#!/usr/bin/env python3
import io
import csv
from model.network import Network
from model.drug import Drug
from model.disease import Disease
from model.edge import Edge
network = Network()
with io.open('../data/PubMed/drug_disease.csv',
'r',
encoding='utf-8',
newline='') as f:
reader = csv.reader(f, delimiter=',', quotechar='"')
next(reader, None)
for row in reader:
drug = Drug(['DrugBank:%s' % row[1]], [row[0]])
disease = Disease([row[4]], [row[3]])
network.add_node(drug)
network.add_node(disease)
network.add_edge(
Edge(drug, disease, row[2], {
'source': 'PubMed',
'pmid': row[5]
}))
network.save('../data/PubMed/graph.json')
gene = Gene(['HGNC:%s' % gene_id], [])
network.add_node(gene)
rel = {
'source': 'PMID:24013639',
'pvalue': row[0],
'snp_chr': row[2],
'cis_trans': row[7]
}
network.add_edge(Edge(gene, variant, 'EQTL', rel))
with io.open(file_trans, 'r', encoding='utf-8', newline='') as f:
reader = csv.reader(f, delimiter='\t', quotechar='"')
next(reader, None)
for row in reader:
if value_empty(row[1]) or value_empty(row[16]):
continue
variant = Variant(['dbSNP:%s' % row[1]], [])
network.add_node(variant)
for gene_id in row[16].split(','):
gene = Gene(['HGNC:%s' % gene_id], [])
network.add_node(gene)
rel = {
'source': 'PMID:24013639',
'pvalue': row[0],
'snp_chr': row[2],
'cis_trans': 'trans' # row[7] TODO: always "-"?
}
network.add_edge(Edge(gene, variant, 'EQTL', rel))
network.save('../data/Westra_etal_2017/graph.json')
network = Network()
# 0 Location
# 1 Phenotype
# 2 Phenotype MIM number
# 3 Inheritance
# 4 Phenotype mapping key
# 5 Gene/Locus
# 6 Gene/Locus MIM number
with io.open('../data/OMIM/filtered_associations.csv',
'r',
encoding='utf-8',
newline='') as f:
reader = csv.reader(f, delimiter=',', quotechar='"')
next(reader, None)
for row in reader:
disease = Disease(['OMIM:%s' % row[2]], [])
network.add_node(disease)
gene = Gene(['HGNC:%s' % row[5]], []) # , 'OMIM:%s' % row[6]
network.add_node(gene)
rel = {
'source': 'OMIM',
'location': row[0],
'phenotype': row[1],
'inheritance': row[2],
'phenotype_mapping_key': row[4]
}
network.add_edge(Edge(gene, disease, 'ASSOCIATES_WITH', rel))
network.save('../data/OMIM/graph.json')
gene_hgnc_id = 'HGNC:' + row[3]
gene_entrez_id = int(row[4])
gene_entrez_id = 'Entrez:' + str(gene_entrez_id)
pmid = int(row[8])
pmid = str(pmid)
with io.open(mirna_to_URS_mapping_file, 'r', encoding='utf-8', newline='') as mapping_file:
mapping_reader = csv.reader(mapping_file, delimiter='\t')
next(mapping_reader, None)
for mapping_row in mapping_reader:
if mirna_name == mapping_row[2]:
mirna_rnacentral_id = mapping_row[0]
mirna = MiRNA([mirna_rnacentral_id], [mirna_name])
network.add_node(mirna)
gene = Gene([gene_hgnc_id, gene_entrez_id], [])
network.add_node(gene)
if (mirna_rnacentral_id + '$' + gene_hgnc_id) in edge_source_target_lookup:
edges = network.get_edges_from_to(mirna, gene, 'REGULATES')
for edge in edges:
pmid = edge.attributes['pmid'] + ', ' + str(pmid)
network.delete_edge(edge)
e = Edge(mirna, gene, 'REGULATES', {'source': 'miRTarBase', 'pmid': pmid})
network.add_edge(e)
edge_source_target_lookup.append(mirna_rnacentral_id + '$' + gene_hgnc_id)
else:
e = Edge(mirna, gene, 'REGULATES', {'source': 'miRTarBase', 'pmid': pmid})
network.add_edge(e)
edge_source_target_lookup.append(mirna_rnacentral_id + '$' + gene_hgnc_id)
break
network.save('data/miRTarBase/graph.json')
