def _get_gtf(config):
gtf = config["annotation"].get("file", None)
#gtf = config.get("gtf", None)
if not gtf or not file_exists(gtf):
logger.error("genebody_coverage needs a GTF file passed to it.")
exit(1)
return gtf
def align(fastq_file,
pair_file,
ref_file,
out_base,
align_dir,
config,
names=None):
out_dir = os.path.join(align_dir, "%s_tophat" % out_base)
out_file = os.path.join(out_dir, _out_fnames[0])
if file_exists(out_file):
return os.path.join(out_dir, "%s.sam" % out_base)
if not _bowtie_ref_match(ref_file, config):
logger.error("Bowtie version %d was detected but the reference "
"file %s is built for version %d. Download version "
"%d or build it with bowtie-build." %
(_bowtie_major_version(config), ref_file,
_ref_version(ref_file), _bowtie_major_version(config)))
exit(1)
out_files = tophat_align(fastq_file,
pair_file,
ref_file,
out_base,
align_dir,
config,
names=None)
return out_files
def demultiplex_samples(data):
"""
demultiplex a fastqtransformed FASTQ file into separate sample barcode files
"""
work_dir = os.path.join(dd.get_work_dir(data), "umis")
sample_dir = os.path.join(work_dir, dd.get_sample_name(data))
demulti_dir = os.path.join(sample_dir, "demultiplexed")
files = data["files"]
if len(files) == 2:
logger.error(
"Sample demultiplexing doesn't handle paired-end reads, but "
"we can add it. Open an issue here https://github.com/bcbio/bcbio-nextgen/issues if you need this and we'll add it."
)
sys.exit(1)
else:
fq1 = files[0]
# check if samples need to be demultiplexed
with open_fastq(fq1) as in_handle:
read = next(in_handle)
if "SAMPLE_" not in read:
return [[data]]
bcfile = get_sample_barcodes(dd.get_sample_barcodes(data), sample_dir)
demultiplexed = glob.glob(os.path.join(demulti_dir, "*.fq*"))
if demultiplexed:
return [split_demultiplexed_sampledata(data, demultiplexed)]
umis = config_utils.get_program("umis", data, default="umis")
cmd = ("{umis} demultiplex_samples --nedit 1 --barcodes {bcfile} "
"--out_dir {tx_dir} {fq1}")
msg = "Demultiplexing {fq1}."
with file_transaction(data, demulti_dir) as tx_dir:
do.run(cmd.format(**locals()), msg.format(**locals()))
demultiplexed = glob.glob(os.path.join(demulti_dir, "*.fq*"))
return [split_demultiplexed_sampledata(data, demultiplexed)]
def run_rnaseq_variant_calling(data):
"""
run RNA-seq variant calling, variation file is stored in `vrn_file`
in the datadict
"""
variantcaller = dd.get_variantcaller(data)
if isinstance(variantcaller, list) and len(variantcaller) > 1:
logger.error("Only one variantcaller can be run for RNA-seq at "
"this time. Post an issue here "
"(https://github.com/bcbio/bcbio-nextgen/issues) "
"if this is something you need to do.")
sys.exit(1)
if variantcaller:
if "gatk-haplotype" in variantcaller:
data = variation.rnaseq_gatk_variant_calling(data)
if vardict.get_vardict_command(data):
data = variation.rnaseq_vardict_variant_calling(data)
if dd.get_vrn_file(data):
ann_file = vcfanno.run_vcfanno(dd.get_vrn_file(data), ["rnaedit"],
data)
if ann_file:
data = dd.set_vrn_file(data, ann_file)
ann_file = population.run_vcfanno(dd.get_vrn_file(data), data,
population.do_db_build([data]))
if ann_file:
data = dd.set_vrn_file(data, ann_file)
return [[data]]
def singlecell_rnaseq(samples, run_parallel):
quantifier = dd.get_in_samples(samples, dd.get_singlecell_quantifier)
quantifier = quantifier.lower()
samples = run_parallel("run_umi_transform", samples)
demultiplexed = run_parallel("demultiplex_samples", samples)
# break demultiplixed lanes into their own samples
samples = []
for lane in demultiplexed:
for index in lane:
samples.append([index])
samples = run_parallel("run_filter_barcodes", samples)
samples = run_parallel("run_barcode_histogram", samples)
if quantifier == "rapmap":
samples = run_parallel("run_rapmap_index", [samples])
samples = run_parallel("run_rapmap_align", samples)
samples = run_parallel("run_tagcount", samples)
samples = run_parallel("run_concatenate_sparse_counts", [samples])
elif quantifier == "kallisto":
samples = run_parallel("run_kallisto_singlecell", samples)
else:
logger.error(("%s is not supported for singlecell RNA-seq "
"quantification." % quantifier))
sys.exit(1)
samples = scrnaseq_concatenate_metadata(samples)
singlecellexperiment.make_scrnaseq_object(samples)
return samples
def main(config, view):
# make the needed directories
map(safe_makedir, config["dir"].values())
# specific for project
human_input = find_sam_files(config["input_dir_human"])
mouse_input = find_sam_files(config["input_dir_mouse"])
if len(human_input) != len(mouse_input):
logger.error("The length of the number of human SAM files does "
"not match the length of the number of mouse SAM "
"files, aborting.")
sys.exit(1)
input_files = zip(human_input, mouse_input)
curr_files = input_files
logger.info("Running disambiguation pipeline on %s." % (curr_files))
for stage in config["run"]:
if stage == "disambiguate":
logger.info("Disambiguating %s." % (curr_files))
disambiguate = Disambiguate(config)
out_files = list(flatten(view.map(disambiguate, curr_files)))
bam_files = view.map(sam.sam2bam, out_files)
bam_sorted = view.map(sam.bamsort, bam_files)
view.map(sam.bamindex, bam_sorted)
def load_summarizedexperiment(samples):
""" create summarizedexperiment rds object
fails with n_samples = 1 """
# using r36 (4.0) - will eventually drop R3.5
rcmd = Rscript_cmd("r36")
se_script = os.path.join(os.path.dirname(__file__), os.pardir, "scripts",
"R", "bcbio2se.R")
data = samples[0][0]
work_dir = dd.get_work_dir(data)
out_dir = os.path.join(work_dir, "salmon")
summarized_experiment = os.path.join(out_dir, "bcbio-se.rds")
if not file_exists(summarized_experiment):
with file_transaction(summarized_experiment) as tx_out_file:
cmd = f"{rcmd} --vanilla {se_script} {work_dir} {tx_out_file}"
message = f"Loading SummarizedExperiment."
