def merge_reads(reads):
""" Generator over merged reads.
:param reads: iterable of reads from FastqReader
:return: a generator with items (merged_bases may be None if merge fails):
(pair_name,
(read1_name, bases, quality),
(read2_name, bases, quality),
merged_bases)
"""
for pair_name, (r1_name, seq1, qual1), (r2_name, seq2, qual2) in reads:
if not (seq1 and seq2):
score = -1
else:
seq2_rev = reverse_and_complement(seq2)
aligned1, aligned2, score = align_it(seq1, seq2_rev, GAP_OPEN_COST,
GAP_EXTEND_COST,
USE_TERMINAL_COST)
if score >= MIN_PAIR_ALIGNMENT_SCORE and aligned1[0] != '-':
aligned_qual1 = align_quality(aligned1, qual1)
aligned_qual2 = align_quality(aligned2, reversed(qual2))
merged = merge_pairs(aligned1,
aligned2,
aligned_qual1,
aligned_qual2,
q_cutoff=Q_CUTOFF)
else:
merged = None
yield (pair_name, (r1_name, seq1, qual1), (r2_name, seq2, qual2),
merged)
def main():
args = parse_args()
with args.source as source:
reader = csv.DictReader(source)
for (run, sample), rows in groupby(reader, itemgetter('run', 'enum')):
sample_name = format_sample_name(run, sample)
filename1 = os.path.join(args.dest, sample_name + '_R1_001.fastq.gz')
filename2 = os.path.join(args.dest, sample_name + '_R2_001.fastq.gz')
print(filename1)
with open(filename1, 'wb') as dest1, open(filename2, 'wb') as dest2:
dest1_zip = GzipFile(fileobj=dest1)
dest2_zip = GzipFile(fileobj=dest2)
for i, row in enumerate(rows):
seq = row['string'].replace('-', '')
for j in range(3):
# Three duplicates so that G2P doesn't ignore it.
prefix = '@M454:01:000000000-AAAAA:1:1101:{}:{}'.format(
10*i + j,
row['count'])
dest1_zip.write(prefix + ' 1:N:0:1\n')
dest2_zip.write(prefix + ' 2:N:0:1\n')
dest1_zip.write(seq + '\n')
dest2_zip.write(reverse_and_complement(seq) + '\n')
dest1_zip.write('+\n')
dest2_zip.write('+\n')
quality = 'A' * len(seq)
dest1_zip.write(quality + '\n')
dest2_zip.write(quality + '\n')
dest1_zip.close()
dest2_zip.close()
print('Done.')
def testSimple(self):
fwd = 'ACTG'
expected = 'CAGT'
rev = reverse_and_complement(fwd)
self.assertEqual(expected, rev)
def merge_reads(reads):
""" Generator over merged reads.
:param reads: iterable of reads from FastqReader
:return: a generator with items (merged_bases may be None if merge fails):
(pair_name,
(read1_name, bases, quality),
(read2_name, bases, quality),
merged_bases)
"""
for pair_name, (r1_name, seq1, qual1), (r2_name, seq2, qual2) in reads:
if not (seq1 and seq2):
score = -1
aligned1 = aligned2 = None
else:
seq2_rev = reverse_and_complement(seq2)
aligned1, aligned2, score = align_it(seq1,
seq2_rev,
GAP_OPEN_COST,
GAP_EXTEND_COST,
USE_TERMINAL_COST)
if score >= MIN_PAIR_ALIGNMENT_SCORE and aligned1[0] != '-':
aligned_qual1 = align_quality(aligned1, qual1)
aligned_qual2 = align_quality(aligned2, reversed(qual2))
merged = merge_pairs(aligned1,
aligned2,
aligned_qual1,
aligned_qual2,
q_cutoff=Q_CUTOFF)
else:
merged = None
yield (pair_name,
(r1_name, seq1, qual1),
(r2_name, seq2, qual2),
merged)
def write_fastq(self, fields, fastq, is_reversed=False):
qname = fields[0]
seq = fields[9]
quality = fields[10]
if is_reversed:
seq = reverse_and_complement(seq)
quality = ''.join(reversed(quality))
fastq.write('@{}\n{}\n+\n{}\n'.format(qname, seq, quality))
def write_fastq(fields, fastq, is_reversed=False):
qname = fields[0]
seq = fields[9]
quality = fields[10]
if is_reversed:
seq = reverse_and_complement(seq)
quality = ''.join(reversed(quality))
fastq.write('@{}\n{}\n+\n{}\n'.format(qname, seq, quality))
def __init__(self, contig_seq: str, target_seq: str):
gap_open_penalty = 15
gap_extend_penalty = 3
use_terminal_gap_penalty = 1
best_acontig = best_atarget = best_target = best_score = None
best_reversed = None
