def standardpipeline(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"]),
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", "qsignature", "kraken", "gatk", "samtools"]),
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):
samples = run_parallel("upload_samples", samples)
for sample in samples:
run_parallel("upload_samples_project", [sample])
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, 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 = 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(_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 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, 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, 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, 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, run_parallel, parallel, dirs, lane_items):
lane_items = run_parallel("trim_lane", lane_items)
samples = run_parallel("process_alignment", lane_items)
samples = run_parallel("generate_transcript_counts", samples)
samples = qcsummary.generate_parallel(samples, run_parallel)
#run_parallel("generate_bigwig", samples, {"programs": ["ucsc_bigwig"]})
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])
logger.info("Timing: finished")
return samples
def run(self, config, config_file, run_parallel, parallel, dirs, lane_items):
lane_items = run_parallel("trim_lane", lane_items)
samples = disambiguate.split(lane_items)
samples = run_parallel("process_alignment", samples)
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, ["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):
samples = run_parallel("upload_samples", samples)
for sample in samples:
run_parallel("upload_samples_project", [sample])
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, 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 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])
logger.info("Timing: finished")
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 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("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, run_parallel, dirs, lane_items):
lane_items = run_parallel("trim_lane", lane_items)
align_items = run_parallel("process_alignment", lane_items)
# process samples, potentially multiplexed across multiple lanes
samples = organize_samples(align_items, dirs, config_file)
samples = run_parallel("merge_sample", samples)
samples = run_parallel("generate_transcript_counts", samples)
run_parallel("generate_bigwig", samples, {"programs": ["ucsc_bigwig"]})
samples = qcsummary.generate_parallel(samples, run_parallel)
return samples
def run(self, config, config_file, run_parallel, dirs, lane_items):
lane_items = run_parallel("trim_lane", lane_items)
align_items = run_parallel("process_alignment", lane_items)
# process samples, potentially multiplexed across multiple lanes
samples = organize_samples(align_items, dirs, config_file)
samples = run_parallel("merge_sample", samples)
samples = run_parallel("generate_transcript_counts", samples)
run_parallel("generate_bigwig", samples, {"programs": ["ucsc_bigwig"]})
samples = qcsummary.generate_parallel(samples, run_parallel)
return samples
def run(self, config, config_file, run_parallel, parallel, dirs,
lane_items):
## Alignment and preparation requiring the entire input file (multicore cluster)
with global_parallel(parallel, "multicore", ["align_prep_full"],
lane_items, dirs, config) as parallel:
run_parallel = parallel_runner(parallel, dirs, config)
logger.info("Timing: alignment")
samples = run_parallel(
"align_prep_full",
[list(x) + [config_file] for x in lane_items])
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 global_parallel(parallel,
"full", ["piped_bamprep", "variantcall_sample"],
samples,
dirs,
config,
multiplier=len(regions["analysis"])) as parallel:
run_parallel = parallel_runner(parallel, dirs, config)
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 global_parallel(parallel, "persample", ["postprocess_variants"],
samples, dirs, config) as parallel:
run_parallel = parallel_runner(parallel, dirs, config)
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)
logger.info("Timing: ensemble calling")
samples = ensemble.combine_calls_parallel(samples, run_parallel)
samples = validate.summarize_grading(samples)
logger.info("Timing: quality control")
samples = qcsummary.generate_parallel(samples, run_parallel)
## Finalizing BAMs and population databases, handle multicore computation
with global_parallel(parallel, "multicore2",
["prep_gemini_db", "delayed_bam_merge"], samples,
dirs, config) as parallel:
run_parallel = parallel_runner(parallel, dirs, config)
logger.info("Timing: prepped BAM merging")
samples = region.delayed_bamprep_merge(samples, run_parallel)
logger.info("Timing: population database")
samples = population.prep_db_parallel(samples, run_parallel)
logger.info("Timing: finished")
return samples
def run(self, config, config_file, run_parallel, parallel, dirs, samples):
## Alignment and preparation requiring the entire input file (multicore cluster)
with global_parallel(parallel, "multicore", ["process_alignment", "postprocess_alignment"],