try:
do.run(cmd, message)
except Exception:
logger.error("SE creation failed")
if file_exists(summarized_experiment):
try:
se_qc_report = generate_se_qc_report(work_dir)
except Exception:
se_qc_report = None
logger.error("SE QC failed")
updated_samples = []
for data in dd.sample_data_iterator(samples):
data = dd.set_summarized_experiment(data, summarized_experiment)
updated_samples.append([data])
return updated_samples
else:
return samples
def singlecell_rnaseq(samples, run_parallel):
quantifier = dd.get_in_samples(samples, dd.get_singlecell_quantifier)
quantifier = quantifier.lower()
samples = run_parallel("run_umi_transform", samples)
demultiplexed = run_parallel("demultiplex_samples", samples)
# break demultiplixed lanes into their own samples
samples = []
for lane in demultiplexed:
for index in lane:
samples.append([index])
samples = run_parallel("run_filter_barcodes", samples)
samples = run_parallel("run_barcode_histogram", samples)
if quantifier == "rapmap":
samples = run_parallel("run_rapmap_index", [samples])
samples = run_parallel("run_rapmap_align", samples)
samples = run_parallel("run_tagcount", samples)
samples = run_parallel("run_concatenate_sparse_counts", [samples])
elif quantifier == "kallisto":
samples = run_parallel("run_kallisto_singlecell", samples)
else:
logger.error(("%s is not supported for singlecell RNA-seq "
"quantification." % quantifier))
sys.exit(1)
samples = scrnaseq_concatenate_metadata(samples)
singlecellexperiment.make_scrnaseq_object(samples)
return samples
def clean_chipseq_alignment(data):
aligner = dd.get_aligner(data)
data["align_bam"] = dd.get_work_bam(data)
if dd.get_mark_duplicates(data):
if aligner:
if aligner == "bowtie2":
filterer = bowtie2.filter_multimappers
elif aligner == "bwa":
filterer = bwa.filter_multimappers
else:
logger.error("ChIP-seq only supported for bowtie2 and bwa.")
sys.exit(-1)
unique_bam = filterer(dd.get_work_bam(data), data)
data["work_bam"] = unique_bam
else:
logger.info(
"Warning: When BAM file is given as input, bcbio skips multimappers removal."
"If BAM is not cleaned for peak calling, can result in downstream errors."
)
# lcr_bed = utils.get_in(data, ("genome_resources", "variation", "lcr"))
data["work_bam"] = _keep_assembled_chrom(dd.get_work_bam(data),
dd.get_ref_file(data),
data["config"])
encode_bed = tz.get_in(
["genome_resources", "variation", "encode_blacklist"], data)
if encode_bed:
data["work_bam"] = _prepare_bam(dd.get_work_bam(data), encode_bed,
data['config'])
bam.index(data["work_bam"], data['config'])
data["bigwig"] = _bam_coverage(dd.get_sample_name(data),
dd.get_work_bam(data), data)
return [[data]]
def make_bcbiornaseq_object(data):
""" load the initial bcb.rda object using bcbioRNASeq """
if "bcbiornaseq" not in dd.get_tools_on(data):
return data
upload_dir = tz.get_in(("upload", "dir"), data)
report_dir = os.path.join(upload_dir, "bcbioRNASeq")
safe_makedir(report_dir)
organism = dd.get_bcbiornaseq(data).get("organism", None)
groups = dd.get_bcbiornaseq(data).get("interesting_groups", None)
loadstring = create_load_string(upload_dir, groups, organism, "gene")
r_file = os.path.join(report_dir, "load_bcbioRNAseq.R")
with file_transaction(r_file) as tmp_file:
memoize_write_file(loadstring, tmp_file)
rcmd = Rscript_cmd(env="rbcbiornaseq")
with chdir(report_dir):
do.run([rcmd, "--vanilla", r_file], "Loading bcbioRNASeq object.")
# bcbiornaseq 0.3.44 writes to data/bcb.rds
write_counts(os.path.join(report_dir, "data", "bcb.rds"), "gene")
loadstring = create_load_string(upload_dir, groups, organism, "transcript")
r_file = os.path.join(report_dir, "load_transcript_bcbioRNAseq.R")
with file_transaction(r_file) as tmp_file:
memoize_write_file(loadstring, tmp_file)
rcmd = Rscript_cmd(env="rbcbiornaseq")
with chdir(report_dir):
do.run([rcmd, "--vanilla", r_file],
"Loading transcript-level bcbioRNASeq object.")
write_counts(os.path.join(report_dir, "data-transcript", "bcb.rds"),
"transcript")
try:
make_quality_report(data)
except:
logger.error("bcbiornaseq error at quality report")
finally:
return data
def clean_chipseq_alignment(data):
aligner = dd.get_aligner(data)
data["align_bam"] = dd.get_work_bam(data)
if dd.get_mark_duplicates(data):
if aligner:
if aligner == "bowtie2":
filterer = bowtie2.filter_multimappers
elif aligner == "bwa":
filterer = bwa.filter_multimappers
else:
logger.error("ChIP-seq only supported for bowtie2 and bwa.")
sys.exit(-1)
unique_bam = filterer(dd.get_work_bam(data), data)
data["work_bam"] = unique_bam
else:
logger.info("Warning: When BAM file is given as input, bcbio skips multimappers removal."
"If BAM is not cleaned for peak calling, can result in downstream errors.")
# lcr_bed = utils.get_in(data, ("genome_resources", "variation", "lcr"))
data["work_bam"] = _keep_assembled_chrom(dd.get_work_bam(data), dd.get_ref_file(data),
data["config"])
encode_bed = tz.get_in(["genome_resources", "variation", "encode_blacklist"], data)
if encode_bed:
data["work_bam"] = _prepare_bam(dd.get_work_bam(data), encode_bed, data['config'])
bam.index(data["work_bam"], data['config'])
data["bigwig"] = _bam_coverage(dd.get_sample_name(data), dd.get_work_bam(data), data)
return [[data]]
def demultiplex_samples(data):
"""
demultiplex a fastqtransformed FASTQ file into separate sample barcode files
"""
files = data["files"]
if len(files) == 2:
logger.error("Sample demultiplexing doesn't handle paired-end reads, but "
"we can add it. Open an issue here https://github.com/chapmanb/bcbio-nextgen/issues if you need this and we'll add it.")
sys.exit(1)
else:
fq1 = files[0]
# check if samples need to be demultiplexed
with open_fastq(fq1) as in_handle:
read = in_handle.next()
if "SAMPLE_" not in read:
return [[data]]
bcfile = dd.get_sample_barcodes(data)
if not bcfile:
logger.error("Sample demultiplexing needs a list of known indexes provided "
"with via the sample_barcodes option in the algorithm section.")
