def run_example_cluster():
"""
run a set of jobs on cluster
"""
print ""
print ""
print "====================================="
print "======== Submit and Wait ========"
print "====================================="
print ""
print ""
functionJobs = make_jobs()
print "output ret field in each job before sending it onto the cluster"
for (i, job) in enumerate(functionJobs):
print "Job #", i, "- ret: ", job.ret
print ""
print "sending function jobs to cluster"
print ""
processedFunctionJobs = process_jobs(functionJobs)
print "ret fields AFTER execution on cluster"
for (i, job) in enumerate(processedFunctionJobs):
print "Job #", i, "- ret: ", str(job.ret)[0:10]
def run_example_local_multithreading():
"""
run a set of jobs on local machine using several cores
"""
print "====================================="
print "====== Local Multithreading ======="
print "====================================="
print ""
print ""
print "generating function jobs"
functionJobs = make_jobs()
# KybJob object start out with an empty ret field, which is only filled after execution
print "output ret field in each job before multithreaded computation"
for (i, job) in enumerate(functionJobs):
print "Job #", i, "- ret: ", job.ret
print ""
print "executing jobs on local machine using 1 thread"
processedFunctionJobs = process_jobs(functionJobs,
local=True,
maxNumThreads=1)
print "ret fields AFTER execution on local machine"
for (i, job) in enumerate(processedFunctionJobs):
print "Job #", i, "- ret: ", str(job.ret)[0:10]
def run_example_local_multithreading():
"""
run a set of jobs on local machine using several cores
"""
print "====================================="
print "====== Local Multithreading ======="
print "====================================="
print ""
print ""
print "generating function jobs"
functionJobs = make_jobs()
# KybJob object start out with an empty ret field, which is only filled after execution
print "output ret field in each job before multithreaded computation"
for (i, job) in enumerate(functionJobs):
print "Job #", i, "- ret: ", job.ret
print ""
print "executing jobs on local machine using 1 thread"
processedFunctionJobs = process_jobs(functionJobs, local=True, maxNumThreads=1)
print "ret fields AFTER execution on local machine"
for (i, job) in enumerate(processedFunctionJobs):
print "Job #", i, "- ret: ", str(job.ret)[0:10]
def align_rnaseq_reads(yaml_config):
"""
wrapper for aligning rnaseq reads using
"""
operation_seleted = "3"
orgdb = expdb.experiment_db(yaml_config, operation_seleted)
Jobs = []
for org_name, det in orgdb.items():
## arguments to pygrid
lib_type = 'PE'
lib_type = 'SE' if len(det['fastq']) == 1 else lib_type
## library insert size
lib_insert_size = 100000
num_cpu = 3
arg = [[det, lib_type, lib_insert_size, num_cpu]]
job = pg.cBioJob(call_align_reads, arg)
job.mem = "90gb"
job.vmem = "90gb"
job.pmem = "30gb"
job.pvmem = "30gb"
job.nodes = 1
job.ppn = num_cpu
job.walltime = "48:00:00"
Jobs.append(job)
print
print "sending read alignment with STAR jobs to worker"
print
processedJobs = pg.process_jobs(Jobs, local=False)
def transcript_prediction_trsk(yaml_config):
"""
transcript prediction using TranscriptSkimmer
"""
operation_seleted = "4"
orgdb = expdb.experiment_db(yaml_config, operation_seleted)
Jobs = []
for org_name, det in orgdb.items():
