def align_transcriptome(fastq_file, pair_file, ref_file, data):
"""
bwa mem with settings for aligning to the transcriptome for eXpress/RSEM/etc
"""
work_bam = dd.get_work_bam(data)
base, ext = os.path.splitext(work_bam)
out_file = base + ".transcriptome" + ext
if utils.file_exists(out_file):
data = dd.set_transcriptome_bam(data, out_file)
return data
# bwa mem needs phred+33 quality, so convert if it is Illumina
if dd.get_quality_format(data).lower() == "illumina":
logger.info("bwa mem does not support the phred+64 quality format, "
"converting %s and %s to phred+33.")
fastq_file = fastq.groom(fastq_file, data, in_qual="fastq-illumina")
if pair_file:
pair_file = fastq.groom(pair_file, data, in_qual="fastq-illumina")
bwa = config_utils.get_program("bwa", data["config"])
gtf_file = dd.get_gtf_file(data)
gtf_fasta = index_transcriptome(gtf_file, ref_file, data)
args = " ".join(_bwa_args_from_config(data["config"]))
num_cores = data["config"]["algorithm"].get("num_cores", 1)
samtools = config_utils.get_program("samtools", data["config"])
cmd = ("{bwa} mem {args} -a -t {num_cores} {gtf_fasta} {fastq_file} "
"{pair_file} ")
with file_transaction(data, out_file) as tx_out_file:
message = "Aligning %s and %s to the transcriptome." % (fastq_file, pair_file)
cmd += "| " + postalign.sam_to_sortbam_cl(data, tx_out_file, name_sort=True)
do.run(cmd.format(**locals()), message)
data = dd.set_transcriptome_bam(data, out_file)
return data
def rapmap_align(fq1, fq2, rapmap_dir, gtf_file, ref_file, algorithm, data):
valid_algorithms = ["pseudo", "quasi"]
assert algorithm in valid_algorithms, \
"RapMap algorithm needs to be one of %s." % valid_algorithms
safe_makedir(rapmap_dir)
samplename = dd.get_sample_name(data)
out_file = os.path.join(rapmap_dir, samplename + ".bam")
if file_exists(out_file):
return out_file
rapmap_index_loc = rapmap_index(gtf_file, ref_file, algorithm, data,
rapmap_dir)
num_cores = dd.get_num_cores(data)
algorithm_subcommand = algorithm + "map"
rapmap = config_utils.get_program("rapmap", dd.get_config(data))
cmd = "{rapmap} {algorithm_subcommand} -t {num_cores} -i {rapmap_index_loc} "
fq1_cmd = "{fq1} " if not is_gzipped(fq1) else "<(gzip -cd {fq1}) "
fq1_cmd = fq1_cmd.format(fq1=fq1)
if not fq2:
cmd += "-r {fq1_cmd} "
else:
fq2_cmd = "{fq2} " if not is_gzipped(fq2) else "<(gzip -cd {fq2}) "
fq2_cmd = fq2_cmd.format(fq2=fq2)
cmd += "-1 {fq2_cmd} -2 {fq2_cmd} "
with file_transaction(out_file) as tx_out_file:
cmd += "| " + postalign.sam_to_sortbam_cl(data, tx_out_file)
run_message = ("%smapping %s and %s to %s with Rapmap. "
% (algorithm, fq1, fq2, rapmap_index))
do.run(cmd.format(**locals()), run_message, None)
return out_file
def align(fastq_file, pair_file, ref_file, names, align_dir, data):
paired = True if pair_file else False
hisat2 = config_utils.get_program("hisat2", data)
num_cores = dd.get_num_cores(data)
quality_flag = _get_quality_flag(data)
stranded_flag = _get_stranded_flag(data, paired)
rg_flags = _get_rg_flags(names)
out_file = os.path.join(align_dir, dd.get_lane(data)) + ".bam"
if file_exists(out_file):
data = dd.set_work_bam(data, out_file)
return data
cmd = (
"{hisat2} -x {ref_file} -p {num_cores} {quality_flag} {stranded_flag} "
"{rg_flags} ")
if paired:
cmd += "-1 {fastq_file} -2 {pair_file} "
else:
cmd += "-U {fastq_file} "
if dd.get_analysis(data).lower() == "smallrna-seq":
