def scrub(self):
"""
The XML file seems to have mixed-encoding;
we scrub out the control characters
from the file for processing.
i.e.?i
omia.xml:1555328.28: PCDATA invalid Char value 2
<field name="journal">Bulletin et Memoires de la Societe Centrale de Medic
:return:
"""
logger.info(
"Scrubbing out the nasty characters that break our parser.")
myfile = '/'.join((self.rawdir, self.files['data']['file']))
tmpfile = '/'.join((self.rawdir, self.files['data']['file']+'.tmp.gz'))
t = gzip.open(tmpfile, 'wb')
du = DipperUtil()
with gzip.open(myfile, 'rb') as f:
filereader = io.TextIOWrapper(f, newline="")
for l in filereader:
l = du.remove_control_characters(l) + '\n'
t.write(l.encode('utf-8'))
t.close()
# TEC I do not like this at all. original data must be preserved as is.
# also may be heavy handed as chars which do not break the parser
# are stripped as well (i.e. tabs and newlines)
# move the temp file
logger.info("Replacing the original data with the scrubbed file.")
shutil.move(tmpfile, myfile)
return
def _add_gene_equivalencies(self, xrefs, gene_id, taxon):
"""
Add equivalentClass and sameAs relationships
Uses external resource map located in
/resources/clique_leader.yaml to determine
if an ID space is a clique leader
"""
clique_map = self.open_and_parse_yaml(self.resources['clique_leader'])
if self.testMode:
graph = self.testgraph
else:
graph = self.graph
filter_out = ['Vega', 'IMGT/GENE-DB', 'Araport']
# These will be made xrefs
taxon_spec_xref_filters = {'10090': ['ENSEMBL'], '9606': ['ENSEMBL']}
if taxon in taxon_spec_xref_filters:
taxon_spec_filters = taxon_spec_xref_filters[taxon]
else:
taxon_spec_filters = []
model = Model(graph)
# deal with the xrefs
# MIM:614444|HGNC:HGNC:16851|Ensembl:ENSG00000136828|HPRD:11479|Vega:OTTHUMG00000020696
for ref in xrefs.strip().split('|'):
xref_curie = self._cleanup_id(ref)
if xref_curie is not None and xref_curie.strip() != '':
if re.match(r'HPRD', xref_curie):
# proteins are not == genes.
model.addTriple(gene_id,
self.properties['has_gene_product'],
xref_curie)
continue
# skip some of these for now
if xref_curie.split(':')[0] in filter_out:
continue
if xref_curie.split(':')[0] in taxon_spec_xref_filters:
model.addXref(gene_id, xref_curie)
if re.match(r'^OMIM', xref_curie):
if DipperUtil.is_omim_disease(xref_curie):
continue
try:
if self.class_or_indiv.get(gene_id) == 'C':
model.addEquivalentClass(gene_id, xref_curie)
if int(taxon) in clique_map:
if clique_map[int(taxon)] == xref_curie.split(
':')[0]:
model.makeLeader(xref_curie)
elif clique_map[int(taxon)] == gene_id.split(
':')[0]:
model.makeLeader(gene_id)
else:
model.addSameIndividual(gene_id, xref_curie)
except AssertionError as e:
logger.warn("Error parsing {0}: {1}".format(gene_id, e))
return
def _add_variant_trait_association(self,
variant_id,
mapped_trait_uri,
mapped_trait,
mondo_data,
pubmed_id,
description=None):
if self.test_mode:
graph = self.testgraph
else:
graph = self.graph
model = Model(graph)
# Use mapped traits for labels, hope that labels do not contain commas
mapped_traits = [trait.strip() for trait in mapped_trait.split(',')]
mapped_trait_uris = [
iri.strip() for iri in mapped_trait_uri.split(',')
]
if mapped_trait_uris:
for index, trait in enumerate(mapped_trait_uris):
trait_curie = trait.replace("http://www.ebi.ac.uk/efo/EFO_",
"EFO:")
if not DipperUtil.is_id_in_mondo(trait_curie, mondo_data):
if re.match(r'^EFO', trait_curie):
model.addClassToGraph(
trait_curie,
mapped_traits[index],
self.globaltt['phenotype'],
class_category=blv.terms['PhenotypicFeature'])
LOG.debug("{} not in mondo".format(trait_curie))
else:
LOG.debug("{} in mondo".format(trait_curie))
pubmed_curie = 'PMID:' + pubmed_id
ref = Reference(graph, pubmed_curie,
self.globaltt['journal article'])
ref.addRefToGraph()
if trait_curie is not None and trait_curie != '' and \
variant_id is not None and variant_id != '':
assoc = G2PAssoc(
graph, self.name, variant_id, trait_curie,
model.globaltt['contributes to condition'])
assoc.add_source(pubmed_curie)
assoc.add_evidence(self.globaltt[
'combinatorial evidence used in automatic assertion'])
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()
def _add_gene_equivalencies(self, xrefs, gene_id, taxon):
"""
Add equivalentClass and sameAs relationships
Uses external resource map located in
/resources/clique_leader.yaml to determine
if an NCBITaxon ID space is a clique leader
"""
clique_map = self.open_and_parse_yaml(self.resources['clique_leader'])
if self.test_mode:
graph = self.testgraph
else:
graph = self.graph
model = Model(graph)
filter_out = ['Vega', 'IMGT/GENE-DB', 'Araport']
# deal with the dbxrefs
# MIM:614444|HGNC:HGNC:16851|Ensembl:ENSG00000136828|HPRD:11479|Vega:OTTHUMG00000020696
for dbxref in xrefs.strip().split('|'):
prefix = ':'.join(dbxref.split(':')[:-1]).strip()
if prefix in self.localtt:
prefix = self.localtt[prefix]
dbxref_curie = ':'.join((prefix, dbxref.split(':')[-1]))
if dbxref_curie is not None and prefix != '':
if prefix == 'HPRD': # proteins are not == genes.
