def postprocess_alignment(data):
"""Perform post-processing steps required on full BAM files.
Prepares list of callable genome regions allowing subsequent parallelization.
"""
data = utils.to_single_data(data)
bam_file = data.get("align_bam") or data.get("work_bam")
if vmulti.bam_needs_processing(data) and bam_file and bam_file.endswith(".bam"):
ref_file = dd.get_ref_file(data)
out_dir = utils.safe_makedir(os.path.join(dd.get_work_dir(data), "align",
dd.get_sample_name(data)))
bam_file_ready = os.path.join(out_dir, os.path.basename(bam_file))
if not utils.file_exists(bam_file_ready):
utils.symlink_plus(bam_file, bam_file_ready)
bam.index(bam_file_ready, data["config"])
callable_region_bed, nblock_bed, callable_bed = \
callable.block_regions(bam_file_ready, ref_file, data)
sample_callable = callable.sample_callable_bed(bam_file_ready, ref_file, data)
offtarget_stats = callable.calculate_offtarget(bam_file_ready, ref_file, data)
data["regions"] = {"nblock": nblock_bed, "callable": callable_bed,
"sample_callable": sample_callable,
"offtarget_stats": offtarget_stats}
data = coverage.assign_interval(data)
highdepth_bed = highdepth.identify(data)
data["regions"]["highdepth"] = highdepth_bed
if (os.path.exists(callable_region_bed) and
not data["config"]["algorithm"].get("variant_regions")):
data["config"]["algorithm"]["variant_regions"] = callable_region_bed
data = bedutils.clean_inputs(data)
data = _recal_no_markduplicates(data)
return [[data]]
def postprocess_alignment(data):
"""Perform post-processing steps required on full BAM files.
Prepares list of callable genome regions allowing subsequent parallelization.
"""
if vmulti.bam_needs_processing(data) and data["work_bam"].endswith(".bam"):
ref_file = dd.get_ref_file(data)
callable_region_bed, nblock_bed, callable_bed = \
callable.block_regions(data["work_bam"], ref_file, data)
highdepth_bed = highdepth.identify(data)
bam.index(data["work_bam"], data["config"])
sample_callable = callable.sample_callable_bed(data["work_bam"],
ref_file, data)
offtarget_stats = callable.calculate_offtarget(data["work_bam"],
ref_file, data)
data["regions"] = {
"nblock": nblock_bed,
"callable": callable_bed,
"highdepth": highdepth_bed,
"sample_callable": sample_callable,
"offtarget_stats": offtarget_stats
}
data = coverage.assign_interval(data)
if (os.path.exists(callable_region_bed)
and not data["config"]["algorithm"].get("variant_regions")):
data["config"]["algorithm"][
"variant_regions"] = callable_region_bed
data = bedutils.clean_inputs(data)
data = _recal_no_markduplicates(data)
return [[data]]
def postprocess_alignment(data):
"""Perform post-processing steps required on full BAM files.
Prepares list of callable genome regions allowing subsequent parallelization.
"""
if vmulti.bam_needs_processing(data) and data["work_bam"].endswith(".bam"):
ref_file = dd.get_ref_file(data)
callable_region_bed, nblock_bed, callable_bed = \
callable.block_regions(data["work_bam"], ref_file, data)
highdepth_bed = highdepth.identify(data)
sample_callable = callable.sample_callable_bed(data["work_bam"], ref_file, data)
offtarget_stats = callable.calculate_offtarget(data["work_bam"], ref_file, data)
data["regions"] = {"nblock": nblock_bed, "callable": callable_bed, "highdepth": highdepth_bed,
"sample_callable": sample_callable,
"offtarget_stats": offtarget_stats}
data = coverage.assign_interval(data)
if (os.path.exists(callable_region_bed) and
not data["config"]["algorithm"].get("variant_regions")):
data["config"]["algorithm"]["variant_regions"] = callable_region_bed
data = bedutils.clean_inputs(data)
data = _recal_no_markduplicates(data)
return [[data]]