cmd += "-k 1000 "
# if assembling transcripts, set flags that cufflinks/stringtie can use
if dd.get_transcript_assembler(data):
cmd += "--dta-cufflinks "
if dd.get_analysis(data).lower() == "rna-seq":
gtf_file = dd.get_gtf_file(data)
splicesites = os.path.join(os.path.dirname(gtf_file),
"ref-transcripts-splicesites.txt")
cmd += "--known-splicesite-infile {splicesites} "
message = "Aligning %s and %s with hisat2." % (fastq_file, pair_file)
with file_transaction(out_file) as tx_out_file:
cmd += " | " + postalign.sam_to_sortbam_cl(data, tx_out_file)
do.run(cmd.format(**locals()), message)
data = dd.set_work_bam(data, out_file)
return data
def align_transcriptome(fastq_file, pair_file, ref_file, data):
"""
bwa mem with settings for aligning to the transcriptome for eXpress/RSEM/etc
"""
work_bam = dd.get_work_bam(data)
base, ext = os.path.splitext(work_bam)
out_file = base + ".transcriptome" + ext
if utils.file_exists(out_file):
data = dd.set_transcriptome_bam(data, out_file)
return data
# bwa mem needs phred+33 quality, so convert if it is Illumina
if dd.get_quality_format(data).lower() == "illumina":
logger.info("bwa mem does not support the phred+64 quality format, "
"converting %s and %s to phred+33.")
fastq_file = fastq.groom(fastq_file, data, in_qual="fastq-illumina")
if pair_file:
pair_file = fastq.groom(pair_file, data, in_qual="fastq-illumina")
bwa = config_utils.get_program("bwa", data["config"])
gtf_file = dd.get_gtf_file(data)
gtf_fasta = index_transcriptome(gtf_file, ref_file, data)
args = " ".join(_bwa_args_from_config(data["config"]))
num_cores = data["config"]["algorithm"].get("num_cores", 1)
samtools = config_utils.get_program("samtools", data["config"])
cmd = ("{bwa} mem {args} -a -t {num_cores} {gtf_fasta} {fastq_file} "
"{pair_file} ")
with file_transaction(data, out_file) as tx_out_file:
message = "Aligning %s and %s to the transcriptome." % (fastq_file,
pair_file)
cmd += "| " + postalign.sam_to_sortbam_cl(
data, tx_out_file, name_sort=True)
do.run(cmd.format(**locals()), message)
data = dd.set_transcriptome_bam(data, out_file)
return data
def align_transcriptome(fastq_file, pair_file, ref_file, data):
"""
bowtie2 with settings for aligning to the transcriptome for eXpress/RSEM/etc
"""
work_bam = dd.get_work_bam(data)
base, ext = os.path.splitext(work_bam)
out_file = base + ".transcriptome" + ext
if utils.file_exists(out_file):
data = dd.set_transcriptome_bam(data, out_file)
return data
bowtie2 = config_utils.get_program("bowtie2", data["config"])
gtf_file = dd.get_gtf_file(data)
gtf_index = index_transcriptome(gtf_file, ref_file, data)
num_cores = data["config"]["algorithm"].get("num_cores", 1)
fastq_cmd = "-1 %s" % fastq_file if pair_file else "-U %s" % fastq_file
pair_cmd = "-2 %s " % pair_file if pair_file else ""
cmd = (
"{bowtie2} -p {num_cores} -a -X 600 --rdg 6,5 --rfg 6,5 --score-min L,-.6,-.4 --no-discordant --no-mixed -x {gtf_index} {fastq_cmd} {pair_cmd} "
)
with file_transaction(data, out_file) as tx_out_file:
message = "Aligning %s and %s to the transcriptome." % (fastq_file,
pair_file)
cmd += "| " + postalign.sam_to_sortbam_cl(
data, tx_out_file, name_sort=True)
do.run(cmd.format(**locals()), message)
data = dd.set_transcriptome_bam(data, out_file)
return data
def align(fastq_file, pair_file, ref_file, names, align_dir, data):
paired = True if pair_file else False