network = Network()
with io.open(file, 'r', encoding='utf-8', newline='') as f:
reader = csv.reader(f, delimiter='\t', quotechar='"')
next(reader, None)
for row in reader:
row = [x.strip() for x in row]
if not row[0] or not row[7] or not row[8]:
continue
gene_ids = {'HGNC:%s' % row[0]}
if row[2]:
gene_ids.add('Entrez:%s' % row[2])
gene = Gene(gene_ids, [])
network.add_node(gene)
drug_name = row[7].replace('(%s)' % row[8], '').replace(row[8],
'').strip()
drug = Drug(['ChEMBL:%s' % row[8]], [drug_name] if drug_name else [])
network.add_node(drug)
rel = {
'source': 'DGIdb,%s' % row[3],
'actions': [row[4]],
}
if row[9]:
pubmed_ids = ','.join(
['PMID:%s' % x for x in row[9].strip().split(',')])
rel['source'] += ',%s' % pubmed_ids
network.add_edge(Edge(drug, gene, 'TARGETS', rel))
network.save('../data/DGIdb/graph.json')
reader = csv.reader(f, delimiter='\t', quotechar='"')
next(reader, None)
for row in reader:
if row[3] == 'H**o sapiens' and row[6] == 'H**o sapiens' and float(
row[7]) > 0.9:
interactor_a_name = row[1]
interactor_a_type = row[2]
interactor_b_name = row[4]
interactor_b_type = row[5]
interactor_a = add_rna(interactor_a_name, interactor_a_type,
node_lookup)
interactor_b = add_rna(interactor_b_name, interactor_b_type,
node_lookup)
if interactor_a is not None and interactor_b is not None:
if interactor_a_type == 'mRNA':
gene = Gene([interactor_a.id], [])
network.add_node(gene)
e = Edge(gene, interactor_a, 'TRANSCRIBES', {})
network.add_edge(e)
elif interactor_b_type == 'mRNA':
gene = Gene([interactor_b.id], [])
network.add_node(gene)
e = Edge(gene, interactor_b, 'TRANSCRIBES', {})
network.add_edge(e)
e = Edge(interactor_a, interactor_b, 'REGULATES',
{'source': 'RNAInter'})
network.add_edge(e)
network.save('../data/RNAInter/graph.json')
with io.open('../data/DrugCentral/drugcentral_indications.csv',
'r',
encoding='utf-8',
newline='') as f:
reader = csv.reader(f, delimiter=',', quotechar='"')
next(reader, None)
for row in reader:
disease = Disease(['SnoMedCT:%s' % row[2],
'UMLS:%s' % row[3]], [row[1]])
network.add_node(disease)
drug = network.get_node_by_id('DrugBank:%s' % row[0], 'Drug')
e = Edge(drug, disease, 'INDICATES', {'source': 'DrugCentral'})
network.add_edge(e)
with io.open('../data/DrugCentral/drugcentral_contraindications.csv',
'r',
encoding='utf-8',
newline='') as f:
reader = csv.reader(f, delimiter=',', quotechar='"')
next(reader, None)
for row in reader:
disease = Disease(['SnoMedCT:%s' % row[2],
'UMLS:%s' % row[3]], [row[1]])
network.add_node(disease)
drug = network.get_node_by_id('DrugBank:%s' % row[0], 'Drug')
e = Edge(drug, disease, 'CONTRAINDICATES', {'source': 'DrugCentral'})
network.add_edge(e)
network.save('../data/DrugCentral/graph.json')
id_mapping_file = '../data/UniprotKB/HUMAN_9606_idmapping.dat'
id_mapping_zip_file = '../data/UniprotKB/HUMAN_9606_idmapping.dat.gz'
id_mapping_url = 'ftp://ftp.uniprot.org/pub/databases/uniprot/current_release/knowledgebase/idmapping/by_organism/HUMAN_9606_idmapping.dat.gz'
if not os.path.exists(id_mapping_file):
print('Database does not exist. Trying to download and extract...')
if not os.path.exists(id_mapping_zip_file):
print('Downloading latest archive...')
with urllib.request.urlopen(id_mapping_url) as response, open(
id_mapping_zip_file, 'wb') as f:
f.write(response.read())
print('Extracting database file...')