## arguments to pygrid
arg = [det]
job = pg.cBioJob(call_transcript_prediction_trsk, arg)
## native specifications
job.mem="32gb"
job.vmem="32gb"
job.pmem="32gb"
job.pvmem="32gb"
job.nodes = 1
job.ppn = 1
job.walltime = "9:00:00"
Jobs.append(job)
print
print "sending transcript assembly trsk jobs to worker"
print
local = True ## cluster compute switch
processedJobs = pg.process_jobs(Jobs, local=local)
def run_example_cluster():
"""
run a set of jobs on cluster
"""
print ""
print ""
print "====================================="
print "======== Submit and Wait ========"
print "====================================="
print ""
print ""
functionJobs = make_jobs()
print "output ret field in each job before sending it onto the cluster"
for (i, job) in enumerate(functionJobs):
print "Job #", i, "- ret: ", job.ret
print ""
print "sending function jobs to cluster"
print ""
processedFunctionJobs = process_jobs(functionJobs)
print "ret fields AFTER execution on cluster"
for (i, job) in enumerate(processedFunctionJobs):
print "Job #", i, "- ret: ", str(job.ret)[0:10]
def alignment_filter(yaml_config):
"""
run multimapper resolution program
"""
operation_seleted = "m"
orgdb = expdb.experiment_db(yaml_config, operation_seleted)
Jobs = []
for org_name, det in orgdb.items():
num_cpus = 5
## arguments to pygrid
arg = [[det['short_name'], det['read_map_dir'], num_cpus]]
job = pg.cBioJob(call_alignment_filter, arg)
## native specifications
job.pmem="90gb"
job.pvmem="90gb"
job.mem="90gb"
job.vmem="90gb"
job.nodes = 1
job.ppn = num_cpus
job.walltime = "48:00:00"
Jobs.append(job)
print
print "sending multi map resolution jobs to worker"
print
processedJobs = pg.process_jobs(Jobs)
def transcript_prediction_cuff(yaml_config):
"""
transcript prediction using cufflinks
"""
operation_seleted = "c"
orgdb = expdb.experiment_db(yaml_config, operation_seleted)
Jobs = []
for org_name, det in orgdb.items():
## arguments to pygrid
arg = [[det, 4]]
job = pg.cBioJob(call_transcript_prediction_cuff, arg)
## native specifications
job.mem="96gb"
job.vmem="96gb"
job.pmem="24gb"
job.pvmem="24gb"
job.nodes = 1
job.ppn = 4
job.walltime = "32:00:00"
Jobs.append(job)
print
print "sending transcript assembly cufflinks jobs to worker"
print
processedJobs = pg.process_jobs(Jobs)
def create_genome_index(yaml_config):
"""
wrapper for calling genome index function
"""
operation_seleted = "2"
orgdb = expdb.experiment_db(yaml_config, operation_seleted)
Jobs = []
for org_name, det in orgdb.items():
## arguments to pygrid
num_cpus = 4
arg = [[det['fasta'], det['genome_index_dir'], det['gtf'], num_cpus, det['read_length']-1]]
job = pg.cBioJob(call_genome_index, arg)
job.mem="46gb"
job.vmem="46gb"
job.pmem="46gb"
job.pvmem="46gb"
job.nodes = 1
job.ppn = num_cpus
job.walltime = "24:00:00"
Jobs.append(job)
print
print "sending star genome index jobs to worker"
print
processedJobs = pg.process_jobs(Jobs)
def align_rnaseq_reads(yaml_config):
"""
wrapper for aligning rnaseq reads using
"""
operation_seleted = "3"
orgdb = expdb.experiment_db(yaml_config, operation_seleted)
Jobs = []
for org_name, det in orgdb.items():
## arguments to pygrid
lib_type = 'PE'
lib_type = 'SE' if len(det['fastq'])==1 else lib_type
## library insert size
lib_insert_size = 100000
num_cpu = 3
arg = [[det, lib_type, lib_insert_size, num_cpu]]