model.addTriple(gene_id, self.globaltt['has gene product'],
dbxref_curie)
continue
# skip some of these for now based on curie prefix
if prefix in filter_out:
continue
if prefix == 'ENSEMBL':
model.addXref(gene_id, dbxref_curie)
if prefix == 'OMIM':
if DipperUtil.is_omim_disease(dbxref_curie):
continue
try:
if self.class_or_indiv.get(gene_id) == 'C':
model.addEquivalentClass(gene_id, dbxref_curie)
if taxon in clique_map:
if clique_map[taxon] == prefix:
model.makeLeader(dbxref_curie)
elif clique_map[taxon] == gene_id.split(':')[0]:
model.makeLeader(gene_id)
else:
model.addSameIndividual(gene_id, dbxref_curie)
except AssertionError as err:
LOG.warning("Error parsing %s: %s", gene_id, err)
return
def _process_haplotype(self, hap_id, hap_label, chrom_num, chrom_pos,
context, risk_allele_frequency, mapped_gene,
so_ontology):
if self.test_mode:
graph = self.testgraph
else:
graph = self.graph
geno = Genotype(graph)
model = Model(graph)
# add the feature to the graph
hap_description = None
if risk_allele_frequency not in ['', 'NR']:
hap_description = str(
risk_allele_frequency) + ' [risk allele frequency]'
model.addIndividualToGraph(hap_id, hap_label.strip(),
self.globaltt['haplotype'], hap_description)
geno.addTaxon(self.globaltt["H**o sapiens"], hap_id)
snp_labels = re.split(r';\s?', hap_label)
chrom_nums = re.split(r';\s?', chrom_num)
chrom_positions = re.split(r';\s?', chrom_pos)
context_list = re.split(r';\s?', context)
mapped_genes = re.split(r';\s?', mapped_gene)
# Not having four "PAX5" as a list might be better, but it breaks unit tests
# mapped_genes = list(set(mapped_genes)) # make uniq
# snp_labels = list(set(snp_labels)) # make uniq
snp_curies = list()
for snp in snp_labels:
snp_curie, snp_type = self._get_curie_and_type_from_id(snp)
if snp_type is None:
LOG.info('cant find type for SNP in %s', snp)
# make blank node
snp_curie = self.make_id(snp, "_")
model.addLabel(snp_curie, snp)
elif snp_curie[0] == '_': # arrived an unlabeled blanknode
model.addLabel(snp_curie, snp)
graph.addTriple(hap_id, self.globaltt['has_variant_part'],
snp_curie)
snp_curies.append(snp_curie)
# courtesy http://stackoverflow.com/a/16720915
# check lengths of mutiple lists
length = len(snp_curies)
if not all(
len(lst) == length for lst in
[snp_labels, chrom_nums, chrom_positions, context_list]):
LOG.warning(
"Incongruous data field(s) for haplotype %s \n "
"will not add snp details", hap_label)
else:
variant_in_gene_count = 0
for index, snp_curie in enumerate(snp_curies):
self._add_snp_to_graph(snp_curie, snp_labels[index],
chrom_nums[index],
chrom_positions[index],
context_list[index])
if mapped_genes and len(mapped_genes) != len(snp_labels):
LOG.warning("More mapped genes than snps,"
" cannot disambiguate for\n%s\n%s",
mapped_genes, snp_labels) # hap_label)
else:
so_class = self.resolve(context_list[index])
so_query = """
SELECT ?variant_label
WHERE {{
{0} rdfs:subClassOf+ {1} ;
rdfs:label ?variant_label .
}}
""".format(so_class, self.globaltt['gene_variant'])
query_result = so_ontology.query(so_query)
gene_id = DipperUtil.get_hgnc_id_from_symbol(
mapped_genes[index])
if gene_id is not None and len(list(query_result)) == 1:
if context_list[index] in [
'upstream_gene_variant',
'downstream_gene_variant'
]:
graph.addTriple(snp_curie,
self.resolve(context_list[index]),
gene_id)
else:
geno.addAffectedLocus(snp_curie, gene_id)
variant_in_gene_count += 1
# Seperate in case we want to apply a different relation
# If not this is redundant with triples added above
if len(mapped_genes) == variant_in_gene_count and \
len(set(mapped_genes)) == 1:
gene_id = DipperUtil.get_hgnc_id_from_symbol(mapped_genes[0])
geno.addAffectedLocus(hap_id, gene_id)
def _process_genes(self, limit=None):
if self.testMode:
graph = self.testgraph
else:
graph = self.graph
geno = Genotype(graph)
model = Model(graph)
raw = '/'.join((self.rawdir, self.files['genes']['file']))
line_counter = 0
logger.info("Processing HGNC genes")
with open(raw, 'r', encoding="utf8") as csvfile:
filereader = csv.reader(csvfile, delimiter='\t', quotechar='\"')
# curl -s ftp://ftp.ebi.ac.uk/pub/databases/genenames/new/tsv/hgnc_complete_set.txt | head -1 | tr '\t' '\n' | grep -n .
for row in filereader:
(hgnc_id, symbol, name, locus_group, locus_type, status,
location, location_sortable, alias_symbol, alias_name,
prev_symbol, prev_name, gene_family, gene_family_id,
date_approved_reserved, date_symbol_changed,
date_name_changed, date_modified, entrez_id, ensembl_gene_id,
vega_id, ucsc_id, ena, refseq_accession, ccds_id, uniprot_ids,
pubmed_id, mgd_id, rgd_id, lsdb, cosmic, omim_id, mirbase,
homeodb, snornabase, bioparadigms_slc, orphanet,
pseudogene_org, horde_id, merops, imgt, iuphar,
kznf_gene_catalog, mamit_trnadb, cd, lncrnadb, enzyme_id,
intermediate_filament_db, rna_central_ids) = row
line_counter += 1
# skip header
if line_counter <= 1:
continue
if self.testMode and entrez_id != '' and \
int(entrez_id) not in self.gene_ids:
continue
if name == '':
name = None
gene_type_id = self.resolve(locus_type,
False) # withdrawn -> None?
