def run(self, network, in_data, out_attributes, user_options, num_cores,
out_path):
import os
from genomicode import filelib
from genomicode import parallel
from Betsy import module_utils
bam_filenames = module_utils.find_bam_files(in_data.identifier)
assert bam_filenames, "No .bam files."
filelib.safe_mkdir(out_path)
jobs = [] # list of (in_filename, out_filename)
for in_filename in bam_filenames:
p, f = os.path.split(in_filename)
s, ext = os.path.splitext(f)
out_filename = os.path.join(out_path, "%s.matches.txt" % s)
x = in_filename, out_filename
jobs.append(x)
jobs2 = [] # list of (function, args, keywds)
for x in jobs:
in_filename, out_filename = x
x = summarize_bam_file, (in_filename, out_filename), None
jobs2.append(x)
parallel.pyfun(jobs2, num_procs=num_cores, DELAY=0.1)
# Make sure the analysis completed successfully.
out_filenames = [x[-1] for x in jobs]
filelib.assert_exists_nz_many(out_filenames)
def run(self, network, antecedents, out_attributes, user_options,
num_cores, outfile):
from genomicode import filelib
from genomicode import alignlib
from Betsy import module_utils as mlib
bam_node, gene_node = antecedents
bam_filenames = mlib.find_bam_files(bam_node.identifier)
gtf_file = gene_node.identifier
filelib.assert_exists_nz(gtf_file)
assert bam_filenames, "No bam files found."
metadata = {}
# Make output filenames.
p, r, e = mlib.splitpath(gtf_file)
bed_file = "%s.bed" % r
# Make bed file.
alignlib.gtf_to_bed(gtf_file, bed_file)
#bed_file = "/data/jchang/biocore/gtf02.txt"
# Figure out the orientation.
x = get_paired_stranded_rseqc(bed_file, bam_filenames[0])
single_or_paired, stranded, frac_failed, frac_first, frac_second = x
x = mlib.Stranded(single_or_paired, stranded, frac_failed, frac_first,
frac_second)
mlib.write_stranded(x, outfile)
return metadata
def run(
self, network, antecedents, out_attributes, user_options, num_cores,
outfile):
from genomicode import parallel
from genomicode import alignlib
from Betsy import module_utils as mlib
bam_node = antecedents
bam_filenames = mlib.find_bam_files(bam_node.identifier)
metadata = {}
metadata["tool"] = "samtools %s" % alignlib.get_samtools_version()
jobs = []
for bam_filename in bam_filenames:
x = count_duplicates, (bam_filename,), {}
jobs.append(x)
results = parallel.pyfun(jobs, num_procs=num_cores)
metadata["num_cores"] = num_cores
assert len(results) == len(bam_filenames)
handle = open(outfile, 'w')
header = "Sample", "Duplicated Reads", "Total Reads", "% Duplicated"
print >>handle, "\t".join(header)
for i in range(len(bam_filenames)):
x, sample, x = mlib.splitpath(bam_filenames[i])
total_reads, dup_reads = results[i]
perc_dup = float(dup_reads) / total_reads * 100
perc_dup = "%.2f" % perc_dup
x = sample, dup_reads, total_reads, perc_dup
print >>handle, "\t".join(map(str, x))
return metadata
def run(
self, network, antecedents, out_attributes, user_options, num_cores,
out_path):
import os
from genomicode import filelib
from genomicode import parallel
from genomicode import alignlib
from Betsy import module_utils as mlib
MAX_RAM = 64 # maximum amount of ram to use in Gb.
bam_node, ref_node = antecedents
bam_filenames = mlib.find_bam_files(bam_node.identifier)
assert bam_filenames, "No .bam files."
ref = alignlib.create_reference_genome(ref_node.identifier)
filelib.safe_mkdir(out_path)
metadata = {}
jobs = [] # list of (in_filename, log_filename, out_filename)
for in_filename in bam_filenames:
p, f = os.path.split(in_filename)
s, ext = os.path.splitext(f)
log_filename = os.path.join(out_path, "%s.log" % s)
out_filename = os.path.join(out_path, f)
x = in_filename, log_filename, out_filename
jobs.append(x)
# java -Xmx5g -jar /usr/local/bin/GATK/GenomeAnalysisTK.jar
# -T SplitNCigarReads -R ../hg19.fa -I $i -o $j
# -rf ReassignOneMappingQuality -RMQF 255 -RMQT 60
# -U ALLOW_N_CIGAR_READS
# Start with 5 Gb RAM.
commands = make_commands(jobs, ref.fasta_file_full, 5)
nc = mlib.calc_max_procs_from_ram(5, upper_max=num_cores)
parallel.pshell(commands, max_procs=nc)
metadata["commands"] = commands
metadata["num_procs"] = nc
# If any of the analyses didn't finish, try again with more
# RAM.
jobs2 = []
for x in jobs:
in_filename, log_filename, out_filename = x
if filelib.exists_nz(out_filename):
continue
jobs2.append(x)
if jobs2:
commands = make_commands(jobs2, ref.fasta_file_full, MAX_RAM)
nc = mlib.calc_max_procs_from_ram(MAX_RAM, upper_max=num_cores)
parallel.pshell(commands, max_procs=nc)
metadata["commands"] += commands
# Make sure the analysis completed successfully.
out_filenames = [x[-1] for x in jobs]
filelib.assert_exists_nz_many(out_filenames)
return metadata
def run(self, network, antecedents, out_attributes, user_options,
num_cores, out_path):
import os
from genomicode import parallel
from genomicode import alignlib
from genomicode import filelib
from Betsy import module_utils as mlib
bam_node, ref_node = antecedents
bam_filenames = mlib.find_bam_files(bam_node.identifier)
assert bam_filenames, "No .bam files."
ref = alignlib.create_reference_genome(ref_node.identifier)
filelib.safe_mkdir(out_path)
metadata = {}
metadata["tool"] = "samtools %s" % alignlib.get_samtools_version()
# list of (in_filename, err_filename, out_filename)
jobs = []
for in_filename in bam_filenames:
p, f = os.path.split(in_filename)
sample, ext = os.path.splitext(f)
err_filename = os.path.join(out_path, "%s.log" % sample)
out_filename = os.path.join(out_path, "%s.pileup" % sample)
x = in_filename, err_filename, out_filename
jobs.append(x)
# samtools mpileup -f [reference sequence] [BAM file(s)]
# > myData.mpileup
samtools = mlib.findbin("samtools")
sq = mlib.sq
commands = []
for x in jobs:
in_filename, err_filename, out_filename = x
x = [
sq(samtools),
"mpileup",
"-f",
sq(ref.fasta_file_full),
]
x.append(sq(in_filename))
x = " ".join(map(str, x))
x = "%s 2> %s 1> %s" % (x, err_filename, out_filename)
commands.append(x)
parallel.pshell(commands, max_procs=num_cores)
metadata["num_cores"] = num_cores
metadata["commands"] = commands
x = [x[-1] for x in jobs]
filelib.assert_exists_nz_many(x)
return metadata
def run(self, network, antecedents, out_attributes, user_options,
num_cores, out_path):
import os
from genomicode import filelib
from genomicode import parallel
from genomicode import alignlib
from Betsy import module_utils
bam_node, ref_node = antecedents
#in_filenames = filelib.list_files_in_path(
# bam_node.identifier, endswith=".bam", case_insensitive=True)
in_filenames = module_utils.find_bam_files(bam_node.identifier)
ref = alignlib.create_reference_genome(ref_node.identifier)
filelib.safe_mkdir(out_path)
# java -Xmx5g -jar /usr/local/bin/picard/picard.jar ReorderSam \
# I=<input.bam> O=<output.bam> REFERENCE=ucsc.hg19.fasta
picard_jar = alignlib.find_picard_jar("picard")
jobs = [] # list of (in_filename, out_filename)
for in_filename in in_filenames:
p, f = os.path.split(in_filename)
out_filename = os.path.join(out_path, f)
x = in_filename, out_filename
jobs.append(x)
# Make a list of commands.
sq = parallel.quote
commands = []
for x in jobs:
in_filename, out_filename = x
x = [
"java",
"-Xmx5g",
"-jar",
sq(picard_jar),
"ReorderSam",
"I=%s" % sq(in_filename),
"O=%s" % sq(out_filename),
"REFERENCE=%s" % ref.fasta_file_full,
]
x = " ".join(x)
commands.append(x)
parallel.pshell(commands, max_procs=num_cores)
# Make sure the analysis completed successfully.
for x in jobs:
in_filename, out_filename = x
filelib.assert_exists_nz(out_filename)
def run(self, network, in_data, out_attributes, user_options, num_cores,
out_path):
import os
from genomicode import filelib
from Betsy import module_utils
align_node = in_data
x = module_utils.find_bam_files(align_node.identifier)
x = [x for x in x if x.endswith("accepted_hits.bam")]
bam_filenames = x
assert bam_filenames, "No accepted_hits.bam files."
filelib.safe_mkdir(out_path)
jobs = [] # list of (in_filename, out_filename)
for in_filename in bam_filenames:
# Names must in the format:
# <path>/<sample>.tophat/accepted_hits.bam
# full_path <path>/<sample>.tophat
# path <path>
# tophat_dir <sample>.tophat
# file_ accepted_hits.bam
# sample <sample>
full_path, file_ = os.path.split(in_filename)
path, tophat_dir = os.path.split(full_path)
assert file_ == "accepted_hits.bam"
assert tophat_dir.endswith(".tophat")
sample = tophat_dir[:-7]
out_filename = os.path.join(out_path, "%s.bam" % sample)
assert in_filename != out_filename
jobs.append((in_filename, out_filename))
# Make sure outfiles are unique.
x = [x[-1] for x in jobs]
x = {}.fromkeys(x)
assert len(jobs) == len(x), "Duplicate sample names."
for x in jobs:
in_filename, out_filename = x
os.symlink(in_filename, out_filename)
# Make sure the analysis completed successfully.
out_filenames = [x[-1] for x in jobs]
filelib.assert_exists_nz_many(out_filenames)
def run(self, network, antecedents, out_attributes, user_options,
num_cores, out_path):
import os
from genomicode import filelib
from genomicode import parallel
from genomicode import alignlib
from Betsy import module_utils
bam_node, ref_node = antecedents
bam_filenames = module_utils.find_bam_files(bam_node.identifier)
assert bam_filenames, "No .bam files."
ref = alignlib.create_reference_genome(ref_node.identifier)
filelib.safe_mkdir(out_path)
metadata = {}
# TODO: Figure out GATK version.
## Figure out whether the user wants SNPs or INDELs.
#assert "vartype" in out_attributes
#vartype = out_attributes["vartype"]
#assert vartype in ["all", "snp", "indel"]
jobs = []
for bam_filename in bam_filenames:
p, f = os.path.split(bam_filename)
sample, ext = os.path.splitext(f)
#raw_outfile = os.path.join(out_path, "%s.raw" % sample)
vcf_outfile = os.path.join(out_path, "%s.vcf" % sample)
log_filename = os.path.join(out_path, "%s.log" % sample)
x = filelib.GenericObject(bam_filename=bam_filename,
vcf_outfile=vcf_outfile,
log_filename=log_filename)
jobs.append(x)
# java -Xmx5g -jar /usr/local/bin/GATK/GenomeAnalysisTK.jar
# -T HaplotypeCaller -R ucsc.hg19.fasta
# -dontUseSoftClippedBases -stand_call_conf 20.0
# -stand_emit_conf 20.0 -I $i -o $j
# Make a list of commands.
commands = []
for j in jobs:
# For debugging. If exists, don't do it again.
#if filelib.exists_nz(j.raw_outfile):
if filelib.exists_nz(j.vcf_outfile):
continue
x = alignlib.make_GATK_command(T="HaplotypeCaller",
R=ref.fasta_file_full,
dontUseSoftClippedBases=None,
stand_call_conf=20.0,
stand_emit_conf=20.0,
I=j.bam_filename,
o=j.vcf_outfile)
x = "%s >& %s" % (x, j.log_filename)
commands.append(x)
parallel.pshell(commands, max_procs=num_cores)
# Filter each of the VCF files.
#for j in jobs:
# filter_by_vartype(vartype, j.raw_outfile, j.vcf_outfile)
#metadata["filter"] = vartype
# Make sure the analysis completed successfully.
x = [j.vcf_outfile for j in jobs]
filelib.assert_exists_nz_many(x)
return metadata
def run(self, network, antecedents, out_attributes, user_options,
num_cores, out_path):
import os
from genomicode import filelib
from genomicode import parallel
from genomicode import alignlib
from Betsy import module_utils as mlib
import call_somatic_varscan
bam_node, nc_node, ref_node, interval_node = antecedents
bam_filenames = mlib.find_bam_files(bam_node.identifier)
assert bam_filenames, "No .bam files."