job = pg.cBioJob(call_align_reads, arg)
job.mem="90gb"
job.vmem="90gb"
job.pmem="30gb"
job.pvmem="30gb"
job.nodes = 1
job.ppn = num_cpu
job.walltime = "48:00:00"
Jobs.append(job)
print
print "sending read alignment with STAR jobs to worker"
print
processedJobs = pg.process_jobs(Jobs, local=False)
def download_gtf(yaml_config):
"""
download gtf/gff file from remote data publishing services
"""
operation_seleted = "a"
orgdb = expdb.experiment_db(yaml_config, operation_seleted)
Jobs = []
for org_name, det in orgdb.items():
## arguments to pygrid
arg = [[det['release_nb'], det['long_name'], det['genome_dir']]]
if det['release_db'] == 'ensembl_metazoa_genome':
job = pg.cBioJob(call_metazoa_gtf, arg)
elif det['release_db'] == 'phytozome_genome':
job = pg.cBioJob(call_phytozome_gtf, arg)
elif det['release_db'] == 'ensembl_genome':
job = pg.cBioJob(call_ensembl_gtf, arg)
else:
print "error: download gtf plugin for %s not available, module works with ensembl_genome, ensembl_metazoa_genome and phytozome_genome servers." % det['release_db']
sys.exit(0)
job.mem="2gb"
job.vmem="2gb"
job.pmem="2gb"
job.pvmem="2gb"
job.nodes = 1
job.ppn = 1
job.walltime = "2:00:00"
Jobs.append(job)
print
print "sending gtf download job to worker"
print
processedJobs = pg.process_jobs(Jobs)
def download_sra_data(yaml_config):
"""
download sra file for the working organism
"""
operation_seleted = "1"
orgdb = expdb.experiment_db(yaml_config, operation_seleted)
Jobs = []
for org_name, det in orgdb.items():
## arguments to pygrid
arg = [[det['sra_run_id'], det['fastq_path']]]
job = pg.cBioJob(call_download_sra_file, arg)
job.mem = "2gb"
job.vmem = "2gb"
job.pmem = "2gb"
job.pvmem = "2gb"
job.nodes = 1
job.ppn = 1
job.walltime = "1:00:00"
Jobs.append(job)
print
print "sending download SRA file jobs to worker"
print
processedJobs = pg.process_jobs(Jobs)
def download_sra_data(yaml_config):
"""
download sra file for the working organism
"""
operation_seleted = "1"
orgdb = expdb.experiment_db(yaml_config, operation_seleted)
Jobs = []
for org_name, det in orgdb.items():
## arguments to pygrid
arg = [[det['sra_run_id'], det['fastq_path']]]
job = pg.cBioJob(call_download_sra_file, arg)
job.mem="2gb"
job.vmem="2gb"
job.pmem="2gb"
job.pvmem="2gb"
job.nodes = 1
job.ppn = 1
job.walltime = "1:00:00"
Jobs.append(job)
print
print "sending download SRA file jobs to worker"
print
processedJobs = pg.process_jobs(Jobs)
def transcript_prediction_stringtie(yaml_config):
"""
transcript prediction using StringTie
"""
operation_seleted = "5"
orgdb = expdb.experiment_db(yaml_config, operation_seleted)
Jobs = []
for org_name, det in orgdb.items():
## arguments to pygrid
arg = [[det["read_map_dir"], det["short_name"], det["read_assembly_dir"]]]
job = pg.cBioJob(call_transcript_prediction_stringtie, arg)
cpus = 1
## native specifications
job.mem="12gb"
job.vmem="12gb"
job.pmem="12gb"
job.pvmem="12gb"
job.nodes = 1
job.ppn = cpus
job.walltime = "24:00:00"
Jobs.append(job)
print("\nsending transcript assembly stringtie jobs to worker\n")
local_compute = False ## switching between local multithreading and cluster computing
processedJobs = pg.process_jobs(Jobs, local=local_compute)
def create_genome_index(yaml_config):
"""
wrapper for calling genome index function
"""
operation_seleted = "2"