for target_nucs, is_reversed in unpack_mixtures_and_reverse(
target_seq):
aligned_contig, aligned_target, score = align_it(
contig_seq, target_nucs, gap_open_penalty, gap_extend_penalty,
use_terminal_gap_penalty)
if best_score is None or score > best_score:
best_acontig = aligned_contig
best_atarget = aligned_target
best_target = target_nucs
best_score = score
best_reversed = is_reversed
aligned_contig = best_acontig
aligned_target = best_atarget
target_nucs = best_target
self.score = best_score
self.is_reversed = best_reversed
if self.is_reversed:
aligned_contig = reverse_and_complement(aligned_contig)
aligned_target = reverse_and_complement(aligned_target)
match = re.match('-*([^-](.*[^-])?)', aligned_target)
self.start = match.start(1)
end = match.end(1)
self.contig_match = aligned_contig[self.start:end].replace('-', '')
self.dist = Levenshtein.distance(target_nucs, self.contig_match)
stripped_contig = aligned_contig.lstrip('-')
overhang = len(aligned_contig) - len(stripped_contig)
if overhang > 0:
stripped_target = target_nucs[overhang:]
self.end_dist = Levenshtein.distance(stripped_target,
self.contig_match)
else:
stripped_contig = aligned_contig.rstrip('-')
overhang = len(aligned_contig) - len(stripped_contig)
if overhang == 0:
self.end_dist = self.dist
else:
stripped_target = target_nucs[:-overhang]
self.end_dist = Levenshtein.distance(stripped_target,
self.contig_match)
def test_probe_finder_reversed():
target_seq = 'ATCGACCTAGCT'
contig_seq = reverse_and_complement('ATCGACCTGGCTAATTCCAGT')
expected_match = 'ATCGACCTGGCT'
finder = ProbeFinder(contig_seq, target_seq)
assert finder.contig_match == expected_match
assert finder.is_reversed
def main():
fastq_files = [
FastqFile('2130A-HCV_S15_L001_R1_001.fastq', '2130', False,
(FastqSection('HCV2-JFH-1-NS5b', 1, 60, 100),
FastqSection('HCV2-JFH-1-NS5b', 117, 176, 100)),
(CodonMutation(159, 'GTC'), )),
FastqFile('2130A-HCV_S15_L001_R2_001.fastq', '2130', True,
(FastqSection('HCV2-JFH-1-NS5b', 57, 116, 100),
FastqSection('HCV2-JFH-1-NS5b', 171, 230, 100)),
(CodonMutation(159, 'GTC'), )),
FastqFile('2130AMIDI-MidHCV_S16_L001_R1_001.fastq', '2130', False,
(FastqSection('HCV2-JFH-1-NS5b', 231, 313, 100),
FastqSection('HCV2-JFH-1-NS5b', 396, 478, 100)),
(CodonMutation(316, 'AGC'), )),
FastqFile('2130AMIDI-MidHCV_S16_L001_R2_001.fastq', '2130', True,
(FastqSection('HCV2-JFH-1-NS5b', 313, 395, 100),
FastqSection('HCV2-JFH-1-NS5b', 479, 561, 100)),
(CodonMutation(316, 'AGC'), ))
]
projects = ProjectConfig.loadDefault()
for fastq_file in fastq_files:
with open(fastq_file.name, 'w') as f:
next_cluster = 1
for section in fastq_file.sections:
ref_name, ref_start, ref_end = find_coord_pos(
projects, section.coord_name, section.start_pos,
section.end_pos)
ref_nuc_seq = projects.getReference(ref_name)
ref_nuc_section = list(ref_nuc_seq[ref_start:ref_end])
for mutation in fastq_file.mutations:
if section.start_pos <= mutation.pos <= section.end_pos:
section_pos = (mutation.pos - section.start_pos) * 3
ref_nuc_section[section_pos:section_pos + 3] = list(
mutation.codon)
ref_nuc_section = ''.join(ref_nuc_section)
if fastq_file.is_reversed:
ref_nuc_section = reverse_and_complement(ref_nuc_section)
phred_scores = 'A' * (ref_end - ref_start)
file_num = '2' if fastq_file.is_reversed else '1'
for cluster in range(section.count):
f.write(
'@M01234:01:000000000-AAAAA:1:1101:{}:{:04} {}:N:0:1\n'
.format(fastq_file.extract_num, cluster + next_cluster,
file_num))
f.write(ref_nuc_section + '\n')
f.write('+\n')
f.write(phred_scores + '\n')
next_cluster += section.count
def unpack_mixtures_and_reverse(
seq: str) -> typing.Set[typing.Tuple[str, bool]]:
""" Unpack mixture nucleotide codes, and add reverse complements.
