def process_pub_xrefs(self, limit=None):
raw = '/'.join((self.rawdir, self.files['pub_xrefs']['file']))
if self.testMode:
g = self.testgraph
else:
g = self.graph
model = Model(g)
logger.info("Processing publication xrefs")
line_counter = 0
with open(raw, 'r') as csvfile:
filereader = csv.reader(csvfile, delimiter='\t', quotechar='\"')
for row in filereader:
line_counter += 1
(wb_ref, xref) = row
# WBPaper00000009 pmid8805<BR>
# WBPaper00000011 doi10.1139/z78-244<BR>
# WBPaper00000012 cgc12<BR>
if self.testMode and wb_ref not in self.test_ids['pub']:
continue
ref_id = 'WormBase:' + wb_ref
xref_id = None
r = None
xref = re.sub(r'<BR>', '', xref)
xref = xref.strip()
if re.match(r'pmid', xref):
xref_id = 'PMID:' + re.sub(r'pmid\s*', '', xref)
reference = Reference(
g, xref_id, Reference.ref_types['journal_article'])
elif re.search(r'[\(\)\<\>\[\]\s]', xref):
continue
elif re.match(r'doi', xref):
xref_id = 'DOI:' + re.sub(r'doi', '', xref.strip())
reference = Reference(g, xref_id)
elif re.match(r'cgc', xref):
# TODO not sure what to do here with cgc xrefs
continue
else:
# logger.debug("Other xrefs like %s", xref)
continue
if xref_id is not None:
reference.addRefToGraph()
model.addSameIndividual(ref_id, xref_id)
if not self.testMode \
and limit is not None and line_counter > limit:
break
return
def _get_pubs(self, entry, g):
"""
Extract mentioned publications from the reference list
:param entry:
:return:
"""
ref_to_pmid = {}
entry_num = entry['mimNumber']
model = Model(g)
if 'referenceList' in entry:
reflist = entry['referenceList']
for r in reflist:
if 'pubmedID' in r['reference']:
pub_id = 'PMID:' + str(r['reference']['pubmedID'])
ref = \
Reference(
g, pub_id,
Reference.ref_types['journal_article'])
else:
# make blank node for internal reference
pub_id = \
'_:OMIM' + str(entry_num) + 'ref' + \
str(r['reference']['referenceNumber'])
ref = Reference(g, pub_id)
title = author_list = source = citation = None
if 'title' in r['reference']:
title = r['reference']['title']
ref.setTitle(title)
if 'authors' in r['reference']:
author_list = r['reference']['authors']
ref.setAuthorList(author_list)
citation = re.split(r'\.\,', author_list)[0] + ' et al'
if 'source' in r['reference']:
source = r['reference']['source']
citation = '; '.join(
list(filter(None.__ne__, [citation, title, source])))
ref.setShortCitation(citation)
ref.addRefToGraph()
ref_to_pmid[r['reference']['referenceNumber']] = pub_id
# add is_about for the pub
omim_id = 'OMIM:'+str(entry_num)
g.addTriple(omim_id, model.object_properties['mentions'],
pub_id)
return ref_to_pmid
def parse(self, limit=None):
zfin_parser = ZFIN(self.graph_type, self.are_bnodes_skized)
model = Model(self.graph)
zp_file = '/'.join((self.rawdir, self.files['zpmap']['file']))
g2p_file = '/'.join((self.rawdir, self.files['g2p_clean']['file']))
zfin_parser.zp_map = zfin_parser._load_zp_mappings(zp_file)
with open(g2p_file, 'r', encoding="utf8") as csvfile:
filereader = csv.reader(csvfile, delimiter='\t', quotechar='\"')
for row in filereader:
(internal_id, symbol, gene_id, subterm1_id, subterm1_label,
pc_rel_id, pc_rel_label, superterm1_id, superterm1_label,
quality_id, quality_name, modifier, subterm2_id,
subterm2_label, pc_rel2_id, pc_rel2_id, superterm2_id,
superterm2_label, fish_id, fish_label, start_stage, end_stage,
environment, pub_id, figure_id, unknown_field) = row
zp_id = zfin_parser._map_sextuple_to_phenotype(
superterm1_id, subterm1_id, quality_id, superterm2_id,
subterm2_id, modifier)
gene_curie = "ZFIN:{0}".format(gene_id)
model.makeLeader(gene_curie)
pub_curie = "ZFIN:{0}".format(pub_id)
if zp_id:
assoc = G2PAssoc(self.graph, self.name, gene_curie, zp_id)
if pub_id:
reference = Reference(self.graph, pub_curie,
Reference.ref_types['document'])
reference.addRefToGraph()
assoc.add_source(pub_curie)
assoc.add_evidence('ECO:0000059')
assoc.add_association_to_graph()
def _parse_curated_chem_disease(self, limit):
model = Model(self.graph)
line_counter = 0
file_path = '/'.join(
(self.rawdir, self.static_files['publications']['file']))
with open(file_path, 'r') as tsvfile:
reader = csv.reader(tsvfile, delimiter="\t")
for row in reader:
# catch comment lines
if re.match(r'^#', ' '.join(row)):
continue
line_counter += 1
self._check_list_len(row, 10)
(pub_id, disease_label, disease_id, disease_cat, evidence,
chem_label, chem_id, cas_rn, gene_symbol, gene_acc) = row
if disease_id.strip() == '' or chem_id.strip() == '':
continue
rel_id = self.resolve(evidence)
chem_id = 'MESH:' + chem_id
model.addClassToGraph(chem_id, chem_label)
model.addClassToGraph(disease_id, None)
if pub_id != '':
pub_id = 'PMID:' + pub_id
r = Reference(pub_id, self.globaltt['journal article'])
r.addRefToGraph(self.graph)
pubids = [pub_id]
else:
pubids = None
self._make_association(chem_id, disease_id, rel_id, pubids)
if not self.testMode and limit is not None and line_counter >= limit:
break
return
def _make_association(self, subject_id, object_id, rel_id, pubmed_ids):
"""
Make a reified association given an array of pubmed identifiers.
Args:
:param subject_id id of the subject of the association (gene/chem)
:param object_id id of the object of the association (disease)
:param rel_id relationship id
:param pubmed_ids an array of pubmed identifiers
Returns:
:return None
"""
# TODO pass in the relevant Assoc class rather than relying on G2P
assoc = G2PAssoc(self.g, self.name, subject_id, object_id, rel_id)
if pubmed_ids is not None and len(pubmed_ids) > 0:
eco = self._get_evidence_code('TAS')
for pmid in pubmed_ids:
r = Reference(
self.g, pmid, Reference.ref_types['journal_article'])
r.addRefToGraph()
assoc.add_source(pmid)
assoc.add_evidence(eco)
assoc.add_association_to_graph()
return
def _process_article_row(self, row):
model = Model(self.g)
# don't bother in test mode
if self.testMode:
return
iarticle_id = self._make_internal_id('article', row['article_id'])
self.id_hash['article'][row['article_id']] = iarticle_id
rtype = None
if row['journal'] != '':
rtype = Reference.ref_types['journal_article']
reference = Reference(self.g, iarticle_id, rtype)
if row['title'] is not None:
reference.setTitle(row['title'].strip())
if row['year'] is not None:
reference.setYear(row['year'])
reference.addRefToGraph()
if row['pubmed_id'] is not None:
pmid = 'PMID:'+str(row['pubmed_id'])
self.id_hash['article'][row['article_id']] = pmid
model.addSameIndividual(iarticle_id, pmid)
model.addComment(pmid, iarticle_id.replace("_:", ''))
return
def _process_pathway_pubmed(self, limit):
"""
Indicate that a pathway is annotated directly to a paper (is about)
via it's pubmed id.