with gzip.open(id_mapping_zip_file, 'rb') as f:
with open(id_mapping_file, 'wb') as out_file:
out_file.write(f.read())
network = Network()
with io.open(id_mapping_file, 'r', encoding='utf-8', newline='') as f:
reader = csv.reader(f, delimiter='\t', quotechar='"')
for row in reader:
if row[1] == 'HGNC':
gene = Gene(['UniProtKB:%s' % row[0], row[2]], [])
network.add_node(gene)
elif row[1] == 'Gene_Name':
gene = Gene(['UniProtKB:%s' % row[0], 'HGNC:%s' % row[2]], [])
network.add_node(gene)
network.save('../data/UniprotKB/graph.json')
'HGNC:%s' % hgnc_id.strip(),
'PharmGKB:%s' % pharmgkb_id.strip(')')
], [])
network.add_node(gene_node)
network.add_edge(
Edge(gene_node, adr, 'ASSOCIATED_WITH_ADR',
{'source': 'PharmGKB'}))
for chemical in split_list(row[11]):
name = chemical[0:chemical.rfind('(')].strip()
pharmgkb_id = chemical[chemical.rfind('(') + 1:].strip(')')
drug_node = Drug(['PharmGKB:%s' % pharmgkb_id], [name])
network.add_node(drug_node)
network.add_edge(
Edge(drug_node, adr, 'HAS_ADR', {'source': 'PharmGKB'}))
for disease in split_list(row[12]):
name = disease[0:disease.rfind('(')].strip()
pharmgkb_id = disease[disease.rfind('(') + 1:].strip(')')
disease_node = Disease(['PharmGKB:%s' % pharmgkb_id], [name])
network.add_node(disease_node)
network.add_edge(
Edge(disease_node, adr, 'ASSOCIATED_WITH_ADR',
{'source': 'PharmGKB'}))
if row[1].startswith('rs'):
variant = Variant(['dbSNP:%s' % row[1]], [])
network.add_node(variant)
network.add_edge(
Edge(variant, adr, 'ASSOCIATED_WITH_ADR',
{'source': 'PharmGKB'}))
network.save('../data/PharmGKB/graph.json')
id_node = owl_class.find(obo_in_owl_ns + 'id')
obo_ns_node = owl_class.find(obo_in_owl_ns + 'hasOBONamespace')
label_node = owl_class.find(rdfs_ns + 'label')
if id_node is not None and obo_ns_node is not None:
go_class = GOClass([id_node.text], [label_node.text])
network.add_node(go_class)
go_class_ns_lookup[id_node.text] = obo_ns_node.text
for alternative_id_node in owl_class.findall(obo_in_owl_ns + 'hasAlternativeId'):
go_class_redirects[alternative_id_node.text] = id_node.text
with io.open(annotations_file, 'r', encoding='utf-8', newline='') as f:
reader = csv.reader(f, delimiter='\t', quotechar='"')
for row in reader:
if not row[0][0].startswith('!') and row[12] == 'taxon:9606':
gene = Gene(['UniProtKB:%s' % row[1], 'HGNC:%s' % row[2]], [])
network.add_node(gene)
if row[4] not in go_class_ns_lookup:
# print('[WARN] GO id %s is obsolete, redirecting to %s' % (row[4], go_class_redirects[row[4]]))
row[4] = go_class_redirects[row[4]]
label = go_class_ns_lookup[row[4]].upper()
if label == 'MOLECULAR_FUNCTION':
label = 'HAS_' + label
elif label == 'BIOLOGICAL_PROCESS':
label = 'BELONGS_TO_' + label
elif label == 'CELLULAR_COMPONENT':
label = 'IN_' + label
e = Edge(gene, network.get_node_by_id(row[4], 'GOClass'), label, {'source': 'GO,%s' % row[5]})
network.add_edge(e)
network.save('../data/GO/graph.json')
to_namespace = association.find('to_namespace').text
to_id = association.find('to_code').text
to_name = association.find('to_name').text
if from_namespace != 'RxNorm' or to_namespace != 'MeSH':
continue
if association_type not in ['induces', 'CI_with', 'may_treat']:
continue
drug_id = 'RxNorm:%s' % from_id
if from_id not in added_rxnorm_drugs:
drug = Drug([drug_id], [from_name])
network.add_node(drug)
added_rxnorm_drugs.add(from_id)
else:
drug = network.get_node_by_id(drug_id, 'Drug')
disease_id = 'MeSH:%s' % to_id
if to_id not in added_mesh_diseases:
disease = Disease([disease_id], [to_name])
network.add_node(disease)
added_mesh_diseases.add(to_id)
else:
disease = network.get_node_by_id(disease_id, 'Disease')
rel = {'source': 'MEDRT'}
if association_type == 'induces':
network.add_edge(Edge(drug, disease, 'INDUCES', rel))
elif association_type == 'CI_with':