samples, dirs, config,
multiplier=alignprep.parallel_multiplier(samples)) as parallel:
run_parallel = parallel_runner(parallel, dirs, config)
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 global_parallel(parallel, "full", ["piped_bamprep", "variantcall_sample"],
samples, dirs, config,
multiplier=len(regions["analysis"]), max_multicore=1) as parallel:
run_parallel = parallel_runner(parallel, dirs, config)
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 global_parallel(parallel, "persample", ["postprocess_variants"],
samples, dirs, config) as parallel:
run_parallel = parallel_runner(parallel, dirs, config)
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)
logger.info("Timing: ensemble calling")
samples = ensemble.combine_calls_parallel(samples, run_parallel)
samples = validate.summarize_grading(samples)
## Finalizing BAMs and population databases, handle multicore computation
with global_parallel(parallel, "multicore2", ["prep_gemini_db", "delayed_bam_merge"],
samples, dirs, config) as parallel:
run_parallel = parallel_runner(parallel, dirs, config)
logger.info("Timing: prepped BAM merging")
samples = region.delayed_bamprep_merge(samples, run_parallel)
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 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, run_parallel, parallel, dirs, lane_items):
lane_items = run_parallel("trim_lane", lane_items)
samples = disambiguate.split(lane_items)
samples = run_parallel("process_alignment", samples)
samples = disambiguate.resolve(samples, run_parallel)
samples = run_parallel("generate_transcript_counts", samples)
combined = combine_count_files([x[0].get("count_file") for x in samples])
for x in samples:
x[0]["combined_counts"] = combined
samples = qcsummary.generate_parallel(samples, run_parallel)
#run_parallel("generate_bigwig", samples, {"programs": ["ucsc_bigwig"]})
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(_wres(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(_wres(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, 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, config_file, run_parallel, parallel, dirs, lane_items):
## Alignment and preparation requiring the entire input file (multicore cluster)
with global_parallel(parallel, "multicore", ["align_prep_full"], lane_items, dirs, config) as parallel:
run_parallel = parallel_runner(parallel, dirs, config)
logger.info("Timing: alignment")
samples = run_parallel("process_alignment", lane_items)
## Finalize (per-sample cluster)
with global_parallel(parallel, "persample", ["postprocess_variants"], samples, dirs, config) as parallel:
run_parallel = parallel_runner(parallel, dirs, config)
logger.info("Timing: quality control")
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 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("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 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 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 run(self, config, config_file, run_parallel, parallel, dirs,
lane_items):
## Alignment and preparation requiring the entire input file (multicore cluster)
with global_parallel(parallel, "multicore", ["align_prep_full"],
lane_items, dirs["work"], config) as parallel:
run_parallel = parallel_runner(parallel, dirs, config)
logger.info("Timing: alignment")
samples = run_parallel(
"align_prep_full",
[list(x) + [config_file] for x in lane_items])
regions = callable.combine_sample_regions(samples)
samples = region.add_region_info(samples, regions)
samples = region.clean_sample_data(samples)
## Variant calling on sub-regions of the input file (full cluster)
with global_parallel(
parallel,
"full", ["piped_bamprep", "variantcall_sample"],
samples,
dirs["work"],
config,
multiplier=len(regions["analysis"])) as parallel:
run_parallel = parallel_runner(parallel, dirs, config)
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 global_parallel(parallel, "persample", ["postprocess_variants"],
samples, dirs["work"], config) as parallel:
run_parallel = parallel_runner(parallel, dirs, config)
logger.info("Timing: variant post-processing")
samples = run_parallel("postprocess_variants", samples)
samples = combine_multiple_callers(samples)
logger.info("Timing: ensemble calling")
samples = ensemble.combine_calls_parallel(samples, run_parallel)
logger.info("Timing: prepped BAM merging")
samples = region.delayed_bamprep_merge(samples, run_parallel)
logger.info("Timing: validation")
samples = run_parallel("compare_to_rm", samples)
samples = validate.summarize_grading(samples)
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 run(self, config, config_file, run_parallel, parallel, dirs, lane_items):
## Alignment and preparation requiring the entire input file (multicore cluster)
with global_parallel(parallel, "multicore", ["process_alignment"],
lane_items, dirs, config) as parallel:
run_parallel = parallel_runner(parallel, dirs, config)
logger.info("Timing: alignment")
