def _get_maxcov_downsample(data):
"""Calculate maximum coverage downsampling for whole genome samples.
Returns None if we're not doing downsampling.
"""
from bcbio.bam import ref
from bcbio.ngsalign import alignprep, bwa
from bcbio.variation import coverage
params = {"min_coverage_for_downsampling": 10,
"maxcov_downsample_multiplier": dd.get_maxcov_downsample(data)}
fastq_file = data["files"][0]
num_reads = alignprep.total_reads_from_grabix(fastq_file)
if num_reads and params["maxcov_downsample_multiplier"] and params["maxcov_downsample_multiplier"] > 0:
vrs = dd.get_variant_regions_merged(data)
total_size = sum([c.size for c in ref.file_contigs(dd.get_ref_file(data), data["config"])])
if vrs:
callable_size = pybedtools.BedTool(vrs).total_coverage()
genome_cov_pct = callable_size / float(total_size)
else:
callable_size = total_size
genome_cov_pct = 1.0
if (genome_cov_pct > coverage.GENOME_COV_THRESH
and dd.get_coverage_interval(data) in ["genome", None, False]):
total_counts, total_sizes = 0, 0
for count, size in bwa.fastq_size_output(fastq_file, 5000):
total_counts += int(count)
total_sizes += (int(size) * int(count))
read_size = float(total_sizes) / float(total_counts)
avg_cov = float(num_reads * read_size) / callable_size
if avg_cov >= params["min_coverage_for_downsampling"]:
return int(avg_cov * params["maxcov_downsample_multiplier"])
return None
def get_downsample_params(data):
ds_mult = dd.get_maxcov_downsample(data)
if ds_mult and ds_mult > 0:
return {
"min_coverage_for_downsampling": 10,
"maxcov_downsample_multiplier": ds_mult
}
def get_downsample_params(data):
ds_mult = dd.get_maxcov_downsample(data)
if ds_mult and ds_mult > 0:
return {"min_coverage_for_downsampling": 10,
"maxcov_downsample_multiplier": ds_mult}