def merge_bam_files(bam_files, work_dir, config):
"""Merge multiple BAM files from a sample into a single BAM for processing.
"""
out_file = os.path.join(work_dir, os.path.basename(bam_files[0]))
if not os.path.exists(out_file):
picard = BroadRunner(config["program"]["picard"])
with utils.curdir_tmpdir() as tmp_dir:
opts = [("OUTPUT", out_file), ("SORT_ORDER", "coordinate"), ("TMP_DIR", tmp_dir)]
for b in bam_files:
opts.append(("INPUT", b))
picard.run("MergeSamFiles", opts)
return out_file
def merge_bam_files(bam_files, work_dir, config):
"""Merge multiple BAM files from a sample into a single BAM for processing.
"""
out_file = os.path.join(work_dir, os.path.basename(bam_files[0]))
if not os.path.exists(out_file):
picard = BroadRunner(config["program"]["picard"])
with utils.curdir_tmpdir() as tmp_dir:
opts = [("OUTPUT", out_file), ("SORT_ORDER", "coordinate"),
("TMP_DIR", tmp_dir)]
for b in bam_files:
opts.append(("INPUT", b))
picard.run("MergeSamFiles", opts)
return out_file
def main(config_file,
align_sam,
ref_file,
fastq_one,
fastq_pair=None,
sample_name="",
rg_name="",
pu_name=""):
with open(config_file) as in_handle:
config = yaml.load(in_handle)
picard = BroadRunner(config["program"]["picard"],
max_memory=config["algorithm"].get("java_memory", ""))
platform = config["algorithm"]["platform"]
if platform.lower() == "illumina":
qual_format = "Illumina"
else:
raise ValueError("Need to specify quality format for %s" % platform)
index_ref_file(picard, ref_file)
base_dir = os.path.split(align_sam)[0]
with curdir_tmpdir() as tmp_dir:
out_fastq_bam = picard_fastq_to_bam(picard, fastq_one, fastq_pair,
base_dir, platform, qual_format,
sample_name, rg_name, pu_name,
tmp_dir)
out_bam = picard_merge_bam(picard, align_sam, out_fastq_bam, ref_file,
tmp_dir, fastq_pair is not None)
sort_bam = picard_sort(picard, out_bam, tmp_dir)
save_diskspace(out_fastq_bam, "Combined into output BAM %s" % out_bam,
config)
save_diskspace(out_bam, "Sorted to %s" % sort_bam, config)
def __init__(self, config):
self.config = config
self.stage_config = config["stage"][self.stage]
self.ribo = self.stage_config["ribo"]
self.picard = BroadRunner(config["program"]["picard"], None, {"algorithm": {}})
self.ref = prepare_ref_file(self.stage_config["ref"],
self.config)
def main(config_file, out_base, ref_file, read1, read2=None, sample_name=""):
with open(config_file) as in_handle:
config = yaml.load(in_handle)
barcode, lane = _get_sample_details(read1)
maq_cmd = config["program"]["maq"]
stringency = config["algorithm"]["stringency"]
picard = BroadRunner(config["program"]["picard"],
config["program"].get("gatk", ""),
max_memory=config["algorithm"].get("java_memory", ""))
bam_reads = fastq_to_bam(picard, sample_name,
config["algorithm"]["quality_format"], read1,
read2)
base_align = picard_run_maq(picard,
maq_cmd,
bam_reads,
ref_file,
barcode,
lane,
out_base,
stringency,
read2 is not None,
limit=1e6)
cal_bam_reads = calibrate_scores(picard, bam_reads, base_align, ref_file)
final_align = picard_run_maq(picard,
maq_cmd,
cal_bam_reads,
ref_file,
barcode,
lane,
out_base,
stringency,
read2 is not None,
ext="-cal")
def main(gatk_dir, vcf_info, ref_file, dbsnp, intervals=None):
picard = BroadRunner(gatk_dir)
if os.path.isdir(vcf_info):
vcf_files = sorted(glob.glob(os.path.join(vcf_info, "*-filter.vcf")))
else:
vcf_files = [vcf_info]
for vcf_in in vcf_files:
eval_file = variant_eval(vcf_in, ref_file, dbsnp, intervals, picard)
stats = extract_eval_stats(eval_file)
print_stats(vcf_in, stats['called'], len(vcf_files) == 1)
def main(config_file):
with open(config_file) as in_handle:
config = yaml.load(in_handle)
# make the needed directories
map(safe_makedir, config["dir"].values())
# specific for thesis pipeline
in_dir = config["dir"]["data"]
id_file = config["id_file"]
curr_files = input_files_from_dir(in_dir, id_file)
logger.info("Running pipeline on %s." % (curr_files))
for stage in config["run"]:
if stage == "fastqc":
logger.info("Running fastqc on %s." % (curr_files))
stage_runner = fastqc.FastQC(config)
view.map(stage_runner, curr_files, block=False)
if stage == "cutadapt":
logger.info("Running cutadapt on %s." % (curr_files))
stage_runner = trim.Cutadapt(config)
curr_files = view.map(stage_runner, curr_files)
if stage == "bowtie":
logger.info("Running bowtie on %s." % (curr_files))
bowtie = Bowtie(config)
curr_files = view.map(bowtie, curr_files)
mapped = view.map(sam.only_mapped, curr_files)
unmapped = view.map(sam.only_unmapped, curr_files)
curr_files = mapped
bam_files = view.map(sam.sam2bam, mapped)
bam_sorted = view.map(sam.bamsort, bam_files)
view.map(sam.bamindex, bam_sorted)
if stage == "coverage":
logger.info("Calculating RNASeq metrics on %s." % (curr_files))
nrun = len(curr_files)
ref = prepare_ref_file(config["stage"][stage]["ref"], config)
ribo = config["stage"][stage]["ribo"]
picard = BroadRunner(config["program"]["picard"])
out_dir = os.path.join(config["dir"]["results"], stage)
safe_makedir(out_dir)
out_files = [
replace_suffix(os.path.basename(x), "metrics")
for x in curr_files
]
out_files = [os.path.join(out_dir, x) for x in out_files]
out_files = view.map(picardrun.picard_rnaseq_metrics,
[picard] * nrun, curr_files, [ref] * nrun,
[ribo] * nrun, out_files)
stop_cluster()
def main(picard_dir,
align_bam,
ref_file,
is_paired,
bait_file=None,
target_file=None,
do_sort=False,
sample_name="",
config=None):
with utils.curdir_tmpdir() as tmp_dir:
work_dir = os.getcwd()
params = {}
java_memory = ""
if config:
with open(config) as in_handle:
info = yaml.load(in_handle)
params = info["program"]
java_memory = info["algorithm"].get("java_memory", "")
picard = BroadRunner(picard_dir, max_memory=java_memory)
if do_sort:
align_bam = picard_sort(picard, align_bam, tmp_dir)
metrics = PicardMetrics(picard, tmp_dir)
summary_table, metrics_graphs = metrics.report(align_bam, ref_file,
is_paired, bait_file,
target_file)
metrics_graphs = [(p, c, 0.75) for p, c in metrics_graphs]