network.add_edge(Edge(drug, disease, 'CONTRAINDICATES', rel))
elif association_type == 'may_treat':
network.add_edge(Edge(drug, disease, 'INDICATES', rel))
network.save('../data/MED-RT/graph.json')
network.add_node(drug2)
rel = {
'source': 'DrugBank',
'description': row[4]
}
network.add_edge(Edge(drug1, drug2, 'INTERACTS', rel))
adr_id_counter = 1
for row in snp_adrs_results:
# drugbank_id, gene_symbol, rs_id, adverse_reaction, description, pubmed_id
adr = AdverseDrugReaction(['GenCoNet:DrugBank_ADR_%s' % adr_id_counter], [])
adr_id_counter += 1
adr.attributes = {
'source': 'DrugBank',
'adverse_reaction': row[3],
'description': row[4],
'pmid': row[5]
}
network.add_node(adr)
drug = Drug(['DrugBank:%s' % row[0]], [])
network.add_node(drug)
network.add_edge(Edge(drug, adr, 'HAS_ADR', {'source': 'DrugBank'}))
if row[2] and len(row[2]) > 0:
variant = Variant(['dbSNP:%s' % row[2]], [])
network.add_node(variant)
network.add_edge(Edge(variant, adr, 'ASSOCIATED_WITH_ADR', {'source': 'DrugBank'}))
gene = Gene(['HGNC:%s' % row[1]], [])
network.add_node(gene)
network.add_edge(Edge(gene, adr, 'ASSOCIATED_WITH_ADR', {'source': 'DrugBank'}))
network.save('../data/DrugBank/graph.json')
next(reader, None)
for row in reader:
# 0 SUPERDRUG_ID
# 1 PREFERRED_NAME
# 2 ATC
# 3 CHEMBL_ID
# 4 DRUGBANK_ID
# 5 KEGG_ID
# 6 PUBCHEM_CID
# 7 CASRN
drug_ids = [] # ['SuperDrug:%s' % row[0]]
# if row[2] != not_available_text:
# drug_ids.extend(['AtcCode:%s' % x for x in row[2].split(';')])
if row[3] != not_available_text:
for chembl_id in {x.strip() for x in row[3].split(';')}:
drug_ids.append('ChEMBL:%s' % chembl_id)
if row[4] != not_available_text:
drug_ids.append('DrugBank:%s' % row[4])
else:
# For now, only use mappings including DrugBank
continue
# if row[5] != not_available_text:
# drug_ids.append('Kegg:%s' % row[5])
if row[6] != not_available_text:
for pubchem_id in {x.strip() for x in row[6].split(';')}:
drug_ids.append('PubChem:CID%s' % pubchem_id)
if len(drug_ids) > 1:
network.add_node(Drug(drug_ids, [row[1]]))
network.save('../data/SuperDrug2/graph.json')
if not os.path.exists(file):
print('Database does not exist. Trying to download...')
data = urllib.parse.urlencode({
'downLoadType': 'all_pheno',
'Mysubmit': 'Download'
}).encode()
with urllib.request.urlopen(urllib.request.Request(url,
data=data)) as response:
with open(file, 'wb') as f:
f.write(response.read())
network = Network()
with io.open(file, 'r', encoding='utf-8', newline='') as f:
for skip in range(0, 4):
f.readline()
for line in f:
parts = [[y.strip() for y in x[:-1].split('(')]
for x in line.strip().split('\t')]
if len(parts) > 1:
disease = Disease(['UMLS:%s' % parts[0][1]], [parts[0][0]])
network.add_node(disease)
for part in parts[1::]:
gene = Gene(['HGNC:%s' % part[0],
'Entrez:%s' % part[1]], [part[0]])
network.add_node(gene)
rel = {'source': 'HuGE Navigator'}
network.add_edge(Edge(gene, disease, 'ASSOCIATES_WITH', rel))
network.save('../data/HuGE-Navigator/graph.json')
def get_given_drugs_related_info(disease_pairs, drugs): # first disease pair with first drug array
all_networks = [] # contains an array for each disease pair
for index, disease_pair in enumerate(disease_pairs):
networks_per_drug = [] # contains a network for each drug
pair_drugs_ids = drugs[index]
temp_id1 = disease_pair[0].replace(':', '-')
temp_id2 = disease_pair[1].replace(':', '-')
path = '../analysis/disease_pairs/' + temp_id1 + '_' + temp_id2
for drug_id in pair_drugs_ids:
try:
os.mkdir(path)
except FileExistsError:
pass
network = Network()
d1 = Disease([disease_pair[0]], [])
network.add_node(d1)
d2 = Disease([disease_pair[1]], [])
network.add_node(d2)
drug = Drug([drug_id], [])
network.add_node(drug)
temp_drug_id = drug_id.replace(':', '-')
with io.open(path + '/' + temp_id1 + '_' + temp_id2 + '_' + temp_drug_id + '_results.txt', 'w', encoding='utf-8', newline='') as results_file:
results_file.write('In this file all information about the connection between ' + disease_pair[0] +
' and ' + disease_pair[1] + ' and the drug ' + drug_id + ' is summarized:\n')
# the drug INDICATES, CONTRAINDICATES or INDUCES the disease
query = """ MATCH (d:Disease)-[a]-(n:Drug) WHERE {d1_id} IN d.ids AND {n_id} in n.ids RETURN distinct(type(a)) """
d1_results = session.run(query, parameters={'d1_id': disease_pair[0], 'n_id': drug_id})
for result in d1_results:
results_file.write(drug_id + ' ' + result['(type(a))'] + ' ' + disease_pair[0] + '\n')
network.add_edge(Edge(drug, d1, result['(type(a))'], {}))
query = """ MATCH (d:Disease)-[a]-(n:Drug) WHERE {d2_id} IN d.ids AND {n_id} in n.ids RETURN distinct(type(a)) """
d2_results = session.run(query, parameters={'d2_id': disease_pair[1], 'n_id': drug_id})
for result in d2_results:
results_file.write(drug_id + ' ' + result['(type(a))'] + ' ' + disease_pair[1] + '\n')
network.add_edge(Edge(drug, d2, result['(type(a))'], {}))
# the drug targets a gene which is associated to the disease
d1_genes = set()
query = """ MATCH (n:Drug)-[:TARGETS]-(g:Gene)-[:ASSOCIATES_WITH]-(d:Disease) WHERE {d1_id} IN d.ids AND {n_id} in n.ids RETURN g.`_id` """
d1_results = session.run(query, parameters={'d1_id': disease_pair[0], 'n_id': drug_id})
for gene in d1_results:
d1_genes.add(gene['g.`_id`'])
g = Gene([gene['g.`_id`']], [])
network.add_node(g)
network.add_edge(Edge(drug, g, 'TARGETS', {'actions': []})) #TODO
network.add_edge(Edge(g, d1, 'ASSOCIATES_WITH', {}))
d2_genes = set()
query = """ MATCH (n:Drug)-[:TARGETS]-(g:Gene)-[:ASSOCIATES_WITH]-(d:Disease) WHERE {d2_id} IN d.ids AND {n_id} in n.ids RETURN g.`_id` """
d2_results = session.run(query, parameters={'d2_id': disease_pair[1], 'n_id': drug_id})
for gene in d2_results:
d2_genes.add(gene['g.`_id`'])
g = Gene([gene['g.`_id`']], [])
network.add_node(g)
network.add_edge(Edge(drug, g, 'TARGETS', {'actions': []})) #TODO
network.add_edge(Edge(g, d2, 'ASSOCIATES_WITH', {}))
common_drug_genes = d1_genes.intersection(d2_genes) # genes associated to the drug and both diseases
# relevant_genes are all genes associated to at least one disease and the drug, below the common genes
# with the most disease associated references are added
relevant_genes = d1_genes.union(d2_genes)
if len(d1_genes) > 0:
nbr = str(len(d1_genes))
d1_genes = str(d1_genes)
d1_genes = d1_genes.replace('{', '')
d1_genes = d1_genes.replace('}', '')
d1_genes = d1_genes.replace('\'', '')
results_file.write(drug_id + ' targets following ' + nbr + ' genes which are associated to ' + disease_pair[0] + ': ' + d1_genes + '\n')
if len(d2_genes) > 0:
nbr = str(len(d2_genes))
d2_genes = str(d2_genes)
d2_genes = d2_genes.replace('{', '')
d2_genes = d2_genes.replace('}', '')
d2_genes = d2_genes.replace('\'', '')
results_file.write(drug_id + ' targets following ' + nbr + ' genes which are associated to ' + disease_pair[1] + ': ' + d2_genes + '\n')
if len(common_drug_genes) > 0:
nbr = str(len(common_drug_genes))
cdgs = str(common_drug_genes)
cdgs = cdgs.replace('{', '')
cdgs = cdgs.replace('}', '')
cdgs = cdgs.replace('\'', '')
results_file.write('The disease pair has ' + nbr + ' common genes which are targeted by the drug: ' + cdgs + '\n')
# add the common genes with the most disease associated references
# no given num_pmids is similar to num_pmids = 0
all_d1_genes, all_d2_genes = get_genes(disease_pair)
all_common_genes = all_d1_genes.intersection(all_d2_genes)
relevant_common_genes = [] # the genes with the most cited gene-disease association, threshold 10
if len(all_common_genes) > 0:
results_file.write('The disease pair has ' + str(len(all_common_genes)) + ' common genes, not considering the connection to the drug.'
