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)