def rnaseq_vardict_variant_calling(data):
sample = dd.get_sample_name(data)
variation_dir = os.path.join(dd.get_work_dir(data), "variation")
safe_makedir(variation_dir)
out_file = os.path.join(variation_dir, sample + "-vardict.vcf.gz")
if file_exists(out_file):
data = dd.set_vrn_file(data, out_file)
return data
vardict_cmd = vardict.get_vardict_command(data)
strandbias = "teststrandbias.R"
var2vcf = "var2vcf_valid.pl"
vcfstreamsort = config_utils.get_program("vcfstreamsort", data)
compress_cmd = "| bgzip -c"
freq = float(dd.get_min_allele_fraction(data, 20) / 100.0)
var2vcf_opts = "-v 50"
fix_ambig = vcfutils.fix_ambiguous_cl()
remove_dup = vcfutils.remove_dup_cl()
r_setup = ("unset R_HOME && export PATH=%s:$PATH && "
% os.path.dirname(Rscript_cmd()))
ref_file = dd.get_ref_file(data)
bamfile = dd.get_work_bam(data)
bed_file = gtf.gtf_to_bed(dd.get_gtf_file(data))
opts = " -c 1 -S 2 -E 3 -g 4 "
with file_transaction(out_file) as tx_out_file:
jvm_opts = vardict._get_jvm_opts(data, tx_out_file)
cmd = ("{r_setup}{jvm_opts}{vardict_cmd} -G {ref_file} -f {freq} "
"-N {sample} -b {bamfile} {opts} {bed_file} "
"| {strandbias}"
"| {var2vcf} -N {sample} -E -f {freq} {var2vcf_opts} "
"| {fix_ambig} | {remove_dup} | {vcfstreamsort} {compress_cmd} "
"> {tx_out_file}")
message = "Calling RNA-seq variants with VarDict"
do.run(cmd.format(**locals()), message)
data = dd.set_vrn_file(data, out_file)
return data
def rnaseq_vardict_variant_calling(data):
sample = dd.get_sample_name(data)
out_dir = utils.safe_makedir(
os.path.join(dd.get_work_dir(data), "variation", "rnaseq", "vardict"))
out_file = os.path.join(out_dir, sample + "-vardict.vcf.gz")
if file_exists(out_file):
data = dd.set_vrn_file(data, out_file)
return data
vardict_cmd = vardict.get_vardict_command(data)
strandbias = "teststrandbias.R"
var2vcf = "var2vcf_valid.pl"
vcfstreamsort = config_utils.get_program("vcfstreamsort", data)
compress_cmd = "| bgzip -c"
freq = float(dd.get_min_allele_fraction(data, 20) / 100.0)
var2vcf_opts = "-v 50"
fix_ambig = vcfutils.fix_ambiguous_cl()
remove_dup = vcfutils.remove_dup_cl()
r_setup = get_R_exports()
ref_file = dd.get_ref_file(data)
bamfile = dd.get_work_bam(data)
data = _setup_variant_regions(data, out_dir)
opts, _ = vardict._vardict_options_from_config(
[data],
data["config"],
out_file,
dd.get_variant_regions(data),
is_rnaseq=True)
cores = dd.get_num_cores(data)
if cores and cores > 1:
opts += " -th %s" % str(cores)
with file_transaction(data, out_file) as tx_out_file:
jvm_opts = vardict._get_jvm_opts(data, tx_out_file)
cmd = ("{r_setup} && {jvm_opts}{vardict_cmd} -G {ref_file} -f {freq} "
"-N {sample} -b {bamfile} {opts} "
"| {strandbias}"
"| {var2vcf} -N {sample} -E -f {freq} {var2vcf_opts} "
"| {fix_ambig} | {remove_dup} | {vcfstreamsort} {compress_cmd} "
"> {tx_out_file}")
message = "Calling RNA-seq variants with VarDict"
do.run(cmd.format(**locals()), message)
out_file = vcfutils.bgzip_and_index(out_file, data["config"])
data = dd.set_vrn_file(data, out_file)
return data
def rnaseq_vardict_variant_calling(data):
sample = dd.get_sample_name(data)
out_dir = utils.safe_makedir(os.path.join(dd.get_work_dir(data),
"variation", "rnaseq", "vardict"))
out_file = os.path.join(out_dir, sample + "-vardict.vcf.gz")
if file_exists(out_file):
data = dd.set_vrn_file(data, out_file)
return data
vardict_cmd = vardict.get_vardict_command(data)
strandbias = "teststrandbias.R"
var2vcf = "var2vcf_valid.pl"
