Skip to content
Permalink
master
Switch branches/tags
Go to file
 
 
Cannot retrieve contributors at this time
""" Generate BpForms for all of the tRNA in MODOMICS, verify
them, and calculate their properties
:Author: Jonathan Karr <karr@mssm.edu>
:Date: 2019-06-20
:Copyright: 2019, Karr Lab
:License: MIT
"""
from matplotlib import pyplot
import bpforms
import bs4
import csv
import matplotlib
import numpy
import os
import requests
import requests_cache
URL = 'https://iimcb.genesilico.pl/modomics/sequences/'
def run():
# create dict of MODOMICS single character monomer codes
modomics_short_code_to_monomer = {}
for monomer in bpforms.rna_alphabet.monomers.values():
for identifier in monomer.identifiers:
if identifier.ns == 'modomics.short_name':
modomics_short_code_to_monomer[identifier.id] = monomer
modomics_short_code_to_monomer['a'] = bpforms.rna_alphabet.monomers.get('A')
modomics_short_code_to_monomer['c'] = bpforms.rna_alphabet.monomers.get('C')
modomics_short_code_to_monomer['g'] = bpforms.rna_alphabet.monomers.get('G')
modomics_short_code_to_monomer['u'] = bpforms.rna_alphabet.monomers.get('U')
modomics_short_code_to_monomer['b'] = bpforms.rna_alphabet.monomers.get('0522U')
modomics_short_code_to_monomer['B'] = bpforms.rna_alphabet.monomers.get('0C')
modomics_short_code_to_monomer['E'] = bpforms.rna_alphabet.monomers.get('662A')
modomics_short_code_to_monomer['h'] = bpforms.rna_alphabet.monomers.get('21511U')
# modomics_short_code_to_monomer['H'] = bpforms.rna_alphabet.monomers.get('0C')
modomics_short_code_to_monomer['J'] = bpforms.rna_alphabet.monomers.get('0U')
modomics_short_code_to_monomer['l'] = bpforms.rna_alphabet.monomers.get('253U')
modomics_short_code_to_monomer['L'] = bpforms.rna_alphabet.monomers.get('2G')
modomics_short_code_to_monomer['K'] = bpforms.rna_alphabet.monomers.get('1G')
modomics_short_code_to_monomer['M'] = bpforms.rna_alphabet.monomers.get('42C')
# modomics_short_code_to_monomer['N'] = bpforms.rna_alphabet.monomers.get('?U')
modomics_short_code_to_monomer['P'] = bpforms.rna_alphabet.monomers.get('9U')
modomics_short_code_to_monomer['R'] = bpforms.rna_alphabet.monomers.get('22G')
modomics_short_code_to_monomer['T'] = bpforms.rna_alphabet.monomers.get('5U')
modomics_short_code_to_monomer['Z'] = bpforms.rna_alphabet.monomers.get('09U')
modomics_short_code_to_monomer['7'] = bpforms.rna_alphabet.monomers.get('7G')
modomics_short_code_to_monomer['#'] = bpforms.rna_alphabet.monomers.get('0G')
modomics_short_code_to_monomer[':'] = bpforms.rna_alphabet.monomers.get('0A')
modomics_short_code_to_monomer['='] = bpforms.rna_alphabet.monomers.get('6A')
modomics_short_code_to_monomer['?'] = bpforms.rna_alphabet.monomers.get('5C')
modomics_short_code_to_monomer['λ'] = bpforms.rna_alphabet.monomers.get('04C')
modomics_short_code_to_monomer['"'] = bpforms.rna_alphabet.monomers.get('1A')
modomics_short_code_to_monomer["'"] = bpforms.rna_alphabet.monomers.get('3C')
modomics_short_code_to_monomer[','] = bpforms.rna_alphabet.monomers.get('522U')
modomics_short_code_to_monomer['\\'] = bpforms.rna_alphabet.monomers.get('05U')
modomics_short_code_to_monomer['ℑ'] = bpforms.rna_alphabet.monomers.get('00G')
modomics_short_code_to_monomer[']'] = bpforms.rna_alphabet.monomers.get('19U')
modomics_short_code_to_monomer['ˆ'] = bpforms.rna_alphabet.monomers.get('00A')
modomics_short_code_to_monomer['gluQtRNA'] = bpforms.rna_alphabet.monomers.get('105G')
modomics_short_code_to_monomer['m22G'] = bpforms.rna_alphabet.monomers.get('22G')
# modomics_short_code_to_monomer[';'] = bpforms.rna_alphabet.monomers.get('?G')
# modomics_short_code_to_monomer['<'] = bpforms.rna_alphabet.monomers.get('?C')
# create cache for web queries
cache_name = os.path.join('examples', 'modomics')
