def run(self, config, config_file, parallel, dirs, lane_items):
## Alignment and preparation requiring the entire input file (multicore cluster)
with prun.start(_wres(parallel, ["aligner"]),
lane_items, config, dirs, "multicore") as run_parallel:
with profile.report("alignment", dirs):
samples = run_parallel("process_alignment", lane_items)
with profile.report("callable regions", dirs):
samples = run_parallel("postprocess_alignment", samples)
regions = run_parallel("combine_sample_regions", [samples])[0]
samples = region.add_region_info(samples, regions)
samples = region.clean_sample_data(samples)
## Processing on sub regions
with prun.start(_wres(parallel, ["gatk", "picard", "samtools"]),
samples, config, dirs, "full",
multiplier=len(regions["analysis"]), max_multicore=1) as run_parallel:
with profile.report("alignment post-processing", dirs):
samples = region.parallel_prep_region(samples, regions, run_parallel)
samples = region.parallel_variantcall_region(samples, run_parallel)
print len(samples)
## Finalize BAMs and QC
with prun.start(_wres(parallel, ["fastqc", "bamtools", "samtools"]),
samples, config, dirs, "multicore2") as run_parallel:
with profile.report("prepped BAM merging", dirs):
samples = region.delayed_bamprep_merge(samples, run_parallel)
print len(samples)
with profile.report("quality control", dirs):
samples = qcsummary.generate_parallel(samples, run_parallel)
logger.info("Timing: finished")
return samples
def run(self, config, run_info_yaml, parallel, dirs, samples):
with prun.start(_wres(parallel, ["aligner"],
ensure_mem={"tophat": 8, "tophat2": 8, "star": 2}),
[samples[0]], config, dirs, "organize_samples") as run_parallel:
with profile.report("organize samples", dirs):
samples = run_parallel("organize_samples", [[dirs, config, run_info_yaml,
[x[0]["description"] for x in samples]]])
with prun.start(_wres(parallel, ["picard", "cutadapt"]),
samples, config, dirs, "trimming") as run_parallel:
with profile.report("adapter trimming", dirs):
samples = run_parallel("prepare_sample", samples)
samples = run_parallel("trim_sample", samples)
with prun.start(_wres(parallel, ["aligner", "picard"],
ensure_mem={"tophat": 8, "tophat2": 8, "star": 2}),
samples, config, dirs, "alignment",
multiplier=alignprep.parallel_multiplier(samples)) as run_parallel:
with profile.report("alignment", dirs):
samples = run_parallel("disambiguate_split", [samples])
samples = run_parallel("process_alignment", samples)
with prun.start(_wres(parallel, ["samtools", "cufflinks"]),
samples, config, dirs, "rnaseqcount") as run_parallel:
with profile.report("disambiguation", dirs):
samples = disambiguate.resolve(samples, run_parallel)
with profile.report("transcript assembly", dirs):
samples = rnaseq.assemble_transcripts(run_parallel, samples)
with profile.report("estimate expression", dirs):
samples = rnaseq.estimate_expression(samples, run_parallel)
with prun.start(_wres(parallel, ["picard", "fastqc", "rnaseqc", "kraken"]),
samples, config, dirs, "qc") as run_parallel:
with profile.report("quality control", dirs):
samples = qcsummary.generate_parallel(samples, run_parallel)
logger.info("Timing: finished")
return samples
def run(self, config, config_file, parallel, dirs, samples):
with prun.start(parallel, samples, config, dirs, "trimming") as run_parallel:
samples = run_parallel("trim_lane", samples)
with prun.start(
_wprogs(parallel, ["aligner"], {"tophat": 8, "tophat2": 8, "star": 30}),
samples,
config,
dirs,
"multicore",
multiplier=alignprep.parallel_multiplier(samples),
) as run_parallel:
samples = disambiguate.split(samples)
samples = run_parallel("process_alignment", samples)
samples = disambiguate.resolve(samples, run_parallel)
with prun.start(
_wprogs(parallel, ["samtools", "gatk", "cufflinks"]), samples, config, dirs, "rnaseqcount"
) as run_parallel:
samples = rnaseq.estimate_expression(samples, run_parallel)
# samples = rnaseq.detect_fusion(samples, run_parallel)
combined = combine_count_files([x[0].get("count_file") for x in samples])
organism = utils.get_in(samples[0][0], ("genome_resources", "aliases", "ensembl"), None)
annotated = annotate_combined_count_file(combined, organism)
for x in samples:
x[0]["combined_counts"] = combined
x[0]["annotated_combined_counts"] = annotated
with prun.start(
_wprogs(parallel, ["picard", "fastqc", "rnaseqc"]), samples, config, dirs, "persample"
) as run_parallel:
samples = qcsummary.generate_parallel(samples, run_parallel)
return samples
def run(self, config, config_file, parallel, dirs, samples):
## Alignment and preparation requiring the entire input file (multicore cluster)
with prun.start(_wres(parallel, ["aligner", "samtools", "sambamba"],
(["reference", "fasta"], ["reference", "aligner"], ["files"])),
samples, config, dirs, "multicore",
multiplier=alignprep.parallel_multiplier(samples)) as run_parallel:
with profile.report("alignment preparation", dirs):
samples = run_parallel("prep_align_inputs", samples)
samples = disambiguate.split(samples)
with profile.report("alignment", dirs):
samples = run_parallel("process_alignment", samples)
samples = alignprep.merge_split_alignments(samples, run_parallel)
samples = disambiguate.resolve(samples, run_parallel)
with profile.report("callable regions", dirs):
samples = run_parallel("postprocess_alignment", samples)
samples = run_parallel("combine_sample_regions", [samples])
samples = region.clean_sample_data(samples)
with profile.report("coverage", dirs):
samples = coverage.summarize_samples(samples, run_parallel)
## Variant calling on sub-regions of the input file (full cluster)
with prun.start(_wres(parallel, ["gatk", "picard", "variantcaller"]),
samples, config, dirs, "full",
multiplier=region.get_max_counts(samples), max_multicore=1) as run_parallel:
with profile.report("alignment post-processing", dirs):
samples = region.parallel_prep_region(samples, run_parallel)
with profile.report("variant calling", dirs):
samples = genotype.parallel_variantcall_region(samples, run_parallel)
## Finalize variants (per-sample cluster)
with prun.start(_wres(parallel, ["gatk", "gatk-vqsr", "snpeff", "bcbio_variation"]),
samples, config, dirs, "persample") as run_parallel:
with profile.report("joint squaring off/backfilling", dirs):
samples = joint.square_off(samples, run_parallel)
with profile.report("variant post-processing", dirs):
samples = run_parallel("postprocess_variants", samples)
samples = run_parallel("split_variants_by_sample", samples)