orgdb = expdb.experiment_db(yaml_config, operation_seleted)
Jobs = []
for org_name, det in orgdb.items():
## arguments to pygrid
num_cpus = 4
arg = [[
det['fasta'], det['genome_index_dir'], det['gtf'], num_cpus,
det['read_length'] - 1
]]
job = pg.cBioJob(call_genome_index, arg)
job.mem = "46gb"
job.vmem = "46gb"
job.pmem = "46gb"
job.pvmem = "46gb"
job.nodes = 1
job.ppn = num_cpus
job.walltime = "24:00:00"
Jobs.append(job)
print
print "sending star genome index jobs to worker"
print
processedJobs = pg.process_jobs(Jobs)
def alignment_filter(yaml_config):
"""
run multimapper resolution program
"""
operation_seleted = "m"
orgdb = expdb.experiment_db(yaml_config, operation_seleted)
Jobs = []
for org_name, det in orgdb.items():
num_cpus = 5
## arguments to pygrid
arg = [[det['short_name'], det['read_map_dir'], num_cpus]]
job = pg.cBioJob(call_alignment_filter, arg)
## native specifications
job.pmem = "90gb"
job.pvmem = "90gb"
job.mem = "90gb"
job.vmem = "90gb"
job.nodes = 1
job.ppn = num_cpus
job.walltime = "48:00:00"
Jobs.append(job)
print
print "sending multi map resolution jobs to worker"
print
processedJobs = pg.process_jobs(Jobs)
def transcript_prediction_stringtie(yaml_config):
"""
transcript prediction using StringTie
"""
operation_seleted = "5"
orgdb = expdb.experiment_db(yaml_config, operation_seleted)
Jobs = []
for org_name, det in orgdb.items():
## arguments to pygrid
arg = [[
det["read_map_dir"], det["short_name"], det["read_assembly_dir"]
]]
job = pg.cBioJob(call_transcript_prediction_stringtie, arg)
cpus = 1
## native specifications
job.mem = "12gb"
job.vmem = "12gb"
job.pmem = "12gb"
job.pvmem = "12gb"
job.nodes = 1
job.ppn = cpus
job.walltime = "24:00:00"
Jobs.append(job)
print("\nsending transcript assembly stringtie jobs to worker\n")
local_compute = False ## switching between local multithreading and cluster computing
processedJobs = pg.process_jobs(Jobs, local=local_compute)
def transcript_prediction_trsk(yaml_config):
"""
transcript prediction using TranscriptSkimmer
"""
operation_seleted = "4"
orgdb = expdb.experiment_db(yaml_config, operation_seleted)
Jobs = []
for org_name, det in orgdb.items():
## arguments to pygrid
arg = [det]
job = pg.cBioJob(call_transcript_prediction_trsk, arg)
## native specifications
job.mem = "32gb"
job.vmem = "32gb"
job.pmem = "32gb"
job.pvmem = "32gb"
job.nodes = 1
job.ppn = 1
job.walltime = "9:00:00"
Jobs.append(job)
print
print "sending transcript assembly trsk jobs to worker"
print
local = True ## cluster compute switch
processedJobs = pg.process_jobs(Jobs, local=local)
def transcript_prediction_cuff(yaml_config):
"""
transcript prediction using cufflinks
"""
operation_seleted = "c"
orgdb = expdb.experiment_db(yaml_config, operation_seleted)
Jobs = []
for org_name, det in orgdb.items():
## arguments to pygrid
arg = [[det, 4]]
job = pg.cBioJob(call_transcript_prediction_cuff, arg)
## native specifications
job.mem = "96gb"
job.vmem = "96gb"
job.pmem = "24gb"
job.pvmem = "24gb"
job.nodes = 1
job.ppn = 4
job.walltime = "32:00:00"
Jobs.append(job)
print
print "sending transcript assembly cufflinks jobs to worker"
print
processedJobs = pg.process_jobs(Jobs)
def filter_genes(yaml_config, data_method):
"""
filter out invalid gene models from the provided genome annotation
"""
operation_seleted = "f"
orgdb = expdb.experiment_db(yaml_config, operation_seleted)
