def quantitate_expression_parallel(samples, run_parallel):
"""
quantitate expression, all programs run here should be multithreaded to
take advantage of the threaded run_parallel environment
"""
data = samples[0][0]
samples = run_parallel("generate_transcript_counts", samples)
if "cufflinks" in dd.get_expression_caller(data):
samples = run_parallel("run_cufflinks", samples)
if "stringtie" in dd.get_expression_caller(data):
samples = run_parallel("run_stringtie_expression", samples)
if ("kallisto" in dd.get_expression_caller(data) or
dd.get_fusion_mode(data) or
"pizzly" in dd.get_fusion_caller(data, [])):
samples = run_parallel("run_kallisto_index", [samples])
samples = run_parallel("run_kallisto_rnaseq", samples)
if "sailfish" in dd.get_expression_caller(data):
samples = run_parallel("run_sailfish_index", [samples])
samples = run_parallel("run_sailfish", samples)
# always run salmon
samples = run_parallel("run_salmon_index", [samples])
samples = run_parallel("run_salmon_reads", samples)
samples = run_parallel("detect_fusions", samples)
return samples
def quantitate(data):
"""CWL target for quantitation.
XXX Needs to be split and parallelized by expression caller, with merging
of multiple calls.
"""
data = to_single_data(to_single_data(data))
data = generate_transcript_counts(data)[0][0]
data["quant"] = {}
if "sailfish" in dd.get_expression_caller(data):
data = to_single_data(sailfish.run_sailfish(data)[0])
data["quant"]["tsv"] = data["sailfish"]
data["quant"]["hdf5"] = os.path.join(os.path.dirname(data["sailfish"]),
"abundance.h5")
if ("kallisto" in dd.get_expression_caller(data)
or "pizzly" in dd.get_fusion_caller(data, [])):
data = to_single_data(kallisto.run_kallisto_rnaseq(data)[0])
data["quant"]["tsv"] = os.path.join(data["kallisto_quant"],
"abundance.tsv")
data["quant"]["hdf5"] = os.path.join(data["kallisto_quant"],
"abundance.h5")
if (os.path.exists(os.path.join(data["kallisto_quant"], "fusion.txt"))):
data["quant"]["fusion"] = os.path.join(data["kallisto_quant"],
"fusion.txt")
else:
data["quant"]["fusion"] = None
if "salmon" in dd.get_expression_caller(data):
data = to_single_data(salmon.run_salmon_reads(data)[0])
data["quant"]["tsv"] = data["salmon"]
data["quant"]["hdf5"] = os.path.join(os.path.dirname(data["salmon"]),
"abundance.h5")
return [[data]]
def quantitate_expression_parallel(samples, run_parallel):
"""
quantitate expression, all programs run here should be multithreaded to
take advantage of the threaded run_parallel environment
"""
data = samples[0][0]
samples = run_parallel("generate_transcript_counts", samples)
if "cufflinks" in dd.get_expression_caller(data):
samples = run_parallel("run_cufflinks", samples)
if "stringtie" in dd.get_expression_caller(data):
samples = run_parallel("run_stringtie_expression", samples)
if ("kallisto" in dd.get_expression_caller(data)
or dd.get_fusion_mode(data)
or "pizzly" in dd.get_fusion_caller(data, [])):
samples = run_parallel("run_kallisto_index", [samples])
samples = run_parallel("run_kallisto_rnaseq", samples)
if "sailfish" in dd.get_expression_caller(data):
samples = run_parallel("run_sailfish_index", [samples])
samples = run_parallel("run_sailfish", samples)
# always run salmon
samples = run_parallel("run_salmon_index", [samples])
samples = run_parallel("run_salmon_reads", samples)
samples = run_parallel("detect_fusions", samples)
return samples
def quantitate(data):
"""CWL target for quantitation.
XXX Needs to be split and parallelized by expression caller, with merging
of multiple calls.
