def _contig_profile(alignment, platform, genome_len):
"""
Computes alignment profile
"""
max_aln_err = cfg.vals["err_modes"][platform]["max_aln_error"]
aln_errors = []
profile = [Profile() for _ in xrange(genome_len)]
for aln in alignment:
#if aln.err_rate > max_aln_err: continue
aln_errors.append(aln.err_rate)
#after gap shifting it is possible that
#two gaps are aligned against each other
qry_seq = shift_gaps(aln.trg_seq, aln.qry_seq)
trg_seq = shift_gaps(qry_seq, aln.trg_seq)
trg_pos = aln.trg_start
for trg_nuc, qry_nuc in izip(trg_seq, qry_seq):
if trg_nuc == "-":
trg_pos -= 1
if trg_pos >= genome_len:
trg_pos -= genome_len
prof_elem = profile[trg_pos]
if trg_nuc == "-" and qry_nuc != "-":
prof_elem.insertions[aln.qry_id] += qry_nuc
else:
prof_elem.nucl = trg_nuc
prof_elem.matches[qry_nuc] += 1
trg_pos += 1
return profile, aln_errors
def _contig_profile(alignment, platform, genome_len):
"""
Computes alignment profile
"""
#max_aln_err = config.vals["err_modes"][platform]["max_aln_error"]
aln_errors = []
profile = [Profile() for _ in xrange(genome_len)]
for aln in alignment:
#if aln.err_rate > max_aln_err: continue
aln_errors.append(aln.err_rate)
qry_seq = shift_gaps(aln.trg_seq, aln.qry_seq)
trg_seq = shift_gaps(qry_seq, aln.trg_seq)
#qry_seq = aln.qry_seq
#trg_seq = aln.trg_seq
trg_pos = aln.trg_start
for trg_nuc, qry_nuc in izip(trg_seq, qry_seq):
if trg_nuc == "-":
trg_pos -= 1
if trg_pos >= genome_len:
trg_pos -= genome_len
prof_elem = profile[trg_pos]
if trg_nuc == "-":
prof_elem.insertions[qry_nuc] += 1
else:
prof_elem.nucl = trg_nuc
prof_elem.matches[qry_nuc] += 1
trg_pos += 1
return profile, aln_errors
def _contig_profile(alignment, platform):
"""
Computes alignment profile
"""
if not alignment:
return []
genome_len = alignment[0].trg_len
aln_errors = []
profile = [Profile() for _ in range(genome_len)]
#max_aln_err = cfg.vals["err_modes"][platform]["max_aln_error"]
for aln in alignment:
#if aln.err_rate > max_aln_err: continue
aln_errors.append(aln.err_rate)
#after gap shifting it is possible that
#two gaps are aligned against each other
qry_seq = shift_gaps(aln.trg_seq, aln.qry_seq)
trg_seq = shift_gaps(qry_seq, aln.trg_seq)
trg_pos = aln.trg_start
for trg_nuc, qry_nuc in zip(trg_seq, qry_seq):
if trg_nuc == "-":
trg_pos -= 1
if trg_pos >= genome_len:
trg_pos -= genome_len
#total += 1
prof_elem = profile[trg_pos]
if trg_nuc == "-" and qry_nuc != "-":
prof_elem.insertions[aln.qry_id] += qry_nuc
else:
prof_elem.nucl = trg_nuc
prof_elem.matches[qry_nuc] += 1
trg_pos += 1
#print "len", genome_len, "median coverage", cov_threshold
#print "total bases: ", total, "discarded bases: ", discarded
#print "filtered", float(discarded) / total
#print ""
return profile, aln_errors
def _compute_profile(alignment, platform, genome_len):
"""
Computes alignment profile
"""
max_aln_err = cfg.vals["err_modes"][platform]["max_aln_error"]
min_aln_len = cfg.vals["min_polish_aln_len"]
aln_errors = []
#filtered = 0
profile = [ProfileInfo() for _ in range(genome_len)]
for aln in alignment:
if aln.err_rate > max_aln_err or len(aln.qry_seq) < min_aln_len:
#filtered += 1
continue
aln_errors.append(aln.err_rate)
qry_seq = shift_gaps(aln.trg_seq, aln.qry_seq)
trg_seq = shift_gaps(qry_seq, aln.trg_seq)
trg_pos = aln.trg_start
for trg_nuc, qry_nuc in zip(trg_seq, qry_seq):
if trg_nuc == "-":
trg_pos -= 1
if trg_pos >= genome_len:
trg_pos -= genome_len
prof_elem = profile[trg_pos]
if trg_nuc == "-":
prof_elem.num_inserts += 1
else:
prof_elem.nucl = trg_nuc
prof_elem.coverage += 1
if qry_nuc == "-":
prof_elem.num_deletions += 1
elif trg_nuc != qry_nuc:
prof_elem.num_missmatch += 1
trg_pos += 1
#logger.debug("Filtered: {0} out of {1}".format(filtered, len(alignment)))
return profile, aln_errors
def _compute_profile(alignment, ref_sequence):
"""
Computes alignment profile
"""
if len(alignment) == 0:
raise Exception("No alignmemnts!")
genome_len = alignment[0].trg_len
#max_aln_err = cfg.vals["err_modes"][platform]["max_aln_error"]
min_aln_len = cfg.vals["min_polish_aln_len"]
aln_errors = []
#filtered = 0
profile = [ProfileInfo() for _ in range(genome_len)]
for i in range(genome_len):
profile[i].nucl = ref_sequence[i]
for aln in alignment:
#if aln.err_rate > max_aln_err or len(aln.qry_seq) < min_aln_len:
if len(aln.qry_seq) < min_aln_len:
#filtered += 1
continue
aln_errors.append(aln.err_rate)
qry_seq = shift_gaps(aln.trg_seq, aln.qry_seq)
trg_seq = shift_gaps(qry_seq, aln.trg_seq)
trg_pos = aln.trg_start
for trg_nuc, qry_nuc in zip(trg_seq, qry_seq):
if trg_nuc == "-":
trg_pos -= 1
#if trg_pos >= genome_len:
# trg_pos -= genome_len
prof_elem = profile[trg_pos]
if trg_nuc == "-":
prof_elem.insertions[aln.qry_id] += qry_nuc
#prof_elem.num_inserts += 1
else:
#prof_elem.nucl = trg_nuc
prof_elem.coverage += 1
if qry_nuc == "-":
prof_elem.num_deletions += 1
elif trg_nuc != qry_nuc:
prof_elem.num_missmatch += 1
trg_pos += 1
for i in range(genome_len):
for ins_read, ins_str in profile[i].insertions.items():
profile[i].propagated_ins += 1
span = len(ins_str)
for j in range(max(0, i - span), i):
profile[j].propagated_ins += 1
for j in range(i + 1, min(i + span + 1, genome_len)):
profile[j].propagated_ins += 1
#logger.debug("Filtered: {0} out of {1}".format(filtered, len(alignment)))
return profile, aln_errors