sys.exit(1)
work_dir = os.path.join(dd.get_work_dir(data), "umis")
sample_dir = os.path.join(work_dir, dd.get_sample_name(data))
demulti_dir = os.path.join(sample_dir, "demultiplexed")
demultiplexed = glob.glob(os.path.join(demulti_dir, "*.fq*"))
if demultiplexed:
return [split_demultiplexed_sampledata(data, demultiplexed)]
umis = config_utils.get_program("umis", data, default="umis")
cmd = ("{umis} demultiplex_samples --nedit 1 --barcodes {bcfile} "
"--out_dir {tx_dir} {fq1}")
msg = "Demultiplexing {fq1}."
with file_transaction(data, demulti_dir) as tx_dir:
do.run(cmd.format(**locals()), msg.format(**locals()))
demultiplexed = glob.glob(os.path.join(demulti_dir, "*.fq*"))
return [split_demultiplexed_sampledata(data, demultiplexed)]
def _cutadapt_trim(fastq_files, quality_format, adapters, out_files):
if quality_format == "illumina":
quality_base = "64"
else:
quality_base = "33"
# --times=2 tries twice remove adapters which will allow things like:
# realsequenceAAAAAAadapter to remove both the poly-A and the adapter
# this behavior might not be what we want; we could also do two or
# more passes of cutadapt
base_cmd = [
"cutadapt", "--times=" + "2", "--quality-base=" + quality_base,
"--quality-cutoff=20", "--format=fastq", "--minimum-length=0"
]
adapter_cmd = map(lambda x: "--adapter=" + x, adapters)
base_cmd.extend(adapter_cmd)
if all(map(file_exists, out_files)):
return out_files
for in_file, out_file in zip(fastq_files, out_files):
# if you pass an output filename, cutadapt will write some stats
# about trimmed adapters to stdout. stat_file captures that.
stat_file = replace_suffix(out_file, ".trim_stats.txt")
with open(stat_file, "w") as stat_handle:
cmd = list(base_cmd)
cmd.extend(["--output=" + out_file, in_file])
try:
return_value = subprocess.check_call(cmd, stdout=stat_handle)
except subprocess.CalledProcessError:
cmd_string = subprocess.list2cmdline(cmd)
logger.error("Cutadapt returned an error. The command "
"used to run cutadapt was: %s." % (cmd_string))
exit(1)
return out_files
def _cutadapt_trim(fastq_files, quality_format, adapters, out_files):
if quality_format == "illumina":
quality_base = "64"
else:
quality_base = "33"
# --times=2 tries twice remove adapters which will allow things like:
# realsequenceAAAAAAadapter to remove both the poly-A and the adapter
# this behavior might not be what we want; we could also do two or
# more passes of cutadapt
base_cmd = ["cutadapt", "--times=" + "2", "--quality-base=" + quality_base,
"--quality-cutoff=20", "--format=fastq", "--minimum-length=0"]
adapter_cmd = map(lambda x: "--adapter=" + x, adapters)
base_cmd.extend(adapter_cmd)
if all(map(file_exists, out_files)):
return out_files
for in_file, out_file in zip(fastq_files, out_files):
# if you pass an output filename, cutadapt will write some stats
# about trimmed adapters to stdout. stat_file captures that.
stat_file = replace_suffix(out_file, ".trim_stats.txt")
with open(stat_file, "w") as stat_handle:
cmd = list(base_cmd)
cmd.extend(["--output=" + out_file, in_file])
try:
return_value = subprocess.check_call(cmd, stdout=stat_handle)
except subprocess.CalledProcessError:
cmd_string = subprocess.list2cmdline(cmd)
logger.error("Cutadapt returned an error. The command "
"used to run cutadapt was: %s." % (cmd_string))
exit(1)
return out_files
def singlecell_rnaseq(samples, run_parallel):
quantifier = dd.get_in_samples(samples, dd.get_singlecell_quantifier)
quantifier = quantifier.lower()
samples = run_parallel("run_umi_transform", samples)
demultiplexed = run_parallel("demultiplex_samples", samples)
# break demultiplixed lanes into their own samples
samples = []
for lane in demultiplexed:
for index in lane:
samples.append([index])
if not samples:
logger.error(f"No samples were found matching the supplied sample barcodes. See "
f"https://github.com/bcbio/bcbio-nextgen/issues/3428#issuecomment-772609904 "
f"for how to debug this issue.")
sys.exit(1)
samples = run_parallel("run_filter_barcodes", samples)
samples = run_parallel("run_barcode_histogram", samples)
if quantifier == "rapmap":
samples = run_parallel("run_rapmap_index", [samples])
samples = run_parallel("run_rapmap_align", samples)
samples = run_parallel("run_tagcount", samples)
samples = run_parallel("run_concatenate_sparse_counts", [samples])
elif quantifier == "kallisto":
samples = run_parallel("run_kallisto_singlecell", samples)
else:
logger.error(("%s is not supported for singlecell RNA-seq "
"quantification." % quantifier))
sys.exit(1)
samples = scrnaseq_concatenate_metadata(samples)
singlecellexperiment.make_scrnaseq_object(samples)
return samples
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 _run_with_possible_error_message(cmd, **kwargs):
try:
subprocess.check_call(cmd, **kwargs)
except subprocess.CalledProcessError:
cmd_string = subprocess.list2cmdline(cmd)
logger.error("Cutadapt returned an error. The command "
"used to run cutadapt was: %s." % (cmd_string))
exit(1)
def _check_bowtie(ref_file, config):
if not _bowtie_ref_match(ref_file, config):
logger.error("Bowtie version %d was detected but the reference "
"file %s is built for version %d. Download version "
"%d or build it with bowtie-build." %
(_bowtie_major_version(config), ref_file,
_ref_version(ref_file), _bowtie_major_version(config)))
exit(1)
def _run_with_possible_error_message(cmd, **kwargs):
try:
subprocess.check_call(cmd, **kwargs)
except subprocess.CalledProcessError:
cmd_string = subprocess.list2cmdline(cmd)
logger.error("Cutadapt returned an error. The command "
"used to run cutadapt was: %s." % (cmd_string))
exit(1)
def combine_pairs(input_files):
""" calls files pairs if they are completely the same except
for one has _1 and the other has _2 returns a list of tuples
of pairs or singles.
From bipy.utils (https://github.com/roryk/bipy/blob/master/bipy/utils.py)
Adjusted to allow different input paths or extensions for matching files.
"""
PAIR_FILE_IDENTIFIERS = set(["1", "2", "3"])
pairs = []
used = set([])
for in_file in input_files:
if in_file in used:
continue
for comp_file in input_files:
if comp_file in used or comp_file == in_file:
continue
a = rstrip_extra(utils.splitext_plus(os.path.basename(in_file))[0])
b = rstrip_extra(
utils.splitext_plus(os.path.basename(comp_file))[0])
if len(a) != len(b):
continue
s = dif(a, b)
# no differences, then its the same file stem
if len(s) == 0:
logger.error(
"%s and %s have the same stem, so we don't know "
"how to assign it to the sample data in the CSV. To "
"get around this you can rename one of the files. "
"If they are meant to be the same sample run in two "
"lanes, combine them first with the "
"bcbio_prepare_samples.py script."