:param seq: nucleotide sequence, possibly including mixture codes
:return: unpacked and reversed sequences, along with is_reversed flag
"""
old_mixtures = {''}
for mixture in seq:
new_mixtures = set()
for nuc in mixture_dict.get(mixture, mixture):
for old_mixture in old_mixtures:
new_mixtures.add(old_mixture + nuc)
old_mixtures = new_mixtures
forward_results = {(mixture, False) for mixture in old_mixtures}
reversed_results = {(reverse_and_complement(mixture), True)
for mixture in old_mixtures}
return forward_results | reversed_results
def test_trim(tmpdir):
read1_content = 'TATCTACTAACTGTCGGTCTAC'
read2_content = reverse_and_complement(read1_content)
expected1 = build_fastq(read1_content)
expected2 = build_fastq(read2_content)
tmp_path = Path(str(tmpdir))
fastq1_path = tmp_path / 'read1.fastq'
fastq2_path = tmp_path / 'read2.fastq'
trimmed1_path = tmp_path / 'trimmed1.fastq'
trimmed2_path = tmp_path / 'trimmed2.fastq'
fastq1_path.write_text(expected1)
fastq2_path.write_text(expected2)
trim([fastq1_path, fastq2_path],
'no_bad_cycles.csv',
[str(trimmed1_path), str(trimmed2_path)],
use_gzip=False)
trimmed1 = trimmed1_path.read_text()
trimmed2 = trimmed2_path.read_text()
assert trimmed1 == expected1
assert trimmed2 == expected2
def main():
projects = ProjectConfig.loadDefault()
sections_2100hcv_1, sections_2100hcv_2 = make_random_sections(
'HCV1A-H77-NS5a', 1, 300, projects, 400)
sections_2100v3_1, sections_2100v3_2 = ([
FastqSection('HIV1-B-FR-K03455-seed', 7056, 7312, 50),
FastqSection('HIV1-B-FR-K03455-seed', 7062, 7312, 50)
], [
FastqSection('HIV1-B-FR-K03455-seed', 7123, 7373, 50),
FastqSection('HIV1-B-FR-K03455-seed', 7123, 7376, 50)
])
sections_2100hiv_1, sections_2100hiv_2 = make_random_sections(
'RT', 1, 300, projects, 400)
sections_2160_1, sections_2160_2 = make_random_sections(
'HCV2-JFH-1-NS5b',
1,
230,
projects,
mutations=(CodonMutation(159, 'GTC'), ))
sections_2160midi_1, sections_2160midi_2 = make_random_sections(
'HCV2-JFH-1-NS5b',
231,
561,
projects,
mutations=(CodonMutation(316, 'AGC'), ))
sections_2170_1a_1, sections_2170_1a_2 = make_random_sections(
'HCV-1a', 6258, 9375)
sections_2170_2_1, sections_2170_2_2 = make_random_sections(
'HCV-2a', 6269, 9440)
sections_2180_1, sections_2180_2 = make_random_sections(
'HIV1-B-FR-K03455-seed', 6225, 7757)
hxb2_ref = projects.getReference('HIV1-B-FR-K03455-seed')
projects.config['regions']['HXB2-with-deletion'] = dict(
reference=hxb2_ref[617:928] + hxb2_ref[9358:9652],
is_nucleotide=True,
seed_group=None)
sections_2210_1, sections_2210_2 = make_random_sections(
'HXB2-with-deletion', projects=projects)
