def gen_reads(vcf, dest_vcf, dest_fq_prefix, ex_snp, gt_policy, read_depth, conf):
"""
Generate fastqs for the given set of input variants. This code is fired when the user supplies the --generate-fqs
arg, and closely mimics the fastq generation code in VariantProcessor
:param vars: List of variants
:param dest_vcf: Destination filename for final VCF (may be gzipped)
:param dest_fq_prefix: Destination prefix for fastq files
:param ex_snp: Info for extra SNP addition
:param gt_policy: Policy describing genotype (hets, homs, from file, etc.)
:param read_depth:
:param conf:
"""
#First, make sure there aren't variants that are too close to process independently...
batches = util.batch_variants(vcf, max_batch_size=1e9)
if len(list(batches))>1:
raise ValueError('The VCF file ' + vcf + ' contains variants that are too close to include in a single set of fastqs, please ensure no two variants are within 2kb of each other')
vars = list(pysam.VariantFile(vcf))
variant_sets = bp.create_variant_sets(vars, ex_snp, gt_policy, pysam.FastaFile( conf.get('main', 'ref_genome')))
allvars = []
for vset in variant_sets:
allvars.extend(vset['vars'])
variant_batch = sorted(allvars, cmp=util.variant_comp)
final_vcf = util.write_vcf(variant_batch, dest_vcf, conf)
logging.info("Writing full VCF to " + final_vcf)
reads = bam_simulation.gen_alt_fq(conf.get('main', 'ref_genome'), variant_sets, read_depth, dest_prefix=dest_fq_prefix)
logging.info("Writing fastqs to " + reads[0] + ", " + reads[1])
def process_vcf(vcf, gt_default, conf, output, callers, fqs=None, snp_info=None, single_batch=False, keep_tmpdir=False, read_depth=250):
"""
Perform analyses for each variant in the VCF file.
:param input_vcf: Path to vcf file containing variants to process
:param single_batch: Assume all variants in VCF are part of one batch and process them all simultaneously
:param keep_tmpdir: Preserve tmpdirs created (otherwise delete them, unless they are flagged)
:param conf: Configuration object
"""
variant_callers = core_callers.get_callers()
variant_callers.update(load_components(conf, 'callers', 'get_callers'))
normalizers = core_norms.get_normalizers()
normalizers.update(load_components(conf, 'normalizers', 'get_normalizers'))
comparators = core_comps.get_comparators()
comparators.update(load_components(conf, 'comparators', 'get_comparators'))
if callers is not None and len(callers)>0:
callers_to_use = {}
for caller in callers:
if caller not in variant_callers:
raise KeyError('No variant caller ' + caller + ' found in callers')
callers_to_use[caller] = variant_callers[caller]
variant_callers = callers_to_use
if fqs is not None:
fqs = [os.path.abspath(fq) for fq in fqs]
processor = bp.VariantProcessor(variant_callers, normalizers, comparators, JsonReporter(output), conf)
logging.info("Processing variants in file " + vcf)
if single_batch:
logging.info("Processing all variants as one batch")
tmp_dir = '{}-tmp'.format(util.strip_extensions(os.path.basename(vcf), ['vcf','gz']))
processor.process_batch(vcf, tmp_dir, gt_default, ex_snp=snp_info, keep_tmpdir=keep_tmpdir, read_depth=read_depth, reads=fqs)
else:
batches = util.batch_variants(vcf, max_batch_size=1000, min_safe_dist=2000)
for batchnum, batch_vcf in enumerate(batches, 1):
logging.info('Processing batch #{} of {}'.format(batchnum, len(batches)))
tmp_dir = '{}-batch{:03d}-tmp'.format(util.strip_extensions(os.path.basename(vcf), ['vcf','gz']), batchnum)
processor.process_batch(batch_vcf, tmp_dir, gt_default, ex_snp=snp_info, keep_tmpdir=keep_tmpdir, read_depth=read_depth, reads=fqs)
os.remove(batch_vcf)
