def find_longest_A_stretch(genes):
threshold = 8
regex = re.compile("(A+A)")
data = []
A_lengths = []
for gene in genes:
stretches = regex.findall(str(gene.seq))
A_lengths.extend([len(s) for s in stretches])
longest_stretch = max(stretches)
if len(longest_stretch) > threshold:
data.append({
'gene_id': gene.id,
'gene_name': utils.extract_gene_name(gene),
'gene_len': len(gene.seq),
'stretch': longest_stretch,
'stretch_len': len(longest_stretch)
})
data = list(reversed(sorted(data, key=operator.itemgetter('stretch'))))
with open('longest_A_stretches.json', 'w') as fd:
json.dump(data, fd)
with open('all_A_stretch_lengths.json', 'w') as fd:
json.dump(A_lengths, fd)
def test_gene_name_extraction(self):
record = SeqRecord(
Seq('AGTC'),
description=
'DDB0191165|DDB_G0267380 |DNA coding sequence|gene: argE on chromosome: 1 position 414980 to 416538'
)
self.assertEqual(utils.extract_gene_name(record), 'argE')
def store_low_CAA_genes(genes):
""" Create list of genes where CAA usage < 0.9
"""
# compute codon usage
print('Computing codon statistics')
dnana = DNAAnalyzer(strict=False)
data = dnana.get_gene_codon_usages(genes)
def compute_norm(gene, *args):
""" Compute normalized occurrence frequency of aa
"""
all_codon_num = dnana._count_codons(str(gene.seq))
aa_num = sum([all_codon_num[codon] for codon in args])
norm = aa_num * 1000 / len(gene.seq)
return norm
avg_codon_freqs = dnana.get_codon_freqs(genes)
print(' LYS freq: %f\n' %
(avg_codon_freqs['AAA'] + avg_codon_freqs['AAG']) +
' GLU freq: %f\n' %
(avg_codon_freqs['GAA'] + avg_codon_freqs['GAG']) +
' GLN freq: %f' % (avg_codon_freqs['CAA'] + avg_codon_freqs['CAG']))
# filter for genes
low_CAA_genes = []
for gene, codu in data.items():
if not codu['CAA'] is None and codu['CAA'] < 0.9:
lys_freq = (compute_norm(gene, 'AAA', 'AAG') / 1000) / (
avg_codon_freqs['AAA'] + avg_codon_freqs['AAG'])
glu_freq = (compute_norm(gene, 'GAA', 'GAG') / 1000) / (
avg_codon_freqs['GAA'] + avg_codon_freqs['GAG'])
gln_freq = (compute_norm(gene, 'CAA', 'CAG') / 1000) / (
avg_codon_freqs['CAA'] + avg_codon_freqs['CAG'])
low_CAA_genes.append(
(gene.id, extract_gene_name(gene), lys_freq, codu['AAA'],
glu_freq, codu['GAA'], gln_freq, codu['CAA']))
# store results
with open('results/low_CAA_genes.csv', 'w') as fd:
wrtr = csv.writer(fd)
wrtr.writerow([
'ID', 'name', 'LYS rel freq', 'CU: AAA', 'GLU rel freq', 'CU: GAA',
'GLN rel freq', 'CU: CAA'
])
for entry in low_CAA_genes:
wrtr.writerow(entry)
def get_direct_annotation(self, record):
""" Extract direct annotation information from amigo query result
"""
soup = self._query_amigo(utils.extract_gene_name(record))
table = soup.find('table',
attrs={'class': 'bbop-js-search-pane-results-table'})
table_body = table.find('tbody')
rows = table_body.find_all('tr')
# only check first row
annotations = []
for ele in rows[0].find_all('a'):
if ele['href'].startswith(
'http://amigo.geneontology.org/amigo/term/GO:'):
annotations.append(ele.text.lower())
return annotations
def get_groupname(self, record):
""" Choose "best" annotation out of list of possible ones
"""
gnames = []
gnames.append('all')
egn = extract_gene_name(record)
if egn.endswith('_RTE'): gnames.append('rte')
annos = ' | '.join(record.annotations['manual'])
for kw in self.keywords:
if kw.lower() in annos.lower():
gnames.append(kw)
if len(gnames) == 1: gnames.append('other')
return gnames
def apply(self, record):
name = extract_gene_name(record)
return not name.endswith('_RTE')
def apply(self, record):
name = extract_gene_name(record)
return not name.startswith('DDB_')