nc_match = mlib.read_normal_cancer_file(nc_node.identifier)
ref = alignlib.create_reference_genome(ref_node.identifier)
filelib.assert_exists_nz(interval_node.identifier)
filelib.safe_mkdir(out_path)
metadata = {}
# TODO: Figure out GATK version.
# Make sure intervals file ends with:
# .bed, .list, .picard, .interval_list, or .intervals
x, x, ext = mlib.splitpath(interval_node.identifier)
assert ext in [
".bed", ".list", ".picard", ".interval_list", ".intervals"
]
cosmic_file = mlib.get_user_option(user_options,
"mutect_cosmic_vcf",
not_empty=True,
check_file=True)
dbsnp_file = mlib.get_user_option(user_options,
"mutect_dbsnp_vcf",
not_empty=True,
check_file=True)
# sample -> bam filename
sample2bamfile = mlib.root2filename(bam_filenames)
# Make sure files exist for all the samples.
mlib.assert_normal_cancer_samples(nc_match, sample2bamfile)
opj = os.path.join
jobs = []
for (normal_sample, cancer_sample) in nc_match:
normal_bamfile = sample2bamfile[normal_sample]
cancer_bamfile = sample2bamfile[cancer_sample]
path, sample, ext = mlib.splitpath(cancer_bamfile)
vcf_outfile = opj(out_path, "%s.vcf" % sample)
log_outfile = opj(out_path, "%s.log" % sample)
x = filelib.GenericObject(normal_sample=normal_sample,
cancer_sample=cancer_sample,
normal_bamfile=normal_bamfile,
cancer_bamfile=cancer_bamfile,
vcf_outfile=vcf_outfile,
log_outfile=log_outfile)
jobs.append(x)
# java -jar GenomeAnalysisTK.jar \
# -T MuTect2 \
# -R reference.fasta \
# -I:tumor tumor.bam \
# -I:normal normal.bam \
# [--dbsnp dbSNP.vcf] \
# [--cosmic COSMIC.vcf] \
# [-L targets.interval_list] \
# -o output.vcf
# Generate the commands.
sq = mlib.sq
commands = []
for j in jobs:
UNHASHABLE = [
("I:normal", sq(normal_bamfile)),
("I:tumor", sq(cancer_bamfile)),
# --dbsnp and --cosmic use two dashes, for some
# reason. Since make_GATK_command only uses one dash,
# add one manually.
("-dbsnp", sq(dbsnp_file)),
("-cosmic", sq(cosmic_file)),
]
x = alignlib.make_GATK_command(
T="MuTect2",
R=sq(ref.fasta_file_full),
L=sq(interval_node.identifier),
o=sq(j.vcf_outfile),
_UNHASHABLE=UNHASHABLE,
)
x = "%s >& %s" % (x, j.log_outfile)
commands.append(x)
assert len(commands) == len(jobs)
nc = mlib.calc_max_procs_from_ram(25, upper_max=num_cores)
parallel.pshell(commands, max_procs=nc)
metadata["num_cores"] = nc
metadata["commands"] = commands
# Make sure log files have no errors. Check the log files
# before the VCF files. If there's an error, the VCF files
# may not be created.
# ##### ERROR -------------------------------------------------------
# ##### ERROR A GATK RUNTIME ERROR has occurred (version 2.2-25-g2a68
# ##### ERROR
# ##### ERROR Please visit the wiki to see if this is a known problem
# ##### ERROR If not, please post the error, with stack trace, to the
# ##### ERROR Visit our website and forum for extensive documentation
# ##### ERROR commonly asked questions http://www.broadinstitute.org/
# ##### ERROR
# ##### ERROR MESSAGE: java.lang.IllegalArgumentException: Comparison
# ##### ERROR -------------------------------------------------------
for i, j in enumerate(jobs):
# Pull out the error lines.
x = [x for x in open(j.log_outfile)]
x = [x for x in x if x.startswith("##### ERROR")]
x = "".join(x)
msg = "MuTect2 error [%s]:\n%s\n%s" % (cancer_sample, commands[i],
x)
assert not x, msg
# Make sure output VCF files exist.
x = [x.vcf_outfile for x in jobs]
filelib.assert_exists_many(x)
# Mutect2 names the samples "NORMAL" and "TUMOR". Replace
# them with the actual names.
for j in jobs:
call_somatic_varscan._fix_normal_cancer_names(
j.vcf_outfile, j.normal_sample, j.cancer_sample)
return metadata
def run(self, network, antecedents, out_attributes, user_options,
num_cores, out_path):
import os
from genomicode import filelib
from genomicode import parallel
from genomicode import alignlib
from Betsy import module_utils as mlib
bam_node, ref_node = antecedents
in_filenames = mlib.find_bam_files(bam_node.identifier)
assert in_filenames, "No .bam files."
ref = alignlib.create_reference_genome(ref_node.identifier)
filelib.safe_mkdir(out_path)
metadata = {}
jobs = [] # list of (in_filename, log_filename, out_filename)
for in_filename in in_filenames:
p, f = os.path.split(in_filename)
f, ext = os.path.splitext(f)
log_filename = os.path.join(out_path, "%s.log" % f)
out_filename = os.path.join(out_path, "%s.intervals" % f)
x = in_filename, log_filename, out_filename
jobs.append(x)
filter_reads_with_N_cigar = mlib.get_user_option(
user_options,
"filter_reads_with_N_cigar",
allowed_values=["no", "yes"])
known_sites = []
x1 = mlib.get_user_option(user_options,
"realign_known_sites1",
check_file=True)
x2 = mlib.get_user_option(user_options,
"realign_known_sites2",
check_file=True)
x3 = mlib.get_user_option(user_options,
"realign_known_sites3",
check_file=True)
x = [x1, x2, x3]
x = [x for x in x if x]
known_sites = x
assert known_sites
# I/O bound, so not likely to get a big speedup with nt.
# java -Xmx5g -jar /usr/local/bin/GATK/GenomeAnalysisTK.jar -nt 4
# -T RealignerTargetCreator -R ../genome.idx/erdman.fa -I $i -o $j
# --known <known_vcf_file>
# RealignerTargetCreator takes ~10Gb per process. Each thread
# takes the full amount of memory.
nc = mlib.calc_max_procs_from_ram(12, upper_max=num_cores)
# Make a list of commands.
commands = []
for x in jobs:
in_filename, log_filename, out_filename = x
n = max(1, nc / len(jobs))
x = [("-known", x) for x in known_sites]
if filter_reads_with_N_cigar == "yes":
x.append(("-filter_reads_with_N_cigar", None))
x = alignlib.make_GATK_command(nt=n,
T="RealignerTargetCreator",
R=ref.fasta_file_full,
I=in_filename,
o=out_filename,
_UNHASHABLE=x)
x = "%s >& %s" % (x, log_filename)
commands.append(x)
parallel.pshell(commands, max_procs=nc)
metadata["num_procs"] = nc
metadata["commands"] = commands
# Make sure the analysis completed successfully.
out_filenames = [x[-1] for x in jobs]
filelib.assert_exists_nz_many(out_filenames)
return metadata
def run(self, network, antecedents, out_attributes, user_options,
num_cores, out_path):
import os
from genomicode import filelib
from genomicode import parallel
from genomicode import alignlib
from Betsy import module_utils as mlib
import call_somatic_varscan
bam_node, nc_node, ref_node = antecedents
bam_filenames = mlib.find_bam_files(bam_node.identifier)
assert bam_filenames, "No .bam files."
nc_match = mlib.read_normal_cancer_file(nc_node.identifier)
ref = alignlib.create_reference_genome(ref_node.identifier)
filelib.safe_mkdir(out_path)
metadata = {}
# TODO: Figure out version.
# sample -> bam filename
sample2bamfile = mlib.root2filename(bam_filenames)
# Make sure files exist for all the samples.
mlib.assert_normal_cancer_samples(nc_match, sample2bamfile)
# list of (normal_sample, cancer_sample, normal_bamfile, tumor_bamfile,
# vcf_outfile)
opj = os.path.join
jobs = []
for (normal_sample, cancer_sample) in nc_match:
normal_bamfile = sample2bamfile[normal_sample]
cancer_bamfile = sample2bamfile[cancer_sample]
path, sample, ext = mlib.splitpath(cancer_bamfile)
vcf_outfile = opj(out_path, "%s.vcf" % sample)
x = normal_sample, cancer_sample, normal_bamfile, cancer_bamfile, \
vcf_outfile
jobs.append(x)
# bam-somaticsniper -q 1 -Q 15 -G -L -F vcf \
# -f genomes/Broad.hg19/Homo_sapiens_assembly19.fa \
# test31/tumor.bam test31/normal.bam test41.vcf
somaticsniper = mlib.get_config("somaticsniper",
which_assert_file=True)
# Generate the commands.
sq = mlib.sq
commands = []
for x in jobs:
normal_sample, cancer_sample, normal_bamfile, cancer_bamfile, \
vcf_outfile = x
x = [
sq(somaticsniper),
"-q",
1,
"-Q",
15,
"-G",
"-L",
"-F",
"vcf",
"-f",
sq(ref.fasta_file_full),
sq(cancer_bamfile),
sq(normal_bamfile),
sq(vcf_outfile),
]
x = " ".join(map(str, x))
commands.append(x)
# Not sure how much RAM this takes.
nc = mlib.calc_max_procs_from_ram(15, upper_max=num_cores)
parallel.pshell(commands, max_procs=nc)
metadata["num_cores"] = nc
metadata["commands"] = commands
# SomaticSniper names the samples "NORMAL" and "TUMOR".
# Replace them with the actual names.
for x in jobs:
normal_sample, cancer_sample, normal_bamfile, cancer_bamfile, \
vcf_outfile = x
call_somatic_varscan._fix_normal_cancer_names(
vcf_outfile, normal_sample, cancer_sample)
x = [x[-1] for x in jobs]
filelib.assert_exists_many(x)
return metadata
def run(
self, network, antecedents, out_attributes, user_options, num_cores,
out_path):
import os
from genomicode import filelib
from genomicode import parallel
from genomicode import alignlib
from Betsy import module_utils as mlib
bam_node, ref_node = antecedents
bam_filenames = mlib.find_bam_files(bam_node.identifier)
assert bam_filenames, "No .bam files."
ref = alignlib.create_reference_genome(ref_node.identifier)
filelib.safe_mkdir(out_path)
metadata = {}
# java -jar picard.jar CollectAlignmentSummaryMetrics \
# R=reference_sequence.fasta \
# I=input.bam \
# O=output.txt
opj = os.path.join
jobs = [] # list of filelib.GenericObject
for bam_filename in bam_filenames:
# <in_path>/<sample>.bam
in_path, sample, ext = mlib.splitpath(bam_filename)
assert ext == ".bam"
out_filename = opj(out_path, "%s.alignment_metrics.txt" % sample)
log_filename = opj(out_path, "%s.log" % sample)
x = filelib.GenericObject(
sample=sample,
bam_filename=bam_filename,
out_filename=out_filename,
log_filename=log_filename)
jobs.append(x)
# Make the commands to run picard.
picard_jar = alignlib.find_picard_jar("picard")
sq = parallel.quote
commands = []
for j in jobs:
# Should have better way of getting java path.
cmd = [
"java",
"-Xmx10g",
"-jar", sq(picard_jar), "CollectAlignmentSummaryMetrics",
"I=%s" % sq(j.bam_filename),
"R=%s" % sq(ref.fasta_file_full),
"O=%s" % sq(j.out_filename),
]
cmd = " ".join(cmd)
cmd = "%s >& %s" % (cmd, sq(j.log_filename))
commands.append(cmd)
metadata["commands"] = commands
parallel.pshell(commands, max_procs=num_cores)
x = [x.out_filename for x in jobs]
filelib.assert_exists_nz_many(x)
# Summarize the insert size files.
outfile = opj(out_path, "summary.txt")
_summarize_alignment_summary_metrics(jobs, outfile)
filelib.assert_exists_nz(outfile)
return metadata
def run(self, network, antecedents, out_attributes, user_options,
num_cores, out_path):
import os
from genomicode import filelib
from genomicode import parallel
from genomicode import alignlib
from Betsy import module_utils as mlib
# For debugging.
RUN_VARIANT_CALLING = True
FILTER_CALLS = True
MERGE_CALLS = True
FIX_VCF_FILES = True
dna_bam_node, rna_bam_node, nc_node, ref_node = antecedents
dna_bam_filenames = mlib.find_bam_files(dna_bam_node.identifier)
assert dna_bam_filenames, "No DNA .bam files."