"(http://bcbio-nextgen.readthedocs.io/en/latest/contents/configuration.html#multiple-files-per-sample)"
% (in_file, comp_file))
sys.exit(1)
if len(s) > 1:
continue #there is only 1 difference
if (a[s[0]] in PAIR_FILE_IDENTIFIERS
and b[s[0]] in PAIR_FILE_IDENTIFIERS):
# if the 1/2 isn't the last digit before a separator, skip
# this skips stuff like 2P 2A, often denoting replicates, not
# read pairings
if len(b) > (s[0] + 1):
if (b[s[0] + 1] not in ("_", "-", ".")):
continue
# if the 1/2 is not a separator or prefaced with R, skip
if b[s[0] - 1] in ("R", "_", "-", "."):
used.add(in_file)
used.add(comp_file)
if b[s[0]] > a[s[0]]:
pairs.append([in_file, comp_file])
else:
pairs.append([comp_file, in_file])
break
if in_file not in used:
pairs.append([in_file])
used.add(in_file)
return pairs
def align(fastq_file, pair_file, ref_file, names, align_dir, data):
assert data["analysis"].lower().startswith("wgbs-seq"), "No comparible alignment."
config = data["config"]
sample = dd.get_sample_name(data)
out_prefix = os.path.join(align_dir, dd.get_lane(data))
out_dir = os.path.join(align_dir, "%s_bismark" % dd.get_lane(data))
if not ref_file:
logger.error("bismark index not found. You can install "
"the index for your genome with: bcbio_nextgen.py upgrade "
"--aligners bismark --genomes genome-build-name --data")
sys.exit(1)
final_out = os.path.join(align_dir, "{0}.bam".format(sample))
if file_exists(final_out):
data = dd.set_work_bam(data, final_out)
data["bam_report"] = glob.glob(os.path.join(out_dir, "*report.txt"))[0]
data = dd.update_summary_qc(data, "bismark", base=data["bam_report"])
return data
bismark = config_utils.get_program("bismark", config)
# bismark uses 5 threads/sample and ~12GB RAM/sample (hg38)
resources = config_utils.get_resources("bismark", data["config"])
max_cores = dd.get_num_cores(data)
max_mem = config_utils.convert_to_bytes(resources.get("memory", "1G")) / (1024.0 * 1024.0)
instances = calculate_bismark_instances(max_cores, max_mem * max_cores)
# override instances if specified in the config
if resources and resources.get("bismark_threads"):
instances = resources.get("bismark_threads")
logger.info(f"Using {instances} bismark instances - overriden by resources")
bowtie_threads = 1
if resources and resources.get("bowtie_threads"):
bowtie_threads = resources.get("bowtie_threads")
logger.info(f"Using {bowtie_threads} bowtie threads per bismark instance")
kit = kits.KITS.get(dd.get_kit(data), None)
directional = "--non_directional" if kit and not kit.is_directional else ""
other_opts = resources.get("options", [])
other_opts = " ".join([str(x) for x in other_opts]).strip()
fastq_files = " ".join([fastq_file, pair_file]) if pair_file else fastq_file
safe_makedir(align_dir)
cmd = "{bismark} {other_opts} {directional} --bowtie2 --temp_dir {tx_out_dir} --gzip --parallel {instances} -p {bowtie_threads} -o {tx_out_dir} --unmapped {ref_file} {fastq_file} "
if pair_file:
fastq_file = "-1 %s -2 %s" % (fastq_file, pair_file)
raw_bam = glob.glob(out_dir + "/*bismark*bt2*bam")
if not raw_bam:
with tx_tmpdir() as tx_out_dir:
run_message = "Running Bismark aligner on %s and %s" % (fastq_file, ref_file)
do.run(cmd.format(**locals()), run_message, None)
shutil.move(tx_out_dir, out_dir)
raw_bam = glob.glob(out_dir + "/*bismark*bt2*bam")
# don't process bam in the bismark pipeline!
utils.symlink_plus(raw_bam[0], final_out)
data = dd.set_work_bam(data, final_out)
data["bam_report"] = glob.glob(os.path.join(out_dir, "*report.txt"))[0]
data = dd.update_summary_qc(data, "bismark", base=data["bam_report"])
return data
def align(fastq_file, pair_file, ref_file, names, align_dir, data):
assert data["analysis"].lower().startswith(
"wgbs-seq"), "No comparible alignment."
config = data["config"]
sample = dd.get_sample_name(data)
out_prefix = os.path.join(align_dir, dd.get_lane(data))
out_dir = os.path.join(align_dir, "%s_bismark" % dd.get_lane(data))
if not ref_file:
logger.error(
"bismark 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 bismark --genomes genome-build-name --data")
sys.exit(1)
final_out = os.path.join(align_dir, "{0}.bam".format(sample))
if file_exists(final_out):
data = dd.set_work_bam(data, final_out)
data["bam_report"] = glob.glob(os.path.join(out_dir, "*report.txt"))[0]
return data
bismark = config_utils.get_program("bismark", config)
# bismark uses 5 threads/sample and ~12GB RAM/sample (hg38)
resources = config_utils.get_resources("bismark", data["config"])
max_cores = resources.get("cores", 1)
max_mem = config_utils.convert_to_bytes(resources.get("memory", "1G"))
n = min(max(int(max_cores / 5), 1),
max(int(max_mem / config_utils.convert_to_bytes("12G")), 1))
kit = kits.KITS.get(dd.get_kit(data), None)
directional = "--non_directional" if kit and not kit.is_directional else ""
other_opts = resources.get("options", [])
other_opts = " ".join([str(x) for x in other_opts]).strip()
fastq_files = " ".join([fastq_file, pair_file
]) if pair_file else fastq_file
safe_makedir(align_dir)
cmd = "{bismark} {other_opts} {directional} --bowtie2 --temp_dir {tx_out_dir} --gzip --multicore {n} -o {tx_out_dir} --unmapped {ref_file} {fastq_file}"
if pair_file:
fastq_file = "-1 %s -2 %s" % (fastq_file, pair_file)
raw_bam = glob.glob(out_dir + "/*bismark*bt2*bam")
if not raw_bam:
with tx_tmpdir() as tx_out_dir:
run_message = "Running Bismark aligner on %s and %s" % (fastq_file,
ref_file)
do.run(cmd.format(**locals()), run_message, None)
shutil.move(tx_out_dir, out_dir)
raw_bam = glob.glob(out_dir + "/*bismark*bt2*bam")
process_bam = _process_bam(raw_bam[0], fastq_files, sample,
dd.get_sam_ref(data), config)
utils.symlink_plus(process_bam, final_out)
data = dd.set_work_bam(data, final_out)
data["bam_report"] = glob.glob(os.path.join(out_dir, "*report.txt"))[0]
return data
def _get_quality_format(config):
SUPPORTED_FORMATS = ["illumina", "standard"]
quality_format = dd.get_quality_format(data).lower()
if quality_format not in SUPPORTED_FORMATS:
logger.error("quality_format is set to an unsupported format. "
"Supported formats are %s."