session = requests_cache.core.CachedSession(cache_name, backend='sqlite', expire_after=None)
session.mount(URL, requests.adapters.HTTPAdapter(max_retries=5))
# parse rRNA and tRNA data
monomer_codes = {}
rrna_forms = run_rrna(session, modomics_short_code_to_monomer, monomer_codes,
os.path.join('examples', 'modomics.rrna.tsv'))
trna_forms, trna_canonical_code_freq, trna_code_freq = run_trna(
session, modomics_short_code_to_monomer, monomer_codes, os.path.join('examples', 'modomics.trna.tsv'))
# plot distribution of tRNA monomeric forms
plot_trna_code_freq(monomer_codes, trna_code_freq)
# return results
return rrna_forms, trna_forms, trna_canonical_code_freq, trna_code_freq
def run_rrna(session, modomics_short_code_to_monomer, monomer_codes, out_filename):
response = session.get(URL, params={
'RNA_type': 'rRNA',
'RNA_subtype': 'all',
'organism': 'all species',
'vis_type': 'Modomics symbols',
})
response.raise_for_status()
doc = bs4.BeautifulSoup(response.text, 'lxml')
table = doc.find('table', {'id': 'tseq'})
tbody = table.find('tbody')
rows = tbody.find_all('tr')
rna_forms = []
for row in rows:
if not isinstance(row, bs4.element.Tag):
continue
cells = row.find_all('td')
rna_form = bpforms.RnaForm()
unsupported_codes = set()
for child in cells[5].children:
if child.name is None or child.name == 'span':
if child.name is None:
text = str(child)
else:
text = child.text
for code in text.strip().replace('-', '').replace('_', ''):
monomer = modomics_short_code_to_monomer.get(code, None)
if monomer is None:
unsupported_codes.add(code)
monomer = bpforms.Monomer(id=code)
else:
monomer_codes[code] = monomer
rna_form.seq.append(monomer)
elif child.name == 'a':
code = child.get('href').replace('/modomics/modifications/', '')
monomer = modomics_short_code_to_monomer.get(code, None)
if monomer is None:
unsupported_codes.add(code)
monomer = bpforms.Monomer(id=code)
else:
monomer_codes[code] = monomer
rna_form.seq.append(monomer)
else:
raise Exception('Unsupported child {}'.format(child.name))
rna_forms.append({
'GenBank': cells[0].find('a').text.strip(),
'Organism': cells[3].text.strip(),
'Organellum': cells[4].text.strip(),
'Type': cells[2].text.strip(),
'Sequence (MODOMICS)': cells[5].text.strip().replace('-', '').replace('_', ''),
})
analyze_form(rna_form, unsupported_codes, rna_forms[-1])
# save results to tab-separated file
save_results(rna_forms, ['GenBank', 'Type'], out_filename)
return rna_forms
def run_trna(session, modomics_short_code_to_monomer, monomer_codes, out_filename):
response = session.get(URL, params={
'RNA_type': 'tRNA',
'RNA_subtype': 'all',
'organism': 'all species',
'vis_type': 'Modomics symbols',
})
response.raise_for_status()
doc = bs4.BeautifulSoup(response.text, 'lxml')
table = doc.find('table', {'id': 'tseq'})
tbody = table.find('tbody')
rows = tbody.find_all('tr')
rna_forms = []
code_freq = {}
canonical_code_freq = {'A': 0, 'C': 0, 'G': 0, 'U': 0}
for row in rows:
cells = row.find_all('td')
rna_form = bpforms.RnaForm()
unsupported_codes = set()
for child in cells[5].children:
if child.name is None or child.name == 'span':
if child.name is None:
text = str(child)
else:
text = child.text
for code in text.strip().replace('-', '').replace('_', ''):
monomer = modomics_short_code_to_monomer.get(code, None)
if monomer is None:
unsupported_codes.add(code)
monomer = bpforms.Monomer(id=code)
else:
monomer_codes[code] = monomer
if code not in code_freq:
code_freq[code] = 0
code_freq[code] += 1
rna_form.seq.append(monomer)
elif child.name == 'a':
code = child.get('href').replace('/modomics/modifications/', '')
monomer = modomics_short_code_to_monomer.get(code, None)
