def count(data):
"""
count reads mapping to genes using featureCounts
http://subread.sourceforge.net
"""
in_bam = dd.get_work_bam(data) or dd.get_align_bam(data)
out_dir = os.path.join(dd.get_work_dir(data), "align",
dd.get_sample_name(data))
if dd.get_aligner(data) == "star":
out_dir = os.path.join(
out_dir,
"%s_%s" % (dd.get_sample_name(data), dd.get_aligner(data)))
sorted_bam = bam.sort(in_bam,
dd.get_config(data),
order="queryname",
out_dir=safe_makedir(out_dir))
gtf_file = dd.get_transcriptome_gtf(data, default=dd.get_gtf_file(data))
work_dir = dd.get_work_dir(data)
out_dir = os.path.join(work_dir, "htseq-count")
safe_makedir(out_dir)
count_file = os.path.join(out_dir, dd.get_sample_name(data)) + ".counts"
summary_file = os.path.join(out_dir,
dd.get_sample_name(data)) + ".counts.summary"
if file_exists(count_file) and _is_fixed_count_file(count_file):
return count_file
featureCounts = config_utils.get_program("featureCounts",
dd.get_config(data))
paired_flag = _paired_flag(in_bam)
strand_flag = _strand_flag(data)
filtered_bam = bam.filter_primary(sorted_bam, data)
cmd = ("{featureCounts} -a {gtf_file} -o {tx_count_file} -s {strand_flag} "
"{paired_flag} {filtered_bam}")
resources = config_utils.get_resources("featureCounts", data["config"])
if resources:
options = resources.get("options")
if options:
cmd += " %s" % " ".join([str(x) for x in options])
message = ("Count reads in {tx_count_file} mapping to {gtf_file} using "
"featureCounts")
with file_transaction(data, [count_file, summary_file]) as tx_files:
tx_count_file, tx_summary_file = tx_files
do.run(cmd.format(**locals()), message.format(**locals()))
fixed_count_file = _format_count_file(count_file, data)
fixed_summary_file = _change_sample_name(summary_file,
dd.get_sample_name(data),
data=data)
shutil.move(fixed_count_file, count_file)
shutil.move(fixed_summary_file, summary_file)
return count_file
def _variant_checkpoints(samples):
"""Check sample configuration to identify required steps in analysis.
"""
checkpoints = {}
checkpoints["vc"] = any([dd.get_variantcaller(d) for d in samples])
checkpoints["sv"] = any([dd.get_svcaller(d) for d in samples])
checkpoints["jointvc"] = any([(dd.get_jointcaller(d) or ("gvcf" in dd.get_tools_on(d))) and dd.get_batch(d)
for d in samples])
checkpoints["hla"] = any([dd.get_hlacaller(d) for d in samples])
checkpoints["align"] = any([(dd.get_aligner(d) or dd.get_bam_clean(d)) for d in samples])
checkpoints["align_split"] = not all([(dd.get_align_split_size(d) is False or
not dd.get_aligner(d))
for d in samples])
return checkpoints
def clean_chipseq_alignment(data):
aligner = dd.get_aligner(data)
data["align_bam"] = dd.get_work_bam(data)
if dd.get_mark_duplicates(data):
if aligner:
if aligner == "bowtie2":
filterer = bowtie2.filter_multimappers
elif aligner == "bwa":
filterer = bwa.filter_multimappers
else:
logger.error("ChIP-seq only supported for bowtie2 and bwa.")
sys.exit(-1)
unique_bam = filterer(dd.get_work_bam(data), data)
data["work_bam"] = unique_bam
else:
logger.info("Warning: When BAM file is given as input, bcbio skips multimappers removal."
"If BAM is not cleaned for peak calling, can result in downstream errors.")
# lcr_bed = utils.get_in(data, ("genome_resources", "variation", "lcr"))
data["work_bam"] = _keep_assembled_chrom(dd.get_work_bam(data), dd.get_ref_file(data),
data["config"])
encode_bed = tz.get_in(["genome_resources", "variation", "encode_blacklist"], data)
if encode_bed:
data["work_bam"] = _prepare_bam(dd.get_work_bam(data), encode_bed, data['config'])
bam.index(data["work_bam"], data['config'])
data["bigwig"] = _bam_coverage(dd.get_sample_name(data), dd.get_work_bam(data), data)
return [[data]]
def clean_chipseq_alignment(data):
aligner = dd.get_aligner(data)
data["align_bam"] = dd.get_work_bam(data)
if dd.get_mark_duplicates(data):
if aligner:
if aligner == "bowtie2":
filterer = bowtie2.filter_multimappers
elif aligner == "bwa":
filterer = bwa.filter_multimappers
else:
logger.error("ChIP-seq only supported for bowtie2 and bwa.")
sys.exit(-1)
unique_bam = filterer(dd.get_work_bam(data), data)
data["work_bam"] = unique_bam
else:
logger.info(
"Warning: When BAM file is given as input, bcbio skips multimappers removal."
"If BAM is not cleaned for peak calling, can result in downstream errors."
)
# lcr_bed = utils.get_in(data, ("genome_resources", "variation", "lcr"))
data["work_bam"] = _keep_assembled_chrom(dd.get_work_bam(data),
dd.get_ref_file(data),
data["config"])
encode_bed = tz.get_in(
["genome_resources", "variation", "encode_blacklist"], data)
if encode_bed:
data["work_bam"] = _prepare_bam(dd.get_work_bam(data), encode_bed,
data['config'])
bam.index(data["work_bam"], data['config'])
data["bigwig"] = _bam_coverage(dd.get_sample_name(data),
dd.get_work_bam(data), data)
return [[data]]
def _variant_checkpoints(samples):
"""Check sample configuration to identify required steps in analysis.