' Following genes have the most references regarding their connection to both diseases:\n')
for gene in all_common_genes:
query = """ MATCH (d1:Disease)-[a]-(g:Gene) WHERE {g_id} IN g.ids AND {d1_id} IN d1.ids RETURN a.num_pmids """
results = session.run(query, parameters={'g_id': gene, 'd1_id': disease_pair[0]})
num_pmids = 0
for result in results: # multiple edges to the same gene
temp = result['a.num_pmids']
if temp is not None:
num_pmids = num_pmids + temp
query = """ MATCH (d2:Disease)-[a]-(g:Gene) WHERE {g_id} IN g.ids AND {d2_id} IN d2.ids RETURN a.num_pmids """
results = session.run(query, parameters={'g_id': gene, 'd2_id': disease_pair[1]})
for result in results: # multiple edges to the same gene
temp = result['a.num_pmids']
if temp is not None:
num_pmids = num_pmids + temp
relevant_common_genes.append([gene, num_pmids])
# sort by number of pmids
relevant_common_genes = sorted(relevant_common_genes, key=lambda item: item[1], reverse=True)
relevant_common_genes = relevant_common_genes[:10] # threshold
rcgs = str(relevant_common_genes)
rcgs = rcgs[1:-1]
rcgs = rcgs.replace('\'', '')
results_file.write(rcgs + '\n')
for g in relevant_common_genes:
gene = Gene([g[0]], [])
network.add_node(gene)
network.add_edge(Edge(gene, d1, 'ASSOCIATES_WITH', {}))
network.add_edge(Edge(gene, d2, 'ASSOCIATES_WITH', {}))
relevant_genes.add(g[0])
# add the common disease associated variants with most references
# no given num_pmids is similar to num_pmids = 0
disease_variants = {}
query = """ MATCH (d1:Disease)-[a]-(v:Variant)--(d2:Disease) WHERE {d1_id} in d1.ids AND {d2_id} in d2.ids RETURN distinct(a.num_pmids), v.`_id` """
results = session.run(query, parameters={'d1_id': disease_pair[0], 'd2_id': disease_pair[1]})
for variant in results:
num_pmids = variant['(a.num_pmids)']
if num_pmids is None:
num_pmids = 0
var_id = variant['v.`_id`']
if var_id in disease_variants:
temp = disease_variants[var_id]
disease_variants[var_id] = temp + num_pmids
else:
disease_variants[var_id] = num_pmids
query = """ MATCH (d2:Disease)-[a]-(v:Variant)--(d1:Disease) WHERE {d1_id} in d1.ids AND {d2_id} in d2.ids RETURN distinct(a.num_pmids), v.`_id` """
results = session.run(query, parameters={'d1_id': disease_pair[0], 'd2_id': disease_pair[1]})
for variant in results:
num_pmids = variant['(a.num_pmids)']
if num_pmids is None:
num_pmids = 0
var_id = variant['v.`_id`']
if var_id in disease_variants:
temp = disease_variants[var_id]
disease_variants[var_id] = temp + num_pmids
else:
disease_variants[var_id] = num_pmids
dvs = ''
i = 0
for key, value in sorted(disease_variants.items(), key=lambda item: item[1], reverse=True):
if i < 9: # threshold
num_pmids = disease_variants[key]
variant = Variant([key], [])
network.add_node(variant)
network.add_edge(Edge(variant, d1, 'ASSOCIATES_WITH', {}))
network.add_edge(Edge(variant, d2, 'ASSOCIATES_WITH', {}))
dvs = dvs + key + ':' + str(num_pmids) + ' PMIDs, '
i += 1
dvs = dvs[:-2]
# add the gene associated variants with smallest pvalues
# if no pvalue is given, pvalue is set to 1
gene_variants = []
for gene in relevant_genes:
query = """ MATCH (g:Gene)-[a]-(v:Variant) WHERE {g_id} in g.ids RETURN v.`_id`, a.pvalue, type(a) """
results = session.run(query, parameters={'g_id': gene})
for variant in results:
pvalue = variant['a.pvalue']
if pvalue is None:
pvalue = 1
else:
pvalue = float(pvalue)
gene_variants.append([variant['v.`_id`'] + '-' + gene, pvalue, variant['type(a)']])
gene_variants = sorted(gene_variants, key=lambda item: item[1])
gene_variants = gene_variants[:10] # threshold
for v in gene_variants:
temp = v[0].split('-')
v_id = temp[0]
g_id = temp[1]
variant = Variant([v_id], [])
network.add_node(variant)
gene = Gene([g_id], [])
network.add_node(gene)
network.add_edge(Edge(gene, variant, v[2], {'pvalue': v[1]}))
if len(gene_variants) > 0:
gvs = str(gene_variants)
gvs = gvs[1:-1]
gvs = gvs.replace('\'', '')
else:
gvs = ''
if len(disease_variants) > 0 or len(gene_variants) > 0:
results_file.write('The disease pair has at least ' + str(i) + ' variants associated to both diseases: ' +
dvs + ' and at least ' + str(len(gene_variants)) + ' gene associated variants: ' + gvs + '\n')
# dict with RNA name as key and an array as value
# first array position is the number of regulated genes, second position is an array with the gene names
relevant_rnas = {}
