def make_reference(data_folder,
adaID,
fragments,
refname,
VERBOSE=0,
summary=True):
'''Make reference sequence trimmed to the necessary parts'''
from hivwholeseq.reference import load_custom_reference
seq = load_custom_reference(refname)
output_filename = get_reference_premap_filename(data_folder, adaID)
if fragments is None:
seq_trim = seq
else:
# Look for the first fwd and the last rev primers to trim the reference
# NOTE: this works even if F1 or F6 are missing (e.g. only F2-5 are seq-ed)!
# If more than one primer is used for the first or last fragment, take the
# longest reference
from hivwholeseq.data.primers import primers_PCR, primers_coordinates_HXB2
if '+' in fragments[0]:
fragment_subs = [
fragments[0][:2] + fsub + fragments[0][-1]
for fsub in fragments[0][2:-1].split('+')
]
fr_pos_subs = [
primers_coordinates_HXB2[fsub][0][0] for fsub in fragment_subs
]
fragments[0] = fragment_subs[np.argmin(fr_pos_subs)]
pr_fwd = primers_PCR[fragments[0]][0]
if '+' in fragments[-1]:
fragment_subs = [
fragments[-1][:2] + fsub + fragments[-1][-1]
for fsub in fragments[-1][2:-1].split('+')
]
fr_pos_subs = [
primers_coordinates_HXB2[fsub][1][1] for fsub in fragment_subs
]
fragments[-1] = fragment_subs[np.argmax(fr_pos_subs)]
pr_rev = primers_PCR[fragments[-1]][1]
smat = np.array(seq)
# Get all possible primers from ambiguous nucleotides and get the best match
from hivwholeseq.utils.sequence import expand_ambiguous_seq as eas
pr_fwd_mat = np.array(map(list, eas(pr_fwd)), 'S1')
n_matches_fwd = [
(smat[i:i + len(pr_fwd)] == pr_fwd_mat).sum(axis=1).max()
for i in xrange(len(seq) - len(pr_fwd))
]
pr_fwd_pos = np.argmax(n_matches_fwd)
pr_rev_mat = np.array(map(list, eas(pr_rev)), 'S1')
n_matches_rev = [
(smat[i:i + len(pr_rev)] == pr_rev_mat).sum(axis=1).max()
for i in xrange(pr_fwd_pos + len(pr_fwd),
len(seq) - len(pr_rev))
]
# Here you come from the right, i.e. look in the 3' LTR first
pr_rev_pos = len(seq) - len(pr_rev) - 1 - np.argmax(
n_matches_rev[::-1])
output = [['Reference name:', refname]]
output.append(['FWD primer:', fragments[0], str(pr_fwd_pos), pr_fwd])
output.append(['REV primer:', fragments[-1], str(pr_rev_pos), pr_rev])
output = '\n'.join(map(' '.join, output))
if VERBOSE:
print output
if summary:
with open(get_premap_summary_filename(data_folder, adaID),
'a') as f:
f.write(output)
f.write('\n')
# The reference includes both the first fwd primer and the last rev one
seq_trim = seq[pr_fwd_pos:pr_rev_pos + len(pr_rev)]
seq_trim.id = '_'.join(
[seq_trim.id,
str(pr_fwd_pos + 1),
str(pr_rev_pos + len(pr_rev))])
seq_trim.name = '_'.join([
seq_trim.name,
str(pr_fwd_pos + 1),
str(pr_rev_pos + len(pr_rev))
])
seq_trim.description = ' '.join([
seq_trim.description, 'from',
str(pr_fwd_pos + 1), 'to',
str(pr_rev_pos + len(pr_rev)),
'(indices from 1, extremes included)'
])
SeqIO.write(seq_trim, output_filename, 'fasta')
if summary:
with open(get_premap_summary_filename(data_folder, adaID), 'a') as f:
f.write('Reference sequence written to: ' + output_filename)
f.write('\n')
def trim_and_divide_reads(data_folder, adaID, n_cycles, fragments,
maxreads=-1, VERBOSE=0,
minisize=100,
include_tests=False, summary=True):
'''Trim reads and divide them into fragments'''
if VERBOSE:
print 'Trim and divide into fragments: adaID '+adaID+', fragments: '+\
' '.join(fragments)
if summary:
with open(get_divide_summary_filename(data_folder, adaID), 'a') as f:
f.write('Fragments used: '+' '.join(fragments)+'\n')
ref_filename = get_reference_premap_filename(data_folder, adaID)
refseq = SeqIO.read(ref_filename, 'fasta')
