def _get_tid_info(tup):
"""For each transcript on this chromosome/strand, identifies every sub-sequence of the appropriate length (fpsize), converts it to an integer,
identifies the number of reads mapping to that position, and outputs all of that information to a pandas HDF store."""
(chrom, strand) = tup
inbams = [pysam.Samfile(infile, 'rb') for infile in opts.bamfiles]
gnd = BAMGenomeArray(inbams, mapping=FivePrimeMapFactory(psite))
# map to roughly the center of each read so that identical sequences that cross different splice sites
# (on different transcripts) still end up mapping to the same place
gnd.add_filter('size', SizeFilterFactory(opts.minlen, opts.maxlen))
tid_seq_info = []
tid_summary = pd.DataFrame(
{'chrom': chrom, 'strand': strand, 'n_psite': -1, 'n_reads': -1, 'peak_reads': -1, 'dropped': ''},
index=pd.Index(bedlinedict[(chrom, strand)].keys(), name='tid'))
for (tid, line) in bedlinedict[(chrom, strand)].iteritems():
currtrans = SegmentChain.from_bed(line)
curr_pos_list = currtrans.get_position_list() # not in stranded order!
if strand == '-':
curr_pos_list = curr_pos_list[::-1]
n_psite = len(curr_pos_list) + 1 - fpsize
tid_summary.at[tid, 'n_psite'] = n_psite
if n_psite > 0:
curr_counts = np.array(currtrans.get_counts(gnd))[psite:n_psite + psite]
# if((curr_counts>0).any()):
sumcounts = curr_counts.sum()
maxcounts = curr_counts.max()
tid_summary.at[tid, 'n_reads'] = sumcounts
tid_summary.at[tid, 'peak_reads'] = maxcounts
if sumcounts >= opts.minreads:
if maxcounts < sumcounts * opts.peakfrac:
numseq = np.array(list(currtrans.get_sequence(genome).upper().translate(str_dict)))
curr_seq = ''.join(numseq)
tid_seq_info.append(pd.DataFrame({'tid': tid,
'genpos': curr_pos_list[psite:n_psite + psite],
'seq': np.array([(int(curr_seq[i:i + fpsize], 4) if 'N' not in curr_seq[i:i + fpsize] else -1)
for i in xrange(n_psite)], dtype=np.int64),
'reads': curr_counts}))
else:
tid_summary.at[tid, 'dropped'] = 'peakfrac'
else:
tid_summary.at[tid, 'dropped'] = 'lowreads'
if tid_seq_info: # don't bother saving anything if there's nothing to save
pd.concat(tid_seq_info, ignore_index=True).to_hdf(seq_info_hdf % (chrom, strand), 'tid_seq_info', format='t',
data_columns=True, complevel=1, complib='blosc')
# sp.call(['ptrepack', orig_store_name, seq_info_hdf%(chrom,strand)]) # repack for efficiency
# os.remove(orig_store_name)
if opts.verbose > 1:
with log_lock:
logprint('%s (%s strand) complete' % (chrom, strand))
for inbam in inbams:
inbam.close()
return tid_summary
def getCountVectorData(bedfile, bamList, bamIDs):
# read BED file to iterator of SegmentChain objects
bed_segmentChains = list(BED_Reader(open(bedfile)))
# create list of tuples (SegmentChain, [count_vectors/BAM])
countVectorData = []
# iterate through ROIs
for index, segment in enumerate(bed_segmentChains):
# prints in terminal progress every 100 ROIs processed
if index % 100 == 0:
#printer.write("Processed %s regions of interest" % index)
print("Processed %s regions of interest" % index)
# create generator of BAMGenomeArray objects
print(bamList)
BAM_al = (BAMGenomeArray(bamfile) for bamfile in bamList)
# list of count vectors per BAM
count_vec = [segment.get_counts(al) for al in BAM_al]
# create recorded array, columns are count vectors
# MUST call by BAM name, NOT ind
count_array = np.core.records.fromarrays(count_vec, names=bamIDs)
# add tuple to countVectorData list
countVectorData.append((segment, count_array))
#printer.write("Completed processing data.")
print("Completed processing data.")
return countVectorData
def __init__(self,bamfiles,mapping):
"""Create HashedReadBAMGenomeArray
Parameters
----------
bamfile : list
An list of open :py:class:`pysam.AlignmentFile` s. Note: the
corresponding `BAM`_ files must be sorted and indexed by `samtools`_.
mapping : func
Mapping function that determines how each read alignment is mapped to a
count at a genomic position. Returns a list of pysam.AlignedReads and a
dict of count vectors corresponding to the number of mapped read counts
at each position, keyed according to a supplied function. Must have a
list of valid keys stored as mapping.read_keys.
Typically generated using ReadKeyMapFactory().
"""
BAMGenomeArray.__init__(self,bamfiles,mapping)
def __init__(self, bamfiles, mapping):
"""Create HashedReadBAMGenomeArray
Parameters
----------
bamfile : list
An list of open :py:class:`pysam.AlignmentFile` s. Note: the
corresponding `BAM`_ files must be sorted and indexed by `samtools`_.
mapping : func
Mapping function that determines how each read alignment is mapped to a
count at a genomic position. Returns a list of pysam.AlignedReads and a
dict of count vectors corresponding to the number of mapped read counts
at each position, keyed according to a supplied function. Must have a
list of valid keys stored as mapping.read_keys.
Typically generated using ReadKeyMapFactory().
"""
BAMGenomeArray.__init__(self, bamfiles, mapping=mapping)
def test_variable_stratified_mapping_plus(self):
offsets = {
26 : 6,
27 : 22,
28 : 13,
29 : 4,
30 : 5
}
chains = {
"fw" : SegmentChain(GenomicSegment('chrII',392959,393180,'+'),
GenomicSegment('chrII',393510,394742,'+'),
GenomicSegment('chrII',394860,394901,'+'),
ID='YBR078W_mRNA'),
"rc" : SegmentChain(GenomicSegment('chrVIII',189061,189749,'-'),
GenomicSegment('chrVIII',189850,190017,'-'),
ID='YHR041C_mRNA')
}
expected = {
"fw" : numpy.loadtxt(resource_filename("plastid","test/data/stratmap/strat_fw_vec.txt"),delimiter="\t"),
"rc" : numpy.loadtxt(resource_filename("plastid","test/data/stratmap/strat_rc_vec.txt"),delimiter="\t"),
}
ga = BAMGenomeArray([resource_filename("plastid","test/data/stratmap/strat.bam")])
ga.set_mapping(StratifiedVariableFivePrimeMapFactory(offsets,26,30))
def _get_tid_info(tup):
"""For each transcript on this chromosome/strand, identifies every sub-sequence of the appropriate length (fpsize), converts it to an integer,
identifies the number of reads mapping to that position, and outputs all of that information to a pandas HDF store."""
(chrom, strand) = tup
inbams = [pysam.Samfile(infile, 'rb') for infile in opts.bamfiles]
gnd = BAMGenomeArray(inbams, mapping=FivePrimeMapFactory(psite))
# map to roughly the center of each read so that identical sequences that cross different splice sites
# (on different transcripts) still end up mapping to the same place
gnd.add_filter('size', SizeFilterFactory(opts.minlen, opts.maxlen))
tid_seq_info = []
tid_summary = pd.DataFrame(
{
'chrom': chrom,
'strand': strand,
'n_psite': -1,
'n_reads': -1,
'peak_reads': -1,
'dropped': ''
},
index=pd.Index(bedlinedict[(chrom, strand)].keys(), name='tid'))
for (tid, line) in bedlinedict[(chrom, strand)].iteritems():
currtrans = SegmentChain.from_bed(line)
curr_pos_list = currtrans.get_position_list() # not in stranded order!
if strand == '-':
curr_pos_list = curr_pos_list[::-1]
n_psite = len(curr_pos_list) + 1 - fpsize
tid_summary.at[tid, 'n_psite'] = n_psite
if n_psite > 0:
curr_counts = np.array(currtrans.get_counts(gnd))[psite:n_psite +
psite]
# if((curr_counts>0).any()):
sumcounts = curr_counts.sum()
maxcounts = curr_counts.max()
tid_summary.at[tid, 'n_reads'] = sumcounts
tid_summary.at[tid, 'peak_reads'] = maxcounts
if sumcounts >= opts.minreads:
if maxcounts < sumcounts * opts.peakfrac:
numseq = np.array(
list(
currtrans.get_sequence(genome).upper().translate(
str_dict)))
curr_seq = ''.join(numseq)
tid_seq_info.append(
pd.DataFrame({
'tid':
tid,
'genpos':
curr_pos_list[psite:n_psite + psite],
'seq':
np.array([(int(curr_seq[i:i + fpsize], 4) if 'N'
not in curr_seq[i:i + fpsize] else -1)
for i in xrange(n_psite)],
dtype=np.int64),
'reads':
curr_counts
}))
else:
tid_summary.at[tid, 'dropped'] = 'peakfrac'
else:
tid_summary.at[tid, 'dropped'] = 'lowreads'
if tid_seq_info: # don't bother saving anything if there's nothing to save
pd.concat(tid_seq_info,
ignore_index=True).to_hdf(seq_info_hdf % (chrom, strand),
'tid_seq_info',
format='t',
data_columns=True,
complevel=1,
complib='blosc')
# sp.call(['ptrepack', orig_store_name, seq_info_hdf%(chrom,strand)]) # repack for efficiency
# os.remove(orig_store_name)
if opts.verbose > 1:
with log_lock:
logprint('%s (%s strand) complete' % (chrom, strand))
for inbam in inbams:
inbam.close()
return tid_summary
