline"] germline.append(cur) # Fix variantcalling specification for only somatic targets somatic_out = [] for data in somatic: vc = dd.get_variantcaller(data) if isinstance(vc, dict) and "somatic" in vc: data["config"]["algorithm"]["variantcaller"] = vc["somatic"] somatic_out.append(data) return non_somatic + somatic_out + germline
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Split somatic batches, adding a germline target. Enables separate germline calling of samples using shared alignments.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/germline.py#L19-L50
train
219,011
bcbio/bcbio-nextgen
bcbio/variation/germline.py
_clean_flat_variantcaller
def _clean_flat_variantcaller(data): """Convert flattened dictionary from CWL representation into dictionary. CWL flattens somatic/germline tags into a set of strings, which we reconstitute as a dictionary. """ vc = dd.get_variantcaller(data) if isinstance(vc, (list, tuple)) and all([x.count(":") == 1 for x in vc]): out = {} for v in vc: k, v = v.split(":") if k in out: out[k].append(v) else: out[k] = [v] data = dd.set_variantcaller(data, out) return data
python
def _clean_flat_variantcaller(data): """Convert flattened dictionary from CWL representation into dictionary. CWL flattens somatic/germline tags into a set of strings, which we reconstitute as a dictionary. """ vc = dd.get_variantcaller(data) if isinstance(vc, (list, tuple)) and all([x.count(":") == 1 for x in vc]): out = {} for v in vc: k, v = v.split(":") if k in out: out[k].append(v) else: out[k] = [v] data = dd.set_variantcaller(data, out) return data
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/germline.py#L52-L68
train
219,012
bcbio/bcbio-nextgen
bcbio/variation/germline.py
remove_align_qc_tools
def remove_align_qc_tools(data): """Remove alignment based QC tools we don't need for data replicates. When we do multiple variant calling on a sample file (somatic/germline), avoid re-running QC. """ align_qc = set(["qsignature", "coverage", "picard", "samtools", "fastqc"]) data["config"]["algorithm"]["qc"] = [t for t in dd.get_algorithm_qc(data) if t not in align_qc] return data
python
def remove_align_qc_tools(data): """Remove alignment based QC tools we don't need for data replicates. When we do multiple variant calling on a sample file (somatic/germline), avoid re-running QC. """ align_qc = set(["qsignature", "coverage", "picard", "samtools", "fastqc"]) data["config"]["algorithm"]["qc"] = [t for t in dd.get_algorithm_qc(data) if t not in align_qc] return data
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Remove alignment based QC tools we don't need for data replicates. When we do multiple variant calling on a sample file (somatic/germline), avoid re-running QC.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/germline.py#L70-L79
train
219,013
bcbio/bcbio-nextgen
bcbio/variation/germline.py
extract
def extract(data, items, out_dir=None): """Extract germline calls for the given sample, if tumor only. """ if vcfutils.get_paired_phenotype(data): if len(items) == 1: germline_vcf = _remove_prioritization(data["vrn_file"], data, out_dir) germline_vcf = vcfutils.bgzip_and_index(germline_vcf, data["config"]) data["vrn_file_plus"] = {"germline": germline_vcf} return data
python
def extract(data, items, out_dir=None): """Extract germline calls for the given sample, if tumor only. """ if vcfutils.get_paired_phenotype(data): if len(items) == 1: germline_vcf = _remove_prioritization(data["vrn_file"], data, out_dir) germline_vcf = vcfutils.bgzip_and_index(germline_vcf, data["config"]) data["vrn_file_plus"] = {"germline": germline_vcf} return data
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/germline.py#L81-L89
train
219,014
bcbio/bcbio-nextgen
bcbio/variation/germline.py
fix_germline_samplename
def fix_germline_samplename(in_file, sample_name, data): """Replace germline sample names, originally from normal BAM file. """ out_file = "%s-fixnames%s" % utils.splitext_plus(in_file) if not utils.file_exists(out_file): with file_transaction(data, out_file) as tx_out_file: sample_file = "%s-samples.txt" % utils.splitext_plus(tx_out_file)[0] with open(sample_file, "w") as out_handle: out_handle.write("%s\n" % sample_name) cmd = ("bcftools reheader -s {sample_file} {in_file} -o {tx_out_file}") do.run(cmd.format(**locals()), "Fix germline samplename: %s" % sample_name) return vcfutils.bgzip_and_index(out_file, data["config"])
python
def fix_germline_samplename(in_file, sample_name, data): """Replace germline sample names, originally from normal BAM file. """ out_file = "%s-fixnames%s" % utils.splitext_plus(in_file) if not utils.file_exists(out_file): with file_transaction(data, out_file) as tx_out_file: sample_file = "%s-samples.txt" % utils.splitext_plus(tx_out_file)[0] with open(sample_file, "w") as out_handle: out_handle.write("%s\n" % sample_name) cmd = ("bcftools reheader -s {sample_file} {in_file} -o {tx_out_file}") do.run(cmd.format(**locals()), "Fix germline samplename: %s" % sample_name) return vcfutils.bgzip_and_index(out_file, data["config"])
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/germline.py#L133-L144
train
219,015
bcbio/bcbio-nextgen
bcbio/variation/germline.py
_remove_prioritization
def _remove_prioritization(in_file, data, out_dir=None): """Remove tumor-only prioritization and return non-filtered calls. """ out_file = "%s-germline.vcf" % utils.splitext_plus(in_file)[0] if out_dir: out_file = os.path.join(out_dir, os.path.basename(out_file)) if not utils.file_uptodate(out_file, in_file) and not utils.file_uptodate(out_file + ".gz", in_file): with file_transaction(data, out_file) as tx_out_file: reader = cyvcf2.VCF(str(in_file)) reader.add_filter_to_header({'ID': 'Somatic', 'Description': 'Variant called as Somatic'}) # with open(tx_out_file, "w") as out_handle: # out_handle.write(reader.raw_header) with contextlib.closing(cyvcf2.Writer(tx_out_file, reader)) as writer: for rec in reader: rec = _update_prioritization_filters(rec) # out_handle.write(str(rec)) writer.write_record(rec) return out_file
python
def _remove_prioritization(in_file, data, out_dir=None): """Remove tumor-only prioritization and return non-filtered calls. """ out_file = "%s-germline.vcf" % utils.splitext_plus(in_file)[0] if out_dir: out_file = os.path.join(out_dir, os.path.basename(out_file)) if not utils.file_uptodate(out_file, in_file) and not utils.file_uptodate(out_file + ".gz", in_file): with file_transaction(data, out_file) as tx_out_file: reader = cyvcf2.VCF(str(in_file)) reader.add_filter_to_header({'ID': 'Somatic', 'Description': 'Variant called as Somatic'}) # with open(tx_out_file, "w") as out_handle: # out_handle.write(reader.raw_header) with contextlib.closing(cyvcf2.Writer(tx_out_file, reader)) as writer: for rec in reader: rec = _update_prioritization_filters(rec) # out_handle.write(str(rec)) writer.write_record(rec) return out_file
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/germline.py#L146-L163
train
219,016
bcbio/bcbio-nextgen
bcbio/variation/germline.py
_extract_germline
def _extract_germline(in_file, data): """Extract germline calls non-somatic, non-filtered calls. """ out_file = "%s-germline.vcf" % utils.splitext_plus(in_file)[0] if not utils.file_uptodate(out_file, in_file) and not utils.file_uptodate(out_file + ".gz", in_file): with file_transaction(data, out_file) as tx_out_file: reader = cyvcf2.VCF(str(in_file)) reader.add_filter_to_header({'ID': 'Somatic', 'Description': 'Variant called as Somatic'}) #with contextlib.closing(cyvcf2.Writer(tx_out_file, reader)) as writer: with open(tx_out_file, "w") as out_handle: out_handle.write(reader.raw_header) for rec in reader: rec = _update_germline_filters(rec) out_handle.write(str(rec)) #writer.write_record(rec) return out_file
python
def _extract_germline(in_file, data): """Extract germline calls non-somatic, non-filtered calls. """ out_file = "%s-germline.vcf" % utils.splitext_plus(in_file)[0] if not utils.file_uptodate(out_file, in_file) and not utils.file_uptodate(out_file + ".gz", in_file): with file_transaction(data, out_file) as tx_out_file: reader = cyvcf2.VCF(str(in_file)) reader.add_filter_to_header({'ID': 'Somatic', 'Description': 'Variant called as Somatic'}) #with contextlib.closing(cyvcf2.Writer(tx_out_file, reader)) as writer: with open(tx_out_file, "w") as out_handle: out_handle.write(reader.raw_header) for rec in reader: rec = _update_germline_filters(rec) out_handle.write(str(rec)) #writer.write_record(rec) return out_file
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Extract germline calls non-somatic, non-filtered calls.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/germline.py#L169-L184
train
219,017
bcbio/bcbio-nextgen
bcbio/variation/germline.py
_is_somatic
def _is_somatic(rec): """Handle somatic classifications from MuTect, MuTect2, VarDict and VarScan """ if _has_somatic_flag(rec): return True if _is_mutect2_somatic(rec): return True ss_flag = rec.INFO.get("SS") if ss_flag is not None: if str(ss_flag) == "2": return True status_flag = rec.INFO.get("STATUS") if status_flag is not None: if str(status_flag).lower() in ["somatic", "likelysomatic", "strongsomatic", "samplespecific"]: return True epr = rec.INFO.get("EPR", "").split(",") if epr and all([p == "pass" for p in epr]): return True return False
python
def _is_somatic(rec): """Handle somatic classifications from MuTect, MuTect2, VarDict and VarScan """ if _has_somatic_flag(rec): return True if _is_mutect2_somatic(rec): return True ss_flag = rec.INFO.get("SS") if ss_flag is not None: if str(ss_flag) == "2": return True status_flag = rec.INFO.get("STATUS") if status_flag is not None: if str(status_flag).lower() in ["somatic", "likelysomatic", "strongsomatic", "samplespecific"]: return True epr = rec.INFO.get("EPR", "").split(",") if epr and all([p == "pass" for p in epr]): return True return False
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Handle somatic classifications from MuTect, MuTect2, VarDict and VarScan
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/germline.py#L210-L228
train
219,018
bcbio/bcbio-nextgen
bcbio/variation/germline.py
_is_germline
def _is_germline(rec): """Handle somatic INFO classifications from MuTect, MuTect2, VarDict, VarScan and Octopus. """ if _has_somatic_flag(rec): return False if _is_mutect2_somatic(rec): return False ss_flag = rec.INFO.get("SS") if ss_flag is not None: if str(ss_flag) == "1": return True # Octopus, assessed for potentially being Germline and not flagged SOMATIC # https://github.com/luntergroup/octopus/wiki/Calling-models:-Cancer#qual-vs-pp pp = rec.INFO.get("PP") if pp and float(pp) / float(rec.QUAL) >= 0.5: return True status_flag = rec.INFO.get("STATUS") if status_flag is not None: if str(status_flag).lower() in ["germline", "likelyloh", "strongloh", "afdiff", "deletion"]: return True return False
python
def _is_germline(rec): """Handle somatic INFO classifications from MuTect, MuTect2, VarDict, VarScan and Octopus. """ if _has_somatic_flag(rec): return False if _is_mutect2_somatic(rec): return False ss_flag = rec.INFO.get("SS") if ss_flag is not None: if str(ss_flag) == "1": return True # Octopus, assessed for potentially being Germline and not flagged SOMATIC # https://github.com/luntergroup/octopus/wiki/Calling-models:-Cancer#qual-vs-pp pp = rec.INFO.get("PP") if pp and float(pp) / float(rec.QUAL) >= 0.5: return True status_flag = rec.INFO.get("STATUS") if status_flag is not None: if str(status_flag).lower() in ["germline", "likelyloh", "strongloh", "afdiff", "deletion"]: return True return False
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Handle somatic INFO classifications from MuTect, MuTect2, VarDict, VarScan and Octopus.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/germline.py#L242-L262
train
219,019
bcbio/bcbio-nextgen
bcbio/variation/bedutils.py
get_sort_cmd
def get_sort_cmd(tmp_dir=None): """Retrieve GNU coreutils sort command, using version-sort if available. Recent versions of sort have alpha-numeric sorting, which provides more natural sorting of chromosomes (chr1, chr2) instead of (chr1, chr10). This also fixes versions of sort, like 8.22 in CentOS 7.1, that have broken sorting without version sorting specified. https://github.com/bcbio/bcbio-nextgen/issues/624 https://github.com/bcbio/bcbio-nextgen/issues/1017 """ has_versionsort = subprocess.check_output("sort --help | grep version-sort; exit 0", shell=True).strip() if has_versionsort: cmd = "sort -V" else: cmd = "sort" if tmp_dir and os.path.exists(tmp_dir) and os.path.isdir(tmp_dir): cmd += " -T %s" % tmp_dir return cmd
python
def get_sort_cmd(tmp_dir=None): """Retrieve GNU coreutils sort command, using version-sort if available. Recent versions of sort have alpha-numeric sorting, which provides more natural sorting of chromosomes (chr1, chr2) instead of (chr1, chr10). This also fixes versions of sort, like 8.22 in CentOS 7.1, that have broken sorting without version sorting specified. https://github.com/bcbio/bcbio-nextgen/issues/624 https://github.com/bcbio/bcbio-nextgen/issues/1017 """ has_versionsort = subprocess.check_output("sort --help | grep version-sort; exit 0", shell=True).strip() if has_versionsort: cmd = "sort -V" else: cmd = "sort" if tmp_dir and os.path.exists(tmp_dir) and os.path.isdir(tmp_dir): cmd += " -T %s" % tmp_dir return cmd
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/bedutils.py#L18-L36
train
219,020
bcbio/bcbio-nextgen
bcbio/variation/bedutils.py
check_bed_contigs
def check_bed_contigs(in_file, data): """Ensure BED file contigs match the reference genome. """ if not dd.get_ref_file(data): return contigs = set([]) with utils.open_gzipsafe(in_file) as in_handle: for line in in_handle: if not line.startswith(("#", "track", "browser", "@")) and line.strip(): contigs.add(line.split()[0]) ref_contigs = set([x.name for x in ref.file_contigs(dd.get_ref_file(data))]) if contigs and len(contigs - ref_contigs) / float(len(contigs)) > 0.25: raise ValueError("Contigs in BED file %s not in reference genome:\n %s\n" % (in_file, list(contigs - ref_contigs)) + "This is typically due to chr1 versus 1 differences in BED file and reference.")
python
def check_bed_contigs(in_file, data): """Ensure BED file contigs match the reference genome. """ if not dd.get_ref_file(data): return contigs = set([]) with utils.open_gzipsafe(in_file) as in_handle: for line in in_handle: if not line.startswith(("#", "track", "browser", "@")) and line.strip(): contigs.add(line.split()[0]) ref_contigs = set([x.name for x in ref.file_contigs(dd.get_ref_file(data))]) if contigs and len(contigs - ref_contigs) / float(len(contigs)) > 0.25: raise ValueError("Contigs in BED file %s not in reference genome:\n %s\n" % (in_file, list(contigs - ref_contigs)) + "This is typically due to chr1 versus 1 differences in BED file and reference.")