"""
checkpoints = {}
checkpoints["vc"] = any([dd.get_variantcaller(d) or d.get("vrn_file") for d in samples])
checkpoints["sv"] = any([dd.get_svcaller(d) for d in samples])
checkpoints["jointvc"] = any([(dd.get_jointcaller(d) or "gvcf" in dd.get_tools_on(d))
for d in samples])
checkpoints["hla"] = any([dd.get_hlacaller(d) for d in samples])
checkpoints["align"] = any([(dd.get_aligner(d) or dd.get_bam_clean(d)) for d in samples])
checkpoints["align_split"] = not all([(dd.get_align_split_size(d) is False or
not dd.get_aligner(d))
for d in samples])
checkpoints["umi"] = any([dd.get_umi_consensus(d) for d in samples])
checkpoints["ensemble"] = any([dd.get_ensemble(d) for d in samples])
checkpoints["cancer"] = any(dd.get_phenotype(d) in ["tumor"] for d in samples)
return checkpoints
def clean_chipseq_alignment(data):
aligner = dd.get_aligner(data)
assert aligner == "bowtie2", "ChIP-seq only supported for bowtie2."
if aligner == "bowtie2":
unique_bam = filter_multimappers(dd.get_work_bam(data), data)
data["work_bam"] = unique_bam
return [[data]]
return [[data]]
def _nosort_tobam_cmd(data):
"""Handle converting to BAM for queryname sorted inputs, correcting HD headers.
"""
if dd.get_aligner(data).startswith("bwa"):
fix_hd = "(echo '@HD VN:1.3 SO:queryname' && cat) | "
else:
fix_hd = "sed 's/SO:unsorted/SO:queryname/g' | "
return fix_hd + "{samtools} view -b - -o {out_file}"
def clean_chipseq_alignment(data):
aligner = dd.get_aligner(data)
assert aligner == "bowtie2", "ChIP-seq only supported for bowtie2."
if aligner == "bowtie2":
unique_bam = filter_multimappers(dd.get_work_bam(data), data)
data["work_bam"] = unique_bam
return [[data]]
return [[data]]
def _variant_checkpoints(samples):
"""Check sample configuration to identify required steps in analysis.
"""
checkpoints = {}
checkpoints["vc"] = any([dd.get_variantcaller(d) or d.get("vrn_file") for d in samples])
checkpoints["sv"] = any([dd.get_svcaller(d) for d in samples])
checkpoints["jointvc"] = any([(dd.get_jointcaller(d) or "gvcf" in dd.get_tools_on(d))
for d in samples])
checkpoints["hla"] = any([dd.get_hlacaller(d) for d in samples])
checkpoints["align"] = any([(dd.get_aligner(d) or dd.get_bam_clean(d)) for d in samples])
checkpoints["align_split"] = not all([(dd.get_align_split_size(d) is False or
not dd.get_aligner(d))
for d in samples])
checkpoints["archive"] = any([dd.get_archive(d) for d in samples])
checkpoints["umi"] = any([dd.get_umi_consensus(d) for d in samples])
checkpoints["ensemble"] = any([dd.get_ensemble(d) for d in samples])
checkpoints["cancer"] = any(dd.get_phenotype(d) in ["tumor"] for d in samples)
return checkpoints
def clean_chipseq_alignment(data):
aligner = dd.get_aligner(data)
data["raw_bam"] = dd.get_work_bam(data)
if aligner:
assert aligner == "bowtie2", "ChIP-seq only supported for bowtie2."
unique_bam = filter_multimappers(dd.get_work_bam(data), data)
data["work_bam"] = unique_bam
else:
logger.info("Warning: When BAM file is given as input, bcbio skips multimappers removal."
"If BAM is not cleaned for peak calling, can result in downstream errors.")
return [[data]]
def _setup_reference_files(data, tx_out_dir):
"""Create a reference directory with fasta and bwa indices.
GRIDSS requires all files in a single directory, so setup with symlinks.
"""
aligner = dd.get_aligner(data)
out_dir = utils.safe_makedir(os.path.join(tx_out_dir, aligner))
ref_fasta = dd.get_ref_file(data)
ref_files = ["%s%s" % (utils.splitext_plus(ref_fasta)[0], ext) for ext in [".fa", ".fa.fai", ".dict"]]
for orig_file in ref_files + tz.get_in(("reference", aligner, "indexes"), data):
utils.symlink_plus(orig_file, os.path.join(out_dir, os.path.basename(orig_file)))
return os.path.join(out_dir, os.path.basename(ref_fasta))
def count(data):
"""
count reads mapping to genes using featureCounts
http://subread.sourceforge.net
"""
in_bam = dd.get_work_bam(data) or dd.get_align_bam(data)
out_dir = os.path.join(dd.get_work_dir(data), "align", dd.get_sample_name(data))
if dd.get_aligner(data) == "star":
out_dir = os.path.join(out_dir, "%s_%s" % (dd.get_sample_name(data), dd.get_aligner(data)))
sorted_bam = bam.sort(in_bam, dd.get_config(data), order="queryname", out_dir=safe_makedir(out_dir))
gtf_file = dd.get_gtf_file(data)
work_dir = dd.get_work_dir(data)
out_dir = os.path.join(work_dir, "htseq-count")
safe_makedir(out_dir)
count_file = os.path.join(out_dir, dd.get_sample_name(data)) + ".counts"
summary_file = os.path.join(out_dir, dd.get_sample_name(data)) + ".counts.summary"
if file_exists(count_file) and _is_fixed_count_file(count_file):
return count_file
featureCounts = config_utils.get_program("featureCounts", dd.get_config(data))
paired_flag = _paired_flag(in_bam)
strand_flag = _strand_flag(data)
filtered_bam = bam.filter_primary(sorted_bam, data)
cmd = ("{featureCounts} -a {gtf_file} -o {tx_count_file} -s {strand_flag} "
"{paired_flag} {filtered_bam}")
message = ("Count reads in {tx_count_file} mapping to {gtf_file} using "
"featureCounts")
with file_transaction(data, [count_file, summary_file]) as tx_files:
tx_count_file, tx_summary_file = tx_files
do.run(cmd.format(**locals()), message.format(**locals()))
fixed_count_file = _format_count_file(count_file, data)
fixed_summary_file = _change_sample_name(
summary_file, dd.get_sample_name(data), data=data)
shutil.move(fixed_count_file, count_file)
shutil.move(fixed_summary_file, summary_file)
return count_file
def _get_bwa_mem_cmd(data,
out_file,
ref_file,
fastq1,
fastq2="",
with_hla=False):
"""Perform piped bwa mem mapping potentially with alternative alleles in GRCh38 + HLA typing.
