def run_sample(tumor, normal, work_dir, cromwell_dir, hla_fa):
hla_fasta, hlas = prep_hla_ref(hla_fa, work_dir)
hla_cromwell_dir = _get_cromwell_execution_dir(cromwell_dir, HLA_GLOB)
sv_cromwell_dir = _get_cromwell_execution_dir(cromwell_dir, SV_GLOB)
tumor_fastq, tumor_calls_orig = get_hla(tumor, hla_cromwell_dir, HLA_GLOB)
tumor_bam = alignment.align_to_sort_bam(tumor_fastq, None, ALIGNER, get_data(tumor, hla_fasta, work_dir))["work_bam"]
normal_fastq, normal_calls_orig = get_hla(normal, hla_cromwell_dir, HLA_GLOB)
normal_bam = alignment.align_to_sort_bam(normal_fastq, None, ALIGNER, get_data(normal, hla_fasta, work_dir))["work_bam"]
tumor_ploidy = prep_ploidy(work_dir, tumor, tumor_bam, sv_cromwell_dir, os.path.join(SV_GLOB, SVCALL_GLOB))
tumor_calls = prep_hla(work_dir, tumor, normal_calls_orig, hlas, normal_bam, tumor_bam)
normal_calls = prep_hla(work_dir, normal, normal_calls_orig, hlas, normal_bam, tumor_bam)
bam_dir, normal_bam_ready = create_tumor_bamdir(tumor, tumor_bam, normal_bam, work_dir)
out_dir = utils.safe_makedir(os.path.join(work_dir, tumor, "lohhla_out"))
prep_bam_inputs(out_dir, tumor, tumor_calls, tumor_bam)
prep_bam_inputs(out_dir, normal, normal_calls, normal_bam)
lohhla_output = os.path.join(out_dir, "%s.%s.DNA.HLAlossPrediction_CI.xls" % (tumor, DEPTH_FILTER))
cmd = ["Rscript", os.path.join(LOHHLA, "LOHHLAscript.R"),
"--patientId", tumor, "--outputDir", out_dir,
"--normalBAMfile", normal_bam_ready, "--BAMDir", bam_dir,
"--hlaPath", normal_calls, "--HLAfastaLoc", hla_fasta,
"--HLAexonLoc", os.path.join(LOHHLA, "data", "hla.dat"),
"--CopyNumLoc", tumor_ploidy,
"--mappingStep", "FALSE",
"--minCoverageFilter", str(DEPTH_FILTER)]
if not os.path.exists(lohhla_output):
subprocess.check_call(cmd)
compare_calls(tumor, lohhla_output, sv_cromwell_dir, os.path.join(SV_GLOB, SVCALL_GLOB))
def process_alignment(fastq1, fastq2, genome_build, lane_name, sample, dirs, config):
"""Do an alignment of fastq files, preparing a sorted BAM output file.
"""
aligner = config["algorithm"].get("aligner", None)
if os.path.exists(fastq1) and aligner:
log.info("Aligning lane %s with %s aligner" % (lane_name, aligner))
align_to_sort_bam(fastq1, fastq2, genome_build, aligner,
lane_name, sample, dirs, config)
def process_alignment(data):
"""Do an alignment of fastq files, preparing a sorted BAM output file.