"""
data = to_single_data(to_single_data(data))
data = generate_transcript_counts(data)[0][0]
data["quant"] = {}
if "sailfish" in dd.get_expression_caller(data):
data = to_single_data(sailfish.run_sailfish(data)[0])
data["quant"]["tsv"] = data["sailfish"]
data["quant"]["hdf5"] = os.path.join(os.path.dirname(data["sailfish"]), "abundance.h5")
if ("kallisto" in dd.get_expression_caller(data) or "pizzly" in dd.get_fusion_caller(data, [])):
data = to_single_data(kallisto.run_kallisto_rnaseq(data)[0])
data["quant"]["tsv"] = os.path.join(data["kallisto_quant"], "abundance.tsv")
data["quant"]["hdf5"] = os.path.join(data["kallisto_quant"], "abundance.h5")
if (os.path.exists(os.path.join(data["kallisto_quant"], "fusion.txt"))):
data["quant"]["fusion"] = os.path.join(data["kallisto_quant"], "fusion.txt")
else:
data["quant"]["fusion"] = None
if "salmon" in dd.get_expression_caller(data):
data = to_single_data(salmon.run_salmon_reads(data)[0])
data["quant"]["tsv"] = data["salmon"]
data["quant"]["hdf5"] = os.path.join(os.path.dirname(data["salmon"]), "abundance.h5")
return [[data]]
def quantitate_expression_noparallel(samples, run_parallel):
"""
run transcript quantitation for algorithms that don't run in parallel
"""
data = samples[0][0]
if "express" in dd.get_expression_caller(data):
samples = run_parallel("run_express", samples)
if "dexseq" in dd.get_expression_caller(data):
samples = run_parallel("run_dexseq", samples)
return samples
def quantitate_expression_noparallel(samples, run_parallel):
"""
run transcript quantitation for algorithms that don't run in parallel
"""
data = samples[0][0]
if "express" in dd.get_expression_caller(data):
samples = run_parallel("run_express", samples)
if "dexseq" in dd.get_expression_caller(data):
samples = run_parallel("run_dexseq", samples)
return samples
def quantitate_expression_parallel(samples, run_parallel):
"""
quantitate expression, all programs run here should be multithreaded to
take advantage of the threaded run_parallel environment
"""
data = samples[0][0]
samples = run_parallel("generate_transcript_counts", samples)
samples = run_parallel("run_sailfish", samples)
samples = sailfish.combine_sailfish(samples)
if "cufflinks" in dd.get_expression_caller(data):
samples = run_parallel("run_cufflinks", samples)
if "stringtie" in dd.get_expression_caller(data):
samples = run_parallel("run_stringtie_expression", samples)
return samples
def quantitate_expression_parallel(samples, run_parallel):
"""
quantitate expression, all programs run here should be multithreaded to
take advantage of the threaded run_parallel environment
"""
data = samples[0][0]
samples = run_parallel("generate_transcript_counts", samples)
samples = run_parallel("run_sailfish", samples)
samples = sailfish.combine_sailfish(samples)
if "cufflinks" in dd.get_expression_caller(data):
samples = run_parallel("run_cufflinks", samples)
if "stringtie" in dd.get_expression_caller(data):
samples = run_parallel("run_stringtie_expression", samples)
return samples
def quantitate(data):
"""CWL target for quantitation.
XXX Needs to be split and parallelized by expression caller, with merging
of multiple calls.
"""
data = to_single_data(to_single_data(data))
data = generate_transcript_counts(data)[0][0]
data["quant"] = {}
if "sailfish" in dd.get_expression_caller(data):
data = to_single_data(sailfish.run_sailfish(data)[0])
data["quant"]["tsv"] = data["sailfish"]
data["quant"]["hdf5"] = os.path.join(os.path.dirname(data["sailfish"]),
"abundance.h5")
if ("kallisto" in dd.get_expression_caller(data)
or "pizzly" in dd.get_fusion_caller(data, [])):
data = to_single_data(kallisto.run_kallisto_rnaseq(data)[0])
data["quant"]["tsv"] = os.path.join(data["kallisto_quant"],
"abundance.tsv")
data["quant"]["hdf5"] = os.path.join(data["kallisto_quant"],
"abundance.h5")
if (os.path.exists(os.path.join(data["kallisto_quant"], "fusion.txt"))):
data["quant"]["fusion"] = os.path.join(data["kallisto_quant"],
"fusion.txt")
else:
data["quant"]["fusion"] = None
if "salmon" in dd.get_expression_caller(data):
if dd.get_quantify_genome_alignments(data):
if dd.get_aligner(data).lower() != "star":
if dd.get_genome_build(data) == "hg38":
logger.warning(
"Whole genome alignment-based Salmon quantification is "
"only supported for the STAR aligner. Since this is hg38 we will fall "
"back to the decoy method")
data = to_single_data(salmon.run_salmon_decoy(data)[0])
else:
logger.warning(
"Whole genome alignment-based Salmon quantification is "
"only supported for the STAR aligner. Falling back to the "
"transcriptome-only method.")