vcfstreamsort = config_utils.get_program("vcfstreamsort", data)
compress_cmd = "| bgzip -c"
freq = float(dd.get_min_allele_fraction(data, 20) / 100.0)
var2vcf_opts = "-v 50"
fix_ambig = vcfutils.fix_ambiguous_cl()
remove_dup = vcfutils.remove_dup_cl()
r_setup = get_R_exports()
ref_file = dd.get_ref_file(data)
bamfile = dd.get_work_bam(data)
data = _setup_variant_regions(data, out_dir)
opts, _ = vardict._vardict_options_from_config([data], data["config"], out_file, dd.get_variant_regions(data),
is_rnaseq=True)
cores = dd.get_num_cores(data)
if cores and cores > 1:
opts += " -th %s" % str(cores)
with file_transaction(data, out_file) as tx_out_file:
jvm_opts = vardict._get_jvm_opts(data, tx_out_file)
cmd = ("{r_setup} && {jvm_opts}{vardict_cmd} -G {ref_file} -f {freq} "
"-N {sample} -b {bamfile} {opts} "
"| {strandbias}"
"| {var2vcf} -N {sample} -E -f {freq} {var2vcf_opts} "
"| {fix_ambig} | {remove_dup} | {vcfstreamsort} {compress_cmd} "
"> {tx_out_file}")
message = "Calling RNA-seq variants with VarDict"
do.run(cmd.format(**locals()), message)
out_file = vcfutils.bgzip_and_index(out_file, data["config"])
data = dd.set_vrn_file(data, out_file)
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
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 _run_vardict_caller(align_bams,
items,
ref_file,
assoc_files,
region=None,
out_file=None):
"""Detect SNPs and indels with VarDict.
"""
config = items[0]["config"]
if out_file is None:
out_file = "%s-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,
do_merge=False)
num_bams = len(align_bams)
sample_vcf_names = [
] # for individual sample names, given batch calling may be required
for bamfile, item in itertools.izip(align_bams, items):
# prepare commands
sample = dd.get_sample_name(item)
vardict = get_vardict_command(items[0])
strandbias = "teststrandbias.R"
var2vcf = "var2vcf_valid.pl"
opts = (" ".join(
_vardict_options_from_config(items, config, out_file,
target))
if _is_bed_file(target) else "")
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
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()
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 {sample} -b {bamfile} {opts} "
"| {strandbias}"
"| {var2vcf} -N {sample} -E -f {freq} {var2vcf_opts} "
"| {fix_ambig_ref} | {fix_ambig_alt} | {remove_dup} | {vcfstreamsort} {compress_cmd}"
)
if num_bams > 1:
temp_file_prefix = out_file.replace(".gz", "").replace(
".vcf", "") + item["name"][1]
tmp_out = temp_file_prefix + ".temp.vcf"
tmp_out += ".gz" if out_file.endswith("gz") else ""
sample_vcf_names.append(tmp_out)
with file_transaction(item, tmp_out) as tx_tmp_file:
if not _is_bed_file(target):
vcfutils.write_empty_vcf(tx_tmp_file,
config,
samples=[sample])
else:
cmd += " > {tx_tmp_file}"
do.run(cmd.format(**locals()),
"Genotyping with VarDict: Inference", {})
else:
if not _is_bed_file(target):
vcfutils.write_empty_vcf(tx_out_file,
config,
samples=[sample])
else:
cmd += " > {tx_out_file}"
do.run(cmd.format(**locals()),
"Genotyping with VarDict: Inference", {})
if num_bams > 1:
# N.B. merge_variant_files wants region in 1-based end-inclusive
# coordinates. Thus use bamprep.region_to_gatk
vcfutils.merge_variant_files(
orig_files=sample_vcf_names,
out_file=tx_out_file,
ref_file=ref_file,
config=config,
region=bamprep.region_to_gatk(region))
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_caller(align_bams,
items,
ref_file,
assoc_files,
region=None,
out_file=None):
"""Detect SNPs and indels with VarDict.