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Ensure BED file contigs match the reference genome.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/bedutils.py#L38-L52
train
219,021
bcbio/bcbio-nextgen
bcbio/variation/bedutils.py
check_bed_coords
def check_bed_coords(in_file, data): """Ensure BED file coordinates match reference genome. Catches errors like using a hg38 BED file for an hg19 genome run. """ if dd.get_ref_file(data): contig_sizes = {} for contig in ref.file_contigs(dd.get_ref_file(data)): contig_sizes[contig.name] = contig.size with utils.open_gzipsafe(in_file) as in_handle: for line in in_handle: if not line.startswith(("#", "track", "browser", "@")) and line.strip(): parts = line.split() if len(parts) > 3: try: end = int(parts[2]) except ValueError: continue contig = parts[0] check_size = contig_sizes.get(contig) if check_size and end > check_size: raise ValueError("Found BED coordinate off the end of the chromosome:\n%s%s\n" "Is the input BED from the right genome build?" % (line, in_file))
python
def check_bed_coords(in_file, data): """Ensure BED file coordinates match reference genome. Catches errors like using a hg38 BED file for an hg19 genome run. """ if dd.get_ref_file(data): contig_sizes = {} for contig in ref.file_contigs(dd.get_ref_file(data)): contig_sizes[contig.name] = contig.size with utils.open_gzipsafe(in_file) as in_handle: for line in in_handle: if not line.startswith(("#", "track", "browser", "@")) and line.strip(): parts = line.split() if len(parts) > 3: try: end = int(parts[2]) except ValueError: continue contig = parts[0] check_size = contig_sizes.get(contig) if check_size and end > check_size: raise ValueError("Found BED coordinate off the end of the chromosome:\n%s%s\n" "Is the input BED from the right genome build?" % (line, in_file))
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Ensure BED file coordinates match reference genome. Catches errors like using a hg38 BED file for an hg19 genome run.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/bedutils.py#L54-L77
train
219,022
bcbio/bcbio-nextgen
bcbio/variation/bedutils.py
clean_file
def clean_file(in_file, data, prefix="", bedprep_dir=None, simple=None): """Prepare a clean sorted input BED file without headers """ # Remove non-ascii characters. Used in coverage analysis, to support JSON code in one column # and be happy with sambamba: simple = "iconv -c -f utf-8 -t ascii | sed 's/ //g' |" if simple else "" if in_file: if not bedprep_dir: bedprep_dir = utils.safe_makedir(os.path.join(data["dirs"]["work"], "bedprep")) # Avoid running multiple times with same prefix if prefix and os.path.basename(in_file).startswith(prefix): return in_file out_file = os.path.join(bedprep_dir, "%s%s" % (prefix, os.path.basename(in_file))) out_file = out_file.replace(".interval_list", ".bed") if out_file.endswith(".gz"): out_file = out_file[:-3] if not utils.file_uptodate(out_file, in_file): check_bed_contigs(in_file, data) check_bed_coords(in_file, data) with file_transaction(data, out_file) as tx_out_file: bcbio_py = sys.executable cat_cmd = "zcat" if in_file.endswith(".gz") else "cat" sort_cmd = get_sort_cmd(os.path.dirname(tx_out_file)) cmd = ("{cat_cmd} {in_file} | grep -v ^track | grep -v ^browser | grep -v ^@ | " "grep -v ^# | {simple} " "{bcbio_py} -c 'from bcbio.variation import bedutils; bedutils.remove_bad()' | " "{sort_cmd} -k1,1 -k2,2n > {tx_out_file}") do.run(cmd.format(**locals()), "Prepare cleaned BED file", data) vcfutils.bgzip_and_index(out_file, data.get("config", {}), remove_orig=False) return out_file
python
def clean_file(in_file, data, prefix="", bedprep_dir=None, simple=None): """Prepare a clean sorted input BED file without headers """ # Remove non-ascii characters. Used in coverage analysis, to support JSON code in one column # and be happy with sambamba: simple = "iconv -c -f utf-8 -t ascii | sed 's/ //g' |" if simple else "" if in_file: if not bedprep_dir: bedprep_dir = utils.safe_makedir(os.path.join(data["dirs"]["work"], "bedprep")) # Avoid running multiple times with same prefix if prefix and os.path.basename(in_file).startswith(prefix): return in_file out_file = os.path.join(bedprep_dir, "%s%s" % (prefix, os.path.basename(in_file))) out_file = out_file.replace(".interval_list", ".bed") if out_file.endswith(".gz"): out_file = out_file[:-3] if not utils.file_uptodate(out_file, in_file): check_bed_contigs(in_file, data) check_bed_coords(in_file, data) with file_transaction(data, out_file) as tx_out_file: bcbio_py = sys.executable cat_cmd = "zcat" if in_file.endswith(".gz") else "cat" sort_cmd = get_sort_cmd(os.path.dirname(tx_out_file)) cmd = ("{cat_cmd} {in_file} | grep -v ^track | grep -v ^browser | grep -v ^@ | " "grep -v ^# | {simple} " "{bcbio_py} -c 'from bcbio.variation import bedutils; bedutils.remove_bad()' | " "{sort_cmd} -k1,1 -k2,2n > {tx_out_file}") do.run(cmd.format(**locals()), "Prepare cleaned BED file", data) vcfutils.bgzip_and_index(out_file, data.get("config", {}), remove_orig=False) return out_file
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/bedutils.py#L79-L108
train
219,023
bcbio/bcbio-nextgen
bcbio/variation/bedutils.py
remove_bad
def remove_bad(): """Remove non-increasing BED lines which will cause variant callers to choke. Also fixes space separated BED inputs. """ for line in sys.stdin: parts = line.strip().split("\t") if len(parts) == 1 and len(line.strip().split()) > 1: parts = line.strip().split() if line.strip() and len(parts) > 2 and int(parts[2]) > int(parts[1]): sys.stdout.write("\t".join(parts) + "\n")
python
def remove_bad(): """Remove non-increasing BED lines which will cause variant callers to choke. Also fixes space separated BED inputs. """ for line in sys.stdin: parts = line.strip().split("\t") if len(parts) == 1 and len(line.strip().split()) > 1: parts = line.strip().split() if line.strip() and len(parts) > 2 and int(parts[2]) > int(parts[1]): sys.stdout.write("\t".join(parts) + "\n")
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Remove non-increasing BED lines which will cause variant callers to choke. Also fixes space separated BED inputs.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/bedutils.py#L134-L144
train
219,024
bcbio/bcbio-nextgen
bcbio/variation/bedutils.py
merge_overlaps
def merge_overlaps(in_file, data, distance=None, out_dir=None): """Merge bed file intervals to avoid overlapping regions. Output is always a 3 column file. Overlapping regions (1:1-100, 1:90-100) cause issues with callers like FreeBayes that don't collapse BEDs prior to using them. """ config = data["config"] if in_file: bedtools = config_utils.get_program("bedtools", config, default="bedtools") work_dir = tz.get_in(["dirs", "work"], data) if out_dir: bedprep_dir = out_dir elif work_dir: bedprep_dir = utils.safe_makedir(os.path.join(work_dir, "bedprep")) else: bedprep_dir = os.path.dirname(in_file) out_file = os.path.join(bedprep_dir, "%s-merged.bed" % (utils.splitext_plus(os.path.basename(in_file))[0])) if not utils.file_uptodate(out_file, in_file): with file_transaction(data, out_file) as tx_out_file: distance = "-d %s" % distance if distance else "" cmd = "{bedtools} merge {distance} -i {in_file} > {tx_out_file}" do.run(cmd.format(**locals()), "Prepare merged BED file", data) vcfutils.bgzip_and_index(out_file, data["config"], remove_orig=False) return out_file
python
def merge_overlaps(in_file, data, distance=None, out_dir=None): """Merge bed file intervals to avoid overlapping regions. Output is always a 3 column file. Overlapping regions (1:1-100, 1:90-100) cause issues with callers like FreeBayes that don't collapse BEDs prior to using them. """ config = data["config"] if in_file: bedtools = config_utils.get_program("bedtools", config, default="bedtools") work_dir = tz.get_in(["dirs", "work"], data) if out_dir: bedprep_dir = out_dir elif work_dir: bedprep_dir = utils.safe_makedir(os.path.join(work_dir, "bedprep")) else: bedprep_dir = os.path.dirname(in_file) out_file = os.path.join(bedprep_dir, "%s-merged.bed" % (utils.splitext_plus(os.path.basename(in_file))[0])) if not utils.file_uptodate(out_file, in_file): with file_transaction(data, out_file) as tx_out_file: distance = "-d %s" % distance if distance else "" cmd = "{bedtools} merge {distance} -i {in_file} > {tx_out_file}" do.run(cmd.format(**locals()), "Prepare merged BED file", data) vcfutils.bgzip_and_index(out_file, data["config"], remove_orig=False) return out_file
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Merge bed file intervals to avoid overlapping regions. Output is always a 3 column file. Overlapping regions (1:1-100, 1:90-100) cause issues with callers like FreeBayes that don't collapse BEDs prior to using them.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/bedutils.py#L146-L170
train
219,025
bcbio/bcbio-nextgen
bcbio/variation/bedutils.py
population_variant_regions
def population_variant_regions(items, merged=False): """Retrieve the variant region BED file from a population of items. If tumor/normal, return the tumor BED file. If a population, return the BED file covering the most bases. """ def _get_variant_regions(data): out = dd.get_variant_regions(data) or dd.get_sample_callable(data) # Only need to merge for variant region inputs, not callable BED regions which don't overlap if merged and dd.get_variant_regions(data): merged_out = dd.get_variant_regions_merged(data) if merged_out: out = merged_out else: out = merge_overlaps(out, data) return out import pybedtools if len(items) == 1: return _get_variant_regions(items[0]) else: paired = vcfutils.get_paired(items) if paired: return _get_variant_regions(paired.tumor_data) else: vrs = [] for data in items: vr_bed = _get_variant_regions(data) if vr_bed: vrs.append((pybedtools.BedTool(vr_bed).total_coverage(), vr_bed)) vrs.sort(reverse=True) if vrs: return vrs[0][1]
python
def population_variant_regions(items, merged=False): """Retrieve the variant region BED file from a population of items. If tumor/normal, return the tumor BED file. If a population, return the BED file covering the most bases. """ def _get_variant_regions(data): out = dd.get_variant_regions(data) or dd.get_sample_callable(data) # Only need to merge for variant region inputs, not callable BED regions which don't overlap if merged and dd.get_variant_regions(data): merged_out = dd.get_variant_regions_merged(data) if merged_out: out = merged_out else: out = merge_overlaps(out, data) return out import pybedtools if len(items) == 1: return _get_variant_regions(items[0]) else: paired = vcfutils.get_paired(items) if paired: return _get_variant_regions(paired.tumor_data) else: vrs = [] for data in items: vr_bed = _get_variant_regions(data) if vr_bed: vrs.append((pybedtools.BedTool(vr_bed).total_coverage(), vr_bed)) vrs.sort(reverse=True) if vrs: return vrs[0][1]
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/bedutils.py#L172-L203
train
219,026
bcbio/bcbio-nextgen
bcbio/variation/bedutils.py
combine
def combine(in_files, out_file, config): """Combine multiple BED files into a single output. """ if not utils.file_exists(out_file): with file_transaction(config, out_file) as tx_out_file: with open(tx_out_file, "w") as out_handle: for in_file in in_files: with open(in_file) as in_handle: shutil.copyfileobj(in_handle, out_handle) return out_file
python
def combine(in_files, out_file, config): """Combine multiple BED files into a single output. """ if not utils.file_exists(out_file): with file_transaction(config, out_file) as tx_out_file: with open(tx_out_file, "w") as out_handle: for in_file in in_files: with open(in_file) as in_handle: shutil.copyfileobj(in_handle, out_handle) return out_file
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/bedutils.py#L205-L214
train
219,027
bcbio/bcbio-nextgen
bcbio/variation/bedutils.py
intersect_two
def intersect_two(f1, f2, work_dir, data): """Intersect two regions, handling cases where either file is not present. """ bedtools = config_utils.get_program("bedtools", data, default="bedtools") f1_exists = f1 and utils.file_exists(f1) f2_exists = f2 and utils.file_exists(f2) if not f1_exists and not f2_exists: return None elif f1_exists and not f2_exists: return f1 elif f2_exists and not f1_exists: return f2 else: out_file = os.path.join(work_dir, "%s-merged.bed" % (utils.splitext_plus(os.path.basename(f1))[0])) if not utils.file_exists(out_file): with file_transaction(data, out_file) as tx_out_file: cmd = "{bedtools} intersect -a {f1} -b {f2} > {tx_out_file}" do.run(cmd.format(**locals()), "Intersect BED files", data) return out_file
python
def intersect_two(f1, f2, work_dir, data): """Intersect two regions, handling cases where either file is not present. """ bedtools = config_utils.get_program("bedtools", data, default="bedtools") f1_exists = f1 and utils.file_exists(f1) f2_exists = f2 and utils.file_exists(f2) if not f1_exists and not f2_exists: return None elif f1_exists and not f2_exists: return f1 elif f2_exists and not f1_exists: return f2 else: out_file = os.path.join(work_dir, "%s-merged.bed" % (utils.splitext_plus(os.path.basename(f1))[0])) if not utils.file_exists(out_file): with file_transaction(data, out_file) as tx_out_file: cmd = "{bedtools} intersect -a {f1} -b {f2} > {tx_out_file}" do.run(cmd.format(**locals()), "Intersect BED files", data) return out_file
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/bedutils.py#L216-L234
train
219,028
bcbio/bcbio-nextgen
bcbio/variation/bedutils.py
subset_to_genome
def subset_to_genome(in_file, out_file, data): """Subset a BED file to only contain contigs present in the reference genome. """ if not utils.file_uptodate(out_file, in_file): contigs = set([x.name for x in ref.file_contigs(dd.get_ref_file(data))]) with utils.open_gzipsafe(in_file) as in_handle: with file_transaction(data, out_file) as tx_out_file: with open(tx_out_file, "w") as out_handle: for line in in_handle: parts = line.split() if parts and parts[0] in contigs: out_handle.write(line) return out_file
python
def subset_to_genome(in_file, out_file, data): """Subset a BED file to only contain contigs present in the reference genome. """ if not utils.file_uptodate(out_file, in_file): contigs = set([x.name for x in ref.file_contigs(dd.get_ref_file(data))]) with utils.open_gzipsafe(in_file) as in_handle: with file_transaction(data, out_file) as tx_out_file: with open(tx_out_file, "w") as out_handle: for line in in_handle: parts = line.split() if parts and parts[0] in contigs: out_handle.write(line) return out_file
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Subset a BED file to only contain contigs present in the reference genome.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/bedutils.py#L247-L259
train
219,029
bcbio/bcbio-nextgen
bcbio/variation/octopus.py
run
def run(align_bams, items, ref_file, assoc_files, region, out_file): """Run octopus variant calling, handling both somatic and germline calling. """ if not utils.file_exists(out_file): paired = vcfutils.get_paired_bams(align_bams, items) vrs = bedutils.population_variant_regions(items) target = shared.subset_variant_regions(vrs, region, out_file, items=items, do_merge=True) if paired: return _run_somatic(paired, ref_file, target, out_file) else: return _run_germline(align_bams, items, ref_file, target, out_file) return out_file
python
def run(align_bams, items, ref_file, assoc_files, region, out_file): """Run octopus variant calling, handling both somatic and germline calling. """ if not utils.file_exists(out_file): paired = vcfutils.get_paired_bams(align_bams, items) vrs = bedutils.population_variant_regions(items) target = shared.subset_variant_regions(vrs, region, out_file, items=items, do_merge=True) if paired: return _run_somatic(paired, ref_file, target, out_file) else: return _run_germline(align_bams, items, ref_file, target, out_file) return out_file
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/octopus.py#L17-L29
train
219,030
bcbio/bcbio-nextgen
bcbio/variation/octopus.py
_produce_compatible_vcf
def _produce_compatible_vcf(out_file, data, is_somatic): """Create a compatible VCF that downstream tools can deal with. - htsjdk and thus GATK and Picard do not support VCF4.3: https://github.com/broadinstitute/gatk/issues/2092 - Use octopus legacy format to avoid incompatibilities. https://github.com/luntergroup/octopus#output-format - Fixes `##contig` lines since octopus only writes contigs used in the BED file region, causing incompatibilies with GatherVcfs when merging - Fixes alleles prefixed with '*' like 'C,*T' which cause downstream failures when reading with GATK. """ base, ext = utils.splitext_plus(out_file) legacy_file = "%s.legacy%s" % (base, ext) if is_somatic: legacy_file = _covert_to_diploid(legacy_file, data) final_file = "%s.vcf.gz" % base cat_cmd = "zcat" if legacy_file.endswith(".gz") else "cat" contig_cl = vcfutils.add_contig_to_header_cl(dd.get_ref_file(data), out_file) remove_problem_alleles = r"sed 's/,\*\([A-Z]\)/,\1/'" cmd = ("{cat_cmd} {legacy_file} | sed 's/fileformat=VCFv4.3/fileformat=VCFv4.2/' | " "{remove_problem_alleles} | {contig_cl} | bgzip -c > {final_file}") do.run(cmd.format(**locals()), "Produce compatible VCF output file from octopus") return vcfutils.bgzip_and_index(out_file, data["config"])
python
def _produce_compatible_vcf(out_file, data, is_somatic): """Create a compatible VCF that downstream tools can deal with. - htsjdk and thus GATK and Picard do not support VCF4.3: https://github.com/broadinstitute/gatk/issues/2092 - Use octopus legacy format to avoid incompatibilities. https://github.com/luntergroup/octopus#output-format - Fixes `##contig` lines since octopus only writes contigs used in the BED file region, causing incompatibilies with GatherVcfs when merging - Fixes alleles prefixed with '*' like 'C,*T' which cause downstream failures when reading with GATK. """ base, ext = utils.splitext_plus(out_file) legacy_file = "%s.legacy%s" % (base, ext) if is_somatic: legacy_file = _covert_to_diploid(legacy_file, data) final_file = "%s.vcf.gz" % base cat_cmd = "zcat" if legacy_file.endswith(".gz") else "cat" contig_cl = vcfutils.add_contig_to_header_cl(dd.get_ref_file(data), out_file) remove_problem_alleles = r"sed 's/,\*\([A-Z]\)/,\1/'" cmd = ("{cat_cmd} {legacy_file} | sed 's/fileformat=VCFv4.3/fileformat=VCFv4.2/' | " "{remove_problem_alleles} | {contig_cl} | bgzip -c > {final_file}") do.run(cmd.format(**locals()), "Produce compatible VCF output file from octopus") return vcfutils.bgzip_and_index(out_file, data["config"])