base, ext = os.path.splitext(align_bam)
base = base.replace(".", "-")
fastqc_graphs, fastqc_stats, fastqc_overrep = \
fastqc_report(align_bam, params)
all_graphs = fastqc_graphs + metrics_graphs
summary_table = _update_summary_table(summary_table, ref_file,
fastqc_stats)
tmpl = Template(section_template)
if sample_name is None:
sample_name = fastqc_stats["Filename"]
sample_name = "%s (%s)" % (sample_name.replace(
"_", "\_"), base.replace("_", " "))
section = tmpl.render(
name=sample_name,
summary=None,
summary_table=summary_table,
figures=[(f, c, i) for (f, c, i) in all_graphs if f],
overrep=fastqc_overrep,
recal_figures=_get_recal_plots(work_dir, align_bam))
out_file = "%s-summary.tex" % base
out_tmpl = Template(base_template)
with open(out_file, "w") as out_handle:
out_handle.write(out_tmpl.render(parts=[section]))
run_pdflatex(out_file, params)
def main(picard_dir,
align_bam,
ref_file,
fastq_one,
fastq_pair=None,
bait_file=None,
target_file=None,
do_sort=False,
sample_name="",
config=None):
tmp_dir = _make_tmpdir()
work_dir = os.getcwd()
if config:
with open(config) as in_handle:
params = yaml.load(in_handle)["program"]
else:
params = PARAM_DEFAULTS
picard = BroadRunner(picard_dir)
if do_sort:
align_bam = picard_sort(picard, align_bam, tmp_dir)
metrics = PicardMetrics(picard, tmp_dir)
summary_table, metrics_graphs = metrics.report(align_bam, ref_file,
fastq_pair is not None,
bait_file, target_file)
base, ext = os.path.splitext(align_bam)
base = base.replace(".", "-")
total_count, read_size, fastq_graphs = plot_fastq_stats(
[fastq_one, fastq_pair], base, params)
qa_graphs = solexaqa_plots([fastq_one, fastq_pair], params, work_dir)
# add read_size to the total summary table
summary_table[0] = (summary_table[0][0], summary_table[0][1],
"%sbp %s" % (read_size, summary_table[0][-1]))
ref_org = os.path.splitext(os.path.split(ref_file)[-1])[0]
summary_table.insert(0,
("Reference organism", ref_org.replace("_", " "), ""))
tmpl = Template(section_template)
sample_name = "%s (%s)" % (sample_name.replace(
"_", "\_"), base.replace("_", " "))
section = tmpl.render(name=sample_name,
summary=None,
summary_table=summary_table,
figures=[(f, c) for (f, c) in metrics_graphs +
fastq_graphs + qa_graphs if f],
recal_figures=_get_recal_plots(work_dir, align_bam))
out_file = "%s-summary.tex" % base
out_tmpl = Template(base_template)
with open(out_file, "w") as out_handle:
out_handle.write(out_tmpl.render(parts=[section]))
run_pdflatex(out_file, params)
shutil.rmtree(tmp_dir)
def main(config_file, ref_file, align_bam, snp_file=None):
with open(config_file) as in_handle:
config = yaml.load(in_handle)
picard = BroadRunner(config["program"]["picard"],
config["program"].get("gatk", ""))
platform = config["algorithm"]["platform"]
ref_dict = index_ref_file(picard, ref_file)
#snp_dict = (index_snp_file(picard, ref_dict, snp_file) if snp_file else
# None)
align_sort_bam = picard_sort(picard, align_bam)
dup_align_bam = mark_duplicates(picard, align_sort_bam)
recal_file = count_covariates(picard, dup_align_bam, ref_file, platform,
snp_file)
recal_bam = gatk_recalibrate(picard, dup_align_bam, ref_file, recal_file,
platform)
def main(config_file, ref_file, align_bam, dbsnp=None):
with open(config_file) as in_handle:
config = yaml.load(in_handle)
picard = BroadRunner(config["program"]["picard"],
config["program"].get("gatk", ""))
ref_dict = index_ref_file(picard, ref_file)
index_bam(align_bam, config["program"]["samtools"])
realign_target_file = realigner_targets(picard, align_bam,
ref_file, dbsnp)
realign_bam = indel_realignment(picard, align_bam, ref_file,
realign_target_file)
realign_sort_bam = picard_fixmate(picard, realign_bam)
index_bam(realign_sort_bam, config["program"]["samtools"])
snp_file = unified_genotyper(picard, realign_sort_bam, ref_file,
dbsnp)
filter_snp = variant_filtration(picard, snp_file, ref_file)
def process_fastq(in_file, ref_index, cur_config, config, config_file):
do_realignment = config["algorithm"].get("realignment", "")
do_kmercorrect = config["algorithm"].get("kmer_correct", "")
trim_three = config["algorithm"].get("trim_three", "")
picard = BroadRunner(config["program"]["picard"], config["program"]["gatk"],
config["algorithm"]["java_memory"])
if trim_three:
in_file = trim_fastq(in_file, three=int(trim_three))
if do_kmercorrect:
in_file = remove_ns(in_file)
in_file = kmer_filter(in_file, do_kmercorrect, config)
unique_file = uniquify_bioplayground(in_file, config)
align_sam = novoalign.align(config["dir"]["align"], ref_index, unique_file,
qual_format=cur_config.get("format", None))
align_bam = to_bamsort(align_sam, unique_file, config, config_file)
if do_realignment == "gatk":
picard.run_fn("picard_index", align_bam)
align_bam = picard.run_fn("gatk_realigner", align_bam, config["ref"],
deep_coverage=True)
picard.run_fn("picard_index", align_bam)
if config["algorithm"].get("range_params", None):
call_analyze_multiple(align_bam, in_file, config)
else:
call_bases_and_analyze(align_bam, in_file, config)
def main(config_file):
with open(config_file) as in_handle:
config = yaml.load(in_handle)
setup_logging(config)
from bipy.log import logger
start_cluster(config)
from bipy.cluster import view
# view.push({'logger': logger})
input_files = [
os.path.join(config["dir"]["data"], x) for x in config["input"]
]
results_dir = config["dir"]["results"]
map(safe_makedir, config["dir"].values())
curr_files = input_files
for stage in config["run"]:
if stage == "fastqc":
nfiles = len(curr_files)
logger.info("Running %s on %s" % (stage, str(curr_files)))
fastqc_config = _get_stage_config(config, stage)
fastqc_outputs = view.map(fastqc.run, curr_files,
[fastqc_config] * nfiles,
[config] * nfiles)
if stage == "cutadapt":
nfiles = len(curr_files)
cutadapt_config = _get_stage_config(config, stage)
cutadapt_outputs = view.map(cutadapt_tool.run, curr_files,
[cutadapt_config] * nfiles,
[config] * nfiles)
curr_files = cutadapt_outputs
if stage == "novoalign":
nfiles = len(curr_files)
