def process_sample(parse_functions, sample, samples, config, amplicon_list):
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('-s', '--samples_file',
help="Input configuration file for samples")
parser.add_argument('-c', '--configuration',
help="Configuration file for various settings")
parser.add_argument('-l', '--list',
help="List file of SNPs to process")
args = parser.parse_args()
sys.stdout.write("Parsing configuration data\n")
config = configuration.configure_runtime(args.configuration)
sys.stdout.write("Parsing sample data\n")
samples = configuration.configure_samples(args.samples_file, config)
sample_cov_data = defaultdict(lambda: defaultdict())
for sample in samples:
sys.stdout.write("Processing sample {}\n".format(sample))
sample_cov_data[sample] = process_sample(parse_functions, sample,
samples, config, snps)
sys.stdout.write("Writing out data\n")
with open("glioma_snp_data.txt", 'wb') as out:
out.write("SNP\tChr\tPos")
for sample in samples:
out.write("\t{} - AAF\t{} - Depth".format(sample, sample))
out.write("\n")
for snp in snps:
out.write("{}".format(snp))
for sample in samples:
if sample_snp_data[sample][snp]:
out.write("\t{}\t{}".format(sample_snp_data[sample][snp]['freq'],
sample_snp_data[sample][snp]['depth']))
else:
out.write("\t-\t-")
out.write("\n")
parser.add_argument('-s', '--samples_file', help="Input configuration file for samples")
parser.add_argument('-c', '--configuration', help="Configuration file for various settings")
parser.add_argument('-a', '--address', help="IP Address for Cassandra connection", default='127.0.0.1')
parser.add_argument('-u', '--username', help='Cassandra username for login', default=None)
argcomplete.autocomplete(parser)
Job.Runner.addToilOptions(parser)
args = parser.parse_args()
fractions = [50, 33, 25]
sys.stdout.write("Parsing configuration data\n")
config = configuration.configure_runtime(args.configuration)
sys.stdout.write("Parsing sample data\n")
samples = configuration.configure_samples(args.samples_file, config)
# Workflow Graph definition. The following workflow definition should create a valid Directed Acyclic Graph (DAG)
root_job = Job.wrapJobFn(pipeline.spawn_batch_jobs, cores=1)
if args.username:
password = getpass.getpass()
auth_provider = PlainTextAuthProvider(username=args.username, password=password)
else:
auth_provider = None
for sample in samples:
for fraction in fractions:
iteration = 0
while iteration < int(args.number):
job = Job.wrapJobFn(subsample_bam, [args.address], "coveragestore", auth_provider, sample, samples,
from ddb import configuration
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', help="Input config file for samples")
parser.add_argument('-c',
'--configuration',
help="Configuration file for various settings")
parser.add_argument('-o', '--output', help="Output file name for CSV file")
args = parser.parse_args()
sys.stdout.write("Parsing configuration data\n")
config = configuration.configure_runtime(args.configuration)
sys.stdout.write("Parsing sample data\n")
samples = configuration.configure_samples(args.input, config)
transcript_counts = defaultdict(
lambda: defaultdict(lambda: defaultdict(int)))
for sample in samples:
sys.stderr.write("Processing sample {}\n".format(sample))
gtf_file = HTSeq.GFF_Reader(samples[sample]['gtf'], end_included=True)
for feature in gtf_file:
# sys.stderr.write("Processing entry: {}\n".format(feature))
if feature.type is 'transcript':
transcript_counts[feature.attr['transcript_id']][sample][
'FPKM'] = feature.attr['FPKM']
transcript_counts[feature.attr['transcript_id']][sample][
'TPM'] = feature.attr['TPM']
parser = argparse.ArgumentParser()
parser.add_argument('-s',
'--samples_file',
help="Input configuration file for samples")
parser.add_argument('-c',
'--configuration',
help="Configuration file for various settings")
Job.Runner.addToilOptions(parser)
args = parser.parse_args()
args.logLevel = "INFO"
sys.stdout.write("Parsing configuration data\n")
config = configuration.configure_runtime(args.configuration)
sys.stdout.write("Parsing sample data\n")
samples = configuration.configure_samples(args.samples_file, config)
# Workflow Graph definition. The following workflow definition should create a valid Directed Acyclic Graph (DAG)
root_job = Job.wrapJobFn(pipeline.spawn_batch_jobs, cores=1)
transcripts_list = list()
flags = ["keep_retained", "max_intron", "stranded"]
# Per sample jobs
for sample in samples:
# Alignment and Refinement Stages
align_job = Job.wrapJobFn(hisat.hisat_unpaired,
config,
sample,
samples,
flags,
cores=int(config['hisat']['num_cores']),
if __name__ == "__main__":
type = "colorectal"
parser = argparse.ArgumentParser()
parser.add_argument('-c',
'--configuration',
help="Configuration file for various settings")
args = parser.parse_args()
sys.stdout.write("Parsing configuration data\n")
config = configuration.configure_runtime(args.configuration)
# type_cases = defaultdict(defaultdict(list))
# counts = defaultdict(int)
for root, dirs, files in os.walk("."):
for samples_file in fnmatch.filter(files, "1*_M0373?.config"):
sys.stderr.write("Reading file: {}\n".format(
os.path.join(root, samples_file)))
sys.stdout.write("Parsing sample data\n")
libraries = configuration.configure_samples(
os.path.join(root, samples_file), config)
samples = configuration.merge_library_configs_samples(libraries)
for sample in samples:
for library in samples[sample]:
if samples[sample][library]['report'].startswith(type):
print "Colorectal case found: {}\n".format(sample)
# sys.stderr.write("Type\tCount\n")
# for report_type in counts:
# sys.stdout.write("{}\t{}\n".format(report_type, counts[report_type]))
from collections import defaultdict
from ddb import configuration
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--input', help="Input config file for samples")
parser.add_argument('-c', '--configuration', help="Configuration file for various settings")
parser.add_argument('-o', '--output', help="Output file name for CSV file")
args = parser.parse_args()
sys.stdout.write("Parsing configuration data\n")
config = configuration.configure_runtime(args.configuration)
sys.stdout.write("Parsing sample data\n")
samples = configuration.configure_samples(args.input, config)
transcript_counts = defaultdict(lambda: defaultdict(lambda: defaultdict(int)))
for sample in samples:
sys.stderr.write("Processing sample {}\n".format(sample))
gtf_file = HTSeq.GFF_Reader(samples[sample]['gtf'], end_included=True)
for feature in gtf_file:
# sys.stderr.write("Processing entry: {}\n".format(feature))
if feature.type is 'transcript':
transcript_counts[feature.attr['transcript_id']][sample]['FPKM'] = feature.attr['FPKM']
transcript_counts[feature.attr['transcript_id']][sample]['TPM'] = feature.attr['TPM']
with open(args.output, 'w') as output:
output.write("Transcript")
for sample in samples:
