def parsePairs(config, databases_directory, qtype, mapping, download=True):
url = config['db_url']
ifile = config['db_files'][qtype]
source = config['db_sources'][qtype]
relationships = set()
directory = os.path.join(databases_directory, "Jensenlab")
builder_utils.checkDirectory(os.path.join(directory, "integration"))
if download:
builder_utils.downloadDB(url.replace("FILE", ifile), os.path.join(directory, "integration"))
ifile = os.path.join(directory,os.path.join("integration", ifile))
with open(ifile, 'r') as idbf:
for line in idbf:
data = line.rstrip("\r\n").split('\t')
id1 = "9606."+data[0]
id2 = data[2]
score = float(data[4])
if id1 in mapping:
for ident in mapping[id1]:
relationships.add((ident, id2, "ASSOCIATED_WITH_INTEGRATED", source, score, "compiled"))
else:
continue
return relationships
def parser(databases_directory, download=True):
config = builder_utils.get_config(config_name="signorConfig.yml",
data_type='databases')
directory = os.path.join(databases_directory, "SIGNOR")
builder_utils.checkDirectory(directory)
url = config['url']
modifications = config['modifications']
amino_acids = config['amino_acids']
accronyms = config['accronyms']
entities_header = config['entities_header']
relationships_headers = config['rel_headers']
entities = set()
relationships = defaultdict(set)
filename = os.path.join(directory, url.split('/')[-1])
if download:
builder_utils.downloadDB(url, directory)
entities, relationships = parse_substrates(filename, modifications,
accronyms, amino_acids)
return entities, relationships, entities_header, relationships_headers
def parser(databases_directory, download=True):
config = builder_utils.get_config(config_name="smpdbConfig.yml", data_type='databases')
urls = config['smpdb_urls']
entities = set()
relationships = defaultdict(set)
entities_header = config['pathway_header']
relationships_headers = config['relationships_header']
directory = os.path.join(databases_directory, "SMPDB")
builder_utils.checkDirectory(directory)
for dataset in urls:
url = urls[dataset]
file_name = url.split('/')[-1]
if download:
builder_utils.downloadDB(url, directory)
zipped_file = os.path.join(directory, file_name)
with zipfile.ZipFile(zipped_file) as rf:
if dataset == "pathway":
entities = parsePathways(config, rf)
elif dataset == "protein":
relationships.update(parsePathwayProteinRelationships(rf))
elif dataset == "metabolite":
relationships.update(parsePathwayMetaboliteDrugRelationships(rf))
builder_utils.remove_directory(directory)
return entities, relationships, entities_header, relationships_headers
def parser(databases_directory, download=True):
relationships = defaultdict(set)
config = builder_utils.get_config(config_name="disgenetConfig.yml",
data_type='databases')
files = config['disgenet_files']
mapping_files = config['disgenet_mapping_files']
url = config['disgenet_url']
directory = os.path.join(databases_directory, "disgenet")
builder_utils.checkDirectory(directory)
header = config['disgenet_header']
output_file = 'disgenet_associated_with.tsv'
if download:
for f in files:
builder_utils.downloadDB(url + files[f], directory)
for f in mapping_files:
builder_utils.downloadDB(url + mapping_files[f], directory)
proteinMapping = readDisGeNetProteinMapping(config, directory)
diseaseMapping = readDisGeNetDiseaseMapping(config, directory)
for f in files:
first = True
associations = gzip.open(os.path.join(directory, files[f]), 'r')
dtype, atype = f.split('_')
if dtype == 'gene':
idType = "Protein"
scorePos = 9
if dtype == 'variant':
idType = "Transcript"
scorePos = 5
for line in associations:
if first:
first = False
continue
try:
data = line.decode('utf-8').rstrip("\r\n").split("\t")
geneId = str(int(data[0]))
#disease_specificity_index = data[2]
#disease_pleiotropy_index = data[3]
diseaseId = data[4]
score = float(data[scorePos])
pmids = data[13]
source = data[-1]
if geneId in proteinMapping:
for identifier in proteinMapping[geneId]:
if diseaseId in diseaseMapping:
for code in diseaseMapping[diseaseId]:
code = "DOID:" + code
relationships[idType].add(
(identifier, code, "ASSOCIATED_WITH",
score, atype, "DisGeNet: " + source,
pmids))
except UnicodeDecodeError:
continue
associations.close()
builder_utils.remove_directory(directory)
return (relationships, header, output_file)
def parser(databases_directory, importDirectory, download=True):
config = builder_utils.get_config(config_name="jensenlabConfig.yml", data_type='databases')
outputfileName = "Publications.tsv"
url = config['db_url']
ifile = config['organisms_file']
organisms = str(config['organisms'])
directory = os.path.join(databases_directory, "Jensenlab")
builder_utils.checkDirectory(os.path.join(directory, "textmining"))
if download:
builder_utils.downloadDB(url.replace("FILE", ifile), os.path.join(directory, "textmining"))
ifile = os.path.join(directory, os.path.join("textmining", ifile))
valid_pubs = read_valid_pubs(organisms, ifile)
entities, header = parse_PMC_list(config, os.path.join(directory, "textmining"), download=download, valid_pubs=valid_pubs)
num_entities = len(entities)
outputfile = os.path.join(importDirectory, outputfileName)
builder_utils.write_entities(entities, header, outputfile)
entities = None
for qtype in config['db_mentions_types']:
parse_mentions(config, directory, qtype, importDirectory, download)
builder_utils.remove_directory(os.path.join(directory, "textmining"))
return (num_entities, outputfile)
def parse_PMC_list(config, directory, download=True, valid_pubs=None):
url = config['PMC_db_url']
plinkout = config['pubmed_linkout']
entities = set()
fileName = os.path.join(directory, url.split('/')[-1])