% (", ".join(SUPPORTED_FORMATS)))
exit(1)
return quality_format
def _get_pipeline(item):
from bcbio.log import logger
analysis_type = item.get("analysis", "").lower()
if analysis_type not in SUPPORTED_PIPELINES:
logger.error("Cannot determine which type of analysis to run, "
"set in the run_info under details.")
sys.exit(1)
else:
return SUPPORTED_PIPELINES[analysis_type]
def index(data, bam_fpath):
cmdl = make_command(data, "index", bam_fpath)
indexed_bam = bam_fpath + ".bai"
if not utils.file_uptodate(indexed_bam, bam_fpath):
do.run(cmdl, "Indexing BAM file using sambamba")
if not utils.file_exists(indexed_bam):
logger.error("Cannot index BAM file " + bam_fpath + " using sambamba.")
return None
return indexed_bam
def umi_transform(data):
"""
transform each read by identifying the barcode and UMI for each read
and putting the information in the read name
"""
fq1 = data["files"][0]
umi_dir = os.path.join(dd.get_work_dir(data), "umis")
safe_makedir(umi_dir)
transform = dd.get_umi_type(data)
if not transform:
logger.info("No UMI transform specified, assuming pre-transformed data.")
if is_transformed(fq1):
logger.info("%s detected as pre-transformed, passing it on unchanged." % fq1)
data["files"] = [fq1]
return data
else:
logger.error("No UMI transform was specified, but %s does not look "
"pre-transformed. Assuming non-umi data." % fq1)
return data
if file_exists(transform):
transform_file = transform
else:
transform_file = get_transform_file(transform)
if not file_exists(transform_file):
logger.error(
"The UMI transform can be specified as either a file or a "
"bcbio-supported transform. Either the file %s does not exist "
"or the transform is not supported by bcbio. Supported "
"transforms are %s."
% (dd.get_umi_type(data), ", ".join(SUPPORTED_TRANSFORMS)))
sys.exit(1)
out_base = dd.get_sample_name(data) + ".umitransformed.fq.gz"
out_file = os.path.join(umi_dir, out_base)
if file_exists(out_file):
data["files"] = [out_file]
return data
umis = config_utils.get_program("umis", data, default="umis")
cores = dd.get_num_cores(data)
# skip transformation if the file already looks transformed
with open_fastq(fq1) as in_handle:
read = next(in_handle)
if "UMI_" in read:
data["files"] = [out_file]
return data
cmd = ("{umis} fastqtransform {transform_file} "
"--cores {cores} "
"{fq1}"
"| seqtk seq -L 20 - | gzip > {tx_out_file}")
message = ("Inserting UMI and barcode information into the read name of %s"
% fq1)
with file_transaction(out_file) as tx_out_file:
do.run(cmd.format(**locals()), message)
data["files"] = [out_file]
return data
def _check_bowtie(ref_file, config):
if not _bowtie_ref_match(ref_file, config):
logger.error("Bowtie version %d was detected but the reference "
"file %s is built for version %d. Download version "
"%d or build it with bowtie-build."
% (_bowtie_major_version(config), ref_file,
_ref_version(ref_file),
_bowtie_major_version(config)))
exit(1)
def _get_quality_format(config):
SUPPORTED_FORMATS = ["illumina", "standard"]
quality_format = config["algorithm"].get("quality_format", "standard").lower()
if quality_format not in SUPPORTED_FORMATS:
logger.error("quality_format is set to an unsupported format. "
"Supported formats are %s."
% (", ".join(SUPPORTED_FORMATS)))
exit(1)
return quality_format
def _get_pipeline(item):
from bcbio.log import logger
analysis_type = item.get("analysis", "").lower()
if analysis_type not in SUPPORTED_PIPELINES:
logger.error("Cannot determine which type of analysis to run, "
"set in the run_info under details.")
sys.exit(1)
else:
return SUPPORTED_PIPELINES[analysis_type]
def combine_pairs(input_files):
""" calls files pairs if they are completely the same except
for one has _1 and the other has _2 returns a list of tuples
of pairs or singles.
From bipy.utils (https://github.com/roryk/bipy/blob/master/bipy/utils.py)
Adjusted to allow different input paths or extensions for matching files.
"""
PAIR_FILE_IDENTIFIERS = set(["1", "2", "3"])
pairs = []
used = set([])
for in_file in input_files:
if in_file in used:
continue
for comp_file in input_files:
if comp_file in used or comp_file == in_file:
continue
a = rstrip_extra(utils.splitext_plus(os.path.basename(in_file))[0])
b = rstrip_extra(utils.splitext_plus(os.path.basename(comp_file))[0])
if len(a) != len(b):
continue
s = dif(a,b)
# no differences, then its the same file stem
if len(s) == 0:
logger.error("%s and %s have the same stem, so we don't know "
"how to assign it to the sample data in the CSV. To "
"get around this you can rename one of the files. "
"If they are meant to be the same sample run in two "
"lanes, combine them first with the "
"bcbio_prepare_samples.py script."
"(http://bcbio-nextgen.readthedocs.io/en/latest/contents/configuration.html#multiple-files-per-sample)"
% (in_file, comp_file))
sys.exit(1)
if len(s) > 1:
continue #there is only 1 difference
if (a[s[0]] in PAIR_FILE_IDENTIFIERS and
b[s[0]] in PAIR_FILE_IDENTIFIERS):
# if the 1/2 isn't the last digit before a separator, skip
# this skips stuff like 2P 2A, often denoting replicates, not
# read pairings
if len(b) > (s[0] + 1):
if (b[s[0]+1] not in ("_", "-", ".")):
continue
# if the 1/2 is not a separator or prefaced with R, skip
if b[s[0]- 1] in ("R", "_", "-", "."):
used.add(in_file)
used.add(comp_file)
if b[s[0]] > a[s[0]]:
pairs.append([in_file, comp_file])
else:
pairs.append([comp_file, in_file])
break
if in_file not in used:
pairs.append([in_file])
used.add(in_file)
return pairs
def index(data, bam_fpath):
cmdl = make_command(data, "index", bam_fpath)
indexed_bam = bam_fpath + ".bai"
if not utils.file_uptodate(indexed_bam, bam_fpath):
do.run(cmdl, "Indexing BAM file using sambamba")
if not utils.file_exists(indexed_bam):
logger.error("Cannot index BAM file " + bam_fpath +
" using sambamba.")
