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from Bio import Entrez
Entrez.email = 'insert_your_email@gmail.com'
import xml.etree.ElementTree as ET
import yaml
import os
from datetime import date
today = date.today().strftime("%Y%m%d")
dir_metadata_today = 'metadata_from_nuccore_{}'.format(today)
dir_fasta_and_yaml_today = 'fasta_and_yaml_{}'.format(today)
dir_dict_ontology_standardization = 'dict_ontology_standardization/'
path_ncbi_virus_accession = 'sequences.acc'
# Take all the ids
id_set = set()
term_list = ['SARS-CoV-2', 'SARS-CoV2', 'SARS CoV2', 'SARSCoV2', 'txid2697049[Organism]']
for term in term_list:
tmp_list = Entrez.read(
Entrez.esearch(db='nuccore', term=term, idtype='acc', retmax='10000')
)['IdList']
# Remove mRNAs, ncRNAs, Proteins, and predicted models (more information here: https://en.wikipedia.org/wiki/RefSeq)
tmp_list = [x for x in tmp_list if x[:2] not in ['NM', 'NR', 'NP', 'XM', 'XR', 'XP', 'WP']]
# Remove the version in the id
tmp_list = [x.split('.')[0] for x in tmp_list]
print(term, len(tmp_list))
id_set.update([x.split('.')[0] for x in tmp_list])
print(term_list, len(id_set))
with open(path_ncbi_virus_accession) as f:
tmp_list = [line.strip('\n') for line in f]
print('NCBI Virus', len(tmp_list))
id_set.update(tmp_list)
print(term_list + ['NCBI Virus'], len(id_set))
def chunks(lst, n):
for i in range(0, len(lst), n):
yield lst[i:i + n]
num_ids_for_request = 100
if not os.path.exists(dir_metadata_today):
os.makedirs(dir_metadata_today)
for i, id_x_list in enumerate(chunks(list(id_set), num_ids_for_request)):
path_metadata_xxx_xml = os.path.join(dir_metadata_today, 'metadata_{}.xml'.format(i))
print('Requesting {} ids --> {}'.format(len(id_x_list), path_metadata_xxx_xml))
with open(path_metadata_xxx_xml, 'w') as fw:
fw.write(
Entrez.efetch(db='nuccore', id=id_x_list, retmode='xml').read()
)
term_to_uri_dict = {}
for path_dict_xxx_csv in [os.path.join(dir_dict_ontology_standardization, name_xxx_csv) for name_xxx_csv in os.listdir(dir_dict_ontology_standardization) if name_xxx_csv.endswith('.csv')]:
print('Read {}'.format(path_dict_xxx_csv))
with open(path_dict_xxx_csv) as f:
for line in f:
if len(line.split(',')) > 2:
term, uri = line.strip('\n').split('",')
term = term.strip('"')
else:
term, uri = line.strip('\n').split(',')
term_to_uri_dict[term] = uri
species_to_taxid_dict = {
'Homo sapiens': 9606
}
if not os.path.exists(dir_fasta_and_yaml_today):
os.makedirs(dir_fasta_and_yaml_today)
for path_metadata_xxx_xml in [os.path.join(dir_metadata_today, name_metadata_xxx_xml) for name_metadata_xxx_xml in os.listdir(dir_metadata_today) if name_metadata_xxx_xml.endswith('.xml')]:
tree = ET.parse(path_metadata_xxx_xml)
GBSet = tree.getroot()
for GBSeq in GBSet:
accession_version = GBSeq.find('GBSeq_accession-version').text
GBSeq_sequence = GBSeq.find('GBSeq_sequence')
if GBSeq_sequence is None:
print(accession_version, ' - sequence not found')
continue
# A general default-empty yaml could be read from the definitive one
info_for_yaml_dict = {
'id': 'placeholder',
'host': {},
'sample': {},
'virus': {},
'technology': {},
'submitter': {}
}
info_for_yaml_dict['sample']['sample_id'] = accession_version
info_for_yaml_dict['submitter']['authors'] = ';'.join([x.text for x in GBSeq.iter('GBAuthor')])
GBSeq_comment = GBSeq.find('GBSeq_comment')
if GBSeq_comment is not None and 'Assembly-Data' in GBSeq_comment.text:
GBSeq_comment_text = GBSeq_comment.text.split('##Assembly-Data-START## ; ')[1].split(' ; ##Assembly-Data-END##')[0]
for info_to_check, field_in_yaml in zip(
['Assembly Method', 'Coverage', 'Sequencing Technology'],
['sequence_assembly_method', 'sequencing_coverage', 'sample_sequencing_technology']
):
if info_to_check in GBSeq_comment_text:
tech_info_to_parse = GBSeq_comment_text.split('{} :: '.format(info_to_check))[1].split(' ;')[0]
if field_in_yaml == 'sequencing_coverage':