hisat2 = config_utils.get_program("hisat2", data)
num_cores = dd.get_num_cores(data)
quality_flag = _get_quality_flag(data)
stranded_flag = _get_stranded_flag(data, paired)
rg_flags = _get_rg_flags(names)
out_file = os.path.join(align_dir, dd.get_lane(data)) + ".bam"
if file_exists(out_file):
data = dd.set_work_bam(data, out_file)
return data
cmd = ("{hisat2} -x {ref_file} -p {num_cores} {quality_flag} {stranded_flag} "
"{rg_flags} ")
if paired:
cmd += "-1 {fastq_file} -2 {pair_file} "
else:
cmd += "-U {fastq_file} "
if dd.get_analysis(data).lower() == "smallrna-seq":
cmd += "-k 1000 "
# if assembling transcripts, set flags that cufflinks can use
if dd.get_assemble_transcripts(data):
cmd += "--dta-cufflinks "
if dd.get_analysis(data) == "rna-seq":
splicesites = os.path.join(os.path.dirname(gtf_file),
"ref-transcripts-splicesites.txt")
cmd += "--known-splicesite-infile {splicesites} "
message = "Aligning %s and %s with hisat2." %(fastq_file, pair_file)
with file_transaction(out_file) as tx_out_file:
cmd += " | " + postalign.sam_to_sortbam_cl(data, tx_out_file)
do.run(cmd.format(**locals()), message)
data = dd.set_work_bam(data, out_file)
return data
def align(fastq_file, pair_file, ref_file, names, align_dir, data):
if not ref_file:
logger.error("STAR index not found. We don't provide the STAR indexes "
"by default because they are very large. You can install "
"the index for your genome with: bcbio_nextgen.py upgrade "
"--aligners star --genomes genome-build-name --data")
sys.exit(1)
max_hits = 10
srna = True if data["analysis"].lower().startswith("smallrna-seq") else False
srna_opts = ""
if srna:
max_hits = 1000
srna_opts = "--alignIntronMax 1"
config = data["config"]
star_dirs = _get_star_dirnames(align_dir, data, names)
if file_exists(star_dirs.final_out):
data = _update_data(star_dirs.final_out, star_dirs.out_dir, names, data)
return data
star_path = config_utils.get_program("STAR", config)
fastq_files = " ".join([fastq_file, pair_file]) if pair_file else fastq_file
num_cores = dd.get_num_cores(data)
gtf_file = dd.get_gtf_file(data)
if ref_file.endswith("chrLength"):
ref_file = os.path.dirname(ref_file)
with file_transaction(data, align_dir) as tx_align_dir:
tx_star_dirnames = _get_star_dirnames(tx_align_dir, data, names)
tx_out_dir, tx_out_file, tx_out_prefix, tx_final_out = tx_star_dirnames
safe_makedir(tx_align_dir)
safe_makedir(tx_out_dir)
cmd = ("{star_path} --genomeDir {ref_file} --readFilesIn {fastq_files} "
"--runThreadN {num_cores} --outFileNamePrefix {tx_out_prefix} "
"--outReadsUnmapped Fastx --outFilterMultimapNmax {max_hits} "
"--outStd SAM {srna_opts} "
"--outSAMunmapped Within --outSAMattributes %s " % " ".join(ALIGN_TAGS))
cmd += _add_sj_index_commands(fastq_file, ref_file, gtf_file) if not srna else ""
cmd += " --readFilesCommand zcat " if is_gzipped(fastq_file) else ""
cmd += _read_group_option(names)
fusion_mode = utils.get_in(data, ("config", "algorithm", "fusion_mode"), False)
if fusion_mode:
cmd += (" --chimSegmentMin 12 --chimJunctionOverhangMin 12 "
"--chimScoreDropMax 30 --chimSegmentReadGapMax 5 "
"--chimScoreSeparation 5 "
"--chimOutType WithinSAM ")
strandedness = utils.get_in(data, ("config", "algorithm", "strandedness"),
"unstranded").lower()
if strandedness == "unstranded" and not srna:
cmd += " --outSAMstrandField intronMotif "
if not srna:
cmd += " --quantMode TranscriptomeSAM "
cmd += " | " + postalign.sam_to_sortbam_cl(data, tx_final_out)
run_message = "Running STAR aligner on %s and %s" % (fastq_file, ref_file)
do.run(cmd.format(**locals()), run_message, None)
print("hello")
data = _update_data(star_dirs.final_out, star_dirs.out_dir, names, data)
return data
def align(fastq_file, pair_file, ref_file, names, align_dir, data):
max_hits = 10
srna = True if data["analysis"].lower().startswith("smallrna-seq") else False
srna_opts = ""
if srna:
max_hits = 1000
srna_opts = "--alignIntronMax 1"
config = data["config"]
out_prefix = os.path.join(align_dir, dd.get_lane(data))
out_file = out_prefix + "Aligned.out.sam"
out_dir = os.path.join(align_dir, "%s_star" % dd.get_lane(data))
if not ref_file:
logger.error("STAR index not found. We don't provide the STAR indexes "
"by default because they are very large. You can install "
"the index for your genome with: bcbio_nextgen.py upgrade "
"--aligners star --genomes genome-build-name --data")
sys.exit(1)
final_out = os.path.join(out_dir, "{0}.bam".format(names["sample"]))
if file_exists(final_out):
data = _update_data(final_out, out_dir, names, data)
return data
star_path = config_utils.get_program("STAR", config)
fastq_files = " ".join([fastq_file, pair_file]) if pair_file else fastq_file
num_cores = dd.get_num_cores(data)
gtf_file = dd.get_gtf_file(data)
safe_makedir(align_dir)
cmd = ("{star_path} --genomeDir {ref_file} --readFilesIn {fastq_files} "
"--runThreadN {num_cores} --outFileNamePrefix {out_prefix} "
"--outReadsUnmapped Fastx --outFilterMultimapNmax {max_hits} "
"--outStd SAM {srna_opts} "
"--outSAMunmapped Within --outSAMattributes %s " % " ".join(ALIGN_TAGS))
cmd += _add_sj_index_commands(fastq_file, ref_file, gtf_file)
cmd += " --readFilesCommand zcat " if is_gzipped(fastq_file) else ""
cmd += _read_group_option(names)
fusion_mode = utils.get_in(data, ("config", "algorithm", "fusion_mode"), False)
if fusion_mode:
cmd += " --chimSegmentMin 15 --chimJunctionOverhangMin 15"
strandedness = utils.get_in(data, ("config", "algorithm", "strandedness"),
"unstranded").lower()
if strandedness == "unstranded" and not srna:
cmd += " --outSAMstrandField intronMotif "
if dd.get_transcriptome_align(data) and not is_transcriptome_broken(data):
cmd += " --quantMode TranscriptomeSAM "
with file_transaction(data, final_out) as tx_final_out:
cmd += " | " + postalign.sam_to_sortbam_cl(data, tx_final_out)
run_message = "Running STAR aligner on %s and %s" % (fastq_file, ref_file)
do.run(cmd.format(**locals()), run_message, None)
data = _update_data(final_out, out_dir, names, data)
return data
def align(fastq_file, pair_file, ref_file, names, align_dir, data):
max_hits = 10
srna = True if data["analysis"].lower().startswith("smallrna-seq") else False
srna_opts = ""
if srna:
max_hits = 1000
srna_opts = "--alignIntronMax 1"
config = data["config"]
out_prefix = os.path.join(align_dir, dd.get_lane(data))
out_file = out_prefix + "Aligned.out.sam"
out_dir = os.path.join(align_dir, "%s_star" % dd.get_lane(data))
if not ref_file:
logger.error("STAR index not found. We don't provide the STAR indexes "
"by default because they are very large. You can install "
"the index for your genome with: bcbio_nextgen.py upgrade "
"--aligners star --genomes genome-build-name --data")
sys.exit(1)
final_out = os.path.join(out_dir, "{0}.bam".format(names["sample"]))