Commands for HLA post-processing:
base=TEST
run-HLA $base.hla > $base.hla.top
cat $base.hla.HLA*.gt | grep ^GT | cut -f2- > $base.hla.all
rm -f $base.hla.HLA*gt
rm -f $base.hla.HLA*gz
"""
alt_file = ref_file + ".alt"
if with_hla:
bwakit_dir = os.path.dirname(
os.path.realpath(utils.which("run-bwamem")))
hla_base = os.path.join(
utils.safe_makedir(os.path.join(os.path.dirname(out_file), "hla")),
os.path.basename(out_file) + ".hla")
alt_cmd = (
" | {bwakit_dir}/k8 {bwakit_dir}/bwa-postalt.js -p {hla_base} {alt_file}"
)
else:
alt_cmd = ""
if dd.get_aligner(data) == "sentieon-bwa":
bwa_exe = "sentieon-bwa"
exports = sentieon.license_export(data)
else:
bwa_exe = "bwa"
exports = ""
bwa = config_utils.get_program(bwa_exe, data["config"])
num_cores = data["config"]["algorithm"].get("num_cores", 1)
bwa_resources = config_utils.get_resources("bwa", data["config"])
bwa_params = (" ".join([str(x) for x in bwa_resources.get("options", [])])
if "options" in bwa_resources else "")
rg_info = novoalign.get_rg_info(data["rgnames"])
# For UMI runs, pass along consensus tags
c_tags = "-C" if "umi_bam" in data else ""
pairing = "-p" if not fastq2 else ""
# Restrict seed occurances to 1/2 of default, manage memory usage for centromere repeats in hg38
# https://sourceforge.net/p/bio-bwa/mailman/message/31514937/
# http://ehc.ac/p/bio-bwa/mailman/message/32268544/
mem_usage = "-c 250"
bwa_cmd = (
"{exports}{bwa} mem {pairing} {c_tags} {mem_usage} -M -t {num_cores} {bwa_params} -R '{rg_info}' "
"-v 1 {ref_file} {fastq1} {fastq2} ")
return (bwa_cmd + alt_cmd).format(**locals())
def generate_transcript_counts(data):
"""Generate counts per transcript and per exon from an alignment"""
data["count_file"] = featureCounts.count(data)
if dd.get_fusion_mode(data, False):
oncofuse_file = oncofuse.run(data)
if oncofuse_file:
data["oncofuse_file"] = oncofuse.run(data)
if dd.get_dexseq_gff(data, None):
data = dd.set_dexseq_counts(data, dexseq.bcbio_run(data))
# if RSEM was run, stick the transcriptome BAM file into the datadict
if dd.get_aligner(data).lower() == "star" and dd.get_rsem(data):
base, ext = os.path.splitext(dd.get_work_bam(data))
data = dd.set_transcriptome_bam(data, base + ".transcriptome" + ext)
return [[data]]
def generate_transcript_counts(data):
"""Generate counts per transcript and per exon from an alignment"""
data["count_file"] = featureCounts.count(data)
if dd.get_fusion_mode(data, False):
oncofuse_file = oncofuse.run(data)
if oncofuse_file:
data["oncofuse_file"] = oncofuse.run(data)
if dd.get_dexseq_gff(data, None):
data = dd.set_dexseq_counts(data, dexseq.bcbio_run(data))
# if RSEM was run, stick the transcriptome BAM file into the datadict
if dd.get_aligner(data).lower() == "star" and dd.get_rsem(data):
base, ext = os.path.splitext(dd.get_work_bam(data))
data = dd.set_transcriptome_bam(data, base + ".transcriptome" + ext)
return [[data]]
def quantitate(data):
"""CWL target for quantitation.
XXX Needs to be split and parallelized by expression caller, with merging
of multiple calls.
"""
data = to_single_data(to_single_data(data))
data = generate_transcript_counts(data)[0][0]
data["quant"] = {}
if "sailfish" in dd.get_expression_caller(data):
data = to_single_data(sailfish.run_sailfish(data)[0])
data["quant"]["tsv"] = data["sailfish"]
data["quant"]["hdf5"] = os.path.join(os.path.dirname(data["sailfish"]),
"abundance.h5")
if ("kallisto" in dd.get_expression_caller(data)
or "pizzly" in dd.get_fusion_caller(data, [])):
data = to_single_data(kallisto.run_kallisto_rnaseq(data)[0])
data["quant"]["tsv"] = os.path.join(data["kallisto_quant"],
"abundance.tsv")
data["quant"]["hdf5"] = os.path.join(data["kallisto_quant"],
"abundance.h5")
if (os.path.exists(os.path.join(data["kallisto_quant"], "fusion.txt"))):
data["quant"]["fusion"] = os.path.join(data["kallisto_quant"],
"fusion.txt")
else:
data["quant"]["fusion"] = None
if "salmon" in dd.get_expression_caller(data):
if dd.get_quantify_genome_alignments(data):
if dd.get_aligner(data).lower() != "star":
if dd.get_genome_build(data) == "hg38":
logger.warning(
"Whole genome alignment-based Salmon quantification is "
"only supported for the STAR aligner. Since this is hg38 we will fall "
"back to the decoy method")
data = to_single_data(salmon.run_salmon_decoy(data)[0])
else:
logger.warning(
"Whole genome alignment-based Salmon quantification is "
"only supported for the STAR aligner. Falling back to the "
"transcriptome-only method.")