"""
fastq1, fastq2 = data["files"]
config = data["config"]
aligner = config["algorithm"].get("aligner", None)
out_bam = ""
if os.path.exists(fastq1) and aligner:
logger.info("Aligning lane %s with %s aligner" % (data["rgnames"]["lane"], aligner))
out_bam = align_to_sort_bam(fastq1, fastq2, aligner, data)
elif os.path.exists(fastq1) and fastq1.endswith(".bam"):
sort_method = config["algorithm"].get("bam_sort")
bamclean = config["algorithm"].get("bam_clean")
if sort_method:
runner = broad.runner_from_config(config)
out_file = os.path.join(data["dirs"]["work"], "{}-sort.bam".format(
os.path.splitext(os.path.basename(fastq1))[0]))
out_bam = runner.run_fn("picard_sort", fastq1, sort_method, out_file)
elif bamclean is True or bamclean == "picard":
out_bam = cleanbam.picard_prep(fastq1, data["rgnames"], data["sam_ref"], data["dirs"], config)
else:
out_bam = link_bam_file(fastq1, os.path.join(data["dirs"]["work"], "prealign",
data["rgnames"]["sample"]))
_check_prealigned_bam(fastq1, data["sam_ref"], config)
if not out_bam and not os.path.exists(fastq1):
raise ValueError("Could not find input file: %s" % fastq1)
data["work_bam"] = out_bam
return [[data]]
def process_alignment(fastq1, fastq2, info, lane_name, lane_desc,
dirs, config):
"""Do an alignment of fastq files, preparing a sorted BAM output file.
"""
aligner = config["algorithm"].get("aligner", None)
out_bam = ""
names = rg_names(lane_name, lane_desc, config)
_, ref_file = get_genome_ref(info["genome_build"], None, dirs["galaxy"])
if os.path.exists(fastq1) and aligner:
logger.info("Aligning lane %s with %s aligner" % (lane_name, aligner))
out_bam, ref_file = align_to_sort_bam(fastq1, fastq2, names,
info["genome_build"], aligner,
dirs, config)
elif os.path.exists(fastq1) and fastq1.endswith(".bam"):
sort_method = config["algorithm"].get("bam_sort")
bamclean = config["algorithm"].get("bam_clean")
if sort_method:
runner = broad.runner_from_config(config)
out_file = os.path.join(dirs["work"], "{}-sort.bam".format(
os.path.splitext(os.path.basename(fastq1))[0]))
out_bam = runner.run_fn("picard_sort", fastq1, sort_method, out_file)
elif bamclean is True or bamclean == "picard":
out_bam = cleanbam.picard_prep(fastq1, names, ref_file, dirs, config)
else:
out_bam = link_bam_file(fastq1, os.path.join(dirs["work"], "prealign",
names["sample"]))
if not out_bam and not os.path.exists(fastq1):
raise ValueError("Could not find input file: %s" % fastq1)
return [{"fastq": [fastq1, fastq2], "work_bam": out_bam, "info": info,
"sam_ref": ref_file, "config": config}]
def process_alignment(fastq1, fastq2, info, lane_name, lane_desc, dirs, config):
"""Do an alignment of fastq files, preparing a sorted BAM output file.
"""
aligner = config["algorithm"].get("aligner", None)
out_bam = ""
if os.path.exists(fastq1) and aligner:
log.info("Aligning lane %s with %s aligner" % (lane_name, aligner))
out_bam = align_to_sort_bam(fastq1, fastq2, info["genome_build"], aligner, lane_name, lane_desc, dirs, config)
return [{"fastq": [fastq1, fastq2], "out_bam": out_bam, "info": info, "config": config}]
def process_alignment(data, alt_input=None):
"""Do an alignment of fastq files, preparing a sorted BAM output file.