return None
return indexed_bam
def _get_pipeline(item):
from bcbio.log import logger
SUPPORTED_PIPELINES = {x.name.lower(): x for x in utils.itersubclasses(AbstractPipeline)}
analysis_type = item.get("analysis", "").lower()
if analysis_type not in SUPPORTED_PIPELINES:
logger.error("Cannot determine which type of analysis to run, " "set in the run_info under details.")
sys.exit(1)
else:
return SUPPORTED_PIPELINES[analysis_type]
def align(fastq_file, pair_file, ref_file, names, align_dir, data):
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 10 "
"--outStd SAM "
"--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":
cmd += " --outSAMstrandField intronMotif "
if dd.get_rsem(data) and not is_transcriptome_broken():
cmd += " --quantMode TranscriptomeSAM "
with tx_tmpdir(data) as tmp_dir:
sam_to_bam = bam.sam_to_bam_stream_cmd(config)
sort = bam.sort_cmd(config, tmp_dir)
cmd += "| {sam_to_bam} | {sort} -o {tx_final_out} "
run_message = "Running STAR aligner on %s and %s" % (fastq_file,
ref_file)
with file_transaction(data, final_out) as tx_final_out:
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_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 run(name, chip_bam, input_bam, genome_build, out_dir, method, resources,
data):
"""
Run macs2 for chip and input samples avoiding
errors due to samples.
"""
# output file name need to have the caller name
config = dd.get_config(data)
out_file = os.path.join(out_dir, name + "_peaks_macs2.xls")
macs2_file = os.path.join(out_dir, name + "_peaks.xls")
if utils.file_exists(out_file):
_compress_and_sort_bdg_files(out_dir, data)
return _get_output_files(out_dir)
macs2 = config_utils.get_program("macs2", config)
antibody = dd.get_antibody(data)
if antibody:
antibody = antibody.lower()
if antibody not in antibodies.SUPPORTED_ANTIBODIES:
logger.error(
f"{antibody} specified, but not listed as a supported antibody. Valid antibodies are {antibodies.SUPPORTED_ANTIBODIES}. If you know your antibody "
f"should be called with narrow or broad peaks, supply 'narrow' or 'broad' as the antibody."
f"It will run 'narrow' if the antibody is not supported.")
antibody = 'narrow'
antibody = antibodies.ANTIBODIES[antibody]
logger.info(
f"{antibody.name} specified, using {antibody.peaktype} peak settings."
)
peaksettings = select_peak_parameters(antibody)
elif method == "atac":
logger.info(f"ATAC-seq specified, using narrow peak settings.")
peaksettings = " "
else:
peaksettings = " "
options = " ".join(resources.get("macs2", {}).get("options", ""))
genome_size = bam.fasta.total_sequence_length(dd.get_ref_file(data))
genome_size = "" if options.find("-g") > -1 else "-g %s" % genome_size
paired = "-f BAMPE" if bam.is_paired(chip_bam) else ""
with utils.chdir(out_dir):
cmd = _macs2_cmd(data)
cmd += peaksettings
try:
do.run(cmd.format(**locals()), "macs2 for %s" % name)
utils.move_safe(macs2_file, out_file)
except subprocess.CalledProcessError:
raise RuntimeWarning(
"macs2 terminated with an error. "
"Please, check the message and report "
"error if it is related to bcbio. "
"You can add specific options for the sample "
"setting resources as explained in docs: "
"https://bcbio-nextgen.readthedocs.org/en/latest/contents/configuration.html#sample-specific-resources"
)
_compress_and_sort_bdg_files(out_dir, data)
return _get_output_files(out_dir)
def _ref_version(ref_file):
_, ext = os.path.splitext(glob.glob(ref_file + "*")[0])
if ext == ".ebwt":
return 1
elif ext == ".bt2":
return 2
else:
logger.error("Cannot detect which reference version %s is. "
"Should end in either .ebwt (bowtie) or .bt2 "
"(bowtie2)." % (ref_file))
exit(1)
def _ref_version(ref_file):
_, ext = os.path.splitext(glob.glob(ref_file + "*")[0])
if ext == ".ebwt":
return 1
elif ext == ".bt2":
return 2
else:
logger.error("Cannot detect which reference version %s is. "
"Should end in either .ebwt (bowtie) or .bt2 "
"(bowtie2)." % (ref_file))
exit(1)
def _get_pipeline(item):
from bcbio.log import logger
SUPPORTED_PIPELINES = {x.name.lower(): x for x in
utils.itersubclasses(AbstractPipeline)}
analysis_type = item.get("analysis", "").lower()
if analysis_type not in SUPPORTED_PIPELINES:
logger.error("Cannot determine which type of analysis to run, "
"set in the run_info under details.")
sys.exit(1)
else:
return SUPPORTED_PIPELINES[analysis_type]
def get_sample_barcodes(fn, out_dir):
if not fn:
logger.error("Sample demultiplexing needs a list of known indexes provided "
"with via the sample_barcodes option in the algorithm section.")
sys.exit(1)
utils.safe_makedir(out_dir)
out_fn = os.path.join(out_dir, "barcodes.csv")
with open(fn) as inh:
with open(out_fn, 'w') as outh:
for line in inh:
outh.write("%s\n" % (line.strip().split(",")[0]))
return out_fn
def htseq_reader(align_file):
"""
returns a read-by-read sequence reader for a BAM or SAM file
"""
if bam.is_sam(align_file):
read_seq = HTSeq.SAM_Reader(align_file)
elif bam.is_bam(align_file):
read_seq = HTSeq.BAM_Reader(align_file)
else:
logger.error("%s is not a SAM or BAM file" % (align_file))
sys.exit(1)
return read_seq
def htseq_reader(align_file):
"""
returns a read-by-read sequence reader for a BAM or SAM file
"""
if bam.is_sam(align_file):
read_seq = HTSeq.SAM_Reader(align_file)
elif bam.is_bam(align_file):
read_seq = HTSeq.BAM_Reader(align_file)
else:
logger.error("%s is not a SAM or BAM file" % (align_file))
sys.exit(1)
return read_seq
def umi_transform(data):
"""
transform each read by identifying the barcode and UMI for each read
and putting the information in the read name
"""
fqfiles = data["files"]
fqfiles.extend(list(repeat("", 4-len(fqfiles))))
fq1, fq2, fq3, fq4 = fqfiles
umi_dir = os.path.join(dd.get_work_dir(data), "umis")
safe_makedir(umi_dir)
transform = dd.get_umi_type(data)
if file_exists(transform):
transform_file = transform
else:
transform_file = get_transform_file(transform)
if not file_exists(transform_file):
logger.error(
"The UMI transform can be specified as either a file or a "
"bcbio-supported transform. Either the file %s does not exist "
"or the transform is not supported by bcbio. Supported "
"transforms are %s."