data = to_single_data(salmon.run_salmon_reads(data)[0])
else:
data = to_single_data(salmon.run_salmon_bam(data)[0])
else:
data = to_single_data(salmon.run_salmon_reads(data)[0])
data["quant"]["tsv"] = data["salmon"]
data["quant"]["hdf5"] = os.path.join(os.path.dirname(data["salmon"]),
"abundance.h5")
return [[data]]
def run_count(bam_file, dexseq_gff, stranded, out_file, data):
"""
run dexseq_count on a BAM file
"""
assert file_exists(bam_file), "%s does not exist." % bam_file
sort_order = bam._get_sort_order(bam_file, {})
assert sort_order, "Cannot determine sort order of %s." % bam_file
strand_flag = _strand_flag(stranded)
assert strand_flag, "%s is not a valid strandedness value." % stranded
if not dexseq_gff:
logger.info(
"No DEXSeq GFF file was found, skipping exon-level counting.")
return None
elif not file_exists(dexseq_gff):
logger.info("%s was not found, so exon-level counting is being "
"skipped." % dexseq_gff)
return None
dexseq_count = _dexseq_count_path()
if not dexseq_count:
logger.info("DEXseq is not installed, skipping exon-level counting.")
return None
if dd.get_aligner(data) == "bwa":
logger.info(
"Can't use DEXSeq with bwa alignments, skipping exon-level counting."
)
return None
sort_flag = "name" if sort_order == "queryname" else "pos"
is_paired = bam.is_paired(bam_file)
paired_flag = "yes" if is_paired else "no"
anaconda = os.path.dirname(os.path.realpath(sys.executable))
r36_python = os.path.join(anaconda, "..", "envs", "r36", "bin", "python")
if file_exists(out_file):
return out_file
cmd = (
"{r36_python} {dexseq_count} -f bam -r {sort_flag} -p {paired_flag} "
"-s {strand_flag} {dexseq_gff} {bam_file} {tx_out_file}")
message = "Counting exon-level counts with %s and %s." % (bam_file,
dexseq_gff)
with file_transaction(data, out_file) as tx_out_file:
do.run(cmd.format(**locals()), message)
return out_file
def remove_multimappers(bam_file, data):
aligner = dd.get_aligner(data)
if aligner:
if aligner == "bowtie2":
filterer = bowtie2.filter_multimappers
elif aligner == "bwa":
filterer = bwa.filter_multimappers
else:
logger.error("ChIP-seq only supported for bowtie2 and bwa.")
sys.exit(-1)
unique_bam = filterer(bam_file, data)
else:
unique_bam = bam_file
logger.warn(
"When a BAM file is given as input, bcbio skips removal of "
"multimappers.")
return unique_bam
def _setup_reference_files(data, tx_out_dir):
"""Create a reference directory with fasta and bwa indices.
GRIDSS requires all files in a single directory, so setup with symlinks.
"""
aligner = dd.get_aligner(data)
out_dir = utils.safe_makedir(os.path.join(tx_out_dir, aligner))
ref_fasta = dd.get_ref_file(data)
ref_files = [
"%s%s" % (utils.splitext_plus(ref_fasta)[0], ext)
for ext in [".fa", ".fa.fai", ".dict"]
]
for orig_file in ref_files + tz.get_in(
("reference", aligner, "indexes"), data):
utils.symlink_plus(orig_file,
os.path.join(out_dir, os.path.basename(orig_file)))
return os.path.join(out_dir, os.path.basename(ref_fasta))
def _check_dedup(data):
"""Check configuration for de-duplication.
Defaults to no de-duplication for RNA-seq and small RNA, the
back compatible default. Allow overwriting with explicit
`mark_duplicates: true` setting.
Also defaults to false for no alignment inputs.
"""
if dd.get_analysis(data).lower() in ["rna-seq", "smallrna-seq"] or not dd.get_aligner(data):
dup_param = utils.get_in(data, ("config", "algorithm", "mark_duplicates"), False)
else:
dup_param = utils.get_in(data, ("config", "algorithm", "mark_duplicates"), True)
if dup_param and isinstance(dup_param, six.string_types):
logger.info("Warning: bcbio no longer support explicit setting of mark_duplicate algorithm. "
"Using best-practice choice based on input data.")
dup_param = True
return dup_param
def quantitate_expression_parallel(samples, run_parallel):
"""
quantitate expression, all programs run here should be multithreaded to
take advantage of the threaded run_parallel environment
"""
data = samples[0][0]
to_index = determine_indexes_to_make(samples)
samples = run_parallel("generate_transcript_counts", samples)
if "cufflinks" in dd.get_expression_caller(data):
samples = run_parallel("run_cufflinks", samples)
if "stringtie" in dd.get_expression_caller(data):
samples = run_parallel("run_stringtie_expression", samples)
if ("kallisto" in dd.get_expression_caller(data)
or dd.get_fusion_mode(data)
or "pizzly" in dd.get_fusion_caller(data, [])):
run_parallel("run_kallisto_index", [to_index])
samples = run_parallel("run_kallisto_rnaseq", samples)
if "sailfish" in dd.get_expression_caller(data):
run_parallel("run_sailfish_index", [to_index])
samples = run_parallel("run_sailfish", samples)
# always run salmon
run_parallel("run_salmon_index", [to_index])
if dd.get_quantify_genome_alignments(data):
if dd.get_aligner(data).lower() != "star":
if dd.get_genome_build(data) == "hg38":
logger.warning(
"Whole genome alignment-based Salmon quantification is "
"only supported for the STAR aligner. Since this is hg38 we will fall "
"back to the decoy method")
samples = run_parallel("run_salmon_decoy", samples)
else:
logger.warning(
"Whole genome alignment-based Salmon quantification is "
"only supported for the STAR aligner. Falling back to the "
"transcriptome-only method.")