var2vcf_valid uses -A flag which reports all alleles and improves sensitivity:
https://github.com/AstraZeneca-NGS/VarDict/issues/35#issuecomment-276738191
"""
config = items[0]["config"]
if out_file is None:
out_file = "%s-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=False)
num_bams = len(align_bams)
sample_vcf_names = [
] # for individual sample names, given batch calling may be required
for bamfile, item in zip(align_bams, items):
# prepare commands
sample = dd.get_sample_name(item)
vardict = get_vardict_command(items[0])
strandbias = "teststrandbias.R"
var2vcf = "var2vcf_valid.pl"
opts, var2vcf_opts = _vardict_options_from_config(
items, config, out_file, target)
vcfstreamsort = config_utils.get_program(
"vcfstreamsort", config)
compress_cmd = "| bgzip -c" if tx_out_file.endswith(
"gz") else ""
freq = float(
utils.get_in(config, ("algorithm", "min_allele_fraction"),
10)) / 100.0
fix_ambig_ref = vcfutils.fix_ambiguous_cl()
fix_ambig_alt = vcfutils.fix_ambiguous_cl(5)
remove_dup = vcfutils.remove_dup_cl()
py_cl = os.path.join(utils.get_bcbio_bin(), "py")
jvm_opts = _get_jvm_opts(items[0], tx_out_file)
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 {sample} -b {bamfile} {opts} "
"| {strandbias}"
"| {var2vcf} -A -N {sample} -E -f {freq} {var2vcf_opts} "
"| {contig_cl} | bcftools filter -i 'QUAL >= 0' "
"| {fix_ambig_ref} | {fix_ambig_alt} | {remove_dup} | {vcfstreamsort} {compress_cmd}"
)
if num_bams > 1:
temp_file_prefix = out_file.replace(".gz", "").replace(
".vcf", "") + item["name"][1]
tmp_out = temp_file_prefix + ".temp.vcf"
tmp_out += ".gz" if out_file.endswith("gz") else ""
sample_vcf_names.append(tmp_out)
with file_transaction(item, tmp_out) as tx_tmp_file:
if not _is_bed_file(target):
vcfutils.write_empty_vcf(tx_tmp_file,
config,
samples=[sample])
else:
cmd += " > {tx_tmp_file}"
do.run(cmd.format(**locals()),
"Genotyping with VarDict: Inference", {})
else:
if not _is_bed_file(target):
vcfutils.write_empty_vcf(tx_out_file,
config,
samples=[sample])
else:
cmd += " > {tx_out_file}"
do.run(cmd.format(**locals()),
"Genotyping with VarDict: Inference", {})
if num_bams > 1:
# N.B. merge_variant_files wants region in 1-based end-inclusive
# coordinates. Thus use bamprep.region_to_gatk
vcfutils.merge_variant_files(
orig_files=sample_vcf_names,
out_file=tx_out_file,
ref_file=ref_file,
config=config,
region=bamprep.region_to_gatk(region))
return out_file
def _run_vardict_caller(align_bams, items, ref_file, assoc_files,
region=None, out_file=None):
"""Detect SNPs and indels with VarDict.
"""
config = items[0]["config"]
if out_file is None:
out_file = "%s-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=False)
num_bams = len(align_bams)
sample_vcf_names = [] # for individual sample names, given batch calling may be required
for bamfile, item in itertools.izip(align_bams, items):
# prepare commands
sample = dd.get_sample_name(item)
vardict = get_vardict_command(items[0])
strandbias = "teststrandbias.R"
var2vcf = "var2vcf_valid.pl"
opts = (" ".join(_vardict_options_from_config(items, config, out_file, target))
if _is_bed_file(target) else "")
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
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()
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 {sample} -b {bamfile} {opts} "
"| {strandbias}"
"| {var2vcf} -N {sample} -E -f {freq} {var2vcf_opts} "
"| {fix_ambig} | {remove_dup} | {vcfstreamsort} {compress_cmd}")
if num_bams > 1:
temp_file_prefix = out_file.replace(".gz", "").replace(".vcf", "") + item["name"][1]
tmp_out = temp_file_prefix + ".temp.vcf"
tmp_out += ".gz" if out_file.endswith("gz") else ""
sample_vcf_names.append(tmp_out)
with file_transaction(item, tmp_out) as tx_tmp_file:
if not _is_bed_file(target):
vcfutils.write_empty_vcf(tx_tmp_file, config, samples=[sample])
else:
cmd += " > {tx_tmp_file}"
do.run(cmd.format(**locals()), "Genotyping with VarDict: Inference", {})
else:
if not _is_bed_file(target):
vcfutils.write_empty_vcf(tx_out_file, config, samples=[sample])
else:
cmd += " > {tx_out_file}"
do.run(cmd.format(**locals()), "Genotyping with VarDict: Inference", {})
if num_bams > 1:
# N.B. merge_variant_files wants region in 1-based end-inclusive
# coordinates. Thus use bamprep.region_to_gatk
vcfutils.merge_variant_files(orig_files=sample_vcf_names,
out_file=tx_out_file, ref_file=ref_file,
config=config, region=bamprep.region_to_gatk(region))
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:
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, bam.aligner_from_header(paired.tumor_bam)))
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} "
"-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 {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 _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, bam.aligner_from_header(paired.tumor_bam)))
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 _run_vardict_caller(align_bams, items, ref_file, assoc_files,
region=None, out_file=None):
"""Detect SNPs and indels with VarDict.