fastq_files = [
FastqFile('2010A-V3LOOP_S3_L001_R1_001.fastq', '2010', False,
(FastqSection('HIV1-CON-XX-Consensus-seed', 855, 906, 10),
FastqSection('HIV1-CON-XX-Consensus-seed', 912, 960, 10))),
FastqFile('2010A-V3LOOP_S3_L001_R2_001.fastq', '2010', True,
(FastqSection('HIV1-CON-XX-Consensus-seed', 855, 906, 10),
FastqSection('HIV1-CON-XX-Consensus-seed', 912, 960, 10))),
FastqFile('2020A-GP41_S4_L001_R1_001.fastq', '2020', False,
(FastqSection('HIV1-B-FR-KF716496-seed', 6957, 7065, 10,
(CodonMutation(6981, 'GGGATA'), )), )),
FastqFile('2020A-GP41_S4_L001_R2_001.fastq', '2020', True,
(FastqSection('HIV1-B-FR-KF716496-seed', 6957, 7065, 10,
(CodonMutation(6981, 'GGGATA'), )), )),
FastqFile('2040A-HLA-B_S6_L001_R1_001.fastq', '2040', False,
(FastqSection('HLA-B-seed', 201, 315, 80),
FastqSection('HLA-B-seed', 201, 315, 20,
(CodonMutation(207, 'TCT'), )))),
FastqFile('2040A-HLA-B_S6_L001_R2_001.fastq', '2040', True,
(FastqSection('HLA-B-seed', 201, 315, 80),
FastqSection('HLA-B-seed', 201, 315, 20,
(CodonMutation(207, 'TCT'), )))),
FastqFile(
'2070A-PR_S9_L001_R1_001.fastq', '2070', False,
(FastqSection('PR', 40, 80, 12, (CodonMutation(45, ''), )),
FastqSection('PR', 40, 80, 3,
(CodonMutation(45, ''), CodonMutation(64, ''))))),
FastqFile(
'2070A-PR_S9_L001_R2_001.fastq', '2070', True,
(FastqSection('PR', 40, 80, 12, (CodonMutation(45, ''), )),
FastqSection('PR', 40, 80, 3,
(CodonMutation(45, ''), CodonMutation(64, ''))))),
FastqFile('2100A-HCV-1337B-V3LOOP-PWND-HIV_S12_L001_R1_001.fastq',
'2100', False,
sections_2100hcv_1 + sections_2100v3_1 + sections_2100hiv_1),
FastqFile('2100A-HCV-1337B-V3LOOP-PWND-HIV_S12_L001_R2_001.fastq',
'2100', True,
sections_2100hcv_2 + sections_2100v3_2 + sections_2100hiv_2),
FastqFile('2130A-HCV_S15_L001_R1_001.fastq', '2130', False,
(FastqSection('HCV2-JFH-1-NS5b', 1, 66, 100),
FastqSection('HCV2-JFH-1-NS5b', 115, 181, 100,
(CodonMutation(159, 'GTC'), )))),
FastqFile('2130A-HCV_S15_L001_R2_001.fastq', '2130', True,
(FastqSection('HCV2-JFH-1-NS5b', 51, 114, 100),
FastqSection('HCV2-JFH-1-NS5b', 165, 230, 100))),
FastqFile('2130AMIDI-MidHCV_S16_L001_R1_001.fastq', '2130', False,
(FastqSection('HCV2-JFH-1-NS5b', 231, 315, 100),
FastqSection('HCV2-JFH-1-NS5b', 398, 485, 100))),
FastqFile('2130AMIDI-MidHCV_S16_L001_R2_001.fastq', '2130', True,
(FastqSection('HCV2-JFH-1-NS5b', 305, 397, 100,
(CodonMutation(316, 'AGC'), )),
FastqSection('HCV2-JFH-1-NS5b', 470, 561, 100))),
FastqFile('2140A-HIV_S17_L001_R1_001.fastq', '2140', False,
(FastqSection('PR', 1, 80, 100,
(CodonMutation(24, 'ATA'), )), )),
FastqFile('2140A-HIV_S17_L001_R2_001.fastq', '2140', True,
(FastqSection('PR', 20, 99, 100,
(CodonMutation(24, 'ATA'), )), )),
# Simplify with one_contig.
FastqFile('2160A-HCV_S19_L001_R1_001.fastq', '2160', False,
sections_2160_1),
FastqFile('2160A-HCV_S19_L001_R2_001.fastq', '2160', True,
sections_2160_2),
# Simplify with one_contig.
FastqFile('2160AMIDI-MidHCV_S20_L001_R1_001.fastq', '2160', False,
sections_2160midi_1),
FastqFile('2160AMIDI-MidHCV_S20_L001_R2_001.fastq', '2160', True,
sections_2160midi_2),
# Simplify with two_long_contigs.
FastqFile('2170A-HCV_S21_L001_R1_001.fastq', '2170', False,
sections_2170_1a_1 + sections_2170_2_1),
FastqFile('2170A-HCV_S21_L001_R2_001.fastq', '2170', True,