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/octopus.py#L31-L55
train
219,031
bcbio/bcbio-nextgen
bcbio/variation/octopus.py
_covert_to_diploid
def _covert_to_diploid(in_file, data): """Converts non-diploid somatic outputs into diploid. https://github.com/luntergroup/octopus/wiki/Case-study:-Tumour-only-UMI#evaluate-variant-calls """ sample = dd.get_sample_name(data) out_file = "%s-diploid.vcf" % utils.splitext_plus(in_file)[0] in_vcf = pysam.VariantFile(in_file) out_vcf = pysam.VariantFile(out_file, 'w', header=in_vcf.header) for record in in_vcf: gt = list(record.samples[sample]['GT']) if 'SOMATIC' in record.info: for allele in set(gt): if allele != gt[0]: record.samples[sample]['GT'] = gt[0], allele out_vcf.write(record) else: if len(gt) == 1: record.samples[sample]['GT'] = gt else: record.samples[sample]['GT'] = gt[0], gt[1] out_vcf.write(record) in_vcf.close() out_vcf.close() return vcfutils.bgzip_and_index(out_file, data["config"])
python
def _covert_to_diploid(in_file, data): """Converts non-diploid somatic outputs into diploid. https://github.com/luntergroup/octopus/wiki/Case-study:-Tumour-only-UMI#evaluate-variant-calls """ sample = dd.get_sample_name(data) out_file = "%s-diploid.vcf" % utils.splitext_plus(in_file)[0] in_vcf = pysam.VariantFile(in_file) out_vcf = pysam.VariantFile(out_file, 'w', header=in_vcf.header) for record in in_vcf: gt = list(record.samples[sample]['GT']) if 'SOMATIC' in record.info: for allele in set(gt): if allele != gt[0]: record.samples[sample]['GT'] = gt[0], allele out_vcf.write(record) else: if len(gt) == 1: record.samples[sample]['GT'] = gt else: record.samples[sample]['GT'] = gt[0], gt[1] out_vcf.write(record) in_vcf.close() out_vcf.close() return vcfutils.bgzip_and_index(out_file, data["config"])
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/octopus.py#L57-L81
train
219,032
bcbio/bcbio-nextgen
bcbio/variation/octopus.py
_run_germline
def _run_germline(align_bams, items, ref_file, target, out_file): """Run germline calling, handling populations. TODO: - We could better handle trio calling with ped inputs as octopus has special support. """ align_bams = " ".join(align_bams) cores = dd.get_num_cores(items[0]) cmd = ("octopus --threads {cores} --reference {ref_file} --reads {align_bams} " "--regions-file {target} " "--working-directory {tmp_dir} " "-o {tx_out_file} --legacy") with file_transaction(items[0], out_file) as tx_out_file: tmp_dir = os.path.dirname(tx_out_file) do.run(cmd.format(**locals()), "Octopus germline calling") _produce_compatible_vcf(tx_out_file, items[0]) return out_file
python
def _run_germline(align_bams, items, ref_file, target, out_file): """Run germline calling, handling populations. TODO: - We could better handle trio calling with ped inputs as octopus has special support. """ align_bams = " ".join(align_bams) cores = dd.get_num_cores(items[0]) cmd = ("octopus --threads {cores} --reference {ref_file} --reads {align_bams} " "--regions-file {target} " "--working-directory {tmp_dir} " "-o {tx_out_file} --legacy") with file_transaction(items[0], out_file) as tx_out_file: tmp_dir = os.path.dirname(tx_out_file) do.run(cmd.format(**locals()), "Octopus germline calling") _produce_compatible_vcf(tx_out_file, items[0]) return out_file
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Run germline calling, handling populations. TODO: - We could better handle trio calling with ped inputs as octopus has special support.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/octopus.py#L83-L100
train
219,033
bcbio/bcbio-nextgen
bcbio/variation/octopus.py
_run_somatic
def _run_somatic(paired, ref_file, target, out_file): """Run somatic calling with octopus, handling both paired and tumor-only cases. Tweaks for low frequency, tumor only and UMI calling documented in: https://github.com/luntergroup/octopus/blob/develop/configs/UMI.config """ align_bams = paired.tumor_bam if paired.normal_bam: align_bams += " %s --normal-sample %s" % (paired.normal_bam, paired.normal_name) cores = dd.get_num_cores(paired.tumor_data) # Do not try to search below 0.4% currently as leads to long runtimes # https://github.com/luntergroup/octopus/issues/29#issuecomment-428167979 min_af = max([float(dd.get_min_allele_fraction(paired.tumor_data)) / 100.0, 0.004]) min_af_floor = min_af / 4.0 cmd = ("octopus --threads {cores} --reference {ref_file} --reads {align_bams} " "--regions-file {target} " "--min-credible-somatic-frequency {min_af_floor} --min-expected-somatic-frequency {min_af} " "--downsample-above 4000 --downsample-target 4000 --min-kmer-prune 5 --min-bubble-score 20 " "--max-haplotypes 200 --somatic-snv-mutation-rate '5e-4' --somatic-indel-mutation-rate '1e-05' " "--target-working-memory 5G --target-read-buffer-footprint 5G --max-somatic-haplotypes 3 " "--caller cancer " "--working-directory {tmp_dir} " "-o {tx_out_file} --legacy") if not paired.normal_bam: cmd += (" --tumour-germline-concentration 5") if dd.get_umi_type(paired.tumor_data) or _is_umi_consensus_bam(paired.tumor_bam): cmd += (" --allow-octopus-duplicates --overlap-masking 0 " "--somatic-filter-expression 'GQ < 200 | MQ < 30 | SB > 0.2 | SD[.25] > 0.1 " "| BQ < 40 | DP < 100 | MF > 0.1 | AD < 5 | CC > 1.1 | GQD > 2'") with file_transaction(paired.tumor_data, out_file) as tx_out_file: tmp_dir = os.path.dirname(tx_out_file) do.run(cmd.format(**locals()), "Octopus somatic calling") _produce_compatible_vcf(tx_out_file, paired.tumor_data, is_somatic=True) return out_file
python
def _run_somatic(paired, ref_file, target, out_file): """Run somatic calling with octopus, handling both paired and tumor-only cases. Tweaks for low frequency, tumor only and UMI calling documented in: https://github.com/luntergroup/octopus/blob/develop/configs/UMI.config """ align_bams = paired.tumor_bam if paired.normal_bam: align_bams += " %s --normal-sample %s" % (paired.normal_bam, paired.normal_name) cores = dd.get_num_cores(paired.tumor_data) # Do not try to search below 0.4% currently as leads to long runtimes # https://github.com/luntergroup/octopus/issues/29#issuecomment-428167979 min_af = max([float(dd.get_min_allele_fraction(paired.tumor_data)) / 100.0, 0.004]) min_af_floor = min_af / 4.0 cmd = ("octopus --threads {cores} --reference {ref_file} --reads {align_bams} " "--regions-file {target} " "--min-credible-somatic-frequency {min_af_floor} --min-expected-somatic-frequency {min_af} " "--downsample-above 4000 --downsample-target 4000 --min-kmer-prune 5 --min-bubble-score 20 " "--max-haplotypes 200 --somatic-snv-mutation-rate '5e-4' --somatic-indel-mutation-rate '1e-05' " "--target-working-memory 5G --target-read-buffer-footprint 5G --max-somatic-haplotypes 3 " "--caller cancer " "--working-directory {tmp_dir} " "-o {tx_out_file} --legacy") if not paired.normal_bam: cmd += (" --tumour-germline-concentration 5") if dd.get_umi_type(paired.tumor_data) or _is_umi_consensus_bam(paired.tumor_bam): cmd += (" --allow-octopus-duplicates --overlap-masking 0 " "--somatic-filter-expression 'GQ < 200 | MQ < 30 | SB > 0.2 | SD[.25] > 0.1 " "| BQ < 40 | DP < 100 | MF > 0.1 | AD < 5 | CC > 1.1 | GQD > 2'") with file_transaction(paired.tumor_data, out_file) as tx_out_file: tmp_dir = os.path.dirname(tx_out_file) do.run(cmd.format(**locals()), "Octopus somatic calling") _produce_compatible_vcf(tx_out_file, paired.tumor_data, is_somatic=True) return out_file
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/octopus.py#L102-L135
train
219,034
bcbio/bcbio-nextgen
bcbio/variation/octopus.py
_is_umi_consensus_bam
def _is_umi_consensus_bam(in_file): """Check if input BAM file generated by fgbio consensus calls on UMIs. Identify these by lack of duplicated reads. This is useful for pre-aligned consensus BAMs feeding into octopus. """ cmd = "samtools view -h %s | head -500000 | samtools view -c -f 1024" count = subprocess.check_output(cmd % in_file, shell=True) return int(count) == 0
python
def _is_umi_consensus_bam(in_file): """Check if input BAM file generated by fgbio consensus calls on UMIs. Identify these by lack of duplicated reads. This is useful for pre-aligned consensus BAMs feeding into octopus. """ cmd = "samtools view -h %s | head -500000 | samtools view -c -f 1024" count = subprocess.check_output(cmd % in_file, shell=True) return int(count) == 0
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/octopus.py#L137-L145
train
219,035
bcbio/bcbio-nextgen
bcbio/upload/s3.py
update_file
def update_file(finfo, sample_info, config): """Update the file to an Amazon S3 bucket, using server side encryption. """ ffinal = filesystem.update_file(finfo, sample_info, config, pass_uptodate=True) if os.path.isdir(ffinal): to_transfer = [] for path, dirs, files in os.walk(ffinal): for f in files: full_f = os.path.join(path, f) k = full_f.replace(os.path.abspath(config["dir"]) + "/", "") to_transfer.append((full_f, k)) else: k = ffinal.replace(os.path.abspath(config["dir"]) + "/", "") to_transfer = [(ffinal, k)] region = "@%s" % config["region"] if config.get("region") else "" fname = "s3://%s%s/%s" % (config["bucket"], region, to_transfer[0][1]) conn = objectstore.connect(fname) bucket = conn.lookup(config["bucket"]) if not bucket: bucket = conn.create_bucket(config["bucket"], location=config.get("region", "us-east-1")) for fname, orig_keyname in to_transfer: keyname = os.path.join(config.get("folder", ""), orig_keyname) key = bucket.get_key(keyname) if bucket else None modified = datetime.datetime.fromtimestamp(email.utils.mktime_tz( email.utils.parsedate_tz(key.last_modified))) if key else None no_upload = key and modified >= finfo["mtime"] if not no_upload: _upload_file_aws_cli(fname, config["bucket"], keyname, config, finfo)
python
def update_file(finfo, sample_info, config): """Update the file to an Amazon S3 bucket, using server side encryption. """ ffinal = filesystem.update_file(finfo, sample_info, config, pass_uptodate=True) if os.path.isdir(ffinal): to_transfer = [] for path, dirs, files in os.walk(ffinal): for f in files: full_f = os.path.join(path, f) k = full_f.replace(os.path.abspath(config["dir"]) + "/", "") to_transfer.append((full_f, k)) else: k = ffinal.replace(os.path.abspath(config["dir"]) + "/", "") to_transfer = [(ffinal, k)] region = "@%s" % config["region"] if config.get("region") else "" fname = "s3://%s%s/%s" % (config["bucket"], region, to_transfer[0][1]) conn = objectstore.connect(fname) bucket = conn.lookup(config["bucket"]) if not bucket: bucket = conn.create_bucket(config["bucket"], location=config.get("region", "us-east-1")) for fname, orig_keyname in to_transfer: keyname = os.path.join(config.get("folder", ""), orig_keyname) key = bucket.get_key(keyname) if bucket else None modified = datetime.datetime.fromtimestamp(email.utils.mktime_tz( email.utils.parsedate_tz(key.last_modified))) if key else None no_upload = key and modified >= finfo["mtime"] if not no_upload: _upload_file_aws_cli(fname, config["bucket"], keyname, config, finfo)
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/upload/s3.py#L20-L49
train
219,036
bcbio/bcbio-nextgen
bcbio/upload/s3.py
_upload_file_aws_cli
def _upload_file_aws_cli(local_fname, bucket, keyname, config=None, mditems=None): """Streaming upload via the standard AWS command line interface. """ s3_fname = "s3://%s/%s" % (bucket, keyname) args = ["--sse", "--expected-size", str(os.path.getsize(local_fname))] if config: if config.get("region"): args += ["--region", config.get("region")] if config.get("reduced_redundancy"): args += ["--storage-class", "REDUCED_REDUNDANCY"] cmd = [os.path.join(os.path.dirname(sys.executable), "aws"), "s3", "cp"] + args + \ [local_fname, s3_fname] do.run(cmd, "Upload to s3: %s %s" % (bucket, keyname))
python
def _upload_file_aws_cli(local_fname, bucket, keyname, config=None, mditems=None): """Streaming upload via the standard AWS command line interface. """ s3_fname = "s3://%s/%s" % (bucket, keyname) args = ["--sse", "--expected-size", str(os.path.getsize(local_fname))] if config: if config.get("region"): args += ["--region", config.get("region")] if config.get("reduced_redundancy"): args += ["--storage-class", "REDUCED_REDUNDANCY"] cmd = [os.path.join(os.path.dirname(sys.executable), "aws"), "s3", "cp"] + args + \ [local_fname, s3_fname] do.run(cmd, "Upload to s3: %s %s" % (bucket, keyname))
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Streaming upload via the standard AWS command line interface.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/upload/s3.py#L69-L81
train
219,037
bcbio/bcbio-nextgen
bcbio/upload/s3.py
upload_file_boto
def upload_file_boto(fname, remote_fname, mditems=None): """Upload a file using boto instead of external tools. """ r_fname = objectstore.parse_remote(remote_fname) conn = objectstore.connect(remote_fname) bucket = conn.lookup(r_fname.bucket) if not bucket: bucket = conn.create_bucket(r_fname.bucket, location=objectstore.get_region(remote_fname)) key = bucket.get_key(r_fname.key, validate=False) if mditems is None: mditems = {} if "x-amz-server-side-encryption" not in mditems: mditems["x-amz-server-side-encryption"] = "AES256" for name, val in mditems.items(): key.set_metadata(name, val) key.set_contents_from_filename(fname, encrypt_key=True)
python
def upload_file_boto(fname, remote_fname, mditems=None): """Upload a file using boto instead of external tools. """ r_fname = objectstore.parse_remote(remote_fname) conn = objectstore.connect(remote_fname) bucket = conn.lookup(r_fname.bucket) if not bucket: bucket = conn.create_bucket(r_fname.bucket, location=objectstore.get_region(remote_fname)) key = bucket.get_key(r_fname.key, validate=False) if mditems is None: mditems = {} if "x-amz-server-side-encryption" not in mditems: mditems["x-amz-server-side-encryption"] = "AES256" for name, val in mditems.items(): key.set_metadata(name, val) key.set_contents_from_filename(fname, encrypt_key=True)
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/upload/s3.py#L83-L98
train
219,038
bcbio/bcbio-nextgen
bcbio/qc/chipseq.py
run
def run(bam_file, sample, out_dir): """Standard QC metrics for chipseq""" out = {} # if "rchipqc" in dd.get_tools_on(sample): # out = chipqc(bam_file, sample, out_dir) peaks = sample.get("peaks_files", {}).get("main") if peaks: out.update(_reads_in_peaks(bam_file, peaks, sample)) return out
python
def run(bam_file, sample, out_dir): """Standard QC metrics for chipseq""" out = {} # if "rchipqc" in dd.get_tools_on(sample): # out = chipqc(bam_file, sample, out_dir) peaks = sample.get("peaks_files", {}).get("main") if peaks: out.update(_reads_in_peaks(bam_file, peaks, sample)) return out
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Standard QC metrics for chipseq
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/qc/chipseq.py#L16-L25
train
219,039
bcbio/bcbio-nextgen
bcbio/qc/chipseq.py
_reads_in_peaks
def _reads_in_peaks(bam_file, peaks_file, sample): """Calculate number of reads in peaks""" if not peaks_file: return {} rip = number_of_mapped_reads(sample, bam_file, bed_file = peaks_file) return {"metrics": {"RiP": rip}}
python
def _reads_in_peaks(bam_file, peaks_file, sample): """Calculate number of reads in peaks""" if not peaks_file: return {} rip = number_of_mapped_reads(sample, bam_file, bed_file = peaks_file) return {"metrics": {"RiP": rip}}
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Calculate number of reads in peaks
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/qc/chipseq.py#L27-L32
train
219,040
bcbio/bcbio-nextgen
bcbio/qc/chipseq.py
chipqc
def chipqc(bam_file, sample, out_dir): """Attempt code to run ChIPQC bioconductor packate in one sample""" sample_name = dd.get_sample_name(sample) logger.warning("ChIPQC is unstable right now, if it breaks, turn off the tool.") if utils.file_exists(out_dir): return _get_output(out_dir) with tx_tmpdir() as tmp_dir: rcode = _sample_template(sample, tmp_dir) if rcode: # local_sitelib = utils.R_sitelib() rscript = utils.Rscript_cmd() do.run([rscript, "--no-environ", rcode], "ChIPQC in %s" % sample_name, log_error=False) shutil.move(tmp_dir, out_dir) return _get_output(out_dir)
python
def chipqc(bam_file, sample, out_dir): """Attempt code to run ChIPQC bioconductor packate in one sample""" sample_name = dd.get_sample_name(sample) logger.warning("ChIPQC is unstable right now, if it breaks, turn off the tool.") if utils.file_exists(out_dir): return _get_output(out_dir) with tx_tmpdir() as tmp_dir: rcode = _sample_template(sample, tmp_dir) if rcode: # local_sitelib = utils.R_sitelib() rscript = utils.Rscript_cmd() do.run([rscript, "--no-environ", rcode], "ChIPQC in %s" % sample_name, log_error=False) shutil.move(tmp_dir, out_dir) return _get_output(out_dir)
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/qc/chipseq.py#L34-L47
train
219,041
bcbio/bcbio-nextgen
bcbio/qc/chipseq.py
_sample_template
def _sample_template(sample, out_dir): """R code to get QC for one sample""" bam_fn = dd.get_work_bam(sample) genome = dd.get_genome_build(sample) if genome in supported: peaks = sample.get("peaks_files", []).get("main") if peaks: r_code = ("library(ChIPQC);\n" "sample = ChIPQCsample(\"{bam_fn}\"," "\"{peaks}\", " "annotation = \"{genome}\"," ");\n" "ChIPQCreport(sample);\n") r_code_fn = os.path.join(out_dir, "chipqc.r") with open(r_code_fn, 'w') as inh: inh.write(r_code.format(**locals())) return r_code_fn
python