data = to_single_data(salmon.run_salmon_reads(data)[0])
else:
data = to_single_data(salmon.run_salmon_bam(data)[0])
else:
data = to_single_data(salmon.run_salmon_reads(data)[0])
data["quant"]["tsv"] = data["salmon"]
data["quant"]["hdf5"] = os.path.join(os.path.dirname(data["salmon"]),
"abundance.h5")
return [[data]]
def sample_annotation(data):
"""
Annotate miRNAs using miRBase database with seqbuster tool
"""
names = data["rgnames"]['sample']
tools = dd.get_expression_caller(data)
work_dir = os.path.join(dd.get_work_dir(data), "mirbase")
out_dir = os.path.join(work_dir, names)
utils.safe_makedir(out_dir)
out_file = op.join(out_dir, names)
if dd.get_mirbase_hairpin(data):
mirbase = op.abspath(op.dirname(dd.get_mirbase_hairpin(data)))
if utils.file_exists(data["collapse"]):
data['transcriptome_bam'] = _align(data["collapse"], dd.get_mirbase_hairpin(data), out_file, data)
data['seqbuster'] = _miraligner(data["collapse"], out_file, dd.get_species(data), mirbase, data['config'])
else:
logger.debug("Trimmed collapsed file is empty for %s." % names)
else:
logger.debug("No annotation file from miRBase.")
sps = dd.get_species(data) if dd.get_species(data) else "None"
logger.debug("Looking for mirdeep2 database for %s" % names)
if file_exists(op.join(dd.get_work_dir(data), "mirdeep2", "novel", "hairpin.fa")):
data['seqbuster_novel'] = _miraligner(data["collapse"], "%s_novel" % out_file, sps, op.join(dd.get_work_dir(data), "mirdeep2", "novel"), data['config'])
if "trna" in tools:
data['trna'] = _mint_trna_annotation(data)
data = spikein.counts_spikein(data)
return [[data]]
def run_cluster(*data):
"""
Run seqcluster cluster to detect smallRNA clusters
"""
sample = data[0][0]
tools = dd.get_expression_caller(data[0][0])
work_dir = dd.get_work_dir(sample)
out_dir = op.join(work_dir, "seqcluster", "cluster")
out_dir = op.abspath(safe_makedir(out_dir))
prepare_dir = op.join(work_dir, "seqcluster", "prepare")
bam_file = data[0][0]["cluster_bam"]
if "seqcluster" in tools:
gtf_file = dd.get_transcriptome_gtf(sample) if dd.get_transcriptome_gtf(sample) else dd.get_srna_gtf_file(sample)
sample["seqcluster"] = _cluster(bam_file, data[0][0]["seqcluster_prepare_ma"],
out_dir, dd.get_ref_file(sample),
gtf_file)
sample["report"] = _report(sample, dd.get_ref_file(sample))
if "mirge" in tools:
sample["mirge"] = mirge.run(data)
out_mirna = _make_isomir_counts(data, out_dir=op.join(work_dir, "mirbase"))
if out_mirna:
sample = dd.set_mirna_counts(sample, out_mirna[0])
sample = dd.set_isomir_counts(sample, out_mirna[1])
out_novel = _make_isomir_counts(data, "seqbuster_novel", op.join(work_dir, "mirdeep2"), "_novel")
if out_novel:
sample = dd.set_novel_mirna_counts(sample, out_novel[0])
sample = dd.set_novel_isomir_counts(sample, out_novel[1])
data[0][0] = sample
data = spikein.combine_spikein(data)
return data
def run_prepare(*data):
"""
Run seqcluster prepare to merge all samples in one file
"""
out_dir = os.path.join(dd.get_work_dir(data[0][0]), "seqcluster", "prepare")
out_dir = os.path.abspath(safe_makedir(out_dir))
prepare_dir = os.path.join(out_dir, "prepare")
tools = dd.get_expression_caller(data[0][0])
if len(tools) == 0:
logger.info("You didn't specify any other expression caller tool."