sections_2170_1a_2 + sections_2170_2_2),
FastqFile('2180A-HIV_S22_L001_R1_001.fastq', '2180', False,
sections_2180_1),
FastqFile('2180A-HIV_S22_L001_R2_001.fastq', '2180', True,
sections_2180_2),
FastqFile('2190A-SARSCOV2_S23_L001_R1_001.fastq', '2190', False,
(FastqSection('SARS-CoV-2-ORF1ab', 4393, 4429, 50,
(CodonMutation(4400, 'TCA'), )),
FastqSection('SARS-CoV-2-ORF1ab', 4393, 4430, 50,
(CodonMutation(4400, 'TCA'), )))),
FastqFile('2190A-SARSCOV2_S23_L001_R2_001.fastq', '2190', True,
(FastqSection('SARS-CoV-2-ORF1ab', 4393, 4429, 50,
(CodonMutation(4400, 'TCA'), )),
FastqSection('SARS-CoV-2-ORF1ab', 4393, 4430, 50,
(CodonMutation(4400, 'TCA'), )))),
FastqFile('2200A-SARSCOV2_S24_L001_R1_001.fastq', '2200', False,
(FastqSection('SARS-CoV-2-nsp1', 20, 66, 100), )),
FastqFile('2200A-SARSCOV2_S24_L001_R2_001.fastq', '2200', True,
(FastqSection('SARS-CoV-2-nsp1', 56, 102, 100), )),
FastqFile('2210A-NFLHIVDNA_S25_L001_R1_001.fastq', '2210', False,
sections_2210_1),
FastqFile('2210A-NFLHIVDNA_S25_L001_R2_001.fastq', '2210', True,
sections_2210_2)
]
for fastq_file in fastq_files:
with open(fastq_file.name, 'w') as f:
next_cluster = 1
for section in fastq_file.sections:
ref_name, ref_start, ref_end = find_coord_pos(
projects, section.coord_name, section.start_pos,
section.end_pos)
ref_nuc_seq = projects.getReference(ref_name)
ref_nuc_section = list(ref_nuc_seq[ref_start:ref_end])
is_nucleotide = ((ref_start, ref_end) == (section.start_pos,
section.end_pos))
for mutation in section.mutations:
if section.start_pos <= mutation.pos <= section.end_pos:
section_pos = mutation.pos - section.start_pos
if not is_nucleotide:
section_pos *= 3
ref_nuc_section[section_pos:section_pos + 3] = list(
mutation.codon)
ref_nuc_section = ''.join(ref_nuc_section)
if fastq_file.is_reversed:
ref_nuc_section = reverse_and_complement(ref_nuc_section)
phred_scores = 'A' * len(ref_nuc_section)
file_num = '2' if fastq_file.is_reversed else '1'
# noinspection PyTypeChecker
for cluster in range(section.count):
f.write(
'@M01234:01:000000000-AAAAA:1:1101:{}:{:04} {}:N:0:1\n'
.format(fastq_file.extract_num, cluster + next_cluster,
file_num))
f.write(ref_nuc_section + '\n')
f.write('+\n')
f.write(phred_scores + '\n')
next_cluster += section.count
""" Reverse a nucleotide sequence and replace with complementary nucleotides.
Mixtures are allowed, as well as *, N, and -.
If you want to compare the result to an expected sequence, put the expected
sequence in reverse_compare.
Source: https://github.com/ArtPoon/bioinfo/blob/master/seqUtils.py#L143
"""
from micall.utils.translation import reverse_and_complement
nuc_seq = ''.join([
"TGTACAAGACCCAACAACAATACAAGAAAAAGTATACATATAGGACCAGGGAGAGCATTTTATGC",
"AACAGGAGAAATAATAGGAGATATAAGACAAGCACATTGT"
])
reverse_compare = ''.join([
"ACAATGTGCTTGTCTTATATCTCCTATTATTTCTCCTGTTGCATAAAATGCTCTCCCTGGTCCTA",
"TATGTATACTTTTTCTTGTATTGTTGTTGGGTCTTGTACA"
])
reverse_seq = reverse_and_complement(nuc_seq)
pairs = zip(reverse_seq, reverse_compare)
diffs = [' ' if a == b else '*' for a, b in pairs]
print 'result ', reverse_seq
print 'diffs ', ''.join(
diffs) if reverse_seq != reverse_compare else 'no diffs'
print 'compare', reverse_compare
""" Reverse a nucleotide sequence and replace with complementary nucleotides.