if gene_type_id != locus_type:
model.addClassToGraph(hgnc_id, symbol, gene_type_id, name)
if locus_type == 'withdrawn':
model.addDeprecatedClass(hgnc_id)
else:
model.makeLeader(hgnc_id)
if entrez_id != '':
model.addEquivalentClass(hgnc_id, 'NCBIGene:' + entrez_id)
if ensembl_gene_id != '':
model.addEquivalentClass(hgnc_id,
'ENSEMBL:' + ensembl_gene_id)
if omim_id != '' and "|" not in omim_id:
omim_curie = 'OMIM:' + omim_id
if not DipperUtil.is_omim_disease(omim_curie):
model.addEquivalentClass(hgnc_id, omim_curie)
geno.addTaxon(self.hs_txid, hgnc_id)
# add pubs as "is about"
if pubmed_id != '':
for p in re.split(r'\|', pubmed_id.strip()):
if str(p) != '':
graph.addTriple('PMID:' + str(p.strip()),
self.globaltt['is_about'], hgnc_id)
# add chr location
# sometimes two are listed, like: 10p11.2 or 17q25
# -- there are only 2 of these FRA10A and MPFD
# sometimes listed like "1 not on reference assembly"
# sometimes listed like 10q24.1-q24.3
# sometimes like 11q11 alternate reference locus
band = chrom = None
chr_pattern = r'(\d+|X|Y|Z|W|MT)[pq$]'
chr_match = re.match(chr_pattern, location)
if chr_match is not None and len(chr_match.groups()) > 0:
chrom = chr_match.group(1)
chrom_id = makeChromID(chrom, self.hs_txid, 'CHR')
band_pattern = r'([pq][A-H\d]?\d?(?:\.\d+)?)'
band_match = re.search(band_pattern, location)
feat = Feature(graph, hgnc_id, None, None)
if band_match is not None and len(band_match.groups()) > 0:
band = band_match.group(1)
band = chrom + band
# add the chr band as the parent to this gene
# as a feature but assume that the band is created
# as a class with properties elsewhere in Monochrom
band_id = makeChromID(band, self.hs_txid, 'CHR')
model.addClassToGraph(band_id, None)
feat.addSubsequenceOfFeature(band_id)
else:
model.addClassToGraph(chrom_id, None)
feat.addSubsequenceOfFeature(chrom_id)
if not self.testMode and limit is not None and line_counter > limit:
break
# end loop through file
return
def _process_data(self, source, limit=None):
"""
This function will process the data files from Coriell.
We make the assumption that any alleles listed are variants
(alternates to w.t.)
Triples: (examples)
:NIGMSrepository a CLO_0000008 #repository
label : NIGMS Human Genetic Cell Repository
foaf:page
https://catalog.coriell.org/0/sections/collections/NIGMS/?SsId=8
line_id a CL_0000057, #fibroblast line
derives_from patient_id
part_of :NIGMSrepository
RO:model_of OMIM:disease_id
patient id a foaf:person,
label: "fibroblast from patient 12345 with disease X"
member_of family_id #what is the right thing here?
SIO:race EFO:caucasian #subclass of EFO:0001799
in_taxon NCBITaxon:9606
dc:description Literal(remark)
RO:has_phenotype OMIM:disease_id
GENO:has_genotype genotype_id
family_id a owl:NamedIndividual
foaf:page
"https://catalog.coriell.org/0/Sections/BrowseCatalog/FamilyTypeSubDetail.aspx?PgId=402&fam=2104&coll=GM"
genotype_id a intrinsic_genotype
GENO:has_alternate_part allelic_variant_id
we don't necessarily know much about the genotype,
other than the allelic variant. also there's the sex here
pub_id mentions cell_line_id
:param raw:
:param limit:
:return:
"""
raw = '/'.join((self.rawdir, self.files[source]['file']))
LOG.info("Processing Data from %s", raw)
if self.testMode: # set the graph to build
graph = self.testgraph
else:
graph = self.graph
family = Family(graph)
model = Model(graph)
line_counter = 1
geno = Genotype(graph)
diputil = DipperUtil()
col = self.files[source]['columns']
# affords access with
# x = row[col.index('x')].strip()
with open(raw, 'r', encoding="iso-8859-1") as csvfile:
filereader = csv.reader(csvfile, delimiter=',', quotechar=r'"')
# we can keep a close watch on changing file formats
fileheader = next(filereader, None)
fileheader = [c.lower() for c in fileheader]
if col != fileheader: # assert
LOG.error('Expected %s to have columns: %s', raw, col)
LOG.error('But Found %s to have columns: %s', raw, fileheader)
raise AssertionError('Incomming data headers have changed.')
