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import numpy as np
# own tools
from deeptools import bamHandler
from deeptools import mapReduce
old_settings = np.seterr(all='ignore')
def getFragmentLength_wrapper(args):
return getFragmentLength_worker(*args)
def getFragmentLength_worker(chrom, start, end, bamFile, distanceBetweenBins):
"""
Queries the reads at the given region for the distance between
reads and the read length
Parameters
----------
chrom : str
chromosome name
start : int
region start
end : int
region end
bamFile : str
BAM file name
distanceBetweenBins : int
the number of bases at the end of each bin to ignore
Returns
-------
np.array
an np.array, where first column is fragment length, the
second is for read length
"""
bam = bamHandler.openBam(bamFile)
end = max(start + 1, end - distanceBetweenBins)
if chrom in bam.references:
reads = np.array([(abs(r.template_length), r.infer_query_length(always=False))
for r in bam.fetch(chrom, start, end)
if r.is_proper_pair and r.is_read1 and not r.is_unmapped])
if not len(reads):
# if the previous operation produces an empty list
# it could be that the data is not paired, then
# we try with out filtering
reads = np.array([(abs(r.template_length), r.infer_query_length(always=False))
for r in bam.fetch(chrom, start, end) if not r.is_unmapped])
else:
raise NameError("chromosome {} not found in bam file".format(chrom))
if not len(reads):
reads = np.array([]).reshape(0, 2)
return reads
def get_read_and_fragment_length(bamFile, return_lengths=False, blackListFileName=None,
binSize=50000, distanceBetweenBins=1000000,
numberOfProcessors=None, verbose=False):
"""
Estimates the fragment length and read length through sampling
Parameters
----------
bamFile : str
BAM file name
return_lengths : bool
numberOfProcessors : int
verbose : bool
binSize : int
distanceBetweenBins : int
Returns
-------
d : dict
tuple of two dictionaries, one for the fragment length and the other
for the read length. The dictionaries summarise the mean, median etc. values
"""
bam_handle = bamHandler.openBam(bamFile)
chrom_sizes = list(zip(bam_handle.references, bam_handle.lengths))
distanceBetweenBins *= 2
fl = []
# Fix issue #522, allow distanceBetweenBins == 0
if distanceBetweenBins == 0:
imap_res = mapReduce.mapReduce((bam_handle.filename, distanceBetweenBins),
getFragmentLength_wrapper,
chrom_sizes,
genomeChunkLength=binSize,
blackListFileName=blackListFileName,
numberOfProcessors=numberOfProcessors,
verbose=verbose)
fl = np.concatenate(imap_res)
# Try to ensure we have at least 1000 regions from which to compute statistics, halving the intra-bin distance as needed
while len(fl) < 1000 and distanceBetweenBins > 1:
distanceBetweenBins /= 2
stepsize = binSize + distanceBetweenBins
imap_res = mapReduce.mapReduce((bam_handle.filename, distanceBetweenBins),
getFragmentLength_wrapper,
chrom_sizes,
genomeChunkLength=stepsize,
blackListFileName=blackListFileName,
numberOfProcessors=numberOfProcessors,
verbose=verbose)
fl = np.concatenate(imap_res)
if len(fl):
fragment_length = fl[:, 0]
read_length = fl[:, 1]
if fragment_length.mean() > 0:
fragment_len_dict = {'sample_size': len(fragment_length),
'min': fragment_length.min(),
'qtile25': np.percentile(fragment_length, 25),
'mean': np.mean(fragment_length),
'median': np.median(fragment_length),
'qtile75': np.percentile(fragment_length, 75),
'max': fragment_length.max(),
'std': np.std(fragment_length),
'mad': np.median(np.abs(fragment_length - np.median(fragment_length))),
'qtile10': np.percentile(fragment_length, 10),
'qtile20': np.percentile(fragment_length, 20),
'qtile30': np.percentile(fragment_length, 30),
'qtile40': np.percentile(fragment_length, 40),
'qtile60': np.percentile(fragment_length, 60),
'qtile70': np.percentile(fragment_length, 70),
'qtile80': np.percentile(fragment_length, 80),
'qtile90': np.percentile(fragment_length, 90),
'qtile99': np.percentile(fragment_length, 99)}
else:
fragment_len_dict = None
if return_lengths and fragment_len_dict is not None:
fragment_len_dict['lengths'] = fragment_length
read_len_dict = {'sample_size': len(read_length),
'min': read_length.min(),
'qtile25': np.percentile(read_length, 25),
'mean': np.mean(read_length),
'median': np.median(read_length),
'qtile75': np.percentile(read_length, 75),
'max': read_length.max(),
'std': np.std(read_length),
'mad': np.median(np.abs(read_length - np.median(read_length))),
'qtile10': np.percentile(read_length, 10),
'qtile20': np.percentile(read_length, 20),
'qtile30': np.percentile(read_length, 30),
'qtile40': np.percentile(read_length, 40),
'qtile60': np.percentile(read_length, 60),
'qtile70': np.percentile(read_length, 70),
'qtile80': np.percentile(read_length, 80),
'qtile90': np.percentile(read_length, 90),
'qtile99': np.percentile(read_length, 99)}
if return_lengths:
read_len_dict['lengths'] = read_length
else:
fragment_len_dict = None
read_len_dict = None
return fragment_len_dict, read_len_dict
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