if monomer is None:
unsupported_codes.add(code)
monomer = bpforms.Monomer(id=code)
else:
monomer_codes[code] = monomer
if code not in code_freq:
code_freq[code] = 0
code_freq[code] += 1
rna_form.seq.append(monomer)
else:
raise Exception('Unsupported child {}'.format(child.name))
rna_forms.append({
'Amino acid type': cells[1].text.strip(),
'Anticodon': cells[2].text.strip(),
'Organism': cells[3].text.strip(),
'Organellum': cells[4].text.strip(),
'Sequence (MODOMICS)': cells[5].text.strip().replace('-', '').replace('_', ''),
})
analyze_form(rna_form, unsupported_codes, rna_forms[-1])
canonical_code_freq['A'] += \
rna_forms[-1]['Sequence (IUPAC)'].count('A') \
- rna_form.seq.count(bpforms.rna_alphabet.monomers.A)
canonical_code_freq['C'] += \
rna_forms[-1]['Sequence (IUPAC)'].count('C') \
- rna_form.seq.count(bpforms.rna_alphabet.monomers.C)
canonical_code_freq['G'] += \
rna_forms[-1]['Sequence (IUPAC)'].count('G') \
- rna_form.seq.count(bpforms.rna_alphabet.monomers.G)
canonical_code_freq['U'] += \
rna_forms[-1]['Sequence (IUPAC)'].count('U') \
- rna_form.seq.count(bpforms.rna_alphabet.monomers.U)
# save results to tab-separated file
save_results(rna_forms, ['Amino acid type', 'Anticodon'], out_filename)
with open(os.path.join('examples', 'modomics.trna.canonical-code-freq.tsv'), 'w') as file:
writer = csv.DictWriter(file, fieldnames=['Code', 'Frequency'], dialect='excel-tab')
writer.writeheader()
for code, freq in canonical_code_freq.items():
writer.writerow({'Code': code, 'Frequency': freq})
with open(os.path.join('examples', 'modomics.trna.code-freq.tsv'), 'w') as file:
writer = csv.DictWriter(file, fieldnames=['Code', 'Frequency'], dialect='excel-tab')
writer.writeheader()
for code, freq in code_freq.items():
writer.writerow({'Code': code, 'Frequency': freq})
return rna_forms, canonical_code_freq, code_freq
def analyze_form(rna_form, unsupported_codes, results_dict):
results_dict['Sequence (BpForms)'] = str(rna_form)
results_dict['Sequence (IUPAC)'] = canonical_seq = rna_form.get_canonical_seq()
results_dict['Length'] = len(rna_form.seq)
results_dict['Number of modifications'] = len(rna_form.seq) \
- rna_form.seq.count(bpforms.rna_alphabet.monomers.A) \
- rna_form.seq.count(bpforms.rna_alphabet.monomers.C) \
- rna_form.seq.count(bpforms.rna_alphabet.monomers.G) \
- rna_form.seq.count(bpforms.rna_alphabet.monomers.U)
results_dict['Number of modified A'] = canonical_seq.count('A') - rna_form.seq.count(bpforms.rna_alphabet.monomers.A)
results_dict['Number of modified C'] = canonical_seq.count('C') - rna_form.seq.count(bpforms.rna_alphabet.monomers.C)
results_dict['Number of modified G'] = canonical_seq.count('G') - rna_form.seq.count(bpforms.rna_alphabet.monomers.G)
results_dict['Number of modified U'] = canonical_seq.count('U') - rna_form.seq.count(bpforms.rna_alphabet.monomers.U)
if unsupported_codes:
results_dict['BpForms errors'] = 'MODOMICS sequence uses monomeric forms {}'.format(
', '.join(unsupported_codes))
else:
results_dict['Formula'] = str(rna_form.get_formula())
results_dict['Molecular weight'] = rna_form.get_mol_wt()
results_dict['Charge'] = rna_form.get_charge()
canonical_form = bpforms.RnaForm().from_str(canonical_seq)
results_dict['Canonical formula'] = str(canonical_form.get_formula())
results_dict['Canonical molecular weight'] = canonical_form.get_mol_wt()
results_dict['Canonical charge'] = canonical_form.get_charge()
results_dict['Extra formula'] = str(rna_form.get_formula() - canonical_form.get_formula())
results_dict['Extra molecular weight'] = rna_form.get_mol_wt() - canonical_form.get_mol_wt()
results_dict['Extra charge'] = rna_form.get_charge() - canonical_form.get_charge()