if download:
builder_utils.downloadDB(url, directory)
entities = pd.read_csv(fileName, sep=',', dtype=str, compression='gzip', low_memory=False)
entities = entities[config['PMC_fields']]
entities = entities[entities.iloc[:, 0].notnull()]
entities = entities.set_index(list(entities.columns)[0])
if valid_pubs is not None:
entities = entities.loc[valid_pubs]
entities['linkout'] = [plinkout.replace("PUBMEDID", str(int(pubmedid))) for pubmedid in list(entities.index)]
entities.index.names = ['ID']
entities['TYPE'] = 'Publication'
entities = entities.reset_index()
header = [c.replace(' ', '_').lower() if c not in ['ID', 'TYPE'] else c for c in list(entities.columns)]
entities = entities.replace('\\\\', '', regex=True)
entities = list(entities.itertuples(index=False))
return entities, header
def parser(databases_directory, download=True):
config = builder_utils.get_config(config_name="gwasCatalogConfig.yml",
data_type='databases')
url = config['GWASCat_url']
entities_header = config['entities_header']
relationships_header = config['relationships_header']
entities = set()
relationships = defaultdict(set)
directory = os.path.join(databases_directory, "GWAScatalog")
builder_utils.checkDirectory(directory)
fileName = os.path.join(directory, url.split('/')[-1])
if download:
builder_utils.downloadDB(url, directory)
with open(fileName, 'r', encoding="utf-8") as catalog:
for line in catalog:
data = line.rstrip("\r\n").split("\t")
if len(data) > 36:
pubmedid = data[1]
date = data[3]
title = data[6]
sample_size = data[8]
replication_size = data[9]
#chromosome = data[11]
#position = data[12]
#genes_mapped = data[14].split(" - ")
snp_id = data[20].split('-')[0]
freq = data[26]
pval = data[27]
odds_ratio = data[30]
trait = data[34]
exp_factor = data[35]
study = data[36]
entities.add((study, "GWAS_study", title, date, sample_size,
replication_size, trait))
if pubmedid != "":
relationships["published_in_publication"].add(
(study, pubmedid, "PUBLISHED_IN", "GWAS Catalog"))
if snp_id != "":
relationships["variant_found_in_gwas"].add(
(re.sub(r"^\W+|\W+$", "",
snp_id), study, "VARIANT_FOUND_IN_GWAS", freq,
pval, odds_ratio, trait, "GWAS Catalog"))
if exp_factor != "":
exp_factor = exp_factor.split('/')[-1].replace('_', ':')
relationships["studies_trait"].add(
(study, exp_factor, "STUDIES_TRAIT", "GWAS Catalog"))
builder_utils.remove_directory(directory)
return (entities, relationships, entities_header, relationships_header)
def extract_metabolites(config, directory, download=True):
metabolites = defaultdict()
prefix = "{http://www.hmdb.ca}"
url = config['HMDB_url']
fileName = os.path.join(directory, url.split('/')[-1])
if download:
builder_utils.downloadDB(url, directory)
fields = config['HMDB_fields']
parentFields = config['HMDB_parentFields']
structuredFields = config['HMDB_structures']
with zipfile.ZipFile(fileName, 'r') as zipped:
for zfile in zipped.namelist():
zipped.extract(member=zfile, path=directory)
xfile = os.path.join(directory, zfile)
with open(xfile, 'rb') as f:
context = etree.iterparse(f,
events=("end", ),
tag=prefix + "metabolite")
for _, elem in context:
values = {
child.tag.replace(prefix, ''): child.text
for child in elem.iterchildren()
if child.tag.replace(prefix, '') in fields
and child.text is not None
}
for child in elem.iterchildren():
if child.tag.replace(prefix, '') in parentFields:
label = child.tag.replace(prefix, '')
values[label] = set()
for intchild in child.iter():
if intchild.text is not None:
text = intchild.text
if text.strip() != "":
if label in structuredFields:
if intchild.tag.replace(
prefix, ''
) in structuredFields[label]:
if len(structuredFields[label]
) > 1:
values[
intchild.tag.replace(
prefix, '')] = text
else:
values[label].add(text)
elif intchild.tag.replace(
prefix, '') in fields and text:
values[label].add(text)
if "accession" in values:
metabolites[values["accession"]] = values
return metabolites
def parser(databases_directory, download=True):
relationships = defaultdict(set)
directory = os.path.join(databases_directory, "FooDB")
builder_utils.checkDirectory(directory)
config = builder_utils.get_config(config_name="foodbConfig.yml", data_type='databases')
database_url = config['database_url']
entities_header = config['entities_header']
relationships_headers = config['relationships_headers']
tar_fileName = os.path.join(directory, database_url.split('/')[-1])
if download:
builder_utils.downloadDB(database_url, directory)
contents = {}
food = set()
compounds = {}
try:
tf = tarfile.open(tar_fileName, 'r')
file_content = tf.getnames()
tar_dir = file_content[1]
tf.extractall(path=directory)
tf.close()
for file_name in config['files']:
path = os.path.join(directory, os.path.join(tar_dir, file_name))
with open(path, 'r', encoding="utf-8", errors='replace') as f:
if file_name == "Content.csv":
contents = parseContents(f)
elif file_name == "Food.csv":
food, mapping = parseFood(f)
elif file_name == "Compound.csv":
compounds = parseCompounds(f)
for food_id, compound_id in contents:
if compound_id in compounds:
compound_code = compounds[compound_id].replace("HMDB", "HMDB00")
relationships[("food", "has_content")].add((food_id, compound_code, "HAS_CONTENT") + contents[(food_id, compound_id)])
mp.reset_mapping(entity="Food")
with open(os.path.join(directory, "mapping.tsv"), 'w', encoding='utf-8') as out:
for food_id in mapping:
for alias in mapping[food_id]:
out.write(str(food_id)+"\t"+str(alias)+"\n")
mp.mark_complete_mapping(entity="Food")
except tarfile.ReadError as err:
raise Exception("Error importing database FooDB.\n {}".format(err))