for row in filereader:
line_counter += 1
if len(row) != len(col):
LOG.warning('Expected %i values but find %i in row %i',
len(col), len(row), line_counter)
continue
# (catalog_id, description, omim_number, sample_type,
# cell_line_available, dna_in_stock, dna_ref, gender, age,
# race, ethnicity, affected, karyotype, relprob, mutation,
# gene, family_id, collection, url, cat_remark, pubmed_ids,
# family_member, variant_id, dbsnp_id, species) = row
# example:
# GM00003,HURLER SYNDROME,607014,Fibroblast,Yes,No,
# ,Female,26 YR,Caucasian,,,,
# parent,,,39,NIGMS Human Genetic Cell Repository,
# http://ccr.coriell.org/Sections/Search/Sample_Detail.aspx?Ref=GM00003,
# 46;XX; clinically normal mother of a child with Hurler syndrome;
# proband not in Repository,,
# 2,,18343,H**o sapiens
catalog_id = row[col.index('catalog_id')].strip()
if self.testMode and catalog_id not in self.test_lines:
# skip rows not in our test lines, when in test mode
continue
# ########### BUILD REQUIRED VARIABLES ###########
# Make the cell line ID
cell_line_id = 'Coriell:' + catalog_id
# Map the cell/sample type
cell_type = self.resolve(row[col.index('sample_type')].strip())
# on fail cell_type = self.globaltt['cell'] ?
# Make a cell line label
collection = row[col.index('collection')].strip()
line_label = collection.partition(' ')[0] + '-' + catalog_id
# Map the repository/collection
repository = self.localtt[collection]
# patients are uniquely identified by one of:
# dbsnp id (which is == an individual haplotype)
# family id + family member (if present) OR
# probands are usually family member zero
# cell line id
# since some patients have >1 cell line derived from them,
# we must make sure that the genotype is attached to
# the patient, and can be inferred to the cell line
# examples of repeated patients are:
# famid=1159, member=1; fam=152,member=1
# Make the patient ID
# make an anonymous patient
patient_id = '_:person'
fam_id = row[col.index('fam')].strip()
fammember = row[col.index('fammember')].strip()
if fam_id != '':
patient_id = '-'.join((patient_id, fam_id, fammember))
else:
# make an anonymous patient
patient_id = '-'.join((patient_id, catalog_id))
# properties of the individual patients: sex, family id,
# member/relproband, description descriptions are
# really long and ugly SCREAMING text, so need to clean up
# the control cases are so odd with this labeling scheme;
# but we'll deal with it as-is for now.
description = row[col.index('description')].strip()
short_desc = (description.split(';')[0]).capitalize()
gender = row[col.index('gender')].strip().lower()
affected = row[col.index('affected')].strip()
relprob = row[col.index('relprob')].strip()
if affected == '':
affected = 'unspecified'
elif affected in self.localtt:
affected = self.localtt[affected]
else:
LOG.warning('Novel Affected status %s at row: %i of %s',
affected, line_counter, raw)
patient_label = ' '.join((affected, gender, relprob))
if relprob == 'proband':
patient_label = ' '.join(
(patient_label.strip(), 'with', short_desc))
else:
patient_label = ' '.join(
(patient_label.strip(), 'of proband with', short_desc))
# ############# BUILD THE CELL LINE #############
# Adding the cell line as a typed individual.
cell_line_reagent_id = self.globaltt['cell line']
model.addIndividualToGraph(cell_line_id, line_label,
cell_line_reagent_id)
# add the equivalent id == dna_ref
dna_ref = row[col.index('dna_ref')].strip()
if dna_ref != '' and dna_ref != catalog_id:
equiv_cell_line = 'Coriell:' + dna_ref
# some of the equivalent ids are not defined
# in the source data; so add them
model.addIndividualToGraph(equiv_cell_line, None,
cell_line_reagent_id)
model.addSameIndividual(cell_line_id, equiv_cell_line)
# Cell line derives from patient
geno.addDerivesFrom(cell_line_id, patient_id)
geno.addDerivesFrom(cell_line_id, cell_type)
# Cell line a member of repository
family.addMember(repository, cell_line_id)
cat_remark = row[col.index('cat_remark')].strip()
if cat_remark != '':
model.addDescription(cell_line_id, cat_remark)
# Cell age_at_sampling
# TODO add the age nodes when modeled properly in #78
# if (age != ''):
# this would give a BNode that is an instance of Age.
# but i don't know how to connect
# the age node to the cell line? we need to ask @mbrush
# age_id = '_'+re.sub('\s+','_',age)
# gu.addIndividualToGraph(
# graph,age_id,age,self.globaltt['age'])
# gu.addTriple(
# graph,age_id,self.globaltt['has measurement value'],age,
# True)
# ############# BUILD THE PATIENT #############
# Add the patient ID as an individual.
model.addPerson(patient_id, patient_label)
# TODO map relationship to proband as a class
# (what ontology?)
# Add race of patient
# FIXME: Adjust for subcategories based on ethnicity field
# EDIT: There are 743 different entries for ethnicity...
# Too many to map?
# Add ethnicity as literal in addition to the mapped race?
# Adjust the ethnicity txt (if using)
# to initial capitalization to remove ALLCAPS
# TODO race should go into the individual's background
# and abstracted out to the Genotype class punting for now.
# if race != '':
# mapped_race = self.resolve(race)
# if mapped_race is not None:
# gu.addTriple(
# g,patient_id,self.globaltt['race'], mapped_race)
# model.addSubClass(
# mapped_race,self.globaltt['ethnic_group'])
# ############# BUILD THE FAMILY #############
# Add triples for family_id, if present.
if fam_id != '':
family_comp_id = 'CoriellFamily:' + fam_id
family_label = ' '.join(
('Family of proband with', short_desc))
# Add the family ID as a named individual
model.addIndividualToGraph(family_comp_id, family_label,
self.globaltt['family'])
# Add the patient as a member of the family
family.addMemberOf(patient_id, family_comp_id)