with profile.report("validation", dirs):
samples = run_parallel("compare_to_rm", samples)
samples = genotype.combine_multiple_callers(samples)
## Finalizing BAMs and population databases, handle multicore computation
with prun.start(_wres(parallel, ["gemini", "samtools", "fastqc", "bamtools", "bcbio_variation",
"bcbio-variation-recall"]),
samples, config, dirs, "multicore2") as run_parallel:
with profile.report("prepped BAM merging", dirs):
samples = region.delayed_bamprep_merge(samples, run_parallel)
with profile.report("ensemble calling", dirs):
samples = ensemble.combine_calls_parallel(samples, run_parallel)
with profile.report("validation summary", dirs):
samples = validate.summarize_grading(samples)
with profile.report("structural variation", dirs):
samples = structural.run(samples, run_parallel)
with profile.report("population database", dirs):
samples = population.prep_db_parallel(samples, run_parallel)
with profile.report("quality control", dirs):
samples = qcsummary.generate_parallel(samples, run_parallel)
with profile.report("archive", dirs):
samples = archive.compress(samples, run_parallel)
logger.info("Timing: finished")
return samples
def run(self, config, config_file, parallel, dirs, samples):
with prun.start(_wres(parallel, ["picard"]),
samples, config, dirs, "trimming") as run_parallel:
samples = run_parallel("process_lane", samples)
samples = run_parallel("trim_lane", samples)
with prun.start(_wres(parallel, ["aligner"],
ensure_mem={"tophat": 8, "tophat2": 8, "star": 30}),
samples, config, dirs, "multicore",
multiplier=alignprep.parallel_multiplier(samples)) as run_parallel:
samples = disambiguate.split(samples)
samples = run_parallel("process_alignment", samples)
samples = disambiguate.resolve(samples, run_parallel)
with prun.start(_wres(parallel, ["samtools", "cufflinks"]),
samples, config, dirs, "rnaseqcount") as run_parallel:
samples = rnaseq.estimate_expression(samples, run_parallel)
#samples = rnaseq.detect_fusion(samples, run_parallel)
combined = combine_count_files([x[0].get("count_file") for x in samples])
gtf_file = utils.get_in(samples[0][0], ('genome_resources', 'rnaseq',
'transcripts'), None)
annotated = annotate_combined_count_file(combined, gtf_file)
for x in samples:
x[0]["combined_counts"] = combined
if annotated:
x[0]["annotated_combined_counts"] = annotated
with prun.start(_wres(parallel, ["picard", "fastqc", "rnaseqc"]),
samples, config, dirs, "persample") as run_parallel:
samples = qcsummary.generate_parallel(samples, run_parallel)
return samples
def chipseqpipeline(config, run_info_yaml, parallel, dirs, samples):
with prun.start(_wres(parallel, ["aligner", "picard"]),
samples, config, dirs, "multicore",
multiplier=alignprep.parallel_multiplier(samples)) as run_parallel:
with profile.report("organize samples", dirs):
samples = run_parallel("organize_samples", [[dirs, config, run_info_yaml,
[x[0]["description"] for x in samples]]])
with profile.report("alignment", dirs):
samples = run_parallel("prepare_sample", samples)
samples = run_parallel("trim_sample", samples)
samples = run_parallel("disambiguate_split", [samples])
samples = run_parallel("process_alignment", samples)
with profile.report("disambiguation", dirs):
samples = disambiguate.resolve(samples, run_parallel)
samples = run_parallel("clean_chipseq_alignment", samples)
with prun.start(_wres(parallel, ["peakcaller"]),
samples, config, dirs, "peakcalling",
multiplier = peaks._get_multiplier(samples)) as run_parallel:
with profile.report("peakcalling", dirs):
samples = peaks.peakcall_prepare(samples, run_parallel)
with prun.start(_wres(parallel, ["picard", "fastqc"]),
samples, config, dirs, "qc") as run_parallel:
with profile.report("quality control", dirs):
samples = qcsummary.generate_parallel(samples, run_parallel)
with profile.report("upload", dirs):
samples = run_parallel("upload_samples", samples)
for sample in samples:
run_parallel("upload_samples_project", [sample])
return samples
def run(self, config, config_file, parallel, dirs, samples):
with prun.start(_wres(parallel, ["picard", "AlienTrimmer"]),
samples, config, dirs, "trimming") as run_parallel:
with profile.report("adapter trimming", dirs):
samples = run_parallel("prepare_sample", samples)
samples = run_parallel("trim_sample", samples)
with prun.start(_wres(parallel, ["aligner", "picard"],
ensure_mem={"tophat": 8, "tophat2": 8, "star": 40}),
samples, config, dirs, "multicore",
multiplier=alignprep.parallel_multiplier(samples)) as run_parallel:
with profile.report("alignment", dirs):
samples = disambiguate.split(samples)
samples = run_parallel("process_alignment", samples)
with prun.start(_wres(parallel, ["samtools", "cufflinks"]),
samples, config, dirs, "rnaseqcount") as run_parallel:
with profile.report("disambiguation", dirs):
samples = disambiguate.resolve(samples, run_parallel)
with profile.report("transcript assembly", dirs):
samples = rnaseq.assemble_transcripts(run_parallel, samples)
with profile.report("estimate expression", dirs):
samples = rnaseq.estimate_expression(samples, run_parallel)
with prun.start(_wres(parallel, ["picard", "fastqc", "rnaseqc","kraken"]),
samples, config, dirs, "persample") as run_parallel:
with profile.report("quality control", dirs):
samples = qcsummary.generate_parallel(samples, run_parallel)
logger.info("Timing: finished")
return samples
def run(self, config, run_info_yaml, parallel, dirs, samples):
## Alignment and preparation requiring the entire input file (multicore cluster)
with prun.start(_wres(parallel, ["aligner"]), samples, config, dirs, "multicore") as run_parallel:
with profile.report("organize samples", dirs):
samples = run_parallel(
"organize_samples", [[dirs, config, run_info_yaml, [x[0]["description"] for x in samples]]]
)
with profile.report("alignment", dirs):
samples = run_parallel("process_alignment", samples)
with profile.report("callable regions", dirs):
samples = run_parallel("prep_samples", [samples])
samples = run_parallel("postprocess_alignment", samples)
samples = run_parallel("combine_sample_regions", [samples])
samples = region.clean_sample_data(samples)
## Quality control
with prun.start(
_wres(parallel, ["fastqc", "bamtools", "samtools", "qsignature", "kraken"]),
samples,
config,
dirs,
"multicore2",
) as run_parallel:
with profile.report("quality control", dirs):
samples = qcsummary.generate_parallel(samples, run_parallel)
with profile.report("upload", dirs):
for sample in samples:
run_parallel("upload_samples", [sample])
logger.info("Timing: finished")
return samples
def run(self, config, run_info_yaml, parallel, dirs, samples):
with prun.start(
_wres(parallel, ["aligner", "picard"]),
samples,
config,
dirs,
"multicore",
multiplier=alignprep.parallel_multiplier(samples),
) as run_parallel:
with profile.report("organize samples", dirs):
samples = run_parallel(
"organize_samples", [[dirs, config, run_info_yaml, [x[0]["description"] for x in samples]]]
)
samples = run_parallel("prepare_sample", samples)
samples = run_parallel("trim_sample", samples)
samples = run_parallel("disambiguate_split", [samples])
samples = run_parallel("process_alignment", samples)
with prun.start(_wres(parallel, ["picard", "fastqc"]), samples, config, dirs, "persample") as run_parallel:
with profile.report("disambiguation", dirs):
samples = disambiguate.resolve(samples, run_parallel)
samples = run_parallel("clean_chipseq_alignment", samples)
samples = qcsummary.generate_parallel(samples, run_parallel)
with profile.report("upload", dirs):
for sample in samples:
run_parallel("upload_samples", [sample])
return samples
def run(self, config, config_file, parallel, dirs, samples):
with prun.start(_wres(parallel, ["picard", "AlienTrimmer"]), samples, config, dirs, "trimming") as run_parallel:
with profile.report("adapter trimming", dirs):
samples = run_parallel("prepare_sample", samples)
samples = run_parallel("trim_sample", samples)
with prun.start(_wres(parallel, ["sailfish"]), samples, config, dirs, "sailfish") as run_parallel:
with profile.report("sailfish", dirs):
samples = run_parallel("run_sailfish", samples)
return samples
def run(self, config, config_file, parallel, dirs, lane_items):
## Alignment and preparation requiring the entire input file (multicore cluster)
with prun.start(_wprogs(parallel, ["aligner"]), lane_items, config, dirs, "multicore") as run_parallel:
logger.info("Timing: alignment")
samples = run_parallel("process_alignment", lane_items)
## Finalize (per-sample cluster)
with prun.start(_wprogs(parallel, ["fastqc", "bamtools"]), samples, config, dirs, "persample") as run_parallel:
logger.info("Timing: quality control")
samples = qcsummary.generate_parallel(samples, run_parallel)
logger.info("Timing: finished")
return samples
def run(self, config, config_file, parallel, dirs, samples):
with prun.start(_wres(parallel, ["aligner", "picard"]),
samples, config, dirs, "multicore",
multiplier=alignprep.parallel_multiplier(samples)) as run_parallel:
samples = run_parallel("process_lane", samples)
samples = run_parallel("trim_lane", samples)
samples = disambiguate.split(samples)
samples = run_parallel("process_alignment", samples)
with prun.start(_wres(parallel, ["picard", "fastqc"]),
samples, config, dirs, "persample") as run_parallel:
samples = run_parallel("clean_chipseq_alignment", samples)
samples = qcsummary.generate_parallel(samples, run_parallel)
return samples
def run(self, config, run_info_yaml, parallel, dirs, samples):
with prun.start(_wres(parallel, ["picard", "cutadapt"]),
samples, config, dirs, "trimming") as run_parallel:
with profile.report("organize samples", dirs):
samples = run_parallel("organize_samples", [[dirs, config, run_info_yaml,
[x[0]["description"] for x in samples]]])
with profile.report("adapter trimming", dirs):
samples = run_parallel("prepare_sample", samples)
samples = run_parallel("trim_sample", samples)
with prun.start(_wres(parallel, ["sailfish"]), samples, config, dirs,
"sailfish") as run_parallel:
with profile.report("sailfish", dirs):
samples = run_parallel("run_sailfish", samples)
return samples
def rnaseq_prep_samples(config, run_info_yaml, parallel, dirs, samples):
"""
organizes RNA-seq and small-RNAseq samples, converting from BAM if
necessary and trimming if necessary
"""
pipeline = dd.get_in_samples(samples, dd.get_analysis)
trim_reads_set = any([tz.get_in(["algorithm", "trim_reads"], d) for d in dd.sample_data_iterator(samples)])
resources = ["picard"]
needs_trimming = (_is_smallrnaseq(pipeline) or trim_reads_set)
if needs_trimming:
resources.append("atropos")
with prun.start(_wres(parallel, resources),
samples, config, dirs, "trimming",
max_multicore=1 if not needs_trimming else None) as run_parallel:
with profile.report("organize samples", dirs):
samples = run_parallel("organize_samples", [[dirs, config, run_info_yaml,
[x[0]["description"] for x in samples]]])
samples = run_parallel("prepare_sample", samples)
if needs_trimming:
with profile.report("adapter trimming", dirs):
if _is_smallrnaseq(pipeline):
samples = run_parallel("trim_srna_sample", samples)
else:
samples = run_parallel("trim_sample", samples)
return samples
def test_1_parallel_vcf_combine(self):
"""Parallel combination of VCF files, split by chromosome.
"""
from bcbio.variation import vcfutils
files = [
os.path.join(self.var_dir, "S1-variants.vcf"),
os.path.join(self.var_dir, "S2-variants.vcf")
]
with make_workdir() as workdir:
config = load_config(
get_post_process_yaml(self.automated_dir, workdir))
config["algorithm"] = {}
region_dir = os.path.join(self.var_dir, "S1_S2-combined-regions")
if os.path.exists(region_dir):
shutil.rmtree(region_dir)
if os.path.exists(self.combo_file):
os.remove(self.combo_file)
reqs = {"type": "local", "cores": 1}
with prun.start(reqs, [[config]], config) as run_parallel:
vcfutils.parallel_combine_variants(
files, self.combo_file, self.ref_file, config, run_parallel)
for fname in files:
if os.path.exists(fname + ".gz"):
subprocess.check_call(["gunzip", fname + ".gz"])
if os.path.exists(fname + ".gz.tbi"):
os.remove(fname + ".gz.tbi")
def run(self, config, config_file, parallel, dirs, samples):
## Alignment and preparation requiring the entire input file (multicore cluster)
with prun.start(_wres(parallel, ["aligner"]),
samples, config, dirs, "multicore") as run_parallel:
with profile.report("alignment", dirs):
samples = run_parallel("process_alignment", samples)
with profile.report("callable regions", dirs):
samples = run_parallel("postprocess_alignment", samples)
samples = run_parallel("combine_sample_regions", [samples])
samples = region.clean_sample_data(samples)
## Quality control
with prun.start(_wres(parallel, ["fastqc", "bamtools", "samtools"]),
samples, config, dirs, "multicore2") as run_parallel:
with profile.report("quality control", dirs):
samples = qcsummary.generate_parallel(samples, run_parallel)
logger.info("Timing: finished")
return samples