builder_utils.remove_directory(directory)
return food, relationships, entities_header, relationships_headers
def parse_mentions(config, directory, qtype, importDirectory, download=True):
url = config['db_url']
ifile = config['db_mentions_files'][qtype]
if qtype == "9606":
mapping = mp.getSTRINGMapping(download=download)
elif qtype == "-1":
mapping = mp.getSTRINGMapping(source=config['db_sources']["Drug"], download=download, db="STITCH")
filters = []
if qtype in config['db_mentions_filters']:
filters = config['db_mentions_filters'][qtype]
entity1, entity2 = config['db_mentions_types'][qtype]
outputfile = os.path.join(importDirectory, entity1 + "_" + entity2 + "_mentioned_in_publication.tsv")
if download:
builder_utils.downloadDB(url.replace("FILE", ifile), os.path.join(directory, "textmining"))
ifile = os.path.join(directory, os.path.join("textmining", ifile))
with open(outputfile, 'w') as f:
f.write("START_ID\tEND_ID\tTYPE\n")
with open(ifile, 'r') as idbf:
for line in idbf:
data = line.rstrip("\r\n").split('\t')
id1 = data[0]
pubmedids = data[1].split(" ")
ident = []
if qtype == "9606":
id1 = "9606."+id1
if id1 in mapping:
ident = mapping[id1]
elif qtype == "-1":
if id1 in mapping:
ident = mapping[id1]
elif qtype == "-26":
if id1.startswith("DOID"):
ident = [id1]
else:
ident = [id1]
for i in ident:
if i not in filters:
aux = pd.DataFrame(data={"Pubmedids": list(pubmedids)})
aux["START_ID"] = i
aux["TYPE"] = "MENTIONED_IN_PUBLICATION"
aux.to_csv(path_or_buf=f, sep='\t', header=False, index=False, quotechar='"', line_terminator='\n', escapechar='\\')
aux = None
def parser(databases_directory, download=True):
relationships = set()
config = builder_utils.get_config(config_name="mutationDsConfig.yml",
data_type='databases')
header = config['header']
output_file_name = "mutation_curated_affects_interaction_with.tsv"
regex = r":(\w+)\("
url = config['mutations_url']
directory = os.path.join(databases_directory, "MutationDs")
builder_utils.checkDirectory(directory)
file_name = os.path.join(directory, url.split('/')[-1])
if download:
builder_utils.downloadDB(url, directory)
with open(file_name, 'r') as mf:
first = True
for line in mf:
if first:
first = False
continue
data = line.rstrip("\r\n").split("\t")
if len(data) > 12:
internal_id = data[0]
pvariant = data[1]
effect = data[5]
protein = data[7].split(':')
organism = data[10]
interaction = data[11]
evidence = data[12]
if organism.startswith("9606") and len(protein) > 1:
protein = protein[1]
pvariant = protein + "_" + pvariant
matches = re.finditer(regex, interaction)
for matchNum, match in enumerate(matches, start=1):
interactor = match.group(1)
relationships.add((pvariant, interactor,
"CURATED_AFFECTS_INTERACTION_WITH",
effect, interaction, evidence,
internal_id, "Intact-MutationDs"))
builder_utils.remove_directory(directory)
return (relationships, header, output_file_name)
def getSTRINGMapping(source="BLAST_UniProt_AC", download=True, db="STRING"):
"""
Parses database (db) and extracts relationships between identifiers to order databases (source).
:param str url: link to download database raw file.
:param str source: name of the source database for selecting aliases.
:param bool download: wether to download the file or not.
:param str db: name of the database to be parsed.
:return: Dictionary of database identifers (keys) and set of unique aliases to other databases (values).
"""
url = get_STRING_mapping_url(db=db)
mapping = defaultdict(set)
directory = os.path.join(dbconfig["databasesDir"], db)
file_name = os.path.join(directory, url.split('/')[-1])
builder_utils.checkDirectory(directory)
print(download)
if download:
print("Downloading", url, directory)
builder_utils.downloadDB(url, directory)
f = os.path.join(directory, file_name)
first = True
with gzip.open(f, 'rb') as mf:
for line in mf:
if first:
first = False
continue
data = line.decode('utf-8').rstrip("\r\n").split("\t")
if db == "STRING":
stringID = data[0]
alias = data[1]
sources = data[2].split(' ')
else:
stringID = data[0]
alias = data[2]
sources = data[3].split(' ')
if not alias.startswith('DB'):
continue
if source in sources:
mapping[stringID].add(alias)
return mapping
def parser(databases_dir, download=True):
config = builder_utils.get_config(config_name="goaConfig.yml",
data_type='databases')
url = config['url']
rel_header = config['header']
protein_mapping = mp.getMappingForEntity(entity="Protein")
valid_proteins = list(set(protein_mapping.values))
directory = os.path.join(databases_dir, "GOA")
builder_utils.checkDirectory(directory)
file_name = os.path.join(directory, url.split('/')[-1])
if download:
builder_utils.downloadDB(url, directory)
annotations = parse_annotations_with_pandas(file_name, valid_proteins)
builder_utils.remove_directory(directory)
return annotations, rel_header
def parser(databases_directory, download=True):
config = builder_utils.get_config(config_name="pathwayCommonsConfig.yml",
data_type='databases')
url = config['pathwayCommons_pathways_url']
entities = set()
relationships = set()
directory = os.path.join(databases_directory, "PathwayCommons")
builder_utils.checkDirectory(directory)
fileName = url.split('/')[-1]
entities_header = config['pathways_header']
relationships_header = config['relationships_header']
if download:
builder_utils.downloadDB(url, directory)
f = os.path.join(directory, fileName)
associations = gzip.open(f, 'r')
for line in associations:
data = line.decode('utf-8').rstrip("\r\n").split("\t")
linkout = data[0]
code = data[0].split("/")[-1]
ptw_dict = dict([item.split(": ")[0], ":".join(item.split(": ")[1:])]
