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, 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, 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, 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