"""
data = utils.to_single_data(data)
fastq1, fastq2 = dd.get_input_sequence_files(data)
if alt_input:
fastq1, fastq2 = alt_input
config = data["config"]
aligner = config["algorithm"].get("aligner", None)
if fastq1 and objectstore.file_exists_or_remote(fastq1) and aligner:
logger.info("Aligning lane %s with %s aligner" % (data["rgnames"]["lane"], aligner))
data = align_to_sort_bam(fastq1, fastq2, aligner, data)
data = _add_supplemental_bams(data)
elif fastq1 and objectstore.file_exists_or_remote(fastq1) and fastq1.endswith(".bam"):
sort_method = config["algorithm"].get("bam_sort")
bamclean = config["algorithm"].get("bam_clean")
if bamclean is True or bamclean == "picard":
if sort_method and sort_method != "coordinate":
raise ValueError("Cannot specify `bam_clean: picard` with `bam_sort` other than coordinate: %s"
% sort_method)
out_bam = cleanbam.picard_prep(fastq1, data["rgnames"], dd.get_ref_file(data), data["dirs"],
data)
elif bamclean == "fixrg":
out_bam = cleanbam.fixrg(fastq1, data["rgnames"], dd.get_ref_file(data), data["dirs"], data)
elif sort_method:
runner = broad.runner_from_path("picard", config)
out_file = os.path.join(data["dirs"]["work"], "{}-sort.bam".format(
os.path.splitext(os.path.basename(fastq1))[0]))
out_bam = runner.run_fn("picard_sort", fastq1, sort_method, out_file)
else:
out_bam = link_bam_file(fastq1, os.path.join(data["dirs"]["work"], "prealign",
data["rgnames"]["sample"]))
bam.index(out_bam, data["config"])
bam.check_header(out_bam, data["rgnames"], dd.get_ref_file(data), data["config"])
dedup_bam = postalign.dedup_bam(out_bam, data)
bam.index(dedup_bam, data["config"])
data["work_bam"] = dedup_bam
elif fastq1 and objectstore.file_exists_or_remote(fastq1) and fastq1.endswith(".cram"):
data["work_bam"] = fastq1
elif fastq1 is None and "vrn_file" in data:
data["config"]["algorithm"]["variantcaller"] = False
data["work_bam"] = None
elif not fastq1:
raise ValueError("No 'files' specified for input sample: %s" % dd.get_sample_name(data))
else:
raise ValueError("Could not process input file from sample configuration. \n" +
fastq1 +
"\nIs the path to the file correct or is empty?\n" +
"If it is a fastq file (not pre-aligned BAM or CRAM), "
"is an aligner specified in the input configuration?")
if data.get("work_bam"):
# Add stable 'align_bam' target to use for retrieving raw alignment
data["align_bam"] = data["work_bam"]
data = _add_hla_files(data)
return [[data]]
def process_alignment(data):
"""Do an alignment of fastq files, preparing a sorted BAM output file.
"""
if "files" not in data:
fastq1, fastq2 = None, None
elif len(data["files"]) == 2:
fastq1, fastq2 = data["files"]
else:
assert len(data["files"]) == 1, data["files"]
fastq1, fastq2 = data["files"][0], None
config = data["config"]
aligner = config["algorithm"].get("aligner", None)
if fastq1 and os.path.exists(fastq1) and aligner:
logger.info("Aligning lane %s with %s aligner" % (data["rgnames"]["lane"], aligner))
data = align_to_sort_bam(fastq1, fastq2, aligner, data)
data = _add_supplemental_bams(data)
elif fastq1 and os.path.exists(fastq1) and fastq1.endswith(".bam"):
sort_method = config["algorithm"].get("bam_sort")
bamclean = config["algorithm"].get("bam_clean")
if bamclean is True or bamclean == "picard":
if sort_method and sort_method != "coordinate":
raise ValueError(
"Cannot specify `bam_clean: picard` with `bam_sort` other than coordinate: %s" % sort_method
)
out_bam = cleanbam.picard_prep(fastq1, data["rgnames"], data["sam_ref"], data["dirs"], data)
elif sort_method:
runner = broad.runner_from_config(config)
out_file = os.path.join(
data["dirs"]["work"], "{}-sort.bam".format(os.path.splitext(os.path.basename(fastq1))[0])
)
out_bam = runner.run_fn("picard_sort", fastq1, sort_method, out_file)
else:
out_bam = link_bam_file(fastq1, os.path.join(data["dirs"]["work"], "prealign", data["rgnames"]["sample"]))
bam.check_header(out_bam, data["rgnames"], data["sam_ref"], data["config"])
dedup_bam = postalign.dedup_bam(out_bam, data)
data["work_bam"] = dedup_bam
elif fastq1 and os.path.exists(fastq1) and fastq1.endswith(".cram"):
data["work_bam"] = fastq1
elif fastq1 is None and "vrn_file" in data:
data["config"]["algorithm"]["variantcaller"] = False
data["work_bam"] = None
else:
raise ValueError("Could not process input file: %s" % fastq1)
return [[data]]
def process_alignment(data):
"""Do an alignment of fastq files, preparing a sorted BAM output file.