samples = run_parallel("run_salmon_reads", samples)
else:
samples = run_parallel("run_salmon_bam", samples)
else:
samples = run_parallel("run_salmon_reads", samples)
samples = run_parallel("detect_fusions", samples)
return samples
def run(data):
if not aligner_supports_fusion(data):
aligner = dd.get_aligner(data)
logger.warning("Oncofuse is not supported for the %s aligner, "
"skipping. " % aligner)
return None
config = data["config"]
genome_build = data.get("genome_build", "")
input_type, input_dir, input_file = _get_input_para(data)
if genome_build == "GRCh37": # assume genome_build is hg19 otherwise
if config["algorithm"].get("aligner") in ["star"]:
if file_exists(input_file):
input_file = _fix_star_junction_output(input_file)
if config["algorithm"].get("aligner") in ["tophat", "tophat2"]:
if file_exists(input_file):
input_file = _fix_tophat_junction_output(input_file)
elif "hg19" not in genome_build:
return None
#handle cases when fusion file doesn't exist
if not file_exists(input_file):
return None
out_file = os.path.join(input_dir, "oncofuse_out.txt")
if file_exists(out_file):
return out_file
oncofuse = config_utils.get_program("oncofuse", config)
tissue_type = _oncofuse_tissue_arg_from_config(data)
resources = config_utils.get_resources("oncofuse", config)
if not file_exists(out_file):
cl = [oncofuse]
cl += resources.get("jvm_opts", ["-Xms750m", "-Xmx5g"])
with file_transaction(data, out_file) as tx_out_file:
cl += [input_file, input_type, tissue_type, tx_out_file]
cmd = " ".join(cl)
try:
do.run(cmd, "oncofuse fusion detection", data)
except:
do.run(
"touch %s && echo '# failed' >> %s" %
(tx_out_file, tx_out_file), "oncofuse failed", data)
#return out_file
return out_file
def run(data):
if not aligner_supports_fusion(data):
aligner = dd.get_aligner(data)
logger.warning("Oncofuse is not supported for the %s aligner, "
"skipping. " % aligner)
return None
config = data["config"]
genome_build = data.get("genome_build", "")
input_type, input_dir, input_file = _get_input_para(data)
if genome_build == "GRCh37": # assume genome_build is hg19 otherwise
if config["algorithm"].get("aligner") in ["star"]:
if file_exists(input_file):
input_file = _fix_star_junction_output(input_file)
if config["algorithm"].get("aligner") in ["tophat", "tophat2"]:
if file_exists(input_file):
input_file = _fix_tophat_junction_output(input_file)
elif "hg19" not in genome_build:
return None
#handle cases when fusion file doesn't exist
if not file_exists(input_file):
return None
out_file = os.path.join(input_dir, "oncofuse_out.txt")
if file_exists(out_file):
return out_file
oncofuse = config_utils.get_program("oncofuse", config)
tissue_type = _oncofuse_tissue_arg_from_config(data)
resources = config_utils.get_resources("oncofuse", config)
if not file_exists(out_file):
cl = [oncofuse]
cl += resources.get("jvm_opts", ["-Xms750m", "-Xmx5g"])
with file_transaction(data, out_file) as tx_out_file:
cl += [input_file, input_type, tissue_type, tx_out_file]
cmd = " ".join(cl)
try:
do.run(cmd, "oncofuse fusion detection", data)
except:
do.run("touch %s && echo '# failed' >> %s" % (tx_out_file, tx_out_file), "oncofuse failed", data)
#return out_file
return out_file
def _get_bwa_mem_cmd(data, out_file, ref_file, fastq1, fastq2=""):
"""Perform piped bwa mem mapping potentially with alternative alleles in GRCh38 + HLA typing.
Commands for HLA post-processing:
base=TEST
run-HLA $base.hla > $base.hla.top
cat $base.hla.HLA*.gt | grep ^GT | cut -f2- > $base.hla.all
rm -f $base.hla.HLA*gt
rm -f $base.hla.HLA*gz
"""
alt_file = ref_file + ".alt"
if utils.file_exists(alt_file) and dd.get_hlacaller(data):
bwakit_dir = os.path.dirname(os.path.realpath(utils.which("run-bwamem")))
hla_base = os.path.join(utils.safe_makedir(os.path.join(os.path.dirname(out_file), "hla")),
os.path.basename(out_file) + ".hla")
alt_cmd = (" | {bwakit_dir}/k8 {bwakit_dir}/bwa-postalt.js -p {hla_base} {alt_file}")
else:
alt_cmd = ""
if dd.get_aligner(data) == "sentieon-bwa":
bwa_exe = "sentieon-bwa"
exports = sentieon.license_export(data)
else:
bwa_exe = "bwa"
exports = ""
bwa = config_utils.get_program(bwa_exe, data["config"])
num_cores = data["config"]["algorithm"].get("num_cores", 1)
bwa_resources = config_utils.get_resources("bwa", data["config"])
bwa_params = (" ".join([str(x) for x in bwa_resources.get("options", [])])
if "options" in bwa_resources else "")
rg_info = novoalign.get_rg_info(data["rgnames"])
# For UMI runs, pass along consensus tags
c_tags = "-C" if "umi_bam" in data else ""
pairing = "-p" if not fastq2 else ""
# Restrict seed occurances to 1/2 of default, manage memory usage for centromere repeats in hg38
# https://sourceforge.net/p/bio-bwa/mailman/message/31514937/
# http://ehc.ac/p/bio-bwa/mailman/message/32268544/
mem_usage = "-c 250"
bwa_cmd = ("{exports}{bwa} mem {pairing} {c_tags} {mem_usage} -M -t {num_cores} {bwa_params} -R '{rg_info}' "
"-v 1 {ref_file} {fastq1} {fastq2} ")
return (bwa_cmd + alt_cmd).format(**locals())
def get_maxcov_downsample_cl(data, in_pipe=None):
"""Retrieve command line for max coverage downsampling, fitting into bamsormadup output.