for gene in relevant_genes:
query = """ MATCH (r:RNA)--(g:Gene) WHERE {g_id} in g.ids AND NOT r.label_id CONTAINS "MRNA" return r.`_id` """
results = session.run(query, parameters={'g_id': gene})
for result in results:
key = result['r.`_id`']
if key in relevant_rnas:
value = relevant_rnas[key]
genes = value[1]
if gene not in genes:
genes.add(gene)
relevant_rnas[key] = [value[0] + 1, genes]
else:
genes = set()
genes.add(gene)
relevant_rnas[key] = [1, genes]
if len(relevant_rnas) > 0:
i = 0
for key, value in sorted(relevant_rnas.items(), key=lambda item: item[1], reverse=True):
# sort by the number of regulated genes
if i > 9: # threshold
break
elif value[0] > 1: # only add and print RNAs which regulate more than one gene
if i == 0:
results_file.write('RNAs with the number and names of the genes they regulate: \n')
rna_id = key
for gene_id in value[1]:
rna = RNA([rna_id], [])
network.add_node(rna)
gene = Gene([gene_id], [])
network.add_node(gene)
network.add_edge(Edge(rna, gene, 'REGULATES', {}))
regulated_genes = str(value[1])
regulated_genes = regulated_genes[1:-1]
regulated_genes = regulated_genes.replace('\'', '')
results_file.write(rna_id + '\t' + str(value[0]) + '\t' + regulated_genes + '\n')
i += 1
# append regulating RNAs to one RNA which regulates the most genes, MRNAs are not added
for key, value in sorted(relevant_rnas.items(), key=lambda item: item[1], reverse=True):
if value[0] > 1:
most_relevant_rna = RNA([key], [])
network.add_node(most_relevant_rna)
query = """ MATCH (r:RNA)--(n:RNA) WHERE {r_id} in r.ids AND NOT n.label_id CONTAINS "MRNA" RETURN n.`_id`, labels(n) """
results = session.run(query, parameters={'r_id': key})
reg_rnas = ''
for result in results:
rna_id = result['n.`_id`']
types = result['labels(n)']
for type in types:
if type != 'RNA':
if type == 'CircRNA':
rna = CircRNA([rna_id], [])
if type == 'ERNA':
rna = ERNA([rna_id], [])
if type == 'LncRNA':
rna = LncRNA([rna_id], [])
if type == 'MiRNA':
rna = MiRNA([rna_id], [])
if type == 'NcRNA':
rna = NcRNA([rna_id], [])
if type == 'PiRNA':
rna = PiRNA([rna_id], [])
if type == 'Pseudogene':
rna = Pseudogene([rna_id], [])
if type == 'Ribozyme':
rna = Ribozyme([rna_id], [])
if type == 'RRNA':
rna = RRNA([rna_id], [])
if type == 'ScaRNA':
rna = ScaRNA([rna_id], [])
if type == 'ScRNA':
rna = ScRNA([rna_id], [])
if type == 'SnoRNA':
rna = SnoRNA([rna_id], [])
if type == 'SnRNA':
rna = SnRNA([rna_id], [])
network.add_node(rna)
network.add_edge(Edge(rna, most_relevant_rna, 'REGULATES', {}))
reg_rnas = reg_rnas + rna_id + ', '
reg_rnas = reg_rnas[:-2]
results_file.write(key + ' is the RNA which regulates the most genes in this subgraph. It is regulated by ' + reg_rnas + '.\n')
break
json_file = path + '/' + temp_id1 + '_' + temp_id2 + '_' + temp_drug_id + '_graph.json'
network.save(json_file)
draw_drug_subgraph(json_file)
networks_per_drug.append(network)
all_networks.append(networks_per_drug)
return all_networks
network.add_edge(
Edge(drug, ('NDFRT:%s' % role[1], 'Disease'), 'INDUCES',
rel))
elif role_name == 'CI_with {NDFRT}':
network.add_edge(
Edge(drug, ('NDFRT:%s' % role[1], 'Disease'),
'CONTRAINDICATES', rel))
elif role_name == 'may_treat {NDFRT}':
network.add_edge(
Edge(drug, ('NDFRT:%s' % role[1], 'Disease'), 'INDICATES',
rel))
elif concept['kind'] == kind_defs_rev['DISEASE_KIND']:
disease_ids = ['NDFRT:%s' % concept['code']]
disease_names = [concept['name']]
for prop in concept['properties']:
if property_defs[prop[0]] == 'SNOMED_CID':
disease_ids.append('SnoMedCT:%s' % prop[1])
elif property_defs[prop[0]] == 'UMLS_CUI':
disease_ids.append('UMLS:%s' % prop[1])
elif property_defs[prop[0]] == 'MeSH_CUI':
disease_ids.append('MeSH:%s' % prop[1])
elif property_defs[prop[0]] == 'Synonym':
disease_names.append(prop[1])
disease_names = [
x.replace('[Disease/Finding]', '').strip() for x in disease_names
]
disease = Disease(disease_ids, disease_names)
network.add_node(disease)
network.save('../data/NDF-RT/graph.json')
network = Network()
drug_lookup = {}
with io.open(drug_file, 'r', encoding='utf-8', newline='') as f:
reader = csv.reader(f, delimiter='\t', quotechar='"')
for row in reader:
drug_lookup[row[0].strip()] = row[1].strip()