%(dd.get_umi_type(data), ", ".join(SUPPORTED_TRANSFORMS)))
sys.exit(1)
out_base = dd.get_sample_name(data) + ".umitransformed.fq.gz"
out_file = os.path.join(umi_dir, out_base)
if file_exists(out_file):
data["files"] = [out_file]
return [[data]]
cellular_barcodes = get_cellular_barcodes(data)
if len(cellular_barcodes) > 1:
split_option = "--separate_cb"
else:
split_option = ""
umis = config_utils.get_program("umis", data, default="umis")
cores = dd.get_num_cores(data)
# skip transformation if the file already looks transformed
with open_fastq(fq1) as in_handle:
read = in_handle.next()
if "UMI_" in read:
data["files"] = [out_file]
return [[data]]
cmd = ("{umis} fastqtransform {split_option} {transform_file} "
"--cores {cores} "
"{fq1} {fq2} {fq3} {fq4}"
"| seqtk seq -L 20 - | gzip > {tx_out_file}")
message = ("Inserting UMI and barcode information into the read name of %s"
% fq1)
with file_transaction(out_file) as tx_out_file:
do.run(cmd.format(**locals()), message)
data["files"] = [out_file]
return [[data]]
def umi_transform(data):
"""
transform each read by identifying the barcode and UMI for each read
and putting the information in the read name
"""
fqfiles = data["files"]
fqfiles.extend(list(repeat("", 4 - len(fqfiles))))
fq1, fq2, fq3, fq4 = fqfiles
umi_dir = os.path.join(dd.get_work_dir(data), "umis")
safe_makedir(umi_dir)
transform = dd.get_umi_type(data)
if file_exists(transform):
transform_file = transform
else:
transform_file = get_transform_file(transform)
if not file_exists(transform_file):
logger.error(
"The UMI transform can be specified as either a file or a "
"bcbio-supported transform. Either the file %s does not exist "
"or the transform is not supported by bcbio. Supported "
"transforms are %s." %
(transform_file, ", ".join(SUPPORTED_TRANSFORMS)))
sys.exit(1)
out_base = dd.get_sample_name(data) + ".umitransformed.fq.gz"
out_file = os.path.join(umi_dir, out_base)
if file_exists(out_file):
data["files"] = [out_file]
return [[data]]
cellular_barcodes = get_cellular_barcodes(data)
if len(cellular_barcodes) == 2:
split_option = "--separate_cb"
else:
split_option = ""
umis = config_utils.get_program("umis", data, default="umis")
cores = dd.get_num_cores(data)
# skip transformation if the file already looks transformed
with open_fastq(fq1) as in_handle:
read = in_handle.next()
if "UMI_" in read:
data["files"] = [out_file]
return [[data]]
cmd = ("{umis} fastqtransform {split_option} {transform_file} "
"--cores {cores} "
"{fq1} {fq2} {fq3} {fq4}"
"| seqtk seq -L 20 - | gzip > {tx_out_file}")
message = (
"Inserting UMI and barcode information into the read name of %s" % fq1)
with file_transaction(out_file) as tx_out_file:
do.run(cmd.format(**locals()), message)
data["files"] = [out_file]
return [[data]]
def includes_missingalt(data):
"""
As of GATK 4.1.0.0, variants with missing alts are generated
(see https://github.com/broadinstitute/gatk/issues/5650)
"""
MISSINGALT_VERSION = LooseVersion("4.1.0.0")
version = LooseVersion(broad.get_gatk_version(config=dd.get_config(data)))
try:
return version >= MISSINGALT_VERSION
except TypeError:
logger.error(
f"LooseVersion failing with {version} as the detected version.")
sys.exit(1)
def get_sample_barcodes(fn, out_dir):
if not fn:
logger.error(
"Sample demultiplexing needs a list of known indexes provided "
"with via the sample_barcodes option in the algorithm section.")
sys.exit(1)
utils.safe_makedir(out_dir)
out_fn = os.path.join(out_dir, "barcodes.csv")
with open(fn) as inh:
with open(out_fn, 'w') as outh:
for line in inh:
outh.write("%s\n" % (line.strip().split(",")[0]))
return out_fn
def _cut_file(self, in_file):
"""
run cutadapt on a single file
"""
adapters = self._get_adapters(self.chemistry)
out_file = self.in2trimmed(in_file)
if file_exists(out_file):
return out_file
cutadapt = sh.Command(self.stage_config.get("program",
"cutadapt"))
quality_format = self.quality_format
if not quality_format:
quality_format = self._detect_fastq_format(in_file)
if quality_format == "sanger":
logger.info("Quality format detected as sanger.")
quality_base = 33
elif quality_format == "illumina":
logger.info("Quality format set to illumina 1.5/1.3")
quality_base = 64
else:
logger.error("Quality format could not be detected. Quality "
"Detected or set as %s. It should be illumina "
"or sanger.")
exit(1)
# if we want to trim the polya tails we have to first remove
# the adapters and then trim the tail
if self.stage_config.get("trim_polya", True):
temp_cut = tempfile.NamedTemporaryFile(suffix=".fastq",
dir=self.out_dir)
# trim off adapters
cmd = str(cutadapt.bake(in_file, self.options, adapters,
quality_base=quality_base, out=temp_cut.name))
do.run(cmd, "Cutadapt trim of adapters of %s." % (in_file), None)
with file_transaction(out_file) as temp_out:
polya = ADAPTERS.get("polya")
# trim off polya
cmd = str(cutadapt.bake(temp_cut.name, self.options, "-a",
polya, "-a", self._rc_adapters(polya),
quality_base=quality_base, out=temp_out))
do.run(cmd, "Cutadapt trim of polyA tail of %s." % (temp_cut.name),
None)
return out_file
else:
with file_transaction(out_file) as temp_out:
cmd = str(cutadapt.bake(in_file, self.options, adapters,
out=temp_out))
do.run(cmd, "Cutadapt trim of %s." % (in_file))
return out_file
def align(fastq_file, pair_file, ref_file, names, align_dir, data):
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 10 "
"--outStd SAM "
"--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":
cmd += " --outSAMstrandField intronMotif "
if dd.get_rsem(data) and not is_transcriptome_broken():
cmd += " --quantMode TranscriptomeSAM "
with tx_tmpdir(data) as tmp_dir:
sam_to_bam = bam.sam_to_bam_stream_cmd(config)
sort = bam.sort_cmd(config, tmp_dir)
cmd += "| {sam_to_bam} | {sort} -o {tx_final_out} "
run_message = "Running STAR aligner on %s and %s" % (fastq_file, ref_file)
with file_transaction(data, final_out) as tx_final_out:
do.run(cmd.format(**locals()), run_message, None)
data = _update_data(final_out, out_dir, names, data)
return data
def _fetch_chrom_sizes(config):
PROGRAM = "fetchChromSizes"
if not program_exists(PROGRAM):
logger.error("%s is not in the path or is not executable. Make sure "
"it is installed or go to "
"http://hgdownload.cse.ucsc.edu/admin/exe/"
"to download it." % (PROGRAM))
exit(1)
if "annotation" not in config:
logger.error("'annotation' must be in the yaml file. See example "
" configuration files")
exit(1)
if "name" not in config["annotation"]:
logger.error("'name' must be in the yaml file under "
" 'annotation'. See example configuration files.")