Mixtures are allowed, as well as *, N, and -.
If you want to compare the result to an expected sequence, put the expected
sequence in reverse_compare.
Source: https://github.com/ArtPoon/bioinfo/blob/master/seqUtils.py#L143
"""
from micall.utils.translation import reverse_and_complement
nuc_seq = ''.join([
"TGTACAAGACCCAACAACAATACAAGAAAAAGTATACATATAGGACCAGGGAGAGCATTTTATGC",
"AACAGGAGAAATAATAGGAGATATAAGACAAGCACATTGT"
])
reverse_compare = ''.join([
"ACAATGTGCTTGTCTTATATCTCCTATTATTTCTCCTGTTGCATAAAATGCTCTCCCTGGTCCTA",
"TATGTATACTTTTTCTTGTATTGTTGTTGGGTCTTGTACA"
])
reverse_seq = reverse_and_complement(nuc_seq)
pairs = zip(reverse_seq, reverse_compare)
diffs = [' ' if a == b else '*' for a, b in pairs]
print 'result ', reverse_seq
print 'diffs ', ''.join(diffs) if reverse_seq != reverse_compare else 'no diffs'
print 'compare', reverse_compare
def main():
fastq_files = [FastqFile('2130A-HCV_S15_L001_R1_001.fastq',
'2130',
False,
(FastqSection('HCV2-JFH-1-NS5b', 1, 60, 100),
FastqSection('HCV2-JFH-1-NS5b', 117, 176, 100)),
(CodonMutation(159, 'GTC'),)),
FastqFile('2130A-HCV_S15_L001_R2_001.fastq',
'2130',
True,
(FastqSection('HCV2-JFH-1-NS5b', 57, 116, 100),
FastqSection('HCV2-JFH-1-NS5b', 171, 230, 100)),
(CodonMutation(159, 'GTC'),)),
FastqFile('2130AMIDI-MidHCV_S16_L001_R1_001.fastq',
'2130',
False,
(FastqSection('HCV2-JFH-1-NS5b', 231, 313, 100),
FastqSection('HCV2-JFH-1-NS5b', 396, 478, 100)),
(CodonMutation(316, 'AGC'),)),
FastqFile('2130AMIDI-MidHCV_S16_L001_R2_001.fastq',
'2130',
True,
(FastqSection('HCV2-JFH-1-NS5b', 313, 395, 100),
FastqSection('HCV2-JFH-1-NS5b', 479, 561, 100)),
(CodonMutation(316, 'AGC'),)),
FastqFile('2140A-HIV_S17_L001_R1_001.fastq',
'2140',
False,
(FastqSection('PR', 1, 80, 100),),
(CodonMutation(24, 'ATA'),)),
FastqFile('2140A-HIV_S17_L001_R2_001.fastq',
'2140',
True,
(FastqSection('PR', 20, 99, 100),),
(CodonMutation(24, 'ATA'),))]
projects = ProjectConfig.loadDefault()
for fastq_file in fastq_files:
with open(fastq_file.name, 'w') as f:
next_cluster = 1
for section in fastq_file.sections:
ref_name, ref_start, ref_end = find_coord_pos(projects,
section.coord_name,
section.start_pos,
section.end_pos)
ref_nuc_seq = projects.getReference(ref_name)
ref_nuc_section = list(ref_nuc_seq[ref_start:ref_end])
for mutation in fastq_file.mutations:
if section.start_pos <= mutation.pos <= section.end_pos:
section_pos = (mutation.pos - section.start_pos) * 3
ref_nuc_section[section_pos:section_pos+3] = list(mutation.codon)
ref_nuc_section = ''.join(ref_nuc_section)
if fastq_file.is_reversed:
ref_nuc_section = reverse_and_complement(ref_nuc_section)
phred_scores = 'A' * (ref_end-ref_start)
file_num = '2' if fastq_file.is_reversed else '1'
for cluster in range(section.count):
f.write('@M01234:01:000000000-AAAAA:1:1101:{}:{:04} {}:N:0:1\n'.format(
fastq_file.extract_num,
cluster + next_cluster,
file_num))
f.write(ref_nuc_section+'\n')
f.write('+\n')
f.write(phred_scores+'\n')
next_cluster += section.count