def run(self, config, run_info_yaml, parallel, dirs, samples):
# causes a circular import at the top level
from bcbio.srna.group import report as srna_report
with prun.start(_wres(parallel, ["picard", "cutadapt"]),
samples, config, dirs, "trimming") as run_parallel:
with profile.report("organize samples", dirs):
samples = run_parallel("organize_samples", [[dirs, config, run_info_yaml,
[x[0]["description"] for x in samples]]])
with profile.report("adapter trimming", dirs):
samples = run_parallel("prepare_sample", samples)
samples = run_parallel("trim_srna_sample", samples)
with prun.start(_wres(parallel, ["aligner", "picard", "samtools"],
ensure_mem={"bowtie": 8, "bowtie2": 8, "star": 2}),
[samples[0]], config, dirs, "alignment") as run_parallel:
with profile.report("prepare", dirs):
samples = run_parallel("seqcluster_prepare", [samples])
with profile.report("alignment", dirs):
samples = run_parallel("srna_alignment", [samples])
with prun.start(_wres(parallel, ["picard", "miraligner"]),
samples, config, dirs, "annotation") as run_parallel:
with profile.report("small RNA annotation", dirs):
samples = run_parallel("srna_annotation", samples)
with prun.start(_wres(parallel, ["seqcluster"],
ensure_mem={"seqcluster": 8}),
[samples[0]], config, dirs, "cluster") as run_parallel:
with profile.report("cluster", dirs):
samples = run_parallel("seqcluster_cluster", [samples])
with prun.start(_wres(parallel, ["picard", "fastqc"]),
samples, config, dirs, "qc") as run_parallel:
with profile.report("quality control", dirs):
samples = qcsummary.generate_parallel(samples, run_parallel)
with profile.report("report", dirs):
srna_report(samples)
with profile.report("upload", dirs):
samples = run_parallel("upload_samples", samples)
for sample in samples:
run_parallel("upload_samples_project", [sample])
return samples
def smallrnaseqpipeline(config, run_info_yaml, parallel, dirs, samples):
# causes a circular import at the top level
from bcbio.srna.group import report as srna_report
samples = rnaseq_prep_samples(config, run_info_yaml, parallel, dirs, samples)
with prun.start(_wres(parallel, ["aligner", "picard", "samtools"],
ensure_mem={"bowtie": 8, "bowtie2": 8, "star": 2}),
[samples[0]], config, dirs, "alignment") as run_parallel:
with profile.report("prepare", dirs):
samples = run_parallel("seqcluster_prepare", [samples])
with profile.report("seqcluster alignment", dirs):
samples = run_parallel("srna_alignment", [samples])
with prun.start(_wres(parallel, ["aligner", "picard", "samtools"],
ensure_mem={"tophat": 10, "tophat2": 10, "star": 2, "hisat2": 8}),
samples, config, dirs, "alignment_samples",
multiplier=alignprep.parallel_multiplier(samples)) as run_parallel:
with profile.report("alignment", dirs):
samples = run_parallel("process_alignment", samples)
with prun.start(_wres(parallel, ["picard", "miraligner"]),
samples, config, dirs, "annotation") as run_parallel:
with profile.report("small RNA annotation", dirs):
samples = run_parallel("srna_annotation", samples)
with prun.start(_wres(parallel, ["seqcluster", "mirge"],
ensure_mem={"seqcluster": 8}),
[samples[0]], config, dirs, "cluster") as run_parallel:
with profile.report("cluster", dirs):
samples = run_parallel("seqcluster_cluster", [samples])
with prun.start(_wres(parallel, ["picard", "fastqc"]),
samples, config, dirs, "qc") as run_parallel:
with profile.report("quality control", dirs):
samples = qcsummary.generate_parallel(samples, run_parallel)
with profile.report("report", dirs):
srna_report(samples)
with profile.report("upload", dirs):
samples = run_parallel("upload_samples", samples)
for sample in samples:
run_parallel("upload_samples_project", [sample])
return samples
def process_all_lanes(lanes, parallel, dirs, config):
"""Process all input lanes, avoiding starting a cluster if not needed.
"""
lanes = list(lanes)
if _item_needs_compute(lanes):
with prun.start(_wprogs(parallel, ["picard"]),
lanes, config, dirs, "laneprocess") as run_parallel:
return run_parallel("process_lane", [[x] for x in lanes])
else:
return [process_lane(x)[0] for x in lanes]
def rnaseqpipeline(config, run_info_yaml, parallel, dirs, samples):
samples = rnaseq_prep_samples(config, run_info_yaml, parallel, dirs, samples)
with prun.start(_wres(parallel, ["aligner", "picard", "samtools"],
ensure_mem={"tophat": 10, "tophat2": 10, "star": 2, "hisat2": 8}),
samples, config, dirs, "alignment",
multiplier=alignprep.parallel_multiplier(samples)) as run_parallel:
with profile.report("alignment", dirs):
samples = run_parallel("disambiguate_split", [samples])
samples = run_parallel("process_alignment", samples)
with prun.start(_wres(parallel, ["samtools", "cufflinks"]),
samples, config, dirs, "rnaseqcount") as run_parallel:
with profile.report("disambiguation", dirs):
samples = disambiguate.resolve(samples, run_parallel)
with profile.report("transcript assembly", dirs):
samples = rnaseq.assemble_transcripts(run_parallel, samples)
with profile.report("estimate expression (threaded)", dirs):
samples = rnaseq.quantitate_expression_parallel(samples, run_parallel)
with prun.start(_wres(parallel, ["dexseq", "express"]), samples, config,
dirs, "rnaseqcount-singlethread", max_multicore=1) as run_parallel:
with profile.report("estimate expression (single threaded)", dirs):
samples = rnaseq.quantitate_expression_noparallel(samples, run_parallel)
samples = rnaseq.combine_files(samples)
with prun.start(_wres(parallel, ["gatk", "vardict"]), samples, config,
dirs, "rnaseq-variation") as run_parallel:
with profile.report("RNA-seq variant calling", dirs):
samples = rnaseq.rnaseq_variant_calling(samples, run_parallel)
with prun.start(_wres(parallel, ["samtools", "fastqc", "qualimap",
"kraken", "gatk", "preseq"], ensure_mem={"qualimap": 4}),
samples, config, dirs, "qc") as run_parallel:
with profile.report("quality control", dirs):
samples = qcsummary.generate_parallel(samples, run_parallel)
with profile.report("upload", dirs):
samples = run_parallel("upload_samples", samples)
for sample in samples:
run_parallel("upload_samples_project", [sample])
with profile.report("bcbioRNAseq loading", dirs):
run_parallel("run_bcbiornaseqload", [sample])
logger.info("Timing: finished")
return samples
def fastrnaseqpipeline(config, run_info_yaml, parallel, dirs, samples):
samples = rnaseq_prep_samples(config, run_info_yaml, parallel, dirs, samples)
with prun.start(_wres(parallel, ["samtools"]), samples, config,
dirs, "fastrnaseq") as run_parallel:
with profile.report("fastrnaseq", dirs):
samples = rnaseq.fast_rnaseq(samples, run_parallel)
with profile.report("upload", dirs):
samples = run_parallel("upload_samples", samples)
for samples in samples:
run_parallel("upload_samples_project", [samples])
logger.info("Timing: finished")
return samples
def parallel_callable_loci(in_bam, ref_file, data):
config = copy.deepcopy(data["config"])
num_cores = config["algorithm"].get("num_cores", 1)
data = {"work_bam": in_bam, "config": config, "reference": data["reference"]}
parallel = {"type": "local", "cores": num_cores, "module": "bcbio.distributed"}
items = [[data]]
with prun.start(parallel, items, config, multiplier=int(num_cores)) as runner:
split_fn = shared.process_bam_by_chromosome("-callable.bed", "work_bam", remove_alts=True)
out = parallel_split_combine(
items, split_fn, runner, "calc_callable_loci", "combine_bed", "callable_bed", ["config"]
)[0]
return out[0]["callable_bed"]
def parallel_callable_loci(in_bam, ref_file, config):
num_cores = config["algorithm"].get("num_cores", 1)
config = copy.deepcopy(config)
config["algorithm"]["memory_adjust"] = {"direction": "decrease", "magnitude": 2}
data = {"work_bam": in_bam, "sam_ref": ref_file, "config": config}
parallel = {"type": "local", "cores": num_cores, "module": "bcbio.distributed"}
items = [[data]]
with prun.start(parallel, items, config) as runner:
split_fn = shared.process_bam_by_chromosome("-callable.bed", "work_bam")
out = parallel_split_combine(
items, split_fn, runner, "calc_callable_loci", "combine_bed", "callable_bed", ["config"]
)[0]
return out[0]["callable_bed"]
def singlecellrnaseqpipeline(config, run_info_yaml, parallel, dirs, samples):
samples = rnaseq_prep_samples(config, run_info_yaml, parallel, dirs, samples)
with prun.start(_wres(parallel, ["samtools", "rapmap"]), samples, config,
dirs, "singlecell-rnaseq") as run_parallel:
with profile.report("singlecell-rnaseq", dirs):
samples = rnaseq.singlecell_rnaseq(samples, run_parallel)
with profile.report("quality control", dirs):
samples = qcsummary.generate_parallel(samples, run_parallel)
with profile.report("upload", dirs):
samples = run_parallel("upload_samples", samples)
for samples in samples:
run_parallel("upload_samples_project", [samples])
logger.info("Timing: finished")
return samples
def fastrnaseqpipeline(config, run_info_yaml, parallel, dirs, samples):
samples = rnaseq_prep_samples(config, run_info_yaml, parallel, dirs, samples)
ww = initialize_watcher(samples)
with prun.start(_wres(parallel, ["samtools"]), samples, config,
dirs, "fastrnaseq") as run_parallel:
with profile.report("fastrnaseq", dirs):
samples = rnaseq.fast_rnaseq(samples, run_parallel)
ww.report("fastrnaseq", samples)
with profile.report("quality control", dirs):
samples = qcsummary.generate_parallel(samples, run_parallel)
ww.report("qcsummary", samples)
with profile.report("upload", dirs):
samples = run_parallel("upload_samples", samples)
for samples in samples:
run_parallel("upload_samples_project", [samples])
logger.info("Timing: finished")
return samples
def run(self, config, run_info_yaml, parallel, dirs, samples):
with prun.start(_wres(parallel, ["picard", "cutadapt"]), samples, config, dirs, "trimming") as run_parallel:
with profile.report("organize samples", dirs):
samples = run_parallel(
"organize_samples", [[dirs, config, run_info_yaml, [x[0]["description"] for x in samples]]]
)
with profile.report("adapter trimming", dirs):
samples = run_parallel("prepare_sample", samples)
samples = run_parallel("trim_sample", samples)
with prun.start(
_wres(parallel, ["aligner", "picard"], ensure_mem={"tophat": 8, "tophat2": 8, "star": 2}),
samples,
config,
dirs,
"alignment",
multiplier=alignprep.parallel_multiplier(samples),
) as run_parallel:
with profile.report("alignment", dirs):
samples = run_parallel("disambiguate_split", [samples])
samples = run_parallel("process_alignment", samples)
with prun.start(
_wres(parallel, ["samtools", "cufflinks"]), samples, config, dirs, "rnaseqcount"
) as run_parallel:
with profile.report("disambiguation", dirs):
samples = disambiguate.resolve(samples, run_parallel)
with profile.report("transcript assembly", dirs):
samples = rnaseq.assemble_transcripts(run_parallel, samples)
with profile.report("estimate expression (threaded)", dirs):
samples = rnaseq.quantitate_expression_parallel(samples, run_parallel)
with prun.start(
_wres(parallel, ["dexseq", "express"]), samples, config, dirs, "rnaseqcount-singlethread", max_multicore=1
) as run_parallel:
with profile.report("estimate expression (single threaded)", dirs):
samples = rnaseq.quantitate_expression_noparallel(samples, run_parallel)
samples = rnaseq.combine_files(samples)
with prun.start(_wres(parallel, ["gatk"]), samples, config, dirs, "rnaseq-variation") as run_parallel:
with profile.report("RNA-seq variant calling", dirs):
samples = rnaseq.rnaseq_variant_calling(samples, run_parallel)
with prun.start(
_wres(parallel, ["picard", "fastqc", "rnaseqc", "kraken"]), samples, config, dirs, "qc"
) as run_parallel:
with profile.report("quality control", dirs):
samples = qcsummary.generate_parallel(samples, run_parallel)
with profile.report("upload", dirs):
for sample in samples:
run_parallel("upload_samples", [sample])
logger.info("Timing: finished")
return samples
def test_1_parallel_vcf_combine(self):
"""Parallel combination of VCF files, split by chromosome.
"""
# Be back compatible with 0.7.6 -- remove after 0.7.7 release
if prun is None:
return
files = [os.path.join(self.var_dir, "S1-variants.vcf"), os.path.join(self.var_dir, "S2-variants.vcf")]
ref_file = os.path.join(self.data_dir, "genomes", "hg19", "seq", "hg19.fa")
config = load_config(os.path.join(self.data_dir, "automated",
"post_process-sample.yaml"))
region_dir = os.path.join(self.var_dir, "S1_S2-combined-regions")
if os.path.exists(region_dir):
shutil.rmtree(region_dir)
if os.path.exists(self.combo_file):
os.remove(self.combo_file)
with prun.start({"type": "local", "cores": 1}, [[config]], config) as run_parallel:
vcfutils.parallel_combine_variants(files, self.combo_file, ref_file, config, run_parallel)
for fname in files:
if os.path.exists(fname + ".gz"):
subprocess.check_call(["gunzip", fname + ".gz"])
if os.path.exists(fname + ".gz.tbi"):
os.remove(fname + ".gz.tbi")
def _get_machine_info(parallel, sys_config, dirs, config):
"""Get machine resource information from the job scheduler via either the command line or the queue.