var2vcf_valid uses -A flag which reports all alleles and improves sensitivity:
https://github.com/AstraZeneca-NGS/VarDict/issues/35#issuecomment-276738191
"""
config = items[0]["config"]
if out_file is None:
out_file = "%s-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=False)
num_bams = len(align_bams)
sample_vcf_names = [] # for individual sample names, given batch calling may be required
for bamfile, item in zip(align_bams, items):
# prepare commands
sample = dd.get_sample_name(item)
vardict = get_vardict_command(items[0])
opts, var2vcf_opts = _vardict_options_from_config(items, config, out_file, target)
vcfstreamsort = config_utils.get_program("vcfstreamsort", config)
compress_cmd = "| bgzip -c" if tx_out_file.endswith("gz") else ""
freq = float(utils.get_in(config, ("algorithm", "min_allele_fraction"), 10)) / 100.0
fix_ambig_ref = vcfutils.fix_ambiguous_cl()
fix_ambig_alt = vcfutils.fix_ambiguous_cl(5)
remove_dup = vcfutils.remove_dup_cl()
py_cl = os.path.join(utils.get_bcbio_bin(), "py")
jvm_opts = _get_jvm_opts(items[0], tx_out_file)
setup = ("%s && unset JAVA_HOME &&" % utils.get_R_exports())
contig_cl = vcfutils.add_contig_to_header_cl(ref_file, tx_out_file)
lowfreq_filter = _lowfreq_linear_filter(0, False)
cmd = ("{setup}{jvm_opts}{vardict} -G {ref_file} -f {freq} "
"-N {sample} -b {bamfile} {opts} "
"| teststrandbias.R "
"| var2vcf_valid.pl -A -N {sample} -E -f {freq} {var2vcf_opts} "
"| {contig_cl} | bcftools filter -i 'QUAL >= 0' | {lowfreq_filter} "
"| {fix_ambig_ref} | {fix_ambig_alt} | {remove_dup} | {vcfstreamsort} {compress_cmd}")
if num_bams > 1:
temp_file_prefix = out_file.replace(".gz", "").replace(".vcf", "") + item["name"][1]
tmp_out = temp_file_prefix + ".temp.vcf"
tmp_out += ".gz" if out_file.endswith("gz") else ""
sample_vcf_names.append(tmp_out)
with file_transaction(item, tmp_out) as tx_tmp_file:
if not _is_bed_file(target):
vcfutils.write_empty_vcf(tx_tmp_file, config, samples=[sample])
else:
cmd += " > {tx_tmp_file}"
do.run(cmd.format(**locals()), "Genotyping with VarDict: Inference", {})
else:
if not _is_bed_file(target):
vcfutils.write_empty_vcf(tx_out_file, config, samples=[sample])
else:
cmd += " > {tx_out_file}"
do.run(cmd.format(**locals()), "Genotyping with VarDict: Inference", {})
if num_bams > 1:
# N.B. merge_variant_files wants region in 1-based end-inclusive
# coordinates. Thus use bamprep.region_to_gatk
vcfutils.merge_variant_files(orig_files=sample_vcf_names,
out_file=tx_out_file, ref_file=ref_file,
config=config, region=bamprep.region_to_gatk(region))
return out_file
