def insert_best_seqid_column(params):
csv = params['csv']
uniprot_ids_header = params['uniprot_ids_header']
delimiter = params['delimiter']
out_csv = params['output_csv']
uniprot_cache = params['cache_dir']
if not csv:
raise IOError('No file selected')
if not os.path.isfile(csv):
raise IOError(csv + ' not found')
headers = get_headers(csv)
if uniprot_ids_header not in headers:
s = "Column header '%s' not found, available headers:\n" % uniprot_ids_header
for header in headers:
s += ' ' + header + '\n'
raise IOError(s)
logging('Reading %s\n' % csv)
entries = read_csv(csv)
all_seqids = []
for entry in entries:
tokens = entry[uniprot_ids_header].split(delimiter)
entry['seqids'] = [s.strip() for s in tokens]
all_seqids.extend(entry['seqids'])
logging('Found %d potential Uniprot IDs\n' % len(all_seqids))
uniprot_data = uniprot.batch_uniprot_metadata(
all_seqids, uniprot_cache)
for entry in entries:
best_seqid = uniprot.sort_seqids_by_uniprot(
entry['seqids'], uniprot_data)[0]
entry['best_seqid'] = best_seqid
entry['is_reviewed'] = False
if best_seqid in uniprot_data:
entry['is_reviewed'] = \
uniprot_data[best_seqid]['is_reviewed']
logging('Writing ')
logging('%s\n' % os.path.abspath(out_csv), lambda: open_file(out_csv))
headers = ['best_seqid', 'is_reviewed'] + get_headers(csv)
rows = [headers]
for entry in entries:
rows.append([entry[h] for h in headers])
write_csv(out_csv, rows)
def add_uniprot_data(protxml_groups, cache_file=None):
"""
Processes the data from an PROTXML file, reads the
seqids, and attempts to mapt to a UNIPROT ID
and then, fetch the metadata of that protein from
the uniprot.org website: organism, gene, description
etc.
"""
seqids = get_all_seqids(protxml_groups)
if is_url_connected('http://uniprot.org'):
uniprot_dict = uniprot.get_metadata_with_some_seqid_conversions(
seqids, cache_file)
else:
print "Can't connect to www.uniprot.org, won't use uniprot metatdata"
uniprot_dict = {}
for group_id, protxml_group in protxml_groups.items():
for protein in protxml_group['proteins']:
protein['id'] = ''
protein['acc'] = protein['protein_name']
names = [protein['protein_name']] + protein['other_seqids']
new_seqid = uniprot.sort_seqids_by_uniprot(names, uniprot_dict)[0]
if new_seqid != protein['protein_name']:
print "Protein group %s%s is better represented with %s than %s" % \
(group_id,
protein['group_sibling_id'],
uniprot.get_naked_seqid(new_seqid),
uniprot.get_naked_seqid(protein['protein_name']))
protein['protein_name'] = new_seqid
protein['other_seqids'] = names[1:]
protein['acc'] = new_seqid
protein['other_seqids'] = ';'.join(protein['other_seqids'])
if new_seqid not in uniprot_dict:
print "No uniprot metadata for protein group %s%s seqid %s" % \
(group_id,
protein['group_sibling_id'],
uniprot.get_naked_seqid(new_seqid))
continue
protein['link'] = ''
uniprot_entry = uniprot_dict[new_seqid]
protein['id'] = uniprot_entry['id']
protein['acc'] = uniprot_entry['accs'][0]
protein['link'] = \
'=HYPERLINK("http://uniprot.org/uniprot/%s")' % \
uniprot_dict[new_seqid]['id']
if 'gene' in uniprot_entry:
protein['gene'] = uniprot_entry['gene']
if 'organism' in uniprot_entry:
protein['organism'] = uniprot_entry['organism']
protein['description'] = '; '.join(uniprot_entry['descriptions'])
if 'length' in uniprot_entry:
protein['length'] = uniprot_entry['length']
def add_uniprot_data(protxml_groups, cache_file=None):
"""
Processes the data from an PROTXML file, reads the
seqids, and attempts to mapt to a UNIPROT ID
and then, fetch the metadata of that protein from
the uniprot.org website: organism, gene, description
etc.