# ############# BUILD THE GENOTYPE #############
# the important things to pay attention to here are:
# karyotype = chr rearrangements (somatic?)
# mutation = protein-level mutation as a label,
# often from omim
# gene = gene symbol - TODO get id
# variant_id = omim variant ids (; delimited)
# dbsnp_id = snp individual ids = full genotype?
# note GM00633 is a good example of chromosomal variation
# - do we have enough to capture this?
# GM00325 has both abnormal karyotype and variation
# make an assumption that if the taxon is blank,
# that it is human!
species = row[col.index('species')].strip()
if species is None or species == '':
species = 'H**o sapiens'
taxon = self.resolve(species)
# if there's a dbSNP id,
# this is actually the individual's genotype
genotype_id = None
genotype_label = None
dbsnp_id = row[col.index('dbsnp_id')].strip()
if dbsnp_id != '':
genotype_id = 'dbSNPIndividual:' + dbsnp_id
omim_map = {}
gvc_id = None
# some of the karyotypes are encoded
# with terrible hidden codes. remove them here
# i've seen a <98> character
karyotype = row[col.index('karyotype')].strip()
karyotype = diputil.remove_control_characters(karyotype)
karyotype_id = None
if karyotype.strip() != '':
karyotype_id = '_:' + re.sub('MONARCH:', '',
self.make_id(karyotype))
# add karyotype as karyotype_variation_complement
model.addIndividualToGraph(
karyotype_id, karyotype,
self.globaltt['karyotype_variation_complement'])
# TODO break down the karyotype into parts
# and map into GENO. depends on #77
# place the karyotype in a location(s).
karyo_chrs = self._get_affected_chromosomes_from_karyotype(
karyotype)
for chrom in karyo_chrs:
chr_id = makeChromID(chrom, taxon, 'CHR')
# add an anonymous sequence feature,
# each located on chr
karyotype_feature_id = '-'.join((karyotype_id, chrom))
karyotype_feature_label = \
'some karyotype alteration on chr' + str(chrom)
feat = Feature(graph, karyotype_feature_id,
karyotype_feature_label,
self.globaltt['sequence_alteration'])
feat.addFeatureStartLocation(None, chr_id)
feat.addFeatureToGraph()
geno.addParts(karyotype_feature_id, karyotype_id,
self.globaltt['has_variant_part'])
gene = row[col.index('gene')].strip()
mutation = row[col.index('mutation')].strip()
if gene != '':
vl = gene + '(' + mutation + ')'
# fix the variant_id so it's always in the same order
variant_id = row[col.index('variant_id')].strip()
vids = variant_id.split(';')
variant_id = ';'.join(sorted(list(set(vids))))
if karyotype.strip() != '' and not self._is_normal_karyotype(
karyotype):
gvc_id = karyotype_id
if variant_id != '':
gvc_id = '_:' + variant_id.replace(';', '-') + '-' \
+ re.sub(r'\w*:', '', karyotype_id)
if mutation.strip() != '':
gvc_label = '; '.join((vl, karyotype))
else:
gvc_label = karyotype
elif variant_id.strip() != '':
gvc_id = '_:' + variant_id.replace(';', '-')
gvc_label = vl
else:
# wildtype?
pass
# add the karyotype to the gvc.
# use reference if normal karyotype
karyo_rel = self.globaltt['has_variant_part']
if self._is_normal_karyotype(karyotype):
karyo_rel = self.globaltt['has_reference_part']
if karyotype_id is not None \
and not self._is_normal_karyotype(karyotype) \
and gvc_id is not None and karyotype_id != gvc_id:
geno.addParts(karyotype_id, gvc_id, karyo_rel)
if variant_id.strip() != '':
# split the variants & add them as part of the genotype
# we don't necessarily know their zygosity,
# just that they are part of the genotype variant ids
# are from OMIM, so prefix as such we assume that the
# sequence alts will be defined in OMIM not here
# TODO sort the variant_id list, if the omim prefix is
# the same, then assume it's the locus make a hashmap
# of the omim id to variant id list;
# then build the genotype hashmap is also useful for
# removing the "genes" from the list of "phenotypes"
# will hold gene/locus id to variant list
omim_map = {}
locus_num = None
for var in variant_id.split(';'):
# handle omim-style and odd var ids
# like 610661.p.R401X
mch = re.match(r'(\d+)\.+(.*)', var.strip())
if mch is not None and len(mch.groups()) == 2:
(locus_num, var_num) = mch.groups()
if locus_num is not None and locus_num not in omim_map:
omim_map[locus_num] = [var_num]
else:
omim_map[locus_num] += [var_num]
for omim in omim_map:
# gene_id = 'OMIM:' + omim # TODO unused
vslc_id = '_:' + '-'.join(
[omim + '.' + a for a in omim_map.get(omim)])