def _sample_template(sample, out_dir): """R code to get QC for one sample""" bam_fn = dd.get_work_bam(sample) genome = dd.get_genome_build(sample) if genome in supported: peaks = sample.get("peaks_files", []).get("main") if peaks: r_code = ("library(ChIPQC);\n" "sample = ChIPQCsample(\"{bam_fn}\"," "\"{peaks}\", " "annotation = \"{genome}\"," ");\n" "ChIPQCreport(sample);\n") r_code_fn = os.path.join(out_dir, "chipqc.r") with open(r_code_fn, 'w') as inh: inh.write(r_code.format(**locals())) return r_code_fn
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R code to get QC for one sample
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/qc/chipseq.py#L56-L72
train
219,042
bcbio/bcbio-nextgen
bcbio/rnaseq/featureCounts.py
_change_sample_name
def _change_sample_name(in_file, sample_name, data=None): """Fix name in feature counts log file to get the same name in multiqc report. """ out_file = append_stem(in_file, "_fixed") with file_transaction(data, out_file) as tx_out: with open(tx_out, "w") as out_handle: with open(in_file) as in_handle: for line in in_handle: if line.startswith("Status"): line = "Status\t%s.bam" % sample_name out_handle.write("%s\n" % line.strip()) return out_file
python
def _change_sample_name(in_file, sample_name, data=None): """Fix name in feature counts log file to get the same name in multiqc report. """ out_file = append_stem(in_file, "_fixed") with file_transaction(data, out_file) as tx_out: with open(tx_out, "w") as out_handle: with open(in_file) as in_handle: for line in in_handle: if line.startswith("Status"): line = "Status\t%s.bam" % sample_name out_handle.write("%s\n" % line.strip()) return out_file
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/rnaseq/featureCounts.py#L57-L69
train
219,043
bcbio/bcbio-nextgen
bcbio/rnaseq/featureCounts.py
_format_count_file
def _format_count_file(count_file, data): """ this cuts the count file produced from featureCounts down to a two column file of gene ids and number of reads mapping to each gene """ COUNT_COLUMN = 5 out_file = os.path.splitext(count_file)[0] + ".fixed.counts" if file_exists(out_file): return out_file df = pd.io.parsers.read_table(count_file, sep="\t", index_col=0, header=1) df_sub = df.ix[:, COUNT_COLUMN] with file_transaction(data, out_file) as tx_out_file: df_sub.to_csv(tx_out_file, sep="\t", index_label="id", header=False) return out_file
python
def _format_count_file(count_file, data): """ this cuts the count file produced from featureCounts down to a two column file of gene ids and number of reads mapping to each gene """ COUNT_COLUMN = 5 out_file = os.path.splitext(count_file)[0] + ".fixed.counts" if file_exists(out_file): return out_file df = pd.io.parsers.read_table(count_file, sep="\t", index_col=0, header=1) df_sub = df.ix[:, COUNT_COLUMN] with file_transaction(data, out_file) as tx_out_file: df_sub.to_csv(tx_out_file, sep="\t", index_label="id", header=False) return out_file
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this cuts the count file produced from featureCounts down to a two column file of gene ids and number of reads mapping to each gene
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/rnaseq/featureCounts.py#L71-L86
train
219,044
bcbio/bcbio-nextgen
bcbio/variation/peddy.py
run_qc
def run_qc(_, data, out_dir): """Run quality control in QC environment on a single sample. Enables peddy integration with CWL runs. """ if cwlutils.is_cwl_run(data): qc_data = run_peddy([data], out_dir) if tz.get_in(["summary", "qc", "peddy"], qc_data): return tz.get_in(["summary", "qc", "peddy"], qc_data)
python
def run_qc(_, data, out_dir): """Run quality control in QC environment on a single sample. Enables peddy integration with CWL runs. """ if cwlutils.is_cwl_run(data): qc_data = run_peddy([data], out_dir) if tz.get_in(["summary", "qc", "peddy"], qc_data): return tz.get_in(["summary", "qc", "peddy"], qc_data)
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Run quality control in QC environment on a single sample. Enables peddy integration with CWL runs.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/peddy.py#L35-L43
train
219,045
bcbio/bcbio-nextgen
bcbio/chipseq/macs2.py
run
def run(name, chip_bam, input_bam, genome_build, out_dir, method, resources, data): """ Run macs2 for chip and input samples avoiding errors due to samples. """ # output file name need to have the caller name config = dd.get_config(data) out_file = os.path.join(out_dir, name + "_peaks_macs2.xls") macs2_file = os.path.join(out_dir, name + "_peaks.xls") if utils.file_exists(out_file): _compres_bdg_files(out_dir) return _get_output_files(out_dir) macs2 = config_utils.get_program("macs2", config) options = " ".join(resources.get("macs2", {}).get("options", "")) genome_size = bam.fasta.total_sequence_length(dd.get_ref_file(data)) genome_size = "" if options.find("-g") > -1 else "-g %s" % genome_size paired = "-f BAMPE" if bam.is_paired(chip_bam) else "" with utils.chdir(out_dir): cmd = _macs2_cmd(method) try: do.run(cmd.format(**locals()), "macs2 for %s" % name) utils.move_safe(macs2_file, out_file) except subprocess.CalledProcessError: raise RuntimeWarning("macs2 terminated with an error.\n" "Please, check the message and report " "error if it is related to bcbio.\n" "You can add specific options for the sample " "setting resources as explained in docs: " "https://bcbio-nextgen.readthedocs.org/en/latest/contents/configuration.html#sample-specific-resources") _compres_bdg_files(out_dir) return _get_output_files(out_dir)
python
def run(name, chip_bam, input_bam, genome_build, out_dir, method, resources, data): """ Run macs2 for chip and input samples avoiding errors due to samples. """ # output file name need to have the caller name config = dd.get_config(data) out_file = os.path.join(out_dir, name + "_peaks_macs2.xls") macs2_file = os.path.join(out_dir, name + "_peaks.xls") if utils.file_exists(out_file): _compres_bdg_files(out_dir) return _get_output_files(out_dir) macs2 = config_utils.get_program("macs2", config) options = " ".join(resources.get("macs2", {}).get("options", "")) genome_size = bam.fasta.total_sequence_length(dd.get_ref_file(data)) genome_size = "" if options.find("-g") > -1 else "-g %s" % genome_size paired = "-f BAMPE" if bam.is_paired(chip_bam) else "" with utils.chdir(out_dir): cmd = _macs2_cmd(method) try: do.run(cmd.format(**locals()), "macs2 for %s" % name) utils.move_safe(macs2_file, out_file) except subprocess.CalledProcessError: raise RuntimeWarning("macs2 terminated with an error.\n" "Please, check the message and report " "error if it is related to bcbio.\n" "You can add specific options for the sample " "setting resources as explained in docs: " "https://bcbio-nextgen.readthedocs.org/en/latest/contents/configuration.html#sample-specific-resources") _compres_bdg_files(out_dir) return _get_output_files(out_dir)
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/chipseq/macs2.py#L11-L41
train
219,046
bcbio/bcbio-nextgen
bcbio/chipseq/macs2.py
_macs2_cmd
def _macs2_cmd(method="chip"): """Main command for macs2 tool.""" if method.lower() == "chip": cmd = ("{macs2} callpeak -t {chip_bam} -c {input_bam} {paired} " " {genome_size} -n {name} -B {options}") elif method.lower() == "atac": cmd = ("{macs2} callpeak -t {chip_bam} --nomodel " " {paired} {genome_size} -n {name} -B {options}" " --nolambda --keep-dup all") else: raise ValueError("chip_method should be chip or atac.") return cmd
python
def _macs2_cmd(method="chip"): """Main command for macs2 tool.""" if method.lower() == "chip": cmd = ("{macs2} callpeak -t {chip_bam} -c {input_bam} {paired} " " {genome_size} -n {name} -B {options}") elif method.lower() == "atac": cmd = ("{macs2} callpeak -t {chip_bam} --nomodel " " {paired} {genome_size} -n {name} -B {options}" " --nolambda --keep-dup all") else: raise ValueError("chip_method should be chip or atac.") return cmd
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/chipseq/macs2.py#L60-L71
train
219,047
bcbio/bcbio-nextgen
bcbio/pipeline/archive.py
to_cram
def to_cram(data): """Convert BAM archive files into indexed CRAM. """ data = utils.to_single_data(data) cram_file = cram.compress(dd.get_work_bam(data) or dd.get_align_bam(data), data) out_key = "archive_bam" if cwlutils.is_cwl_run(data) else "work_bam" data[out_key] = cram_file return [[data]]
python
def to_cram(data): """Convert BAM archive files into indexed CRAM. """ data = utils.to_single_data(data) cram_file = cram.compress(dd.get_work_bam(data) or dd.get_align_bam(data), data) out_key = "archive_bam" if cwlutils.is_cwl_run(data) else "work_bam" data[out_key] = cram_file return [[data]]
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/pipeline/archive.py#L11-L18
train
219,048
bcbio/bcbio-nextgen
bcbio/pipeline/archive.py
compress
def compress(samples, run_parallel): """Perform compression of output files for long term storage. """ to_cram = [] finished = [] for data in [x[0] for x in samples]: if "cram" in dd.get_archive(data) or "cram-lossless" in dd.get_archive(data): to_cram.append([data]) else: finished.append([data]) crammed = run_parallel("archive_to_cram", to_cram) return finished + crammed
python
def compress(samples, run_parallel): """Perform compression of output files for long term storage. """ to_cram = [] finished = [] for data in [x[0] for x in samples]: if "cram" in dd.get_archive(data) or "cram-lossless" in dd.get_archive(data): to_cram.append([data]) else: finished.append([data]) crammed = run_parallel("archive_to_cram", to_cram) return finished + crammed
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/pipeline/archive.py#L20-L31
train
219,049
bcbio/bcbio-nextgen
bcbio/qc/samtools.py
run
def run(_, data, out_dir=None): """Run samtools stats with reports on mapped reads, duplicates and insert sizes. """ stats_file, idxstats_file = _get_stats_files(data, out_dir) samtools = config_utils.get_program("samtools", data["config"]) bam_file = dd.get_align_bam(data) or dd.get_work_bam(data) if not utils.file_exists(stats_file): utils.safe_makedir(out_dir) with file_transaction(data, stats_file) as tx_out_file: cores = dd.get_num_cores(data) cmd = "{samtools} stats -@ {cores} {bam_file}" cmd += " > {tx_out_file}" do.run(cmd.format(**locals()), "samtools stats", data) if not utils.file_exists(idxstats_file): utils.safe_makedir(out_dir) with file_transaction(data, idxstats_file) as tx_out_file: cmd = "{samtools} idxstats {bam_file}" cmd += " > {tx_out_file}" do.run(cmd.format(**locals()), "samtools index stats", data) out = {"base": idxstats_file, "secondary": [stats_file]} out["metrics"] = _parse_samtools_stats(stats_file) return out
python
def run(_, data, out_dir=None): """Run samtools stats with reports on mapped reads, duplicates and insert sizes. """ stats_file, idxstats_file = _get_stats_files(data, out_dir) samtools = config_utils.get_program("samtools", data["config"]) bam_file = dd.get_align_bam(data) or dd.get_work_bam(data) if not utils.file_exists(stats_file): utils.safe_makedir(out_dir) with file_transaction(data, stats_file) as tx_out_file: cores = dd.get_num_cores(data) cmd = "{samtools} stats -@ {cores} {bam_file}" cmd += " > {tx_out_file}" do.run(cmd.format(**locals()), "samtools stats", data) if not utils.file_exists(idxstats_file): utils.safe_makedir(out_dir) with file_transaction(data, idxstats_file) as tx_out_file: cmd = "{samtools} idxstats {bam_file}" cmd += " > {tx_out_file}" do.run(cmd.format(**locals()), "samtools index stats", data) out = {"base": idxstats_file, "secondary": [stats_file]} out["metrics"] = _parse_samtools_stats(stats_file) return out
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Run samtools stats with reports on mapped reads, duplicates and insert sizes.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/qc/samtools.py#L13-L34
train
219,050
bcbio/bcbio-nextgen
bcbio/qc/samtools.py
run_and_save
def run_and_save(data): """Run QC, saving file outputs in data dictionary. """ run(None, data) stats_file, idxstats_file = _get_stats_files(data) data = tz.update_in(data, ["depth", "samtools", "stats"], lambda x: stats_file) data = tz.update_in(data, ["depth", "samtools", "idxstats"], lambda x: idxstats_file) return data
python
def run_and_save(data): """Run QC, saving file outputs in data dictionary. """ run(None, data) stats_file, idxstats_file = _get_stats_files(data) data = tz.update_in(data, ["depth", "samtools", "stats"], lambda x: stats_file) data = tz.update_in(data, ["depth", "samtools", "idxstats"], lambda x: idxstats_file) return data
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Run QC, saving file outputs in data dictionary.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/qc/samtools.py#L36-L43
train
219,051
bcbio/bcbio-nextgen
bcbio/qc/samtools.py
_get_stats_files
def _get_stats_files(data, out_dir=None): """Retrieve stats files from pre-existing dictionary or filesystem. """ if not out_dir: out_dir = utils.safe_makedir(os.path.join(dd.get_work_dir(data), "qc", dd.get_sample_name(data), "samtools")) stats_file = tz.get_in(["depth", "samtools", "stats"], data) idxstats_file = tz.get_in(["depth", "samtools", "idxstats"], data) if not stats_file: stats_file = os.path.join(out_dir, "%s.txt" % dd.get_sample_name(data)) if not idxstats_file: idxstats_file = os.path.join(out_dir, "%s-idxstats.txt" % dd.get_sample_name(data)) return stats_file, idxstats_file
python
def _get_stats_files(data, out_dir=None): """Retrieve stats files from pre-existing dictionary or filesystem. """ if not out_dir: out_dir = utils.safe_makedir(os.path.join(dd.get_work_dir(data), "qc", dd.get_sample_name(data), "samtools")) stats_file = tz.get_in(["depth", "samtools", "stats"], data) idxstats_file = tz.get_in(["depth", "samtools", "idxstats"], data) if not stats_file: stats_file = os.path.join(out_dir, "%s.txt" % dd.get_sample_name(data)) if not idxstats_file: idxstats_file = os.path.join(out_dir, "%s-idxstats.txt" % dd.get_sample_name(data)) return stats_file, idxstats_file
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Retrieve stats files from pre-existing dictionary or filesystem.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/qc/samtools.py#L45-L57
train
219,052
bcbio/bcbio-nextgen
bcbio/provenance/do.py
_descr_str
def _descr_str(descr, data, region): """Add additional useful information from data to description string. """ if data: name = dd.get_sample_name(data) if name: descr = "{0} : {1}".format(descr, name) elif "work_bam" in data: descr = "{0} : {1}".format(descr, os.path.basename(data["work_bam"])) if region: descr = "{0} : {1}".format(descr, region) return descr
python
def _descr_str(descr, data, region): """Add additional useful information from data to description string. """ if data: name = dd.get_sample_name(data) if name: descr = "{0} : {1}".format(descr, name) elif "work_bam" in data: descr = "{0} : {1}".format(descr, os.path.basename(data["work_bam"])) if region: descr = "{0} : {1}".format(descr, region) return descr
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Add additional useful information from data to description string.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/provenance/do.py#L35-L46
train
219,053
bcbio/bcbio-nextgen
bcbio/variation/vcfutils.py
get_indelcaller
def get_indelcaller(d_or_c): """Retrieve string for indelcaller to use, or empty string if not specified. """ config = d_or_c if isinstance(d_or_c, dict) and "config" in d_or_c else d_or_c indelcaller = config["algorithm"].get("indelcaller", "") if not indelcaller: indelcaller = "" if isinstance(indelcaller, (list, tuple)): indelcaller = indelcaller[0] if (len(indelcaller) > 0) else "" return indelcaller
python
def get_indelcaller(d_or_c): """Retrieve string for indelcaller to use, or empty string if not specified. """ config = d_or_c if isinstance(d_or_c, dict) and "config" in d_or_c else d_or_c indelcaller = config["algorithm"].get("indelcaller", "") if not indelcaller: indelcaller = "" if isinstance(indelcaller, (list, tuple)): indelcaller = indelcaller[0] if (len(indelcaller) > 0) else "" return indelcaller
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Retrieve string for indelcaller to use, or empty string if not specified.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/vcfutils.py#L140-L149
train
219,054
bcbio/bcbio-nextgen
bcbio/variation/vcfutils.py
split_snps_indels
def split_snps_indels(orig_file, ref_file, config): """Split a variant call file into SNPs and INDELs for processing. """ base, ext = utils.splitext_plus(orig_file) snp_file = "{base}-snp{ext}".format(base=base, ext=ext) indel_file = "{base}-indel{ext}".format(base=base, ext=ext) for out_file, select_arg in [(snp_file, "--types snps"), (indel_file, "--exclude-types snps")]: if not utils.file_exists(out_file): with file_transaction(config, out_file) as tx_out_file: bcftools = config_utils.get_program("bcftools", config) output_type = "z" if out_file.endswith(".gz") else "v" cmd = "{bcftools} view -O {output_type} {orig_file} {select_arg} > {tx_out_file}" do.run(cmd.format(**locals()), "Subset to SNPs and indels") if out_file.endswith(".gz"): bgzip_and_index(out_file, config) return snp_file, indel_file
python
def split_snps_indels(orig_file, ref_file, config): """Split a variant call file into SNPs and INDELs for processing. """ base, ext = utils.splitext_plus(orig_file) snp_file = "{base}-snp{ext}".format(base=base, ext=ext) indel_file = "{base}-indel{ext}".format(base=base, ext=ext) for out_file, select_arg in [(snp_file, "--types snps"), (indel_file, "--exclude-types snps")]: if not utils.file_exists(out_file): with file_transaction(config, out_file) as tx_out_file: bcftools = config_utils.get_program("bcftools", config) output_type = "z" if out_file.endswith(".gz") else "v" cmd = "{bcftools} view -O {output_type} {orig_file} {select_arg} > {tx_out_file}" do.run(cmd.format(**locals()), "Subset to SNPs and indels") if out_file.endswith(".gz"): bgzip_and_index(out_file, config) return snp_file, indel_file
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/vcfutils.py#L183-L199
train
219,055
bcbio/bcbio-nextgen
bcbio/variation/vcfutils.py
get_samples