"You can add to the YAML file:"
"expression_caller:[trna, seqcluster, mirdeep2]")
fn = []
for sample in data:
name = sample[0]["rgnames"]['sample']
fn.append("%s\t%s" % (sample[0]['collapse'], name))
args = namedtuple('args', 'debug print_debug minc minl maxl out')
args = args(False, False, 2, 17, 40, out_dir)
ma_out = op.join(out_dir, "seqs.ma")
seq_out = op.join(out_dir, "seqs.fastq")
min_shared = max(int(len(fn) / 10.0), 1)
if not file_exists(ma_out):
seq_l, sample_l = prepare._read_fastq_files(fn, args)
with file_transaction(ma_out) as ma_tx:
with open(ma_tx, 'w') as ma_handle:
with open(seq_out, 'w') as seq_handle:
logger.info("Prepare seqs.fastq with -minl 17 -maxl 40 -minc 2 --min_shared 0.1")
prepare._create_matrix_uniq_seq(sample_l, seq_l, ma_handle, seq_handle, min_shared)
for sample in data:
sample[0]["seqcluster_prepare_ma"] = ma_out
sample[0]["seqcluster_prepare_fastq"] = seq_out
return data
def run_cluster(*data):
"""
Run seqcluster cluster to detect smallRNA clusters
"""
sample = data[0][0]
tools = dd.get_expression_caller(data[0][0])
work_dir = dd.get_work_dir(sample)
out_dir = op.join(work_dir, "seqcluster", "cluster")
out_dir = op.abspath(safe_makedir(out_dir))
prepare_dir = op.join(work_dir, "seqcluster", "prepare")
bam_file = data[0][0]["work_bam"]
if "seqcluster" in tools:
sample["seqcluster"] = _cluster(bam_file, data[0][0]["seqcluster_prepare_ma"], out_dir, dd.get_ref_file(sample), dd.get_srna_gtf_file(sample))
sample["report"] = _report(sample, dd.get_ref_file(sample))
out_mirna = _make_isomir_counts(data, out_dir=op.join(work_dir, "mirbase"))
if out_mirna:
sample = dd.set_mirna_counts(sample, out_mirna[0])
sample = dd.set_isomir_counts(sample, out_mirna[1])
out_novel = _make_isomir_counts(data, "seqbuster_novel", op.join(work_dir, "mirdeep2"), "_novel")
if out_novel:
sample = dd.set_novel_mirna_counts(sample, out_novel[0])
sample = dd.set_novel_isomir_counts(sample, out_novel[1])
data[0][0] = sample
return data
def sample_annotation(data):
"""
Annotate miRNAs using miRBase database with seqbuster tool
"""
names = data["rgnames"]['sample']
tools = dd.get_expression_caller(data)
work_dir = os.path.join(dd.get_work_dir(data), "mirbase")
out_dir = os.path.join(work_dir, names)
utils.safe_makedir(out_dir)
out_file = op.join(out_dir, names)
if dd.get_mirbase_hairpin(data):
mirbase = op.abspath(op.dirname(dd.get_mirbase_hairpin(data)))
data['seqbuster'] = _miraligner(data["collapse"], out_file,
dd.get_species(data), mirbase,
data['config'])
else:
logger.debug("No annotation file from miRBase.")
sps = dd.get_species(data) if dd.get_species(data) else "None"
logger.debug("Looking for mirdeep2 database for %s" % names)
if file_exists(
op.join(dd.get_work_dir(data), "mirdeep2", "novel", "hairpin.fa")):
data['seqbuster_novel'] = _miraligner(
data["collapse"], "%s_novel" % out_file, sps,
op.join(dd.get_work_dir(data), "mirdeep2", "novel"),
data['config'])
if "trna" in tools:
data['trna'] = _trna_annotation(data)
data = spikein.counts_spikein(data)
return [[data]]
def run_prepare(*data):
"""
Run seqcluster prepare to merge all samples in one file
"""
out_dir = os.path.join(dd.get_work_dir(data[0][0]), "seqcluster", "prepare")
out_dir = os.path.abspath(safe_makedir(out_dir))
prepare_dir = os.path.join(out_dir, "prepare")
tools = dd.get_expression_caller(data[0][0])
if len(tools) == 0:
logger.info("You didn't specify any other expression caller tool."