if file_exists(final_out):
data = _update_data(final_out, out_dir, names, data)
return data
star_path = config_utils.get_program("STAR", config)
fastq = " ".join([fastq_file, pair_file]) if pair_file else fastq_file
num_cores = config["algorithm"].get("num_cores", 1)
safe_makedir(align_dir)
cmd = ("{star_path} --genomeDir {ref_file} --readFilesIn {fastq} "
"--runThreadN {num_cores} --outFileNamePrefix {out_prefix} "
"--outReadsUnmapped Fastx --outFilterMultimapNmax {max_hits} "
"--outStd SAM {srna_opts} "
"--outSAMunmapped Within --outSAMattributes %s" % " ".join(ALIGN_TAGS))
cmd = cmd + " --readFilesCommand zcat " if is_gzipped(fastq_file) else cmd
cmd += _read_group_option(names)
fusion_mode = utils.get_in(data, ("config", "algorithm", "fusion_mode"), False)
if fusion_mode:
cmd += " --chimSegmentMin 15 --chimJunctionOverhangMin 15"
strandedness = utils.get_in(data, ("config", "algorithm", "strandedness"),
"unstranded").lower()
if strandedness == "unstranded" and not srna:
cmd += " --outSAMstrandField intronMotif "
if dd.get_transcriptome_align(data) and not is_transcriptome_broken():
cmd += " --quantMode TranscriptomeSAM "
with file_transaction(data, final_out) as tx_final_out:
cmd += " | " + postalign.sam_to_sortbam_cl(data, tx_final_out)
run_message = "Running STAR aligner on %s and %s" % (fastq_file, ref_file)
do.run(cmd.format(**locals()), run_message, None)
data = _update_data(final_out, out_dir, names, data)
return data
def align_transcriptome(fastq_file, pair_file, ref_file, data):
"""
bowtie2 with settings for aligning to the transcriptome for eXpress/RSEM/etc
"""
work_bam = dd.get_work_bam(data)
base, ext = os.path.splitext(work_bam)
out_file = base + ".transcriptome" + ext
if file_exists(out_file):
data = dd.set_transcriptome_bam(data, out_file)
return data
bowtie2 = config_utils.get_program("bowtie2", data["config"])
gtf_file = dd.get_gtf_file(data)
gtf_index = index_transcriptome(gtf_file, ref_file, data)
num_cores = data["config"]["algorithm"].get("num_cores", 1)
fastq_cmd = "-1 %s" % fastq_file if pair_file else "-U %s" % fastq_file
pair_cmd = "-2 %s " % pair_file if pair_file else ""
cmd = ("{bowtie2} -p {num_cores} -a -X 600 --rdg 6,5 --rfg 6,5 --score-min L,-.6,-.4 --no-discordant --no-mixed -x {gtf_index} {fastq_cmd} {pair_cmd} ")
with file_transaction(out_file) as tx_out_file:
message = "Aligning %s and %s to the transcriptome." % (fastq_file, pair_file)
cmd += "| " + postalign.sam_to_sortbam_cl(data, tx_out_file, name_sort=True)
do.run(cmd.format(**locals()), message)
data = dd.set_transcriptome_bam(data, out_file)
return data
def rapmap_pseudoalign(fq1, fq2, rapmap_dir, gtf_file, ref_file, data):
safe_makedir(rapmap_dir)
samplename = dd.get_sample_name(data)
out_file = os.path.join(rapmap_dir, samplename + ".bam")
if file_exists(out_file):
return out_file
rapmap_index = rapmap_pseudoindex(gtf_file, ref_file, data, rapmap_dir)
num_cores = dd.get_num_cores(data)
rapmap = config_utils.get_program("rapmap", dd.get_config(data))
cmd = "{rapmap} pseudomap -t {num_cores} -i {rapmap_index} "
fq1_cmd = "{fq1} " if not is_gzipped(fq1) else "<(gzip -cd {fq1}) "
fq1_cmd = fq1_cmd.format(fq1=fq1)
if not fq2:
cmd += "-r {fq1_cmd} "
else:
fq2_cmd = "{fq2} " if not is_gzipped(fq2) else "<(gzip -cd {fq2}) "
fq2_cmd = fq2_cmd.format(fq2=fq2)
