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(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(config_file):
with open(config_file) as in_handle:
config = yaml.load(in_handle)
setup_logging(config)
start_cluster(config)
from bipy.cluster import view
input_files = config["input"]
for stage in config["run"]:
if config["stage"][stage]["program"] == "tagdust":
tagdust_config = config["stage"][stage]
view.map(tagdust.run, input_files,
[tagdust_config] * len(input_files),
[config] * len(input_files))
stop_cluster()
def main(config_file):
with open(config_file) as in_handle:
config = yaml.load(in_handle)
setup_logging(config)
start_cluster(config)
from bipy.cluster import view
input_files = config["input"]
for stage in config["run"]:
if config["stage"][stage]["program"] == "tagdust":
tagdust_config = config["stage"][stage]
view.map(tagdust.run, input_files,
[tagdust_config] * len(input_files),
[config] * len(input_files))
stop_cluster()
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
input_dir = config["dir"]["input_dir"]
results_dir = config["dir"]["results"]
input_files = glob.glob(os.path.join(input_dir, "*.bam"))
""" example running with macs
macs.run_with_config(input_file, config, control_file=None, stage=None)
"""
curr_files = input_files
# first combine all the negative controls into one file
negative_control = _merge_condition(input_files,
config["groups"]["negative"])
test_files = [_merge_condition(input_files, condition) for
condition in config["groups"]["test"]]
test_files = [x for x in test_files if x]
curr_files = test_files
for stage in config["run"]:
# for now just run macs on all of these files without the control
# file
if stage == "macs":
nfiles = len(curr_files)
out_files = view.map(macs.run_with_config, curr_files,
[config] * nfiles,
[negative_control] * nfiles,
[stage] * nfiles)
# just use the peak files going forward
peak_files = [x[0] for x in out_files]
curr_files = peak_files
if stage == "piranha":
nfiles = len(curr_files)
piranha_runner = piranha.PiranhaStage(config)
out_files = view.map(piranha_runner, curr_files)
stop_cluster()
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
input_dir = config["dir"]["input_dir"]
results_dir = config["dir"]["results"]
input_files = glob.glob(os.path.join(input_dir, "*.bam"))
""" example running with macs
macs.run_with_config(input_file, config, control_file=None, stage=None)
"""
curr_files = input_files
# first combine all the negative controls into one file
negative_control = _merge_condition(input_files,
config["groups"]["negative"])
test_files = [
_merge_condition(input_files, condition)
for condition in config["groups"]["test"]
]
test_files = [x for x in test_files if x]
curr_files = test_files
for stage in config["run"]:
# for now just run macs on all of these files without the control
# file
if stage == "macs":
nfiles = len(curr_files)
out_files = view.map(macs.run_with_config, curr_files,
[config] * nfiles,
[negative_control] * nfiles, [stage] * nfiles)
# just use the peak files going forward
peak_files = [x[0] for x in out_files]
curr_files = peak_files
if stage == "piranha":
nfiles = len(curr_files)
piranha_runner = piranha.PiranhaStage(config)
out_files = view.map(piranha_runner, curr_files)
stop_cluster()
def main(config_file):
# load yaml config file
with open(config_file) as in_handle:
config = yaml.load(in_handle)
# setup logging
setup_logging(config)
from bipy.log import logger
# start cluster
start_cluster(config)
from bipy.cluster import view
found = sh.find(config["dir"]["data"], "-name", "Variations")
var_dirs = [str(x).strip() for x in found]
logger.info("Var_dirs: %s" % (var_dirs))
in_dirs = map(os.path.dirname, var_dirs)
logger.info("in_dirs: %s" % (in_dirs))
# XXX for testing only load 3
#curr_files = in_dirs[0:5]
curr_files = in_dirs
# run the illumina fixer
logger.info("Running illumina fixer on %s." % (curr_files))
illf_class = STAGE_LOOKUP.get("illumina_fixer")
illf = illf_class(config)
curr_files = view.map(illf, curr_files)
# sort the vcf files
def sort_vcf(in_file):
from bipy.utils import append_stem
from bcbio.distributed.transaction import file_transaction
from bcbio.utils import file_exists
import sh
out_file = append_stem(in_file, "sorted")
if file_exists(out_file):
return out_file
with file_transaction(out_file) as tmp_out_file:
sh.vcf_sort(in_file, _out=tmp_out_file)
return out_file
# combine
out_file = os.path.join(config["dir"].get("results", "results"),
"geminiloader", "all_combined.vcf")
logger.info("Combining files %s into %s." % (curr_files, out_file))
if file_exists(out_file):
curr_files = [out_file]