def get_samples(in_file): """Retrieve samples present in a VCF file """ with utils.open_gzipsafe(in_file) as in_handle: for line in in_handle: if line.startswith("#CHROM"): parts = line.strip().split("\t") return parts[9:] raise ValueError("Did not find sample header in VCF file %s" % in_file)
python
def get_samples(in_file): """Retrieve samples present in a VCF file """ with utils.open_gzipsafe(in_file) as in_handle: for line in in_handle: if line.startswith("#CHROM"): parts = line.strip().split("\t") return parts[9:] raise ValueError("Did not find sample header in VCF file %s" % in_file)
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/vcfutils.py#L210-L218
train
219,056
bcbio/bcbio-nextgen
bcbio/variation/vcfutils.py
_get_exclude_samples
def _get_exclude_samples(in_file, to_exclude): """Identify samples in the exclusion list which are actually in the VCF. """ include, exclude = [], [] to_exclude = set(to_exclude) for s in get_samples(in_file): if s in to_exclude: exclude.append(s) else: include.append(s) return include, exclude
python
def _get_exclude_samples(in_file, to_exclude): """Identify samples in the exclusion list which are actually in the VCF. """ include, exclude = [], [] to_exclude = set(to_exclude) for s in get_samples(in_file): if s in to_exclude: exclude.append(s) else: include.append(s) return include, exclude
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Identify samples in the exclusion list which are actually in the VCF.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/vcfutils.py#L220-L230
train
219,057
bcbio/bcbio-nextgen
bcbio/variation/vcfutils.py
exclude_samples
def exclude_samples(in_file, out_file, to_exclude, ref_file, config, filters=None): """Exclude specific samples from an input VCF file. """ include, exclude = _get_exclude_samples(in_file, to_exclude) # can use the input sample, all exclusions already gone if len(exclude) == 0: out_file = in_file elif not utils.file_exists(out_file): with file_transaction(config, out_file) as tx_out_file: bcftools = config_utils.get_program("bcftools", config) output_type = "z" if out_file.endswith(".gz") else "v" include_str = ",".join(include) filter_str = "-f %s" % filters if filters is not None else "" # filters could be e.g. 'PASS,.' cmd = "{bcftools} view -O {output_type} -s {include_str} {filter_str} {in_file} > {tx_out_file}" do.run(cmd.format(**locals()), "Exclude samples: {}".format(to_exclude)) return out_file
python
def exclude_samples(in_file, out_file, to_exclude, ref_file, config, filters=None): """Exclude specific samples from an input VCF file. """ include, exclude = _get_exclude_samples(in_file, to_exclude) # can use the input sample, all exclusions already gone if len(exclude) == 0: out_file = in_file elif not utils.file_exists(out_file): with file_transaction(config, out_file) as tx_out_file: bcftools = config_utils.get_program("bcftools", config) output_type = "z" if out_file.endswith(".gz") else "v" include_str = ",".join(include) filter_str = "-f %s" % filters if filters is not None else "" # filters could be e.g. 'PASS,.' cmd = "{bcftools} view -O {output_type} -s {include_str} {filter_str} {in_file} > {tx_out_file}" do.run(cmd.format(**locals()), "Exclude samples: {}".format(to_exclude)) return out_file
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/vcfutils.py#L232-L247
train
219,058
bcbio/bcbio-nextgen
bcbio/variation/vcfutils.py
select_sample
def select_sample(in_file, sample, out_file, config, filters=None): """Select a single sample from the supplied multisample VCF file. """ if not utils.file_exists(out_file): with file_transaction(config, out_file) as tx_out_file: if len(get_samples(in_file)) == 1: shutil.copy(in_file, tx_out_file) else: if in_file.endswith(".gz"): bgzip_and_index(in_file, config) bcftools = config_utils.get_program("bcftools", config) output_type = "z" if out_file.endswith(".gz") else "v" filter_str = "-f %s" % filters if filters is not None else "" # filters could be e.g. 'PASS,.' cmd = "{bcftools} view -O {output_type} {filter_str} {in_file} -s {sample} > {tx_out_file}" do.run(cmd.format(**locals()), "Select sample: %s" % sample) if out_file.endswith(".gz"): bgzip_and_index(out_file, config) return out_file
python
def select_sample(in_file, sample, out_file, config, filters=None): """Select a single sample from the supplied multisample VCF file. """ if not utils.file_exists(out_file): with file_transaction(config, out_file) as tx_out_file: if len(get_samples(in_file)) == 1: shutil.copy(in_file, tx_out_file) else: if in_file.endswith(".gz"): bgzip_and_index(in_file, config) bcftools = config_utils.get_program("bcftools", config) output_type = "z" if out_file.endswith(".gz") else "v" filter_str = "-f %s" % filters if filters is not None else "" # filters could be e.g. 'PASS,.' cmd = "{bcftools} view -O {output_type} {filter_str} {in_file} -s {sample} > {tx_out_file}" do.run(cmd.format(**locals()), "Select sample: %s" % sample) if out_file.endswith(".gz"): bgzip_and_index(out_file, config) return out_file
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Select a single sample from the supplied multisample VCF file.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/vcfutils.py#L249-L266
train
219,059
bcbio/bcbio-nextgen
bcbio/variation/vcfutils.py
merge_variant_files
def merge_variant_files(orig_files, out_file, ref_file, config, region=None): """Combine multiple VCF files with different samples into a single output file. Uses bcftools merge on bgzipped input files, handling both tricky merge and concatenation of files. Does not correctly handle files with the same sample (use combine_variant_files instead). """ in_pipeline = False if isinstance(orig_files, dict): file_key = config["file_key"] in_pipeline = True orig_files = orig_files[file_key] out_file = _do_merge(orig_files, out_file, config, region) if in_pipeline: return [{file_key: out_file, "region": region, "sam_ref": ref_file, "config": config}] else: return out_file
python
def merge_variant_files(orig_files, out_file, ref_file, config, region=None): """Combine multiple VCF files with different samples into a single output file. Uses bcftools merge on bgzipped input files, handling both tricky merge and concatenation of files. Does not correctly handle files with the same sample (use combine_variant_files instead). """ in_pipeline = False if isinstance(orig_files, dict): file_key = config["file_key"] in_pipeline = True orig_files = orig_files[file_key] out_file = _do_merge(orig_files, out_file, config, region) if in_pipeline: return [{file_key: out_file, "region": region, "sam_ref": ref_file, "config": config}] else: return out_file
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Combine multiple VCF files with different samples into a single output file. Uses bcftools merge on bgzipped input files, handling both tricky merge and concatenation of files. Does not correctly handle files with the same sample (use combine_variant_files instead).
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/vcfutils.py#L288-L304
train
219,060
bcbio/bcbio-nextgen
bcbio/variation/vcfutils.py
_do_merge
def _do_merge(orig_files, out_file, config, region): """Do the actual work of merging with bcftools merge. """ if not utils.file_exists(out_file): with file_transaction(config, out_file) as tx_out_file: _check_samples_nodups(orig_files) prep_files = run_multicore(p_bgzip_and_index, [[x, config] for x in orig_files], config) input_vcf_file = "%s-files.txt" % utils.splitext_plus(out_file)[0] with open(input_vcf_file, "w") as out_handle: for fname in prep_files: out_handle.write(fname + "\n") bcftools = config_utils.get_program("bcftools", config) output_type = "z" if out_file.endswith(".gz") else "v" region_str = "-r {}".format(region) if region else "" cmd = "{bcftools} merge -O {output_type} {region_str} `cat {input_vcf_file}` > {tx_out_file}" do.run(cmd.format(**locals()), "Merge variants") if out_file.endswith(".gz"): bgzip_and_index(out_file, config) return out_file
python
def _do_merge(orig_files, out_file, config, region): """Do the actual work of merging with bcftools merge. """ if not utils.file_exists(out_file): with file_transaction(config, out_file) as tx_out_file: _check_samples_nodups(orig_files) prep_files = run_multicore(p_bgzip_and_index, [[x, config] for x in orig_files], config) input_vcf_file = "%s-files.txt" % utils.splitext_plus(out_file)[0] with open(input_vcf_file, "w") as out_handle: for fname in prep_files: out_handle.write(fname + "\n") bcftools = config_utils.get_program("bcftools", config) output_type = "z" if out_file.endswith(".gz") else "v" region_str = "-r {}".format(region) if region else "" cmd = "{bcftools} merge -O {output_type} {region_str} `cat {input_vcf_file}` > {tx_out_file}" do.run(cmd.format(**locals()), "Merge variants") if out_file.endswith(".gz"): bgzip_and_index(out_file, config) return out_file
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Do the actual work of merging with bcftools merge.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/vcfutils.py#L306-L324
train
219,061
bcbio/bcbio-nextgen
bcbio/variation/vcfutils.py
_check_samples_nodups
def _check_samples_nodups(fnames): """Ensure a set of input VCFs do not have duplicate samples. """ counts = defaultdict(int) for f in fnames: for s in get_samples(f): counts[s] += 1 duplicates = [s for s, c in counts.items() if c > 1] if duplicates: raise ValueError("Duplicate samples found in inputs %s: %s" % (duplicates, fnames))
python
def _check_samples_nodups(fnames): """Ensure a set of input VCFs do not have duplicate samples. """ counts = defaultdict(int) for f in fnames: for s in get_samples(f): counts[s] += 1 duplicates = [s for s, c in counts.items() if c > 1] if duplicates: raise ValueError("Duplicate samples found in inputs %s: %s" % (duplicates, fnames))
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/vcfutils.py#L326-L335
train
219,062
bcbio/bcbio-nextgen
bcbio/variation/vcfutils.py
_sort_by_region
def _sort_by_region(fnames, regions, ref_file, config): """Sort a set of regionally split files by region for ordered output. """ contig_order = {} for i, sq in enumerate(ref.file_contigs(ref_file, config)): contig_order[sq.name] = i sitems = [] assert len(regions) == len(fnames), (regions, fnames) added_fnames = set([]) for region, fname in zip(regions, fnames): if fname not in added_fnames: if isinstance(region, (list, tuple)): c, s, e = region elif isinstance(region, six.string_types) and region.find(":") >= 0: c, coords = region.split(":") s, e = [int(x) for x in coords.split("-")] else: c = region s, e = 0, 0 sitems.append(((contig_order[c], s, e), c, fname)) added_fnames.add(fname) sitems.sort() return [(x[1], x[2]) for x in sitems]
python
def _sort_by_region(fnames, regions, ref_file, config): """Sort a set of regionally split files by region for ordered output. """ contig_order = {} for i, sq in enumerate(ref.file_contigs(ref_file, config)): contig_order[sq.name] = i sitems = [] assert len(regions) == len(fnames), (regions, fnames) added_fnames = set([]) for region, fname in zip(regions, fnames): if fname not in added_fnames: if isinstance(region, (list, tuple)): c, s, e = region elif isinstance(region, six.string_types) and region.find(":") >= 0: c, coords = region.split(":") s, e = [int(x) for x in coords.split("-")] else: c = region s, e = 0, 0 sitems.append(((contig_order[c], s, e), c, fname)) added_fnames.add(fname) sitems.sort() return [(x[1], x[2]) for x in sitems]
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/vcfutils.py#L337-L359
train
219,063
bcbio/bcbio-nextgen
bcbio/variation/vcfutils.py
concat_variant_files
def concat_variant_files(orig_files, out_file, regions, ref_file, config): """Concatenate multiple variant files from regions into a single output file. Uses GATK4's GatherVcfs, falling back to bcftools concat --naive if it fails. These both only combine samples and avoid parsing, allowing scaling to large file sizes. """ if not utils.file_exists(out_file): input_file_list = _get_file_list(orig_files, out_file, regions, ref_file, config) try: out_file = _run_concat_variant_files_gatk4(input_file_list, out_file, config) except subprocess.CalledProcessError as msg: if ("We require all VCFs to have complete VCF headers" in str(msg) or "Features added out of order" in str(msg) or "The reference allele cannot be missing" in str(msg)): out_file = _run_concat_variant_files_bcftools(input_file_list, out_file, config, naive=True) else: raise if out_file.endswith(".gz"): bgzip_and_index(out_file, config) return out_file
python
def concat_variant_files(orig_files, out_file, regions, ref_file, config): """Concatenate multiple variant files from regions into a single output file. Uses GATK4's GatherVcfs, falling back to bcftools concat --naive if it fails. These both only combine samples and avoid parsing, allowing scaling to large file sizes. """ if not utils.file_exists(out_file): input_file_list = _get_file_list(orig_files, out_file, regions, ref_file, config) try: out_file = _run_concat_variant_files_gatk4(input_file_list, out_file, config) except subprocess.CalledProcessError as msg: if ("We require all VCFs to have complete VCF headers" in str(msg) or "Features added out of order" in str(msg) or "The reference allele cannot be missing" in str(msg)): out_file = _run_concat_variant_files_bcftools(input_file_list, out_file, config, naive=True) else: raise if out_file.endswith(".gz"): bgzip_and_index(out_file, config) return out_file
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/vcfutils.py#L361-L381
train
219,064
bcbio/bcbio-nextgen
bcbio/variation/vcfutils.py
_run_concat_variant_files_gatk4
def _run_concat_variant_files_gatk4(input_file_list, out_file, config): """Use GATK4 GatherVcfs for concatenation of scattered VCFs. """ if not utils.file_exists(out_file): with file_transaction(config, out_file) as tx_out_file: params = ["-T", "GatherVcfs", "-I", input_file_list, "-O", tx_out_file] # Use GATK4 for merging, tools_off: [gatk4] applies to variant calling config = utils.deepish_copy(config) if "gatk4" in dd.get_tools_off({"config": config}): config["algorithm"]["tools_off"].remove("gatk4") # Allow specification of verbosity in the unique style this tool uses resources = config_utils.get_resources("gatk", config) opts = [str(x) for x in resources.get("options", [])] if "--verbosity" in opts: params += ["--VERBOSITY:%s" % opts[opts.index("--verbosity") + 1]] broad_runner = broad.runner_from_config(config) broad_runner.run_gatk(params) return out_file
python
def _run_concat_variant_files_gatk4(input_file_list, out_file, config): """Use GATK4 GatherVcfs for concatenation of scattered VCFs. """ if not utils.file_exists(out_file): with file_transaction(config, out_file) as tx_out_file: params = ["-T", "GatherVcfs", "-I", input_file_list, "-O", tx_out_file] # Use GATK4 for merging, tools_off: [gatk4] applies to variant calling config = utils.deepish_copy(config) if "gatk4" in dd.get_tools_off({"config": config}): config["algorithm"]["tools_off"].remove("gatk4") # Allow specification of verbosity in the unique style this tool uses resources = config_utils.get_resources("gatk", config) opts = [str(x) for x in resources.get("options", [])] if "--verbosity" in opts: params += ["--VERBOSITY:%s" % opts[opts.index("--verbosity") + 1]] broad_runner = broad.runner_from_config(config) broad_runner.run_gatk(params) return out_file
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Use GATK4 GatherVcfs for concatenation of scattered VCFs.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/vcfutils.py#L383-L400
train
219,065
bcbio/bcbio-nextgen
bcbio/variation/vcfutils.py
_get_file_list
def _get_file_list(orig_files, out_file, regions, ref_file, config): """Create file with region sorted list of non-empty VCFs for concatenating. """ sorted_files = _sort_by_region(orig_files, regions, ref_file, config) exist_files = [(c, x) for c, x in sorted_files if os.path.exists(x) and vcf_has_variants(x)] if len(exist_files) == 0: # no non-empty inputs, merge the empty ones exist_files = [x for c, x in sorted_files if os.path.exists(x)] elif len(exist_files) > 1: exist_files = _fix_gatk_header(exist_files, out_file, config) else: exist_files = [x for c, x in exist_files] ready_files = run_multicore(p_bgzip_and_index, [[x, config] for x in exist_files], config) input_file_list = "%s-files.list" % utils.splitext_plus(out_file)[0] with open(input_file_list, "w") as out_handle: for fname in ready_files: out_handle.write(fname + "\n") return input_file_list
python
def _get_file_list(orig_files, out_file, regions, ref_file, config): """Create file with region sorted list of non-empty VCFs for concatenating. """ sorted_files = _sort_by_region(orig_files, regions, ref_file, config) exist_files = [(c, x) for c, x in sorted_files if os.path.exists(x) and vcf_has_variants(x)] if len(exist_files) == 0: # no non-empty inputs, merge the empty ones exist_files = [x for c, x in sorted_files if os.path.exists(x)] elif len(exist_files) > 1: exist_files = _fix_gatk_header(exist_files, out_file, config) else: exist_files = [x for c, x in exist_files] ready_files = run_multicore(p_bgzip_and_index, [[x, config] for x in exist_files], config) input_file_list = "%s-files.list" % utils.splitext_plus(out_file)[0] with open(input_file_list, "w") as out_handle: for fname in ready_files: out_handle.write(fname + "\n") return input_file_list
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Create file with region sorted list of non-empty VCFs for concatenating.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/vcfutils.py#L402-L418
train
219,066
bcbio/bcbio-nextgen
bcbio/variation/vcfutils.py
_fix_gatk_header