rna_bam_filenames = mlib.find_bam_files(rna_bam_node.identifier)
assert rna_bam_filenames, "No RNA .bam files."
nc_match = mlib.read_normal_cancer_file(nc_node.identifier)
ref = alignlib.create_reference_genome(ref_node.identifier)
filelib.safe_mkdir(out_path)
metadata = {}
metadata["tool"] = "Radia %s" % alignlib.get_radia_version()
## Make sure the BAM files do not contain spaces in the
## filenames. Radia doesn't work well with spaces.
#filenames = dna_bam_filenames + rna_bam_filenames
#has_spaces = []
#for filename in filenames:
# if filename.find(" ") >= 0:
# has_spaces.append(filename)
#x = has_spaces
#if len(x) > 5:
# x = x[:5] + ["..."]
#x = ", ".join(x)
#msg = "Radia breaks if there are spaces in filenames: %s" % x
#assert not has_spaces, msg
# sample -> bam filename
dnasample2bamfile = mlib.root2filename(dna_bam_filenames)
rnasample2bamfile = mlib.root2filename(rna_bam_filenames)
# Make sure files exist for all the samples. The DNA-Seq
# should have both normal and cancer. RNA is not needed for
# normal sample.
mlib.assert_normal_cancer_samples(nc_match, dnasample2bamfile)
mlib.assert_normal_cancer_samples(nc_match,
rnasample2bamfile,
ignore_normal_sample=True)
# Make sure Radia and snpEff are configured.
radia_genome_assembly = mlib.get_user_option(user_options,
"radia_genome_assembly",
not_empty=True)
assert radia_genome_assembly == "hg19", "Only hg19 handled."
snp_eff_genome = mlib.get_user_option(user_options,
"snp_eff_genome",
not_empty=True)
radia_path = mlib.get_config("radia_path", assert_exists=True)
snp_eff_path = mlib.get_config("snp_eff_path", assert_exists=True)
radia_files = get_radia_files(radia_path, radia_genome_assembly)
# Make a list of the chromosomes to use. Pick an arbitrarily
# BAM file. Look at only the chromosomes that are present in
# all files.
all_bamfiles = dnasample2bamfile.values() + rnasample2bamfile.values()
chroms = list_common_chromosomes(all_bamfiles)
assert chroms, "No chromosomes found in all files."
# Only use the chromosomes that can be filtered by Radia.
chroms = filter_radia_chromosomes(chroms, radia_files)
# Make output directories.
radia_outpath = "radia1.tmp"
filter_outpath = "radia2.tmp"
merge_outpath = "radia3.tmp"
if not os.path.exists(radia_outpath):
os.mkdir(radia_outpath)
if not os.path.exists(filter_outpath):
os.mkdir(filter_outpath)
if not os.path.exists(merge_outpath):
os.mkdir(merge_outpath)
# Steps:
# 1. Call variants (radia.py)
# -o <file.vcf>
# 2. Filter variants (filterRadia.py)
# <outpath>
# Creates a file: <filter_outpath>/<patient_id>_chr<chrom>.vcf
# 3. Merge (mergeChroms.py)
# Takes as input: <filter_outpath>
# Produces: <merge_outpath>/<patient_id>.vcf
# list of (normal_sample, cancer_sample, chrom,
# normal_bamfile, dna_tumor_bamfile, rna_tumor_bamfile,
# radia_vcf_outfile, filter_vcf_outfile, merge_vcf_outfile,
# final_vcf_outfile,
# radia_logfile, filter_logfile, merge_logfile)
opj = os.path.join
jobs = []
for i, (normal_sample, cancer_sample) in enumerate(nc_match):
normal_bamfile = dnasample2bamfile[normal_sample]
dna_tumor_bamfile = dnasample2bamfile[cancer_sample]
rna_tumor_bamfile = rnasample2bamfile[cancer_sample]
merge_vcf_outfile = opj(merge_outpath, "%s.vcf" % cancer_sample)
merge_logfile = opj(merge_outpath, "%s.log" % cancer_sample)
final_vcf_outfile = opj(out_path, "%s.vcf" % cancer_sample)
for chrom in chroms:
radia_vcf_outfile = opj(
radia_outpath, "%s_chr%s.vcf" % (cancer_sample, chrom))
filter_vcf_outfile = opj(
filter_outpath, "%s_chr%s.vcf" % (cancer_sample, chrom))
radia_logfile = opj(radia_outpath,
"%s_chr%s.log" % (cancer_sample, chrom))
filter_logfile = opj(filter_outpath,
"%s_chr%s.log" % (cancer_sample, chrom))
x = normal_sample, cancer_sample, chrom, \
normal_bamfile, dna_tumor_bamfile, rna_tumor_bamfile, \
radia_vcf_outfile, filter_vcf_outfile, merge_vcf_outfile, \
final_vcf_outfile, \
radia_logfile, filter_logfile, merge_logfile
jobs.append(x)
# Since Radia doesn't work well if there are spaces in the
# filenames, symlink these files here to guarantee that there
# are no spaces.
normal_path = "normal.bam"
dna_path = "dna.bam"
rna_path = "rna.bam"
if not os.path.exists(normal_path):
os.mkdir(normal_path)
if not os.path.exists(dna_path):
os.mkdir(dna_path)
if not os.path.exists(rna_path):
os.mkdir(rna_path)
for i, x in enumerate(jobs):
normal_sample, cancer_sample, chrom, \
normal_bamfile, dna_tumor_bamfile, rna_tumor_bamfile, \
radia_vcf_outfile, filter_vcf_outfile, merge_vcf_outfile, \
final_vcf_outfile, \
radia_logfile, filter_logfile, merge_logfile = x
x1 = hash_and_symlink_bamfile(normal_bamfile, normal_path)
x2 = hash_and_symlink_bamfile(dna_tumor_bamfile, dna_path)
x3 = hash_and_symlink_bamfile(rna_tumor_bamfile, rna_path)
clean_normal, clean_dna, clean_rna = x1, x2, x3
x = normal_sample, cancer_sample, chrom, \
clean_normal, clean_dna, clean_rna, \
radia_vcf_outfile, filter_vcf_outfile, merge_vcf_outfile, \
final_vcf_outfile, \
radia_logfile, filter_logfile, merge_logfile
jobs[i] = x
# Generate the commands for doing variant calling.
python = mlib.get_config("python", which_assert_file=True)
# filterRadia.py calls the "blat" command, and there's no way
# to set the path. Make sure "blat" is executable.
if not filelib.which("blat"):
# Find "blat" in the configuration and add it to the path.
x = mlib.get_config("blat", which_assert_file=True)
path, x = os.path.split(x)
if os.environ["PATH"]:
path = "%s:%s" % (os.environ["PATH"], path)
os.environ["PATH"] = path
# Make sure it's findable now.
filelib.which_assert("blat")
# STEP 1. Call variants with radia.py.
# python radia.py test31 5 \
# -n bam04/PIM001_G.bam \
# -t bam04/196B-MG.bam \
# -r bam34/196B-MG.bam \
# -f genomes/Broad.hg19/Homo_sapiens_assembly19.fa \
# -o test32.vcf
# --dnaTumorMitochon MT \
# --rnaTumorMitochon MT \
sq = mlib.sq
commands = []
for x in jobs:
normal_sample, cancer_sample, chrom, \
normal_bamfile, dna_tumor_bamfile, rna_tumor_bamfile, \
radia_vcf_outfile, filter_vcf_outfile, merge_vcf_outfile, \
final_vcf_outfile, \
radia_logfile, filter_logfile, merge_logfile = x
x = [
sq(python),
sq(radia_files.radia_py),
cancer_sample,
chrom,
"-n",
sq(normal_bamfile),
"-t",
sq(dna_tumor_bamfile),
"-r",
sq(rna_tumor_bamfile),
"-f",
sq(ref.fasta_file_full),
"-o",
radia_vcf_outfile,
]
if "MT" in chroms:
x += [
"--dnaNormalMitochon MT",
"--dnaTumorMitochon MT",
"--rnaTumorMitochon MT",
]
x = " ".join(x)
x = "%s >& %s" % (x, radia_logfile)
commands.append(x)
assert len(commands) == len(jobs)
# Only uses ~200 Mb of ram.
if RUN_VARIANT_CALLING:
parallel.pshell(commands, max_procs=num_cores)
metadata["num_cores"] = num_cores
metadata["commands"] = commands
# Make sure log files are empty.
logfiles = [x[10] for x in jobs]
filelib.assert_exists_z_many(logfiles)
# STEP 2. Filter variants with filterRadia.py.
commands = []
for x in jobs:
normal_sample, cancer_sample, chrom, \
normal_bamfile, dna_tumor_bamfile, rna_tumor_bamfile, \
radia_vcf_outfile, filter_vcf_outfile, merge_vcf_outfile, \
final_vcf_outfile, \
radia_logfile, filter_logfile, merge_logfile = x
x = [
sq(python),
sq(radia_files.filterRadia_py),
cancer_sample,
chrom,
sq(radia_vcf_outfile),
sq(filter_outpath),
sq(radia_files.scripts_dir),
"-b",
sq(radia_files.blacklist_dir),
"-d",
sq(radia_files.snp_dir),
"-r",
sq(radia_files.retro_dir),
"-p",
sq(radia_files.pseudo_dir),
"-c",
sq(radia_files.cosmic_dir),
"-t",
sq(radia_files.target_dir),
"-s",
sq(snp_eff_path),
"-e",
snp_eff_genome,
"--rnaGeneBlckFile",
sq(radia_files.rnageneblck_file),
"--rnaGeneFamilyBlckFile",
sq(radia_files.rnagenefamilyblck_file),
]
x = " ".join(x)
x = "%s >& %s" % (x, filter_logfile)
commands.append(x)
assert len(commands) == len(jobs)
# Sometimes samtools crashes in the middle of a run. Detect
# this case, and re-run the analysis if needed.
assert len(commands) == len(jobs)
py_commands = []
for x, cmd in zip(jobs, commands):
normal_sample, cancer_sample, chrom, \
normal_bamfile, dna_tumor_bamfile, rna_tumor_bamfile, \
radia_vcf_outfile, filter_vcf_outfile, merge_vcf_outfile, \
final_vcf_outfile, \
radia_logfile, filter_logfile, merge_logfile = x
args = cmd, cancer_sample, chrom, filter_logfile
x = _run_filterRadia_with_restart, args, {}
py_commands.append(x)
# Takes ~10 Gb each.
nc = mlib.calc_max_procs_from_ram(25, upper_max=num_cores)
if FILTER_CALLS:
parallel.pyfun(py_commands, num_procs=nc)
metadata["commands"] += commands
# Make sure log files are empty.
logfiles = [x[11] for x in jobs]
filelib.assert_exists_z_many(logfiles)
# Make sure filter_vcf_outfile exists.
outfiles = [x[7] for x in jobs]
filelib.assert_exists_nz_many(outfiles)
# STEP 3. Merge the results.
commands = []
for x in jobs:
normal_sample, cancer_sample, chrom, \
normal_bamfile, dna_tumor_bamfile, rna_tumor_bamfile, \
radia_vcf_outfile, filter_vcf_outfile, merge_vcf_outfile, \
final_vcf_outfile, \
radia_logfile, filter_logfile, merge_logfile = x
# python /usr/local/radia/scripts/mergeChroms.py 196B-MG \
# radia2.tmp/ radia3.tmp
# The "/" after radia2.tmp is important. If not given,
# will generate some files with only newlines.
fo = filter_outpath
if not fo.endswith("/"):
fo = "%s/" % fo
x = [
sq(python),
sq(radia_files.mergeChroms_py),
cancer_sample,
fo,
merge_outpath,
]
x = " ".join(x)
x = "%s >& %s" % (x, merge_logfile)
commands.append(x)
assert len(commands) == len(jobs)
# Since the chromosomes were separated for the previous steps,
# this will generate one merge for each chromosome. This is
# unnecessary, since we only need to merge once per sample.
# Get rid of duplicates.
commands = sorted({}.fromkeys(commands))
if MERGE_CALLS:
parallel.pshell(commands, max_procs=num_cores)
metadata["commands"] += commands
# Make sure log files are empty.
logfiles = [x[12] for x in jobs]
logfiles = sorted({}.fromkeys(logfiles))
filelib.assert_exists_z_many(logfiles)
# Fix the VCF files.
commands = []
for x in jobs:
normal_sample, cancer_sample, chrom, \
normal_bamfile, dna_tumor_bamfile, rna_tumor_bamfile, \
radia_vcf_outfile, filter_vcf_outfile, merge_vcf_outfile, \
final_vcf_outfile, \
radia_logfile, filter_logfile, merge_logfile = x
args = normal_sample, cancer_sample, \
merge_vcf_outfile, final_vcf_outfile
x = alignlib.clean_radia_vcf, args, {}
commands.append(x)
if FIX_VCF_FILES:
parallel.pyfun(commands, num_procs=num_cores)
# Make sure output VCF files exist.
x = [x[9] for x in jobs]
filelib.assert_exists_nz_many(x)
return metadata
def run(self, network, antecedents, out_attributes, user_options,
num_cores, outfile):
from genomicode import parselib
from genomicode import parallel
from Betsy import module_utils as mlib
MAX_CORES = 4 # I/O intensive.