novoalign_config = _get_stage_config(config, stage)
#db = novoindex.run(config["ref"],
# _get_stage_config(config, "novoindex"),
# config)
db = config["genome"]["file"]
novoalign_outputs = view.map(novoalign.run, curr_files,
[db] * nfiles,
[novoalign_config] * nfiles,
[config] * nfiles)
picard = BroadRunner(config["program"]["picard"])
args = zip(*itertools.product([picard], novoalign_outputs))
# conver to bam
bamfiles = view.map(picardrun.picard_formatconverter, *args)
args = zip(*itertools.product([picard], bamfiles))
# sort bam
sorted_bf = view.map(picardrun.picard_sort, *args)
# index bam
args = zip(*itertools.product([picard], sorted_bf))
view.map(picardrun.picard_index, *args)
curr_files = novoalign_outputs
if stage == "htseq-count":
nfiles = len(curr_files)
htseq_config = _get_stage_config(config, stage)
htseq_outputs = view.map(htseq_count.run_with_config, curr_files,
[config] * nfiles, [stage] * nfiles)
column_names = _get_short_names(input_files)
logger.info("Column names: %s" % (column_names))
out_file = os.path.join(config["dir"]["results"], stage,
"combined.counts")
combined_out = htseq_count.combine_counts(htseq_outputs,
column_names, out_file)
rpkm = htseq_count.calculate_rpkm(combined_out,
config["annotation"]["file"])
rpkm_file = os.path.join(config["dir"]["results"], stage,
"rpkm.txt")
rpkm.to_csv(rpkm_file, sep="\t")
if stage == "coverage":
logger.info("Calculating RNASeq metrics on %s." % (curr_files))
nrun = len(curr_files)
ref = blastn.prepare_ref_file(config["stage"][stage]["ref"],
config)
ribo = config["stage"][stage]["ribo"]
picard = BroadRunner(config["program"]["picard"])
out_dir = os.path.join(results_dir, stage)
safe_makedir(out_dir)
out_files = [
replace_suffix(os.path.basename(x), "metrics")
for x in curr_files
]
out_files = [os.path.join(out_dir, x) for x in out_files]
out_files = view.map(picardrun.picard_rnaseq_metrics,
[picard] * nrun, curr_files, [ref] * nrun,
[ribo] * nrun, out_files)
if stage == "deseq":
conditions = [
os.path.basename(x).split("_")[0] for x in input_files
]
deseq_config = _get_stage_config(config, stage)
out_dir = os.path.join(results_dir, stage)
safe_makedir(out_dir)
for comparison in deseq_config["comparisons"]:
comparison_name = "_vs_".join(comparison)
out_dir = os.path.join(results_dir, stage, comparison_name)
safe_makedir(out_dir)
# get the of the conditons that match this comparison
indexes = [
x for x, y in enumerate(conditions) if y in comparison
]
# find the htseq_files to combine and combine them
htseq_files = [htseq_outputs[index] for index in indexes]
htseq_columns = [column_names[index] for index in indexes]
logger.info(htseq_files)
logger.info(htseq_columns)
out_file = os.path.join(out_dir,
comparison_name + ".counts.txt")
combined_out = htseq_count.combine_counts(
htseq_files, htseq_columns, out_file)
deseq_conds = [conditions[index] for index in indexes]
deseq_prefix = os.path.join(out_dir, comparison_name)
deseq_out = view.map(deseq.run, [combined_out], [deseq_conds],
[deseq_prefix])
logger.info("Annotating %s." % (deseq_out))
annotated_file = view.map(annotate.annotate_table_with_biomart,
deseq_out, ["id"],
["ensembl_gene_id"], ["human"])
stop_cluster()
def main(config_file):
""" this assumes that we are keeping the same order of the files
throughout """
with open(config_file) as in_handle:
config = yaml.load(in_handle)
# make the needed directories
map(safe_makedir, config["dir"].values())
input_dict = config["input"]
curr_files = _make_current_files(input_dict.keys())
input_meta = input_dict.values()
for stage in config["run"]:
if stage == "fastqc":
_emit_stage_message(stage, curr_files)
fastqc_config = _get_stage_config(config, stage)
fastqc_args = zip(*product(curr_files, [fastqc_config], [config]))
view.map(fastqc.run, *fastqc_args)
if stage == "cutadapt":
_emit_stage_message(stage, curr_files)
cutadapt_config = _get_stage_config(config, stage)
cutadapt_args = zip(
*product(curr_files, [cutadapt_config], [config]))
cutadapt_outputs = view.map(cutadapt_tool.run, *cutadapt_args)
curr_files = _make_current_files(cutadapt_outputs)
if stage == "tophat":
_emit_stage_message(stage, curr_files)
tophat_config = _get_stage_config(config, stage)
tophat_args = zip(*product(curr_files, [None], [config["ref"]],
["tophat"], [config]))
tophat_outputs = view.map(tophat.run_with_config, *tophat_args)
bamfiles = view.map(sam.sam2bam, tophat_outputs)
bamsort = view.map(sam.bamsort, bamfiles)
view.map(sam.bamindex, bamsort)
final_bamfiles = bamsort
curr_files = tophat_outputs
if stage == "htseq-count":
_emit_stage_message(stage, curr_files)
htseq_config = _get_stage_config(config, stage)
htseq_args = zip(*product(curr_files, [config], [stage]))
htseq_outputs = view.map(htseq_count.run_with_config, *htseq_args)
combined_out = os.path.join(config["dir"]["results"], stage,
"all_combined.counts")
combined_out = htseq_count.combine_counts(htseq_outputs,
None,
out_file=combined_out)
if stage == "rseqc":
_emit_stage_message(stage, curr_files)
rseqc_config = _get_stage_config(config, stage)
rseq_args = zip(*product(curr_files, [config]))
view.map(rseqc.bam_stat, *rseq_args)
view.map(rseqc.genebody_coverage, *rseq_args)
view.map(rseqc.junction_annotation, *rseq_args)
view.map(rseqc.junction_saturation, *rseq_args)
RPKM_args = zip(*product(final_bamfiles, [config]))
RPKM_count_out = view.map(rseqc.RPKM_count, *RPKM_args)
RPKM_count_fixed = view.map(rseqc.fix_RPKM_count_file,
RPKM_count_out)
annotate_args = zip(*product(RPKM_count_fixed, ["gene_id"],
["ensembl_transcript_id"], ["mouse"]))
view.map(annotate.annotate_table_with_biomart, *annotate_args)
view.map(rseqc.RPKM_saturation, *RPKM_args)
if stage == "coverage":
logger.info("Calculating RNASeq metrics on %s." % (curr_files))
nrun = len(curr_files)
ref = prepare_ref_file(config["stage"][stage]["ref"], config)
ribo = config["stage"][stage]["ribo"]
picard = BroadRunner(config["program"]["picard"])
out_dir = os.path.join(config["dir"]["results"], stage)