"""
if "files" not in data:
fastq1, fastq2 = None, None
elif len(data["files"]) == 2:
fastq1, fastq2 = data["files"]
else:
assert len(data["files"]) == 1, data["files"]
fastq1, fastq2 = data["files"][0], None
config = data["config"]
aligner = config["algorithm"].get("aligner", None)
if fastq1 and os.path.exists(fastq1) and aligner:
logger.info("Aligning lane %s with %s aligner" % (data["rgnames"]["lane"], aligner))
data = align_to_sort_bam(fastq1, fastq2, aligner, data)
data = _add_supplemental_bams(data)
elif fastq1 and os.path.exists(fastq1) and fastq1.endswith(".bam"):
sort_method = config["algorithm"].get("bam_sort")
bamclean = config["algorithm"].get("bam_clean")
if bamclean is True or bamclean == "picard":
if sort_method and sort_method != "coordinate":
raise ValueError("Cannot specify `bam_clean: picard` with `bam_sort` other than coordinate: %s"
% sort_method)
out_bam = cleanbam.picard_prep(fastq1, data["rgnames"], data["sam_ref"], data["dirs"],
config)
elif sort_method:
runner = broad.runner_from_config(config)
out_file = os.path.join(data["dirs"]["work"], "{}-sort.bam".format(
os.path.splitext(os.path.basename(fastq1))[0]))
out_bam = runner.run_fn("picard_sort", fastq1, sort_method, out_file)
else:
out_bam = link_bam_file(fastq1, os.path.join(data["dirs"]["work"], "prealign",
data["rgnames"]["sample"]))
bam.check_header(out_bam, data["rgnames"], data["sam_ref"], data["config"])
dedup_bam = postalign.dedup_bam(out_bam, data)
data["work_bam"] = dedup_bam
elif fastq1 and os.path.exists(fastq1) and fastq1.endswith(".cram"):
data["work_bam"] = fastq1
elif fastq1 is None and "vrn_file" in data:
data["config"]["algorithm"]["variantcaller"] = False
data["work_bam"] = None
else:
raise ValueError("Could not process input file: %s" % fastq1)
return [[data]]
def process_alignment(fastq1, fastq2, info, lane_name, lane_desc, dirs,
config):
"""Do an alignment of fastq files, preparing a sorted BAM output file.
"""
aligner = config["algorithm"].get("aligner", None)
out_bam = ""
if os.path.exists(fastq1) and aligner:
logger.info("Aligning lane %s with %s aligner" % (lane_name, aligner))
out_bam = align_to_sort_bam(fastq1, fastq2, info["genome_build"],
aligner, lane_name, lane_desc, dirs,
config)
elif os.path.exists(fastq1) and fastq1.endswith(".bam"):
out_bam = fastq1
return [{
"fastq": [fastq1, fastq2],
"out_bam": out_bam,
"info": info,
"config": config
}]
def process_alignment(fastq1, fastq2, info, lane_name, lane_desc,
dirs, config):
"""Do an alignment of fastq files, preparing a sorted BAM output file.