cmd += "-1 {fq2_cmd} -2 {fq2_cmd} "
with file_transaction(out_file) as tx_out_file:
cmd += "| " + postalign.sam_to_sortbam_cl(data, tx_out_file)
run_message = ("Pseudomapping %s and %s to %s with Rapmap. "
% (fq1, fq2, rapmap_index))
do.run(cmd.format(**locals()), run_message, None)
return out_file
def align(fastq_file, pair_file, ref_file, names, align_dir, data):
if not ref_file:
logger.error(
"STAR index not found. We don't provide the STAR indexes "
"by default because they are very large. You can install "
"the index for your genome with: bcbio_nextgen.py upgrade "
"--aligners star --genomes genome-build-name --data")
sys.exit(1)
max_hits = 10
srna = True if data["analysis"].lower().startswith(
"smallrna-seq") else False
srna_opts = ""
if srna:
max_hits = 1000
srna_opts = "--alignIntronMax 1"
config = data["config"]
star_dirs = _get_star_dirnames(align_dir, data, names)
if file_exists(star_dirs.final_out):
data = _update_data(star_dirs.final_out, star_dirs.out_dir, names,
data)
return data
star_path = config_utils.get_program("STAR", config)
def _unpack_fastq(f):
"""Use process substitution instead of readFilesCommand for gzipped inputs.
Prevents issues on shared filesystems that don't support FIFO:
https://github.com/alexdobin/STAR/issues/143
"""
if f and is_gzipped(f):
return "<(gunzip -c %s)" % f
else:
return f
fastq_files = (" ".join([
_unpack_fastq(fastq_file),
_unpack_fastq(pair_file)
]) if pair_file else _unpack_fastq(fastq_file))
num_cores = dd.get_num_cores(data)
gtf_file = dd.get_gtf_file(data)
if ref_file.endswith("chrLength"):
ref_file = os.path.dirname(ref_file)
with file_transaction(data, align_dir) as tx_align_dir:
tx_star_dirnames = _get_star_dirnames(tx_align_dir, data, names)
tx_out_dir, tx_out_file, tx_out_prefix, tx_final_out = tx_star_dirnames
safe_makedir(tx_align_dir)
safe_makedir(tx_out_dir)
cmd = (
"{star_path} --genomeDir {ref_file} --readFilesIn {fastq_files} "
"--runThreadN {num_cores} --outFileNamePrefix {tx_out_prefix} "
"--outReadsUnmapped Fastx --outFilterMultimapNmax {max_hits} "
"--outStd BAM_Unsorted {srna_opts} "
"--limitOutSJcollapsed 2000000 "
"--outSAMtype BAM Unsorted "
"--outSAMmapqUnique 60 "
"--outSAMunmapped Within --outSAMattributes %s " %
" ".join(ALIGN_TAGS))
cmd += _add_sj_index_commands(fastq_file, ref_file,
gtf_file) if not srna else ""
cmd += _read_group_option(names)
if dd.get_fusion_caller(data):
cmd += (" --chimSegmentMin 12 --chimJunctionOverhangMin 12 "
"--chimScoreDropMax 30 --chimSegmentReadGapMax 5 "
"--chimScoreSeparation 5 ")
if "oncofuse" in dd.get_fusion_caller(data):
cmd += "--chimOutType Junctions "
else:
cmd += "--chimOutType WithinBAM "
strandedness = utils.get_in(data,
("config", "algorithm", "strandedness"),
"unstranded").lower()
if strandedness == "unstranded" and not srna:
cmd += " --outSAMstrandField intronMotif "
if not srna:
cmd += " --quantMode TranscriptomeSAM "
resources = config_utils.get_resources("star", data["config"])
if resources.get("options", []):
cmd += " " + " ".join(
[str(x) for x in resources.get("options", [])])
cmd += " | " + postalign.sam_to_sortbam_cl(data, tx_final_out)
cmd += " > {tx_final_out} "
run_message = "Running STAR aligner on %s and %s" % (fastq_file,
ref_file)
do.run(cmd.format(**locals()), run_message, None)
data = _update_data(star_dirs.final_out, star_dirs.out_dir, names, data)
return data