for item in data[1].split("; "))
proteins = data[2:]
if "organism" in ptw_dict and ptw_dict["organism"] == "9606":
name = ptw_dict["name"]
source = ptw_dict["datasource"]
else:
continue
entities.add((code, "Pathway", name, name, ptw_dict["organism"],
source, linkout))
for protein in proteins:
relationships.add((protein, code, "ANNOTATED_IN_PATHWAY", linkout,
"PathwayCommons: " + source))
associations.close()
builder_utils.remove_directory(directory)
return (entities, relationships, entities_header, relationships_header)
def parser(databases_directory, download=True):
config = builder_utils.get_config(
config_name="drugGeneInteractionDBConfig.yml", data_type='databases')
url = config['DGIdb_url']
header = config['header']
output_file = "dgidb_targets.tsv"
drugmapping = mp.getMappingForEntity("Drug")
relationships = set()
directory = os.path.join(databases_directory, "DGIdb")
builder_utils.checkDirectory(directory)
fileName = os.path.join(directory, url.split('/')[-1])
if download:
builder_utils.downloadDB(url, directory)
with open(fileName, 'r', encoding='utf-8') as associations:
first = True
for line in associations:
if first:
first = False
continue
data = line.rstrip("\r\n").split("\t")
gene = data[0]
source = data[3]
interactionType = data[4] if data[4] != '' else 'unknown'
drug = data[8].lower()
if drug == "":
drug = data[7]
if drug == "" and data[6] != "":
drug = data[6]
else:
continue
if gene != "":
if drug in drugmapping:
drug = drugmapping[drug]
relationships.add((drug, gene, "TARGETS", "NA", "NA", "NA",
interactionType, "DGIdb: " + source))
builder_utils.remove_directory(directory)
return (relationships, header, output_file)
def parser(databases_directory, download=True):
directory = os.path.join(databases_directory, "ExposomeExplorer")
builder_utils.checkDirectory(directory)
config = builder_utils.get_config(config_name="exposomeConfig.yml", data_type='databases')
database_urls = config['database_urls']
relationships_header = config['relationships_header']
mapping = mp.getMappingForEntity("Food")
correlations = {}
for url in database_urls:
zipped_fileName = os.path.join(directory, url.split('/')[-1])
file_name = '.'.join(url.split('/')[-1].split('.')[0:2])
if download:
builder_utils.downloadDB(url, directory)
with zipfile.ZipFile(zipped_fileName) as z:
if file_name == "biomarkers.csv":
biomarkers = parseBiomarkersFile(z, file_name)
elif file_name == "correlations.csv":
correlations = parseCorrelationsFile(z, file_name, biomarkers, mapping)
builder_utils.remove_directory(directory)
return correlations, relationships_header
def parser(databases_directory, download=True):
config = builder_utils.get_config(config_name="hgncConfig.yml",
data_type='databases')
url = config['hgnc_url']
entities = set()
directory = os.path.join(databases_directory, "HGNC")
builder_utils.checkDirectory(directory)
fileName = os.path.join(directory, url.split('/')[-1])
taxid = 9606
entities_header = config['header']
if download:
builder_utils.downloadDB(url, directory)
with open(fileName, 'r', encoding="utf-8") as df:
first = True
for line in df:
if first:
first = False
continue
data = line.rstrip("\r\n").split("\t")
geneSymbol = data[1]
geneName = data[2]
status = data[5]
geneFamily = data[12]
synonyms = data[18:23]
transcript = data[23]
if status != "Approved":
continue
entities.add((geneSymbol, "Gene", geneName, geneFamily,
",".join(synonyms), taxid))
#relationships.add((geneSymbol, transcript, "TRANSCRIBED_INTO"))
builder_utils.remove_directory(directory)
return entities, entities_header
def parser(databases_directory, download=True):
config = builder_utils.get_config(config_name="hpaConfig.yml",
data_type='databases')
url = config['hpa_pathology_url']
disease_mapping = mp.getMappingFromOntology(ontology="Disease",
source=None)
protein_mapping = mp.getMultipleMappingForEntity("Protein")
directory = os.path.join(databases_directory, "HPA")
builder_utils.checkDirectory(directory)
compressed_fileName = os.path.join(directory, url.split('/')[-1])
file_name = '.'.join(url.split('/')[-1].split('.')[0:2])
relationships_headers = config['relationships_headers']
if download:
builder_utils.downloadDB(url, directory)
with zipfile.ZipFile(compressed_fileName) as z:
if file_name == "pathology.tsv":
pathology = parsePathologyFile(config, z, file_name,
protein_mapping, disease_mapping)
builder_utils.remove_directory(directory)
return (pathology, relationships_headers)
def parser(databases_directory,
import_directory,
download=True,
updated_on=None):
config = builder_utils.get_config(config_name="pfamConfig.yml",
data_type='databases')
entity_header = config['entity_header']
relationship_headers = config['relationship_headers']
directory = os.path.join(databases_directory, 'Pfam')
builder_utils.checkDirectory(directory)
protein_mapping = mp.getMappingForEntity(entity="Protein")
valid_proteins = list(set(protein_mapping.values()))
ftp_url = config['ftp_url']
filename = config['full_uniprot_file']
# url = config['test']
if not os.path.exists(os.path.join(directory, filename)):
if download:
builder_utils.downloadDB(ftp_url + filename, directory)
stats = set()
if os.path.exists(os.path.join(directory, filename)):
fhandler = builder_utils.read_gzipped_file(
os.path.join(directory, filename))
identifier = None
description = []
lines = []
missed = 0
entities = set()
relationships = defaultdict(set)
is_first = True
i = 0
read_lines = 0
num_entities = 0
num_relationships = {}
try:
for line in fhandler:
i += 1
read_lines += 1
if line.startswith("# STOCKHOLM"):
if identifier is not None:
entities.add((identifier, 'Functional_region', name,