safe_makedir(out_dir)
out_files = [
replace_suffix(os.path.basename(x), "metrics")
for x in curr_files
]
out_files = [os.path.join(out_dir, x) for x in out_files]
out_files = view.map(picardrun.picard_rnaseq_metrics,
[picard] * nrun, curr_files, [ref] * nrun,
[ribo] * nrun, out_files)
if stage == "deseq":
_emit_stage_message(stage, curr_files)
deseq_config = _get_stage_config(config, stage)
out_dir = os.path.join(config["dir"]["results"], stage)
safe_makedir(out_dir)
for test in deseq_config["tests"]:
indexes = [
_find_file_index_for_test(input_meta, condition)
for condition in test
]
files = [htseq_outputs[x] for x in indexes]
conditions = [input_meta[x]["condition"] for x in indexes]
combined_out = os.path.join(
out_dir, "_".join(conditions) + "_combined.counts")
logger.info("Combining %s to %s." % (files, combined_out))
count_file = htseq_count.combine_counts(files,
None,
out_file=combined_out)
out_file = os.path.join(out_dir,
"_".join(conditions) + "_deseq.txt")
logger.info("Running deseq on %s with conditions %s "
"and writing to %s" %
(count_file, conditions, out_file))
view.map(deseq.run, [count_file], [conditions], [out_file])
#deseq.run(count_file, conditions, out_file=out_file)
# end gracefully
stop_cluster()
def main(config_file):
if config_file:
with open(config_file) as in_handle:
config = yaml.load(in_handle)
dirs = config["in_dir"]
conditions = config["conditions"]
glob_string = config["glob_string"]
files = list(flatten([glob.glob(os.path.join(x, glob_string)) for x in dirs]))
out_dir = config["dir"]["results"]
safe_makedir(out_dir)
curr_files = []
for condition in conditions:
condition_files = [x for x in files if condition in x]
out_file = os.path.join(out_dir, condition + "_v2_v3.bam")
print "Combining %s into %s." % (condition_files, out_file)
sh.samtools.merge(list(flatten([out_file, condition_files])))
# bsub_call = list(flatten(["-q", "hsph", "-o", "out" + condition, "-e", "err" + condition, "samtools", "merge", out_file, condition_files]))
#sh.bsub(bsub_call)
sorted_prefix = remove_suffix(out_file) + ".sorted"
sorted_file = sorted_prefix + ".bam"
sh.samtools.sort(out_file, sorted_prefix)
sh.samtools.index(sorted_file)
mapped_file = append_stem(sorted_file, "mapped")
sh.samtools.view(sorted_file, F=4, b=True, o=mapped_file)
sh.samtools.index(mapped_file)
# find the reads that don't intersect with the rrna
in_file = mapped_file
out_file = os.path.join(out_dir, condition + "_noribo" + "_v2_v3.bam")
ribo = config["ribo"]
print "Filtering %s for rRNA in %s into %s." % (in_file, ribo, out_file)
sh.bedtools.intersect("-abam", in_file, "-v", "-b", ribo, _out=out_file)
filtered_file = out_file
print "Calculating RNASeq metrics on %s." % (out_file)
in_file = out_file
ref = blastn.prepare_ref_file(config["stage"]["new_coverage"]["ref"],
config)
ribo = config["stage"]["new_coverage"]["ribo"]
picard = BroadRunner(config["program"]["picard"])
out_dir = os.path.join(config["dir"]["results"], "new_coverage")
safe_makedir(out_dir)
out_file = replace_suffix(os.path.basename(in_file), "metrics")
out_file = os.path.join(out_dir, out_file)
metrics_file = picardrun.picard_rnaseq_metrics(picard, in_file, ref,
ribo, out_file)
jelly_dir = os.path.join(config["dir"]["results"], "jellyfish")
safe_makedir(jelly_dir)
# convert the filtered file to fastq for jellyfish counting
fastq_file = os.path.join(jelly_dir,
os.path.basename(replace_suffix(filtered_file,
"fastq")))
sh.bam2fastx(filtered_file, fastq=True, _out=fastq_file)
for mer in config["stage"]["jellyfish"]["mer_lengths"]:
base, _ = os.path.splitext(os.path.basename(fastq_file))
out_prefix = base + "_%dmer" % (mer)
out_file = os.path.join(jelly_dir, out_prefix)
if not file_exists(out_file):
sh.jellyfish.count(fastq_file,
config["stage"]["jellyfish"]["options"],
m=mer, o=out_file)
def main(config_file):
with open(config_file) as in_handle:
config = yaml.load(in_handle)
# make the needed directories
map(safe_makedir, config["dir"].values())
# specific for thesis pipeline
in_dir = config["dir"]["data"]
curr_files = input_files_from_dir(in_dir)
for stage in config["run"]:
if stage == "fastqc":
stage_runner = fastqc.FastQCStage(config)
view.map(stage_runner, curr_files)
if stage == "cutadapt":
stage_runner = trim.Cutadapt(config)
curr_files = view.map(stage_runner, curr_files)
if stage == "tophat":
_emit_stage_message(stage, curr_files)
tophat_config = _get_stage_config(config, stage)
tophat_outputs = view.map(tophat.run_with_config,
first, [None] * len(curr_files),
[config["ref"]] * len(curr_files),
["tophat"] * len(curr_files),
[config] * len(curr_files))
bamfiles = view.map(sam.sam2bam, tophat_outputs)
bamsort = view.map(sam.bamsort, bamfiles)
view.map(sam.bamindex, bamsort)
final_bamfiles = bamsort
curr_files = tophat_outputs
if stage == "coverage":
logger.info("Calculating RNASeq metrics on %s." % (curr_files))
nrun = len(curr_files)
ref = prepare_ref_file(config["stage"][stage]["ref"], config)
ribo = config["stage"][stage]["ribo"]
picard = BroadRunner(config["program"]["picard"])
out_dir = os.path.join(results_dir, stage)
safe_makedir(out_dir)
out_files = [replace_suffix(os.path.basename(x),
"metrics") for x in curr_files]
out_files = [os.path.join(out_dir, x) for x in out_files]
out_files = view.map(picardrun.picard_rnaseq_metrics,
[picard] * nrun,
curr_files,
[ref] * nrun,
[ribo] * nrun,
out_files)
if stage == "rseqc":
_emit_stage_message(stage, curr_files)
rseqc_config = _get_stage_config(config, stage)
rseq_args = zip(*product(curr_files, [config]))
view.map(rseqc.bam_stat, *rseq_args)
view.map(rseqc.genebody_coverage, *rseq_args)
view.map(rseqc.junction_annotation, *rseq_args)
view.map(rseqc.junction_saturation, *rseq_args)
RPKM_args = zip(*product(final_bamfiles, [config]))
RPKM_count_out = view.map(rseqc.RPKM_count, *RPKM_args)
RPKM_count_fixed = view.map(rseqc.fix_RPKM_count_file,
RPKM_count_out)
"""
i annotate_args = zip(*product(RPKM_count_fixed,
["gene_id"],
["ensembl_gene_id"],