def align(fastq_file, pair_file, ref_file, names, align_dir, data):
if not ref_file:
logger.error(
"STAR index not found. We don't provide the STAR indexes "
"by default because they are very large. You can install "
"the index for your genome with: bcbio_nextgen.py upgrade "
"--aligners star --genomes genome-build-name --data")
sys.exit(1)
max_hits = 10
srna = True if data["analysis"].lower().startswith(
"smallrna-seq") else False
srna_opts = ""
if srna:
max_hits = 1000
srna_opts = "--alignIntronMax 1"
config = data["config"]
star_dirs = _get_star_dirnames(align_dir, data, names)
if file_exists(star_dirs.final_out):
data = _update_data(star_dirs.final_out, star_dirs.out_dir, names,
data)
return data
star_path = config_utils.get_program("STAR", config)
fastq_files = " ".join([fastq_file, pair_file
]) if pair_file else fastq_file
num_cores = dd.get_num_cores(data)
gtf_file = dd.get_gtf_file(data)
if ref_file.endswith("chrLength"):
ref_file = os.path.dirname(ref_file)
with file_transaction(data, align_dir) as tx_align_dir:
tx_star_dirnames = _get_star_dirnames(tx_align_dir, data, names)
tx_out_dir, tx_out_file, tx_out_prefix, tx_final_out = tx_star_dirnames
safe_makedir(tx_align_dir)
safe_makedir(tx_out_dir)
cmd = (
"{star_path} --genomeDir {ref_file} --readFilesIn {fastq_files} "
"--runThreadN {num_cores} --outFileNamePrefix {tx_out_prefix} "
"--outReadsUnmapped Fastx --outFilterMultimapNmax {max_hits} "
"--outStd SAM {srna_opts} "
"--outSAMunmapped Within --outSAMattributes %s " %
" ".join(ALIGN_TAGS))
cmd += _add_sj_index_commands(fastq_file, ref_file,
gtf_file) if not srna else ""
cmd += " --readFilesCommand zcat " if is_gzipped(fastq_file) else ""
cmd += _read_group_option(names)
fusion_mode = utils.get_in(data,
("config", "algorithm", "fusion_mode"),
False)
if fusion_mode:
cmd += (" --chimSegmentMin 12 --chimJunctionOverhangMin 12 "
"--chimScoreDropMax 30 --chimSegmentReadGapMax 5 "
"--chimScoreSeparation 5 "
"--chimOutType WithinSAM ")
strandedness = utils.get_in(data,
("config", "algorithm", "strandedness"),
"unstranded").lower()
if strandedness == "unstranded" and not srna:
cmd += " --outSAMstrandField intronMotif "
if not srna:
cmd += " --quantMode TranscriptomeSAM "
cmd += " | " + postalign.sam_to_sortbam_cl(data, tx_final_out)
run_message = "Running STAR aligner on %s and %s" % (fastq_file,
ref_file)
do.run(cmd.format(**locals()), run_message, None)
print("hello")
data = _update_data(star_dirs.final_out, star_dirs.out_dir, names, data)
return data
def align(fastq_file, pair_file, ref_file, names, align_dir, data):
if not ref_file:
logger.error("STAR index not found. We don't provide the STAR indexes "
"by default because they are very large. You can install "
"the index for your genome with: bcbio_nextgen.py upgrade "
"--aligners star --genomes genome-build-name --data")
sys.exit(1)
max_hits = 10
srna = True if data["analysis"].lower().startswith("smallrna-seq") else False
srna_opts = ""
if srna:
max_hits = 1000
srna_opts = "--alignIntronMax 1"
config = data["config"]
star_dirs = _get_star_dirnames(align_dir, data, names)
if file_exists(star_dirs.final_out):
data = _update_data(star_dirs.final_out, star_dirs.out_dir, names, data)
return data
star_path = config_utils.get_program("STAR", config)
def _unpack_fastq(f):
"""Use process substitution instead of readFilesCommand for gzipped inputs.
