def hpo_to_tree(cls, hpo_terms, hpo_graph, tree, path):
tree_path = copy.copy(path)
tree_path.append(cls)
curie_util = CurieUtil(curie_map.get())
if cls not in hpo_terms:
hpo_terms[cls] = {
'label': hpo_graph.label(URIRef(curie_util.get_uri(cls)))
}
parents = hpo_graph.objects(URIRef(curie_util.get_uri(cls)),
RDFS.subClassOf)
hpo_terms[cls]['parents'] = len(list(parents))
lay_person = get_lay_person(cls, hpo_graph)
hpo_terms[cls]["lay_person"] = lay_person
# Traverse the tree to get to the input class
position = tree[tree_path[0]]
for term in tree_path[1:]:
position = position[term]
for sub_class in hpo_graph.subjects(
RDFS.subClassOf, URIRef(curie_util.get_uri(tree_path[-1]))):
curie = curie_util.get_curie(sub_class).replace("OBO:HP_", "HP:")
position[curie] = {}
hpo_to_tree(curie, hpo_terms, hpo_graph, tree, tree_path)
def hpo_to_tree(cls, hpo_terms, hpo_graph, tree, path):
tree_path = copy.copy(path)
tree_path.append(cls)
curie_util = CurieUtil(curie_map.get())
if cls not in hpo_terms:
hpo_terms[cls] = {
'label': hpo_graph.label(URIRef(curie_util.get_uri(cls)))
}
parents = hpo_graph.objects(URIRef(curie_util.get_uri(cls)), RDFS.subClassOf)
hpo_terms[cls]['parents'] = len(list(parents))
lay_person = get_lay_person(cls, hpo_graph)
hpo_terms[cls]["lay_person"] = lay_person
# Traverse the tree to get to the input class
position = tree[tree_path[0]]
for term in tree_path[1:]:
position = position[term]
for sub_class in hpo_graph.subjects(RDFS.subClassOf, URIRef(curie_util.get_uri(tree_path[-1]))):
curie = curie_util.get_curie(sub_class).replace("OBO:HP_", "HP:")
position[curie] = {}
hpo_to_tree(curie, hpo_terms, hpo_graph, tree, tree_path)
def process_catalog(self, limit=None):
"""
:param limit:
:return:
"""
raw = '/'.join((self.rawdir, self.files['catalog']['file']))
logger.info("Processing Data from %s", raw)
gu = GraphUtils(curie_map.get())
if self.testMode: # set the graph to build
g = self.testgraph
else:
g = self.graph
line_counter = 0
geno = Genotype(g)
gu.loadProperties(g, geno.object_properties, gu.OBJPROP)
gu.loadAllProperties(g)
tax_id = 'NCBITaxon:9606' # hardcode
genome_version = 'GRCh38' # hardcode
# build a hashmap of genomic location to identifiers,
# to try to get the equivalences
loc_to_id_hash = {}
with open(raw, 'r', encoding="iso-8859-1") as csvfile:
filereader = csv.reader(csvfile, delimiter='\t', quotechar='\"')
next(filereader, None) # skip the header row
for row in filereader:
if not row:
pass
else:
line_counter += 1
(date_added_to_catalog, pubmed_num, first_author, pub_date,
journal, link, study_name, disease_or_trait,
initial_sample_description, replicate_sample_description,
region, chrom_num, chrom_pos, reported_gene_nums,
mapped_gene, upstream_gene_num, downstream_gene_num,
snp_gene_nums, upstream_gene_distance,
downstream_gene_distance, strongest_snp_risk_allele, snps,
merged, snp_id_current, context, intergenic_flag,
risk_allele_frequency, pvalue, pvalue_mlog, pvalue_text,
or_or_beta, confidence_interval_95,
platform_with_snps_passing_qc, cnv_flag, mapped_trait,
mapped_trait_uri) = row
intersect = \
list(set([str(i) for i in self.test_ids['gene']]) &
set(re.split(r',', snp_gene_nums)))
# skip if no matches found in test set
if self.testMode and len(intersect) == 0:
continue
# 06-May-2015 25917933 Zai CC 20-Nov-2014 J Psychiatr Res http://europepmc.org/abstract/MED/25917933
# A genome-wide association study of suicide severity scores in bipolar disorder.
# Suicide in bipolar disorder
# 959 European ancestry individuals NA
# 10p11.22 10 32704340 C10orf68, CCDC7, ITGB1 CCDC7
# rs7079041-A rs7079041 0 7079041 intron 0 2E-6 5.698970
if chrom_num != '' and chrom_pos != '':
loc = 'chr'+str(chrom_num)+':'+str(chrom_pos)
if loc not in loc_to_id_hash:
loc_to_id_hash[loc] = set()
else:
loc = None
if re.search(r' x ', strongest_snp_risk_allele) \
or re.search(r',', strongest_snp_risk_allele):
# TODO deal with haplotypes
logger.warning(
"We can't deal with haplotypes yet: %s",
strongest_snp_risk_allele)
continue
elif re.match(r'rs', strongest_snp_risk_allele):
rs_id = 'dbSNP:'+strongest_snp_risk_allele.strip()
# remove the alteration
elif re.match(r'kgp', strongest_snp_risk_allele):
# FIXME this isn't correct
rs_id = 'dbSNP:'+strongest_snp_risk_allele.strip()
# http://www.1000genomes.org/faq/what-are-kgp-identifiers
# for some information
# They were created by Illumina for their genotyping
# platform before some variants identified during the
# pilot phase of the project had been assigned
# rs numbers.
elif re.match(r'chr', strongest_snp_risk_allele):
# like: chr10:106180121-G
rs_id = ':gwas-' + \
re.sub(
r':', '-', strongest_snp_risk_allele.strip())
elif strongest_snp_risk_allele.strip() == '':
# logger.debug(
# "No strongest SNP risk allele for %s:\n%s",
# pubmed_num, str(row))
# FIXME still consider adding in the EFO terms
# for what the study measured?
continue
else:
logger.warning(
"There's a snp id i can't manage: %s",
strongest_snp_risk_allele)
continue
alteration = re.search(r'-(.*)$', rs_id)
if alteration is not None \
and re.match(r'[ATGC]', alteration.group(1)):
# add variation to snp
pass # TODO
rs_id = re.sub(r'-.*$', '', rs_id).strip()
if loc is not None:
loc_to_id_hash[loc].add(rs_id)
pubmed_id = 'PMID:'+pubmed_num
r = Reference(
pubmed_id, Reference.ref_types['journal_article'])
r.addRefToGraph(g)
# create the chromosome
chrom_id = makeChromID(chrom_num, genome_version, 'CHR')
# add the feature to the graph
snp_description = None
if risk_allele_frequency != '' and \
risk_allele_frequency != 'NR':
snp_description = \
str(risk_allele_frequency) + \
' [risk allele frequency]'
f = Feature(
rs_id, strongest_snp_risk_allele.strip(),
Feature.types[r'SNP'], snp_description)
if chrom_num != '' and chrom_pos != '':
f.addFeatureStartLocation(chrom_pos, chrom_id)
f.addFeatureEndLocation(chrom_pos, chrom_id)
f.addFeatureToGraph(g)
f.addTaxonToFeature(g, tax_id)
# TODO consider adding allele frequency as property;
# but would need background info to do that
# also want to add other descriptive info about
# the variant from the context
for c in re.split(r';', context):
cid = self._map_variant_type(c.strip())
if cid is not None:
gu.addType(g, rs_id, cid)
# add deprecation information
if merged == 1 and str(snp_id_current.strip()) != '':
# get the current rs_id
current_rs_id = 'dbSNP:'
if not re.match(r'rs', snp_id_current):
current_rs_id += 'rs'
if loc is not None:
loc_to_id_hash[loc].append(current_rs_id)
current_rs_id += str(snp_id_current)
gu.addDeprecatedIndividual(g, rs_id, current_rs_id)