fastq_node, sample_node, bam_node = antecedents
bam_filenames = mlib.find_bam_files(bam_node.identifier)
sample2fastq = mlib.find_merged_fastq_files(sample_node.identifier,
fastq_node.identifier,
as_dict=True)
metadata = {}
jobs = [] # list of (sample, bam_file, fastq_file)
for filename in bam_filenames:
path, sample, ext = mlib.splitpath(filename)
assert sample in sample2fastq, "Missing fastq: %s" % sample
fastq1, fastq2 = sample2fastq[sample]
x = sample, filename, fastq1
jobs.append(x)
funcalls = []
for x in jobs:
sample, bam_filename, fastq_filename = x
# Count the number of reads.
x1 = count_reads, (fastq_filename, ), {}
# Count the number of alignments.
x2 = count_alignments, (bam_filename, ), {}
funcalls.append(x1)
funcalls.append(x2)
assert len(funcalls) == len(jobs) * 2
nc = min(num_cores, MAX_CORES)
results = parallel.pyfun(funcalls, num_procs=nc)
metadata["num_cores"] = nc
# list of (sample, aligns, aligned_reads, total_reads, perc_aligned).
results2 = []
for i, x in enumerate(jobs):
sample, bam_filename, fastq_filename = x
x1 = results[i * 2]
x2 = results[i * 2 + 1]
total_reads = x1
aligned_reads, alignments = x2
perc_aligned = float(aligned_reads) / total_reads
x = sample, alignments, aligned_reads, total_reads, perc_aligned
results2.append(x)
results = results2
# sort by sample name
results.sort()
# Make table where the rows are the samples and the columns
# are the statistics.
table = []
header = ("Sample", "Alignments", "Aligned Reads", "Total Reads",
"Perc Aligned")
table.append(header)
for x in results:
sample, alignments, aligned_reads, total_reads, perc_aligned = x
x1 = parselib.pretty_int(alignments)
x2 = parselib.pretty_int(aligned_reads)
x3 = parselib.pretty_int(total_reads)
x4 = "%.2f%%" % (perc_aligned * 100)
x = sample, x1, x2, x3, x4
assert len(x) == len(header)
table.append(x)
# Write out the table as text file.
TXT_FILE = "summary.txt"
handle = open(TXT_FILE, 'w')
for x in table:
print >> handle, "\t".join(x)
handle.close()
txt2xls = mlib.findbin("txt2xls", quote=True)
parallel.sshell("%s -b %s > %s" % (txt2xls, TXT_FILE, outfile))
return metadata
def run(self, network, antecedents, out_attributes, user_options,
num_cores, out_path):
import os
from genomicode import filelib
from genomicode import parallel
from genomicode import alignlib
from Betsy import module_utils as mlib
bam_node, ref_node, insert_size_node, alignment_node = antecedents
bam_filenames = mlib.find_bam_files(bam_node.identifier)
assert bam_filenames, "No .bam files."
ref = alignlib.create_reference_genome(ref_node.identifier)
filelib.safe_mkdir(out_path)
metadata = {}
# ./pindel -f <reference.fa> -i <bam_configuration_file>
# -c <chromosome_name> -o <out_prefix>
# -T <num threads>
#
# Creates files:
# <out_prefix>_D Deletion
# <out_prefix>_SI Short insertion
# <out_prefix>_LI Long insertion
# <out_prefix>_INV Inversion
# <out_prefix>_TD Tandem deletion
# <out_prefix>_BP Breakpoint
# <out_prefix>_RP ??? read pair???
# <out_prefix>_CloseEndMapped Only on end could be mapped.
# Pindel cannot handle spaces in the BAM filenames (because of
# the config file). Symlink the file to a local directory to make
# sure there are no spaces.
bam_path = "bam"
opj = os.path.join
jobs = [] # list of filelib.GenericObject
for bam_filename in bam_filenames:
p, f = os.path.split(bam_filename)
sample, ext = os.path.splitext(f)
bai_filename = "%s.bai" % bam_filename
filelib.assert_exists_nz(bai_filename)
x = sample.replace(" ", "_")
local_bam = opj(bam_path, "%s.bam" % x)
local_bai = opj(bam_path, "%s.bam.bai" % x)
config_filename = opj(out_path, "%s.config.txt" % sample)
out_prefix = opj(out_path, sample)
log_filename = opj(out_path, "%s.log" % sample)
x = filelib.GenericObject(sample=sample,
bam_filename=bam_filename,
bai_filename=bai_filename,
local_bam=local_bam,
local_bai=local_bai,
config_filename=config_filename,
out_prefix=out_prefix,
log_filename=log_filename)
jobs.append(x)
filelib.safe_mkdir(bam_path)
for j in jobs:
assert " " not in j.local_bam
filelib.assert_exists_nz(j.bam_filename)
filelib.assert_exists_nz(j.bai_filename)
if not os.path.exists(j.local_bam):
os.symlink(j.bam_filename, j.local_bam)
if not os.path.exists(j.local_bai):
os.symlink(j.bai_filename, j.local_bai)
# Read the insert sizes.
summary_file = opj(insert_size_node.identifier, "summary.txt")
filelib.assert_exists_nz(summary_file)
sample2size = _read_insert_sizes(summary_file)
# Make sure all the samples have inserts.
for j in jobs:
assert j.sample in sample2size, \
"Missing in insert size file: %s" % j.sample
# Read the fragment sizes.
summary_file = opj(alignment_node.identifier, "summary.txt")
filelib.assert_exists_nz(summary_file)
sample2readlen = _read_fragment_sizes(summary_file)
# Make sure all the samples have read lengths.
for j in jobs:
assert j.sample in sample2readlen, \
"Missing in alignment summary file: %s" % j.sample
# Make the config file.
for j in jobs:
# <insert size> is the whole length to be sequenced, including
# the length of the pair of reads. Picard only counts the
# sequence between the reads.
size = sample2size[j.sample]
read_length = sample2readlen[j.sample]
insert_size = size + read_length * 2
handle = open(j.config_filename, 'w')
print >> handle, "%s %s %s" % (j.local_bam, insert_size, j.sample)
handle.close()
# Make a list of commands.
pindel = mlib.get_config("pindel", which_assert_file=True)
sq = parallel.quote
commands = []
for j in jobs:
cmd = [
sq(pindel),
"-f",
sq(ref.fasta_file_full),
"-i",
sq(j.config_filename),
"-c",
"ALL",
"-T",
1,
"-o",
sq(j.out_prefix),
]
cmd = " ".join(map(str, cmd))
cmd = "%s >& %s" % (cmd, j.log_filename)
commands.append(cmd)
parallel.pshell(commands, max_procs=num_cores)
metadata["num_cores"] = num_cores
metadata["commands"] = commands
# Make sure the analysis completed successfully. If not, try
# to diagnose.
x = [x.log_filename for x in jobs]
filelib.assert_exists_nz_many(x)
x1 = ["%s_D" % x.out_prefix for x in jobs]
x2 = ["%s_SI" % x.out_prefix for x in jobs]
x3 = ["%s_LI" % x.out_prefix for x in jobs]
x4 = ["%s_INV" % x.out_prefix for x in jobs]
x5 = ["%s_TD" % x.out_prefix for x in jobs]
x6 = ["%s_BP" % x.out_prefix for x in jobs]
x = x1 + x2 + x3 + x4 + x5 + x6
filelib.assert_exists_many(x)
return metadata
def run(self, network, antecedents, out_attributes, user_options,
num_cores, out_path):
import os
from genomicode import filelib
from genomicode import parallel
from genomicode import alignlib
from Betsy import module_utils as mlib
bam_node, nc_node, ref_node = antecedents
bam_filenames = mlib.find_bam_files(bam_node.identifier)
assert bam_filenames, "No .bam files."
nc_match = mlib.read_normal_cancer_file(nc_node.identifier)
ref = alignlib.create_reference_genome(ref_node.identifier)
filelib.safe_mkdir(out_path)
metadata = {}
metadata["tool"] = "MuSE %s" % alignlib.get_muse_version()
wgs_or_wes = mlib.get_user_option(user_options,
"wgs_or_wes",
not_empty=True,
allowed_values=["wgs", "wes"])
dbsnp_file = mlib.get_user_option(user_options,
"muse_dbsnp_vcf",
not_empty=True,
check_file=True)
# Make sure dbsnp_file is compressed and indexed.
assert dbsnp_file.endswith(".vcf.gz"), \
"muse_dbsnp_vcf must be bgzip compressed."
x = "%s.tbi" % dbsnp_file
assert filelib.exists_nz(x), "muse_dbsnp_vcf must be tabix indexed."
# sample -> bam filename
sample2bamfile = mlib.root2filename(bam_filenames)
# Make sure files exist for all the samples.
mlib.assert_normal_cancer_samples(nc_match, sample2bamfile)
# list of (normal_sample, cancer_sample, normal_bamfile, tumor_bamfile,
# muse_call_stem, muse_call_file, raw_vcf_outfile, vcf_outfile,
# logfile1, logfile2)
opj = os.path.join
jobs = []
for (normal_sample, cancer_sample) in nc_match:
normal_bamfile = sample2bamfile[normal_sample]
cancer_bamfile = sample2bamfile[cancer_sample]
path, sample, ext = mlib.splitpath(cancer_bamfile)
muse_call_stem = opj(out_path, "%s.call" % cancer_sample)
muse_call_file = "%s.MuSE.txt" % muse_call_stem
raw_vcf_outfile = opj(out_path, "%s.vcf.raw" % cancer_sample)
vcf_outfile = opj(out_path, "%s.vcf" % cancer_sample)
log_outfile1 = opj(out_path, "%s.call.log" % cancer_sample)
log_outfile2 = opj(out_path, "%s.sump.log" % cancer_sample)
x = normal_sample, cancer_sample, normal_bamfile, cancer_bamfile, \
muse_call_stem, muse_call_file, raw_vcf_outfile, vcf_outfile, \
log_outfile1, log_outfile2
jobs.append(x)
# Generate the commands.
# MuSE call -O test11 -f genomes/Broad.hg19/Homo_sapiens_assembly19.fa\
# bam04/196B-MG.bam bam04/PIM001_G.bam
# MuSE sump -I test11.MuSE.txt -E -O test12.vcf \
# -D MuSE/dbsnp_132_b37.leftAligned.vcf.gz
MuSE = mlib.findbin("muse")
sq = mlib.sq
commands = []
for x in jobs:
normal_sample, cancer_sample, normal_bamfile, cancer_bamfile, \
muse_call_stem, muse_call_file, raw_vcf_outfile, vcf_outfile, \
log_outfile1, log_outfile2 = x
x = [
sq(MuSE),
"call",
"-O",
muse_call_stem,
"-f",
sq(ref.fasta_file_full),
cancer_bamfile,
normal_bamfile,
]
x = " ".join(x)
x = "%s >& %s" % (x, log_outfile1)
commands.append(x)
assert len(commands) == len(jobs)
# Not sure about RAM.
nc = mlib.calc_max_procs_from_ram(10, upper_max=num_cores)
parallel.pshell(commands, max_procs=nc)
metadata["num_cores"] = nc
metadata["commands"] = commands
# Make sure the log files have no errors. The files should be
# empty.
log_files = [x[8] for x in jobs]
filelib.assert_exists_z_many(log_files)
# Make sure the call files are created and not empty.
call_files = [x[5] for x in jobs]
filelib.assert_exists_nz_many(call_files)
# Run the "sump" step.
commands = []
for x in jobs:
normal_sample, cancer_sample, normal_bamfile, cancer_bamfile, \
muse_call_stem, muse_call_file, raw_vcf_outfile, vcf_outfile, \
log_outfile1, log_outfile2 = x
x = [
sq(MuSE),
"sump",
"-I",
sq(muse_call_file),
]
assert wgs_or_wes in ["wgs", "wes"]
if wgs_or_wes == "wgs":
x += ["-G"]
else:
x += ["-E"]
x += [
"-O",
sq(raw_vcf_outfile),
"-D",
sq(dbsnp_file),
]
x = " ".join(x)
x = "%s >& %s" % (x, log_outfile2)
commands.append(x)
assert len(commands) == len(jobs)
# Not sure about RAM.
nc = mlib.calc_max_procs_from_ram(10, upper_max=num_cores)
parallel.pshell(commands, max_procs=nc)
metadata["commands"] = metadata["commands"] + commands
# Make sure the log files have no errors. The files should be
# empty.
log_files = [x[9] for x in jobs]
filelib.assert_exists_z_many(log_files)
# Make sure the raw files are created and not empty.
vcf_files = [x[6] for x in jobs]
filelib.assert_exists_nz_many(vcf_files)
# Fix the files.
commands = [] # Should be python commands.