# 1: STITCH compound id (flat, see above)
# 2: UMLS concept id as it was found on the label
# 3: method of detection: NLP_indication / NLP_precondition / text_mention
# 4: concept name
# 5: MedDRA concept type (LLT = lowest level term, PT = preferred term; in a few cases the term is neither LLT nor PT)
# 6: UMLS concept id for MedDRA term
# 7: MedDRA concept name
# All side effects found on the labels are given as LLT. Additionally, the PT is shown. There is at least one
# PT for every LLT, but sometimes the PT is the same as the LLT.
with io.open(file, 'r', encoding='utf-8', newline='') as f:
reader = csv.reader(f, delimiter='\t', quotechar='"')
for row in reader:
pubchem_id = row[0][4::].lstrip('0')
drug = Drug(['PubChem:CID%s' % pubchem_id], [drug_lookup[row[0]]] if row[0] in drug_lookup else [])
network.add_node(drug)
disease = Disease(['UMLS:%s' % row[1], 'UMLS:%s' % row[5]], [row[3], row[6]])
network.add_node(disease)
network.add_edge(Edge(drug, disease, 'INDICATES', {'source': 'SIDER'}))
network.save('../data/SIDER/graph.json')
# 30 OR or BETA
# 31 95% CI (TEXT)
# 32 PLATFORM [SNPS PASSING QC]
# 33 CNV
loc_pattern = re.compile(r'LOC[0-9]+')
with io.open(file, 'r', encoding='utf-8', newline='') as f:
reader = csv.reader(f, delimiter='\t', quotechar='"')
next(reader, None)
for row in reader:
if not row[14]:
continue
gene_ids = row[14].replace(' x ', ', ').replace(' - ',
', ').split(', ')
print(row[14])
print('\t', gene_ids)
for gene_id in gene_ids:
if loc_pattern.fullmatch(gene_id) is not None:
continue
gene = Gene(['HGNC:%s' % gene_id], [])
network.add_node(gene)
for variant_id in {x.strip() for x in row[21].split(';')}:
variant = Variant(['dbSNP:%s' % variant_id], [])
network.add_node(variant)
network.add_edge(
Edge(gene, variant, 'CODES', {
'source': 'GWASCatalog',
'pmid': row[1]
}))
network.save('../data/GWAS-Catalog/graph.json')
elif go_id == 'GO:1903231':
label = 'HAS_MOLECULAR_FUNCTION'
if (mirna_rnacentral_id + '$' +
go_id) in edge_source_target_lookup:
edges = network.get_edges_from_to(mirna, go_class, label)
for edge in edges:
if edge.attributes['source'] == ('EBI-GOA-miRNA, ' +
source_database):
pmid = edge.attributes['pmid'] + ', ' + pmid
network.delete_edge(edge)
e_go = Edge(
mirna, go_class, label, {
'source':
'EBI-GOA-miRNA, ' + source_database,
'pmid': pmid
})
network.add_edge(e_go)
edge_source_target_lookup.append(
mirna_rnacentral_id + '$' + go_id)
else:
e_go = Edge(
mirna, go_class, label, {
'source': 'EBI-GOA-miRNA, ' + source_database,
'pmid': pmid
})
network.add_edge(e_go)
edge_source_target_lookup.append(mirna_rnacentral_id +
'$' + go_id)
network.save('../data/EBI-GOA-miRNA/graph.json')
# 0 - snpId
# 1 - chromosome
# 2 - position
# 3 - DSI
# 4 - DPI
# 5 - diseaseId
# 6 - diseaseName
# 7 - diseaseType
# 8 - diseaseClass
# 9 - diseaseSemanticType
# 10 - score
# 11 - EI
# 12 - YearInitial
# 13 - YearFinal
# 14 - NofPmids
# 15 - source
if int(row[14]) >= PUBMED_COUNT_THRESHOLD:
variant = Variant(['dbSNP:%s' % row[0]], [])
network.add_node(variant)
disease = Disease(['UMLS:%s' % row[5]], [row[6]])
network.add_node(disease)
rel = {
'source': 'DisGeNet,%s' % row[15],
'num_pmids': int(row[14]),
'score': row[10]
}
network.add_edge(Edge(variant, disease, 'ASSOCIATES_WITH', rel))
network.save('../data/DisGeNet/graph.json')