vslc_label = vl
# we don't really know the zygosity of
# the alleles at all.
# so the vslcs are just a pot of them
model.addIndividualToGraph(
vslc_id, vslc_label,
self.globaltt['variant single locus complement'])
for var in omim_map.get(omim):
# this is actually a sequence alt
allele1_id = 'OMIM:' + omim + '.' + var
geno.addSequenceAlteration(allele1_id, None)
# assume that the sa -> var_loc -> gene
# is taken care of in OMIM
geno.addPartsToVSLC(
vslc_id, allele1_id, None,
self.globaltt['indeterminate'],
self.globaltt['has_variant_part'])
if vslc_id != gvc_id:
geno.addVSLCtoParent(vslc_id, gvc_id)
if affected == 'unaffected':
# let's just say that this person is wildtype
model.addType(patient_id, self.globaltt['wildtype'])
elif genotype_id is None:
# make an anonymous genotype id (aka blank node)
genotype_id = '_:geno' + catalog_id.strip()
# add the gvc
if gvc_id is not None:
model.addIndividualToGraph(
gvc_id, gvc_label,
self.globaltt['genomic_variation_complement'])
# add the gvc to the genotype
if genotype_id is not None:
if affected == 'unaffected':
rel = self.globaltt['has_reference_part']
else:
rel = self.globaltt['has_variant_part']
geno.addParts(gvc_id, genotype_id, rel)
if karyotype_id is not None \
and self._is_normal_karyotype(karyotype):
if gvc_label is not None and gvc_label != '':
genotype_label = '; '.join((gvc_label, karyotype))
elif karyotype is not None:
genotype_label = karyotype
if genotype_id is None:
genotype_id = karyotype_id
else:
geno.addParts(karyotype_id, genotype_id,
self.globaltt['has_reference_part'])
else:
genotype_label = gvc_label
# use the catalog id as the background
genotype_label += ' [' + catalog_id.strip() + ']'
if genotype_id is not None and gvc_id is not None:
# only add the genotype if it has some parts
geno.addGenotype(genotype_id, genotype_label,
self.globaltt['intrinsic_genotype'])
geno.addTaxon(taxon, genotype_id)
# add that the patient has the genotype
# TODO check if the genotype belongs to
# the cell line or to the patient
graph.addTriple(patient_id, self.globaltt['has_genotype'],
genotype_id)
else:
geno.addTaxon(taxon, patient_id)
# TODO: Add sex/gender (as part of the karyotype?)
# = row[col.index('')].strip()
# ############# DEAL WITH THE DISEASES #############
omim_num = row[col.index('omim_num')].strip()
# we associate the disease to the patient
if affected == 'affected' and omim_num != '':
for d in omim_num.split(';'):
if d is not None and d != '':
# if the omim number is in omim_map,
# then it is a gene not a pheno
# TEC - another place to use the mimTitle omim
# classifier omia & genereviews are using
if d not in omim_map:
disease_id = 'OMIM:' + d.strip()
# assume the label is taken care of in OMIM
model.addClassToGraph(disease_id, None)
# add the association:
# the patient has the disease
assoc = G2PAssoc(graph, self.name, patient_id,
disease_id)
assoc.add_association_to_graph()
# this line is a model of this disease
# TODO abstract out model into
# it's own association class?
graph.addTriple(cell_line_id,
self.globaltt['is model of'],
disease_id)
else:
LOG.info('drop gene %s from disease list', d)
# ############# ADD PUBLICATIONS #############
pubmed_ids = row[col.index('pubmed_ids')].strip()
if pubmed_ids != '':
for s in pubmed_ids.split(';'):
pubmed_id = 'PMID:' + s.strip()
ref = Reference(graph, pubmed_id)
ref.setType(self.globaltt['journal article'])
ref.addRefToGraph()
graph.addTriple(pubmed_id, self.globaltt['mentions'],
cell_line_id)
if not self.testMode and (limit is not None
and line_counter > limit):
break
return
def _add_variant_gene_relationship(self, patient_var_map,
gene_coordinate_map):
"""
Right now it is unclear the best approach on how to connect
variants to genes. In most cases has_affected_locus/GENO:0000418
is accurate; however, there are cases where a variant is in the intron
on one gene and is purported to causally affect another gene down or
upstream. In these cases we must first disambiguate which gene
is the affected locus, and which gene(s) are predicated to be
causully influenced by (RO:0002566)
UPDATE 8-30: In the latest dataset we no longer have 1-many mappings
between variants and genes, but leaving this here in case we see
these in the future
The logic followed here is:
if mutation type contains downstream/upstream and more than one
gene of interest, investigate coordinates of all genes to
see if we can disambiguate which genes are which
:return: None
"""
# genotype = Genotype(self.graph)
dipper_util = DipperUtil()
model = Model(self.graph)
# Note this could be compressed in someway to remove one level of for looping
for patient in patient_var_map:
for variant_id, variant in patient_var_map[patient].items():
variant_bnode = self.make_id("{0}".format(variant_id), "_")
genes_of_interest = variant['genes_of_interest']
if len(genes_of_interest) == 1:
# Assume variant is variant allele of gene
gene = genes_of_interest[0]
gene_id = dipper_util.get_hgnc_id_from_symbol(gene)
self._add_gene_to_graph(
gene, variant_bnode, gene_id,
self.globaltt['has_affected_feature'])
elif re.search(r'upstream|downstream',
variant['type'],
flags=re.I):
# Attempt to disambiguate
ref_gene = []
up_down_gene = []
unmatched_genes = []
for gene in variant['genes_of_interest']:
if gene_id and gene_id != '' and gene_id in gene_coordinate_map:
if gene_coordinate_map[gene_id]['start'] \
<= variant['position']\
<= gene_coordinate_map[gene_id]['end']:
gene_info = {
'symbol':
gene,
'strand':
gene_coordinate_map[gene_id]['strand']
}
ref_gene.append(gene_info)
else:
up_down_gene.append(gene)
else:
unmatched_genes.append(gene)
if len(ref_gene) == 1:
self._add_gene_to_graph(
ref_gene[0]['symbol'], variant_bnode, gene_id,
self.globaltt['has_affected_feature'])
# update label with gene
gene_list = [ref_gene[0]['symbol']
] # build label expects list
variant_label = self._build_variant_label(
variant['build'], variant['chromosome'],
variant['position'], variant['reference_allele'],