:param limit:
:return:
"""
LOG.info("Processing KEGG pathways to pubmed ids")
if self.test_mode:
graph = self.testgraph
else:
graph = self.graph
line_counter = 0
raw = '/'.join((self.rawdir, self.files['pathway_pubmed']['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
(pubmed_id, kegg_pathway_num) = row
if self.test_mode and kegg_pathway_num not in self.test_ids['pathway']:
continue
pubmed_id = pubmed_id.upper()
# will look like KEGG-path:map04130
kegg_id = 'KEGG-' + kegg_pathway_num
r = Reference(graph, pubmed_id, self.globaltt['journal article'])
r.addRefToGraph()
graph.addTriple(pubmed_id, self.globaltt['is_about'], kegg_id)
if not self.test_mode and limit is not None and line_counter > limit:
break
return
def _process_collection(self, collection_id, label, page):
"""
This function will process the data supplied internally
about the repository from Coriell.
Triples:
Repository a ERO:collection
rdf:label Literal(label)
foaf:page Literal(page)
:param collection_id:
:param label:
:param page:
:return:
"""
# ############# BUILD THE CELL LINE REPOSITORY #############
for graph in [self.graph, self.testgraph]:
# TODO: How to devise a label for each repository?
model = Model(graph)
reference = Reference(graph)
repo_id = 'CoriellCollection:' + collection_id
repo_label = label
repo_page = page
model.addIndividualToGraph(repo_id, repo_label,
self.globaltt['collection'])
reference.addPage(repo_id, repo_page)
return
def _make_association(self, subject_id, object_id, rel_id, pubmed_ids):
"""
Make a reified association given an array of pubmed identifiers.
Args:
:param subject_id id of the subject of the association (gene/chem)
:param object_id id of the object of the association (disease)
:param rel_id relationship id
:param pubmed_ids an array of pubmed identifiers
Returns:
:return None
"""
# TODO pass in the relevant Assoc class rather than relying on G2P
assoc = G2PAssoc(self.graph, self.name, subject_id, object_id, rel_id)
if pubmed_ids is not None and len(pubmed_ids) > 0:
for pmid in pubmed_ids:
ref = Reference(
self.graph, pmid, self.globaltt['journal article'])
ref.addRefToGraph()
assoc.add_source(pmid)
assoc.add_evidence(self.globaltt['traceable author statement'])
assoc.add_association_to_graph()
return
def _get_pubs(self, entry, graph):
"""
Extract mentioned publications from the reference list
:param entry:
:return:
"""
ref_to_pmid = {}
entry_num = entry['mimNumber']
if 'referenceList' in entry:
reflist = entry['referenceList']
for rlst in reflist:
if 'pubmedID' in rlst['reference']:
pub_id = 'PMID:' + str(rlst['reference']['pubmedID'])
ref = Reference(graph, pub_id,
self.globaltt['journal article'])
else:
# make blank node for internal reference
pub_id = '_:OMIM' + str(entry_num) + 'ref' + str(
rlst['reference']['referenceNumber'])
ref = Reference(graph, pub_id)
title = author_list = source = citation = None
if 'title' in rlst['reference']:
title = rlst['reference']['title']
ref.setTitle(title)
if 'authors' in rlst['reference']:
author_list = rlst['reference']['authors']
ref.setAuthorList(author_list)
citation = re.split(r'\.\,', author_list)[0] + ' et al'
if 'source' in rlst['reference']:
source = rlst['reference']['source']
citation = '; '.join([
tok for tok in [citation, title, source]
if tok is not None
])
ref.setShortCitation(citation)
ref.addRefToGraph()
ref_to_pmid[rlst['reference']['referenceNumber']] = pub_id
# add is_about for the pub
omim_id = 'OMIM:' + str(entry_num)
graph.addTriple(omim_id, self.globaltt['mentions'], pub_id)
return ref_to_pmid
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 parse(self, limit=None):
count = 0
for num in range(10, 100):
fuzzy_gene = "MGI:{0}*".format(num)
gene = "MGI:{0}".format(num)
service = Service("http://www.mousemine.org/mousemine/service")
logging.getLogger('Model').setLevel(logging.CRITICAL)
logging.getLogger('JSONIterator').setLevel(logging.CRITICAL)
query = service.new_query("OntologyAnnotation")
query.add_constraint("subject", "SequenceFeature")
query.add_constraint("ontologyTerm", "MPTerm")
query.add_view("subject.primaryIdentifier", "subject.symbol",
"subject.sequenceOntologyTerm.name",
"ontologyTerm.identifier", "ontologyTerm.name",
"evidence.publications.pubMedId",
"evidence.comments.type",
"evidence.comments.description")
query.add_sort_order("OntologyAnnotation.ontologyTerm.name", "ASC")
query.add_constraint("subject.organism.taxonId",
"=",
"10090",
code="A")
query.add_constraint("subject", "LOOKUP", fuzzy_gene, code="B")
query.add_constraint("subject.primaryIdentifier",
"CONTAINS",
gene,
code="C")
query.outerjoin("evidence.comments")
for row in query.rows():
mgi_curie = row["subject.primaryIdentifier"]
mp_curie = row["ontologyTerm.identifier"]
pub_curie = "PMID:{0}".format(
row["evidence.publications.pubMedId"])
assoc = G2PAssoc(self.graph, self.name, mgi_curie, mp_curie)
if row["evidence.publications.pubMedId"]:
reference = Reference(
self.graph, pub_curie,
Reference.ref_types['journal_article'])
reference.addRefToGraph()
assoc.add_source(pub_curie)
assoc.add_evidence('ECO:0000059')
assoc.add_association_to_graph()
if not count % 10 and count != 0:
count_from = count - 10
logger.info(
"{0} processed ids from MGI:{1}* to MGI:{2}*".format(
datetime.datetime.now(), count_from, count))
count += 1
if limit and count >= limit:
break
return
def _add_variant_trait_association(self,
variant_id,
mapped_trait_uri,
efo_ontology,
pubmed_id,
description=None):
if self.test_mode:
graph = self.testgraph
else:
graph = self.graph
model = Model(graph)
# 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()
trait_curie = trait.replace("http://www.ebi.ac.uk/efo/EFO_",
"EFO:")
phenotype_query = """
SELECT ?trait
WHERE {{
<{0}> rdfs:subClassOf+ <http://www.ebi.ac.uk/efo/EFO_0000651> .
<{0}> rdfs:label ?trait .