results_dict['BpForms errors'] = ' '.join(rna_form.validate())
def save_results(rna_forms, variable_field_names, out_filename):
with open(out_filename, 'w') as file:
writer = csv.DictWriter(file,
fieldnames=variable_field_names + [
'Organism', 'Organellum',
'Sequence (MODOMICS)',
'Sequence (BpForms)',
'Sequence (IUPAC)',
'Length',
'Number of modifications',
'Number of modified A',
'Number of modified C',
'Number of modified G',
'Number of modified U',
'Formula', 'Molecular weight', 'Charge',
'Canonical formula', 'Canonical molecular weight', 'Canonical charge',
'Extra formula', 'Extra molecular weight', 'Extra charge',
'BpForms errors'],
dialect='excel-tab')
writer.writeheader()
for rna_form in rna_forms:
writer.writerow(rna_form)
def plot_trna_code_freq(monomer_codes, trna_code_freq):
monomer_ids = {}
for code, monomer in bpforms.rna_alphabet.monomers.items():
monomer_ids[monomer] = code
trna_canonical_code_freq = {'A': 0, 'C': 0, 'G': 0, 'U': 0}
for code, count in trna_code_freq.items():
if code in ['A', 'C', 'G', 'U']:
continue
canonical_code = monomer_codes[code].get_canonical_code(monomer_ids)
trna_canonical_code_freq[canonical_code] += count
pyplot.style.use('ggplot')
fig, axes = pyplot.subplots(nrows=1, ncols=2, gridspec_kw={'width_ratios': [1, 16]})
fig.set_size_inches(9.3, 1.5)
plot_codes(trna_canonical_code_freq, monomer_codes,
axes[0], ignore_canonical=False, title='Frequency of modifications')
plot_codes(trna_code_freq, monomer_codes,
axes[1], ignore_canonical=True, title='Frequency of modified monomeric forms')
if not os.path.isdir(os.path.join('bpforms', 'web', 'img', 'example')):
os.makedirs(os.path.join('bpforms', 'web', 'img', 'example'))
fig.savefig(os.path.join('bpforms', 'web', 'img', 'example', 'modomics.trna.code-freq.svg'),
transparent=True,
bbox_inches=matplotlib.transforms.Bbox([[0.4, -0.1], [8.35, 1.5]]))
pyplot.close(fig)
def plot_codes(code_freq, monomer_codes, axis, ignore_canonical=False, use_rel=True,
title=None, x_axis_label=None, y_axis_label='Frequency (%)',
axis_font_size=10, tick_font_size=6, font_family='Raleway',
x_label_pad=None):
monomer_ids = {}
for code, monomer in bpforms.rna_alphabet.monomers.items():
monomer_ids[monomer] = code
id_freqs = []
for code, count in code_freq.items():
if ignore_canonical and code in ['A', 'C', 'G', 'U']:
continue
id_freqs.append((monomer_codes[code].get_canonical_code(monomer_ids),
monomer_codes[code].id,
count))
id_freqs.sort()
y_pos = numpy.arange(len(id_freqs))
freq = numpy.array([id_freq[2] for id_freq in id_freqs])
if use_rel:
freq = freq / numpy.sum(freq) * 100.
x_tick_labels = {id: y_pos for y_pos, (_, id, _) in enumerate(id_freqs)}
bars = axis.bar(y_pos, freq, align='center', alpha=1.0, edgecolor="none")
axis.set_xticks(y_pos)
axis.set_xticklabels(x_tick_labels, rotation=270, fontsize=tick_font_size, fontfamily=font_family)
axis.tick_params(axis='x', pad=2)
if x_axis_label:
axis.set_xlabel(x_axis_label, fontdict={
'fontsize': axis_font_size,
'fontweight': 'regular',
'fontfamily': 'Raleway',
}, labelpad=x_label_pad)
if use_rel:
y_tick_labels = (str(int(tick)) for tick in axis.get_yticks())
else:
y_tick_labels = [str(int(tick * 1e-5)) for tick in axis.get_yticks()]
y_tick_labels[0] = '0'
axis.set_yticklabels(y_tick_labels, fontsize=tick_font_size, fontfamily=font_family)
axis.tick_params(axis='y', pad=2)
axis.set_ylabel(y_axis_label, fontdict={
'fontsize': axis_font_size,
'fontweight': 'regular',
'fontfamily': 'Raleway',
})
axis.set_title(title, fontdict={
'fontsize': axis_font_size,
'fontweight': 'regular',
'fontfamily': 'Raleway',
})
axis.set_xlim((-0.75, len(id_freqs) - 0.25))
axis.spines['right'].set_visible(False)
axis.spines['top'].set_visible(False)
return axis, bars