"""
max_cov = _get_maxcov_downsample(data) if dd.get_aligner(data) not in ["snap"] else None
if max_cov:
if in_pipe == "bamsormadup":
prefix = "level=0"
elif in_pipe == "samtools":
prefix = "-l 0"
else:
prefix = ""
# Swap over to multiple cores until after testing
#core_arg = "-t %s" % dd.get_num_cores(data)
core_arg = ""
return ("%s | variant - -b %s --mark-as-qc-fail --max-coverage %s"
% (prefix, core_arg, max_cov))
else:
if in_pipe == "bamsormadup":
prefix = "indexfilename={tx_out_file}.bai"
else:
prefix = ""
return prefix
def run_count(bam_file, dexseq_gff, stranded, out_file, data):
"""
run dexseq_count on a BAM file
"""
assert file_exists(bam_file), "%s does not exist." % bam_file
sort_order = bam._get_sort_order(bam_file, {})
assert sort_order, "Cannot determine sort order of %s." % bam_file
strand_flag = _strand_flag(stranded)
assert strand_flag, "%s is not a valid strandedness value." % stranded
if not dexseq_gff:
logger.info("No DEXSeq GFF file was found, skipping exon-level counting.")
return None
elif not file_exists(dexseq_gff):
logger.info("%s was not found, so exon-level counting is being "
"skipped." % dexseq_gff)
return None
dexseq_count = _dexseq_count_path()
if not dexseq_count:
logger.info("DEXseq is not installed, skipping exon-level counting.")
return None
if dd.get_aligner(data) == "bwa":
logger.info("Can't use DEXSeq with bwa alignments, skipping exon-level counting.")
return None
sort_flag = "name" if sort_order == "queryname" else "pos"
is_paired = bam.is_paired(bam_file)
paired_flag = "yes" if is_paired else "no"
bcbio_python = sys.executable
if file_exists(out_file):
return out_file
cmd = ("{bcbio_python} {dexseq_count} -f bam -r {sort_flag} -p {paired_flag} "
"-s {strand_flag} {dexseq_gff} {bam_file} {tx_out_file}")
message = "Counting exon-level counts with %s and %s." % (bam_file, dexseq_gff)
with file_transaction(data, out_file) as tx_out_file:
do.run(cmd.format(**locals()), message)
return out_file
def get_maxcov_downsample_cl(data, in_pipe=None):
"""Retrieve command line for max coverage downsampling, fitting into bamsormadup output.
"""
max_cov = _get_maxcov_downsample(data) if dd.get_aligner(data) not in ["snap"] else None
if max_cov:
if in_pipe == "bamsormadup":
prefix = "level=0"
elif in_pipe == "samtools":
prefix = "-l 0"
else:
prefix = ""
# Swap over to multiple cores until after testing
#core_arg = "-t %s" % dd.get_num_cores(data)
core_arg = ""
return ("%s | variant - -b %s --mark-as-qc-fail --max-coverage %s"
% (prefix, core_arg, max_cov))
else:
if in_pipe == "bamsormadup":
prefix = "indexfilename={tx_out_file}.bai"
else:
prefix = ""
return prefix
def aligner_supports_fusion(data):
SUPPORTED_ALIGNERS = ["tophat2", "tophat", "star"]
aligner = dd.get_aligner(data)
return aligner and aligner.lower() in SUPPORTED_ALIGNERS
def _run_vardict_paired(align_bams,
items,
ref_file,
assoc_files,
region=None,
out_file=None):
"""Detect variants with Vardict.
This is used for paired tumor / normal samples.