" ".join(description), "PFam"))
if len(entities) == 100:
print_files(entities,
entity_header,
outputfile=os.path.join(
import_directory,
'Functional_region.tsv'),
is_first=is_first)
num_entities += len(entities)
if 'mentioned_in_publication' in relationships:
print_files(
relationships['mentioned_in_publication'],
relationship_headers[
'mentioned_in_publication'],
outputfile=os.path.join(
import_directory,
'Functional_region_mentioned_in_publication.tsv'
),
is_first=is_first)
if 'mentioned_in_publication' not in num_relationships:
num_relationships[
'mentioned_in_publication'] = 0
num_relationships[
'mentioned_in_publication'] += len(
relationships[
'mentioned_in_publication'])
if 'found_in_protein' in relationships:
print_files(
relationships['found_in_protein'],
relationship_headers['found_in_protein'],
outputfile=os.path.join(
import_directory,
'Functional_region_found_in_protein.tsv'
),
is_first=is_first,
filter_for=('END_ID', valid_proteins))
if 'found_in_protein' not in num_relationships:
num_relationships['found_in_protein'] = 0
num_relationships['found_in_protein'] += len(
relationships['found_in_protein'])
entities = set()
relationships = defaultdict(set)
is_first = False
identifier = None
description = []
elif line.startswith("#=GF"):
data = line.rstrip('\r\n').split()
if 'AC' in data:
identifier = data[2].split('.')[0]
elif 'DE' in data:
name = " ".join(data[2:])
elif 'RM' in data:
relationships['mentioned_in_publication'].add(
(identifier, data[2], "MENTIONED_IN_PUBLICATION",
"PFam"))
elif 'CC' in data:
description.append(" ".join(data[2:]))
elif not line.startswith('//'):
data = line.rstrip('\r\n').split()
protein, positions = data[0].split('/')
protein = protein.replace('.', '-')
start, end = positions.split('-')
sequence = data[1]
relationships['found_in_protein'].add(
(identifier, protein, "FOUND_IN_PROTEIN", start, end,
sequence, "PFam"))
if protein.split('-')[0] != protein:
relationships['found_in_protein'].add(
(identifier, protein.split('-')[0],
"FOUND_IN_PROTEIN", start, end, sequence, "PFam"))
except UnicodeDecodeError:
lines.append(i)
missed += 1
fhandler.close()
if len(entities) > 0:
print_files(entities,
entity_header,
outputfile=os.path.join(import_directory,
'Functional_region.tsv'),
is_first=is_first)
num_entities += len(entities)
print_files(relationships['mentioned_in_publication'],
relationship_headers['mentioned_in_publication'],
outputfile=os.path.join(
import_directory,
'Functional_region_mentioned_in_publication.tsv'),
is_first=is_first)
num_relationships['mentioned_in_publication'] += len(
relationships['mentioned_in_publication'])
print_files(relationships['found_in_protein'],
relationship_headers['found_in_protein'],
outputfile=os.path.join(
import_directory,
'Functional_region_found_in_protein.tsv'),
is_first=is_first)
num_relationships['found_in_protein'] += len(
relationships['found_in_protein'])
stats.add(
builder_utils.buildStats(num_entities, "entity",
"Functional_region", "Pfam",
'Functional_region.tsv', updated_on))
for rel in num_relationships:
stats.add(
builder_utils.buildStats(num_relationships[rel],
"relationship", rel.upper(), "Pfam",
'Functional_region_' + rel + '.tsv',
updated_on))
builder_utils.remove_directory(directory)
return stats
def parser(databases_directory, download=True):
config = builder_utils.get_config(config_name="oncokbConfig.yml",
data_type='databases')
url_actionable = config['OncoKB_actionable_url']
url_annotation = config['OncoKB_annotated_url']
amino_acids = config['amino_acids']
entities_header = config['entities_header']
relationships_headers = config['relationships_headers']
mapping = mp.getMappingFromOntology(ontology="Disease", source=None)
drug_mapping = mp.getMappingForEntity("Drug")
protein_mapping = mp.getMultipleMappingForEntity("Protein")
levels = config['OncoKB_levels']
entities = set()
relationships = defaultdict(set)
directory = os.path.join(databases_directory, "OncoKB")
builder_utils.checkDirectory(directory)
acfileName = os.path.join(directory, url_actionable.split('/')[-1])
anfileName = os.path.join(directory, url_annotation.split('/')[-1])
if download:
builder_utils.downloadDB(url_actionable, directory)
builder_utils.downloadDB(url_annotation, directory)
variant_regex = r"(\D\d+\D)$"
with open(anfileName, 'r', errors='replace') as variants:
first = True
for line in variants:
if first:
first = False
continue
data = line.rstrip("\r\n").split("\t")
gene = data[3]
variant = data[4]
oncogenicity = data[5]
effect = data[6]
if gene in protein_mapping:
for protein in protein_mapping[gene]:
match = re.search(variant_regex, variant)
if match:
if variant[0] in amino_acids and variant[
-1] in amino_acids:
valid_variant = protein + '_p.' + amino_acids[
variant[0]] + ''.join(
variant[1:-1]) + amino_acids[variant[-1]]
entities.add(
(valid_variant, "Clinically_relevant_variant",
"", "", "", "", "", effect, oncogenicity))
with open(acfileName, 'r', errors='replace') as associations:
first = True
for line in associations:
if first:
first = False
continue
data = line.rstrip("\r\n").split("\t")
isoform = data[1]
gene = data[3]
variant = data[5]
disease = data[6]
level = data[7]
drugs = data[8].split(', ')
pubmed_ids = data[9].split(',')
if level in levels:
level = levels[level]
valid_variants = []
if gene in protein_mapping:
for protein in protein_mapping[gene]:
match = re.search(variant_regex, variant)
if match:
if variant[0] in amino_acids and variant[
-1] in amino_acids:
valid_variants.append(protein + '_p.' +
amino_acids[variant[0]] +
''.join(variant[1:-1]) +
amino_acids[variant[-1]])