variant['variant_allele'], gene_list)
model.addLabel(variant_bnode, variant_label)
# In some cases there are multiple instances
# of same gene from dupe rows in the source
# Credit http://stackoverflow.com/a/3844832
elif len(ref_gene) > 0 and ref_gene[1:] == ref_gene[:-1]:
self._add_gene_to_graph(
ref_gene[0]['symbol'], variant_bnode, gene_id,
self.globaltt['has_affected_feature'])
# build label function expects list
gene_list = [ref_gene[0]['symbol']]
variant_label = self._build_variant_label(
variant['build'], variant['chromosome'],
variant['position'], variant['reference_allele'],
variant['variant_allele'], gene_list)
model.addLabel(variant_bnode, variant_label)
# Check if reference genes are on different strands
elif len(ref_gene) == 2:
strands = [st['strand'] for st in ref_gene]
if "minus" in strands and "plus" in strands:
for r_gene in ref_gene:
self._add_gene_to_graph(
r_gene['symbol'], variant_bnode, gene_id,
self.globaltt['has_affected_feature'])
else:
LOG.warning(
"unable to map intron variant to gene coordinates: %s",
variant)
for r_gene in ref_gene:
self._add_gene_to_graph(
r_gene['symbol'], variant_bnode, gene_id,
self.globaltt['causally_influences'])
elif re.search(r'intron', variant['type'], flags=re.I):
LOG.warning(
"unable to map intron variant to gene coordinates_2: %s",
variant)
for neighbor in up_down_gene:
self._add_gene_to_graph(
neighbor, variant_bnode, gene_id,
self.globaltt['causally_influences'])
# Unmatched genes are likely because we cannot map to an NCBIGene
# or we do not have coordinate information
for unmatched_gene in unmatched_genes:
self._add_gene_to_graph(
unmatched_gene, variant_bnode, gene_id,
self.globaltt['causally_influences'])
return
def _process_omim2gene(self, limit=None):
"""
This method maps the OMIM IDs and KEGG gene ID.
Currently split based on the link_type field.
Equivalent link types are mapped as gene XRefs.
Reverse link types are mapped as disease to gene associations.
Original link types are currently skipped.
Triples created:
<kegg_gene_id> is a Gene
<omim_gene_id> is a Gene
<kegg_gene_id>> hasXref <omim_gene_id>
<assoc_id> has subject <omim_disease_id>
<assoc_id> has object <kegg_gene_id>
:param limit:
:return:
"""
LOG.info("Processing OMIM to KEGG gene")
if self.test_mode:
graph = self.testgraph
else:
graph = self.graph
model = Model(graph)
line_counter = 0
geno = Genotype(graph)
raw = '/'.join((self.rawdir, self.files['omim2gene']['file']))
with open(raw, 'r', encoding="iso-8859-1") as csvfile:
filereader = csv.reader(csvfile, delimiter='\t', quotechar='\"')
for row in filereader:
line_counter += 1
(kegg_gene_id, omim_id, link_type) = row
if self.test_mode and kegg_gene_id not in self.test_ids['genes']:
continue
kegg_gene_id = 'KEGG-' + kegg_gene_id.strip()
omim_id = re.sub(r'omim', 'OMIM', omim_id)
if link_type == 'equivalent':
# these are genes!
# so add them as a class then make equivalence
model.addClassToGraph(omim_id, None)
geno.addGene(kegg_gene_id, None)
if not DipperUtil.is_omim_disease(omim_id):
model.addEquivalentClass(kegg_gene_id, omim_id)
elif link_type == 'reverse':
# make an association between an OMIM ID & the KEGG gene ID
# we do this with omim ids because
# they are more atomic than KEGG ids
alt_locus_id = self._make_variant_locus_id(kegg_gene_id, omim_id)
alt_label = self.label_hash[alt_locus_id]
model.addIndividualToGraph(
alt_locus_id, alt_label, self.globaltt['variant_locus'])
geno.addAffectedLocus(alt_locus_id, kegg_gene_id)
model.addBlankNodeAnnotation(alt_locus_id)
# Add the disease to gene relationship.
rel = self.globaltt['is marker for']
assoc = G2PAssoc(graph, self.name, alt_locus_id, omim_id, rel)
assoc.add_association_to_graph()
elif link_type == 'original':
# these are sometimes a gene, and sometimes a disease
LOG.info(
'Unable to handle original link for %s-%s',
kegg_gene_id, omim_id)
else:
# don't know what these are
LOG.warning(
'Unhandled link type for %s-%s: %s',
kegg_gene_id, omim_id, link_type)
if (not self.test_mode) and (
limit is not None and line_counter > limit):
break
LOG.info("Done with OMIM to KEGG gene")
return
def _process_haplotype(
self, hap_id, hap_label, chrom_num, chrom_pos, context,
risk_allele_frequency, mapped_gene, so_ontology):
tax_id = 'NCBITaxon:9606'
if self.testMode:
g = self.testgraph
else:
g = self.graph
geno = Genotype(g)
model = Model(g)
# add the feature to the graph
hap_description = None
if risk_allele_frequency != '' and \
risk_allele_frequency != 'NR':
hap_description = \
str(risk_allele_frequency) + \
' [risk allele frequency]'
model.addIndividualToGraph(hap_id, hap_label.strip(),
Feature.types['haplotype'], hap_description)
geno.addTaxon(tax_id, hap_id)
snp_labels = re.split(r';\s?', hap_label)
chrom_nums = re.split(r';\s?', chrom_num)
chrom_positions = re.split(r';\s?', chrom_pos)
context_list = re.split(r';\s?', context)
mapped_genes = re.split(r';\s?', mapped_gene)
snp_curies = list()
for index, snp in enumerate(snp_labels):
snp_curie, snp_type = self._get_curie_and_type_from_id(snp)
if snp_type is None:
# make blank node
snp_curie = self.make_id(snp, "_")
g.addTriple(hap_id, geno.object_properties['has_variant_part'],
snp_curie)
snp_curies.append(snp_curie)
# courtesy http://stackoverflow.com/a/16720915
length = len(snp_labels)
if not all(len(lst) == length
for lst in [chrom_nums, chrom_positions, context_list]):
logger.warn(
"Unexpected data field for haplotype {} \n "
"will not add snp details".format(hap_label))
return
variant_in_gene_count = 0
for index, snp_curie in enumerate(snp_curies):
self._add_snp_to_graph(
snp_curie, snp_labels[index], chrom_nums[index],
chrom_positions[index], context_list[index])
if len(mapped_genes) == len(snp_labels):
so_class = self._map_variant_type(context_list[index])
if so_class is None:
raise ValueError("Unknown SO class {} in haplotype {}"
.format(context_list[index], hap_label))
so_query = """
SELECT ?variant_label
WHERE {{
{0} rdfs:subClassOf+ SO:0001564 ;
rdfs:label ?variant_label .