Prevents issues on shared filesystems that don't support FIFO:
https://github.com/alexdobin/STAR/issues/143
"""
if f and is_gzipped(f):
return "<(gunzip -c %s)" % f
else:
return f
fastq_files = (" ".join([_unpack_fastq(fastq_file), _unpack_fastq(pair_file)])
if pair_file else _unpack_fastq(fastq_file))
num_cores = dd.get_num_cores(data)
gtf_file = dd.get_gtf_file(data)
if ref_file.endswith("chrLength"):
ref_file = os.path.dirname(ref_file)
with file_transaction(data, align_dir) as tx_align_dir:
tx_star_dirnames = _get_star_dirnames(tx_align_dir, data, names)
tx_out_dir, tx_out_file, tx_out_prefix, tx_final_out = tx_star_dirnames
safe_makedir(tx_align_dir)
safe_makedir(tx_out_dir)
cmd = ("{star_path} --genomeDir {ref_file} --readFilesIn {fastq_files} "
"--runThreadN {num_cores} --outFileNamePrefix {tx_out_prefix} "
"--outReadsUnmapped Fastx --outFilterMultimapNmax {max_hits} "
"--outStd BAM_Unsorted {srna_opts} "
"--limitOutSJcollapsed 2000000 "
"--outSAMtype BAM Unsorted "
"--outSAMmapqUnique 60 "
"--outSAMunmapped Within --outSAMattributes %s " % " ".join(ALIGN_TAGS))
cmd += _add_sj_index_commands(fastq_file, ref_file, gtf_file) if not srna else ""
cmd += _read_group_option(names)
if dd.get_fusion_caller(data):
cmd += (" --chimSegmentMin 12 --chimJunctionOverhangMin 12 "
"--chimScoreDropMax 30 --chimSegmentReadGapMax 5 "
"--chimScoreSeparation 5 ")
if "oncofuse" in dd.get_fusion_caller(data):
cmd += "--chimOutType Junctions "
else:
cmd += "--chimOutType WithinBAM "
strandedness = utils.get_in(data, ("config", "algorithm", "strandedness"),
"unstranded").lower()
if strandedness == "unstranded" and not srna:
cmd += " --outSAMstrandField intronMotif "
if not srna:
cmd += " --quantMode TranscriptomeSAM "
resources = config_utils.get_resources("star", data["config"])
if resources.get("options", []):
cmd += " " + " ".join([str(x) for x in resources.get("options", [])])
cmd += " | " + postalign.sam_to_sortbam_cl(data, tx_final_out)
cmd += " > {tx_final_out} "
run_message = "Running STAR aligner on %s and %s" % (fastq_file, ref_file)
do.run(cmd.format(**locals()), run_message, None)
data = _update_data(star_dirs.final_out, star_dirs.out_dir, names, data)
return data