for drug in drugs:
for d in drug.split(' + '):
if d.lower() in drug_mapping:
drug = drug_mapping[d.lower()]
relationships["targets"].add(
(drug, gene, "CURATED_TARGETS", "curated", "NA",
"NA", "curated", "OncoKB"))
for valid_variant in valid_variants:
relationships[
"targets_clinically_relevant_variant"].add(
(drug, valid_variant,
"TARGETS_KNOWN_VARIANT", level[0],
level[1], disease, "curated", "OncoKB"))
for valid_variant in valid_variants:
if disease.lower() in mapping:
disease = mapping[disease.lower()]
relationships["associated_with"].add(
(valid_variant, disease, "ASSOCIATED_WITH", "curated",
"curated", "OncoKB", len(pubmed_ids)))
else:
pass
relationships["known_variant_is_clinically_relevant"].add(
(valid_variant, valid_variant,
"KNOWN_VARIANT_IS_CLINICALLY_RELEVANT", "OncoKB"))
builder_utils.remove_directory(directory)
return (entities, relationships, entities_header, relationships_headers)
def parser(databases_directory, download=True):
variant_regex = r"(\D\d+\D)$"
regex = r"(chr\d+)\:g\.(\d+)(\w)>(\w)"
config = builder_utils.get_config(
config_name="cancerGenomeInterpreterConfig.yml", data_type='databases')
url = config['cancerBiomarkers_url']
entities_header = config['entities_header']
relationships_headers = config['relationships_headers']
amino_acids = config['amino_acids']
mapping = mp.getMappingFromOntology(ontology="Disease", source=None)
drugmapping = mp.getMappingForEntity("Drug")
protein_mapping = mp.getMultipleMappingForEntity("Protein")
fileName = config['cancerBiomarkers_variant_file']
relationships = defaultdict(set)
entities = set()
directory = os.path.join(databases_directory, "CancerGenomeInterpreter")
builder_utils.checkDirectory(directory)
zipFile = os.path.join(directory, url.split('/')[-1])
if download:
builder_utils.downloadDB(url, directory)
with zipfile.ZipFile(zipFile) as z:
if fileName in z.namelist():
with z.open(fileName, 'r') as responses:
first = True
for line in responses:
if first:
first = False
continue
data = line.decode('utf-8').rstrip("\r\n").split("\t")
gene_variant = data[0].split(':')
if len(gene_variant) < 2:
continue
gene = gene_variant[0]
variants = gene_variant[1].split(',')
#alterationType = data[1]
response = data[3]
drugs = data[10].split(';')
#status = data[11].split(';')
evidence = data[12]
tumors = data[16].split(';')
publications = data[17].split(';')
identifier = data[21]
prot_variant = data[22]
matches = re.match(regex, identifier)
alternative_names = [identifier]
if matches is not None:
cpra = matches.groups()
chromosome, position, reference, alternative = cpra
variant = chromosome + ":g." + position + reference + ">" + alternative
if prot_variant != "":
prot_variant = prot_variant.split(':')[1]
alternative_names.append(prot_variant)
valid_variants = []
if gene in protein_mapping:
for protein in protein_mapping[gene]:
for variant in variants:
match = re.search(variant_regex, variant)
if match:
if variant[0] in amino_acids and variant[
-1] in amino_acids:
valid_variant = protein + '_p.' + amino_acids[
variant[0]] + ''.join(
variant[1:-1]) + amino_acids[
variant[-1]]
valid_variants.append(valid_variant)
entities.add(
(valid_variant,
"Clinically_relevant_variant",
",".join(alternative_names),
chromosome, position, reference,
alternative, "", "", "CGI"))
relationships[
"known_variant_is_clinically_relevant"].add(
(valid_variant, valid_variant,
"KNOWN_VARIANT_IS_CLINICALLY_RELEVANT",
"CGI"))
for drug in drugs:
if drug.lower() in drugmapping:
drug = drugmapping[drug.lower()]
elif drug.split(" ")[0].lower() in drugmapping:
drug = drugmapping[drug.split(" ")[0].lower()]
elif " ".join(
drug.split(" ")[1:]).lower() in drugmapping:
drug = drugmapping[" ".join(
drug.split(" ")[1:]).lower()]
relationships["targets"].add(
(drug, gene, "CURATED_TARGETS", evidence, response,
",".join(tumors), "curated", "CGI"))
for valid_variant in valid_variants:
relationships[
"targets_clinically_relevant_variant"].add(
(drug, valid_variant,
"TARGETS_CLINICALLY_RELEVANT_VARIANT",
evidence, response, "".join(tumors),
"curated", "CGI"))
for tumor in tumors:
if tumor.lower() in mapping:
tumor = mapping[tumor.lower()]
for valid_variant in valid_variants:
relationships["associated_with"].add(
(valid_variant, tumor, "ASSOCIATED_WITH",
"curated", "curated", "CGI",
len(publications)))
builder_utils.remove_directory(directory)
return (entities, relationships, entities_header, relationships_headers)
def parser(databases_directory, download=True):
entities = set()
relationships = defaultdict(set)
directory = os.path.join(databases_directory, "CORUM")
builder_utils.checkDirectory(directory)
try:
config = builder_utils.get_config(config_name="corumConfig.yml",
data_type='databases')
except Exception as err:
raise Exception("Reading configuration > {}.".format(err))
database_url = config['database_url']
entities_header = config['entities_header']
relationships_headers = config['relationships_headers']
zipped_fileName = os.path.join(directory, database_url.split('/')[-1])
fileName = '.'.join(database_url.split('/')[-1].split('.')[0:2])
if download:
builder_utils.downloadDB(database_url, directory)
names = set()
first = True
with zipfile.ZipFile(zipped_fileName) as z:
with z.open(fileName) as f:
for line in f:
if first:
first = False
continue
data = line.decode("utf-8").rstrip("\r\n").split("\t")
identifier = data[0]
name = data[1]
organism = data[2]
synonyms = data[3].split(';') if data[3] != "None" else [""]
cell_lines = data[4].join(';')
subunits = data[5].split(';')
evidences = data[7].split(';')
processes = data[8].split(';')
pubmedid = data[14]
if organism == "Human":