}}
""".format(trait)
query_result = efo_ontology.query(phenotype_query)
if len(list(query_result)) > 0:
if re.match(r'^EFO', trait_curie):
model.addClassToGraph(trait_curie,
list(query_result)[0][0],
self.globaltt['Phenotype'])
pubmed_curie = 'PMID:' + pubmed_id
ref = Reference(graph, pubmed_curie,
self.globaltt['journal article'])
ref.addRefToGraph()
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)
if trait_curie is not None:
assoc.add_association_to_graph()
def process_pub_xrefs(self, limit=None):
src_key = 'pub_xrefs'
raw = '/'.join((self.rawdir, self.files[src_key]['file']))
LOG.info("Processing: %s", self.files[src_key]['file'])
graph = self.graph
model = Model(graph)
col = self.files[src_key]['columns']
with open(raw, 'r') as csvfile:
reader = csv.reader(csvfile, delimiter='\t', quotechar='\"')
for row in reader:
wb_ref = row[col.index('wb_ref')].strip()
xref = row[col.index(
'xref')].strip()[:-4] # strips trailing '<BR>'
# WBPaper00000009 pmid8805<BR>
# WBPaper00000011 doi10.1139/z78-244<BR>
# WBPaper00000012 cgc12<BR>
ref_curie = 'WormBase:' + wb_ref
dbxref_curie = None
if xref[:4] == 'pmid':
dbxref_curie = 'PMID:' + xref[4:]
reference = Reference(graph, dbxref_curie,
self.globaltt['journal article'])
elif re.search(r'[\(\)\<\>\[\]\s]', xref):
continue
elif xref[:3] == 'doi':
dbxref_curie = 'DOI:' + xref[3:]
reference = Reference(graph, dbxref_curie)
else:
# LOG.debug("Other xrefs like %s", xref)
continue
if dbxref_curie is not None:
reference.addRefToGraph()
model.addSameIndividual(ref_curie, dbxref_curie)
if limit is not None and reader.line_num > limit:
break
def make_association(self, record):
"""
contstruct the association
:param record:
:return: modeled association of genotype to mammalian phenotype
"""
model = Model(self.graph)
record['relation']['id'] = self.resolve("has phenotype")
# define the triple
gene = record['subject']['id']
relation = record['relation']['id']
phenotype = record['object']['id']
# instantiate the association
g2p_assoc = Assoc(self.graph,
self.name,
sub=gene,
obj=phenotype,
pred=relation)
# add the references
references = record['evidence']['has_supporting_reference']
# created RGDRef prefix in curie map to route to proper reference URL in RGD
references = [
x.replace('RGD', 'RGDRef') if 'PMID' not in x else x
for x in references
]
if len(references) > 0:
# make first ref in list the source
g2p_assoc.add_source(identifier=references[0])
ref_model = Reference(self.graph, references[0],
self.globaltt['publication'])
ref_model.addRefToGraph()
if len(references) > 1:
# create equivalent source for any other refs in list
# This seems to be specific to this source and
# there could be non-equivalent references in this list
for ref in references[1:]:
model.addSameIndividual(sub=references[0], obj=ref)
# add the date created on
g2p_assoc.add_date(date=record['date'])
g2p_assoc.add_evidence(self.resolve(
record['evidence']['type'])) # ?set where?
g2p_assoc.add_association_to_graph()
return
def _parse_curated_chem_disease(self, file_path, limit):
model = Model(self.graph)
src_key = 'publications'
col = self.api_fetch[src_key]['columns']
with open(file_path, 'r') as tsvfile:
reader = csv.reader(tsvfile, delimiter="\t")
row = next(reader)
row[0] = row[0][2:].strip()
if not self.check_fileheader(col, row):
pass
for row in reader:
# catch comment lines
if row[0][0] == '#':
continue
self._check_list_len(row, len(col))
pub_id = row[col.index('Input')]
# disease_label = row[col.index('DiseaseName')]
disease_id = row[col.index('DiseaseID')]
# disease_cat = row[col.index('DiseaseCategories')]
evidence = row[col.index('DirectEvidence')]
chem_label = row[col.index('ChemicalName')]
chem_id = row[col.index('ChemicalID')]
# cas_rn = row[col.index('CasRN')]
# gene_symbol = row[col.index('GeneSymbol')]
# gene_acc = row[col.index('GeneAcc')]
if disease_id.strip() == '' or chem_id.strip() == '':
continue
rel_id = self.resolve(evidence)
chem_id = 'MESH:' + chem_id
model.addClassToGraph(chem_id, chem_label)
model.addClassToGraph(disease_id, None)
if pub_id != '':
pub_id = 'PMID:' + pub_id
ref = Reference(self.graph,
pub_id,
ref_type=self.globaltt['journal article'])
ref.addRefToGraph()
pubids = [pub_id]
else:
pubids = None
self._make_association(chem_id, disease_id, rel_id, pubids)
if not self.test_mode and limit is not None and \
reader.line_num >= limit:
break
def process_gaf(self, gaffile, limit, id_map=None, eco_map=None):
if self.test_mode:
graph = self.testgraph
else:
graph = self.graph
model = Model(graph)
geno = Genotype(graph)
LOG.info("Processing Gene Associations from %s", gaffile)
uniprot_hit = 0
uniprot_miss = 0
col = self.gaf_columns
with gzip.open(gaffile, 'rb') as csvfile:
reader = csv.reader(
io.TextIOWrapper(csvfile, newline=""), delimiter='\t', quotechar='\"')
for row in reader:
# comments start with exclamation
if row[0][0] == '!':
continue
if len(row) != len(col):
LOG.error(
"Wrong number of columns %i, expected ... got:\n\t%s",
len(col), row)
exit(1)
dbase = row[col.index('DB')].strip()
gene_num = row[col.index('DB_Object_ID')].strip()
gene_symbol = row[col.index('DB_Object_Symbol')].strip()
qualifier = row[col.index('Qualifier')]
go_id = row[col.index('GO_ID')].strip()
ref = row[col.index('DB:Reference')].strip()
eco_symbol = row[col.index('Evidence Code')].strip()
with_or_from = row[col.index('With (or) From')]
aspect = row[col.index('Aspect')].strip()
gene_name = row[col.index('DB_Object_Name')]
gene_synonym = row[col.index('DB_Object_Synonym')]
# object_type = row[col.index('DB_Object_Type')].strip()
taxon = row[col.index('Taxon and Interacting taxon')].strip()