for x in jobs:
normal_sample, cancer_sample, normal_bamfile, cancer_bamfile, \
muse_call_stem, muse_call_file, raw_vcf_outfile, vcf_outfile, \
log_outfile1, log_outfile2 = x
args = normal_sample, cancer_sample, raw_vcf_outfile, vcf_outfile
x = alignlib.clean_muse_vcf, args, {}
commands.append(x)
parallel.pyfun(commands, num_procs=num_cores)
# Delete the log_outfiles if empty.
for x in jobs:
normal_sample, cancer_sample, normal_bamfile, cancer_bamfile, \
muse_call_stem, muse_call_file, raw_vcf_outfile, vcf_outfile, \
log_outfile1, log_outfile2 = x
if os.path.exists(log_outfile1):
os.unlink(log_outfile1)
if os.path.exists(log_outfile2):
os.unlink(log_outfile2)
# Make sure output VCF files exist.
x = [x[7] for x in jobs]
filelib.assert_exists_many(x)
return metadata
def run(self, network, antecedents, out_attributes, user_options,
num_cores, out_path):
import os
from genomicode import filelib
from genomicode import ngslib
from genomicode import parallel
from genomicode import alignlib
from Betsy import module_utils as mlib
bam_node, ref_node = antecedents
bam_filenames = mlib.find_bam_files(bam_node.identifier)
ref = alignlib.create_reference_genome(ref_node.identifier)
filelib.safe_mkdir(out_path)
metadata = {}
features_bed = mlib.get_user_option(user_options,
"features_bed",
check_file=True)
if features_bed:
metadata["features_bed"] = features_bed
# Applies to genomecov.
min_coverage = user_options.get("ignore_coverage_below")
if min_coverage == "":
min_coverage = None
if min_coverage is not None:
min_coverage = int(min_coverage)
assert min_coverage >= 0
metadata["tool"] = "bedtools %s" % ngslib.get_bedtools_version()
metadata["num_cores"] = num_cores
metadata["commands"] = []
# Set up the filenames.
# list of (
# sample,
# orig_bam_filename, Original bam filename.
# bam_filename, bam file, after filtering out unmapped reads.
# genomecov_filename, Generated by genomecov. Histogram.
# histo_datafile, Data file to generate histogram (from cov).
# histo_plotfile, Histogram plot.
# histo_prismfile, To make histogram in PRISM.
#
# ONLY USED IF features_bed
# intervallist_file, Made from BED file.
# cov_filename, Generated by Picard.
# targetcov_filename, Generated by Picard. Per target coverage.
# log_filename, Output from Picard.
# )
opj = os.path.join
jobs = [] # list of filelib.GenericObject
for bam_filename in bam_filenames:
# <in_path>/<sample>.bam
in_path, sample, ext = mlib.splitpath(bam_filename)
assert ext == ".bam"
clean_bam_filename = opj(out_path, "%s.bam" % sample)
assert clean_bam_filename != bam_filename
genomecov_filename = opj(out_path, "%s.genomecov.txt" % sample)
histo_datafile = opj(out_path, "%s.histo.txt" % sample)
histo_plotfile = opj(out_path, "%s.histo.png" % sample)
histo_prismfile = opj(out_path, "%s.prism.txt" % sample)
intervallist_file = opj(out_path, "%s.interval.txt" % sample)
cov_filename = opj(out_path, "%s.coverage.txt" % sample)
targetcov_filename = opj(out_path, "%s.targetcov.txt" % sample)
log_filename = opj(out_path, "%s.picard.log" % sample)
x = filelib.GenericObject(sample=sample,
orig_bam_filename=bam_filename,
bam_filename=clean_bam_filename,
genomecov_filename=genomecov_filename,
histo_datafile=histo_datafile,
histo_plotfile=histo_plotfile,
histo_prismfile=histo_prismfile,
intervallist_file=intervallist_file,
cov_filename=cov_filename,
targetcov_filename=targetcov_filename,
log_filename=log_filename)
#x = sample, bam_filename, genomecov_filename, \
# histo_datafile, histo_plotfile, histo_prismfile, \
# intervallist_file, cov_filename, targetcov_filename, \
# log_filename
jobs.append(x)
# Remove unmapped reads from the BAM files.
# Need to remove the unmapped reads or Picard might complain:
# Exception in thread "main"
# htsjdk.samtools.SAMFormatException: SAM validation error:
# ERROR: Record 154286082, Read name
# DF9F08P1:326:C5KJFACXX:5:1304:12068:90850, MAPQ should be 0
# for unmapped read.
#
# This can happen with BWA generated alignments.
cmds = []
for x in jobs:
x = _make_samtools_filter_cmd(x.orig_bam_filename, x.bam_filename)
cmds.append(x)
parallel.pshell(cmds, max_procs=num_cores)
x = [x.bam_filename for x in jobs]
filelib.assert_exists_nz_many(x)
# Generate the intervallist_file(s).
if features_bed:
cmds = []
for x in jobs:
args = x.intervallist_file, features_bed, x.bam_filename
x = _make_intervallist_file, args, {}
cmds.append(x)
parallel.pyfun(cmds, num_procs=num_cores)
# Make the commands to run picard.
if features_bed:
commands = []
for x in jobs:
x = _make_calculatehsmetrics_command(
x.intervallist_file, x.bam_filename, x.cov_filename,
x.targetcov_filename, ref.fasta_file_full, x.log_filename)
commands.append(x)
metadata["commands"].append(commands)
parallel.pshell(commands, max_procs=num_cores)
x1 = [x.cov_filename for x in jobs]
x2 = [x.targetcov_filename for x in jobs]
filelib.assert_exists_nz_many(x1 + x2)
# Use genomecov to count read depth.
x = _run_genomecov(jobs, ref_node.identifier, num_cores)
metadata["commands"].append(x)
# Summarize the average read depth.
summary_file = opj(out_path, "summary.xls")
_summarize_average_read_depth(jobs, min_coverage, summary_file)
# Make histograms of the distribution of the read depth for
# each sample.
for x in jobs:
_make_histo_file(x.genomecov_filename, x.histo_datafile)
# Delete the filtered BAM files to save space.
for x in jobs:
filelib.assert_exists_nz(x.bam_filename)
os.unlink(x.bam_filename)
return metadata
def run(self, network, antecedents, out_attributes, user_options,
num_cores, out_path):
import os
from genomicode import parallel
from genomicode import filelib
from genomicode import genomelib
from genomicode import config
from Betsy import module_utils as mlib
fasta_node, bam_node, sample_node, orient_node = antecedents
fasta_data = mlib.find_merged_fastq_files(sample_node.identifier,
fasta_node.identifier,
find_fasta=True)
bam_filenames = mlib.find_bam_files(bam_node.identifier)
orient = mlib.read_orientation(orient_node.identifier)
filelib.safe_mkdir(out_path)
# TODO: Try to figure out version.
metadata = {}
metadata["tool"] = "RSeQC (unknown version)"
pyrseqc = mlib.findbin("pyrseqc")
gene_model = mlib.get_user_option(user_options,
"gene_model",
not_empty=True,
allowed_values=["hg19"])
if gene_model == "hg19":
gene_path = config.rseqc_hg19
else:
raise AssertionError, "Unhandled: %s" % gene_model
filelib.dir_exists(gene_path)
gene_model_bed = os.path.join(gene_path, "RefSeq.bed12")
housekeeping_model_bed = os.path.join(gene_path,
"HouseKeepingGenes.bed")
sample2fastadata = {}
for x in fasta_data:
sample, f1, f2 = x
sample2fastadata[sample] = x
is_paired = orient.orientation.startswith("paired")
# Guess the read length. Read the first fasta.
assert sample2fastadata
x = sample2fastadata.keys()[0]
filename = sample2fastadata[x][1]
lengths = {} # length -> count
for i, x in enumerate(genomelib.read_fasta_many(filename)):
if i >= 100:
break
title, sequence = x
l = len(sequence)
lengths[l] = lengths.get(l, 0) + 1
# Use the most common length.
c_length = c_count = None
for (l, c) in lengths.iteritems():
if c_count is None or c > c_count:
c_length, c_count = l, c
assert c_length
read_length = c_length
jobs = [] # sample, bam_filename, fasta_file1, fasta_file2, outdir
for bam_filename in bam_filenames:
# <path>/<sample>.bam
p, sample, e = mlib.splitpath(bam_filename)
assert sample in sample2fastadata
x, f1, f2 = sample2fastadata[sample]
outdir = os.path.join(out_path, sample)
x = sample, bam_filename, f1, f2, outdir
jobs.append(x)
# Some of the modules of RSeQC uses a lot of memory. Have
# seen a Python process take 33 Gb, and an R process take 200
# Gb. However, most of the modules use much less memory. So
# run one pyrseqc at a time, and run each one of those
# processes in parallel. Is probably slower than running
# multiple pyrseqc, but takes less memory.
commands = []
for x in jobs:
sample, bam_filename, fasta_filename1, fasta_filename2, outdir = x
# pyrseqc.py -j 20 --paired_end rqc11.bam rqc14.fa 76 \
# mod07.txt hg19.HouseKeepingGenes.bed rqc21 --dry_run
x = [
mlib.sq(pyrseqc),
"-j",
str(num_cores),
]
if is_paired:
x += ["--paired_end"]
x += [
mlib.sq(bam_filename),
mlib.sq(fasta_filename1),
str(read_length),
mlib.sq(gene_model_bed),
mlib.sq(housekeeping_model_bed),
mlib.sq(outdir),
]
x = " ".join(x)
commands.append(x)
metadata["commands"] = commands
metadata["num_cores"] = num_cores
# pyrseqc takes up to ~40 Gb per process.
# read_distribution.py takes 33 Gb.
# read_quality.py spins off an R process that takes ~200 Gb.
# Make sure we don't use up more memory than is available on
# the machine.
#nc = mlib.calc_max_procs_from_ram(60, upper_max=num_cores)
#metadata["num cores"] = nc
#x = parallel.pshell(commands, max_procs=nc)
# Because of memory, just run one at a time, but each one, use
# multiple cores.
for cmd in commands:
x = parallel.sshell(cmd)
assert x.find("Traceback") < 0, x
filelib.assert_exists_nz(out_path)
return metadata
def run(self, network, antecedents, out_attributes, user_options,
num_cores, out_path):
import os
from genomicode import filelib
from genomicode import parallel
from genomicode import alignlib
from Betsy import module_utils as mlib
bam_node, nc_node, ref_node = antecedents
bam_filenames = mlib.find_bam_files(bam_node.identifier)
assert bam_filenames, "No .bam files."