else:
curr_files = [
genotype.combine_variant_files(curr_files, out_file,
config["ref"]["fasta"], config)
]
# break the VCF files up by chromosome for speed
logger.info("Breaking up %s by chromosome." % (curr_files))
breakvcf_class = STAGE_LOOKUP.get("breakvcf")
breakvcf = breakvcf_class(config)
curr_files = view.map(breakvcf, curr_files)
# run VEP on the separate files in parallel
logger.info("Running VEP on %s." % (curr_files))
vep_class = STAGE_LOOKUP.get("vep")
vep = vep_class(config)
curr_files = view.map(vep, list(flatten(curr_files)))
curr_files = filter(file_exists, curr_files)
# load the files into gemini not in parallel
# don't run in parallel
# sort the vcf files
logger.info("Sorting %s." % (curr_files))
curr_files = view.map(sort_vcf, curr_files)
# don't run the rest of this in parallel, so take the cluster down
stop_cluster()
out_file = os.path.join(config["dir"].get("results", "results"),
"geminiloader", "all_combined.vep.vcf")
logger.info("Combining files %s into %s." % (curr_files, out_file))
if file_exists(out_file):
curr_files = [out_file]
else:
curr_files = [
genotype.combine_variant_files(curr_files, out_file,
config["ref"]["fasta"], config)
]
logger.info("Loading %s into gemini." % (curr_files))
gemini_class = STAGE_LOOKUP.get("geminiloader")
geminiloader = gemini_class(config)
curr_files = map(geminiloader, curr_files)
logger.info("Run complete.")
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 project
input_dir = config["dir"]["data"]
logger.info("Loading files from %s" % (input_dir))
input_files = list(locate("*.fq", input_dir))
input_files += list(locate("*.fastq", input_dir))
logger.info("Input files: %s" % (input_files))
results_dir = config["dir"]["results"]
safe_makedir(results_dir)
# make the stage repository
repository = StageRepository(config)
logger.info("Stages found: %s" % (repository.plugins))
if config.get("test_pipeline", False):
logger.info("Running a test pipeline on a subset of the reads.")
results_dir = os.path.join(results_dir, "test_pipeline")
config["dir"]["results"] = results_dir
safe_makedir(results_dir)
curr_files = map(make_test, input_files, [config] * len(input_files))
logger.info("Converted %s to %s. " % (input_files, curr_files))
else:
curr_files = input_files
logger.info("Running RNASeq alignment pipeline on %s." % (curr_files))
for stage in config["run"]:
if stage == "fastqc":
logger.info("Running fastqc on %s." % (curr_files))
stage_runner = FastQC(config)
view.map(stage_runner, curr_files)
if stage == "cutadapt":
curr_files = combine_pairs(curr_files)
logger.info("Running cutadapt on %s." % (curr_files))
stage_runner = Cutadapt(config)
curr_files = view.map(stage_runner, curr_files)
if stage == "tophat":
logger.info("Running Tophat on %s." % (curr_files))
#tophat = repository["tophat"](config)
tophat = Tophat(config)
tophat_outputs = view.map(tophat, curr_files)
sortsam = view.map(sam.coordinate_sort_sam, tophat_outputs,
[config] * len(tophat_outputs))
bamfiles = view.map(sam.sam2bam, sortsam)
bamsort = view.map(sam.bamsort, bamfiles)
view.map(sam.bamindex, bamsort)
final_bamfiles = bamsort
curr_files = tophat_outputs
if stage == "disambiguate":
logger.info("Disambiguating %s." % (curr_files))
disambiguate = repository[stage](config)
view.map(disambiguate, curr_files)
if stage == "htseq-count":
logger.info("Running htseq-count on %s." % (bamfiles))
name_sorted = view.map(sam.bam_name_sort, bamfiles)
curr_files = view.map(sam.bam2sam, name_sorted)
htseq_args = zip(*product(curr_files, [config], [stage]))
htseq_outputs = view.map(htseq_count.run_with_config,
*htseq_args)
htseq_count.combine_counts(htseq_outputs)
if stage == "rnaseq_metrics":
logger.info("Calculating RNASeq metrics on %s." % (curr_files))
#coverage = repository[stage](config)
coverage = RNASeqMetrics(config)
view.map(coverage, curr_files)
if stage == "rseqc":
logger.info("Running rseqc on %s." % (curr_files))
#rseq_args = zip(*product(curr_files, [config]))
rseq_args = zip(*product(final_bamfiles, [config]))
view.map(rseqc.bam_stat, *rseq_args)
down_args = zip(*product(final_bamfiles, [40000000]))
down_bam = view.map(sam.downsample_bam, *down_args)
view.map(rseqc.genebody_coverage, down_bam,
[config] * len(down_bam))
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
# end gracefully
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()
parser = argparse.ArgumentParser(description='generic launcher')
parser.add_argument('--profile',
required=True,
help="IPython profile name to use")
parser.add_argument('--cores',
required=True,
help="Number of IPython engines to start.")