# date = row[col.index('Date')].strip()
# assigned_by = row[col.index('Assigned_By')].strip()
# annotation_extension = row[col.index('Annotation_Extension')]
# gene_product_form_id = row[col.index('Gene_Product_Form_ID')]
# test for required fields
if '' in [row[:10], row[12]]:
LOG.error(
"Missing required part of annotation on row %i:\n%s",
reader.line_num, str(row[:-4]))
continue
# (Don't) deal with qualifier NOT, contributes_to, colocalizes_with
if re.search(r'NOT', qualifier):
continue
if dbase in self.localtt:
dbase = self.localtt[dbase]
uniprotid = None
gene_id = None
if dbase == 'UniProtKB':
if id_map is not None and gene_num in id_map:
gene_id = id_map[gene_num]
uniprotid = ':'.join((dbase, gene_num))
(dbase, gene_num) = gene_id.split(':')
uniprot_hit += 1
else:
# LOG.warning(
# "UniProt id %s is without a 1:1 mapping to entrez/ensembl",
# gene_num)
uniprot_miss += 1
continue
else:
gene_num = gene_num.split(':')[-1] # last
gene_id = ':'.join((dbase, gene_num))
if self.test_mode and gene_id[:9] != 'NCBIGene:' and\
gene_num not in self.test_ids:
continue
model.addClassToGraph(gene_id, gene_symbol)
if gene_name != '':
model.addDescription(gene_id, gene_name)
if gene_synonym != '':
for syn in re.split(r'\|', gene_synonym):
syn = syn.strip()
if syn[:10] == 'UniProtKB:':
model.addTriple(
gene_id, self.globaltt['has gene product'], syn)
elif re.fullmatch(graph.curie_regexp, syn) is not None:
LOG.warning(
'possible curie "%s" as a literal synomym for %s',
syn, gene_id)
model.addSynonym(gene_id, syn)
else:
model.addSynonym(gene_id, syn)
for txid in taxon.split('|'):
tax_curie = re.sub(r'taxon:', 'NCBITaxon:', txid)
geno.addTaxon(tax_curie, gene_id)
assoc = Assoc(graph, self.name)
assoc.set_subject(gene_id)
assoc.set_object(go_id)
try:
eco_id = eco_map[eco_symbol]
assoc.add_evidence(eco_id)
except KeyError:
LOG.error("Evidence code (%s) not mapped", eco_symbol)
refs = re.split(r'\|', ref)
for ref in refs:
ref = ref.strip()
if ref != '':
prefix = ref.split(':')[0] # sidestep 'MGI:MGI:'
if prefix in self.localtt:
prefix = self.localtt[prefix]
ref = ':'.join((prefix, ref.split(':')[-1]))
refg = Reference(graph, ref)
if prefix == 'PMID':
ref_type = self.globaltt['journal article']
refg.setType(ref_type)
refg.addRefToGraph()
assoc.add_source(ref)
# TODO add the source of the annotations from assigned by?
rel = self.resolve(aspect, mandatory=False)
if rel is not None and aspect == rel:
if aspect == 'F' and re.search(r'contributes_to', qualifier):
assoc.set_relationship(self.globaltt['contributes to'])
else:
LOG.error(
"Aspect: %s with qualifier: %s is not recognized",
aspect, qualifier)
elif rel is not None:
assoc.set_relationship(rel)
assoc.add_association_to_graph()
else:
LOG.warning("No predicate for association \n%s\n", str(assoc))
if uniprotid is not None:
assoc.set_description('Mapped from ' + uniprotid)
# object_type should be one of:
# protein_complex; protein; transcript; ncRNA; rRNA; tRNA;
# snRNA; snoRNA; any subtype of ncRNA in the Sequence Ontology.
# If the precise product type is unknown,
# gene_product should be used
########################################################################
# Derive G2P Associations from IMP annotations
# in version 2.1 Pipe will indicate 'OR'
# and Comma will indicate 'AND'.
# in version 2.0, multiple values are separated by pipes
# where the pipe has been used to mean 'AND'
if eco_symbol == 'IMP' and with_or_from != '':
withitems = with_or_from.split('|')
phenotypeid = go_id + 'PHENOTYPE'
# create phenotype associations
for itm in withitems:
if itm == '' or re.match(
r'(UniProtKB|WBPhenotype|InterPro|HGNC)', itm):
LOG.warning(
"Skipping %s from or with %s", uniprotid, itm)
continue
itm = re.sub(r'MGI\:MGI\:', 'MGI:', itm)
itm = re.sub(r'WB:', 'WormBase:', itm)
# for worms and fish, they might give a RNAi or MORPH
# in these cases make a reagent-targeted gene
if re.search('MRPHLNO|CRISPR|TALEN', itm):
targeted_gene_id = self.zfin.make_targeted_gene_id(
gene_id, itm)
geno.addReagentTargetedGene(itm, gene_id, targeted_gene_id)
# TODO PYLINT why is this needed?
# Redefinition of assoc type from
# dipper.models.assoc.Association.Assoc to
# dipper.models.assoc.G2PAssoc.G2PAssoc
assoc = G2PAssoc(
graph, self.name, targeted_gene_id, phenotypeid)
elif re.search(r'WBRNAi', itm):
targeted_gene_id = self.wbase.make_reagent_targeted_gene_id(
gene_id, itm)
geno.addReagentTargetedGene(itm, gene_id, targeted_gene_id)
assoc = G2PAssoc(
graph, self.name, targeted_gene_id, phenotypeid)
else:
assoc = G2PAssoc(graph, self.name, itm, phenotypeid)
for ref in refs:
ref = ref.strip()
if ref != '':
prefix = ref.split(':')[0]
if prefix in self.localtt:
prefix = self.localtt[prefix]
ref = ':'.join((prefix, ref.split(':')[-1]))
assoc.add_source(ref)
# experimental phenotypic evidence
assoc.add_evidence(
self.globaltt['experimental phenotypic evidence'])
assoc.add_association_to_graph()
# TODO should the G2PAssoc be the evidence for the GO assoc?
if not self.test_mode and limit is not None and \
reader.line_num > limit:
break
uniprot_tot = (uniprot_hit + uniprot_miss)
uniprot_per = 0.0
if uniprot_tot != 0:
uniprot_per = 100.0 * uniprot_hit / uniprot_tot
LOG.info(
"Uniprot: %.2f%% of %i benefited from the 1/4 day id mapping download",
uniprot_per, uniprot_tot)
def process_nbk_html(self, limit):
"""
Here we process the gene reviews books to fetch
the clinical descriptions to include in the ontology.
We only use books that have been acquired manually,
as NCBI Bookshelf does not permit automated downloads.
This parser will only process the books that are found in
the ```raw/genereviews/books``` directory,
permitting partial completion.