samples = run_parallel("process_alignment", lane_items)
## Finalize (per-sample cluster)
with global_parallel(parallel, "persample", ["postprocess_variants"],
samples, dirs, config) as parallel:
run_parallel = parallel_runner(parallel, dirs, config)
logger.info("Timing: quality control")
samples = qcsummary.generate_parallel(samples, run_parallel)
logger.info("Timing: finished")
return samples
def run(self, config, config_file, run_parallel, dirs, lane_items):
lane_items = run_parallel("trim_lane", lane_items)
align_items = run_parallel("process_alignment", lane_items)
# process samples, potentially multiplexed across multiple lanes
samples = organize_samples(align_items, dirs, config_file)
samples = run_parallel("merge_sample", samples)
samples = run_parallel("prep_recal", samples)
samples = recalibrate.parallel_write_recal_bam(samples, run_parallel)
samples = parallel_realign_sample(samples, run_parallel)
samples = parallel_variantcall(samples, run_parallel)
samples = run_parallel("postprocess_variants", samples)
samples = combine_multiple_callers(samples)
samples = ensemble.combine_calls_parallel(samples, run_parallel)
samples = run_parallel("detect_sv", samples)
samples = qcsummary.generate_parallel(samples, run_parallel)
run_parallel("generate_bigwig", samples, {"programs": ["ucsc_bigwig"]})
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, 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)
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, config_file, run_parallel, dirs, lane_items):
# Handle alignment and preparation requiring the entire input file
samples = run_parallel("align_prep_full", (list(x) + [config_file] for x in lane_items))
regions = callable.combine_sample_regions(samples)
samples = region.add_region_info(samples, regions)
# Handle all variant calling on sub-regions of the input file
samples = region.clean_sample_data(samples)
samples = region.parallel_prep_region(samples, regions, run_parallel)
samples = region.parallel_variantcall_region(samples, run_parallel)
samples = run_parallel("postprocess_variants", samples)
samples = combine_multiple_callers(samples)
samples = ensemble.combine_calls_parallel(samples, run_parallel)
samples = population.prep_db_parallel(samples, run_parallel)
samples = region.delayed_bamprep_merge(samples, run_parallel)
samples = qcsummary.generate_parallel(samples, run_parallel)
samples = validate.summarize_grading(samples)
return samples
def run(self, config, config_file, run_parallel, dirs, lane_items):
lane_items = run_parallel("trim_lane", lane_items)
align_items = run_parallel("process_alignment", lane_items)
# process samples, potentially multiplexed across multiple lanes
samples = organize_samples(align_items, dirs, config_file)
samples = run_parallel("merge_sample", samples)
samples = run_parallel("prep_recal", samples)
samples = recalibrate.parallel_write_recal_bam(samples, run_parallel)
samples = parallel_realign_sample(samples, run_parallel)
samples = parallel_variantcall(samples, run_parallel)
samples = run_parallel("postprocess_variants", samples)
samples = combine_multiple_callers(samples)
samples = ensemble.combine_calls_parallel(samples, run_parallel)
samples = run_parallel("detect_sv", samples)
samples = qcsummary.generate_parallel(samples, run_parallel)
run_parallel("generate_bigwig", samples, {"programs": ["ucsc_bigwig"]})
return samples
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, ["ericscript"]), samples, config,
dirs, "fusion-standalone-callers") as run_parallel:
with profile.report("Detect gene fusions", dirs):
rnaseq.detect_fusions(samples)
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, ["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])
logger.info("Timing: finished")
return samples
def wgbsseqpipeline(config, run_info_yaml, parallel, dirs, samples):
with prun.start(
_wres(parallel, ["fastqc", "picard"], ensure_mem={"fastqc": 4}),
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]
]])
samples = run_parallel("prepare_sample", samples)
samples = run_parallel("trim_bs_sample", samples)
with prun.start(
_wres(parallel, ["aligner", "bismark", "picard", "samtools"]),
samples,
config,
dirs,
"multicore",
multiplier=alignprep.parallel_multiplier(samples)) as run_parallel:
with profile.report("alignment", dirs):
samples = run_parallel("process_alignment", samples)
with prun.start(_wres(parallel, ['samtools']), samples, config, dirs,
'deduplication') as run_parallel:
with profile.report('deduplicate', dirs):
samples = run_parallel('deduplicate_bismark', samples)
with prun.start(_wres(parallel, ["caller"], ensure_mem={"caller": 5}),
samples,
config,
dirs,
"multicore2",
multiplier=24) as run_parallel:
with profile.report("cpg calling", dirs):
samples = run_parallel("cpg_calling", samples)
with prun.start(_wres(parallel, ["picard", "fastqc", "samtools"]), 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])
logger.info("Timing: finished")
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("process_lane", samples)