"""
if parallel.get("queue") and parallel.get("scheduler"):
# dictionary as switch statement; can add new scheduler implementation functions as (lowercase) keys
sched_info_dict = {
"slurm": _slurm_info,
"torque": _torque_info,
"sge": _sge_info
}
try:
return sched_info_dict[parallel["scheduler"].lower()](parallel["queue"])
except KeyError:
logger.info("Resource query function not implemented for scheduler \"{0}\"; "
"submitting job to queue".format(parallel["scheduler"]))
except:
# If something goes wrong, just hit the queue
logger.warn("Couldn't get machine information from resource query function for queue "
"'{0}' on scheduler \"{1}\"; "
"submitting job to queue".format(parallel["queue"], parallel["scheduler"]))
from bcbio.distributed import prun
with prun.start(parallel, [[sys_config]], config, dirs) as run_parallel:
return run_parallel("machine_info", [[sys_config]])
def rnaseqpipeline(config, run_info_yaml, parallel, dirs, samples):
samples = rnaseq_prep_samples(config, run_info_yaml, parallel, dirs,
samples)
with prun.start(
_wres(parallel, ["aligner", "picard", "samtools"],
ensure_mem={
"tophat": 10,
"tophat2": 10,
"star": 2,
"hisat2": 8
}),
samples,
config,
dirs,
"alignment",
multiplier=alignprep.parallel_multiplier(samples)) as run_parallel:
with profile.report("alignment", dirs):
samples = run_parallel("disambiguate_split", [samples])
samples = run_parallel("process_alignment", samples)
with prun.start(_wres(parallel, ["samtools", "cufflinks"]), samples,
config, dirs, "rnaseqcount") as run_parallel:
with profile.report("disambiguation", dirs):
samples = disambiguate.resolve(samples, run_parallel)
with profile.report("transcript assembly", dirs):
samples = rnaseq.assemble_transcripts(run_parallel, samples)
with profile.report("estimate expression (threaded)", dirs):
samples = rnaseq.quantitate_expression_parallel(
samples, run_parallel)
with prun.start(_wres(parallel, ["dexseq", "express"]),
samples,
config,
dirs,
"rnaseqcount-singlethread",
max_multicore=1) as run_parallel:
with profile.report("estimate expression (single threaded)", dirs):
samples = rnaseq.quantitate_expression_noparallel(
samples, run_parallel)
samples = rnaseq.combine_files(samples)
with prun.start(_wres(parallel, ["gatk", "vardict"]), samples, config,
dirs, "rnaseq-variation") as run_parallel:
with profile.report("RNA-seq variant calling", dirs):
samples = rnaseq.rnaseq_variant_calling(samples, run_parallel)
with prun.start(
_wres(
parallel,
["samtools", "fastqc", "qualimap", "kraken", "gatk", "preseq"],
ensure_mem={"qualimap": 4}), samples, config, dirs,
"qc") as run_parallel:
with profile.report("quality control", dirs):
samples = qcsummary.generate_parallel(samples, run_parallel)
with profile.report("upload", dirs):
samples = run_parallel("upload_samples", samples)
for sample in samples:
run_parallel("upload_samples_project", [sample])
with profile.report("bcbioRNAseq loading", dirs):
run_parallel("run_bcbiornaseqload", [sample])
logger.info("Timing: finished")
return samples
def run(self, config, run_info_yaml, parallel, dirs, samples):
## Alignment and preparation requiring the entire input file (multicore cluster)
with prun.start(_wres(parallel, ["aligner", "samtools", "sambamba"],
(["reference", "fasta"], ["reference", "aligner"], ["files"])),
samples, config, dirs, "multicore",
multiplier=alignprep.parallel_multiplier(samples)) as run_parallel:
with profile.report("organize samples", dirs):
samples = run_parallel("organize_samples", [[dirs, config, run_info_yaml,
[x[0]["description"] for x in samples]]])
with profile.report("alignment preparation", dirs):
samples = run_parallel("prep_align_inputs", samples)
samples = disambiguate.split(samples)
with profile.report("alignment", dirs):
samples = run_parallel("process_alignment", samples)
samples = alignprep.merge_split_alignments(samples, run_parallel)
samples = disambiguate.resolve(samples, run_parallel)
with profile.report("callable regions", dirs):
samples = run_parallel("prep_samples", [samples])
samples = run_parallel("postprocess_alignment", samples)
samples = run_parallel("combine_sample_regions", [samples])
samples = region.clean_sample_data(samples)
with profile.report("coverage", dirs):
samples = coverage.summarize_samples(samples, run_parallel)
## Variant calling on sub-regions of the input file (full cluster)
with prun.start(_wres(parallel, ["gatk", "picard", "variantcaller"]),
samples, config, dirs, "full",
multiplier=region.get_max_counts(samples), max_multicore=1) as run_parallel:
with profile.report("alignment post-processing", dirs):
samples = region.parallel_prep_region(samples, run_parallel)
with profile.report("variant calling", dirs):
samples = genotype.parallel_variantcall_region(samples, run_parallel)
## Finalize variants, BAMs and population databases (per-sample multicore cluster)
with prun.start(_wres(parallel, ["gatk", "gatk-vqsr", "snpeff", "bcbio_variation",
"gemini", "samtools", "fastqc", "bamtools",
"bcbio-variation-recall", "qsignature"]),
samples, config, dirs, "multicore2") as run_parallel:
with profile.report("joint squaring off/backfilling", dirs):
samples = joint.square_off(samples, run_parallel)
with profile.report("variant post-processing", dirs):
samples = run_parallel("postprocess_variants", samples)
samples = run_parallel("split_variants_by_sample", samples)
with profile.report("prepped BAM merging", dirs):
samples = region.delayed_bamprep_merge(samples, run_parallel)
with profile.report("validation", dirs):
samples = run_parallel("compare_to_rm", samples)
samples = genotype.combine_multiple_callers(samples)
with profile.report("ensemble calling", dirs):
samples = ensemble.combine_calls_parallel(samples, run_parallel)
with profile.report("validation summary", dirs):
samples = validate.summarize_grading(samples)
with profile.report("structural variation", dirs):
samples = structural.run(samples, run_parallel)
with profile.report("population database", dirs):
samples = population.prep_db_parallel(samples, run_parallel)
with profile.report("quality control", dirs):
samples = qcsummary.generate_parallel(samples, run_parallel)
with profile.report("archive", dirs):
samples = archive.compress(samples, run_parallel)
logger.info("Timing: finished")
return samples
def variant2pipeline(config, run_info_yaml, parallel, dirs, samples):
## Alignment and preparation requiring the entire input file (multicore cluster)
# Assign GATK supplied memory if required for post-process recalibration
align_programs = ["aligner", "samtools", "sambamba"]
if any(tz.get_in(["algorithm", "recalibrate"], utils.to_single_data(d)) in [True, "gatk"] for d in samples):
align_programs.append("gatk")
with prun.start(_wres(parallel, align_programs,