# A regular expression would be better!
info_for_yaml_dict['technology'][field_in_yaml] = ';'.join(
[x.strip('(average)').strip("reads/nt").replace(',', '.').strip(' xX>') for x in tech_info_to_parse.split(';')]
)
elif field_in_yaml == 'sample_sequencing_technology':
new_seq_tec_list = []
for seq_tec in tech_info_to_parse.split(';'):
seq_tec = seq_tec.strip()
if seq_tec in term_to_uri_dict:
seq_tec = term_to_uri_dict[seq_tec]
else:
print(accession_version, 'missing technologies:', seq_tec)
new_seq_tec_list.append(seq_tec)
for n, seq_tec in enumerate(new_seq_tec_list):
info_for_yaml_dict['technology'][field_in_yaml + ('' if n == 0 else str(n + 1))] = seq_tec
else:
info_for_yaml_dict['technology'][field_in_yaml] = tech_info_to_parse
#term_to_uri_dict
for GBFeature in GBSeq.iter('GBFeature'):
if GBFeature.find('GBFeature_key').text != 'source':
continue
for GBQualifier in GBFeature.iter('GBQualifier'):
GBQualifier_value = GBQualifier.find('GBQualifier_value')
if GBQualifier_value is None:
continue
GBQualifier_value_text = GBQualifier_value.text
GBQualifier_name_text = GBQualifier.find('GBQualifier_name').text
if GBQualifier_name_text == 'host':
GBQualifier_value_text_list = GBQualifier_value_text.split('; ')
info_for_yaml_dict['host']['host_common_name'] = GBQualifier_value_text_list[0]
if GBQualifier_value_text_list[0] in species_to_taxid_dict:
info_for_yaml_dict['host']['host_species'] = species_to_taxid_dict[GBQualifier_value_text_list[0]]
if len(GBQualifier_value_text_list) > 1:
if GBQualifier_value_text_list[1] in ['male', 'female']:
info_for_yaml_dict['host']['host_sex'] = GBQualifier_value_text_list[1]
else:
info_for_yaml_dict['host']['host_health_status'] = GBQualifier_value_text_list[1]
if 'age' in GBQualifier_value_text:
info_for_yaml_dict['host']['host_age'] = int(GBQualifier_value_text_list[2].split('age ')[1])
info_for_yaml_dict['host']['host_age_unit'] = 'year'
elif GBQualifier_name_text == 'collected_by':
if any([x in GBQualifier_value_text.lower() for x in ['institute', 'hospital', 'city', 'center']]):
info_for_yaml_dict['sample']['collecting_institution'] = GBQualifier_value_text
else:
info_for_yaml_dict['sample']['collector_name'] = GBQualifier_value_text
elif GBQualifier_name_text == 'isolation_source':
if GBQualifier_value_text in term_to_uri_dict:
info_for_yaml_dict['sample']['specimen_source'] = term_to_uri_dict[GBQualifier_value_text]
else:
if GBQualifier_value_text in ['NP/OP swab', 'nasopharyngeal and oropharyngeal swab', 'nasopharyngeal/oropharyngeal swab', 'np/np swab']:
info_for_yaml_dict['sample']['specimen_source'] = term_to_uri_dict['nasopharyngeal swab']
info_for_yaml_dict['sample']['specimen_source2'] = term_to_uri_dict['oropharyngeal swab']
else:
print(accession_version, 'missing specimen_source:', GBQualifier_value_text)
elif GBQualifier_name_text == 'collection_date':
# TO_DO: which format we will use?
info_for_yaml_dict['sample']['collection_date'] = GBQualifier_value_text
elif GBQualifier_name_text in ['lat_lon', 'country']:
if GBQualifier_value_text in term_to_uri_dict:
GBQualifier_value_text = term_to_uri_dict[GBQualifier_value_text]
else:
print(accession_version, 'missing {}:'.format(GBQualifier_name_text), GBQualifier_value_text)
info_for_yaml_dict['sample']['collection_location'] = GBQualifier_value_text
elif GBQualifier_name_text == 'note':
info_for_yaml_dict['sample']['additional_collection_information'] = GBQualifier_value_text
elif GBQualifier_name_text == 'isolate':
info_for_yaml_dict['virus']['virus_strain'] = GBQualifier_value_text
elif GBQualifier_name_text == 'db_xref':
info_for_yaml_dict['virus']['virus_species'] = int(GBQualifier_value_text.split('taxon:')[1])
with open(os.path.join(dir_fasta_and_yaml_today, '{}.fasta'.format(accession_version)), 'w') as fw:
fw.write('>{}\n{}'.format(accession_version, GBSeq_sequence.text.upper()))
with open(os.path.join(dir_fasta_and_yaml_today, '{}.yaml'.format(accession_version)), 'w') as fw:
yaml.dump(info_for_yaml_dict, fw, default_flow_style=False)
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