smat = np.array(refseq, 'S1')
len_reference = len(refseq)
# Get the positions of fragment start/end, w/ and w/o primers
frags_pos = get_fragment_positions(smat, fragments)
store_reference_fragmented(data_folder, adaID, refseq,
dict(zip(fragments, frags_pos['trim'])))
if summary:
with open(get_divide_summary_filename(data_folder, adaID), 'a') as f:
f.write('Primer positions (for fragments):\n')
for (fragment, poss_full, poss_trim) in izip(fragments,
frags_pos['full'],
frags_pos['trim']):
f.write(fragment+': fwd '+str(poss_full[0])+' '+str(poss_trim[0])+\
', rev '+str(poss_trim[1])+' '+str(poss_full[1])+'\n')
write_fragment_positions(data_folder, adaID, fragments, frags_pos)
# Get the positions of the unwanted outer primers (in case we DO nested PCR
# for that fragment)
# NOTE: the LTRs make no problem, because the rev outer primer of F6
# is not in the reference anymore if F6 has undergone nested PCR
# FIXME: this might not work if we have mixed fragments (e.g. F5a+b) AND nesting
from re import findall
primers_out = {'fwd': [], 'rev': []}
for i, fr in enumerate(fragments):
if (i != 0) and findall(r'F[2-6][a-z]?i', fr):
primers_out['fwd'].append(fr[:-1]+'o')
if (i != len(fragments) - 1) and findall(r'F[1-5][a-z]?i', fr):
primers_out['rev'].append(fr[:-1]+'o')
# Get all possible unambiguous primers for the unwanted outer primers
from hivwholeseq.data.primers import primers_PCR
from hivwholeseq.utils.sequence import expand_ambiguous_seq as eas
primers_out_seq = {'fwd': [np.array(map(list, eas(primers_PCR[fr][0])),
'S1', ndmin=2)
for fr in primers_out['fwd']],
'rev': [np.array(map(list, eas(primers_PCR[fr][1])),
'S1', ndmin=2)
for fr in primers_out['rev']],
}
primers_out_pos = {'fwd': [], 'rev': []}
if primers_out['fwd']:
primers_out_pos['fwd'] = map(itemgetter(0),
get_primer_positions(smat,
primers_out['fwd'], 'fwd'))
if primers_out['rev']:
primers_out_pos['rev'] = map(itemgetter(1),
get_primer_positions(smat,
primers_out['rev'], 'rev'))
# Input and output files
input_filename = get_premapped_filename(data_folder, adaID, type='bam')
if not os.path.isfile(input_filename):
convert_sam_to_bam(input_filename)
output_filenames = get_divided_filenames(data_folder, adaID, fragments, type='bam')
with pysam.Samfile(input_filename, 'rb') as bamfile:
try:
file_handles = [pysam.Samfile(ofn, 'wb', template=bamfile)
for ofn in output_filenames[:len(fragments)]]
fo_am = pysam.Samfile(output_filenames[-4], 'wb', template=bamfile)
fo_cm = pysam.Samfile(output_filenames[-3], 'wb', template=bamfile)
fo_um = pysam.Samfile(output_filenames[-2], 'wb', template=bamfile)
fo_lq = pysam.Samfile(output_filenames[-1], 'wb', template=bamfile)
# Iterate over the mapped reads and assign fragments
n_mapped = [0 for fragment in fragments]
n_unmapped = 0
n_crossfrag = 0
n_ambiguous = 0
n_outer = 0
n_lowq = 0
for irp, reads in enumerate(pair_generator(bamfile)):
if irp == maxreads:
if VERBOSE:
print 'Maximal number of read pairs reached:', maxreads
break
if VERBOSE >= 2:
if not ((irp+1) % 10000):
print irp+1
i_fwd = reads[0].is_reverse
# If unmapped or unpaired, mini, or insert size mini, or
# divergent read pair (fully cross-overlapping), discard
if reads[0].is_unmapped or (not reads[0].is_proper_pair) or \
reads[1].is_unmapped or (not reads[1].is_proper_pair) or \
(reads[0].rlen < 50) or (reads[1].rlen < 50) or \
(reads[i_fwd].isize < minisize):
if VERBOSE >= 3:
print 'Read pair unmapped/unpaired/tiny/divergent:', reads[0].qname
n_unmapped += 1
fo_um.write(reads[0])
fo_um.write(reads[1])