"""
aligner = config["algorithm"].get("aligner", None)
out_bam = ""
if os.path.exists(fastq1) and aligner:
logger.info("Aligning lane %s with %s aligner" % (lane_name, aligner))
out_bam, ref_file = align_to_sort_bam(fastq1, fastq2, info["genome_build"], aligner,
lane_name, lane_desc, dirs, config)
elif os.path.exists(fastq1) and fastq1.endswith(".bam"):
sort_method = config["algorithm"].get("bam_sort")
if sort_method:
runner = broad.runner_from_config(config)
out_file = os.path.join(dirs["work"], "{}-sort.bam".format(
os.path.splitext(os.path.basename(fastq1))[0]))
out_bam = runner.run_fn("picard_sort", fastq1, sort_method, out_file)
else:
out_bam = fastq1
return [{"fastq": [fastq1, fastq2], "work_bam": out_bam, "info": info,
"sam_ref": ref_file, "config": config}]
def tiered_alignment(in_bam, tier_num, multi_mappers, extra_args,
genome_build, pair_stats,
work_dir, dirs, config):
"""Perform the alignment of non-mapped reads from previous tier.
"""
nomap_fq1, nomap_fq2 = select_unaligned_read_pairs(in_bam, "tier{}".format(tier_num),
work_dir, config)
if nomap_fq1 is not None:
base_name = "{}-tier{}out".format(os.path.splitext(os.path.basename(in_bam))[0],
tier_num)
config = copy.deepcopy(config)
dirs = copy.deepcopy(dirs)
config["algorithm"]["bam_sort"] = "queryname"
config["algorithm"]["multiple_mappers"] = multi_mappers
config["algorithm"]["extra_align_args"] = ["-i", int(pair_stats["mean"]),
int(pair_stats["std"])] + extra_args
return align_to_sort_bam(nomap_fq1, nomap_fq2, genome_build, "novoalign",
base_name, base_name,
dirs, config, dir_ext=os.path.join("hydra", os.path.split(nomap_fq1)[0]))
else:
return None
def process_alignment(fastq1, fastq2, info, lane_name, lane_desc, dirs,
config):
"""Do an alignment of fastq files, preparing a sorted BAM output file.
"""
aligner = config["algorithm"].get("aligner", None)
out_bam = ""
names = rg_names(lane_name, lane_desc, config)
_, ref_file = get_genome_ref(info["genome_build"], None, dirs["galaxy"])
if os.path.exists(fastq1) and aligner:
logger.info("Aligning lane %s with %s aligner" % (lane_name, aligner))
out_bam, ref_file = align_to_sort_bam(fastq1, fastq2, names,
info["genome_build"], aligner,
dirs, config)
elif os.path.exists(fastq1) and fastq1.endswith(".bam"):
sort_method = config["algorithm"].get("bam_sort")
bamclean = config["algorithm"].get("bam_clean")
if sort_method:
runner = broad.runner_from_config(config)
out_file = os.path.join(
dirs["work"], "{}-sort.bam".format(
os.path.splitext(os.path.basename(fastq1))[0]))
out_bam = runner.run_fn("picard_sort", fastq1, sort_method,
out_file)
elif bamclean is True or bamclean == "picard":
out_bam = cleanbam.picard_prep(fastq1, names, ref_file, dirs,
config)
else:
out_bam = link_bam_file(
fastq1, os.path.join(dirs["work"], "prealign",
names["sample"]))
if not out_bam and not os.path.exists(fastq1):
raise ValueError("Could not find input file: %s" % fastq1)
return [{
"fastq": [fastq1, fastq2],
"work_bam": out_bam,
"info": info,
"sam_ref": ref_file,
"config": config
}]
def tiered_alignment(in_bam, tier_num, multi_mappers, extra_args,
genome_build, pair_stats,
work_dir, dirs, config):
"""Perform the alignment of non-mapped reads from previous tier.
