def main(args):
config = cli.load_pypeliner_config(args)
pyp = pypeliner.app.Pypeline([], config)
workflow = Workflow()
workflow.subworkflow(name='snpeff',
func=snpeff.create_snpeff_annotation_workflow,
args=(pypeliner.managed.InputFile(
args.target_vcf_file),
pypeliner.managed.TempOutputFile('snpeff.h5')),
kwargs={
'data_base': args.data_base,
'split_size': args.split_size,
'table_name': 'snpeff'
})
workflow.transform(name='convert_to_tsv',
func=convert_hdf5_to_tsv,
ctx={'mem': 2},
args=(pypeliner.managed.TempInputFile('snpeff.h5'),
'snpeff',
pypeliner.managed.OutputFile(args.out_file)),
kwargs={
'compress': True,
'index': False
})
pyp.run(workflow)
def main(args):
make_directory(args.out_dir)
with open(args.config_file) as fh:
# Replace {ref_path_db} in config with desired path
config_str = fh.read()
config_str = config_str.format(ref_db_dir=args.ref_db_dir)
# Load config
config = yaml.load(config_str)
if args.exome:
config['strelka']['kwargs']['use_depth_thresholds'] = False
tumour_bam_files = dict(zip(args.tumour_sample_ids, args.tumour_bam_files))
raw_data_dir = os.path.join(args.out_dir, 'raw_data')
results_dir = os.path.join(args.out_dir, 'results.h5')
workflow = call_and_annotate_pipeline(config,
args.normal_bam_file,
tumour_bam_files,
raw_data_dir,
results_dir,
chromosomes=args.chromosomes)
pyp_config = cli.load_pypeliner_config(args)
pyp = pypeliner.app.Pypeline([], pyp_config)
pyp.run(workflow)
def main(args):
config = cli.load_pypeliner_config(args)
pyp = pypeliner.app.Pypeline([], config)
workflow = dollo.get_tree_search_workflow(
args.in_file,
args.search_file,
args.tree_file,
grid_search=args.grid_search,
grid_size=args.grid_size,
max_probability_of_loss=args.max_probability_of_loss,
min_probability_of_loss=args.min_probability_of_loss)
pyp.run(workflow)
def main(args):
config = cli.load_pypeliner_config(args)
pyp = pypeliner.app.Pypeline([], config)
workflow = mutect.create_mutect_workflow(args.normal_bam_file,
args.tumour_bam_file,
args.ref_genome_fasta_file,
args.cosmic_file,
args.dbsnp_file,
args.out_file,
chromosomes=args.chromosomes,
split_size=args.split_size)
pyp.run(workflow)
def main(args):
utils.make_directory(args.raw_data_dir)
config = cli.load_pypeliner_config(args)
pyp = pypeliner.app.Pypeline(config=config)
workflow = delly.delly_pipeline(
args.normal_bam_file,
cli.get_tumour_bam_file_dict(args),
args.ref_genome_fasta_file,
args.delly_excl_chrom,
args.out_file,
args.raw_data_dir,
)
pyp.run(workflow)
def main(args):
with open(args.config_file) as fh:
# Replace {ref_path_db} in config with desired path
config_str = fh.read()
config_str = config_str.format(ref_db_dir=args.ref_db_dir)
# Load config
config = yaml.load(config_str)
workflow = create_setup_reference_dbs_workflow(config['databases'])
pyp_config = cli.load_pypeliner_config(args)
pyp = pypeliner.app.Pypeline([], pyp_config)
pyp.run(workflow)
def main(args):
config = cli.load_pypeliner_config(args)
pyp = pypeliner.app.Pypeline([], config)
workflow = nuseq.create_nuseq_classify_workflow(
args.normal_bam_file,
args.tumour_bam_files,
args.ref_genome_fasta_file,
args.out_file,
chromosomes=args.chromosomes,
indel_threshold=args.indel_threshold,
chunk_size=args.chunk_size,
min_normal_depth=args.min_normal_depth,
min_tumour_depth=args.min_tumour_depth,
min_somatic_probability=args.min_somatic_probability,
split_size=args.split_size
)
pyp.run(workflow)
def main(args):
config = cli.load_pypeliner_config(args)
pyp = pypeliner.app.Pypeline([], config)
indel_vcf_file = args.out_prefix + '.indel.vcf.gz'
snv_vcf_file = args.out_prefix + '.snv.vcf.gz'
workflow = strelka.create_strelka_workflow(
args.normal_bam_file,
args.tumour_bam_file,
args.ref_genome_fasta_file,
indel_vcf_file,
snv_vcf_file,
chromosomes=args.chromosomes,
split_size=args.split_size,
use_depth_thresholds=not args.no_depth_thresholds)
pyp.run(workflow)
def main(args):
utils.make_directory(args.raw_data_dir)
destruct_config = {}
if args.destruct_config is not None:
with open(args.destruct_config) as fh:
destruct_config = yaml.load(fh)
pypeliner_config = cli.load_pypeliner_config(args)
pyp = pypeliner.app.Pypeline(config=pypeliner_config)
workflow = destruct.destruct_pipeline(
args.normal_bam_file,
cli.get_tumour_bam_file_dict(args),
destruct_config,
args.ref_data_dir,
args.out_file,
args.raw_data_dir,
)
pyp.run(workflow)