continue
# If the insert is a misamplification from the outer primers
# in fragments that underwent nested PCR,
# trash it (it will have skewed amplification anyway). We cannot
# find all of those, rather only the ones still carrying the
# primer itself (some others have lost it while shearing). For
# those, no matter what happens at the end (reading into adapters,
# etc.), ONE of the reads in the pair will start exactly with one
# outer primer: if the rev read with a rev primer, if the fwd
# with a fwd one. Test all six.
if (len(primers_out_pos['fwd']) or len(primers_out_pos['rev'])) and \
test_outer_primer(reads,
primers_out_pos, primers_out_seq,
len_reference):
if VERBOSE >= 3:
print 'Read pair from outer primer:', reads[0].qname
n_outer += 1
fo_um.write(reads[0])
fo_um.write(reads[1])
continue
# FIXME: the following becomes a bit harder when we mix parallel
# PCRs, e.g. F5a+b, to get more product
# Assign to a fragment now, so that primer trimming is faster
pair_identity = assign_to_fragment(reads, frags_pos['full'],
VERBOSE=VERBOSE)
# 1. If no fragments are possible (e.g. one read crosses the
# fragment boundary, they map to different fragments), dump it
# into a special bucket
if pair_identity == 'cross':
n_crossfrag += 1
fo_cm.write(reads[0])
fo_cm.write(reads[1])
continue
# 2. If 2+ fragments are possible (tie), put into a special bucket
# (essentially excluded, because we want two independent measurements
# in the overlapping region, but we might want to recover them)
elif pair_identity == 'ambiguous':
n_ambiguous += 1
fo_am.write(reads[0])
fo_am.write(reads[1])
continue
# 3. If the intersection is a single fragment, good: trim the primers
# NB: n_frag is the index IN THE POOL. If we sequence only F2-F5, F2 is n_frag = 0
n_frag = int(pair_identity)
frag_pos = frags_pos['trim'][n_frag]
if not np.isscalar(frag_pos[0]):
frag_pos = [frag_pos[0]['inner'], frag_pos[1]['inner']]
trashed_primers = trim_primers(reads, frag_pos,
include_tests=include_tests)
if trashed_primers or (reads[i_fwd].isize < 100):
n_unmapped += 1
if VERBOSE >= 3:
print 'Read pair is mismapped:', reads[0].qname
fo_um.write(reads[0])
fo_um.write(reads[1])
continue
# Quality trimming: if no decently long pair survives, trash
#trashed_quality = main_block_low_quality(reads, phred_min=20,
# include_tests=include_tests)
trashed_quality = trim_low_quality(reads, phred_min=20,
include_tests=include_tests)
if trashed_quality or (reads[i_fwd].isize < 100):
n_lowq += 1
if VERBOSE >= 3:
print 'Read pair has low phred quality:', reads[0].qname
fo_lq.write(reads[0])
fo_lq.write(reads[1])
continue
# Check for cross-overhangs or COH (reading into the adapters)
# --------------->
# <-----------
# In that case, trim to perfect overlap.
if test_coh(reads, VERBOSE=False):
trim_coh(reads, trim=0, include_tests=include_tests)
# Change coordinates into the fragmented reference (primer-trimmed)
for read in reads:
read.pos -= frag_pos[0]
read.mpos -= frag_pos[0]
# Here the tests
if include_tests:
lfr = frags_pos['trim'][n_frag][1] - frags_pos['trim'][n_frag][0]
if test_sanity(reads, n_frag, lfr):
print 'Tests failed:', reads[0].qname
import ipdb; ipdb.set_trace()
# There we go!
n_mapped[n_frag] += 1
file_handles[n_frag].write(reads[0])
file_handles[n_frag].write(reads[1])
finally:
for f in file_handles:
f.close()
fo_am.close()
fo_cm.close()
fo_um.close()
fo_lq.close()
if VERBOSE:
print 'Trim and divide results: adaID '+adaID
print 'Total:\t\t', irp
print 'Mapped:\t\t', sum(n_mapped), n_mapped
print 'Unmapped/unpaired/tiny:\t', n_unmapped
print 'Outer primer\t', n_outer
print 'Crossfrag:\t', n_crossfrag
print 'Ambiguous:\t', n_ambiguous
print 'Low-quality:\t', n_lowq
# Write summary to file
if summary:
with open(get_divide_summary_filename(data_folder, adaID), 'a') as f:
f.write('\n')
f.write('Trim and divide results: adaID '+adaID+'\n')
f.write('Total:\t\t'+str(irp + 1)+'\n')
f.write('Mapped:\t\t'+str(sum(n_mapped))+' '+str(n_mapped)+'\n')
f.write('Unmapped/unpaired/tiny insert:\t'+str(n_unmapped)+'\n')
f.write('Outer primer\t'+str(n_outer)+'\n')
f.write('Crossfrag:\t'+str(n_crossfrag)+'\n')
f.write('Ambiguous:\t'+str(n_ambiguous)+'\n')
f.write('Low-quality:\t'+str(n_lowq)+'\n')
def get_primer_positions(smat, fragments, type='both'):
'''Get the primer positions for fwd, rev, or both primers'''
from hivwholeseq.data.primers import primers_PCR
from hivwholeseq.utils.sequence import expand_ambiguous_seq as eas
# j controls the direction: j = 0 --> FWD, j = 1 --> REV
types = {'fwd': [0], 'rev': [1], 'both': [0, 1]}
js = types[type]
primer_poss = [[], []]
for j in js:
pr_old_pos = 0
pr_old = ''
for ifr, fragment in enumerate(fragments):
# Sometimes, the PCR is performed with different primer sets in parallel
# to get more DNA, so we must take the innermost to be conservative (we
# lose some reads, but the ones we keep are good).
# NOTE: F6 is not ambiguous, so we do not treat it specially here (see below)
if '+' in fragment:
if fragment[-1] == 'o':
pcs = pcos
elif fragment[-1] == 'i':
pcs = pcis
else:
raise ValueError('Neither PCR1 nor PCR2??')
fragment_subs = [fragment[:2]+fsub for fsub in fragment[2:-1].split('+')]
pco_inn = [pcs[fsub][j][not j] for fsub in fragment_subs]
if not j:
fragment_inn = fragment_subs[np.argmax(pco_inn)]+fragment[-1]
fragment_out = fragment_subs[np.argmin(pco_inn)]+fragment[-1]
else:
fragment_inn = fragment_subs[np.argmin(pco_inn)]+fragment[-1]
fragment_out = fragment_subs[np.argmax(pco_inn)]+fragment[-1]
if VERBOSE >= 3:
print j, fragment_subs, pco_inn, fragment_inn, fragment_out
pr_pos_pair = {}
# Get the left first
if not j:
fragment = fragment_out
label = 'outer'
else:
fragment = fragment_inn
label = 'inner'
# Expand ambiguous primers in a list of all possible unambiguous ones,
# and look for the best approximate match between all primers and a
# sliding window in the HIV genome
pr = primers_PCR[fragment][j]
pr_mat = np.array(map(list, eas(pr)), 'S1')
n_matches = [(smat[i: i + len(pr)] == pr_mat).sum(axis=1).max()
for i in xrange(pr_old_pos + len(pr_old),
len(smat) - len(pr) + 1)]
pr_pos = pr_old_pos + len(pr_old) + np.argmax(n_matches)
pr_pos_pair[label] = [pr_pos, pr_pos + len(pr)]
# Get the right one second
if not j:
fragment = fragment_inn
label = 'inner'
else:
fragment = fragment_out
label = 'outer'
pr = primers_PCR[fragment][j]
pr_mat = np.array(map(list, eas(pr)), 'S1')
n_matches = [(smat[i: i + len(pr)] == pr_mat).sum(axis=1).max()
for i in xrange(pr_pos,
len(smat) - len(pr) + 1)]
pr_pos = pr_pos + np.argmax(n_matches)
pr_pos_pair[label] = [pr_pos, pr_pos + len(pr)]
else:
# Expand ambiguous primers in a list of all possible unambiguous ones,
# and look for the best approximate match between all primers and a
# sliding window in the HIV genome
pr = primers_PCR[fragment][j]
pr_mat = np.array(map(list, eas(pr)), 'S1')
n_matches = [(smat[i: i + len(pr)] == pr_mat).sum(axis=1).max()
for i in xrange(pr_old_pos + len(pr_old),
len(smat) - len(pr) + 1)]
# NOTE: F6 rev lies in the LTR, so we risk reading the other LTR.
# Treat it as a special case: come from the right!
if j and ('F6' in fragment):
pr_pos = len(smat) - len(pr) - np.argmax(n_matches[::-1])
else:
pr_pos = pr_old_pos + len(pr_old) + np.argmax(n_matches)
pr_pos_pair = [pr_pos, pr_pos + len(pr)]
primer_poss[j].append(pr_pos_pair)
pr_old_pos = pr_pos
pr_old = pr
if type != 'both':
return primer_poss[type == 'rev']
else:
return primer_poss
def make_reference(data_folder,
adaID,
fragments,
refname,
VERBOSE=0,
summary=True):
'''Make reference sequence trimmed to the necessary parts'''
from hivwholeseq.reference import load_custom_reference
seq = load_custom_reference(refname)
output_filename = get_reference_premap_filename(data_folder, adaID)
if fragments is None:
seq_trim = seq
else:
# Look for the first fwd and the last rev primers to trim the reference
# NOTE: this works even if F1 or F6 are missing (e.g. only F2-5 are seq-ed)!
# If more than one primer is used for the first or last fragment, take the
# longest reference
from hivwholeseq.data.primers import primers_PCR, primers_coordinates_HXB2
if '+' in fragments[0]:
fragment_subs = [
fragments[0][:2] + fsub + fragments[0][-1]
for fsub in fragments[0][2:-1].split('+')
]
fr_pos_subs = [
primers_coordinates_HXB2[fsub][0][0] for fsub in fragment_subs
]
fragments[0] = fragment_subs[np.argmin(fr_pos_subs)]
pr_fwd = primers_PCR[fragments[0]][0]
if '+' in fragments[-1]:
fragment_subs = [
fragments[-1][:2] + fsub + fragments[-1][-1]
for fsub in fragments[-1][2:-1].split('+')
]
fr_pos_subs = [
primers_coordinates_HXB2[fsub][1][1] for fsub in fragment_subs
]
fragments[-1] = fragment_subs[np.argmax(fr_pos_subs)]
pr_rev = primers_PCR[fragments[-1]][1]
smat = np.array(seq)
# Get all possible primers from ambiguous nucleotides and get the best match
from hivwholeseq.utils.sequence import expand_ambiguous_seq as eas
pr_fwd_mat = np.array(map(list, eas(pr_fwd)), 'S1')
n_matches_fwd = [
(smat[i:i + len(pr_fwd)] == pr_fwd_mat).sum(axis=1).max()
for i in xrange(len(seq) - len(pr_fwd))
]
pr_fwd_pos = np.argmax(n_matches_fwd)
pr_rev_mat = np.array(map(list, eas(pr_rev)), 'S1')
n_matches_rev = [
(smat[i:i + len(pr_rev)] == pr_rev_mat).sum(axis=1).max()
for i in xrange(pr_fwd_pos + len(pr_fwd),
len(seq) - len(pr_rev))
]
# Here you come from the right, i.e. look in the 3' LTR first
pr_rev_pos = len(seq) - len(pr_rev) - 1 - np.argmax(
n_matches_rev[::-1])
output = [['Reference name:', refname]]
output.append(['FWD primer:', fragments[0], str(pr_fwd_pos), pr_fwd])
output.append(['REV primer:', fragments[-1], str(pr_rev_pos), pr_rev])
output = '\n'.join(map(' '.join, output))
if VERBOSE:
print output
if summary:
with open(get_premap_summary_filename(data_folder, adaID),
'a') as f:
f.write(output)
f.write('\n')
# The reference includes both the first fwd primer and the last rev one
seq_trim = seq[pr_fwd_pos:pr_rev_pos + len(pr_rev)]
seq_trim.id = '_'.join(
[seq_trim.id,
str(pr_fwd_pos + 1),
str(pr_rev_pos + len(pr_rev))])
seq_trim.name = '_'.join([
seq_trim.name,
str(pr_fwd_pos + 1),
str(pr_rev_pos + len(pr_rev))
])
seq_trim.description = ' '.join([
seq_trim.description, 'from',
str(pr_fwd_pos + 1), 'to',
str(pr_rev_pos + len(pr_rev)),
'(indices from 1, extremes included)'
])
SeqIO.write(seq_trim, output_filename, 'fasta')
if summary:
with open(get_premap_summary_filename(data_folder, adaID), 'a') as f:
f.write('Reference sequence written to: ' + output_filename)
f.write('\n')