def handle_codon_usage(genes):
""" Generate codon usage histograms
"""
def extract(marker, data):
""" Extract marked codon usage from each gene
"""
out = []
for gene, codu in data.items():
if not codu[marker] is None:
out.append(codu[marker])
return out
print('Computing codon statistics')
dnana = DNAAnalyzer(strict=False)
data = dnana.get_gene_codon_usages(genes)
plot_data = []
bin_width = 0.01
for marker in ['AAA', 'GAA', 'CAA']:
cur = {}
usage = extract(marker, data)
cur['marker'] = marker
cur['counts'], cur['edges'] = do_binning(usage, bin_width)
plot_data.append(cur)
json.dump(plot_data, open('results/gene_codon_usages.json', 'w'))
print('Plotting')
subprocess.check_call(
['Rscript', 'plotting/codon_usage_histogram_maker.R'],
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL)
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)