def _fix_gatk_header(exist_files, out_file, config): """Ensure consistent headers for VCF concatenation. Fixes problems for genomes that start with chrM by reheadering the first file. These files do haploid variant calling which lack the PID phasing key/value pair in FORMAT, so initial chrM samples cause errors during concatenation due to the lack of header merging. This fixes this by updating the first header. """ from bcbio.variation import ploidy c, base_file = exist_files[0] replace_file = base_file items = [{"config": config}] if ploidy.get_ploidy(items, region=(c, 1, 2)) == 1: for c, x in exist_files[1:]: if ploidy.get_ploidy(items, (c, 1, 2)) > 1: replace_file = x break base_fix_file = os.path.join(os.path.dirname(out_file), "%s-fixheader%s" % utils.splitext_plus(os.path.basename(base_file))) with file_transaction(config, base_fix_file) as tx_out_file: header_file = "%s-header.vcf" % utils.splitext_plus(tx_out_file)[0] do.run("zgrep ^# %s > %s" % (replace_file, header_file), "Prepare header file for merging") resources = config_utils.get_resources("picard", config) ropts = [] if "options" in resources: ropts += [str(x) for x in resources.get("options", [])] do.run("%s && picard FixVcfHeader HEADER=%s INPUT=%s OUTPUT=%s %s" % (utils.get_java_clprep(), header_file, base_file, base_fix_file, " ".join(ropts)), "Reheader initial VCF file in merge") bgzip_and_index(base_fix_file, config) return [base_fix_file] + [x for (c, x) in exist_files[1:]]
python
def _fix_gatk_header(exist_files, out_file, config): """Ensure consistent headers for VCF concatenation. Fixes problems for genomes that start with chrM by reheadering the first file. These files do haploid variant calling which lack the PID phasing key/value pair in FORMAT, so initial chrM samples cause errors during concatenation due to the lack of header merging. This fixes this by updating the first header. """ from bcbio.variation import ploidy c, base_file = exist_files[0] replace_file = base_file items = [{"config": config}] if ploidy.get_ploidy(items, region=(c, 1, 2)) == 1: for c, x in exist_files[1:]: if ploidy.get_ploidy(items, (c, 1, 2)) > 1: replace_file = x break base_fix_file = os.path.join(os.path.dirname(out_file), "%s-fixheader%s" % utils.splitext_plus(os.path.basename(base_file))) with file_transaction(config, base_fix_file) as tx_out_file: header_file = "%s-header.vcf" % utils.splitext_plus(tx_out_file)[0] do.run("zgrep ^# %s > %s" % (replace_file, header_file), "Prepare header file for merging") resources = config_utils.get_resources("picard", config) ropts = [] if "options" in resources: ropts += [str(x) for x in resources.get("options", [])] do.run("%s && picard FixVcfHeader HEADER=%s INPUT=%s OUTPUT=%s %s" % (utils.get_java_clprep(), header_file, base_file, base_fix_file, " ".join(ropts)), "Reheader initial VCF file in merge") bgzip_and_index(base_fix_file, config) return [base_fix_file] + [x for (c, x) in exist_files[1:]]
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Ensure consistent headers for VCF concatenation. Fixes problems for genomes that start with chrM by reheadering the first file. These files do haploid variant calling which lack the PID phasing key/value pair in FORMAT, so initial chrM samples cause errors during concatenation due to the lack of header merging. This fixes this by updating the first header.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/vcfutils.py#L420-L451
train
219,067
bcbio/bcbio-nextgen
bcbio/variation/vcfutils.py
_run_concat_variant_files_bcftools
def _run_concat_variant_files_bcftools(in_list, out_file, config, naive=False): """Concatenate variant files using bcftools concat, potentially using the fast naive option. """ if not utils.file_exists(out_file): with file_transaction(config, out_file) as tx_out_file: bcftools = config_utils.get_program("bcftools", config) output_type = "z" if out_file.endswith(".gz") else "v" if naive: args = "--naive" else: args = "--allow-overlaps" cmd = "{bcftools} concat {args} -O {output_type} --file-list {in_list} -o {tx_out_file}" do.run(cmd.format(**locals()), "bcftools concat variants") if out_file.endswith(".gz"): bgzip_and_index(out_file, config) return out_file
python
def _run_concat_variant_files_bcftools(in_list, out_file, config, naive=False): """Concatenate variant files using bcftools concat, potentially using the fast naive option. """ if not utils.file_exists(out_file): with file_transaction(config, out_file) as tx_out_file: bcftools = config_utils.get_program("bcftools", config) output_type = "z" if out_file.endswith(".gz") else "v" if naive: args = "--naive" else: args = "--allow-overlaps" cmd = "{bcftools} concat {args} -O {output_type} --file-list {in_list} -o {tx_out_file}" do.run(cmd.format(**locals()), "bcftools concat variants") if out_file.endswith(".gz"): bgzip_and_index(out_file, config) return out_file
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Concatenate variant files using bcftools concat, potentially using the fast naive option.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/vcfutils.py#L465-L480
train
219,068
bcbio/bcbio-nextgen
bcbio/variation/vcfutils.py
combine_variant_files
def combine_variant_files(orig_files, out_file, ref_file, config, quiet_out=True, region=None): """Combine VCF files from the same sample into a single output file. Handles cases where we split files into SNPs/Indels for processing then need to merge back into a final file. """ in_pipeline = False if isinstance(orig_files, dict): file_key = config["file_key"] in_pipeline = True orig_files = orig_files[file_key] if not utils.file_exists(out_file): with file_transaction(config, out_file) as tx_out_file: exist_files = [x for x in orig_files if os.path.exists(x)] ready_files = run_multicore(p_bgzip_and_index, [[x, config] for x in exist_files], config) dict_file = "%s.dict" % utils.splitext_plus(ref_file)[0] cores = dd.get_num_cores({"config": config}) memscale = {"magnitude": 0.9 * cores, "direction": "increase"} if cores > 1 else None cmd = ["picard"] + broad.get_picard_opts(config, memscale) + \ ["MergeVcfs", "D=%s" % dict_file, "O=%s" % tx_out_file] + \ ["I=%s" % f for f in ready_files] cmd = "%s && %s" % (utils.get_java_clprep(), " ".join(cmd)) do.run(cmd, "Combine variant files") if out_file.endswith(".gz"): bgzip_and_index(out_file, config) if in_pipeline: return [{file_key: out_file, "region": region, "sam_ref": ref_file, "config": config}] else: return out_file
python
def combine_variant_files(orig_files, out_file, ref_file, config, quiet_out=True, region=None): """Combine VCF files from the same sample into a single output file. Handles cases where we split files into SNPs/Indels for processing then need to merge back into a final file. """ in_pipeline = False if isinstance(orig_files, dict): file_key = config["file_key"] in_pipeline = True orig_files = orig_files[file_key] if not utils.file_exists(out_file): with file_transaction(config, out_file) as tx_out_file: exist_files = [x for x in orig_files if os.path.exists(x)] ready_files = run_multicore(p_bgzip_and_index, [[x, config] for x in exist_files], config) dict_file = "%s.dict" % utils.splitext_plus(ref_file)[0] cores = dd.get_num_cores({"config": config}) memscale = {"magnitude": 0.9 * cores, "direction": "increase"} if cores > 1 else None cmd = ["picard"] + broad.get_picard_opts(config, memscale) + \ ["MergeVcfs", "D=%s" % dict_file, "O=%s" % tx_out_file] + \ ["I=%s" % f for f in ready_files] cmd = "%s && %s" % (utils.get_java_clprep(), " ".join(cmd)) do.run(cmd, "Combine variant files") if out_file.endswith(".gz"): bgzip_and_index(out_file, config) if in_pipeline: return [{file_key: out_file, "region": region, "sam_ref": ref_file, "config": config}] else: return out_file
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Combine VCF files from the same sample into a single output file. Handles cases where we split files into SNPs/Indels for processing then need to merge back into a final file.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/vcfutils.py#L482-L511
train
219,069
bcbio/bcbio-nextgen
bcbio/variation/vcfutils.py
sort_by_ref
def sort_by_ref(vcf_file, data): """Sort a VCF file by genome reference and position, adding contig information. """ out_file = "%s-prep.vcf.gz" % utils.splitext_plus(vcf_file)[0] if not utils.file_uptodate(out_file, vcf_file): with file_transaction(data, out_file) as tx_out_file: header_file = "%s-header.txt" % utils.splitext_plus(tx_out_file)[0] with open(header_file, "w") as out_handle: for region in ref.file_contigs(dd.get_ref_file(data), data["config"]): out_handle.write("##contig=<ID=%s,length=%s>\n" % (region.name, region.size)) cat_cmd = "zcat" if vcf_file.endswith("vcf.gz") else "cat" cmd = ("{cat_cmd} {vcf_file} | grep -v ^##contig | bcftools annotate -h {header_file} | " "vt sort -m full -o {tx_out_file} -") with utils.chdir(os.path.dirname(tx_out_file)): do.run(cmd.format(**locals()), "Sort VCF by reference") return bgzip_and_index(out_file, data["config"])
python
def sort_by_ref(vcf_file, data): """Sort a VCF file by genome reference and position, adding contig information. """ out_file = "%s-prep.vcf.gz" % utils.splitext_plus(vcf_file)[0] if not utils.file_uptodate(out_file, vcf_file): with file_transaction(data, out_file) as tx_out_file: header_file = "%s-header.txt" % utils.splitext_plus(tx_out_file)[0] with open(header_file, "w") as out_handle: for region in ref.file_contigs(dd.get_ref_file(data), data["config"]): out_handle.write("##contig=<ID=%s,length=%s>\n" % (region.name, region.size)) cat_cmd = "zcat" if vcf_file.endswith("vcf.gz") else "cat" cmd = ("{cat_cmd} {vcf_file} | grep -v ^##contig | bcftools annotate -h {header_file} | " "vt sort -m full -o {tx_out_file} -") with utils.chdir(os.path.dirname(tx_out_file)): do.run(cmd.format(**locals()), "Sort VCF by reference") return bgzip_and_index(out_file, data["config"])
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Sort a VCF file by genome reference and position, adding contig information.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/vcfutils.py#L513-L528
train
219,070
bcbio/bcbio-nextgen
bcbio/variation/vcfutils.py
add_contig_to_header
def add_contig_to_header(line, ref_file): """Streaming target to add contigs to a VCF file header. """ if line.startswith("##fileformat=VCF"): out = [line] for region in ref.file_contigs(ref_file): out.append("##contig=<ID=%s,length=%s>" % (region.name, region.size)) return "\n".join(out) else: return line
python
def add_contig_to_header(line, ref_file): """Streaming target to add contigs to a VCF file header. """ if line.startswith("##fileformat=VCF"): out = [line] for region in ref.file_contigs(ref_file): out.append("##contig=<ID=%s,length=%s>" % (region.name, region.size)) return "\n".join(out) else: return line
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Streaming target to add contigs to a VCF file header.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/vcfutils.py#L539-L548
train
219,071
bcbio/bcbio-nextgen
bcbio/variation/vcfutils.py
parallel_combine_variants
def parallel_combine_variants(orig_files, out_file, ref_file, config, run_parallel): """Combine variants in parallel by chromosome, concatenating final outputs. """ file_key = "vcf_files" def split_by_region(data): base, ext = utils.splitext_plus(os.path.basename(out_file)) args = [] for region in [x.name for x in ref.file_contigs(ref_file, config)]: region_out = os.path.join(os.path.dirname(out_file), "%s-regions" % base, "%s-%s%s" % (base, region, ext)) utils.safe_makedir(os.path.dirname(region_out)) args.append((region_out, ref_file, config, region)) return out_file, args config = copy.deepcopy(config) config["file_key"] = file_key prep_files = run_multicore(p_bgzip_and_index, [[x, config] for x in orig_files], config) items = [[{file_key: prep_files}]] parallel_split_combine(items, split_by_region, run_parallel, "merge_variant_files", "concat_variant_files", file_key, ["region", "sam_ref", "config"], split_outfile_i=0) return out_file
python
def parallel_combine_variants(orig_files, out_file, ref_file, config, run_parallel): """Combine variants in parallel by chromosome, concatenating final outputs. """ file_key = "vcf_files" def split_by_region(data): base, ext = utils.splitext_plus(os.path.basename(out_file)) args = [] for region in [x.name for x in ref.file_contigs(ref_file, config)]: region_out = os.path.join(os.path.dirname(out_file), "%s-regions" % base, "%s-%s%s" % (base, region, ext)) utils.safe_makedir(os.path.dirname(region_out)) args.append((region_out, ref_file, config, region)) return out_file, args config = copy.deepcopy(config) config["file_key"] = file_key prep_files = run_multicore(p_bgzip_and_index, [[x, config] for x in orig_files], config) items = [[{file_key: prep_files}]] parallel_split_combine(items, split_by_region, run_parallel, "merge_variant_files", "concat_variant_files", file_key, ["region", "sam_ref", "config"], split_outfile_i=0) return out_file
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/vcfutils.py#L552-L572
train
219,072
bcbio/bcbio-nextgen
bcbio/variation/vcfutils.py
move_vcf
def move_vcf(orig_file, new_file): """Move a VCF file with associated index. """ for ext in ["", ".idx", ".tbi"]: to_move = orig_file + ext if os.path.exists(to_move): shutil.move(to_move, new_file + ext)
python
def move_vcf(orig_file, new_file): """Move a VCF file with associated index. """ for ext in ["", ".idx", ".tbi"]: to_move = orig_file + ext if os.path.exists(to_move): shutil.move(to_move, new_file + ext)
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Move a VCF file with associated index.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/vcfutils.py#L576-L582
train
219,073
bcbio/bcbio-nextgen
bcbio/variation/vcfutils.py
bgzip_and_index
def bgzip_and_index(in_file, config=None, remove_orig=True, prep_cmd="", tabix_args=None, out_dir=None): """bgzip and tabix index an input file, handling VCF and BED. """ if config is None: config = {} out_file = in_file if in_file.endswith(".gz") else in_file + ".gz" if out_dir: remove_orig = False out_file = os.path.join(out_dir, os.path.basename(out_file)) if (not utils.file_exists(out_file) or not os.path.lexists(out_file) or (utils.file_exists(in_file) and not utils.file_uptodate(out_file, in_file))): assert not in_file == out_file, "Input file is bgzipped but not found: %s" % in_file assert os.path.exists(in_file), "Input file %s not found" % in_file if not utils.file_uptodate(out_file, in_file): with file_transaction(config, out_file) as tx_out_file: bgzip = tools.get_bgzip_cmd(config) cat_cmd = "zcat" if in_file.endswith(".gz") else "cat" if prep_cmd: prep_cmd = "| %s " % prep_cmd cmd = "{cat_cmd} {in_file} {prep_cmd} | {bgzip} -c > {tx_out_file}" try: do.run(cmd.format(**locals()), "bgzip %s" % os.path.basename(in_file)) except subprocess.CalledProcessError: # Race conditions: ignore errors where file has been deleted by another if os.path.exists(in_file) and not os.path.exists(out_file): raise if remove_orig: try: os.remove(in_file) except OSError: # Handle cases where run in parallel and file has been deleted pass tabix_index(out_file, config, tabix_args=tabix_args) return out_file
python
def bgzip_and_index(in_file, config=None, remove_orig=True, prep_cmd="", tabix_args=None, out_dir=None): """bgzip and tabix index an input file, handling VCF and BED. """ if config is None: config = {} out_file = in_file if in_file.endswith(".gz") else in_file + ".gz" if out_dir: remove_orig = False out_file = os.path.join(out_dir, os.path.basename(out_file)) if (not utils.file_exists(out_file) or not os.path.lexists(out_file) or (utils.file_exists(in_file) and not utils.file_uptodate(out_file, in_file))): assert not in_file == out_file, "Input file is bgzipped but not found: %s" % in_file assert os.path.exists(in_file), "Input file %s not found" % in_file if not utils.file_uptodate(out_file, in_file): with file_transaction(config, out_file) as tx_out_file: bgzip = tools.get_bgzip_cmd(config) cat_cmd = "zcat" if in_file.endswith(".gz") else "cat" if prep_cmd: prep_cmd = "| %s " % prep_cmd cmd = "{cat_cmd} {in_file} {prep_cmd} | {bgzip} -c > {tx_out_file}" try: do.run(cmd.format(**locals()), "bgzip %s" % os.path.basename(in_file)) except subprocess.CalledProcessError: # Race conditions: ignore errors where file has been deleted by another if os.path.exists(in_file) and not os.path.exists(out_file): raise if remove_orig: try: os.remove(in_file) except OSError: # Handle cases where run in parallel and file has been deleted pass tabix_index(out_file, config, tabix_args=tabix_args) return out_file
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bgzip and tabix index an input file, handling VCF and BED.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/vcfutils.py#L584-L616
train
219,074
bcbio/bcbio-nextgen
bcbio/variation/vcfutils.py
tabix_index
def tabix_index(in_file, config, preset=None, tabix_args=None): """Index a file using tabix. """ in_file = os.path.abspath(in_file) out_file = in_file + ".tbi" if not utils.file_exists(out_file) or not utils.file_uptodate(out_file, in_file): # Remove old index files to prevent linking into tx directory utils.remove_safe(out_file) with file_transaction(config, out_file) as tx_out_file: tabix = tools.get_tabix_cmd(config) tx_in_file = os.path.splitext(tx_out_file)[0] utils.symlink_plus(in_file, tx_in_file) if tabix_args: cmd = "{tabix} -f {tabix_args} {tx_in_file}" else: preset = _guess_preset(in_file) if preset is None else preset cmd = "{tabix} -f -p {preset} {tx_in_file}" do.run(cmd.format(**locals()), "tabix index %s" % os.path.basename(in_file)) return out_file
python
def tabix_index(in_file, config, preset=None, tabix_args=None): """Index a file using tabix. """ in_file = os.path.abspath(in_file) out_file = in_file + ".tbi" if not utils.file_exists(out_file) or not utils.file_uptodate(out_file, in_file): # Remove old index files to prevent linking into tx directory utils.remove_safe(out_file) with file_transaction(config, out_file) as tx_out_file: tabix = tools.get_tabix_cmd(config) tx_in_file = os.path.splitext(tx_out_file)[0] utils.symlink_plus(in_file, tx_in_file) if tabix_args: cmd = "{tabix} -f {tabix_args} {tx_in_file}" else: preset = _guess_preset(in_file) if preset is None else preset cmd = "{tabix} -f -p {preset} {tx_in_file}" do.run(cmd.format(**locals()), "tabix index %s" % os.path.basename(in_file)) return out_file