parser.add_argument('--queue', help="Name of queue to use.")
parser.add_argument('--scheduler',
default="",
help="Name of scheduler to use (LSF or SGE)")
args = parser.parse_args()
cluster_config = {
"cluster": {
"profile": args.profile,
"cores": int(args.cores),
"queue": args.queue,
"scheduler": args.scheduler
}
}
setup_logging(cluster_config)
if _args_valid_for_scheduler(args) or _args_valid_for_local(args):
start_cluster(cluster_config)
from bipy.cluster import view
main()
stop_cluster()
else:
print parser.usage()
sys.exit(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
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()
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)
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):
with open(config_file) as in_handle:
config = yaml.load(in_handle)
# make the needed directories
map(safe_makedir, config["dir"].values())
# specific for project
input_dir = config["input_dir"]
logger.info("Loading files from %s" % (input_dir))
input_files = list(locate("*.sam", os.path.join(input_dir, "tophat_control")))
input_files += list(locate("*.sam", os.path.join(input_dir, "tophat_exposed")))
print input_files
input_files = [x for x in input_files if "accepted" not in x]
input_files = [x for x in input_files if "innerdist_estimate" not in x]
logger.info("Input files: %s" % (input_files))
results_dir = config["dir"]["results"]
safe_makedir(results_dir)
# make the stage repository
repository = StageRepository(config)
logger.info("Stages found: %s" % (repository.plugins))
curr_files = input_files
logger.info("Running quantitation on %s." % (curr_files))
for stage in config["run"]:
if stage == "htseq-count":
logger.info("Running htseq-count on %s." % (curr_files))
htseq_args = zip(*product(curr_files, [config], [stage]))
htseq_outputs = view.map(htseq_count.run_with_config,
*htseq_args)
htseq_count.combine_counts(htseq_outputs)
if stage == "rnaseq_metrics":
logger.info("Calculating RNASeq metrics on %s." % (curr_files))
#coverage = repository[stage](config)
curr_files = view.map(sam.bam2sam, curr_files)
coverage = RNASeqMetrics(config)
view.map(coverage, curr_files)
if stage == "rseqc":
logger.info("Running rseqc on %s." % (curr_files))
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)
# 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 project
input_dir = config["input_dir"]
logger.info("Loading files from %s" % (input_dir))
input_files = list(locate("*.fq", input_dir))
input_files += list(locate("*.fastq", input_dir))
logger.info("Input files: %s" % (input_files))
results_dir = config["dir"]["results"]
safe_makedir(results_dir)
if config.get("test_pipeline", False):
logger.info("Running a test pipeline on a subset of the reads.")