(["reference", "fasta"], ["reference", "aligner"], ["files"])),
samples, config, dirs, "multicore",
multiplier=alignprep.parallel_multiplier(samples)) as run_parallel:
with profile.report("organize samples", dirs):
samples = run_parallel("organize_samples", [[dirs, config, run_info_yaml,
[x[0]["description"] for x in samples]]])
with profile.report("alignment preparation", dirs):
samples = run_parallel("prep_align_inputs", samples)
samples = run_parallel("disambiguate_split", [samples])
with profile.report("alignment", dirs):
samples = run_parallel("process_alignment", samples)
samples = disambiguate.resolve(samples, run_parallel)
samples = alignprep.merge_split_alignments(samples, run_parallel)
with profile.report("callable regions", dirs):
samples = run_parallel("prep_samples", [samples])
samples = run_parallel("postprocess_alignment", samples)
samples = run_parallel("combine_sample_regions", [samples])
samples = run_parallel("calculate_sv_bins", [samples])
samples = run_parallel("calculate_sv_coverage", samples)
samples = run_parallel("normalize_sv_coverage", [samples])
samples = region.clean_sample_data(samples)
with profile.report("hla typing", dirs):
samples = hla.run(samples, run_parallel)
## Variant calling on sub-regions of the input file (full cluster)
with prun.start(_wres(parallel, ["gatk", "picard", "variantcaller"]),
samples, config, dirs, "full",
multiplier=region.get_max_counts(samples), max_multicore=1) as run_parallel:
with profile.report("alignment post-processing", dirs):
samples = region.parallel_prep_region(samples, run_parallel)
with profile.report("variant calling", dirs):
samples = genotype.parallel_variantcall_region(samples, run_parallel)
## Finalize variants, BAMs and population databases (per-sample multicore cluster)
with prun.start(_wres(parallel, ["gatk", "gatk-vqsr", "snpeff", "bcbio_variation",
"gemini", "samtools", "fastqc", "sambamba",
"bcbio-variation-recall", "qsignature",
"svcaller", "kraken", "preseq"]),
samples, config, dirs, "multicore2",
multiplier=structural.parallel_multiplier(samples)) as run_parallel:
with profile.report("joint squaring off/backfilling", dirs):
samples = joint.square_off(samples, run_parallel)
with profile.report("variant post-processing", dirs):
samples = run_parallel("postprocess_variants", samples)
samples = run_parallel("split_variants_by_sample", samples)
with profile.report("prepped BAM merging", dirs):
samples = region.delayed_bamprep_merge(samples, run_parallel)
with profile.report("validation", dirs):
samples = run_parallel("compare_to_rm", samples)
samples = genotype.combine_multiple_callers(samples)
with profile.report("ensemble calling", dirs):
samples = ensemble.combine_calls_parallel(samples, run_parallel)
with profile.report("validation summary", dirs):
samples = validate.summarize_grading(samples)
with profile.report("structural variation", dirs):
samples = structural.run(samples, run_parallel, "initial")
with profile.report("structural variation", dirs):
samples = structural.run(samples, run_parallel, "standard")
with profile.report("structural variation ensemble", dirs):
samples = structural.run(samples, run_parallel, "ensemble")
with profile.report("structural variation validation", dirs):
samples = run_parallel("validate_sv", samples)
with profile.report("heterogeneity", dirs):
samples = heterogeneity.run(samples, run_parallel)
with profile.report("population database", dirs):
samples = population.prep_db_parallel(samples, run_parallel)
with profile.report("peddy check", dirs):
samples = peddy.run_peddy_parallel(samples, run_parallel)
with profile.report("quality control", dirs):
samples = qcsummary.generate_parallel(samples, run_parallel)
with profile.report("archive", dirs):
samples = archive.compress(samples, run_parallel)
with profile.report("upload", dirs):
samples = run_parallel("upload_samples", samples)
for sample in samples:
run_parallel("upload_samples_project", [sample])
logger.info("Timing: finished")
return samples
parser.add_argument("-p", "--tag", help="Tag name to label jobs on the cluster", default="bcb-prep")
parser.add_argument("-t", "--paralleltype",
choices=["local", "ipython"],
default="local", help="Run with iptyhon")
args = parser.parse_args()
out_dir = os.path.abspath(args.out)
utils.safe_makedir(out_dir)
system_config = os.path.join(_get_data_dir(), "galaxy", "bcbio_system.yaml")
with open(system_config) as in_handle:
config = yaml.load(in_handle)
res = {'cores': args.cores_per_job}
config["algorithm"] = {"num_cores": args.cores_per_job}
config["resources"].update({'sambamba': res,
'samtools': res})
parallel = clargs.to_parallel(args)
parallel.update({'progs': ['samtools', 'sambamba']})
parallel = log.create_base_logger(config, parallel)
log.setup_local_logging(config, parallel)
dirs = {'work': os.path.abspath(os.getcwd())}
system.write_info(dirs, parallel, config)
sysinfo = system.machine_info()[0]
samples = _get_samples_to_process(args.csv, out_dir, config)
parallel = resources.calculate(parallel, [samples], sysinfo, config)
with prun.start(parallel, samples, config, dirs) as run_parallel:
with profile.report("prepare bcbio samples", dirs):
samples = run_parallel("prepare_bcbio_samples", samples)
create_new_csv(samples, args)
parser.add_argument("-t", "--paralleltype",
choices=["local", "ipython"],
default="local", help="Run with iptyhon")
args = parser.parse_args()
out_dir = os.path.abspath(args.out)
utils.safe_makedir(out_dir)
system_config = os.path.join(_get_data_dir(), "galaxy", "bcbio_system.yaml")
with open(system_config) as in_handle:
config = yaml.load(in_handle)
res = {'cores': args.cores_per_job}
config["algorithm"] = {"num_cores": args.cores_per_job}
config["resources"].update({'sambamba': res,
'samtools': res})
config["log_dir"] = os.path.join(os.path.abspath(os.getcwd()), "log")
parallel = clargs.to_parallel(args)
parallel.update({'progs': ['samtools', 'sambamba']})
parallel = log.create_base_logger(config, parallel)
log.setup_local_logging(config, parallel)
dirs = {'work': os.path.abspath(os.getcwd())}
system.write_info(dirs, parallel, config)
sysinfo = system.machine_info()[0]
samples = _get_samples_to_process(args.csv, out_dir, config)
parallel = resources.calculate(parallel, [samples], sysinfo, config)
with prun.start(parallel, samples, config, dirs) as run_parallel:
with profile.report("prepare bcbio samples", dirs):
samples = run_parallel("prepare_bcbio_samples", samples)
create_new_csv(samples, args)
def run(self, config, config_file, parallel, dirs, samples):
## Alignment and preparation requiring the entire input file (multicore cluster)
with prun.start(_wres(
parallel, ["aligner", "gatk"],
(["reference", "fasta"], ["reference", "aligner"], ["files"])),
samples,
config,
dirs,
"multicore",