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/vcfutils.py#L635-L653
train
219,075
bcbio/bcbio-nextgen
bcbio/variation/vcfutils.py
is_gvcf_file
def is_gvcf_file(in_file): """Check if an input file is raw gVCF """ to_check = 100 n = 0 with utils.open_gzipsafe(in_file) as in_handle: for line in in_handle: if not line.startswith("##"): if n > to_check: break n += 1 parts = line.split("\t") # GATK if parts[4] == "<NON_REF>": return True # strelka2 if parts[4] == "." and parts[7].startswith("BLOCKAVG"): return True # freebayes if parts[4] == "<*>": return True # platypue if parts[4] == "N" and parts[6] == "REFCALL": return True
python
def is_gvcf_file(in_file): """Check if an input file is raw gVCF """ to_check = 100 n = 0 with utils.open_gzipsafe(in_file) as in_handle: for line in in_handle: if not line.startswith("##"): if n > to_check: break n += 1 parts = line.split("\t") # GATK if parts[4] == "<NON_REF>": return True # strelka2 if parts[4] == "." and parts[7].startswith("BLOCKAVG"): return True # freebayes if parts[4] == "<*>": return True # platypue if parts[4] == "N" and parts[6] == "REFCALL": return True
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Check if an input file is raw gVCF
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/vcfutils.py#L655-L678
train
219,076
bcbio/bcbio-nextgen
bcbio/variation/vcfutils.py
cyvcf_add_filter
def cyvcf_add_filter(rec, name): """Add a FILTER value to a cyvcf2 record """ if rec.FILTER: filters = rec.FILTER.split(";") else: filters = [] if name not in filters: filters.append(name) rec.FILTER = filters return rec
python
def cyvcf_add_filter(rec, name): """Add a FILTER value to a cyvcf2 record """ if rec.FILTER: filters = rec.FILTER.split(";") else: filters = [] if name not in filters: filters.append(name) rec.FILTER = filters return rec
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Add a FILTER value to a cyvcf2 record
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/vcfutils.py#L680-L690
train
219,077
bcbio/bcbio-nextgen
bcbio/variation/vcfutils.py
cyvcf_remove_filter
def cyvcf_remove_filter(rec, name): """Remove filter with the given name from a cyvcf2 record """ if rec.FILTER: filters = rec.FILTER.split(";") else: filters = [] new_filters = [x for x in filters if not str(x) == name] if len(new_filters) == 0: new_filters = ["PASS"] rec.FILTER = new_filters return rec
python
def cyvcf_remove_filter(rec, name): """Remove filter with the given name from a cyvcf2 record """ if rec.FILTER: filters = rec.FILTER.split(";") else: filters = [] new_filters = [x for x in filters if not str(x) == name] if len(new_filters) == 0: new_filters = ["PASS"] rec.FILTER = new_filters return rec
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/variation/vcfutils.py#L692-L703
train
219,078
bcbio/bcbio-nextgen
bcbio/pipeline/alignment.py
organize_noalign
def organize_noalign(data): """CWL target to skip alignment and organize input data. """ data = utils.to_single_data(data[0]) work_dir = utils.safe_makedir(os.path.join(dd.get_work_dir(data), "align", dd.get_sample_name(data))) work_bam = os.path.join(work_dir, "%s-input.bam" % dd.get_sample_name(data)) if data.get("files"): if data["files"][0].endswith(".cram"): work_bam = cram.to_bam(data["files"][0], work_bam, data) else: assert data["files"][0].endswith(".bam"), data["files"][0] utils.copy_plus(data["files"][0], work_bam) bam.index(work_bam, data["config"]) else: work_bam = None data["align_bam"] = work_bam return data
python
def organize_noalign(data): """CWL target to skip alignment and organize input data. """ data = utils.to_single_data(data[0]) work_dir = utils.safe_makedir(os.path.join(dd.get_work_dir(data), "align", dd.get_sample_name(data))) work_bam = os.path.join(work_dir, "%s-input.bam" % dd.get_sample_name(data)) if data.get("files"): if data["files"][0].endswith(".cram"): work_bam = cram.to_bam(data["files"][0], work_bam, data) else: assert data["files"][0].endswith(".bam"), data["files"][0] utils.copy_plus(data["files"][0], work_bam) bam.index(work_bam, data["config"]) else: work_bam = None data["align_bam"] = work_bam return data
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CWL target to skip alignment and organize input data.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/pipeline/alignment.py#L53-L69
train
219,079
bcbio/bcbio-nextgen
bcbio/pipeline/alignment.py
align_to_sort_bam
def align_to_sort_bam(fastq1, fastq2, aligner, data): """Align to the named genome build, returning a sorted BAM file. """ names = data["rgnames"] align_dir_parts = [data["dirs"]["work"], "align", names["sample"]] if data.get("disambiguate"): align_dir_parts.append(data["disambiguate"]["genome_build"]) aligner_index = _get_aligner_index(aligner, data) align_dir = utils.safe_makedir(os.path.join(*align_dir_parts)) ref_file = tz.get_in(("reference", "fasta", "base"), data) if fastq1.endswith(".bam"): data = _align_from_bam(fastq1, aligner, aligner_index, ref_file, names, align_dir, data) else: data = _align_from_fastq(fastq1, fastq2, aligner, aligner_index, ref_file, names, align_dir, data) if data["work_bam"] and utils.file_exists(data["work_bam"]): if data.get("align_split") and dd.get_mark_duplicates(data): # If merging later with with bamsormadup need query sorted inputs # but CWL requires a bai file. Create a fake one to make it happy. bam.fake_index(data["work_bam"], data) else: bam.index(data["work_bam"], data["config"]) for extra in ["-sr", "-disc"]: extra_bam = utils.append_stem(data['work_bam'], extra) if utils.file_exists(extra_bam): bam.index(extra_bam, data["config"]) return data
python
def align_to_sort_bam(fastq1, fastq2, aligner, data): """Align to the named genome build, returning a sorted BAM file. """ names = data["rgnames"] align_dir_parts = [data["dirs"]["work"], "align", names["sample"]] if data.get("disambiguate"): align_dir_parts.append(data["disambiguate"]["genome_build"]) aligner_index = _get_aligner_index(aligner, data) align_dir = utils.safe_makedir(os.path.join(*align_dir_parts)) ref_file = tz.get_in(("reference", "fasta", "base"), data) if fastq1.endswith(".bam"): data = _align_from_bam(fastq1, aligner, aligner_index, ref_file, names, align_dir, data) else: data = _align_from_fastq(fastq1, fastq2, aligner, aligner_index, ref_file, names, align_dir, data) if data["work_bam"] and utils.file_exists(data["work_bam"]): if data.get("align_split") and dd.get_mark_duplicates(data): # If merging later with with bamsormadup need query sorted inputs # but CWL requires a bai file. Create a fake one to make it happy. bam.fake_index(data["work_bam"], data) else: bam.index(data["work_bam"], data["config"]) for extra in ["-sr", "-disc"]: extra_bam = utils.append_stem(data['work_bam'], extra) if utils.file_exists(extra_bam): bam.index(extra_bam, data["config"]) return data
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/pipeline/alignment.py#L71-L98
train
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bcbio/bcbio-nextgen
bcbio/pipeline/alignment.py
get_aligner_with_aliases
def get_aligner_with_aliases(aligner, data): """Retrieve aligner index retriever, including aliases for shared. Handles tricky cases like gridss where we need bwa indices even with no aligner specified since they're used internally within GRIDSS. """ aligner_aliases = {"sentieon-bwa": "bwa"} from bcbio import structural if not aligner and "gridss" in structural.get_svcallers(data): aligner = "bwa" return aligner_aliases.get(aligner) or aligner
python
def get_aligner_with_aliases(aligner, data): """Retrieve aligner index retriever, including aliases for shared. Handles tricky cases like gridss where we need bwa indices even with no aligner specified since they're used internally within GRIDSS. """ aligner_aliases = {"sentieon-bwa": "bwa"} from bcbio import structural if not aligner and "gridss" in structural.get_svcallers(data): aligner = "bwa" return aligner_aliases.get(aligner) or aligner
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/pipeline/alignment.py#L100-L110
train
219,081
bcbio/bcbio-nextgen
bcbio/pipeline/alignment.py
_get_aligner_index
def _get_aligner_index(aligner, data): """Handle multiple specifications of aligner indexes, returning value to pass to aligner. Original bcbio case -- a list of indices. CWL case: a single file with secondaryFiles staged in the same directory. """ aligner_indexes = tz.get_in(("reference", get_aligner_with_aliases(aligner, data), "indexes"), data) # standard bcbio case if aligner_indexes and isinstance(aligner_indexes, (list, tuple)): aligner_index = os.path.commonprefix(aligner_indexes) if aligner_index.endswith("."): aligner_index = aligner_index[:-1] return aligner_index # single file -- check for standard naming or directory elif aligner_indexes and os.path.exists(aligner_indexes): aligner_dir = os.path.dirname(aligner_indexes) aligner_prefix = os.path.splitext(aligner_indexes)[0] if len(glob.glob("%s.*" % aligner_prefix)) > 0: return aligner_prefix else: return aligner_dir if aligner not in allow_noindices(): raise ValueError("Did not find reference indices for aligner %s in genome: %s" % (aligner, data["reference"]))
python
def _get_aligner_index(aligner, data): """Handle multiple specifications of aligner indexes, returning value to pass to aligner. Original bcbio case -- a list of indices. CWL case: a single file with secondaryFiles staged in the same directory. """ aligner_indexes = tz.get_in(("reference", get_aligner_with_aliases(aligner, data), "indexes"), data) # standard bcbio case if aligner_indexes and isinstance(aligner_indexes, (list, tuple)): aligner_index = os.path.commonprefix(aligner_indexes) if aligner_index.endswith("."): aligner_index = aligner_index[:-1] return aligner_index # single file -- check for standard naming or directory elif aligner_indexes and os.path.exists(aligner_indexes): aligner_dir = os.path.dirname(aligner_indexes) aligner_prefix = os.path.splitext(aligner_indexes)[0] if len(glob.glob("%s.*" % aligner_prefix)) > 0: return aligner_prefix else: return aligner_dir if aligner not in allow_noindices(): raise ValueError("Did not find reference indices for aligner %s in genome: %s" % (aligner, data["reference"]))
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/pipeline/alignment.py#L115-L139
train
219,082
bcbio/bcbio-nextgen
bcbio/pipeline/alignment.py
_align_from_fastq
def _align_from_fastq(fastq1, fastq2, aligner, align_ref, sam_ref, names, align_dir, data): """Align from fastq inputs, producing sorted BAM output. """ config = data["config"] align_fn = TOOLS[aligner].align_fn out = align_fn(fastq1, fastq2, align_ref, names, align_dir, data) # handle align functions that update the main data dictionary in place if isinstance(out, dict): assert out.get("work_bam"), (dd.get_sample_name(data), out.get("work_bam")) return out # handle output of raw SAM files that need to be converted to BAM else: work_bam = bam.sam_to_bam(out, config) data["work_bam"] = bam.sort(work_bam, config) return data
python
def _align_from_fastq(fastq1, fastq2, aligner, align_ref, sam_ref, names, align_dir, data): """Align from fastq inputs, producing sorted BAM output. """ config = data["config"] align_fn = TOOLS[aligner].align_fn out = align_fn(fastq1, fastq2, align_ref, names, align_dir, data) # handle align functions that update the main data dictionary in place if isinstance(out, dict): assert out.get("work_bam"), (dd.get_sample_name(data), out.get("work_bam")) return out # handle output of raw SAM files that need to be converted to BAM else: work_bam = bam.sam_to_bam(out, config) data["work_bam"] = bam.sort(work_bam, config) return data
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/pipeline/alignment.py#L155-L170
train
219,083
bcbio/bcbio-nextgen
bcbio/structural/gridss.py
_finalize_memory
def _finalize_memory(jvm_opts): """GRIDSS does not recommend setting memory between 32 and 48Gb. https://github.com/PapenfussLab/gridss#memory-usage """ avoid_min = 32 avoid_max = 48 out_opts = [] for opt in jvm_opts: if opt.startswith("-Xmx"): spec = opt[4:] val = int(spec[:-1]) mod = spec[-1] if mod.upper() == "M": adjust = 1024 min_val = avoid_min * 1024 max_val = avoid_max * 1024 else: adjust = 1 min_val, max_val = avoid_min, avoid_max if val >= min_val and val < max_val: val = min_val - adjust opt = "%s%s%s" % (opt[:4], val, mod) out_opts.append(opt) return out_opts
python
def _finalize_memory(jvm_opts): """GRIDSS does not recommend setting memory between 32 and 48Gb. https://github.com/PapenfussLab/gridss#memory-usage """ avoid_min = 32 avoid_max = 48 out_opts = [] for opt in jvm_opts: if opt.startswith("-Xmx"): spec = opt[4:] val = int(spec[:-1]) mod = spec[-1] if mod.upper() == "M": adjust = 1024 min_val = avoid_min * 1024 max_val = avoid_max * 1024 else: adjust = 1 min_val, max_val = avoid_min, avoid_max if val >= min_val and val < max_val: val = min_val - adjust opt = "%s%s%s" % (opt[:4], val, mod) out_opts.append(opt) return out_opts
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/structural/gridss.py#L70-L94
train
219,084
bcbio/bcbio-nextgen
bcbio/structural/gridss.py
_setup_reference_files
def _setup_reference_files(data, tx_out_dir): """Create a reference directory with fasta and bwa indices. GRIDSS requires all files in a single directory, so setup with symlinks. This needs bwa aligner indices available, which we ensure with `get_aligner_with_aliases` during YAML sample setup. """ aligner = dd.get_aligner(data) or "bwa" out_dir = utils.safe_makedir(os.path.join(tx_out_dir, aligner)) ref_fasta = dd.get_ref_file(data) ref_files = ["%s%s" % (utils.splitext_plus(ref_fasta)[0], ext) for ext in [".fa", ".fa.fai", ".dict"]] for orig_file in ref_files + tz.get_in(("reference", aligner, "indexes"), data): utils.symlink_plus(orig_file, os.path.join(out_dir, os.path.basename(orig_file))) return os.path.join(out_dir, os.path.basename(ref_fasta))
python
def _setup_reference_files(data, tx_out_dir): """Create a reference directory with fasta and bwa indices. GRIDSS requires all files in a single directory, so setup with symlinks. This needs bwa aligner indices available, which we ensure with `get_aligner_with_aliases` during YAML sample setup. """ aligner = dd.get_aligner(data) or "bwa" out_dir = utils.safe_makedir(os.path.join(tx_out_dir, aligner)) ref_fasta = dd.get_ref_file(data) ref_files = ["%s%s" % (utils.splitext_plus(ref_fasta)[0], ext) for ext in [".fa", ".fa.fai", ".dict"]] for orig_file in ref_files + tz.get_in(("reference", aligner, "indexes"), data): utils.symlink_plus(orig_file, os.path.join(out_dir, os.path.basename(orig_file))) return os.path.join(out_dir, os.path.basename(ref_fasta))
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/structural/gridss.py#L96-L109
train
219,085
bcbio/bcbio-nextgen
bcbio/qc/multiqc.py
_add_versions
def _add_versions(samples): """Add tool and data versions to the summary. """ samples[0]["versions"] = {"tools": programs.write_versions(samples[0]["dirs"], samples[0]["config"]), "data": provenancedata.write_versions(samples[0]["dirs"], samples)} return samples
python
def _add_versions(samples): """Add tool and data versions to the summary. """ samples[0]["versions"] = {"tools": programs.write_versions(samples[0]["dirs"], samples[0]["config"]), "data": provenancedata.write_versions(samples[0]["dirs"], samples)} return samples
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/qc/multiqc.py#L109-L114
train
219,086
bcbio/bcbio-nextgen
bcbio/qc/multiqc.py
_summarize_inputs
def _summarize_inputs(samples, out_dir): """Summarize inputs for MultiQC reporting in display. """ logger.info("summarize target information") if samples[0].get("analysis", "").lower() in ["variant", "variant2"]: metrics_dir = utils.safe_makedir(os.path.join(out_dir, "report", "metrics")) samples = _merge_target_information(samples, metrics_dir) logger.info("summarize fastqc") out_dir = utils.safe_makedir(os.path.join(out_dir, "report", "fastqc")) with utils.chdir(out_dir): _merge_fastqc(samples) preseq_samples = [s for s in samples if tz.get_in(["config", "algorithm", "preseq"], s)] if preseq_samples: logger.info("summarize preseq") out_dir = utils.safe_makedir(os.path.join(out_dir, "report", "preseq")) with utils.chdir(out_dir): _merge_preseq(preseq_samples) return samples
python
def _summarize_inputs(samples, out_dir): """Summarize inputs for MultiQC reporting in display. """ logger.info("summarize target information") if samples[0].get("analysis", "").lower() in ["variant", "variant2"]: metrics_dir = utils.safe_makedir(os.path.join(out_dir, "report", "metrics")) samples = _merge_target_information(samples, metrics_dir) logger.info("summarize fastqc") out_dir = utils.safe_makedir(os.path.join(out_dir, "report", "fastqc")) with utils.chdir(out_dir): _merge_fastqc(samples) preseq_samples = [s for s in samples if tz.get_in(["config", "algorithm", "preseq"], s)] if preseq_samples: logger.info("summarize preseq") out_dir = utils.safe_makedir(os.path.join(out_dir, "report", "preseq")) with utils.chdir(out_dir): _merge_preseq(preseq_samples) return samples
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Summarize inputs for MultiQC reporting in display.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/qc/multiqc.py#L116-L135
train
219,087
bcbio/bcbio-nextgen
bcbio/qc/multiqc.py
_work_path_to_rel_final_path
def _work_path_to_rel_final_path(path, upload_path_mapping, upload_base_dir): """ Check if `path` is a work-rooted path, and convert to a relative final-rooted path """ if not path or not isinstance(path, str): return path upload_path = None # First, check in the mapping: if it's there is a direct reference and # it's a file, we immediately return it (saves lots of iterations) if upload_path_mapping.get(path) is not None and os.path.isfile(path): upload_path = upload_path_mapping[path] else: # Not a file: check for elements in the mapping that contain # it paths_to_check = [key for key in upload_path_mapping if path.startswith(key)] if paths_to_check: for work_path in paths_to_check: if os.path.isdir(work_path): final_path = upload_path_mapping[work_path] upload_path = path.replace(work_path, final_path) break if upload_path is not None: return os.path.relpath(upload_path, upload_base_dir) else: return None