"""
seqids = get_all_seqids(protxml_groups)
if is_url_connected('http://uniprot.org'):
uniprot_dict = uniprot.get_metadata_with_some_seqid_conversions(seqids, cache_file)
else:
print "Can't connect to www.uniprot.org, won't use uniprot metatdata"
uniprot_dict = {}
for group_id, protxml_group in protxml_groups.items():
for protein in protxml_group['proteins']:
protein['id'] = ''
protein['acc'] = protein['protein_name']
names = [protein['protein_name']] + protein['other_seqids']
new_seqid = uniprot.sort_seqids_by_uniprot(names, uniprot_dict)[0]
if new_seqid != protein['protein_name']:
print "Protein group %s%s is better represented with %s than %s" % \
(group_id,
protein['group_sibling_id'],
uniprot.get_naked_seqid(new_seqid),
uniprot.get_naked_seqid(protein['protein_name']))
protein['protein_name'] = new_seqid
protein['other_seqids'] = names[1:]
protein['acc'] = new_seqid
protein['other_seqids'] = ';'.join(protein['other_seqids'])
if new_seqid not in uniprot_dict:
print "No uniprot metadata for protein group %s%s seqid %s" % \
(group_id,
protein['group_sibling_id'],
uniprot.get_naked_seqid(new_seqid))
continue
protein['link'] = ''
uniprot_entry = uniprot_dict[new_seqid]
protein['id'] = uniprot_entry['id']
protein['acc'] = uniprot_entry['accs'][0]
protein['link'] = \
'=HYPERLINK("http://uniprot.org/uniprot/%s")' % \
uniprot_dict[new_seqid]['id']
if 'gene' in uniprot_entry:
protein['gene'] = uniprot_entry['gene']
if 'organism' in uniprot_entry:
protein['organism'] = uniprot_entry['organism']
protein['description'] = '; '.join(uniprot_entry['descriptions'])
if 'length' in uniprot_entry:
protein['length'] = uniprot_entry['length']
def insert_best_seqid_column(params):
csv = params['csv']
uniprot_ids_header = params['uniprot_ids_header']
delimiter = params['delimiter']
out_csv = params['output_csv']
uniprot_cache = params['cache_dir']
if not csv:
raise IOError('No file selected')
if not os.path.isfile(csv):
raise IOError(csv + ' not found')
headers = get_headers(csv)
if uniprot_ids_header not in headers:
s = "Column header '%s' not found, available headers:\n" % uniprot_ids_header
for header in headers:
s += ' ' + header + '\n'
raise IOError(s)
logging('Reading %s\n' % csv)
entries = read_csv(csv)
all_seqids = []
for entry in entries:
tokens = entry[uniprot_ids_header].split(delimiter)
entry['seqids'] = [s.strip() for s in tokens]
all_seqids.extend(entry['seqids'])
logging('Found %d potential Uniprot IDs\n' % len(all_seqids))
uniprot_data = uniprot.batch_uniprot_metadata(all_seqids, uniprot_cache)
for entry in entries:
best_seqid = uniprot.sort_seqids_by_uniprot(entry['seqids'],
uniprot_data)[0]
entry['best_seqid'] = best_seqid
entry['is_reviewed'] = False
if best_seqid in uniprot_data:
entry['is_reviewed'] = \
uniprot_data[best_seqid]['is_reviewed']
logging('Writing ')
logging('%s\n' % os.path.abspath(out_csv), lambda: open_file(out_csv))
headers = ['best_seqid', 'is_reviewed'] + get_headers(csv)
rows = [headers]
for entry in entries:
rows.append([entry[h] for h in headers])
write_csv(out_csv, rows)