"""
nomap_fq1, nomap_fq2 = select_unaligned_read_pairs(in_bam, "tier{}".format(tier_num),
work_dir, config)
if nomap_fq1 is not None:
base_name = "{}-tier{}out".format(os.path.splitext(os.path.basename(in_bam))[0],
tier_num)
config = copy.deepcopy(config)
dirs = copy.deepcopy(dirs)
config["algorithm"]["bam_sort"] = "queryname"
config["algorithm"]["multiple_mappers"] = multi_mappers
config["algorithm"]["extra_align_args"] = ["-i", int(pair_stats["mean"]),
int(pair_stats["std"])] + extra_args
dirs["align"] = os.path.split(nomap_fq1)[0]
return align_to_sort_bam(nomap_fq1, nomap_fq2, genome_build, "novoalign",
base_name, base_name,
dirs, config)
else:
return None
def process_alignment(data, alt_input=None):
"""Do an alignment of fastq files, preparing a sorted BAM output file.
"""
data = cwlutils.normalize_missing(utils.to_single_data(data))
data = cwlutils.unpack_tarballs(data, data)
fastq1, fastq2 = dd.get_input_sequence_files(data)
if alt_input:
fastq1, fastq2 = alt_input
config = data["config"]
aligner = config["algorithm"].get("aligner", None)
if fastq1 and objectstore.file_exists_or_remote(fastq1) and aligner:
logger.info("Aligning lane %s with %s aligner" %
(data["rgnames"]["lane"], aligner))
data = align_to_sort_bam(fastq1, fastq2, aligner, data)
if dd.get_correct_umis(data):
data["work_bam"] = postalign.correct_umis(data)
if dd.get_umi_consensus(data):
data["umi_bam"] = dd.get_work_bam(data)
if fastq2:
f1, f2, avg_cov = postalign.umi_consensus(data)
data["config"]["algorithm"]["rawumi_avg_cov"] = avg_cov
del data["config"]["algorithm"]["umi_type"]
data["config"]["algorithm"]["mark_duplicates"] = False
data = align_to_sort_bam(f1, f2, aligner, data)
else:
raise ValueError(
"Single fastq input for UMI processing; fgbio needs paired reads: %s"
% dd.get_sample_name(data))
data = _add_supplemental_bams(data)
elif fastq1 and objectstore.file_exists_or_remote(
fastq1) and fastq1.endswith(".bam"):
sort_method = config["algorithm"].get("bam_sort")
bamclean = config["algorithm"].get("bam_clean")
if bamclean is True or bamclean == "picard":
if sort_method and sort_method != "coordinate":
raise ValueError(
"Cannot specify `bam_clean: picard` with `bam_sort` other than coordinate: %s"
% sort_method)
ref_file = dd.get_ref_file(data)
out_bam = cleanbam.picard_prep(fastq1, data["rgnames"], ref_file,
data["dirs"], data)
elif bamclean == "fixrg":
out_bam = cleanbam.fixrg(fastq1, data["rgnames"],
dd.get_ref_file(data), data["dirs"], data)
elif bamclean == "remove_extracontigs":
out_bam = cleanbam.remove_extracontigs(fastq1, data)
elif sort_method:
runner = broad.runner_from_path("picard", config)
out_file = os.path.join(
data["dirs"]["work"], "{}-sort.bam".format(
os.path.splitext(os.path.basename(fastq1))[0]))
if not utils.file_exists(out_file):
work_dir = utils.safe_makedir(
os.path.join(dd.get_work_dir(data), "bamclean",
dd.get_sample_name(data)))
out_file = os.path.join(
work_dir, "{}-sort.bam".format(dd.get_sample_name(data)))
out_bam = runner.run_fn("picard_sort", fastq1, sort_method,
out_file)
else:
out_bam = _link_bam_file(
fastq1,
os.path.join(dd.get_work_dir(data), "prealign",
dd.get_sample_name(data)), data)
bam.index(out_bam, data["config"])
bam.check_header(out_bam, data["rgnames"], dd.get_ref_file(data),
data["config"])
dedup_bam = postalign.dedup_bam(out_bam, data)
bam.index(dedup_bam, data["config"])
data["work_bam"] = dedup_bam
elif fastq1 and objectstore.file_exists_or_remote(
fastq1) and fastq1.endswith(".cram"):
data["work_bam"] = fastq1
elif fastq1 is None and not dd.get_aligner(data):
data["config"]["algorithm"]["variantcaller"] = False
data["work_bam"] = None
elif not fastq1:
raise ValueError("No 'files' specified for input sample: %s" %
dd.get_sample_name(data))
elif "kraken" in config["algorithm"]: # kraken doesn's need bam
pass
else:
raise ValueError(
"Could not process input file from sample configuration. \n" +
fastq1 + "\nIs the path to the file correct or is empty?\n" +
"If it is a fastq file (not pre-aligned BAM or CRAM), "
"is an aligner specified in the input configuration?")