results_dir = os.path.join(results_dir, "test_pipeline")
config["dir"]["results"] = results_dir
safe_makedir(results_dir)
curr_files = map(make_test, input_files, [config] * len(input_files))
logger.info("Converted %s to %s. " % (input_files, curr_files))
else:
curr_files = input_files
logger.info("Running RNASeq alignment pipeline on %s." % (curr_files))
for stage in config["run"]:
if stage == "fastqc":
logger.info("Running fastqc on %s." % (curr_files))
stage_runner = FastQC(config)
view.map(stage_runner, curr_files)
if stage == "cutadapt":
curr_files = combine_pairs(curr_files)
logger.info("Running cutadapt on %s." % (curr_files))
stage_runner = Cutadapt(config)
curr_files = view.map(stage_runner, curr_files)
logger.info("Output of cutadapt: %s." % (curr_files))
if stage == "bowtie":
logger.info("Running Bowtie on %s." % (curr_files))
bowtie = Bowtie(config)
bowtie_outputs = view.map(bowtie, curr_files)
bamfiles = view.map(sam.sam2bam, bowtie_outputs)
bamsort = view.map(sam.bamsort, bamfiles)
view.map(sam.bamindex, bamsort)
if stage == "htseq-count":
logger.info("Running htseq-count on %s." % (curr_files))
htseq_args = zip(*product(curr_files, [config], [stage]))
htseq_outputs = view.map(htseq_count.run_with_config,
*htseq_args)
htseq.combine_counts(htseq_outputs)
if stage == "rnaseq_metrics":
logger.info("Calculating RNASeq metrics on %s." % (curr_files))
coverage = RNASeqMetrics(config)
view.map(coverage, curr_files)
if stage == "rseqc":
logger.info("Running rseqc on %s." % (curr_files))
#rseq_args = zip(*product(curr_files, [config]))
rseq_args = zip(*product(final_bamfiles, [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
# end gracefully
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):
with open(config_file) as in_handle:
config = yaml.load(in_handle)
# make the needed directories
map(safe_makedir, config["dir"].values())
# specific for project
input_dir = config["input_dir"]
logger.info("Loading files from %s" % (input_dir))
input_files = list(
locate("*.sam", os.path.join(input_dir, "tophat_control")))
input_files += list(
locate("*.sam", os.path.join(input_dir, "tophat_exposed")))
print input_files
input_files = [x for x in input_files if "accepted" not in x]
input_files = [x for x in input_files if "innerdist_estimate" not in x]
logger.info("Input files: %s" % (input_files))
results_dir = config["dir"]["results"]
safe_makedir(results_dir)
# make the stage repository
repository = StageRepository(config)
logger.info("Stages found: %s" % (repository.plugins))
curr_files = input_files
logger.info("Running quantitation on %s." % (curr_files))
for stage in config["run"]:
if stage == "htseq-count":
logger.info("Running htseq-count on %s." % (curr_files))
htseq_args = zip(*product(curr_files, [config], [stage]))
htseq_outputs = view.map(htseq_count.run_with_config, *htseq_args)
htseq_count.combine_counts(htseq_outputs)
if stage == "rnaseq_metrics":
logger.info("Calculating RNASeq metrics on %s." % (curr_files))
#coverage = repository[stage](config)
curr_files = view.map(sam.bam2sam, curr_files)
coverage = RNASeqMetrics(config)
view.map(coverage, curr_files)
if stage == "rseqc":
logger.info("Running rseqc on %s." % (curr_files))
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)
# 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":
logger.info("Running fastqc on %s." % (curr_files))
stage_runner = FastQC(config)
view.map(stage_runner, curr_files)
if stage == "cutadapt":
logger.info("Running cutadapt on %s." % (curr_files))
stage_runner = Cutadapt(config)
curr_files = view.map(stage_runner, curr_files)
if stage == "tophat":
logger.info("Running tophat on %s." % (curr_files))
stage_runner = Tophat(config)
tophat_outputs = view.map(stage_runner, 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 == "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")
# end gracefully
stop_cluster()
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)
data_dir = config["dir"]["data"]
from bipy.cluster import view
input_files = [glob.glob(os.path.join(data_dir, x, "*_rep*")) for x in
config["input_dirs"]]
input_files = list(flatten(input_files))
logger.info("Input files to process: %s" % (input_files))
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":
logger.info("Running htseq-count on %s" %(curr_files))
htseq_outputs = curr_files
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")
out_dir = os.path.join(results_dir, stage)
safe_makedir(out_dir)
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"])
if stage == "dss":
conditions = [os.path.basename(x).split("_")[0] for x in
input_files]
dss_config = _get_stage_config(config, stage)
out_dir = os.path.join(results_dir, stage)
safe_makedir(out_dir)
for comparison in dss_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]
out_file = os.path.join(out_dir,
comparison_name + ".counts.txt")
combined_out = htseq_count.combine_counts(htseq_files,
htseq_columns,
out_file)
dss_conds = [conditions[index] for index in indexes]
dss_prefix = os.path.join(out_dir, comparison_name)
logger.info("Running DSS on %s with conditions %s and comparison %s." % (combined_out, dss_conds, comparison))
dss_out = dss.run(combined_out, dss_conds, comparison,
dss_prefix)
stop_cluster()
def _args_valid_for_local(args):
return not args.scheduler and not args.queue
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='generic launcher')
parser.add_argument('--profile', required=True,
help="IPython profile name to use")
parser.add_argument('--cores', required=True,
help="Number of IPython engines to start.")