nc_match = mlib.read_normal_cancer_file(nc_node.identifier)
ref = alignlib.create_reference_genome(ref_node.identifier)
filelib.safe_mkdir(out_path)
metadata = {}
# TODO: Figure out version.
# Figure out whether the user wants SNPs or INDELs.
#assert "vartype" in out_attributes
#vartype = out_attributes["vartype"]
#assert vartype in ["all", "snp", "indel"]
# sample -> bam filename
sample2bamfile = mlib.root2filename(bam_filenames)
# Make sure files exist for all the samples.
mlib.assert_normal_cancer_samples(nc_match, sample2bamfile)
# list of (cancer_sample, normal_bamfile, tumor_bamfile, orig_outfile,
# fixed_outfile, filtered_outfile)
opj = os.path.join
jobs = []
for (normal_sample, cancer_sample) in nc_match:
normal_bamfile = sample2bamfile[normal_sample]
cancer_bamfile = sample2bamfile[cancer_sample]
path, sample, ext = mlib.splitpath(cancer_bamfile)
orig_outfile = opj(out_path, "%s.raw" % sample)
fix_outfile = opj(out_path, "%s.vcf" % sample)
#filter_outfile = opj(out_path, "%s.vcf" % sample)
x = cancer_sample, normal_bamfile, cancer_bamfile, \
orig_outfile, fix_outfile
x = filelib.GenericObject(cancer_sample=cancer_sample,
normal_bamfile=normal_bamfile,
cancer_bamfile=cancer_bamfile,
orig_outfile=orig_outfile,
fix_outfile=fix_outfile)
jobs.append(x)
# python /usr/local/museq/classify.py \
# normal:test31/normal.bam tumour:test31/tumor.bam \
# reference:genomes/Broad.hg19/Homo_sapiens_assembly19.fa \
# model:/usr/local/museq/model_v4.1.2.npz \
# --config /usr/local/museq/metadata.config \
# -o test51.vcf
opj = os.path.join
museq = mlib.get_config("museq", assert_exists=True)
classify_py = opj(museq, "classify.py")
model_file = opj(museq, "model_v4.1.2.npz")
config_file = opj(museq, "metadata.config")
filelib.assert_exists_nz(classify_py)
filelib.assert_exists_nz(model_file)
filelib.assert_exists_nz(config_file)
# museq's config file generates a broken VCF file. Fix it.
fixed_config_file = "fixed.config"
fix_config_file(config_file, fixed_config_file)
# Generate the commands.
sq = mlib.sq
commands = []
for j in jobs:
#cancer_sample, normal_bamfile, cancer_bamfile, \
# raw_outfile, fix_outfile, vcf_outfile = x
x = [
"python", # should allow user to specify python
sq(classify_py),
sq("normal:%s" % j.normal_bamfile),
sq("tumour:%s" % j.cancer_bamfile),
sq("reference:%s" % ref.fasta_file_full),
sq("model:%s" % model_file),
"--config",
sq(fixed_config_file),
"-o",
sq(j.orig_outfile),
]
x = " ".join(map(str, x))
commands.append(x)
# Not sure how much RAM this takes. On Thunderbolts test,
# took < 1 Gb.
nc = mlib.calc_max_procs_from_ram(5, upper_max=num_cores)
parallel.pshell(commands, max_procs=nc)
metadata["num_cores"] = nc
metadata["commands"] = commands
# JointSNVMix produces non-standard VCF files. Fix this so it
# will work with other programs downstream.
for j in jobs:
#cancer_sample, normal_bamfile, cancer_bamfile, \
# raw_outfile, fix_outfile, vcf_outfile = x
fix_vcf_file(j.cancer_sample, j.orig_outfile, j.fix_outfile)
# Filter each of the VCF files.
#for x in jobs:
# cancer_sample, normal_bamfile, cancer_bamfile, \
# raw_outfile, fix_outfile, vcf_outfile = x
# filter_by_vartype(vartype, fix_outfile, vcf_outfile)
#metadata["filter"] = vartype
#x = [x[-1] for x in jobs]
x = [j.fix_outfile for x in jobs]
filelib.assert_exists_many(x)
return metadata
def run(self, network, in_data, out_attributes, user_options, num_cores,
out_path):
import os
from genomicode import filelib
from genomicode import parallel
from genomicode import alignlib
from Betsy import module_utils as mlib
bam_filenames = mlib.find_bam_files(in_data.identifier)
assert bam_filenames, "No .bam files."
filelib.safe_mkdir(out_path)
metadata = {}
#in_path = module_utils.unzip_if_zip(in_data.identifier)
#x = filelib.list_files_in_path(in_path)
#x = [x for x in x if x.lower().endswith(".bam")]
#in_filenames = x
#assert in_filenames, "No .bam files."
jobs = [] # list of (in_filename, log_filename, out_filename)
for in_filename in bam_filenames:
p, f = os.path.split(in_filename)
s, ext = os.path.splitext(f)
log_filename = os.path.join(out_path, "%s.log" % s)
out_filename = os.path.join(out_path, f)
x = in_filename, log_filename, out_filename
jobs.append(x)
# java -Xmx5g -jar MarkDuplicates.jar
# I=<input.sam or .bam> O=<output.bam>
# METRICS_FILE=metricsFile CREATE_INDEX=true
# VALIDATION_STRINGENCY=LENIENT REMOVE_DUPLICATES=true
#picard_jar = module_utils.find_picard_jar("MarkDuplicates")
picard_jar = alignlib.find_picard_jar("picard")
# Make a list of commands.
sq = parallel.quote
commands = []
for x in jobs:
in_filename, log_filename, out_filename = x
x = [
"java",
"-Xmx20g",
"-jar",
sq(picard_jar),
"MarkDuplicates",
"I=%s" % sq(in_filename),
"O=%s" % sq(out_filename),
"METRICS_FILE=metricsFile",
#"CREATE_INDEX=true",
"VALIDATION_STRINGENCY=LENIENT",
#"REMOVE_DUPLICATES=true",
"REMOVE_DUPLICATES=false",
# BAM files should be sorted.
# Actuallly, DEPRECATED now.
#"ASSUME_SORTED=true",
"TMP_DIR=%s" % sq(out_path),
]
x = " ".join(x)
x = "%s >& %s" % (x, sq(log_filename))
commands.append(x)
# java may use additional threads for garbage collection.
# https://sourceforge.net/p/picard/wiki/Main_Page/
# Takes ~10 Gb per process (with -Xmx5g). Increased the RAM
# used to 20 Gb because 5 Gb was not enough for some files.
nc = mlib.calc_max_procs_from_ram(25, upper_max=num_cores)
parallel.pshell(commands, max_procs=nc)
metadata["commands"] = commands
metadata["num_cores"] = nc
# Make sure the analysis completed successfully.
out_filenames = [x[-1] for x in jobs]
filelib.assert_exists_nz_many(out_filenames)
return metadata
def run(self, network, antecedents, out_attributes, user_options,
num_cores, out_path):
import os
from genomicode import filelib
from genomicode import parallel
from genomicode import alignlib
from Betsy import module_utils
bam_node, ref_node, target_node = antecedents
bam_filenames = module_utils.find_bam_files(bam_node.identifier)
assert bam_filenames, "No .bam files."
target_filenames = filelib.list_files_in_path(target_node.identifier,
endswith=".intervals")
assert target_filenames, "No .intervals files."
ref = alignlib.create_reference_genome(ref_node.identifier)
filelib.safe_mkdir(out_path)
assert len(bam_filenames) == len(target_filenames), \
"Should have an .intervals file for each bam file."
sample2bamfilename = {}
for filename in bam_filenames:
p, f = os.path.split(filename)
sample, ext = os.path.splitext(f)
assert sample not in sample2bamfilename
sample2bamfilename[sample] = filename
sample2targetfilename = {}
for filename in target_filenames:
p, f = os.path.split(filename)
sample, ext = os.path.splitext(f)
assert sample not in sample2targetfilename
sample2targetfilename[sample] = filename
assert len(sample2bamfilename) == len(sample2targetfilename)
missing = [
x for x in sample2bamfilename if x not in sample2targetfilename
]
assert not missing, "Missing interval files for %d bam files." % \
len(missing)
# list of (bam_filename, target_filename, log_filename, out_filename)
jobs = []
for sample in sample2bamfilename:
bam_filename = sample2bamfilename[sample]
target_filename = sample2targetfilename[sample]
p, f = os.path.split(bam_filename)
sample, ext = os.path.splitext(f)
out_filename = os.path.join(out_path, "%s.bam" % sample)
log_filename = os.path.join(out_path, "%s.log" % sample)
x = bam_filename, target_filename, log_filename, out_filename
jobs.append(x)
known_sites = []
x1 = module_utils.get_user_option(user_options,
"realign_known_sites1",
check_file=True)
x2 = module_utils.get_user_option(user_options,
"realign_known_sites2",
check_file=True)
x3 = module_utils.get_user_option(user_options,
"realign_known_sites3",
check_file=True)
x = [x1, x2, x3]
x = [x for x in x if x]
known_sites = x
assert known_sites
# java -Xmx5g -jar /usr/local/bin/GATK/GenomeAnalysisTK.jar \
# -T IndelRealigner -R <ref.fa> \
# -I <bam_file> -targetIntervals <target_file> -o <bam_file>
# Make a list of commands.
commands = []
for x in jobs:
bam_filename, target_filename, log_filename, out_filename = x
x = [("known", x) for x in known_sites]
x = alignlib.make_GATK_command(T="IndelRealigner",
R=ref.fasta_file_full,
I=bam_filename,
targetIntervals=target_filename,
o=out_filename,
_UNHASHABLE=x)
x = "%s >& %s" % (x, log_filename)
commands.append(x)
#for x in commands:
# print x
#import sys; sys.exit(0)
parallel.pshell(commands, max_procs=num_cores)
# Make sure the analysis completed successfully.
out_filenames = [x[-1] for x in jobs]
filelib.assert_exists_nz_many(out_filenames)
def run(self, network, antecedents, out_attributes, user_options,
num_cores, out_path):
import os
from genomicode import filelib
from genomicode import parallel
from genomicode import alignlib
from Betsy import module_utils as mlib
bam_node, nc_node, ref_node = antecedents
bam_filenames = mlib.find_bam_files(bam_node.identifier)
assert bam_filenames, "No .bam files."
nc_match = mlib.read_normal_cancer_file(nc_node.identifier)
ref = alignlib.create_reference_genome(ref_node.identifier)
filelib.safe_mkdir(out_path)
metadata = {}
# TODO: Figure out Strelka version.
skip_depth_filter = False
x = mlib.get_user_option(user_options,
"strelka_skip_depth_filter",
allowed_values=["no", "yes"],
not_empty=True)
if x == "yes":
skip_depth_filter = True
assert "vartype" in out_attributes, "Missing attribute: vartype"
x = out_attributes["vartype"]
assert x in ["snp", "indel"]
vartype = x
# sample -> bam filename
sample2bamfile = mlib.root2filename(bam_filenames)
# Make sure files exist for all the samples.
mlib.assert_normal_cancer_samples(nc_match, sample2bamfile)
# Make sure each cancer sample is unique. Otherwise, the
# analysis directories will conflict.
tumor_samples = [x[-1] for x in nc_match]
dups = {}
for i in range(1, len(tumor_samples)):
if tumor_samples[i] in tumor_samples[:i]:
dups[tumor_samples[i]] = 1
assert not dups, "NormalCancerFile contains multiple instances of: %s"\
% ", ".join(sorted(dups))
# list of (normal_sample, cancer_sample, normal_bamfile, tumor_bamfile,
# config_file, output_dir
opj = os.path.join
jobs = []
for (normal_sample, cancer_sample) in nc_match:
normal_bamfile = sample2bamfile[normal_sample]
cancer_bamfile = sample2bamfile[cancer_sample]
path, sample, ext = mlib.splitpath(cancer_bamfile)
config_file = opj(out_path, "config.%s.ini" % cancer_sample)
analysis_path = opj(out_path, "analysis.%s" % cancer_sample)
x = normal_sample, cancer_sample, normal_bamfile, cancer_bamfile, \
config_file, analysis_path
jobs.append(x)
# Make each of the config files.
for x in jobs:
normal_sample, cancer_sample, normal_bamfile, cancer_bamfile, \
config_file, analysis_path = x
_make_config_file(config_file, skip_depth_filter=skip_depth_filter)
# Make the analysis directories.
jobs2 = []
for x in jobs:
normal_sample, cancer_sample, normal_bamfile, cancer_bamfile, \
config_file, analysis_path = x
fn = _make_analysis_directory
args = (analysis_path, config_file, ref.fasta_file_full,
normal_bamfile, cancer_bamfile)
keywds = None
jobs2.append((fn, args, keywds))
parallel.pyfun(jobs2, num_procs=num_cores)
# Run the analysis.
for x in jobs:
normal_sample, cancer_sample, normal_bamfile, cancer_bamfile, \
config_file, analysis_path = x
cmd = "make -j %d" % num_cores
parallel.sshell(cmd, path=analysis_path)
metadata["num_cores"] = num_cores
# Make sure files exists.
x = [x[-1] for x in jobs]
x = [os.path.join(x, "results", "all.somatic.snvs.vcf") for x in x]
filelib.assert_exists_nz_many(x)
# Clean the VCF files and save into the out_path.
for x in jobs:
normal_sample, cancer_sample, normal_bamfile, cancer_bamfile, \
config_file, analysis_path = x
# <analysis_path>/results/all.somatic.snvs.vcf
# <analysis_path>/results/all.somatic.indels.vcf
vartype2file = {
"snp": "all.somatic.snvs.vcf",
"indel": "all.somatic.indels.vcf",
}
assert vartype in vartype2file
x = vartype2file[vartype]
src_file = os.path.join(analysis_path, "results", x)
dst_file = os.path.join(out_path, "%s.vcf" % cancer_sample)
alignlib.clean_strelka_vcf(normal_sample, cancer_sample, src_file,
dst_file)
#metadata["commands"] = commands
return metadata
def run(self, network, antecedents, out_attributes, user_options,
num_cores, out_path):
import os
from genomicode import config
from genomicode import filelib
from genomicode import parallel
from genomicode import alignlib
from Betsy import module_utils
## Importing pysam is hard!
#import sys
#sys_path_old = sys.path[:]
#sys.path = [x for x in sys.path if x.find("RSeQC") < 0]
#import pysam
#sys.path = sys_path_old
bam_node, ref_node = antecedents
bam_filenames = module_utils.find_bam_files(bam_node.identifier)
assert bam_filenames, "No .bam files."