if data.get("work_bam"):
# Add stable 'align_bam' target to use for retrieving raw alignment
data["align_bam"] = data["work_bam"]
data = _add_hla_files(data)
return [[data]]
def process_alignment(data, alt_input=None):
"""Do an alignment of fastq files, preparing a sorted BAM output file.
"""
data = utils.to_single_data(data)
fastq1, fastq2 = dd.get_input_sequence_files(data)
if alt_input:
fastq1, fastq2 = alt_input
config = data["config"]
aligner = config["algorithm"].get("aligner", None)
if fastq1 and objectstore.file_exists_or_remote(fastq1) and aligner:
logger.info("Aligning lane %s with %s aligner" %
(data["rgnames"]["lane"], aligner))
data = align_to_sort_bam(fastq1, fastq2, aligner, data)
data = _add_supplemental_bams(data)
elif fastq1 and objectstore.file_exists_or_remote(
fastq1) and fastq1.endswith(".bam"):
sort_method = config["algorithm"].get("bam_sort")
bamclean = config["algorithm"].get("bam_clean")
if bamclean is True or bamclean == "picard":
if sort_method and sort_method != "coordinate":
raise ValueError(
"Cannot specify `bam_clean: picard` with `bam_sort` other than coordinate: %s"
% sort_method)
out_bam = cleanbam.picard_prep(fastq1, data["rgnames"],
dd.get_ref_file(data), data["dirs"],
data)
elif bamclean == "fixrg":
out_bam = cleanbam.fixrg(fastq1, data["rgnames"],
dd.get_ref_file(data), data["dirs"], data)
elif sort_method:
runner = broad.runner_from_path("picard", config)
out_file = os.path.join(
data["dirs"]["work"], "{}-sort.bam".format(
os.path.splitext(os.path.basename(fastq1))[0]))
out_bam = runner.run_fn("picard_sort", fastq1, sort_method,
out_file)
else:
out_bam = link_bam_file(
fastq1,
os.path.join(data["dirs"]["work"], "prealign",
data["rgnames"]["sample"]))
bam.index(out_bam, data["config"])
bam.check_header(out_bam, data["rgnames"], dd.get_ref_file(data),
data["config"])
dedup_bam = postalign.dedup_bam(out_bam, data)
bam.index(dedup_bam, data["config"])
data["work_bam"] = dedup_bam
elif fastq1 and objectstore.file_exists_or_remote(
fastq1) and fastq1.endswith(".cram"):
data["work_bam"] = fastq1
elif fastq1 is None and "vrn_file" in data:
data["config"]["algorithm"]["variantcaller"] = False
data["work_bam"] = None
elif not fastq1:
raise ValueError("No 'files' specified for input sample: %s" %
dd.get_sample_name(data))
else:
raise ValueError(
"Could not process input file from sample configuration. \n" +
fastq1 + "\nIs the path to the file correct or is empty?\n" +
"If it is a fastq file (not pre-aligned BAM or CRAM), "
"is an aligner specified in the input configuration?")
if data.get("work_bam"):
# Add stable 'align_bam' target to use for retrieving raw alignment
data["align_bam"] = data["work_bam"]
data = _add_hla_files(data)
return [[data]]