Jobs = []
for org_name, det in orgdb.items():
if data_method == "cufflinks":
gff_file = "%s/transcripts.gtf" % det["read_assembly_dir"] ## cufflinks run output file
outFile = "%s/%s_cufflinks_genes.gff" % (
det["read_assembly_dir"],
org_name,
) ## example: A_thaliana_cufflinks_genes.gff
elif data_method == "trsk":
gff_file = "%s/tmp_trsk_genes.gff" % det["read_assembly_dir"] ## trsk run output file
outFile = "%s/%s_trsk_genes.gff" % (
det["read_assembly_dir"],
org_name,
) ## example: A_thaliana_trsk_genes.gff
else:
gff_file = det["gtf"] ## public database genome annotation file
outFile = "%s/%s_%s.gff" % (
det["read_assembly_dir"],
org_name,
det["genome_release_db"],
) ## example: A_thaliana_arabidopsis-tair10.gff
## arguments to pygrid
arg = [[gff_file, det["fasta"], outFile]]
job = pg.cBioJob(call_filter_genes, arg)
## native specifications
job.mem = "6gb"
job.vmem = "6gb"
job.pmem = "6gb"
job.pvmem = "6gb"
job.nodes = 1
job.ppn = 1
job.walltime = "2:00:00"
Jobs.append(job)
print
print "sending filter gene models jobs to worker"
print
processedJobs = pg.process_jobs(Jobs)
def filter_genes(yaml_config, data_method):
"""
filter out invalid gene models from the provided genome annotation
"""
operation_seleted = "f"
orgdb = expdb.experiment_db(yaml_config, operation_seleted)
Jobs = []
for org_name, det in orgdb.items():
if data_method == "cufflinks":
gff_file = "%s/transcripts.gtf" % det[
'read_assembly_dir'] ## cufflinks run output file
outFile = "%s/%s_cufflinks_genes.gff" % (
det['read_assembly_dir'], org_name
) ## example: A_thaliana_cufflinks_genes.gff
elif data_method == "trsk":
gff_file = "%s/tmp_trsk_genes.gff" % det[
'read_assembly_dir'] ## trsk run output file
outFile = "%s/%s_trsk_genes.gff" % (
det['read_assembly_dir'], org_name
) ## example: A_thaliana_trsk_genes.gff
else:
gff_file = det['gtf'] ## public database genome annotation file
outFile = "%s/%s_%s.gff" % (
det['read_assembly_dir'], org_name, det['genome_release_db']
) ## example: A_thaliana_arabidopsis-tair10.gff
## arguments to pygrid
arg = [[gff_file, det['fasta'], outFile]]
job = pg.cBioJob(call_filter_genes, arg)
## native specifications
job.mem = "6gb"
job.vmem = "6gb"
job.pmem = "6gb"
job.pvmem = "6gb"
job.nodes = 1
job.ppn = 1
job.walltime = "2:00:00"
Jobs.append(job)
print
print "sending filter gene models jobs to worker"
print
processedJobs = pg.process_jobs(Jobs)
def download_gtf(yaml_config):
"""
download gtf/gff file from remote data publishing services
"""
operation_seleted = "a"
orgdb = expdb.experiment_db(yaml_config, operation_seleted)
Jobs = []
for org_name, det in orgdb.items():
## arguments to pygrid
arg = [[det['release_nb'], det['long_name'], det['genome_dir']]]
if det['release_db'] == 'ensembl_metazoa_genome':
job = pg.cBioJob(call_metazoa_gtf, arg)
elif det['release_db'] == 'phytozome_genome':
job = pg.cBioJob(call_phytozome_gtf, arg)
elif det['release_db'] == 'ensembl_genome':
job = pg.cBioJob(call_ensembl_gtf, arg)
elif det['release_db'] == 'ensembl_fungi_genome':
job = pg.cBioJob(call_fungi_gtf, arg)
elif det['release_db'] == 'ensembl_protists_genome':
job = pg.cBioJob(call_protists_gtf, arg)
else:
exit(
"error: download gtf plugin for %s not available, module works with ensembl_genome, ensembl_metazoa_genome and phytozome_genome servers."