"You can add to the YAML file:"
"expression_caller:[trna, seqcluster, mirdeep2]")
fn = []
for sample in data:
name = sample[0]["rgnames"]['sample']
fn.append("%s\t%s" % (sample[0]['collapse'], name))
args = namedtuple('args', 'debug print_debug minc minl maxl out')
args = args(False, False, 2, 17, 40, out_dir)
ma_out = op.join(out_dir, "seqs.ma")
seq_out = op.join(out_dir, "seqs.fastq")
min_shared = max(int(len(fn) / 10.0), 1)
if not file_exists(ma_out):
seq_l, sample_l = prepare._read_fastq_files(fn, args)
with file_transaction(ma_out) as ma_tx:
with open(ma_tx, 'w') as ma_handle:
with open(seq_out, 'w') as seq_handle:
prepare._create_matrix_uniq_seq(sample_l, seq_l, ma_handle, seq_handle, min_shared)
for sample in data:
sample[0]["seqcluster_prepare_ma"] = ma_out
sample[0]["seqcluster_prepare_fastq"] = seq_out
return data
def quantitate_expression_parallel(samples, run_parallel):
"""
quantitate expression, all programs run here should be multithreaded to
take advantage of the threaded run_parallel environment
"""
data = samples[0][0]
to_index = determine_indexes_to_make(samples)
samples = run_parallel("generate_transcript_counts", samples)
if "cufflinks" in dd.get_expression_caller(data):
samples = run_parallel("run_cufflinks", samples)
if "stringtie" in dd.get_expression_caller(data):
samples = run_parallel("run_stringtie_expression", samples)
if ("kallisto" in dd.get_expression_caller(data)
or dd.get_fusion_mode(data)
or "pizzly" in dd.get_fusion_caller(data, [])):
run_parallel("run_kallisto_index", [to_index])
samples = run_parallel("run_kallisto_rnaseq", samples)
if "sailfish" in dd.get_expression_caller(data):
run_parallel("run_sailfish_index", [to_index])
samples = run_parallel("run_sailfish", samples)
# always run salmon
run_parallel("run_salmon_index", [to_index])
if dd.get_quantify_genome_alignments(data):
if dd.get_aligner(data).lower() != "star":
if dd.get_genome_build(data) == "hg38":
logger.warning(
"Whole genome alignment-based Salmon quantification is "
"only supported for the STAR aligner. Since this is hg38 we will fall "
"back to the decoy method")
samples = run_parallel("run_salmon_decoy", samples)
else:
logger.warning(
"Whole genome alignment-based Salmon quantification is "
"only supported for the STAR aligner. Falling back to the "
"transcriptome-only method.")
samples = run_parallel("run_salmon_reads", samples)
else:
samples = run_parallel("run_salmon_bam", samples)
else:
samples = run_parallel("run_salmon_reads", samples)
samples = run_parallel("detect_fusions", samples)
return samples
def run_align(*data):
"""
Prepare data to run alignment step, only once for each project
"""
work_dir = dd.get_work_dir(data[0][0])
out_dir = op.join(work_dir, "seqcluster", "prepare")
seq_out = op.join(out_dir, "seqs.fastq")
bam_dir = op.join(work_dir, "align")
new_bam_file = op.join(bam_dir, "seqs.bam")
tools = dd.get_expression_caller(data[0][0])
if not file_exists(new_bam_file):
sample = process_alignment(data[0][0], [seq_out, None])
bam_file = dd.get_work_bam(sample[0][0])
shutil.move(bam_file, new_bam_file)
shutil.move(bam_file + ".bai", new_bam_file + ".bai")
shutil.rmtree(op.join(bam_dir, sample[0][0]["rgnames"]['sample']))
for sample in data:
sample[0]["align_bam"] = sample[0]["clean_fastq"]
if "mirdeep2" in tools:
novel_db = mirdeep.run(data)
return data
def run_align(*data):
"""
Prepare data to run alignment step, only once for each project
"""
work_dir = dd.get_work_dir(data[0][0])
out_dir = op.join(work_dir, "seqcluster", "prepare")
seq_out = op.join(out_dir, "seqs.fastq")
bam_dir = op.join(work_dir, "align")
new_bam_file = op.join(bam_dir, "seqs.bam")
tools = dd.get_expression_caller(data[0][0])
if not file_exists(new_bam_file):
sample = process_alignment(data[0][0], [seq_out, None])
bam_file = dd.get_work_bam(sample[0][0])
shutil.move(bam_file, new_bam_file)
shutil.move(bam_file + ".bai", new_bam_file + ".bai")
shutil.rmtree(op.join(bam_dir, sample[0][0]["rgnames"]['sample']))
for sample in data:
# sample[0]["align_bam"] = sample[0]["clean_fastq"]
sample[0]["cluster_bam"] = new_bam_file
if "mirdeep2" in tools:
novel_db = mirdeep.run(data)
return data
def _maybe_add_sailfish_files(algorithm, sample, out):
if "sailfish" in dd.get_expression_caller(sample):
out.append({"path": dd.get_sailfish_dir(sample),
"type": "directory",
"ext": os.path.join("sailfish", dd.get_sample_name(sample))})
return out