python
def _work_path_to_rel_final_path(path, upload_path_mapping, upload_base_dir): """ Check if `path` is a work-rooted path, and convert to a relative final-rooted path """ if not path or not isinstance(path, str): return path upload_path = None # First, check in the mapping: if it's there is a direct reference and # it's a file, we immediately return it (saves lots of iterations) if upload_path_mapping.get(path) is not None and os.path.isfile(path): upload_path = upload_path_mapping[path] else: # Not a file: check for elements in the mapping that contain # it paths_to_check = [key for key in upload_path_mapping if path.startswith(key)] if paths_to_check: for work_path in paths_to_check: if os.path.isdir(work_path): final_path = upload_path_mapping[work_path] upload_path = path.replace(work_path, final_path) break if upload_path is not None: return os.path.relpath(upload_path, upload_base_dir) else: return None
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/qc/multiqc.py#L195-L222
train
219,088
bcbio/bcbio-nextgen
bcbio/qc/multiqc.py
_one_exists
def _one_exists(input_files): """ at least one file must exist for multiqc to run properly """ for f in input_files: if os.path.exists(f): return True return False
python
def _one_exists(input_files): """ at least one file must exist for multiqc to run properly """ for f in input_files: if os.path.exists(f): return True return False
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/qc/multiqc.py#L224-L231
train
219,089
bcbio/bcbio-nextgen
bcbio/qc/multiqc.py
_get_input_files
def _get_input_files(samples, base_dir, tx_out_dir): """Retrieve input files, keyed by sample and QC method name. Stages files into the work directory to ensure correct names for MultiQC sample assessment when running with CWL. """ in_files = collections.defaultdict(list) for data in samples: sum_qc = tz.get_in(["summary", "qc"], data, {}) if sum_qc in [None, "None"]: sum_qc = {} elif isinstance(sum_qc, six.string_types): sum_qc = {dd.get_algorithm_qc(data)[0]: sum_qc} elif not isinstance(sum_qc, dict): raise ValueError("Unexpected summary qc: %s" % sum_qc) for program, pfiles in sum_qc.items(): if isinstance(pfiles, dict): pfiles = [pfiles["base"]] + pfiles.get("secondary", []) # CWL: presents output files as single file plus associated secondary files elif isinstance(pfiles, six.string_types): if os.path.exists(pfiles): pfiles = [os.path.join(basedir, f) for basedir, subdir, filenames in os.walk(os.path.dirname(pfiles)) for f in filenames] else: pfiles = [] in_files[(dd.get_sample_name(data), program)].extend(pfiles) staged_files = [] for (sample, program), files in in_files.items(): cur_dir = utils.safe_makedir(os.path.join(base_dir, "inputs", sample, program)) for f in files: if _check_multiqc_input(f) and _is_good_file_for_multiqc(f): if _in_temp_directory(f) or any([cwlutils.is_cwl_run(d) for d in samples]): staged_f = os.path.join(cur_dir, os.path.basename(f)) shutil.copy(f, staged_f) staged_files.append(staged_f) else: staged_files.append(f) staged_files.extend(get_qsig_multiqc_files(samples)) # Back compatible -- to migrate to explicit specifications in input YAML if not any([cwlutils.is_cwl_run(d) for d in samples]): staged_files += ["trimmed", "htseq-count/*summary"] # Add in created target_info file if os.path.isfile(os.path.join(base_dir, "report", "metrics", "target_info.yaml")): staged_files += [os.path.join(base_dir, "report", "metrics", "target_info.yaml")] return sorted(list(set(staged_files)))
python
def _get_input_files(samples, base_dir, tx_out_dir): """Retrieve input files, keyed by sample and QC method name. Stages files into the work directory to ensure correct names for MultiQC sample assessment when running with CWL. """ in_files = collections.defaultdict(list) for data in samples: sum_qc = tz.get_in(["summary", "qc"], data, {}) if sum_qc in [None, "None"]: sum_qc = {} elif isinstance(sum_qc, six.string_types): sum_qc = {dd.get_algorithm_qc(data)[0]: sum_qc} elif not isinstance(sum_qc, dict): raise ValueError("Unexpected summary qc: %s" % sum_qc) for program, pfiles in sum_qc.items(): if isinstance(pfiles, dict): pfiles = [pfiles["base"]] + pfiles.get("secondary", []) # CWL: presents output files as single file plus associated secondary files elif isinstance(pfiles, six.string_types): if os.path.exists(pfiles): pfiles = [os.path.join(basedir, f) for basedir, subdir, filenames in os.walk(os.path.dirname(pfiles)) for f in filenames] else: pfiles = [] in_files[(dd.get_sample_name(data), program)].extend(pfiles) staged_files = [] for (sample, program), files in in_files.items(): cur_dir = utils.safe_makedir(os.path.join(base_dir, "inputs", sample, program)) for f in files: if _check_multiqc_input(f) and _is_good_file_for_multiqc(f): if _in_temp_directory(f) or any([cwlutils.is_cwl_run(d) for d in samples]): staged_f = os.path.join(cur_dir, os.path.basename(f)) shutil.copy(f, staged_f) staged_files.append(staged_f) else: staged_files.append(f) staged_files.extend(get_qsig_multiqc_files(samples)) # Back compatible -- to migrate to explicit specifications in input YAML if not any([cwlutils.is_cwl_run(d) for d in samples]): staged_files += ["trimmed", "htseq-count/*summary"] # Add in created target_info file if os.path.isfile(os.path.join(base_dir, "report", "metrics", "target_info.yaml")): staged_files += [os.path.join(base_dir, "report", "metrics", "target_info.yaml")] return sorted(list(set(staged_files)))
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/qc/multiqc.py#L233-L276
train
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bcbio/bcbio-nextgen
bcbio/qc/multiqc.py
_group_by_sample_and_batch
def _group_by_sample_and_batch(samples): """Group samples split by QC method back one per sample-batch. """ out = collections.defaultdict(list) for data in samples: out[(dd.get_sample_name(data), dd.get_align_bam(data), tuple(_get_batches(data)))].append(data) return [xs[0] for xs in out.values()]
python
def _group_by_sample_and_batch(samples): """Group samples split by QC method back one per sample-batch. """ out = collections.defaultdict(list) for data in samples: out[(dd.get_sample_name(data), dd.get_align_bam(data), tuple(_get_batches(data)))].append(data) return [xs[0] for xs in out.values()]
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/qc/multiqc.py#L287-L293
train
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bcbio/bcbio-nextgen
bcbio/qc/multiqc.py
_has_bcftools_germline_stats
def _has_bcftools_germline_stats(data): """Check for the presence of a germline stats file, CWL compatible. """ stats_file = tz.get_in(["summary", "qc"], data) if isinstance(stats_file, dict): stats_file = tz.get_in(["variants", "base"], stats_file) if not stats_file: stats_file = "" return stats_file.find("bcftools_stats_germline") > 0
python
def _has_bcftools_germline_stats(data): """Check for the presence of a germline stats file, CWL compatible. """ stats_file = tz.get_in(["summary", "qc"], data) if isinstance(stats_file, dict): stats_file = tz.get_in(["variants", "base"], stats_file) if not stats_file: stats_file = "" return stats_file.find("bcftools_stats_germline") > 0
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Check for the presence of a germline stats file, CWL compatible.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/qc/multiqc.py#L397-L405
train
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bcbio/bcbio-nextgen
bcbio/qc/multiqc.py
_is_good_file_for_multiqc
def _is_good_file_for_multiqc(fpath): """Returns False if the file is binary or image.""" # Use mimetypes to exclude binary files where possible (ftype, encoding) = mimetypes.guess_type(fpath) if encoding is not None: return False if ftype is not None and ftype.startswith('image'): return False return True
python
def _is_good_file_for_multiqc(fpath): """Returns False if the file is binary or image.""" # Use mimetypes to exclude binary files where possible (ftype, encoding) = mimetypes.guess_type(fpath) if encoding is not None: return False if ftype is not None and ftype.startswith('image'): return False return True
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/qc/multiqc.py#L414-L422
train
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bcbio/bcbio-nextgen
bcbio/qc/multiqc.py
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def _parse_disambiguate(disambiguatestatsfilename): """Parse disambiguation stats from given file. """ disambig_stats = [0, 0, 0] with open(disambiguatestatsfilename, "r") as in_handle: for i, line in enumerate(in_handle): fields = line.strip().split("\t") if i == 0: assert fields == ['sample', 'unique species A pairs', 'unique species B pairs', 'ambiguous pairs'] else: disambig_stats = [x + int(y) for x, y in zip(disambig_stats, fields[1:])] return disambig_stats
python
def _parse_disambiguate(disambiguatestatsfilename): """Parse disambiguation stats from given file. """ disambig_stats = [0, 0, 0] with open(disambiguatestatsfilename, "r") as in_handle: for i, line in enumerate(in_handle): fields = line.strip().split("\t") if i == 0: assert fields == ['sample', 'unique species A pairs', 'unique species B pairs', 'ambiguous pairs'] else: disambig_stats = [x + int(y) for x, y in zip(disambig_stats, fields[1:])] return disambig_stats
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Parse disambiguation stats from given file.
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/qc/multiqc.py#L424-L435
train
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bcbio/bcbio-nextgen
bcbio/qc/multiqc.py
_merge_metrics
def _merge_metrics(samples, out_dir): """Merge metrics from multiple QC steps """ logger.info("summarize metrics") out_dir = utils.safe_makedir(os.path.join(out_dir, "report", "metrics")) sample_metrics = collections.defaultdict(dict) for s in samples: s = _add_disambiguate(s) m = tz.get_in(['summary', 'metrics'], s) if isinstance(m, six.string_types): m = json.loads(m) if m: for me in m.keys(): if isinstance(m[me], list) or isinstance(m[me], dict) or isinstance(m[me], tuple): m.pop(me, None) sample_metrics[dd.get_sample_name(s)].update(m) out = [] for sample_name, m in sample_metrics.items(): sample_file = os.path.join(out_dir, "%s_bcbio.txt" % sample_name) with file_transaction(samples[0], sample_file) as tx_out_file: dt = pd.DataFrame(m, index=['1']) dt.columns = [k.replace(" ", "_").replace("(", "").replace(")", "") for k in dt.columns] dt['sample'] = sample_name dt['rRNA_rate'] = m.get('rRNA_rate', "NA") dt['RiP_pct'] = "%.3f" % (int(m.get("RiP", 0)) / float(m.get("Total_reads", 1)) * 100) dt = _fix_duplicated_rate(dt) dt.transpose().to_csv(tx_out_file, sep="\t", header=False) out.append(sample_file) return out
python
def _merge_metrics(samples, out_dir): """Merge metrics from multiple QC steps """ logger.info("summarize metrics") out_dir = utils.safe_makedir(os.path.join(out_dir, "report", "metrics")) sample_metrics = collections.defaultdict(dict) for s in samples: s = _add_disambiguate(s) m = tz.get_in(['summary', 'metrics'], s) if isinstance(m, six.string_types): m = json.loads(m) if m: for me in m.keys(): if isinstance(m[me], list) or isinstance(m[me], dict) or isinstance(m[me], tuple): m.pop(me, None) sample_metrics[dd.get_sample_name(s)].update(m) out = [] for sample_name, m in sample_metrics.items(): sample_file = os.path.join(out_dir, "%s_bcbio.txt" % sample_name) with file_transaction(samples[0], sample_file) as tx_out_file: dt = pd.DataFrame(m, index=['1']) dt.columns = [k.replace(" ", "_").replace("(", "").replace(")", "") for k in dt.columns] dt['sample'] = sample_name dt['rRNA_rate'] = m.get('rRNA_rate', "NA") dt['RiP_pct'] = "%.3f" % (int(m.get("RiP", 0)) / float(m.get("Total_reads", 1)) * 100) dt = _fix_duplicated_rate(dt) dt.transpose().to_csv(tx_out_file, sep="\t", header=False) out.append(sample_file) return out
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Merge metrics from multiple QC steps
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/qc/multiqc.py#L456-L484
train
219,095
bcbio/bcbio-nextgen
bcbio/qc/multiqc.py
_merge_fastqc
def _merge_fastqc(samples): """ merge all fastqc samples into one by module """ fastqc_list = collections.defaultdict(list) seen = set() for data in samples: name = dd.get_sample_name(data) if name in seen: continue seen.add(name) fns = glob.glob(os.path.join(dd.get_work_dir(data), "qc", dd.get_sample_name(data), "fastqc") + "/*") for fn in fns: if fn.endswith("tsv"): metric = os.path.basename(fn) fastqc_list[metric].append([name, fn]) for metric in fastqc_list: dt_by_sample = [] for fn in fastqc_list[metric]: dt = pd.read_csv(fn[1], sep="\t") dt['sample'] = fn[0] dt_by_sample.append(dt) dt = utils.rbind(dt_by_sample) dt.to_csv(metric, sep="\t", index=False, mode ='w') return samples
python
def _merge_fastqc(samples): """ merge all fastqc samples into one by module """ fastqc_list = collections.defaultdict(list) seen = set() for data in samples: name = dd.get_sample_name(data) if name in seen: continue seen.add(name) fns = glob.glob(os.path.join(dd.get_work_dir(data), "qc", dd.get_sample_name(data), "fastqc") + "/*") for fn in fns: if fn.endswith("tsv"): metric = os.path.basename(fn) fastqc_list[metric].append([name, fn]) for metric in fastqc_list: dt_by_sample = [] for fn in fastqc_list[metric]: dt = pd.read_csv(fn[1], sep="\t") dt['sample'] = fn[0] dt_by_sample.append(dt) dt = utils.rbind(dt_by_sample) dt.to_csv(metric, sep="\t", index=False, mode ='w') return samples
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merge all fastqc samples into one by module
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/bcbio/qc/multiqc.py#L486-L511
train
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bcbio/bcbio-nextgen
scripts/utils/hla_loh_comparison.py
_create_plot
def _create_plot(tumor, in_glob, out_ext, page=1): """Create an output plot for the given PDF in the images directory. """ out_dir = utils.safe_makedir("images") out_name = os.path.join(out_dir, "%s-%s" % (tumor, out_ext)) in_file = glob.glob(in_glob)[0] cmd = ["pdftoppm", in_file, out_name, "-png", "-f", page, "-singlefile"] if not os.path.exists(out_name + ".png"): subprocess.check_call([str(x) for x in cmd]) return out_name + ".png"
python
def _create_plot(tumor, in_glob, out_ext, page=1): """Create an output plot for the given PDF in the images directory. """ out_dir = utils.safe_makedir("images") out_name = os.path.join(out_dir, "%s-%s" % (tumor, out_ext)) in_file = glob.glob(in_glob)[0] cmd = ["pdftoppm", in_file, out_name, "-png", "-f", page, "-singlefile"] if not os.path.exists(out_name + ".png"): subprocess.check_call([str(x) for x in cmd]) return out_name + ".png"
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/scripts/utils/hla_loh_comparison.py#L64-L73
train
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bcbio/bcbio-nextgen
scripts/utils/hla_loh_comparison.py
_get_cromwell_execution_dir
def _get_cromwell_execution_dir(base_dir, target_glob): """Retrieve the baseline directory with cromwell output files. Handles Cromwell restarts where there are multiple work directories and we traverse symlinks back to the original. """ cur_dir = glob.glob(os.path.join(base_dir, target_glob))[0] if os.path.exists(os.path.join(cur_dir, "cwl.output.json")): return base_dir else: symlink_dir = os.path.dirname(os.path.realpath(os.path.join(cur_dir, "script"))) ref_base = os.path.dirname(base_dir) new_guid = symlink_dir[symlink_dir.find(ref_base) + len(ref_base) + 1:].split("/")[0] return _get_cromwell_execution_dir(os.path.join(ref_base, new_guid), target_glob)
python
def _get_cromwell_execution_dir(base_dir, target_glob): """Retrieve the baseline directory with cromwell output files. Handles Cromwell restarts where there are multiple work directories and we traverse symlinks back to the original. """ cur_dir = glob.glob(os.path.join(base_dir, target_glob))[0] if os.path.exists(os.path.join(cur_dir, "cwl.output.json")): return base_dir else: symlink_dir = os.path.dirname(os.path.realpath(os.path.join(cur_dir, "script"))) ref_base = os.path.dirname(base_dir) new_guid = symlink_dir[symlink_dir.find(ref_base) + len(ref_base) + 1:].split("/")[0] return _get_cromwell_execution_dir(os.path.join(ref_base, new_guid), target_glob)
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/scripts/utils/hla_loh_comparison.py#L232-L245
train
219,098
bcbio/bcbio-nextgen
scripts/utils/hla_loh_comparison.py
prep_bam_inputs
def prep_bam_inputs(out_dir, sample, call_file, bam_file): """Prepare expected input BAM files from pre-aligned. """ base = utils.splitext_plus(os.path.basename(bam_file))[0] with open(call_file) as in_handle: for cur_hla in (x.strip() for x in in_handle): out_file = os.path.join(utils.safe_makedir(os.path.join(out_dir, base)), "%s.type.%s.filtered.bam" % (base, cur_hla)) if not os.path.exists(out_file): cmd = ["samtools", "view", "-b","-o", out_file, bam_file, cur_hla] subprocess.check_call(cmd)
python
def prep_bam_inputs(out_dir, sample, call_file, bam_file): """Prepare expected input BAM files from pre-aligned. """ base = utils.splitext_plus(os.path.basename(bam_file))[0] with open(call_file) as in_handle: for cur_hla in (x.strip() for x in in_handle): out_file = os.path.join(utils.safe_makedir(os.path.join(out_dir, base)), "%s.type.%s.filtered.bam" % (base, cur_hla)) if not os.path.exists(out_file): cmd = ["samtools", "view", "-b","-o", out_file, bam_file, cur_hla] subprocess.check_call(cmd)
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6a9348c0054ccd5baffd22f1bb7d0422f6978b20
https://github.com/bcbio/bcbio-nextgen/blob/6a9348c0054ccd5baffd22f1bb7d0422f6978b20/scripts/utils/hla_loh_comparison.py#L247-L257
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