["human"]))
view.map(annotate.annotate_table_with_biomart,
*annotate_args)
"""
view.map(rseqc.RPKM_saturation, *rseq_args)
curr_files = tophat_outputs
def coordinate_sort_sam(in_file, config, out_file=None):
out_file = append_stem(in_file, "sorted")
picard = BroadRunner(config["program"]["picard"], None, {"algorithm": {}})
picardrun.picard_sort(picard, in_file, sort_order="coordinate",
out_file=out_file)
return out_file
def main(config_file):
with open(config_file) as in_handle:
config = yaml.load(in_handle)
# make the needed directories
map(safe_makedir, config["dir"].values())
stage_dict = {"download_encode": _download_encode,
"fastqc": _run_fastqc}
curr_files = config["encode_file"]
results_dir = config["dir"].get("results", "results")
for cell_type in config["cell_types"]:
cell_type_dir = os.path.join(results_dir, cell_type)
safe_makedir(cell_type_dir)
config["dir"]["results"] = cell_type_dir
in_files = glob.glob(os.path.join(config["dir"]["data"],
cell_type, "*"))
curr_files = in_files
for stage in config["run"]:
if stage == "fastqc":
_emit_stage_message(stage, curr_files)
fastqc_config = _get_stage_config(config, stage)
fastqc_args = zip(*product(curr_files, [fastqc_config],
[config]))
view.map(fastqc.run, *fastqc_args)
if stage == "cutadapt":
_emit_stage_message(stage, curr_files)
cutadapt_config = _get_stage_config(config, stage)
cutadapt_args = zip(*product(curr_files, [cutadapt_config],
[config]))
cutadapt_outputs = view.map(cutadapt_tool.run, *cutadapt_args)
curr_files = cutadapt_outputs
if stage == "tophat":
_emit_stage_message(stage, curr_files)
tophat_config = _get_stage_config(config, stage)
tophat_args = zip(*product(curr_files, [None], [config["ref"]],
["tophat"], [config]))
tophat_outputs = view.map(tophat.run_with_config, *tophat_args)
picard = BroadRunner(config["program"]["picard"])
# convert to bam
#args = zip(*product([picard], tophat_outputs))
#bamfiles = view.map(picardrun.picard_formatconverter,
# *args)
bamfiles = view.map(sam.sam2bam, tophat_outputs)
sorted_bf = view.map(sam.bamsort, bamfiles)
view.map(sam.bamindex, sorted_bf)
curr_files = sorted_bf
if stage == "rseqc":
_emit_stage_message(stage, curr_files)
rseqc_config = _get_stage_config(config, stage)
rseq_args = zip(*product(curr_files, [config]))
view.map(rseqc.bam2bigwig, *rseq_args, block=False)
view.map(rseqc.bam_stat, *rseq_args, block=False)
view.map(rseqc.clipping_profile, *rseq_args, block=False)
view.map(rseqc.genebody_coverage, *rseq_args, block=False)
view.map(rseqc.junction_annotation, *rseq_args, block=False)
view.map(rseqc.junction_saturation, *rseq_args, block=False)
RPKM_count_files = view.map(rseqc.RPKM_count,
*rseq_args)
dirs_to_process = list(set(map(os.path.dirname,
RPKM_count_files)))
logger.info("Count files: %s" % (RPKM_count_files))
logger.info("dirnames to process: %s" % (dirs_to_process))
RPKM_merged = view.map(rseqc.merge_RPKM, dirs_to_process)
view.map(rseqc.RPKM_saturation, *rseq_args, block=False)
curr_files = tophat_outputs
if stage == "htseq-count":
_emit_stage_message(stage, curr_files)
htseq_config = _get_stage_config(config, stage)
htseq_args = zip(*product(curr_files, [config], [stage]))
htseq_outputs = view.map(htseq_count.run_with_config,
*htseq_args)
column_names = in_files
out_file = os.path.join(config["dir"]["results"], stage,
cell_type + ".combined.counts")
combined_out = htseq_count.combine_counts(htseq_outputs,
column_names,
out_file)
rpkm = htseq_count.calculate_rpkm(combined_out,
config["annotation"]["file"])
rpkm_file = os.path.join(config["dir"]["results"], stage,
cell_type + ".rpkm.txt")
rpkm.to_csv(rpkm_file, sep="\t")
if stage == "coverage":
logger.info("Calculating RNASeq metrics on %s." % (curr_files))
nrun = len(curr_files)
ref = prepare_ref_file(config["stage"][stage]["ref"],
config)
ribo = config["stage"][stage]["ribo"]
picard = BroadRunner(config["program"]["picard"])
out_dir = os.path.join(config["dir"]["results"], stage)
safe_makedir(out_dir)
out_files = [replace_suffix(os.path.basename(x),
"metrics") for x in curr_files]
out_files = [os.path.join(out_dir, x) for x in out_files]
out_files = view.map(picardrun.picard_rnaseq_metrics,
[picard] * nrun,
curr_files,
[ref] * nrun,
[ribo] * nrun,
out_files)
# end gracefully, wait for jobs to finish, then exit
view.wait()
stop_cluster()
def main(config_file):
with open(config_file) as in_handle:
config = yaml.load(in_handle)
setup_logging(config)
start_cluster(config)
# after the cluster is up, import the view to i
from bipy.cluster import view
input_files = config["input"]
results_dir = config["dir"]["results"]
# make the needed directories
map(safe_makedir, config["dir"].values())
curr_files = input_files
## qc steps
for stage in config["run"]:
if stage == "fastqc":
# run the basic fastqc
logger.info("Running %s on %s" % (stage, str(curr_files)))
fastqc_config = config["stage"][stage]
fastqc_outputs = view.map(fastqc.run, curr_files,
[fastqc_config] * len(curr_files),
[config] * len(curr_files))
# this does nothing for now, not implemented yet
summary_file = _combine_fastqc(fastqc_outputs)
if stage == "trim":
logger.info("Trimming poor quality ends "
" from %s" % (str(curr_files)))
nlen = len(curr_files)
min_length = str(config["stage"][stage].get("min_length", 20))
# trim low quality ends of reads
# do this dirty for now
out_dir = os.path.join(results_dir, "trimmed")
safe_makedir(out_dir)
out_files = [
append_stem(os.path.basename(x), "trim") for x in curr_files
]
out_files = [os.path.join(out_dir, x) for x in out_files]
# XXX remove the magic number of 10 the length of the
# minimum read to keep
out_files = view.map(sickle.run, curr_files, ["se"] * nlen,
["sanger"] * nlen, [min_length] * nlen,
out_files)
curr_files = out_files
if stage == "tagdust":
input_files = curr_files
# remove tags matching the other miRNA tested
logger.info("Running %s on %s." % (stage, input_files))
tagdust_config = config["stage"][stage]
tagdust_outputs = view.map(tagdust.run, input_files,