% det['release_db'])
job.mem = "2gb"
job.vmem = "2gb"
job.pmem = "2gb"
job.pvmem = "2gb"
job.nodes = 1
job.ppn = 1
job.walltime = "2:00:00"
Jobs.append(job)
print
print "sending gtf download job to worker"
print
local_compute = True
processedJobs = pg.process_jobs(Jobs, local=local_compute)
def download_fasta(yaml_config):
"""
download fasta file from remote data publishing services
"""
operation_seleted = "g"
orgdb = expdb.experiment_db(yaml_config, operation_seleted)
Jobs = []
for org_name, det in orgdb.items():
## arguments to pygrid
arg = [[det["release_nb"], det["long_name"], det["genome_dir"]]]
if det["release_db"] == "ensembl_metazoa_genome":
job = pg.cBioJob(call_metazoa_fasta, arg)
elif det["release_db"] == "phytozome_genome":
job = pg.cBioJob(call_phytozome_fasta, arg)
elif det["release_db"] == "ensembl_genome":
job = pg.cBioJob(call_ensembl_fasta, arg)
elif det["release_db"] == "ensembl_fungi_genome":
job = pg.cBioJob(call_fungi_fasta, arg)
else:
exit(
"error: download fasta plugin for %s not available, module works with ensembl_genome, ensembl_metazoa_genome and phytozome_genome servers."
% det["release_db"]
)
job.mem = "2gb"
job.vmem = "2gb"
job.pmem = "2gb"
job.pvmem = "2gb"
job.nodes = 1
job.ppn = 1
job.walltime = "2:00:00"
Jobs.append(job)
print
print "sending fasta download job to worker"
print
local_compute = True
processedJobs = pg.process_jobs(Jobs, local=local_compute)
def main(yaml_config):
"""
"""
config_map = yaml.safe_load(open(yaml_config, "rU"))
exp_path_pfx = config_map['experiment_data_path']['dir']
org_db = defaultdict()
for ent in config_map['experiment']:
species_name = ent['organism_name']
genus, species = species_name.strip().split("_")
short_name = "%s_%s" % (genus[0].upper(), species)
org_db[short_name] = dict(short_name=short_name)
org_db[short_name]['work_dir'] = "%s/%s/set_union_refix" % (
exp_path_pfx, short_name)
org_db[short_name]['data_dir'] = "%s/%s/set_2" % (exp_path_pfx,
short_name)
## prepare jobs
Jobs = []
for org_name, det in org_db.items():
arg = [[org_name, det['work_dir'], det['data_dir']]]
job = pg.cBioJob(distribute_model_train, arg)
job.mem = "4gb"
job.vmem = "4gb"
job.pmem = "4gb"
job.pvmem = "4gb"
job.nodes = 1
job.ppn = 1
job.walltime = "2:00:00"
Jobs.append(job)
compute_local = True
print "sending jobs to worker"
processedJobs = pg.process_jobs(Jobs, local=compute_local)
def main(yaml_config):
"""
"""
config_map = yaml.safe_load(open(yaml_config, "rU"))
exp_path_pfx = config_map["experiment_data_path"]["dir"]
org_db = defaultdict()
for ent in config_map["experiment"]:
species_name = ent["organism_name"]
genus, species = species_name.strip().split("_")
short_name = "%s_%s" % (genus[0].upper(), species)
org_db[short_name] = dict(short_name=short_name)
org_db[short_name]["work_dir"] = "%s/%s/set_union_refix" % (exp_path_pfx, short_name)
org_db[short_name]["data_dir"] = "%s/%s/set_2" % (exp_path_pfx, short_name)
## prepare jobs
Jobs = []
for org_name, det in org_db.items():
arg = [[org_name, det["work_dir"], det["data_dir"]]]
job = pg.cBioJob(distribute_model_train, arg)
job.mem = "4gb"
job.vmem = "4gb"
job.pmem = "4gb"
job.pvmem = "4gb"
job.nodes = 1
job.ppn = 1
job.walltime = "2:00:00"
Jobs.append(job)
compute_local = True
