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 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 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 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_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 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 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 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 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_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_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 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 calculate_insert_size(yaml_config):
"""
wrapper for calling calculate insert size function
"""
operation_seleted = "i"
orgdb = expdb.experiment_db(yaml_config, operation_seleted)
print "NOT YET IMPLEMENTED."
sys.exit(0)
def calculate_insert_size(yaml_config):
"""
wrapper for calling calculate insert size function
"""
operation_seleted = "i"
orgdb = expdb.experiment_db(yaml_config, operation_seleted)
print "NOT YET IMPLEMENTED."
sys.exit(0)
def find_uniq_reads(yaml_config):
"""
find uniquely mapped reads from a bam file
"""
operation_seleted = "u"
orgdb = expdb.experiment_db(yaml_config, operation_seleted)
print "NOT YET IMPLEMENTED."
sys.exit(0)
def find_uniq_reads(yaml_config):
"""
find uniquely mapped reads from a bam file
"""
operation_seleted = "u"
orgdb = expdb.experiment_db(yaml_config, operation_seleted)
print "NOT YET IMPLEMENTED."
sys.exit(0)
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 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)
def fetch_db_signals(yaml_config, data_method):
"""
get the genomic signal labels bases on the annotation from external database
"""
operation_seleted = "6"
orgdb = expdb.experiment_db(yaml_config, operation_seleted)
Jobs = []
for org_name, det in orgdb.items():
if data_method == "trsk":
gff_file = "%s/%s_trsk_genes.gff" % (det['read_assembly_dir'],
org_name)
out_dir = "%s/trsk_4K_labels" % det[
'labels_dir'] ## new label sequence dir
elif data_method == "cufflinks":
gff_file = "%s/%s_cufflinks_genes.gff" % (det['read_assembly_dir'],
org_name)
out_dir = "%s/cuff_4K_labels" % det['labels_dir']
elif data_method == "onlinedb":
gff_file = "%s/%s_%s.gff" % (det['read_assembly_dir'], org_name,
det['genome_release_db']) ## db_anno
out_dir = "%s/jmlr_1K_sm_labels" % det['labels_dir']
if not os.path.isfile(gff_file): ## check the file present or not
exit("error: genome annotation file missing %s" % gff_file)
if not os.path.exists(out_dir): ## create the new label sequence dir
os.makedirs(out_dir)
for the_file in os.listdir(out_dir): ## cleaning the existing one
file_path = os.path.join(out_dir, the_file)
try:
if os.path.isfile(file_path):
os.unlink(file_path)
except Exception, e:
print e
#import subprocess
## get the label count for each organisms, essentially the max number of genes available
#cmd = "grep -P \"\tgene\t\" %s | wc -l" % gff_file
#proc = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
#count, err = proc.communicate()
#count = int(count.strip())
## depends on the genomic signal type
count = 5000
signal_type = "tss"
poslabels_cnt = 1000
neglabels_cnt = 3000
flank_nts = 1200
## arguments to pygrid
arg = [[
det['fasta'], gff_file, signal_type, count, poslabels_cnt,
neglabels_cnt, flank_nts, out_dir
]]
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)
def fetch_db_signals(yaml_config, data_method):
"""
get the genomic signal labels bases on the annotation from external database
"""
operation_seleted = "6"
orgdb = expdb.experiment_db(yaml_config, operation_seleted)
Jobs = []
for org_name, det in orgdb.items():
if data_method == "trsk":
gff_file = "%s/%s_trsk_genes.gff" % (det['read_assembly_dir'], org_name)
out_dir = "%s/trsk_4K_labels" % det['labels_dir']## new label sequence dir
elif data_method == "cufflinks":
gff_file = "%s/%s_cufflinks_genes.gff" % (det['read_assembly_dir'], org_name)
out_dir = "%s/cuff_4K_labels" % det['labels_dir']
elif data_method == "onlinedb":
gff_file = "%s/%s_%s.gff" % (det['read_assembly_dir'], org_name, det['genome_release_db']) ## db_anno
out_dir = "%s/jmlr_1K_sm_labels" % det['labels_dir']
if not os.path.isfile(gff_file):## check the file present or not
exit("error: genome annotation file missing %s" % gff_file)
if not os.path.exists(out_dir): ## create the new label sequence dir
os.makedirs(out_dir)
for the_file in os.listdir(out_dir): ## cleaning the existing one
file_path = os.path.join(out_dir, the_file)
try:
if os.path.isfile(file_path):
os.unlink(file_path)
except Exception, e:
print e
#import subprocess
## get the label count for each organisms, essentially the max number of genes available
#cmd = "grep -P \"\tgene\t\" %s | wc -l" % gff_file
#proc = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
#count, err = proc.communicate()
#count = int(count.strip())
## depends on the genomic signal type
count = 5000
signal_type = "tss"
poslabels_cnt = 1000
neglabels_cnt = 3000
flank_nts = 1200
## arguments to pygrid
arg = [[det['fasta'], gff_file, signal_type, count, poslabels_cnt, neglabels_cnt, flank_nts, out_dir]]
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)