:param limit:
:return:
"""
model = Model(self.graph)
cnt = 0
books_not_found = set()
clin_des_regx = re.compile(r".*Summary.sec0")
lit_cite_regex = re.compile(r".*Literature_Cited")
pubmed_regex = re.compile(r"pubmed") # ??? for a static string?
for nbk in self.book_ids:
cnt += 1
nbk_id = 'GeneReviews:' + nbk
book_item = self.all_books.get(nbk)
url = '/'.join((self.rawdir, book_item['file']))
# figure out if the book is there; if so, process, otherwise skip
book_dir = '/'.join((self.rawdir, 'books'))
book_files = os.listdir(book_dir)
if ''.join((nbk, '.html')) not in book_files:
# LOG.warning("No book found locally for %s; skipping", nbk)
books_not_found.add(nbk)
continue
LOG.info("Processing %s", nbk)
page = open(url)
soup = BeautifulSoup(page.read())
# sec0 == clinical description
clin_summary = soup.find('div', id=clin_des_regx)
if clin_summary is not None:
ptext = clin_summary.find('p').text
ptext = re.sub(r'\s+', ' ', ptext)
unlst = clin_summary.find('ul')
if unlst is not None:
item_text = list()
for lst_itm in unlst.find_all('li'):
item_text.append(re.sub(r'\s+', ' ', lst_itm.text))
ptext += ' '.join(item_text)
# add in the copyright and citation info to description
ptext = ' '.join(
(ptext, '[GeneReviews:NBK1116, GeneReviews:NBK138602, ' +
nbk_id + ']'))
model.addDefinition(nbk_id, ptext.strip())
# get the pubs
pmid_set = set()
pub_div = soup.find('div', id=lit_cite_regex)
if pub_div is not None:
ref_list = pub_div.find_all('div', attrs={'class': "bk_ref"})
for ref in ref_list:
for anchor in ref.find_all('a',
attrs={'href': pubmed_regex}):
if re.match(r'PubMed:', anchor.text):
pmnum = re.sub(r'PubMed:\s*', '', anchor.text)
else:
pmnum = re.search(r'\/pubmed\/(\d+)$',
anchor['href']).group(1)
if pmnum is not None:
pmid = 'PMID:' + str(pmnum)
self.graph.addTriple(pmid,
self.globaltt['is_about'],
nbk_id)
pmid_set.add(pmnum)
reference = Reference(
self.graph, pmid,
self.globaltt['journal article'])
reference.addRefToGraph()
# TODO add author history, copyright, license to dataset
# TODO get PMID-NBKID equivalence (near foot of page),
# and make it "is about" link
# self.gu.addTriple(
# self.graph, pmid,
# self.globaltt['is_about'], nbk_id)
# for example: NBK1191 PMID:20301370
# add the book to the dataset
self.dataset.setFileAccessUrl(book_item['url'])
if limit is not None and cnt > limit:
break
# finish looping through books
bknfd = len(books_not_found)
if len(books_not_found) > 0:
if bknfd > 100:
LOG.warning("There were %d books not found.", bknfd)
else:
LOG.warning(
"The following %d books were not found locally: %s", bknfd,
str(books_not_found))
LOG.info("Finished processing %d books for clinical descriptions",
cnt - bknfd)
return
def process_gaf(self, file, limit, id_map=None, eco_map=None):
if self.test_mode:
graph = self.testgraph
else:
graph = self.graph
model = Model(graph)
geno = Genotype(graph)
LOG.info("Processing Gene Associations from %s", file)
line_counter = 0
uniprot_hit = 0
uniprot_miss = 0
if '7955' in self.tax_ids:
zfin = ZFIN(self.graph_type, self.are_bnodes_skized)
if '6239' in self.tax_ids:
wbase = WormBase(self.graph_type, self.are_bnodes_skized)
with gzip.open(file, 'rb') as csvfile:
filereader = csv.reader(io.TextIOWrapper(csvfile, newline=""),
delimiter='\t',
quotechar='\"')
for row in filereader:
line_counter += 1
# comments start with exclamation
if re.match(r'!', ''.join(row)):
continue
if len(row) > 17 or len(row) < 15:
LOG.warning(
"Wrong number of columns %i, expected 15 or 17\n%s",
len(row), row)
continue
if 17 > len(row) >= 15:
row += [""] * (17 - len(row))
(dbase, gene_num, gene_symbol, qualifier, go_id, ref,
eco_symbol, with_or_from, aspect, gene_name, gene_synonym,
object_type, taxon, date, assigned_by, annotation_extension,
gene_product_form_id) = row
# test for required fields
if (dbase == '' or gene_num == '' or gene_symbol == ''
or go_id == '' or ref == '' or eco_symbol == ''
or aspect == '' or object_type == '' or taxon == ''
or date == '' or assigned_by == ''):
LOG.error(
"Missing required part of annotation on row %d:\n" +
'\t'.join(row), line_counter)
continue
# deal with qualifier NOT, contributes_to, colocalizes_with
if re.search(r'NOT', qualifier):
continue
if dbase in self.localtt:
dbase = self.localtt[dbase]
uniprotid = None
gene_id = None
if dbase == 'UniProtKB':
if id_map is not None and gene_num in id_map:
gene_id = id_map[gene_num]
uniprotid = ':'.join((dbase, gene_num))
(dbase, gene_num) = gene_id.split(':')
uniprot_hit += 1
else:
# LOG.warning(
# "UniProt id %s is without a 1:1 mapping to entrez/ensembl",
# gene_num)
uniprot_miss += 1
continue
else:
gene_num = gene_num.split(':')[-1] # last
gene_id = ':'.join((dbase, gene_num))
if self.test_mode and not (re.match(r'NCBIGene', gene_id)
and int(gene_num) in self.test_ids):
continue
model.addClassToGraph(gene_id, gene_symbol)
if gene_name != '':
model.addDescription(gene_id, gene_name)
if gene_synonym != '':
for syn in re.split(r'\|', gene_synonym):
model.addSynonym(gene_id, syn.strip())
if re.search(r'\|', taxon):
# TODO add annotations with >1 taxon
LOG.info(">1 taxon (%s) on line %d. skipping", taxon,
line_counter)
else:
tax_id = re.sub(r'taxon:', 'NCBITaxon:', taxon)
geno.addTaxon(tax_id, gene_id)
assoc = Assoc(graph, self.name)
assoc.set_subject(gene_id)
assoc.set_object(go_id)
try:
eco_id = eco_map[eco_symbol]
assoc.add_evidence(eco_id)
except KeyError:
LOG.error("Evidence code (%s) not mapped", eco_symbol)
refs = re.split(r'\|', ref)
for ref in refs:
ref = ref.strip()
if ref != '':
prefix = ref.split(':')[0] # sidestep 'MGI:MGI:'
if prefix in self.localtt:
prefix = self.localtt[prefix]
ref = ':'.join((prefix, ref.split(':')[-1]))
refg = Reference(graph, ref)
if prefix == 'PMID':
ref_type = self.globaltt['journal article']
refg.setType(ref_type)
refg.addRefToGraph()
assoc.add_source(ref)
# TODO add the source of the annotations from assigned by?
rel = self.resolve(aspect, mandatory=False)
if rel is not None and aspect == rel:
if aspect == 'F' and re.search(r'contributes_to',
qualifier):
assoc.set_relationship(self.globaltt['contributes to'])
else:
LOG.error(
"Aspect: %s with qualifier: %s is not recognized",
aspect, qualifier)
elif rel is not None:
assoc.set_relationship(rel)
assoc.add_association_to_graph()
else:
LOG.warning("No predicate for association \n%s\n",
str(assoc))
if uniprotid is not None:
assoc.set_description('Mapped from ' + uniprotid)
