# out_file = get_r2_prepped_outfile(sample, args.alignment_dir)
# print ("barcode-prepping %s and %s to %s." % (fq1, fq2, out_file))
# prepped.append(view.apply_async(barcode.prep_r2_with_barcode,
# sample["r1_path"],
# sample["r2_path"],
# get_r2_prepped_outfile(sample, args.alignment_dir)))
# prepped = cluster.wait_until_complete(prepped)
# print "Finshed barcode preparation."
print "Beginning alignment."
aligned = []
for sample in samples:
print ("aligning %s to %s with star." % (sample['sample_id'], args.aligner_index))
aligned.append(view.apply_async(align.star_align, sample['r1_path'], args.aligner_index,
get_star_prefix(sample['r1_path']), args.cores_per_job))
aligned = cluster.wait_until_complete(aligned)
print "Finished alignment."
print "Beginning QC."
qc = []
for sam_file in aligned:
print ("QC for file %s" % sam_file)
qc.append(view.apply_async(align.qc, sam_file))
qc = cluster.wait_until_complete(qc)
print "Finished QC."
print "Begin cleaning of poorly mapped reads."
cleaned = []
for sam_file in aligned:
print ("Cleaning %s, removing poorly mapped reads." % sam_file)
cleaned.append(view.apply_async(align.clean_align, sam_file, get_cleaned_outfile(sam_file)))
cleaned = cluster.wait_until_complete(cleaned)
print "Finished cleaning."
from tailseq import cluster
from argparse import ArgumentParser
if __name__ == "__main__":
parser = ArgumentParser(description="Run a single cell analysis.")
parser.add_argument("--num-jobs", type=int,
default=1, help="Number of concurrent jobs to process.")
parser.add_argument("--cores-per-job", type=int,
default=1, help="Number of cores to use.")
parser.add_argument("--memory-per-job", default=2, help="Memory in GB to reserve per job.")
parser.add_argument("--timeout", default=15, help="Time to wait before giving up starting.")
parser.add_argument("--scheduler", default=None, help="Type of scheduler to use.",
choices=["lsf", "slurm", "torque", "sge"])
parser.add_argument("--resources", default=None, help="Extra scheduler resource flags.")
parser.add_argument("--queue", default=None, help="Queue to submit jobs to.")
parser.add_argument("--parallel", choices = ["local", "ipython"], default="ipython",
help="Run in parallel on a local machine.")
parser.add_argument("--local", action="store_true",
default=True, help="Run parallel locally")
args = parser.parse_args()
res = []
with cluster.get_cluster_view(args) as view:
for sample in [1, 2, 3]:
res.append(view.apply_async(sum, [1, 2, 3]))
res = cluster.wait_until_complete(res)
print res
parser.add_argument("--timeout",
default=15,
help="Time to wait before giving up starting.")
parser.add_argument("--scheduler",
default=None,
help="Type of scheduler to use.",
choices=["lsf", "slurm", "torque", "sge"])
parser.add_argument("--resources",
default=None,
help="Extra scheduler resource flags.")
parser.add_argument("--queue",
default=None,
help="Queue to submit jobs to.")
parser.add_argument("--parallel",
choices=["local", "ipython"],
default="ipython",
help="Run in parallel on a local machine.")
parser.add_argument("--local",
action="store_true",
default=True,
help="Run parallel locally")
args = parser.parse_args()
res = []
with cluster.get_cluster_view(args) as view:
for sample in [1, 2, 3]:
res.append(view.apply_async(sum, [1, 2, 3]))
res = cluster.wait_until_complete(res)
print res