# TODO check on this
# should we add the annotations to the current
# or orig?
gu.makeLeader(g, current_rs_id)
else:
gu.makeLeader(g, rs_id)
# add the feature as a sequence alteration
# affecting various genes
# note that intronic variations don't necessarily list
# the genes such as for rs10448080 FIXME
if snp_gene_nums != '':
for s in re.split(r',', snp_gene_nums):
s = s.strip()
# still have to test for this,
# because sometimes there's a leading comma
if s != '':
gene_id = 'NCBIGene:'+s
geno.addAlleleOfGene(rs_id, gene_id)
# add the up and downstream genes if they are available
if upstream_gene_num != '':
downstream_gene_id = 'NCBIGene:'+downstream_gene_num
gu.addTriple(
g, rs_id,
Feature.object_properties[
r'upstream_of_sequence_of'],
downstream_gene_id)
if downstream_gene_num != '':
upstream_gene_id = 'NCBIGene:'+upstream_gene_num
gu.addTriple(
g, rs_id,
Feature.object_properties[
'downstream_of_sequence_of'],
upstream_gene_id)
description = 'A study of ' + disease_or_trait + \
' in ' + initial_sample_description
if replicate_sample_description != '':
description = \
' '.join(
(description, 'with',
replicate_sample_description))
if platform_with_snps_passing_qc != '':
description = ' '.join(
(description, 'on platform',
platform_with_snps_passing_qc))
description = ' '.join((description, '(p='+pvalue+')'))
# make associations to the EFO terms; there can be >1
if mapped_trait_uri.strip() != '':
for t in re.split(r',', mapped_trait_uri):
t = t.strip()
cu = CurieUtil(curie_map.get())
tid = cu.get_curie(t)
assoc = G2PAssoc(
self.name, rs_id, tid,
gu.object_properties['contributes_to'])
assoc.add_source(pubmed_id)
# combinatorial evidence
# used in automatic assertion
eco_id = 'ECO:0000213'
assoc.add_evidence(eco_id)
# assoc.set_description(description)
# FIXME score should get added to provenance/study
# assoc.set_score(pvalue)
assoc.add_association_to_graph(g)
if not self.testMode and\
(limit is not None and line_counter > limit):
break
Assoc(self.name).load_all_properties(g)
# loop through the location hash,
# and make all snps at that location equivalent
for l in loc_to_id_hash:
snp_ids = loc_to_id_hash[l]
if len(snp_ids) > 1:
logger.info("%s has >1 snp id: %s", l, str(snp_ids))
return
def _add_variant_trait_association(self,
variant_id,
mapped_trait_uri,
efo_ontology,
pubmed_id,
description=None):
if self.testMode:
g = self.testgraph
else:
g = self.graph
model = Model(g)
# make associations to the EFO terms; there can be >1
if mapped_trait_uri.strip() != '':
for trait in re.split(r',', mapped_trait_uri):
trait = trait.strip()
cu = CurieUtil(curie_map.get())
trait_id = cu.get_curie(trait)
dis_query = """
SELECT ?trait
WHERE {{
{0} rdfs:subClassOf+ EFO:0000408 .
{0} rdfs:label ?trait .
}}
""".format(trait_id)
query_result = efo_ontology.query(dis_query)
if len(list(query_result)) > 0:
if re.match(r'^EFO', trait_id):
model.addClassToGraph(trait_id,
list(query_result)[0][0],
'DOID:4')
phenotype_query = """
SELECT ?trait
WHERE {{
{0} rdfs:subClassOf+ EFO:0000651 .
{0} rdfs:label ?trait .
}}
""".format(trait_id)
query_result = efo_ontology.query(phenotype_query)
if len(list(query_result)) > 0:
if re.match(r'^EFO', trait_id):
model.addClassToGraph(trait_id,
list(query_result)[0][0],
'UPHENO:0001001')
pubmed_curie = 'PMID:' + pubmed_id
ref = Reference(g, pubmed_curie,
Reference.ref_types['journal_article'])
ref.addRefToGraph()
assoc = G2PAssoc(g, self.name, variant_id, trait_id,
model.object_properties['contributes_to'])
assoc.add_source(pubmed_curie)
# combinatorial evidence
# used in automatic assertion
eco_id = 'ECO:0000213'
assoc.add_evidence(eco_id)
if description is not None:
assoc.set_description(description)
# FIXME score should get added to provenance/study
# assoc.set_score(pvalue)
assoc.add_association_to_graph()