samples = run_parallel("trim_lane", 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("estimate expression", dirs):
samples = rnaseq.estimate_expression(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:
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):
## 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, config_file, run_parallel, parallel, dirs, lane_items):
lane_items = run_parallel("trim_lane", lane_items)
samples = disambiguate.split(lane_items)
samples = run_parallel("process_alignment", samples)
samples = disambiguate.resolve(samples, 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
samples = qcsummary.generate_parallel(samples, run_parallel)
#run_parallel("generate_bigwig", samples, {"programs": ["ucsc_bigwig"]})
return samples
def run(self, config, config_file, run_parallel, dirs, lane_items):
# Handle alignment and preparation requiring the entire input file
samples = run_parallel("align_prep_full",
(list(x) + [config_file] for x in lane_items))
regions = callable.combine_sample_regions(samples)
samples = region.add_region_info(samples, regions)
# Handle all variant calling on sub-regions of the input file
samples = region.clean_sample_data(samples)
samples = region.parallel_prep_region(samples, regions, run_parallel)
samples = region.parallel_variantcall_region(samples, run_parallel)
samples = run_parallel("postprocess_variants", samples)
samples = combine_multiple_callers(samples)
samples = ensemble.combine_calls_parallel(samples, run_parallel)
samples = population.prep_db_parallel(samples, run_parallel)
samples = region.delayed_bamprep_merge(samples, run_parallel)
samples = qcsummary.generate_parallel(samples, run_parallel)
samples = validate.summarize_grading(samples)
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 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 run(self, config, config_file, run_parallel, parallel, dirs, lane_items):
raise NotImplementedError("`variant` processing is deprecated: please use `variant2`"
"The next version will alias variant to the new variant2 pipeline")
lane_items = run_parallel("trim_lane", lane_items)
align_items = run_parallel("process_alignment", lane_items)
# process samples, potentially multiplexed across multiple lanes
samples = organize_samples(align_items, dirs, config_file)
samples = run_parallel("merge_sample", samples)
samples = run_parallel("prep_recal", samples)
samples = recalibrate.parallel_write_recal_bam(samples, run_parallel)
samples = parallel_realign_sample(samples, run_parallel)
samples = parallel_variantcall(samples, run_parallel)
samples = run_parallel("postprocess_variants", samples)
samples = combine_multiple_callers(samples)
samples = ensemble.combine_calls_parallel(samples, run_parallel)
samples = run_parallel("detect_sv", samples)
samples = qcsummary.generate_parallel(samples, run_parallel)
run_parallel("generate_bigwig", samples, {"programs": ["ucsc_bigwig"]})
return samples
def run(self, config, config_file, run_parallel, parallel, dirs, lane_items):
raise NotImplementedError("`variant` processing is deprecated: please use `variant2`"
"The next version will alias variant to the new variant2 pipeline")
lane_items = run_parallel("trim_lane", lane_items)
align_items = run_parallel("process_alignment", lane_items)
# process samples, potentially multiplexed across multiple lanes
samples = organize_samples(align_items, dirs, config_file)
samples = run_parallel("merge_sample", samples)
samples = run_parallel("prep_recal", samples)
samples = recalibrate.parallel_write_recal_bam(samples, run_parallel)
samples = parallel_realign_sample(samples, run_parallel)
samples = parallel_variantcall(samples, run_parallel)
samples = run_parallel("postprocess_variants", samples)
samples = combine_multiple_callers(samples)
samples = ensemble.combine_calls_parallel(samples, run_parallel)
samples = run_parallel("detect_sv", samples)
samples = qcsummary.generate_parallel(samples, run_parallel)
run_parallel("generate_bigwig", samples, {"programs": ["ucsc_bigwig"]})
return samples
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("create SummarizedExperiment object", dirs):
samples = rnaseq.load_summarizedexperiment(samples)
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):
tools_on = dd.get_in_samples(samples, dd.get_tools_on)
bcbiornaseq_on = tools_on and "bcbiornaseq" in tools_on
if bcbiornaseq_on:
if len(samples) < 3:
logger.warn(
"bcbioRNASeq needs at least three samples total, skipping."
)
elif len(samples) > 100:
logger.warn("Over 100 samples, skipping bcbioRNASeq.")
else:
run_parallel("run_bcbiornaseqload", [sample])
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 = initialize_watcher(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("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", "sambamba", "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 precall", dirs):
samples = structural.run(samples, run_parallel, "precall")
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("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
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