ref = alignlib.create_reference_genome(ref_node.identifier)
filelib.safe_mkdir(out_path)
# list of (in_filename, err_filename, out_filename)
jobs = []
for in_filename in bam_filenames:
p, f = os.path.split(in_filename)
s, ext = os.path.splitext(f)
log_filename = os.path.join(out_path, "%s.log" % s)
out_filename = os.path.join(out_path, f)
assert in_filename != out_filename
x = in_filename, log_filename, out_filename
jobs.append(x)
# Don't do this. Need MD, NM, NH in
# summarize_alignment_cigar. To be sure, just redo it.
## If the files already have MD tags, then just symlink the
## files. Don't add again.
#i = 0
#while i < len(jobs):
# in_filename, out_filename = jobs[i]
#
# handle = pysam.AlignmentFile(in_filename, "rb")
# align = handle.next()
# tag_dict = dict(align.tags)
# if "MD" not in tag_dict:
# i += 1
# continue
# # Has MD tags. Just symlink and continue.
# os.symlink(in_filename, out_filename)
# del jobs[i]
# Make a list of samtools commands.
# Takes ~200 Mb per process, so should not be a big issue.
samtools = filelib.which_assert(config.samtools)
sq = parallel.quote
commands = []
for x in jobs:
in_filename, log_filename, out_filename = x
# samtools calmd -b <in.bam> <ref.fasta> > <out.bam>
# May generate error:
# [bam_fillmd1] different NM for read
# 'ST-J00106:118:H75L3BBXX:3:2128:21846:47014': 0 -> 19
# Pipe stderr to different file.
x = [
samtools,
"calmd",
"-b",
sq(in_filename),
sq(ref.fasta_file_full),
]
x = " ".join(x)
x = "%s 2> %s 1> %s" % (x, sq(log_filename), sq(out_filename))
commands.append(x)
parallel.pshell(commands, max_procs=num_cores)
# Make sure the analysis completed successfully.
x = [x[-1] for x in jobs]
filelib.assert_exists_nz_many(x)
def run(
self, network, antecedents, out_attributes, user_options, num_cores,
out_path):
import os
from genomicode import config
from genomicode import filelib
from genomicode import parallel
from genomicode import alignlib
from genomicode import hashlib
from Betsy import module_utils
bam_node, ref_node = antecedents
bam_filenames = module_utils.find_bam_files(bam_node.identifier)
ref = alignlib.create_reference_genome(ref_node.identifier)
filelib.safe_mkdir(out_path)
# java -jar /usr/local/bin/RNA-SeQC_v1.1.8.jar \
# -o <sample> -r <reference_file> -s "<sample>|<in_filename>|NA"
# -t <gtf_file> >& <log_filename>"
# <out_path> Output directory. Will be created if not exists.
# <in_filename> BAM file
# <reference_file> /data/biocore/genomes/UCSC/mm10.fa
# <gtf_file> /data/biocore/rsem/mouse_refseq_mm10/UCSC_knownGenes.gtf
#
# <reference_file> must be indexed and have a dict file.
rna_seqc_jar = filelib.which_assert(config.rna_seqc_jar)
GTF = module_utils.get_user_option(
user_options, "rna_seqc_gtf_file", not_empty=True)
assert os.path.exists(GTF), "File not found: %s" % GTF
# list of infile, out_path, ref_file, gtf_file, sample, log_file
jobs = []
for in_filename in bam_filenames:
p, file_ = os.path.split(in_filename)
f, e = os.path.splitext(file_)
sample = hashlib.hash_var(f)
out_path_rna_seqc = os.path.join(out_path, sample)
log_filename = os.path.join(out_path, "%s.log" % sample)
x = in_filename, out_path_rna_seqc, ref.fasta_file_full, GTF, \
sample, log_filename
jobs.append(x)
sq = parallel.quote
commands = []
for x in jobs:
(in_filename, out_path_rna_seqc, ref_filename, gtf_filename, \
sample, log_filename) = x
x = [sample, in_filename, "NA"]
x = "|".join(x)
x = [
'java',
'-jar', rna_seqc_jar,
'-o', sq(out_path_rna_seqc),
'-r', sq(ref_filename),
'-s', "'%s'" % x,
'-t', gtf_filename,
]
x = " ".join(x)
cmd = "%s >& %s" % (x, log_filename)
commands.append(cmd)
# Gets lots of errors.
x = parallel.pshell(commands, max_procs=num_cores)
run_log = os.path.join(out_path, "run.log")
open(run_log, 'w').write(x)
# Check for outfile.
# Make sure the analysis completed successfully.
for x in jobs:
(in_filename, out_path_rna_seqc, ref_filename, gtf_filename, \
sample, log_filename) = x
filelib.assert_exists_nz(out_path_rna_seqc)
def run(
self, network, in_data, out_attributes, user_options, num_cores,
out_path):
import os
import shutil
from genomicode import parallel
from genomicode import filelib
from genomicode import alignlib
from Betsy import module_utils as mlib
bam_filenames = mlib.find_bam_files(in_data.identifier)
filelib.safe_mkdir(out_path)
metadata = {}
metadata["tool"] = "bam2fastx (unknown version)"
# Somehow bam2fastx doesn't work if there are spaces in the
# filename. Make a temporary filename with no spaces, and
# then rename it later.
# Actually, may not be bam2fastx's fault.
jobs = []
for i, bam_filename in enumerate(bam_filenames):
p, f, e = mlib.splitpath(bam_filename)
#bai_filename = alignlib.find_bai_file(bam_filename)
#assert bai_filename, "Missing index for: %s" % bam_filename
#temp_bam_filename = "%d.bam" % i
#temp_bai_filename = "%d.bam.bai" % i
#temp_fa_filename = "%d.fa" % i
fa_filename = os.path.join(out_path, "%s.fa" % f)
x = filelib.GenericObject(
bam_filename=bam_filename,
#bai_filename=bai_filename,
#temp_bam_filename=temp_bam_filename,
#temp_bai_filename=temp_bai_filename,
#temp_fa_filename=temp_fa_filename,
fa_filename=fa_filename)
jobs.append(x)
bam2fastx = mlib.findbin("bam2fastx")
# Link all the bam files.
#for j in jobs:
# assert not os.path.exists(j.temp_bam_filename)
# #assert not os.path.exists(j.temp_bai_filename)
# os.symlink(j.bam_filename, j.temp_bam_filename)
# #os.symlink(j.bai_filename, j.temp_bai_filename)
commands = []
for j in jobs:
# bam2fastx -A --fasta -o rqc14.fa rqc11.bam
x = [
mlib.sq(bam2fastx),
"-A",
"--fasta",
#"-o", mlib.sq(j.temp_fa_filename),
#mlib.sq(j.temp_bam_filename),
"-o", mlib.sq(j.fa_filename),
mlib.sq(j.bam_filename),
]
x = " ".join(x)
commands.append(x)
metadata["commands"] = commands
metadata["num_cores"] = num_cores
parallel.pshell(commands, max_procs=num_cores)
#for j in jobs:
# # Move the temporary files to the final location.
# shutil.move(j.temp_fa_filename, j.fa_filename)
# # Remove the link to the BAM file.
# os.unlink(j.temp_bam_filename)
x = [j.fa_filename for x in jobs]
filelib.assert_exists_nz_many(x)
return metadata
def run(self, network, in_data, out_attributes, user_options, num_cores,
out_path):
import os
from genomicode import config
from genomicode import filelib
from genomicode import parallel
from genomicode import alignlib
#from genomicode import hashlib
from Betsy import module_utils
in_filenames = module_utils.find_bam_files(in_data.identifier)
assert in_filenames, "No .bam files."
filelib.safe_mkdir(out_path)
metadata = {}
metadata["tool"] = "samtools %s" % alignlib.get_samtools_version()
jobs = []
#seen = {}
for i, in_filename in enumerate(in_filenames):
p, f = os.path.split(in_filename)
temp_prefix = "temp_%s" % f
#temp_prefix = "temp_%s" % hashlib.hash_var(f)
# Make sure no duplicates.
#assert temp_prefix not in seen
#seen[temp_prefix] = 1
#temp_outfilename = "%d.bam" % i
out_filename = os.path.join(out_path, f)
x = filelib.GenericObject(
in_filename=in_filename,
temp_prefix=temp_prefix,
#temp_outfilename=temp_outfilename,
out_filename=out_filename)
jobs.append(x)
samtools = filelib.which_assert(config.samtools)
# Calculate the number of threads per process.
nc = module_utils.calc_max_procs_from_ram(4, upper_max=num_cores)
num_threads = max(nc / len(jobs), 1)
# Make a list of samtools commands.
# Without -m, takes ~1 Gb per process.
sq = parallel.quote
commands = []
for j in jobs:
# Usage has changed. Below no longer valid.
# samtools sort <in_filename> <out_filestem>
# .bam automatically added to <out_filestem>, so don't
# need it.
#x = out_filename
#assert x.endswith(".bam")
#x = x[:-4]
#out_filestem = x
x = [
sq(samtools),
"sort",
"-O",
"bam",
"-T",
sq(j.temp_prefix),
"-m",
"4G", # Crashing, so try increasing memory.
sq(j.in_filename),
#"-o", sq(j.temp_outfilename),
"-o",
sq(j.out_filename),
]
if num_threads > 1:
x += ["-@", num_threads]
x = " ".join(map(str, x))
commands.append(x)
metadata["commands"] = commands
metadata["num_cores"] = nc
parallel.pshell(commands, max_procs=nc)
#for cmd in commands:
# parallel.sshell(cmd)
#for j in jobs:
# # Move the temporary files to the final location.
# shutil.move(j.temp_outfilename, j.out_filename)
# Make sure the analysis completed successfully.
x = [j.out_filename for j in jobs]
filelib.assert_exists_nz_many(x)
return metadata
def run(
self, network, antecedents, out_attributes, user_options, num_cores,
out_path):
import os
from genomicode import filelib
from genomicode import parallel
from genomicode import alignlib
from Betsy import module_utils as mlib
bam_node, nc_node, ref_node, interval_node = antecedents
bam_filenames = mlib.find_bam_files(bam_node.identifier)
assert bam_filenames, "No .bam files."
nc_match = mlib.read_normal_cancer_file(nc_node.identifier)
ref = alignlib.create_reference_genome(ref_node.identifier)
filelib.assert_exists_nz(interval_node.identifier)
filelib.safe_mkdir(out_path)
metadata = {}
# TODO: Figure out MuTect version.
# Make sure intervals file ends with:
# .bed, .list, .picard, .interval_list, or .intervals
x, x, ext = mlib.splitpath(interval_node.identifier)
assert ext in [
".bed", ".list", ".picard", ".interval_list", ".intervals"]
cosmic_file = mlib.get_user_option(
user_options, "mutect_cosmic_vcf", not_empty=True, check_file=True)
dbsnp_file = mlib.get_user_option(
user_options, "mutect_dbsnp_vcf", not_empty=True, check_file=True)
# sample -> bam filename
sample2bamfile = mlib.root2filename(bam_filenames)
# Make sure files exist for all the samples.
mlib.assert_normal_cancer_samples(nc_match, sample2bamfile)
# list of (cancer_sample, normal_bamfile, tumor_bamfile, call_outfile,
# coverage_outfile, vcf_outfile, logfile)
opj = os.path.join
jobs = []
for (normal_sample, cancer_sample) in nc_match:
normal_bamfile = sample2bamfile[normal_sample]
cancer_bamfile = sample2bamfile[cancer_sample]
path, sample, ext = mlib.splitpath(cancer_bamfile)
call_outfile = opj(out_path, "%s.call_stats.out" % sample)
cov_outfile = opj(out_path, "%s.coverage.wig.txt" % sample)
raw_vcf_outfile = opj(out_path, "%s.vcf.raw" % sample)
vcf_outfile = opj(out_path, "%s.vcf" % sample)
log_outfile = opj(out_path, "%s.log" % sample)
x = normal_sample, cancer_sample, normal_bamfile, cancer_bamfile, \
call_outfile, cov_outfile, raw_vcf_outfile, vcf_outfile, \
log_outfile
jobs.append(x)
# java -Xmx2g -jar muTect.jar
# --analysis_type MuTect
# --reference_sequence <reference>
# --cosmic <cosmic.vcf>
# --dbsnp <dbsnp.vcf>
# --intervals <intervals_to_process>
# --input_file:normal <normal.bam>
# --input_file:tumor <tumor.bam>
# --out <call_stats.out>
# --coverage_file <coverage.wig.txt>
# Generate the commands.
sq = mlib.sq
commands = []
for x in jobs:
normal_sample, cancer_sample, normal_bamfile, cancer_bamfile, \
call_outfile, cov_outfile, raw_vcf_outfile, vcf_outfile, \
log_outfile = x
UNHASHABLE = [
("input_file:normal", sq(normal_bamfile)),
("input_file:tumor", sq(cancer_bamfile)),
]
x = alignlib.make_MuTect_command(
analysis_type="MuTect",
reference_sequence=sq(ref.fasta_file_full),
cosmic=sq(cosmic_file),
dbsnp=sq(dbsnp_file),
intervals=sq(interval_node.identifier),
out=sq(call_outfile),
coverage_file=sq(cov_outfile),
vcf=sq(raw_vcf_outfile),
_UNHASHABLE=UNHASHABLE,
)
x = "%s >& %s" % (x, log_outfile)
commands.append(x)
assert len(commands) == len(jobs)
nc = mlib.calc_max_procs_from_ram(15, upper_max=num_cores)
parallel.pshell(commands, max_procs=nc)