[tagdust_config] * len(input_files),
[config] * len(input_files))
curr_files = [x[0] for x in tagdust_outputs]
if stage == "filter_length":
# filter out reads below or above a certain length
filter_config = config["stage"][stage]
min_length = filter_config.get("min_length", 0)
max_length = filter_config.get("max_length", MAX_READ_LENGTH)
# length predicate
def length_filter(x):
return min_length < len(x.seq) < max_length
# filter the input reads based on length
# parallelizing this doesn't seem to work
# ipython can't accept closures as an argument to view.map()
"""
filtered_fastq = view.map(filter_seqio, tagdust_outputs,
[lf] * len(tagdust_outputs),
["filt"] * len(tagdust_outputs),
["fastq"] * len(tagdust_outputs))"""
out_files = [
append_stem(os.path.basename(input_file[0]), "filt")
for input_file in tagdust_outputs
]
out_dir = os.path.join(config["dir"]["results"], "length_filtered")
safe_makedir(out_dir)
out_files = [os.path.join(out_dir, x) for x in out_files]
filtered_fastq = [
filter_seqio(x[0], length_filter, y, "fastq")
for x, y in zip(tagdust_outputs, out_files)
]
curr_files = filtered_fastq
if stage == "count_ends":
logger.info("Compiling nucleotide counts at 3' and 5' ends.")
# count the nucleotide at the end of each read
def count_ends(x, y):
""" keeps a running count of an arbitrary set of keys
during the reduce step """
x[y] = x.get(y, 0) + 1
return x
def get_3prime_end(x):
return str(x.seq[-1])
def get_5prime_end(x):
return str(x.seq[0])
def output_counts(end_function, count_file):
# if the count_file already exists, skip
outdir = os.path.join(config["dir"]["results"], stage)
safe_makedir(outdir)
count_file = os.path.join(outdir, count_file)
if os.path.exists(count_file):
return count_file
# outputs a tab file of the counts at the end
# of the fastq files kj
counts = [
reduce(count_ends,
apply_seqio(x, end_function, kind="fastq"), {})
for x in curr_files
]
df = pd.DataFrame(counts, index=map(_short_name, curr_files))
df = df.astype(float)
total = df.sum(axis=1)
df = df.div(total, axis=0)
df["total"] = total
df.to_csv(count_file, sep="\t")
output_counts(get_3prime_end, "3prime_counts.tsv")
output_counts(get_5prime_end, "5prime_counts.tsv")
if stage == "tophat":
tophat_config = config["stage"][stage]
logger.info("Running tophat on %s" % (str(curr_files)))
nlen = len(curr_files)
pair_file = None
ref_file = tophat_config["annotation"]
out_base = os.path.join(results_dir, "mirna")
align_dir = os.path.join(results_dir, "tophat")
config = config
tophat_files = view.map(tophat.align, curr_files,
[pair_file] * nlen, [ref_file] * nlen,
[out_base] * nlen, [align_dir] * nlen,
[config] * nlen)
curr_files = tophat_files
if stage == "novoalign":
logger.info("Running novoalign on %s" % (str(curr_files)))
# align
ref = config["genome"]["file"]
novoalign_config = config["stage"][stage]
aligned_outputs = view.map(novoalign.run, curr_files,
[ref] * len(curr_files),
[novoalign_config] * len(curr_files),
[config] * len(curr_files))
# convert sam to bam, sort and index
picard = BroadRunner(config["program"]["picard"], None, {})
bamfiles = view.map(picardrun.picard_formatconverter,
[picard] * len(aligned_outputs),
aligned_outputs)
sorted_bf = view.map(picardrun.picard_sort,
[picard] * len(bamfiles), bamfiles)
view.map(picardrun.picard_index, [picard] * len(sorted_bf),
sorted_bf)
# these files are the new starting point for the downstream
# analyses, so copy them over into the data dir and setting
# them to read only
#data_dir = os.path.join(config["dir"]["data"], stage)
#safe_makedir(data_dir)
#view.map(shutil.copy, sorted_bf, [data_dir] * len(sorted_bf))
#new_files = [os.path.join(data_dir, x) for x in
# map(os.path.basename, sorted_bf)]
#[os.chmod(x, stat.S_IREAD | stat.S_IRGRP) for x in new_files]
# index the bam files for later use
#view.map(picardrun.picard_index, [picard] * len(new_files),
# new_files)
curr_files = sorted_bf
if stage == "new_coverage":
logger.info("Calculating RNASeq metrics on %s." % (curr_files))
nrun = len(curr_files)
ref = blastn.prepare_ref_file(config["stage"][stage]["ref"],
config)
ribo = config["stage"][stage]["ribo"]
picard = BroadRunner(config["program"]["picard"], None, {})
out_dir = os.path.join(results_dir, "new_coverage")
safe_makedir(out_dir)
out_files = [
replace_suffix(os.path.basename(x), "metrics")
for x in curr_files
]
out_files = [os.path.join(out_dir, x) for x in out_files]
out_files = view.map(picardrun.picard_rnaseq_metrics,
[picard] * nrun, curr_files, [ref] * nrun,
[ribo] * nrun, out_files)
curr_files = out_files
if stage == "coverage":
gtf = blastn.prepare_ref_file(config["annotation"], config)
logger.info("Calculating coverage of features in %s for %s" %
(gtf, str(sorted_bf)))
out_files = [replace_suffix(x, "counts.bed") for x in sorted_bf]
out_dir = os.path.join(results_dir, stage)
safe_makedir(out_dir)
logger.info(out_files)
out_files = [
os.path.join(out_dir, os.path.basename(x)) for x in out_files
]
logger.info(out_files)
view.map(bedtools.count_overlaps, sorted_bf,
[gtf] * len(sorted_bf), out_files)
if stage == "htseq-count":
nfiles = len(curr_files)
htseq_config = _get_stage_config(config, stage)
htseq_outputs = view.map(htseq_count.run_with_config,
aligned_outputs, [config] * nfiles,
[stage] * nfiles)
column_names = _get_short_names(input_files)
logger.info("Column names: %s" % (column_names))
out_file = os.path.join(config["dir"]["results"], stage,
"combined.counts")
combined_out = htseq_count.combine_counts(htseq_outputs,
column_names, out_file)
if stage == "bedtools_intersect":
bedfiles = config["stage"]["bedtools_intersect"].get("bed", None)
out_dir = os.path.join(results_dir, stage)
safe_makedir(out_dir)
for bedfile in bedfiles:
bedbase, bedext = os.path.splitext(bedfile)
out_files = [remove_suffix(x) for x in sorted_bf]
out_files = [
os.path.join(out_dir, os.path.basename(x))
for x in out_files
]
out_files = [
"_vs_".join([x, os.path.basename(bedbase)])
for x in out_files
]