parser.add_argument('--queue',
help="Name of queue to use.")
parser.add_argument('--scheduler', default="",
help="Name of scheduler to use (LSF or SGE)")
args = parser.parse_args()
cluster_config = {"cluster":
{"profile": args.profile,
"cores": int(args.cores),
"queue": args.queue,
"scheduler": args.scheduler}}
setup_logging(cluster_config)
if _args_valid_for_scheduler(args) or _args_valid_for_local(args):
start_cluster(cluster_config)
from bipy.cluster import view
main()
stop_cluster()
else:
print parser.usage()
sys.exit(1)
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):
""" 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):
# load yaml config file
with open(config_file) as in_handle:
config = yaml.load(in_handle)
# setup logging
setup_logging(config)
from bipy.log import logger
# start cluster
start_cluster(config)
from bipy.cluster import view
found = sh.find(config["dir"]["data"], "-name", "Variations")
var_dirs = [str(x).strip() for x in found]
logger.info("Var_dirs: %s" % (var_dirs))
in_dirs = map(os.path.dirname, var_dirs)
logger.info("in_dirs: %s" % (in_dirs))
# XXX for testing only load 3
#curr_files = in_dirs[0:5]
curr_files = in_dirs
# run the illumina fixer
logger.info("Running illumina fixer on %s." % (curr_files))
illf_class = STAGE_LOOKUP.get("illumina_fixer")
illf = illf_class(config)
curr_files = view.map(illf, curr_files)
# sort the vcf files
def sort_vcf(in_file):
from bipy.utils import append_stem
from bcbio.distributed.transaction import file_transaction
from bcbio.utils import file_exists
import sh
out_file = append_stem(in_file, "sorted")
if file_exists(out_file):
return out_file
with file_transaction(out_file) as tmp_out_file:
sh.vcf_sort(in_file, _out=tmp_out_file)
return out_file
# combine
out_file = os.path.join(config["dir"].get("results", "results"),
"geminiloader",
"all_combined.vcf")
logger.info("Combining files %s into %s." % (curr_files, out_file))
if file_exists(out_file):
curr_files = [out_file]
else:
curr_files = [genotype.combine_variant_files(curr_files, out_file,
config["ref"]["fasta"],
config)]
# break the VCF files up by chromosome for speed
logger.info("Breaking up %s by chromosome." % (curr_files))
breakvcf_class = STAGE_LOOKUP.get("breakvcf")
breakvcf = breakvcf_class(config)
curr_files = view.map(breakvcf, curr_files)
# run VEP on the separate files in parallel
logger.info("Running VEP on %s." % (curr_files))
vep_class = STAGE_LOOKUP.get("vep")
vep = vep_class(config)
curr_files = view.map(vep, list(flatten(curr_files)))
curr_files = filter(file_exists, curr_files)
# load the files into gemini not in parallel
# don't run in parallel
# sort the vcf files
logger.info("Sorting %s." % (curr_files))
curr_files = view.map(sort_vcf, curr_files)
# don't run the rest of this in parallel, so take the cluster down
stop_cluster()
out_file = os.path.join(config["dir"].get("results", "results"),
"geminiloader",
"all_combined.vep.vcf")
logger.info("Combining files %s into %s." % (curr_files, out_file))
if file_exists(out_file):
curr_files = [out_file]
else:
curr_files = [genotype.combine_variant_files(curr_files, out_file,
config["ref"]["fasta"],
config)]
logger.info("Loading %s into gemini." % (curr_files))
gemini_class = STAGE_LOOKUP.get("geminiloader")
geminiloader = gemini_class(config)
curr_files = map(geminiloader, curr_files)
logger.info("Run complete.")
