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 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 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 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 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="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):
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):
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 run_processing(n_clicks, project_id):
message = None
style = {'display': 'none'}
table = None
if n_clicks > 0:
session_cookie = flask.request.cookies.get('custom-auth-session')
destDir = os.path.join(experimentDir, project_id)
builder_utils.checkDirectory(destDir)
temporaryDirectory = os.path.join(tmpDirectory,
session_cookie + "upload")
datasets = builder_utils.listDirectoryFoldersNotEmpty(
temporaryDirectory)
res_n = dataUpload.check_samples_in_project(driver, project_id)
if 'experimental_design' in datasets:
dataset = 'experimental_design'
directory = os.path.join(temporaryDirectory, dataset)
experimental_files = os.listdir(directory)
if config['file_design'].replace('PROJECTID',
project_id) in experimental_files:
experimental_filename = config['file_design'].replace(
'PROJECTID', project_id)
designData = builder_utils.readDataset(
os.path.join(directory, experimental_filename))
designData = designData.astype(str)
if 'subject external_id' in designData.columns and 'biological_sample external_id' in designData.columns and 'analytical_sample external_id' in designData.columns:
if (res_n > 0).any().values.sum() > 0:
res = dataUpload.remove_samples_nodes_db(
driver, project_id)
res_n = dataUpload.check_samples_in_project(
driver, project_id)
if (res_n > 0).any().values.sum() > 0:
message = 'ERROR: There is already an experimental design loaded into the database and there was an error when trying to delete it. Contact your administrator.'.format(
experimental_filename, ','.join([
'subject external_id',
'biological_sample external_id',
'analytical_sample external_id'
]))
return message, style, table
res_n = None
result = create_new_identifiers.apply_async(
args=[
project_id,
designData.to_json(), directory,
experimental_filename
],
task_id='data_upload_' + session_cookie +
datetime.now().strftime('%Y%m-%d%H-%M%S-'))
result_output = result.wait(timeout=None,
propagate=True,
interval=0.2)
res_n = pd.DataFrame.from_dict(result_output['res_n'])
else:
message = 'ERROR: The Experimental design file provided ({}) is missing some of the required fields: {}'.format(
experimental_filename, ','.join([
'subject external_id',
'biological_sample external_id',
'analytical_sample external_id'
]))
builder_utils.remove_directory(directory)
return message, style, table
if 'clinical' in datasets:
dataset = 'clinical'
directory = os.path.join(temporaryDirectory, dataset)
clinical_files = os.listdir(directory)
if config['file_clinical'].replace('PROJECTID',
project_id) in clinical_files:
clinical_filename = config['file_clinical'].replace(
'PROJECTID', project_id)
data = builder_utils.readDataset(
os.path.join(directory, clinical_filename))
external_ids = {}
if 'subject external_id' in data and 'biological_sample external_id' in data and 'analytical_sample external_id' in data:
external_ids['subjects'] = data[
'subject external_id'].astype(str).unique().tolist()
external_ids['biological_samples'] = data[
'biological_sample external_id'].astype(
str).unique().tolist()
external_ids['analytical_samples'] = data[
'analytical_sample external_id'].astype(
str).unique().tolist()
dataUpload.create_mapping_cols_clinical(
driver,
data,
directory,
clinical_filename,
separator=separator)
if 0 in res_n.values:
samples = ', '.join([k for (k, v) in res_n if v == 0])
message = 'ERROR: No {} for project {} in the database. Please upload first the experimental design (ExperimentalDesign_{}.xlsx)'.format(
samples, project_id, project_id)
builder_utils.remove_directory(directory)
return message, style, table
else:
db_ids = dataUpload.check_external_ids_in_db(
driver, project_id).to_dict()
message = ''
intersections = {}
differences_in = {}
differences_out = {}
for col in external_ids:
intersect = list(
set(db_ids[col].values()).intersection(
external_ids[col]))
difference_in = list(
set(db_ids[col].values()).difference(
external_ids[col]))
difference_out = list(
set(external_ids[col]).difference(
set(db_ids[col].values())))
if len(difference_in) > 0 or len(
difference_out) > 0:
intersections[col] = intersect
differences_in[col] = difference_in
differences_out[col] = difference_out
for col in intersections:
message += 'WARNING: Some {} identifiers were not matched:\n Matching: {}\n No information provided: {} \n Non-existing in the database: {}\n'.format(
col, len(intersections[col]),
','.join(differences_in[col]),
','.join(differences_out[col]))
else:
message = 'ERROR: Format of the Clinical Data file is not correct. Check template in the documentation. Check columns: subject external_id, biological_sample external_id and analytical_sample external_id'
builder_utils.remove_directory(directory)
return message, style, table
try:
for dataset in datasets:
source = os.path.join(temporaryDirectory, dataset)
destination = os.path.join(destDir, dataset)
builder_utils.copytree(source, destination)
datasetPath = os.path.join(
os.path.join(experimentsImportDir, project_id), dataset)
if dataset != "experimental_design":
eh.generate_dataset_imports(project_id, dataset,
datasetPath)
loader.partialUpdate(imports=['project', 'experiment'],
specific=[project_id])
filename = os.path.join(tmpDirectory,
'Uploaded_files_' + project_id)
utils.compress_directory(filename,
temporaryDirectory,
compression_format='zip')
style = {'display': 'block'}
message = 'Files successfully uploaded.'
table = dataUpload.get_project_information(driver, project_id)
except Exception as err:
style = {'display': 'block'}
message = str(err)
return message, style, table
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 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)