out_files = [".".join([x, "bam"]) for x in out_files]
test_out = map(bedtools.intersectbam2bed, sorted_bf,
[bedfile] * len(sorted_bf),
[False] * len(sorted_bf), out_files)
count_files = [replace_suffix(x, "stats") for x in out_files]
map(write_ratios, sorted_bf, out_files, count_files)
if stage == "piranha":
piranha_runner = piranha.PiranhaStage(config)
out_files = view.map(piranha_runner, curr_files)
stop_cluster()
def main(config_file):
""" this assumes that we are keeping the same order of the files
throughout """
with open(config_file) as in_handle:
config = yaml.load(in_handle)
# make the needed directories
map(safe_makedir, config["dir"].values())
input_dir = config["input_dir"]
results_dir = config["dir"].get("results", "results")
input_files = glob.glob(os.path.join(input_dir, "*.fq"))
curr_files = _make_current_files(input_files)
conditions = [os.path.basename(x).split("_")[0] for x in input_files]
for stage in config["run"]:
if stage == "fastqc":
_emit_stage_message(stage, curr_files)
fastqc_config = _get_stage_config(config, stage)
fastqc_args = zip(*product(curr_files, [fastqc_config], [config]))
fastqc_out = view.map(fastqc.run, *fastqc_args)
logger.info("fastqc outfiles: %s" % (fastqc_out))
if stage == "cutadapt":
_emit_stage_message(stage, curr_files)
cutadapt_config = _get_stage_config(config, stage)
cutadapt_args = zip(
*product(curr_files, [cutadapt_config], [config]))
cutadapt_outputs = view.map(cutadapt_tool.run, *cutadapt_args)
curr_files = _make_current_files(cutadapt_outputs)
if stage == "tophat":
_emit_stage_message(stage, curr_files)
tophat_config = _get_stage_config(config, stage)
tophat_args = zip(*product(curr_files, [None], [config["ref"]],
["tophat"], [config]))
tophat_outputs = view.map(tophat.run_with_config, *tophat_args)
# convert to bam, sort and index
bamfiles = view.map(sam.sam2bam, tophat_outputs)
sorted_bf = view.map(sam.bamsort, bamfiles)
view.map(sam.bamindex, sorted_bf)
curr_files = sorted_bf
if stage == "rseqc":
_emit_stage_message(stage, curr_files)
rseqc_config = _get_stage_config(config, stage)
rseq_args = zip(*product(curr_files, [config]))
view.map(rseqc.bam2bigwig, *rseq_args, block=False)
view.map(rseqc.bam_stat, *rseq_args, block=False)
view.map(rseqc.clipping_profile, *rseq_args, block=False)
view.map(rseqc.genebody_coverage, *rseq_args, block=False)
view.map(rseqc.junction_annotation, *rseq_args, block=False)
view.map(rseqc.junction_saturation, *rseq_args, block=False)
view.map(rseqc.RPKM_count, *rseq_args, block=False)
view.map(rseqc.RPKM_saturation, *rseq_args, block=False)
curr_files = tophat_outputs
if stage == "coverage":
logger.info("Calculating RNASeq metrics on %s." % (curr_files))
nrun = len(curr_files)
ref = prepare_ref_file(config["stage"][stage]["ref"], config)
ribo = config["stage"][stage]["ribo"]
picard = BroadRunner(config["program"]["picard"])
out_dir = os.path.join(results_dir, stage)
safe_makedir(out_dir)
out_files = [
replace_suffix(os.path.basename(x), "metrics")
for x in curr_files
]
out_files = [os.path.join(out_dir, x) for x in out_files]
out_files = view.map(picardrun.picard_rnaseq_metrics,
[picard] * nrun, curr_files, [ref] * nrun,
[ribo] * nrun, out_files)
if stage == "htseq-count":
_emit_stage_message(stage, curr_files)
htseq_config = _get_stage_config(config, stage)
htseq_args = zip(*product(curr_files, [config], [stage]))
htseq_outputs = view.map(htseq_count.run_with_config, *htseq_args)
combined_out = os.path.join(config["dir"]["results"], stage,
"all_combined.counts")
combined_out = htseq_count.combine_counts(htseq_outputs,
None,
out_file=combined_out)
if stage == "deseq":
_emit_stage_message(stage, curr_files)
deseq_config = _get_stage_config(config, stage)
out_dir = os.path.join(config["dir"]["results"], stage)
safe_makedir(out_dir)
for comparison in deseq_config["comparisons"]:
comparison_name = "_vs_".join(comparison)
out_dir = os.path.join(results_dir, stage, comparison_name)
safe_makedir(out_dir)
indexes = [
x for x, y in enumerate(conditions) if y in comparison
]
htseq_files = [htseq_outputs[index] for index in indexes]
htseq_columns = [conditions[index] for index in indexes]
out_file = os.path.join(out_dir,
comparison_name + ".counts.txt")
combined_out = htseq_count.combine_counts(
htseq_files, htseq_columns, out_file)
deseq_conds = [conditions[index] for index in indexes]
deseq_out = os.path.join(out_dir,
comparison_name + ".deseq.txt")
logger.info("Running deseq on %s with conditions %s "
"and writing to %s" %
(combined_out, conditions, deseq_out))
view.map(deseq.run, [combined_out], [deseq_conds], [deseq_out])
annotated_file = view.map(annotate.annotate_table_with_biomart,
[deseq_out], ["id"],
["ensembl_gene_id"], ["zebrafish"])
# end gracefully
stop_cluster()
def main(config_file):
with open(config_file) as in_handle:
config = yaml.load(in_handle)
# make the needed directories
map(safe_makedir, config["dir"].values())
# specific for thesis pipeline
input_dirs = config["input_dirs"]
results_dir = config["dir"].get("results", "results")
input_files = _find_input_files(config)
conditions = _group_input_by_condition(input_files)
logger.info("Input_files: %s" % (input_files))
logger.info("Condition groups %s" % (conditions))
htseq_outdict = {}
for condition, curr_files in conditions.items():
condition_dir = os.path.join(results_dir, condition)
safe_makedir(condition_dir)
config["dir"]["results"] = condition_dir
for stage in config["run"]:
if stage == "fastqc":
_emit_stage_message(stage, curr_files)
fastqc_config = _get_stage_config(config, stage)
fastqc_args = zip(
*product(curr_files, [fastqc_config], [config]))
view.map(fastqc.run, *fastqc_args)
if stage == "cutadapt":
_emit_stage_message(stage, curr_files)
cutadapt_config = _get_stage_config(config, stage)
cutadapt_args = zip(
*product(curr_files, [cutadapt_config], [config]))
cutadapt_outputs = view.map(cutadapt_tool.run, *cutadapt_args)
curr_files = cutadapt_outputs
logger.info("Fixing mate pair information.")