}}
""".format(so_class)
query_result = so_ontology.query(so_query)
if len(list(query_result)) > 0:
gene_id = DipperUtil.get_ncbi_id_from_symbol(
mapped_genes[index])
if gene_id is not None:
geno.addAffectedLocus(snp_curie, gene_id)
geno.addAffectedLocus(hap_id, gene_id)
variant_in_gene_count += 1
if context_list[index] == 'upstream_gene_variant':
gene_id = DipperUtil.get_ncbi_id_from_symbol(
mapped_genes[index])
if gene_id is not None:
g.addTriple(
snp_curie,
Feature.object_properties[
'upstream_of_sequence_of'],
gene_id)
elif context_list[index] == 'downstream_gene_variant':
gene_id = DipperUtil.get_ncbi_id_from_symbol(
mapped_genes[index])
if gene_id is not None:
g.addTriple(
snp_curie,
Feature.object_properties[
'downstream_of_sequence_of'],
gene_id)
else:
logger.warn("More mapped genes than snps, "
"cannot disambiguate for {}".format(hap_label))
# Seperate in case we want to apply a different relation
# If not this is redundant with triples added above
if len(mapped_genes) == variant_in_gene_count \
and len(set(mapped_genes)) == 1:
gene_id = DipperUtil.get_ncbi_id_from_symbol(mapped_genes[0])
geno.addAffectedLocus(hap_id, gene_id)
return
def _process_haplotype(self, hap_id, hap_label, chrom_num, chrom_pos,
context, risk_allele_frequency, mapped_gene,
so_ontology):
if self.test_mode:
graph = self.testgraph
else:
graph = self.graph
geno = Genotype(graph)
model = Model(graph)
# add the feature to the graph
hap_description = None
if risk_allele_frequency != '' and risk_allele_frequency != 'NR':
hap_description = str(
risk_allele_frequency) + ' [risk allele frequency]'
model.addIndividualToGraph(hap_id, hap_label.strip(),
self.globaltt['haplotype'], hap_description)
geno.addTaxon(self.globaltt["H**o sapiens"], hap_id)
snp_labels = re.split(r';\s?', hap_label)
chrom_nums = re.split(r';\s?', chrom_num)
chrom_positions = re.split(r';\s?', chrom_pos)
context_list = re.split(r';\s?', context)
mapped_genes = re.split(r';\s?', mapped_gene)
snp_curies = list()
for index, snp in enumerate(snp_labels):
snp_curie, snp_type = self._get_curie_and_type_from_id(snp)
if snp_type is None:
# make blank node
snp_curie = self.make_id(snp, "_")
graph.addTriple(hap_id, self.globaltt['has_variant_part'],
snp_curie)
snp_curies.append(snp_curie)
# courtesy http://stackoverflow.com/a/16720915
length = len(snp_labels)
if not all(
len(lst) == length
for lst in [chrom_nums, chrom_positions, context_list]):
LOG.warning(
"Unexpected data field for haplotype %s \n "
"will not add snp details", hap_label)
return
variant_in_gene_count = 0
for index, snp_curie in enumerate(snp_curies):
self._add_snp_to_graph(snp_curie, snp_labels[index],
chrom_nums[index], chrom_positions[index],
context_list[index])
if len(mapped_genes) == len(snp_labels):
so_class = self.resolve(context_list[index])
# removed the '+' for recursive one-or-more rdfs:subClassOf paths
# just so it did not return an empty graph
so_query = """
SELECT ?variant_label
WHERE {{
{0} rdfs:subClassOf {1} ;
rdfs:label ?variant_label .
}}
""".format(so_class, self.globaltt['gene_variant'])
query_result = so_ontology.query(so_query)
if len(list(query_result)) == 1:
gene_id = DipperUtil.get_ncbi_id_from_symbol(
mapped_genes[index])
if gene_id is not None:
geno.addAffectedLocus(snp_curie, gene_id)
geno.addAffectedLocus(hap_id, gene_id)
variant_in_gene_count += 1
gene_id = DipperUtil.get_ncbi_id_from_symbol(
mapped_genes[index])
if gene_id is not None:
graph.addTriple(snp_curie,
self.resolve(context_list[index]), gene_id)
else:
LOG.warning(
"More mapped genes than snps, cannot disambiguate for %s",
hap_label)
# Seperate in case we want to apply a different relation
# If not this is redundant with triples added above
if len(mapped_genes) == variant_in_gene_count and len(
set(mapped_genes)) == 1:
gene_id = DipperUtil.get_ncbi_id_from_symbol(mapped_genes[0])
geno.addAffectedLocus(hap_id, gene_id)
return