#ID name organism synonyms source
if name not in names:
entities.add((identifier, name, "9606",
",".join(synonyms), "CORUM"))
names.add(name)
for subunit in subunits:
#START_ID END_ID type cell_lines evidences publication source
relationships[("Protein", "is_subunit_of")].add(
(subunit, identifier, "IS_SUBUNIT_OF",
",".join(cell_lines), ",".join(evidences),
pubmedid, "CORUM"))
for process in processes:
#START_ID END_ID type evidence_type score source
relationships["Biological_process",
"associated_with"].add(
(identifier, process,
"ASSOCIATED_WITH", "CURATED", 5,
"CORUM"))
builder_utils.remove_directory(directory)
return entities, relationships, entities_header, relationships_headers
def parser(databases_directory, download=True):
intact_dictionary = defaultdict()
stored = set()
relationships = set()
config = builder_utils.get_config(config_name="intactConfig.yml",
data_type='databases')
header = config['header']
outputfileName = "intact_interacts_with.tsv"
regex = r"\((.*)\)"
taxid_regex = r"\:(\d+)"
url = config['intact_psimitab_url']
directory = os.path.join(databases_directory, "Intact")
builder_utils.checkDirectory(directory)
fileName = os.path.join(directory, url.split('/')[-1])
if download:
builder_utils.downloadDB(url, directory)
with open(fileName, 'r', encoding="utf-8") as idf:
first = True
for line in idf:
if first:
first = False
continue
data = line.rstrip("\r\n").split("\t")
intA = data[0].split(":")[1]
intB = data[1].split(':')
if len(intB) > 1:
intB = intB[1]
else:
continue
methodMatch = re.search(regex, data[6])
method = methodMatch.group(1) if methodMatch else "unknown"
publications = data[8]
tAmatch = re.search(taxid_regex, data[9])
tBmatch = re.search(taxid_regex, data[10])
taxidA = ""
taxidB = ""
if tAmatch and tBmatch:
taxidA = tAmatch.group(1)
taxidB = tBmatch.group(1)
itypeMatch = re.search(regex, data[11])
itype = itypeMatch.group(1) if itypeMatch else "unknown"
sourceMatch = re.search(regex, data[12])
source = sourceMatch.group(1) if sourceMatch else "unknown"
score = data[14].split(":")[1]
if builder_utils.is_number(score):
score = float(score)
else:
continue
if taxidA == "9606" and taxidB == "9606":
if (intA, intB) in intact_dictionary:
intact_dictionary[(intA, intB)]['methods'].add(method)
intact_dictionary[(intA, intB)]['sources'].add(source)
intact_dictionary[(intA, intB)]['publications'].add(
publications.replace('|', ','))
intact_dictionary[(intA, intB)]['itype'].add(itype)
else:
intact_dictionary[(intA, intB)] = {
'methods': set([method]),
'sources': set([source]),
'publications': set([publications]),
'itype': set([itype]),
'score': score
}
for (intA, intB) in intact_dictionary:
if (intA, intB, intact_dictionary[(intA,
intB)]["score"]) not in stored:
relationships.add(
(intA, intB, "CURATED_INTERACTS_WITH",
intact_dictionary[(intA, intB)]['score'],
",".join(intact_dictionary[(intA, intB)]['itype']),
",".join(intact_dictionary[(intA, intB)]['methods']),
",".join(intact_dictionary[(intA, intB)]['sources']),
",".join(intact_dictionary[(intA, intB)]['publications'])))
stored.add((intA, intB, intact_dictionary[(intA, intB)]["score"]))
builder_utils.remove_directory(directory)
return (relationships, header, outputfileName)
def parse_ontology(ontology, download=True):
"""
Parses and extracts data from a given ontology file(s), and returns a tuple with multiple dictionaries.
:param str ontology: acronym of the ontology to be parsed (e.g. Disease Ontology:'DO').
:param bool download: wether database is to be downloaded.
:return: Tuple with three nested dictionaries: terms, relationships between terms, and definitions of the terms.\
For more information on the returned dictionaries, see the documentation for any ontology parser.
"""
directory = config["ontologies_directory"]
ontology_directory = os.path.join(directory, ontology)
builder_utils.checkDirectory(ontology_directory)
ontology_files = []
ontologyData = None
mappings = None
extra_entities = set()
extra_rels = set()
if ontology in config["ontology_types"]:
otype = config["ontology_types"][ontology]
if 'urls' in config:
if otype in config['urls']:
urls = config['urls'][otype]
for url in urls:
f = url.split('/')[-1].replace('?', '_').replace('=', '_')
ontology_files.append(os.path.join(ontology_directory, f))
if download:
builder_utils.downloadDB(url,
directory=ontology_directory,
file_name=f)
elif otype in config["files"]:
ofiles = config["files"][otype]
for f in ofiles:
if '*' not in f:
if os.path.isfile(os.path.join(directory, f)):
ontology_files.append(os.path.join(directory, f))
else:
logger.error(
"Error: file {} is not in the directory {}".
format(f, directory))
else:
ontology_files.append(os.path.join(directory, f))
filters = None
if otype in config["parser_filters"]:
filters = config["parser_filters"][otype]
extra_entities, extra_rels = get_extra_entities_rels(
ontology_directory)
if len(ontology_files) > 0:
if ontology == "SNOMED-CT":
ontologyData = snomedParser.parser(ontology_files, filters)
elif ontology == "ICD":
ontologyData = icdParser.parser(ontology_files)
elif ontology == 'EFO':
ontologyData, mappings = efoParser.parser(ontology_files)
else:
ontologyData = oboParser.parser(ontology, ontology_files)
mp.buildMappingFromOBO(ontology_files[0], ontology)
else:
if ontology == "SNOMED-CT":
logger.info(
"WARNING: SNOMED-CT terminology needs to be downloaded manually since it requires UMLS License. More information available here: https://www.nlm.nih.gov/databases/umls.html"
)
else:
logger.info(
"WARNING: Ontology {} could not be downloaded. Check that the link in configuration works."