pairs = combine_pairs(curr_files)
first = [x[0] for x in pairs]
second = [x[1] for x in pairs]
logger.info("Forward: %s" % (first))
logger.info("Reverse: %s" % (second))
fixed = view.map(fastq.fix_mate_pairs_with_config, first,
second, [config] * len(first))
curr_files = list(flatten(fixed))
if stage == "sickle":
_emit_stage_message(stage, curr_files)
pairs = combine_pairs(curr_files)
first = [x[0] for x in pairs]
second = [x[1] for x in pairs]
fixed = view.map(sickle.run_with_config, first, second,
[config] * len(first))
curr_files = list(flatten(fixed))
if stage == "tophat":
_emit_stage_message(stage, curr_files)
tophat_config = _get_stage_config(config, stage)
pairs = combine_pairs(curr_files)
first = [x[0] for x in pairs]
second = [x[1] for x in pairs]
logger.info("first %s" % (first))
logger.info("second %s" % (second))
#tophat_args = zip(*product(first, second, [config["ref"]],
# ["tophat"], [config]))
tophat_outputs = view.map(tophat.run_with_config, first,
second, [config["ref"]] * len(first),
["tophat"] * len(first),
[config] * len(first))
bamfiles = view.map(sam.sam2bam, tophat_outputs)
bamsort = view.map(sam.bamsort, bamfiles)
view.map(sam.bamindex, bamsort)
final_bamfiles = bamsort
curr_files = tophat_outputs
if stage == "htseq-count":
_emit_stage_message(stage, curr_files)
htseq_config = _get_stage_config(config, stage)
htseq_args = zip(*product(curr_files, [config], [stage]))
htseq_outputs = view.map(htseq_count.run_with_config,
*htseq_args)
htseq_outdict[condition] = htseq_outputs
if stage == "coverage":
logger.info("Calculating RNASeq metrics on %s." % (curr_files))
nrun = len(curr_files)
ref = prepare_ref_file(config["stage"][stage]["ref"], config)
ribo = config["stage"][stage]["ribo"]
picard = BroadRunner(config["program"]["picard"])
out_dir = os.path.join(results_dir, stage)
safe_makedir(out_dir)
out_files = [
replace_suffix(os.path.basename(x), "metrics")
for x in curr_files
]
out_files = [os.path.join(out_dir, x) for x in out_files]
out_files = view.map(picardrun.picard_rnaseq_metrics,
[picard] * nrun, curr_files, [ref] * nrun,
[ribo] * nrun, out_files)
if stage == "rseqc":
_emit_stage_message(stage, curr_files)
rseqc_config = _get_stage_config(config, stage)
rseq_args = zip(*product(curr_files, [config]))
view.map(rseqc.bam_stat, *rseq_args)
view.map(rseqc.genebody_coverage, *rseq_args)
view.map(rseqc.junction_annotation, *rseq_args)
view.map(rseqc.junction_saturation, *rseq_args)
RPKM_args = zip(*product(final_bamfiles, [config]))
RPKM_count_out = view.map(rseqc.RPKM_count, *RPKM_args)
RPKM_count_fixed = view.map(rseqc.fix_RPKM_count_file,
RPKM_count_out)
"""
annotate_args = zip(*product(RPKM_count_fixed,
["gene_id"],
["ensembl_gene_id"],
["human"]))
view.map(annotate.annotate_table_with_biomart,
*annotate_args)
"""
view.map(rseqc.RPKM_saturation, *rseq_args)
curr_files = tophat_outputs
# combine htseq-count files and run deseq on them
conditions, htseq_files = dict_to_vectors(htseq_outdict)
deseq_config = _get_stage_config(config, "deseq")
cell_types = _group_input_by_cell_type(htseq_files)
for cell_type, files in cell_types.items():
for comparison in deseq_config["comparisons"]:
comparison_name = "_vs_".join(comparison)
deseq_dir = os.path.join(results_dir, "deseq", cell_type,
comparison_name)
safe_makedir(deseq_dir)
out_file = os.path.join(deseq_dir, comparison_name + ".counts.txt")
files_by_condition = _group_input_by_condition(files)
_emit_stage_message("deseq", files_by_condition)
c, f = dict_to_vectors(files_by_condition)
combined_out = htseq_count.combine_counts(f, None, out_file)
deseq_out = os.path.join(deseq_dir, comparison_name)
logger.info("Running deseq on %s with conditions %s "
"and writing ot %s" %
(combined_out, conditions, deseq_out))
deseq_out = view.map(deseq.run, [combined_out], [c], [deseq_out])
annotate.annotate_table_with_biomart(deseq_out[0], "id",
"ensembl_gene_id", "human")
#annotated_file = view.map(annotate.annotate_table_with_biomart,
# [deseq_out],
# ["id"],
# ["ensembl_gene_id"],
# ["human"])
# end gracefully
stop_cluster()