multiplier=alignprep.parallel_multiplier(
samples)) as run_parallel:
logger.info("Timing: alignment")
samples = run_parallel("prep_align_inputs", samples)
samples = disambiguate.split(samples)
samples = run_parallel("process_alignment", samples)
samples = alignprep.merge_split_alignments(samples, run_parallel)
samples = disambiguate.resolve(samples, run_parallel)
samples = run_parallel("postprocess_alignment", samples)
regions = callable.combine_sample_regions(samples)
samples = region.add_region_info(samples, regions)
samples = region.clean_sample_data(samples)
logger.info("Timing: coverage")
samples = coverage.summarize_samples(samples, run_parallel)
## Variant calling on sub-regions of the input file (full cluster)
with prun.start(_wres(parallel, ["gatk", "picard", "variantcaller"]),
samples,
config,
dirs,
"full",
multiplier=len(regions["analysis"]),
max_multicore=1) as run_parallel:
logger.info("Timing: alignment post-processing")
samples = region.parallel_prep_region(samples, regions,
run_parallel)
logger.info("Timing: variant calling")
samples = region.parallel_variantcall_region(samples, run_parallel)
## Finalize variants (per-sample cluster)
with prun.start(
_wres(parallel,
["gatk", "gatk-vqsr", "snpeff", "bcbio_variation"]),
samples, config, dirs, "persample") as run_parallel:
logger.info("Timing: variant post-processing")
samples = run_parallel("postprocess_variants", samples)
logger.info("Timing: validation")
samples = run_parallel("compare_to_rm", samples)
samples = combine_multiple_callers(samples)
## Finalizing BAMs and population databases, handle multicore computation
with prun.start(
_wres(parallel, [
"gemini", "samtools", "fastqc", "bamtools",
"bcbio_variation", "bcbio-variation-recall"
]), samples, config, dirs, "multicore2") as run_parallel:
logger.info("Timing: prepped BAM merging")
samples = region.delayed_bamprep_merge(samples, run_parallel)
logger.info("Timing: ensemble calling")
samples = ensemble.combine_calls_parallel(samples, run_parallel)
samples = validate.summarize_grading(samples)
logger.info("Timing: structural variation")
samples = structural.run(samples, run_parallel)
logger.info("Timing: population database")
samples = population.prep_db_parallel(samples, run_parallel)
logger.info("Timing: quality control")
samples = qcsummary.generate_parallel(samples, run_parallel)
logger.info("Timing: finished")
return samples
def variant2pipeline(config, run_info_yaml, parallel, dirs, samples):
## Alignment and preparation requiring the entire input file (multicore cluster)
with prun.start(_wres(parallel, ["aligner", "samtools", "sambamba"],
(["reference", "fasta"], ["reference", "aligner"], ["files"])),
samples, config, dirs, "multicore",
multiplier=alignprep.parallel_multiplier(samples)) as run_parallel:
with profile.report("organize samples", dirs):
samples = run_parallel("organize_samples", [[dirs, config, run_info_yaml,
[x[0]["description"] for x in samples]]])
ww = WorldWatcher(dirs["work"], is_on=any([dd.get_cwl_reporting(d[0]) for d in samples]))
ww.initialize(samples)
with profile.report("alignment preparation", dirs):
samples = run_parallel("prep_align_inputs", samples)
ww.report("prep_align_inputs", samples)
samples = run_parallel("disambiguate_split", [samples])
with profile.report("alignment", dirs):
samples = run_parallel("process_alignment", samples)
ww.report("process_alignment", samples)
samples = disambiguate.resolve(samples, run_parallel)
samples = alignprep.merge_split_alignments(samples, run_parallel)
with profile.report("callable regions", dirs):
samples = run_parallel("prep_samples", [samples])
ww.report("prep_samples", samples)
samples = run_parallel("postprocess_alignment", samples)
ww.report("postprocess_alignment", samples)
samples = run_parallel("combine_sample_regions", [samples])
samples = region.clean_sample_data(samples)
ww.report("combine_sample_regions", samples)
with profile.report("structural variation initial", dirs):
samples = structural.run(samples, run_parallel, "initial")
ww.report("sv_initial", samples)
with profile.report("hla typing", dirs):
samples = hla.run(samples, run_parallel)
ww.report("call_hla", samples)
## Variant calling on sub-regions of the input file (full cluster)
with prun.start(_wres(parallel, ["gatk", "picard", "variantcaller"]),
samples, config, dirs, "full",
multiplier=region.get_max_counts(samples), max_multicore=1) as run_parallel:
with profile.report("alignment post-processing", dirs):
samples = region.parallel_prep_region(samples, run_parallel)
with profile.report("variant calling", dirs):
samples = genotype.parallel_variantcall_region(samples, run_parallel)
## Finalize variants, BAMs and population databases (per-sample multicore cluster)
with prun.start(_wres(parallel, ["gatk", "gatk-vqsr", "snpeff", "bcbio_variation",
"gemini", "samtools", "fastqc", "bamtools",
"bcbio-variation-recall", "qsignature",
"svcaller"]),
samples, config, dirs, "multicore2",
multiplier=structural.parallel_multiplier(samples)) as run_parallel:
with profile.report("joint squaring off/backfilling", dirs):
samples = joint.square_off(samples, run_parallel)
with profile.report("variant post-processing", dirs):
samples = run_parallel("postprocess_variants", samples)
samples = run_parallel("split_variants_by_sample", samples)
with profile.report("prepped BAM merging", dirs):
samples = region.delayed_bamprep_merge(samples, run_parallel)
with profile.report("validation", dirs):
samples = run_parallel("compare_to_rm", samples)
samples = genotype.combine_multiple_callers(samples)
with profile.report("ensemble calling", dirs):
samples = ensemble.combine_calls_parallel(samples, run_parallel)
with profile.report("validation summary", dirs):
samples = validate.summarize_grading(samples)
with profile.report("structural variation final", dirs):
samples = structural.run(samples, run_parallel, "standard")
with profile.report("structural variation ensemble", dirs):
samples = structural.run(samples, run_parallel, "ensemble")
with profile.report("structural variation validation", dirs):
samples = run_parallel("validate_sv", samples)
with profile.report("heterogeneity", dirs):
samples = heterogeneity.run(samples, run_parallel)
with profile.report("population database", dirs):
samples = population.prep_db_parallel(samples, run_parallel)
with profile.report("quality control", dirs):
ww.report("pre_qc", samples)
samples = qcsummary.generate_parallel(samples, run_parallel)
ww.report("qc_summary", samples)
with profile.report("archive", dirs):
samples = archive.compress(samples, run_parallel)
with profile.report("upload", dirs):
samples = run_parallel("upload_samples", samples)
for sample in samples:
run_parallel("upload_samples_project", [sample])
logger.info("Timing: finished")
return samples