# object_type should be one of:
# protein_complex; protein; transcript; ncRNA; rRNA; tRNA;
# snRNA; snoRNA; any subtype of ncRNA in the Sequence Ontology.
# If the precise product type is unknown,
# gene_product should be used
#######################################################################
# Derive G2P Associations from IMP annotations
# in version 2.1 Pipe will indicate 'OR'
# and Comma will indicate 'AND'.
# in version 2.0, multiple values are separated by pipes
# where the pipe has been used to mean 'AND'
if eco_symbol == 'IMP' and with_or_from != '':
withitems = re.split(r'\|', with_or_from)
phenotypeid = go_id + 'PHENOTYPE'
# create phenotype associations
for i in withitems:
if i == '' or re.match(
r'(UniProtKB|WBPhenotype|InterPro|HGNC)', i):
LOG.warning(
"Don't know what having a uniprot id " +
"in the 'with' column means of %s", uniprotid)
continue
i = re.sub(r'MGI\:MGI\:', 'MGI:', i)
i = re.sub(r'WB:', 'WormBase:', i)
# for worms and fish, they might give a RNAi or MORPH
# in these cases make a reagent-targeted gene
if re.search('MRPHLNO|CRISPR|TALEN', i):
targeted_gene_id = zfin.make_targeted_gene_id(
gene_id, i)
geno.addReagentTargetedGene(
i, gene_id, targeted_gene_id)
# TODO PYLINT why is this needed?
# Redefinition of assoc type from
# dipper.models.assoc.Association.Assoc to
# dipper.models.assoc.G2PAssoc.G2PAssoc
assoc = G2PAssoc(graph, self.name,
targeted_gene_id, phenotypeid)
elif re.search(r'WBRNAi', i):
targeted_gene_id = wbase.make_reagent_targeted_gene_id(
gene_id, i)
geno.addReagentTargetedGene(
i, gene_id, targeted_gene_id)
assoc = G2PAssoc(graph, self.name,
targeted_gene_id, phenotypeid)
else:
assoc = G2PAssoc(graph, self.name, i, phenotypeid)
for ref in refs:
ref = ref.strip()
if ref != '':
prefix = ref.split(':')[0]
if prefix in self.localtt:
prefix = self.localtt[prefix]
ref = ':'.join((prefix, ref.split(':')[-1]))
assoc.add_source(ref)
# experimental phenotypic evidence
assoc.add_evidence(self.globaltt[
'experimental phenotypic evidence'])
assoc.add_association_to_graph()
# TODO should the G2PAssoc be
# the evidence for the GO assoc?
if not self.test_mode and limit is not None and line_counter > limit:
break
uniprot_tot = (uniprot_hit + uniprot_miss)
uniprot_per = 0.0
if uniprot_tot != 0:
uniprot_per = 100.0 * uniprot_hit / uniprot_tot
LOG.info(
"Uniprot: %.2f%% of %i benefited from the 1/4 day id mapping download",
uniprot_per, uniprot_tot)
return
def _process_qtls_genomic_location(
self, raw, src_key, txid, build_id, build_label, common_name, limit=None):
"""
This method
Triples created:
:param limit:
:return:
"""
if self.test_mode:
graph = self.testgraph
else:
graph = self.graph
model = Model(graph)
geno = Genotype(graph)
# assume that chrs get added to the genome elsewhere
taxon_curie = 'NCBITaxon:' + txid
eco_id = self.globaltt['quantitative trait analysis evidence']
LOG.info("Processing QTL locations for %s from %s", taxon_curie, raw)
with gzip.open(raw, 'rt', encoding='ISO-8859-1') as tsvfile:
reader = csv.reader(tsvfile, delimiter="\t")
# no header in GFF, so no header checking
col = self.files[src_key]['columns']
col_len = len(col)
for row in reader:
if row[0][0] == '#':
# LOG.info(row)
continue
if len(row) != col_len and ''.join(row[col_len:]) != '':
LOG.warning(
"Problem parsing in %s row %s\n"
"got %s cols but expected %s",
raw, reader.line_num, len(row), col_len)
LOG.info(row)
continue
chromosome = row[col.index('SEQNAME')].strip()
# qtl_source = row[col.index('SOURCE')].strip()
# qtl_type = row[col.index('FEATURE')].strip()
start_bp = row[col.index('START')].strip()
stop_bp = row[col.index('END')].strip()
# score = row[col.index('SCORE')].strip()
strand = row[col.index('STRAND')].strip()
# frame = row[col.index('FRAME')].strip()
attr = row[col.index('ATTRIBUTE')].strip()
example = '''
Chr.Z Animal QTLdb Production_QTL 33954873 34023581...
QTL_ID=2242;Name="Spleen percentage";Abbrev="SPLP";PUBMED_ID=17012160;trait_ID=2234;
trait="Spleen percentage";breed="leghorn";"FlankMarkers=ADL0022";VTO_name="spleen mass";
MO_name="spleen weight to body weight ratio";Map_Type="Linkage";Model="Mendelian";
Test_Base="Chromosome-wise";Significance="Significant";P-value="<0.05";F-Stat="5.52";
Variance="2.94";Dominance_Effect="-0.002";Additive_Effect="0.01
'''
str(example)
# make dictionary of attributes
# keys are:
# QTL_ID,Name,Abbrev,PUBMED_ID,trait_ID,trait,FlankMarkers,
# VTO_name,Map_Type,Significance,P-value,Model,
# Test_Base,Variance, Bayes-value,PTO_name,gene_IDsrc,peak_cM,
# CMO_name,gene_ID,F-Stat,LOD-score,Additive_Effect,
# Dominance_Effect,Likelihood_Ratio,LS-means,Breed,
# trait (duplicate with Name),Variance,Bayes-value,
# F-Stat,LOD-score,Additive_Effect,Dominance_Effect,
# Likelihood_Ratio,LS-means
# deal with poorly formed attributes
if re.search(r'"FlankMarkers";', attr):
attr = re.sub(r'FlankMarkers;', '', attr)
attr_items = re.sub(r'"', '', attr).split(";")
bad_attrs = set()
for attributes in attr_items:
if not re.search(r'=', attributes):
# remove this attribute from the list
bad_attrs.add(attributes)
attr_set = set(attr_items) - bad_attrs
attribute_dict = dict(item.split("=") for item in attr_set)
qtl_num = attribute_dict.get('QTL_ID')
if self.test_mode and int(qtl_num) not in self.test_ids:
continue
# make association between QTL and trait based on taxon
qtl_id = common_name + 'QTL:' + str(qtl_num)
model.addIndividualToGraph(qtl_id, None, self.globaltt['QTL'])
geno.addTaxon(taxon_curie, qtl_id)
#
trait_id = 'AQTLTrait:' + attribute_dict.get('trait_ID')
# if pub is in attributes, add it to the association
pub_id = None
if 'PUBMED_ID' in attribute_dict.keys():
pub_id = attribute_dict.get('PUBMED_ID')
if re.match(r'ISU.*', pub_id):
pub_id = 'AQTLPub:' + pub_id.strip()
reference = Reference(graph, pub_id)
else:
pub_id = 'PMID:' + pub_id.strip()
reference = Reference(
graph, pub_id, self.globaltt['journal article'])
reference.addRefToGraph()
# Add QTL to graph
assoc = G2PAssoc(
graph, self.name, qtl_id, trait_id,
self.globaltt['is marker for'])
assoc.add_evidence(eco_id)
assoc.add_source(pub_id)
if 'P-value' in attribute_dict.keys():
scr = re.sub(r'<', '', attribute_dict.get('P-value'))
if ',' in scr:
scr = re.sub(r',', '.', scr)
if scr.isnumeric():
score = float(scr)
assoc.set_score(score)
assoc.add_association_to_graph()
# TODO make association to breed
# (which means making QTL feature in Breed background)
# get location of QTL
chromosome = re.sub(r'Chr\.', '', chromosome)
chrom_id = makeChromID(chromosome, taxon_curie, 'CHR')
chrom_in_build_id = makeChromID(chromosome, build_id, 'MONARCH')
geno.addChromosomeInstance(
chromosome, build_id, build_label, chrom_id)
qtl_feature = Feature(graph, qtl_id, None, self.globaltt['QTL'])
if start_bp == '':
start_bp = None
qtl_feature.addFeatureStartLocation(
start_bp, chrom_in_build_id, strand,
[self.globaltt['FuzzyPosition']])
if stop_bp == '':
stop_bp = None
qtl_feature.addFeatureEndLocation(
stop_bp, chrom_in_build_id, strand,
[self.globaltt['FuzzyPosition']])
qtl_feature.addTaxonToFeature(taxon_curie)
qtl_feature.addFeatureToGraph()
if not self.test_mode and limit is not None and reader.line_num > limit:
break
# LOG.warning("Bad attribute flags in this file") # what does this even mean??
LOG.info("Done with QTL genomic mappings for %s", taxon_curie)
def _process_phenotype_data(self, limit):
"""
NOTE: If a Strain carries more than one mutation,
then each Mutation description,
i.e., the set: (
Mutation Type - Chromosome - Gene Symbol -
Gene Name - Allele Symbol - Allele Name)
will require a separate line.
Note that MMRRC curates phenotypes to alleles,
even though they distribute only one file with the
phenotypes appearing to be associated with a strain.
So, here we process the allele-to-phenotype relationships separately
from the strain-to-allele relationships.
:param limit:
:return:
"""
src_key = 'catalog'
if self.test_mode:
graph = self.testgraph
else:
graph = self.graph
model = Model(graph)
fname = '/'.join((self.rawdir, self.files[src_key]['file']))
self.strain_hash = {}
self.id_label_hash = {}
genes_with_no_ids = set()
stem_cell_class = self.globaltt['stem cell']
mouse_taxon = self.globaltt['Mus musculus']
geno = Genotype(graph)
with open(fname, 'r', encoding="utf8") as csvfile:
reader = csv.reader(csvfile, delimiter=',', quotechar='\"')
# First line is header not date/version info. This changed recently,
# apparently as of Sep 2019. Also, 3rd line is no longer blank.
row = [x.strip() for x in next(reader)] # messy messy
col = self.files['catalog']['columns']
strain_missing_allele = [] # to count the ones w/insufficent info
if not self.check_fileheader(col, row):
pass
for row in reader:
strain_id = row[col.index('STRAIN/STOCK_ID')].strip()
strain_label = row[col.index('STRAIN/STOCK_DESIGNATION')]
# strain_type_symbol = row[col.index('STRAIN_TYPE')]
strain_state = row[col.index('STATE')]
mgi_allele_id = row[col.index(
'MGI_ALLELE_ACCESSION_ID')].strip()
mgi_allele_symbol = row[col.index('ALLELE_SYMBOL')]
# mgi_allele_name = row[col.index('ALLELE_NAME')]
# mutation_type = row[col.index('MUTATION_TYPE')]
# chrom = row[col.index('CHROMOSOME')]
mgi_gene_id = row[col.index('MGI_GENE_ACCESSION_ID')].strip()
mgi_gene_symbol = row[col.index('GENE_SYMBOL')].strip()
mgi_gene_name = row[col.index('GENE_NAME')]
# sds_url = row[col.index('SDS_URL')]
# accepted_date = row[col.index('ACCEPTED_DATE')]
mpt_ids = row[col.index('MPT_IDS')].strip()
pubmed_nums = row[col.index('PUBMED_IDS')].strip()
research_areas = row[col.index('RESEARCH_AREAS')].strip()
if self.test_mode and (strain_id not in self.test_ids) \
or mgi_gene_name == 'withdrawn':
continue
# strip off stuff after the dash -
# is the holding center important?
# MMRRC:00001-UNC --> MMRRC:00001
strain_id = re.sub(r'-\w+$', '', strain_id)
self.id_label_hash[strain_id] = strain_label
# get the variant or gene to save for later building of
# the genotype
if strain_id not in self.strain_hash:
self.strain_hash[strain_id] = {
'variants': set(),
'genes': set()
}
# flag bad ones
if mgi_allele_id[:4] != 'MGI:' and mgi_allele_id != '':
LOG.error("Erroneous MGI allele id: %s", mgi_allele_id)
if mgi_allele_id[:3] == 'MG:':
mgi_allele_id = 'MGI:' + mgi_allele_id[3:]
else:
mgi_allele_id = ''
if mgi_allele_id != '':
self.strain_hash[strain_id]['variants'].add(mgi_allele_id)
self.id_label_hash[mgi_allele_id] = mgi_allele_symbol
# use the following if needing to add the sequence alteration types
# var_type = self.localtt[mutation_type]
# make a sequence alteration for this variant locus,
# and link the variation type to it
# sa_id = '_'+re.sub(r':','',mgi_allele_id)+'SA'
# if self.nobnodes:
# sa_id = ':'+sa_id
# gu.addIndividualToGraph(g, sa_id, None, var_type)
# geno.addSequenceAlterationToVariantLocus(sa_id, mgi_allele_id)
# scrub out any spaces, fix known issues
mgi_gene_id = re.sub(r'\s+', '', mgi_gene_id)
if mgi_gene_id == 'NULL':
mgi_gene_id = ''
elif mgi_gene_id[:7] == 'GeneID:':
mgi_gene_id = 'NCBIGene:' + mgi_gene_id[7:]
if mgi_gene_id != '':
try:
[curie, localid] = mgi_gene_id.split(':')
except ValueError as verror:
LOG.warning(
"Problem parsing mgi_gene_id %s from file %s: %s",
mgi_gene_id, fname, verror)
if curie not in ['MGI', 'NCBIGene']:
LOG.info("MGI Gene id not recognized: %s", mgi_gene_id)
self.strain_hash[strain_id]['genes'].add(mgi_gene_id)
self.id_label_hash[mgi_gene_id] = mgi_gene_symbol
# catch some errors - too many. report summary at the end
# some things have gene labels, but no identifiers - report
if mgi_gene_symbol != '' and mgi_gene_id == '':
# LOG.error(
# "Gene label with no MGI identifier for strain %s: %s",
# strain_id, mgi_gene_symbol)
genes_with_no_ids.add(mgi_gene_symbol)