exit(1)
genome = config["annotation"]["name"]
chrom_size_file = os.path.join(_results_dir(config),
genome + ".sizes")
if file_exists(chrom_size_file):
return chrom_size_file
with file_transaction(chrom_size_file) as tmp_chrom_size_file:
sh.fetchChromSizes(genome, _out=tmp_chrom_size_file)
if not file_exists(chrom_size_file):
logger.error("chromosome size file does not exist. Check "
"'annotation': 'name' to make sure it is valid.")
exit(1)
return chrom_size_file
def run_with_config(input_file, config, stage, out_file=None):
if out_file is None:
out_dir = os.path.join(config["dir"].get("results", None), stage)
out_file = os.path.join(out_dir, _get_outfilename(input_file))
safe_makedir(out_dir)
if "annotation" not in config:
logger.error("annotation must appear in the config file, see example "
"configuration files.")
exit(1)
ref = prepare_ref_file(config["annotation"], config)
out_file = run(input_file, ref, out_file)
return out_file
def run_rnaseq_variant_calling(data):
variantcaller = dd.get_variantcaller(data)
if isinstance(variantcaller, list) and len(variantcaller) > 1:
logger.error("Only one variantcaller can be run for RNA-seq at "
"this time. Post an issue here "
"(https://github.com/chapmanb/bcbio-nextgen/issues) "
"if this is something you need to do.")
sys.exit(1)
if variantcaller and "gatk" in variantcaller:
data = variation.rnaseq_gatk_variant_calling(data)
if vardict.get_vardict_command(data):
data = variation.rnaseq_vardict_variant_calling(data)
return [[data]]
def run_with_config(input_file, config, stage, out_file=None):
if out_file is None:
out_dir = os.path.join(config["dir"].get("results", None), stage)
out_file = os.path.join(out_dir, _get_outfilename(input_file))
safe_makedir(out_dir)
if "annotation" not in config:
logger.error("annotation must appear in the config file, see example "
"configuration files.")
exit(1)
ref = prepare_ref_file(config["annotation"], config)
out_file = run(input_file, ref, out_file)
return out_file
def run_rnaseq_variant_calling(data):
variantcaller = dd.get_variantcaller(data)
if isinstance(variantcaller, list) and len(variantcaller) > 1:
logger.error("Only one variantcaller can be run for RNA-seq at "
"this time. Post an issue here "
"(https://github.com/chapmanb/bcbio-nextgen/issues) "
"if this is something you need to do.")
sys.exit(1)
if variantcaller and "gatk" in variantcaller:
data = variation.rnaseq_gatk_variant_calling(data)
if vardict.get_vardict_command(data):
data = variation.rnaseq_vardict_variant_calling(data)
return [[data]]
def singlecell_rnaseq(samples, run_parallel):
quantifier = dd.get_in_samples(samples, dd.get_singlecell_quantifier)
quantifier = quantifier.lower()
samples = run_parallel("run_umi_transform", samples)
samples = run_parallel("run_barcode_histogram", samples)
samples = run_parallel("run_filter_barcodes", samples)
if quantifier == "rapmap":
samples = run_parallel("run_rapmap_align", samples)
samples = run_parallel("run_tagcount", samples)
elif quantifier == "kallisto":
samples = run_parallel("run_kallisto_singlecell", samples)
else:
logger.error(("%s is not supported for singlecell RNA-seq "
"quantification." % quantifier))
sys.exit(1)
return samples
def _check_stems(files):
"""check if stem names are the same and use full path then"""
used = set()
for fn in files:
if os.path.basename(fn) in used:
logger.error("%s stem is multiple times in your file list, "
"so we don't know "
"how to assign it to the sample data in the CSV. "
"We are gonna use full path to make a difference, "
"that means paired files should be in the same folder. "
"If this is a problem, you should rename the files you want "
"to merge. Sorry, no possible magic here." % os.path.basename(fn)
)
return True
used.add(os.path.basename(fn))
return False
def bamindex(in_file, samtools="samtools"):
"""
index a bam file
avoids use of pysam.index which is not working for indexing as of 0.7.4
with ipython
"""
assert(is_bam(in_file)), "bamindex requires a BAM file, got %s" % in_file
out_file = replace_suffix(in_file, ".bai")
if file_exists(out_file):
return out_file
cmd = ["samtools", "index", in_file]
try:
subprocess.check_call(cmd)
except subprocess.CalledProcessError:
cmd_string = subprocess.list2cmdline(cmd)
logger.error("bamindex returned an error. The command "
"used to run bamindex was: %s." % (cmd_string))
return out_file
def bam2sam(in_file, samtools="samtools"):
"""
converts a bam file to a sam file
bam2sam("file.bam") -> "file.sam"
"""
assert(is_bam(in_file)), "bam2sam requires a BAM file, got %s" % in_file
out_file = replace_suffix(in_file, ".sam")
if file_exists(out_file):
return out_file
with file_transaction(out_file) as tmp_out_file:
#pysam.view("-h", "-o" + tmp_out_file, in_file)
cmd = "{samtools} view -h -o {tmp_out_file} {in_file}".format(**locals())
try:
subprocess.check_call(cmd)
except subprocess.CalledProcessError:
cmd_string = subprocess.list2cmdline(cmd)
logger.error("bam2sam returned an error. The command "
"used to run bam2sam was: %s." % (cmd_string))
return out_file
def wig2bigwig(wiggle_file, chrom_size_file, out_file):
"""
convert wiggle file to bigwig file using the UCSC tool
"""
PROGRAM = "wigToBigWig"
if not program_exists(PROGRAM):
logger.error("%s is not in the path or is not executable. Make sure "
"it is installed or go to "
"http://hgdownload.cse.ucsc.edu/admin/exe/"
"to download it." % (PROGRAM))
exit(1)
if file_exists(out_file):
return out_file
wigToBigWig = sh.Command(which(PROGRAM))
with file_transaction(out_file) as tx_out_file:
cmd = str(wigToBigWig.bake(wiggle_file, chrom_size_file, tx_out_file))
do.run(cmd, "Converting %s from wig to bigwig." % (wiggle_file), None)
return out_file