print "sending jobs to worker"
processedJobs = pg.process_jobs(Jobs, local=compute_local)
def decompose_sra_file(yaml_config):
"""
decompress the .sra file from ncbi sra
"""
operation_seleted = "d"
orgdb = expdb.experiment_db(yaml_config, operation_seleted)
Jobs = []
for org_name, det in orgdb.items():
sra_file = "%s/%s.sra" % (det['fastq_path'], det['sra_run_id'])
if not os.path.isfile(sra_file):## check the file present or not
print "error: missing sequencing read file %s" % sra_file
sys.exit(0)
## TODO can be consider to the yaml file options
#library_type = "pe"
library_type = "pe"
compress_format = "gzip"
## arguments to pygrid
arg = [[sra_file, det['fastq_path']]]
job = pg.cBioJob(call_decompose_sra_file, arg)
job.mem="6gb"
job.vmem="6gb"
job.pmem="6gb"
job.pvmem="6gb"
job.nodes = 1
job.ppn = 1
job.walltime = "24:00:00"
Jobs.append(job)
print
print "sending decompress SRA file jobs to worker"
print
processedJobs = pg.process_jobs(Jobs)
def decompose_sra_file(yaml_config):
"""
decompress the .sra file from ncbi sra
"""
operation_seleted = "d"
orgdb = expdb.experiment_db(yaml_config, operation_seleted)
Jobs = []
for org_name, det in orgdb.items():
sra_file = "%s/%s.sra" % (det['fastq_path'], det['sra_run_id'])
if not os.path.isfile(sra_file): ## check the file present or not
print "error: missing sequencing read file %s" % sra_file
sys.exit(0)
## TODO can be consider to the yaml file options
#library_type = "pe"
library_type = "pe"
compress_format = "gzip"
## arguments to pygrid
arg = [[sra_file, det['fastq_path']]]
job = pg.cBioJob(call_decompose_sra_file, arg)
job.mem = "6gb"
job.vmem = "6gb"
job.pmem = "6gb"
job.pvmem = "6gb"
job.nodes = 1
job.ppn = 1
job.walltime = "24:00:00"
Jobs.append(job)
print
print "sending decompress SRA file jobs to worker"
print
processedJobs = pg.process_jobs(Jobs)
job = pg.cBioJob(call_fetch_db_signals, arg)
## native specifications
job.mem = "5gb"
job.vmem = "5gb"
job.pmem = "5gb"
job.pvmem = "5gb"
job.nodes = 1
job.ppn = 1
job.walltime = "1:00:00"
Jobs.append(job)
print
print "sending genomic signal fetch jobs to worker"
print
processedJobs = pg.process_jobs(Jobs)
def call_filter_genes(args_list):
"""
wrapper for submitting jobs to pygrid
"""
from rnaseq_align_assembly import refine_transcript_models as filter_tool
gtf_file, fasta_file, result_file = args_list
filter_tool.filter_gene_models(gtf_file, fasta_file, result_file)
return "done"
def filter_genes(yaml_config, data_method):
"""
filter out invalid gene models from the provided genome annotation
job = pg.cBioJob(call_fetch_db_signals, arg)
## native specifications
job.mem="5gb"
job.vmem="5gb"
job.pmem="5gb"
job.pvmem="5gb"
job.nodes = 1
job.ppn = 1
job.walltime = "1:00:00"
Jobs.append(job)
print
print "sending genomic signal fetch jobs to worker"
print
processedJobs = pg.process_jobs(Jobs)
def call_filter_genes(args_list):
"""
wrapper for submitting jobs to pygrid
"""
from rnaseq_align_assembly import refine_transcript_models as filter_tool
gtf_file, fasta_file, result_file = args_list
filter_tool.filter_gene_models(gtf_file, fasta_file, result_file)
return "done"
def filter_genes(yaml_config, data_method):
"""
filter out invalid gene models from the provided genome annotation