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):
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):
with open(config_file) as in_handle:
config = yaml.load(in_handle)
# make the needed directories
map(safe_makedir, config["dir"].values())
# specific for project
input_dir = config["dir"]["data"]
logger.info("Loading files from %s" % (input_dir))
input_files = list(locate("*.fq", input_dir))
input_files += list(locate("*.fastq", input_dir))
logger.info("Input files: %s" % (input_files))
results_dir = config["dir"]["results"]
safe_makedir(results_dir)
# make the stage repository
repository = StageRepository(config)
logger.info("Stages found: %s" % (repository.plugins))
if config.get("test_pipeline", False):
logger.info("Running a test pipeline on a subset of the reads.")
results_dir = os.path.join(results_dir, "test_pipeline")
config["dir"]["results"] = results_dir
safe_makedir(results_dir)
curr_files = map(make_test, input_files, [config] * len(input_files))
logger.info("Converted %s to %s. " % (input_files, curr_files))
else:
curr_files = input_files
logger.info("Running RNASeq alignment pipeline on %s." % (curr_files))
for stage in config["run"]:
if stage == "fastqc":
logger.info("Running fastqc on %s." % (curr_files))
stage_runner = FastQC(config)
view.map(stage_runner, curr_files)
if stage == "cutadapt":
curr_files = combine_pairs(curr_files)
logger.info("Running cutadapt on %s." % (curr_files))
stage_runner = Cutadapt(config)
curr_files = view.map(stage_runner, curr_files)
if stage == "tophat":
logger.info("Running Tophat on %s." % (curr_files))
#tophat = repository["tophat"](config)
tophat = Tophat(config)
tophat_outputs = view.map(tophat, 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 == "disambiguate":
logger.info("Disambiguating %s." % (curr_files))
disambiguate = repository[stage](config)
view.map(disambiguate, curr_files)
if stage == "htseq-count":
logger.info("Running htseq-count on %s." % (bamfiles))
name_sorted = view.map(sam.bam_name_sort, bamfiles)
curr_files = view.map(sam.bam2sam, name_sorted)
htseq_args = zip(*product(curr_files, [config], [stage]))
htseq_outputs = view.map(htseq_count.run_with_config, *htseq_args)
htseq_count.combine_counts(htseq_outputs)
if stage == "rnaseq_metrics":
logger.info("Calculating RNASeq metrics on %s." % (curr_files))
#coverage = repository[stage](config)
coverage = RNASeqMetrics(config)
view.map(coverage, curr_files)
if stage == "rseqc":
logger.info("Running rseqc on %s." % (curr_files))
#rseq_args = zip(*product(curr_files, [config]))
rseq_args = zip(*product(final_bamfiles, [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
# end gracefully
stop_cluster()
def main(config, view):
# make the needed directories
map(safe_makedir, config["dir"].values())
# specific for project
input_dir = config["input_dir"]
logger.info("Loading files from %s" % (input_dir))
input_files = list(locate("*.disambiguous*.sorted.bam", input_dir))
logger.info("Input files: %s" % (input_files))
results_dir = config["dir"]["results"]
safe_makedir(results_dir)
# make the stage repository
repository = StageRepository(config)
logger.info("Stages found: %s" % (repository.plugins))
curr_files = input_files
logger.info("Running quantitation on %s." % (curr_files))
for stage in config["run"]:
if stage == "htseq-count":
logger.info("Running htseq-count on %s." % (input_files))
name_sorted = view.map(sam.bam_name_sort, input_files)
curr_files = view.map(sam.bam2sam, name_sorted)
htseq_args = zip(*product(curr_files, [config], [stage]))
htseq_outputs = view.map(htseq_count.run_with_config,
*htseq_args)
htseq_count.combine_counts(htseq_outputs)
if stage == "rnaseq_metrics":
logger.info("Calculating RNASeq metrics on %s." % (curr_files))
#coverage = repository[stage](config)
#curr_files = view.map(sam.bam2sam, curr_files)
coverage = RNASeqMetrics(config)
view.map(coverage, curr_files)
if stage == "rseqc":
logger.info("Running rseqc on %s." % (curr_files))
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(sam.bamindex, curr_files)
RPKM_count_out = view.map(rseqc.RPKM_count, *rseq_args)
view.map(rseqc.fix_RPKM_count_file, RPKM_count_out)
#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)
#RPKM_count_fixed = view.map(rseqc.fix_RPKM_count_file,
"""
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)
# end gracefully
stop_cluster()