"""
config = items[0]["config"]
if out_file is None:
out_file = "%s-paired-variants.vcf.gz" % os.path.splitext(
align_bams[0])[0]
if not utils.file_exists(out_file):
with file_transaction(items[0], out_file) as tx_out_file:
target = shared.subset_variant_regions(dd.get_variant_regions(
items[0]),
region,
out_file,
do_merge=True)
paired = vcfutils.get_paired_bams(align_bams, items)
if not _is_bed_file(target):
vcfutils.write_empty_vcf(
tx_out_file,
config,
samples=[
x for x in [paired.tumor_name, paired.normal_name] if x
])
else:
if not paired.normal_bam:
ann_file = _run_vardict_caller(align_bams, items, ref_file,
assoc_files, region,
out_file)
return ann_file
vardict = get_vardict_command(items[0])
vcfstreamsort = config_utils.get_program(
"vcfstreamsort", config)
strandbias = "testsomatic.R"
var2vcf = "var2vcf_paired.pl"
compress_cmd = "| bgzip -c" if out_file.endswith("gz") else ""
freq = float(
utils.get_in(config, ("algorithm", "min_allele_fraction"),
10)) / 100.0
# merge bed file regions as amplicon VarDict is only supported in single sample mode
opts = " ".join(
_vardict_options_from_config(items, config, out_file,
target))
coverage_interval = utils.get_in(
config, ("algorithm", "coverage_interval"), "exome")
# for deep targeted panels, require 50 worth of coverage
var2vcf_opts = " -v 50 " if highdepth.get_median_coverage(
items[0]) > 5000 else ""
fix_ambig_ref = vcfutils.fix_ambiguous_cl()
fix_ambig_alt = vcfutils.fix_ambiguous_cl(5)
remove_dup = vcfutils.remove_dup_cl()
if any("vardict_somatic_filter" in tz.get_in((
"config", "algorithm", "tools_off"), data, [])
for data in items):
somatic_filter = ""
freq_filter = ""
else:
var2vcf_opts += " -M " # this makes VarDict soft filter non-differential variants
somatic_filter = (
"| sed 's/\\\\.*Somatic\\\\/Somatic/' "
"| sed 's/REJECT,Description=\".*\">/REJECT,Description=\"Not Somatic via VarDict\">/' "
"| %s -x 'bcbio.variation.freebayes.call_somatic(x)'" %
os.path.join(os.path.dirname(sys.executable), "py"))
freq_filter = (
"| bcftools filter -m '+' -s 'REJECT' -e 'STATUS !~ \".*Somatic\"' 2> /dev/null "
"| %s -x 'bcbio.variation.vardict.depth_freq_filter(x, %s, \"%s\")'"
% (os.path.join(os.path.dirname(sys.executable), "py"),
0, dd.get_aligner(paired.tumor_data)))
jvm_opts = _get_jvm_opts(items[0], tx_out_file)
r_setup = "unset R_HOME && export PATH=%s:$PATH && " % os.path.dirname(
utils.Rscript_cmd())
cmd = (
"{r_setup}{jvm_opts}{vardict} -G {ref_file} -f {freq} "
"-N {paired.tumor_name} -b \"{paired.tumor_bam}|{paired.normal_bam}\" {opts} "
"| {strandbias} "
"| {var2vcf} -P 0.9 -m 4.25 -f {freq} {var2vcf_opts} "
"-N \"{paired.tumor_name}|{paired.normal_name}\" "
"{freq_filter} "
"{somatic_filter} | {fix_ambig_ref} | {fix_ambig_alt} | {remove_dup} | {vcfstreamsort} "
"{compress_cmd} > {tx_out_file}")
do.run(cmd.format(**locals()),
"Genotyping with VarDict: Inference", {})
out_file = (annotation.add_dbsnp(out_file, assoc_files["dbsnp"], config)
if assoc_files.get("dbsnp") else out_file)
return out_file
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_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)
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 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 _skip_duplicates(data):
return (dd.get_coverage_interval(data) == "amplicon"
or (dd.get_aligner(data) and not dd.get_mark_duplicates(data)))
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 _run_vardict_paired(align_bams, items, ref_file, assoc_files,
region=None, out_file=None):
"""Detect variants with Vardict.
This is used for paired tumor / normal samples.
"""
config = items[0]["config"]
if out_file is None:
out_file = "%s-paired-variants.vcf.gz" % os.path.splitext(align_bams[0])[0]
if not utils.file_exists(out_file):
with file_transaction(items[0], out_file) as tx_out_file:
target = shared.subset_variant_regions(dd.get_variant_regions(items[0]), region,
out_file, do_merge=True)
paired = vcfutils.get_paired_bams(align_bams, items)
if not _is_bed_file(target):
vcfutils.write_empty_vcf(tx_out_file, config,
samples=[x for x in [paired.tumor_name, paired.normal_name] if x])
else:
if not paired.normal_bam:
ann_file = _run_vardict_caller(align_bams, items, ref_file,
assoc_files, region, out_file)
return ann_file
vcffilter = config_utils.get_program("vcffilter", config)
vardict = get_vardict_command(items[0])
vcfstreamsort = config_utils.get_program("vcfstreamsort", config)
strandbias = "testsomatic.R"
var2vcf = "var2vcf_paired.pl"
compress_cmd = "| bgzip -c" if out_file.endswith("gz") else ""
freq = float(utils.get_in(config, ("algorithm", "min_allele_fraction"), 10)) / 100.0
# merge bed file regions as amplicon VarDict is only supported in single sample mode
opts = " ".join(_vardict_options_from_config(items, config, out_file, target))
coverage_interval = utils.get_in(config, ("algorithm", "coverage_interval"), "exome")
# for deep targeted panels, require 50 worth of coverage
var2vcf_opts = " -v 50 " if highdepth.get_median_coverage(items[0]) > 5000 else ""
fix_ambig = vcfutils.fix_ambiguous_cl()
remove_dup = vcfutils.remove_dup_cl()
if any("vardict_somatic_filter" in tz.get_in(("config", "algorithm", "tools_off"), data, [])
for data in items):
somatic_filter = ""
freq_filter = ""
else:
var2vcf_opts += " -M " # this makes VarDict soft filter non-differential variants
somatic_filter = ("| sed 's/\\\\.*Somatic\\\\/Somatic/' "
"| sed 's/REJECT,Description=\".*\">/REJECT,Description=\"Not Somatic via VarDict\">/' "
"| %s -x 'bcbio.variation.freebayes.call_somatic(x)'" %
os.path.join(os.path.dirname(sys.executable), "py"))
freq_filter = ("| bcftools filter -m '+' -s 'REJECT' -e 'STATUS !~ \".*Somatic\"' 2> /dev/null "
"| %s -x 'bcbio.variation.vardict.depth_freq_filter(x, %s, \"%s\")'" %
(os.path.join(os.path.dirname(sys.executable), "py"),
0, dd.get_aligner(paired.tumor_data)))
jvm_opts = _get_jvm_opts(items[0], tx_out_file)
r_setup = "unset R_HOME && export PATH=%s:$PATH && " % os.path.dirname(utils.Rscript_cmd())
cmd = ("{r_setup}{jvm_opts}{vardict} -G {ref_file} -f {freq} "
"-N {paired.tumor_name} -b \"{paired.tumor_bam}|{paired.normal_bam}\" {opts} "
"| {strandbias} "
"| {var2vcf} -P 0.9 -m 4.25 -f {freq} {var2vcf_opts} "
"-N \"{paired.tumor_name}|{paired.normal_name}\" "
"{freq_filter} "
"{somatic_filter} | {fix_ambig} | {remove_dup} | {vcfstreamsort} "
"{compress_cmd} > {tx_out_file}")
do.run(cmd.format(**locals()), "Genotyping with VarDict: Inference", {})
out_file = (annotation.add_dbsnp(out_file, assoc_files["dbsnp"], config)
if assoc_files.get("dbsnp") else out_file)
return out_file
def _run_vardict_paired(align_bams,
items,
ref_file,
assoc_files,
region=None,
out_file=None):
"""Detect variants with Vardict.