.format(ontology))
return ontologyData, mappings, extra_entities, extra_rels
def parser(databases_directory,
importDirectory,
drug_source=None,
download=True,
db="STRING"):
config = builder_utils.get_config(config_name="stringConfig.yml",
data_type='databases')
mapping = mp.getSTRINGMapping(download=False)
stored = set()
relationship = None
cutoff = config['STRING_cutoff']
header = config['header']
drugmapping = {}
if db == "STITCH":
evidences = [
"experimental", "prediction", "database", "textmining", "score"
]
relationship = "COMPILED_INTERACTS_WITH"
url = config['STITCH_url']
outputfile = os.path.join(importDirectory,
"stitch_associated_with.tsv")
drugmapping = mp.getSTRINGMapping(source=drug_source,
download=download,
db=db)
elif db == "STRING":
evidences = [
"Neighborhood in the Genome", "Gene fusions",
"Co-ocurrence across genomes", "Co-expression",
"Experimental/biochemical data",
"Association in curated databases", "Text-mining"
]
relationship = "COMPILED_TARGETS"
outputfile = os.path.join(importDirectory, "string_interacts_with.tsv")
url = config['STRING_url']
directory = os.path.join(databases_directory, db)
builder_utils.checkDirectory(directory)
fileName = os.path.join(directory, url.split('/')[-1])
if download:
builder_utils.downloadDB(url, directory)
f = os.path.join(directory, fileName)
associations = gzip.open(f, 'r')
first = True
with open(outputfile, 'w') as csvfile:
writer = csv.writer(csvfile,
delimiter='\t',
escapechar='\\',
quotechar='"',
quoting=csv.QUOTE_ALL)
writer.writerow(header)
for line in associations:
if first:
first = False
continue
data = line.decode('utf-8').rstrip("\r\n").split()
intA = data[0]
intB = data[1]
scores = data[2:]
fscores = [str(float(score) / 1000) for score in scores]
if db == "STRING":
if intA in mapping and intB in mapping and float(
fscores[-1]) >= cutoff:
for aliasA in mapping[intA]:
for aliasB in mapping[intB]:
if (aliasA, aliasB) not in stored:
row = (aliasA, aliasB, relationship,
"association", db, ",".join(evidences),
",".join(fscores[0:-1]), fscores[-1])
stored.add((aliasA, aliasB))
stored.add((aliasB, aliasB))
writer.writerow(row)
elif db == "STITCH":
if intA in drugmapping and intB in mapping and float(
fscores[-1]) >= cutoff:
for aliasA in drugmapping[intA]:
for aliasB in mapping[intB]:
if (aliasA, aliasB) not in stored:
row = (aliasA, aliasB, relationship,
"association", db, ",".join(evidences),
",".join(fscores[0:-1]), fscores[-1])
stored.add((aliasA, aliasB))
stored.add((aliasB, aliasB))
writer.writerow(row)
associations.close()
return mapping, drugmapping
def parseActions(databases_directory,
importDirectory,
proteinMapping,
drugMapping=None,
download=True,
db="STRING"):
config = builder_utils.get_config(config_name="stringConfig.yml",
data_type='databases')
url = None
bool_dict = {
't': True,
'T': True,
'True': True,
'TRUE': True,
'f': False,
'F': False,
'False': False,
'FALSE': False
}
header = config['header_actions']
relationship = "COMPILED_ACTS_ON"
stored = set()
if db == "STRING":
url = config['STRING_actions_url']
outputfile = os.path.join(importDirectory,
"string_protein_acts_on_protein.tsv")
elif db == "STITCH":
url = config['STITCH_actions_url']
outputfile = os.path.join(importDirectory,
"stitch_drug_acts_on_protein.tsv")
directory = os.path.join(databases_directory, db)
builder_utils.checkDirectory(directory)
fileName = os.path.join(directory, url.split('/')[-1])
if download:
builder_utils.downloadDB(url, directory)
f = os.path.join(directory, fileName)
associations = gzip.open(f, 'r')
first = True
with open(outputfile, 'w') as csvfile:
writer = csv.writer(csvfile,
delimiter='\t',
escapechar='\\',
quotechar='"',
quoting=csv.QUOTE_ALL)
writer.writerow(header)
for line in associations:
if first:
first = False
continue
data = line.decode('utf-8').rstrip("\r\n").split()
intA = data[0]
intB = data[1]
action = data[2]
score = float(data[-1]) / 1000
directionality = bool_dict[data[-3]] if db == "STRING" else True
if intB in proteinMapping:
aliasesA = []
if intA in drugMapping:
aliasesA = drugMapping[intA]
elif intA in proteinMapping:
aliasesA = proteinMapping[intA]
for aliasA in aliasesA:
for aliasB in proteinMapping[intB]:
if (aliasA, aliasB, action) not in stored:
row = (aliasA, aliasB, relationship, action,
directionality, score, db)
writer.writerow(row)
stored.add((aliasA, aliasB, action))
stored.add((aliasB, aliasA, action))
associations.close()
def parser(databases_directory, download=True):
config = builder_utils.get_config(config_name="refseqConfig.yml",
data_type='databases')
url = config['refseq_url']
ftp_dir = config['refseq_ftp_dir']
entities = defaultdict(set)
relationships = defaultdict(set)
directory = os.path.join(databases_directory, "RefSeq")
builder_utils.checkDirectory(directory)
fileName = os.path.join(directory, url.split('/')[-1])
headers = config['headerEntities']
taxid = 9606
if download:
file_dir = builder_utils.list_ftp_directory(ftp_dir)[0]
new_file = file_dir.split('/')[-1] + "_feature_table.txt.gz"
url = ftp_dir + file_dir.split('/')[-1] + "/" + new_file
builder_utils.downloadDB(url, directory)
fileName = os.path.join(directory, new_file)
if os.path.isfile(fileName):
df = builder_utils.read_gzipped_file(fileName)
first = True
for line in df:
if first:
first = False
continue
data = line.rstrip("\r\n").split("\t")
tclass = data[1]
assembly = data[2]
chrom = data[5]
geneAcc = data[6]
start = data[7]
end = data[8]
strand = data[9]
protAcc = data[10]
name = data[13]
symbol = data[14]
if protAcc != "":
entities["Transcript"].add(
(protAcc, "Transcript", name, tclass, assembly, taxid))
if chrom != "":
entities["Chromosome"].add(
(chrom, "Chromosome", chrom, taxid))
relationships["LOCATED_IN"].add(
(protAcc, chrom, "LOCATED_IN", start, end, strand,
"RefSeq"))
if symbol != "":
relationships["TRANSCRIBED_INTO"].add(
(symbol, protAcc, "TRANSCRIBED_INTO", "RefSeq"))
elif geneAcc != "":
entities["Transcript"].add(
(geneAcc, "Transcript", name, tclass, assembly, taxid))
if chrom != "":
entities["Chromosome"].add(
(chrom, "Chromosome", chrom, taxid))
relationships["LOCATED_IN"].add(
(protAcc, chrom, "LOCATED_IN", start, end, strand,
"RefSeq"))
df.close()
builder_utils.remove_directory(directory)
return (entities, relationships, headers)