metadata["num_cores"] = nc
metadata["commands"] = commands
# Make sure log files have no errors. Check the log files
# before the VCF files. If there's an error, the VCF files
# may not be created.
# ##### ERROR -------------------------------------------------------
# ##### ERROR A GATK RUNTIME ERROR has occurred (version 2.2-25-g2a68
# ##### ERROR
# ##### ERROR Please visit the wiki to see if this is a known problem
# ##### ERROR If not, please post the error, with stack trace, to the
# ##### ERROR Visit our website and forum for extensive documentation
# ##### ERROR commonly asked questions http://www.broadinstitute.org/
# ##### ERROR
# ##### ERROR MESSAGE: java.lang.IllegalArgumentException: Comparison
# ##### ERROR -------------------------------------------------------
for i, x in enumerate(jobs):
normal_sample, cancer_sample, normal_bamfile, cancer_bamfile, \
call_outfile, cov_outfile, raw_vcf_outfile, vcf_outfile, \
log_outfile = x
# Pull out the error lines.
x = [x for x in open(log_outfile)]
x = [x for x in x if x.startswith("##### ERROR")]
x = "".join(x)
msg = "MuTect error [%s]:\n%s\n%s" % (
cancer_sample, commands[i], x)
assert not x, msg
# Make sure output VCF files exist.
x = [x[6] for x in jobs]
filelib.assert_exists_many(x)
# Fix the files.
for x in jobs:
normal_sample, cancer_sample, normal_bamfile, cancer_bamfile, \
call_outfile, cov_outfile, raw_vcf_outfile, vcf_outfile, \
log_outfile = x
alignlib.clean_mutect_vcf(
normal_bamfile, cancer_bamfile, normal_sample, cancer_sample,
raw_vcf_outfile, vcf_outfile)
return metadata
def run(self, network, antecedents, out_attributes, user_options,
num_cores, out_path):
import os
from genomicode import config
from genomicode import parallel
from genomicode import alignlib
from genomicode import filelib
from Betsy import module_utils
bam_node, ref_node, pos_node = antecedents
bam_filenames = module_utils.find_bam_files(bam_node.identifier)
assert bam_filenames, "No .bam files."
ref = alignlib.create_reference_genome(ref_node.identifier)
filelib.safe_mkdir(out_path)
metadata = {}
# Positions file has 0-based coordinates (like BAM files).
# But samtools requires 1-based coordinates. Convert to
# 1-based coordinates.
positions_filename = "positions.txt"
outhandle = open(positions_filename, 'w')
for x in filelib.read_cols(pos_node.identifier):
assert len(x) == 2
chrom, pos = x
pos = int(pos) + 1 # convert from 0- to 1-based coords.
x = chrom, pos
print >> outhandle, "\t".join(map(str, x))
outhandle.close()
# list of (in_filename, err_filename, out_filename)
jobs = []
for in_filename in bam_filenames:
p, f = os.path.split(in_filename)
sample, ext = os.path.splitext(f)
err_filename = os.path.join(out_path, "%s.log" % sample)
out_filename = os.path.join(out_path, "%s.pileup" % sample)
x = filelib.GenericObject(in_filename=in_filename,
err_filename=err_filename,
out_filename=out_filename)
jobs.append(x)
## Get possible positions file.
#positions_filename = module_utils.get_user_option(
# user_options, "positions_file", check_file=True)
# Figure out whether the purpose is to get coverage. Change
# the parameters if it is.
assert "vartype" in out_attributes
vartype = out_attributes["vartype"]
assert vartype in ["all", "snp", "indel", "consensus"]
#if cov == "yes":
# assert positions_filename, "Missing: positions_file"
# samtools mpileup -l freq04.txt -R -B -q 0 -Q 0 -d10000000 \
# -f genomes/Broad.hg19/Homo_sapiens_assembly19.fasta \
# $i > $j"
samtools = filelib.which_assert(config.samtools)
# Get an error if the BAM files are not indexed.
# [W::bam_hdr_read] EOF marker is absent. The input is probably
# truncated.
#if vartype == "consensus":
# args = [
# "-R", # Ignore read group tags.
# "-B", # Disable BAQ (base quality) computation.
# "-q", 0, # Skip bases with mapQ smaller than this.
# "-Q", 0, # Skip bases with BAQ smaller than this.
# "-d10000000", # Allow deep reads.
# ]
#else:
# raise NotImplementedError
args = [
"-R", # Ignore read group tags.
"-B", # Disable BAQ (base quality) computation.
"-q",
0, # Skip bases with mapQ smaller than this.
"-Q",
0, # Skip bases with BAQ smaller than this.
"-d10000000", # Allow deep reads.
]
sq = parallel.quote
commands = []
for j in jobs:
x = [
sq(samtools),
"mpileup",
"-f",
sq(ref.fasta_file_full),
]
if positions_filename:
x.extend(["-l", positions_filename])
x.extend(args)
x.append(sq(j.in_filename))
x = " ".join(map(str, x))
x = "%s 2> %s 1> %s" % (x, j.err_filename, j.out_filename)
commands.append(x)
#for x in commands:
# print x
parallel.pshell(commands, max_procs=num_cores)
metadata["commands"] = commands
# File may be empty if there are no reads.
x = [x.out_filename for x in jobs]
filelib.assert_exists_many(x)
# Make sure there's no errors in the log files.
for j in jobs:
check_log_file(j.err_filename)
return metadata
def run(self, network, antecedents, out_attributes, user_options,
num_cores, out_path):
import os
from genomicode import filelib
from genomicode import parallel
from genomicode import alignlib
from Betsy import module_utils as mlib
#import call_variants_GATK
bam_node, ref_node = antecedents
bam_filenames = mlib.find_bam_files(bam_node.identifier)
assert bam_filenames, "No .bam files."
ref = alignlib.create_reference_genome(ref_node.identifier)
filelib.safe_mkdir(out_path)
metadata = {}
# Figure out whether the user wants SNPs or INDELs.
#assert "vartype" in out_attributes
#vartype = out_attributes["vartype"]
#assert vartype in ["all", "snp", "indel"]
# Platypus generates an error if there are spaces in the BAM
# filename. Symlink the file to a local directory to make
# sure there are no spaces.
bam_path = "bam"
jobs = [] # list of filelib.GenericObject
for bam_filename in bam_filenames:
p, f = os.path.split(bam_filename)
sample, ext = os.path.splitext(f)
bai_filename = "%s.bai" % bam_filename
filelib.assert_exists_nz(bai_filename)
x = sample.replace(" ", "_")
local_bam = os.path.join(bam_path, "%s.bam" % x)
local_bai = os.path.join(bam_path, "%s.bam.bai" % x)
log_filename = os.path.join(out_path, "%s.log" % sample)
err_filename = os.path.join(out_path, "%s.err" % sample)
# Unfiltered file.
#raw_filename = os.path.join(out_path, "%s.raw" % sample)
# Final VCF file.
out_filename = os.path.join(out_path, "%s.vcf" % sample)
x = filelib.GenericObject(bam_filename=bam_filename,
bai_filename=bai_filename,
local_bam=local_bam,
local_bai=local_bai,
log_filename=log_filename,
err_filename=err_filename,
out_filename=out_filename)
jobs.append(x)
filelib.safe_mkdir(bam_path)
for j in jobs:
assert " " not in j.local_bam
filelib.assert_exists_nz(j.bam_filename)
filelib.assert_exists_nz(j.bai_filename)
if not os.path.exists(j.local_bam):
os.symlink(j.bam_filename, j.local_bam)
if not os.path.exists(j.local_bai):
os.symlink(j.bai_filename, j.local_bai)
# TODO: Keep better track of the metadata.
buffer_size = 100000
max_reads = 5E6
# Running into errors sometimes, so increase these numbers.
# WARNING - Too many reads (5000000) in region
# 1:500000-600000. Quitting now. Either reduce --bufferSize or
# increase --maxReads.
buffer_size = buffer_size * 10
max_reads = max_reads * 10
# Make a list of commands.
commands = []
for j in jobs:
#nc = max(1, num_cores/len(jobs))
x = alignlib.make_platypus_command(bam_file=j.local_bam,
ref_file=ref.fasta_file_full,
log_file=j.log_filename,
out_file=j.out_filename,
buffer_size=buffer_size,
max_reads=max_reads)
x = "%s >& %s" % (x, j.err_filename)
commands.append(x)
#for x in commands:
# print x
#import sys; sys.exit(0)
parallel.pshell(commands, max_procs=num_cores)
# Make sure the analysis completed successfully. If not, try
# to diagnose.
for j in jobs:
if filelib.exists_nz(j.out_filename):
continue
for line in open(j.err_filename):
if line.find("WARNING - Too many reads") >= 0:
print line,
x = [j.out_filename for j in jobs]
filelib.assert_exists_nz_many(x)
# Filter each of the VCF files.
#for j in jobs:
# call_variants_GATK.filter_by_vartype(
# vartype, j.raw_filename, j.out_filename)
#metadata["filter"] = vartype
return metadata
def run(
self, network, in_data, out_attributes, user_options, num_cores,
out_path):
import os
from genomicode import filelib
from genomicode import parallel
from genomicode import alignlib
from genomicode import hashlib
from Betsy import module_utils
bam_filenames = module_utils.find_bam_files(in_data.identifier)
assert bam_filenames, "No .bam files."
filelib.safe_mkdir(out_path)
metadata = {}
jobs = []
for in_filename in bam_filenames:
p, f = os.path.split(in_filename)
s, ext = os.path.splitext(f)
sample = hashlib.hash_var(s)
log_filename = os.path.join(out_path, "%s.log" % s)
out_filename = os.path.join(out_path, f)
x = filelib.GenericObject(
in_filename=in_filename,
sample=sample,
log_filename=log_filename,
out_filename=out_filename)
jobs.append(x)
gid = "group1"
library = "library"
platform_unit = "platform"
#sample = "sample"
platform = "illumina"
# java -Xmx5g -jar AddOrReplaceReadGroups.jar
# I=<input.sam or .bam> O=<output.bam> ID=<group ID>
# LB=<group library> PU=<platform unit> SM=<group sample name>
# PL=<platform> CREATE_INDEX=true VALIDATION_STRINGENCY=LENIENT
picard_jar = alignlib.find_picard_jar("picard")
# Make a list of commands.
sq = parallel.quote
commands = []
for j in jobs:
x = [
"java", "-Xmx5g",
"-jar", sq(picard_jar),
"AddOrReplaceReadGroups",
"I=%s" % sq(j.in_filename),
"O=%s" % sq(j.out_filename),
"ID=%s" % gid,
"LB=%s" % library,
"PU=%s" % platform_unit,
"SM=%s" % j.sample,
"PL=%s" % platform,
#"CREATE_INDEX=true",
"VALIDATION_STRINGENCY=LENIENT",
]
x = " ".join(x)
x = "%s >& %s" % (x, sq(j.log_filename))
commands.append(x)
parallel.pshell(commands, max_procs=num_cores)
metadata["commands"] = commands
metadata["num_cores"] = num_cores
# Make sure the analysis completed successfully.
# Make sure outfiles exist.
out_filenames = [j.out_filename for x in jobs]
filelib.assert_exists_nz_many(out_filenames)
# Check the log files to make sure there are no error.
for j in jobs:
check_log_file(j.log_filename)
return metadata