This is used for paired tumor / normal samples.
"""
config = items[0]["config"]
if out_file is None:
out_file = "%s-paired-variants.vcf.gz" % os.path.splitext(
align_bams[0])[0]
if not utils.file_exists(out_file):
with file_transaction(items[0], out_file) as tx_out_file:
vrs = bedutils.population_variant_regions(items)
target = shared.subset_variant_regions(vrs,
region,
out_file,
items=items,
do_merge=True)
paired = vcfutils.get_paired_bams(align_bams, items)
if not _is_bed_file(target):
vcfutils.write_empty_vcf(
tx_out_file,
config,
samples=[
x for x in [paired.tumor_name, paired.normal_name] if x
])
else:
if not paired.normal_bam:
ann_file = _run_vardict_caller(align_bams, items, ref_file,
assoc_files, region,
out_file)
return ann_file
vardict = get_vardict_command(items[0])
vcfstreamsort = config_utils.get_program(
"vcfstreamsort", config)
compress_cmd = "| bgzip -c" if out_file.endswith("gz") else ""
freq = float(
utils.get_in(config, ("algorithm", "min_allele_fraction"),
10)) / 100.0
# merge bed file regions as amplicon VarDict is only supported in single sample mode
opts, var2vcf_opts = _vardict_options_from_config(
items, config, out_file, target)
fix_ambig_ref = vcfutils.fix_ambiguous_cl()
fix_ambig_alt = vcfutils.fix_ambiguous_cl(5)
remove_dup = vcfutils.remove_dup_cl()
if any("vardict_somatic_filter" in tz.get_in((
"config", "algorithm", "tools_off"), data, [])
for data in items):
somatic_filter = ""
freq_filter = ""
else:
var2vcf_opts += " -M " # this makes VarDict soft filter non-differential variants
somatic_filter = (
"| sed 's/\\\\.*Somatic\\\\/Somatic/' "
"| sed 's/REJECT,Description=\".*\">/REJECT,Description=\"Not Somatic via VarDict\">/' "
"""| %s -c 'from bcbio.variation import freebayes; """
"""freebayes.call_somatic("%s", "%s")' """ %
(sys.executable, paired.tumor_name,
paired.normal_name))
freq_filter = (
"| bcftools filter -m '+' -s 'REJECT' -e 'STATUS !~ \".*Somatic\"' 2> /dev/null "
"| %s -x 'bcbio.variation.vardict.add_db_germline_flag(x)' "
"| %s "
"| %s -x 'bcbio.variation.vardict.depth_freq_filter(x, %s, \"%s\")'"
% (os.path.join(os.path.dirname(sys.executable),
"py"), _lowfreq_linear_filter(0, True),
os.path.join(os.path.dirname(sys.executable), "py"),
0, dd.get_aligner(paired.tumor_data)))
jvm_opts = _get_jvm_opts(items[0], tx_out_file)
py_cl = os.path.join(utils.get_bcbio_bin(), "py")
setup = ("%s && unset JAVA_HOME &&" % utils.get_R_exports())
contig_cl = vcfutils.add_contig_to_header_cl(
ref_file, tx_out_file)
cmd = (
"{setup}{jvm_opts}{vardict} -G {ref_file} -f {freq} "
"-N {paired.tumor_name} -b \"{paired.tumor_bam}|{paired.normal_bam}\" {opts} "
"| awk 'NF>=48' | testsomatic.R "
"| var2vcf_paired.pl -P 0.9 -m 4.25 -f {freq} {var2vcf_opts} "
"-N \"{paired.tumor_name}|{paired.normal_name}\" "
"| {contig_cl} {freq_filter} "
"| bcftools filter -i 'QUAL >= 0' "
"{somatic_filter} | {fix_ambig_ref} | {fix_ambig_alt} | {remove_dup} | {vcfstreamsort} "
"{compress_cmd} > {tx_out_file}")
do.run(cmd.format(**locals()),
"Genotyping with VarDict: Inference", {})
return out_file
def aligner_supports_fusion(data):
SUPPORTED_ALIGNERS = ["tophat2", "tophat", "star"]
aligner = dd.get_aligner(data).lower()
return aligner in SUPPORTED_ALIGNERS
def _skip_duplicates(data):
return (dd.get_coverage_interval(data) == "amplicon" or
(dd.get_aligner(data) and not dd.get_mark_duplicates(data)))
def calling(data):
if dd.get_aligner(data) == "bismark":
data = _bismark_calling(data)
if dd.get_aligner(data) == "bsmap":
data = _bsmap_calling(data)
return [[data]]