# make a temp id for genes that aren't identified ... err wow.
# tmp_gene_id = '_' + mgi_gene_symbol
# self.id_label_hash[tmp_gene_id.strip()] = mgi_gene_symbol
# self.strain_hash[strain_id]['genes'].add(tmp_gene_id)
# split apart the mp ids
# ataxia [MP:0001393] ,hypoactivity [MP:0001402] ...
# mpt_ids are a comma delimited list
# labels with MP terms following in brackets
phenotype_ids = []
if mpt_ids != '':
for lb_mp in mpt_ids.split(r','):
lb_mp = lb_mp.strip()
if lb_mp[-1:] == ']' and lb_mp[-12:-8] == '[MP:':
phenotype_ids.append(lb_mp[-11:-2])
# pubmed ids are space delimited
pubmed_ids = []
if pubmed_nums != '':
for pm_num in re.split(r'\s+', pubmed_nums):
pmid = 'PMID:' + pm_num.strip()
pubmed_ids.append(pmid)
ref = Reference(graph, pmid,
self.globaltt['journal article'])
ref.addRefToGraph()
# https://www.mmrrc.org/catalog/sds.php?mmrrc_id=00001
# is a good example of 4 genotype parts
model.addClassToGraph(mouse_taxon, None)
if research_areas == '':
research_areas = None
else:
research_areas = 'Research Areas: ' + research_areas
strain_type = mouse_taxon
if strain_state == 'ES':
strain_type = stem_cell_class
model.addIndividualToGraph( # an inst of mouse??
strain_id, strain_label, strain_type, research_areas)
model.makeLeader(strain_id)
# phenotypes are associated with the alleles
for pid in phenotype_ids:
# assume the phenotype label is in some ontology
model.addClassToGraph(pid, None)
if mgi_allele_id is not None and mgi_allele_id != '':
assoc = G2PAssoc(graph, self.name, mgi_allele_id, pid,
self.globaltt['has phenotype'])
for p in pubmed_ids:
assoc.add_source(p)
assoc.add_association_to_graph()
else:
# too chatty here. report aggregate
# LOG.info("Phenotypes and no allele for %s", strain_id)
strain_missing_allele.append(strain_id)
if not self.test_mode and (limit is not None
and reader.line_num > limit):
break
# report misses
if strain_missing_allele:
LOG.info("Phenotypes and no allele for %i strains",
len(strain_missing_allele))
# now that we've collected all of the variant information, build it
# we don't know their zygosities
for s in self.strain_hash:
h = self.strain_hash.get(s)
variants = h['variants']
genes = h['genes']
vl_set = set()
# make variant loci for each gene
if variants:
for var in variants:
vl_id = var.strip()
vl_symbol = self.id_label_hash[vl_id]
geno.addAllele(vl_id, vl_symbol,
self.globaltt['variant_locus'])
vl_set.add(vl_id)
if len(variants) == 1 and len(genes) == 1:
for gene in genes:
geno.addAlleleOfGene(vl_id, gene)
else:
geno.addAllele(vl_id, vl_symbol)
else: # len(vars) == 0
# it's just anonymous variants in some gene
for gene in genes:
vl_id = '_:' + re.sub(r':', '', gene) + '-VL'
vl_symbol = self.id_label_hash[gene] + '<?>'
self.id_label_hash[vl_id] = vl_symbol
geno.addAllele(vl_id, vl_symbol,
self.globaltt['variant_locus'])
geno.addGene(gene, self.id_label_hash[gene])
geno.addAlleleOfGene(vl_id, gene)
vl_set.add(vl_id)
# make the vslcs
vl_list = sorted(vl_set)
vslc_list = []
for vl in vl_list:
# for unknown zygosity
vslc_id = re.sub(r'^_', '', vl) + 'U'
vslc_id = re.sub(r':', '', vslc_id)
vslc_id = '_:' + vslc_id
vslc_label = self.id_label_hash[vl] + '/?'
self.id_label_hash[vslc_id] = vslc_label
vslc_list.append(vslc_id)
geno.addPartsToVSLC(vslc_id, vl, None,
self.globaltt['indeterminate'],
self.globaltt['has_variant_part'],
None)
model.addIndividualToGraph(
vslc_id, vslc_label,
self.globaltt['variant single locus complement'])
if vslc_list:
if len(vslc_list) > 1:
gvc_id = '-'.join(vslc_list)
gvc_id = re.sub(r'_|:', '', gvc_id)
gvc_id = '_:' + gvc_id
gvc_label = '; '.join(self.id_label_hash[v]
for v in vslc_list)
model.addIndividualToGraph(
gvc_id, gvc_label,
self.globaltt['genomic_variation_complement'])
for vslc_id in vslc_list:
geno.addVSLCtoParent(vslc_id, gvc_id)
else:
# the GVC == VSLC, so don't have to make an extra piece
gvc_id = vslc_list.pop()
gvc_label = self.id_label_hash[gvc_id]
genotype_label = gvc_label + ' [n.s.]'
bkgd_id = re.sub(
r':', '', '-'.join(
(self.globaltt['unspecified_genomic_background'],
s)))
genotype_id = '-'.join((gvc_id, bkgd_id))
bkgd_id = '_:' + bkgd_id
geno.addTaxon(mouse_taxon, bkgd_id)
geno.addGenomicBackground(
bkgd_id, 'unspecified (' + s + ')',
self.globaltt['unspecified_genomic_background'],
"A placeholder for the unspecified genetic background for "
+ s)
geno.addGenomicBackgroundToGenotype(
bkgd_id, genotype_id,
self.globaltt['unspecified_genomic_background'])
geno.addParts(gvc_id, genotype_id,
self.globaltt['has_variant_part'])
geno.addGenotype(genotype_id, genotype_label)
graph.addTriple(s, self.globaltt['has_genotype'],
genotype_id)
else:
# LOG.debug(
# "Strain %s is not making a proper genotype.", s)
pass
LOG.warning(
"The following gene symbols did not list identifiers: %s",
str(sorted(list(genes_with_no_ids))))
LOG.error('%i symbols given are missing their gene identifiers',
len(genes_with_no_ids))
return
def process_allele_phenotype(self, limit=None):
"""
This file compactly lists variant to phenotype associations,
such that in a single row, there may be >1 variant listed
per phenotype and paper. This indicates that each variant is
individually assocated with the given phenotype,
as listed in 1+ papers.
(Not that the combination of variants is producing the phenotype.)
:param limit:
:return:
"""
raw = '/'.join((self.rawdir, self.files['allele_pheno']['file']))
if self.testMode:
g = self.testgraph
else:
g = self.graph
logger.info("Processing Allele phenotype associations")
line_counter = 0
geno = Genotype(g)
with open(raw, 'r') as csvfile:
filereader = csv.reader(csvfile, delimiter='\t', quotechar='\"')
for row in filereader:
if re.match(r'!', ''.join(row)): # header
continue
line_counter += 1
(db, gene_num, gene_symbol, is_not, phenotype_id, ref,
eco_symbol, with_or_from, aspect, gene_name, gene_synonym,
gene_class, taxon, date, assigned_by, blank, blank2) = row
if self.testMode and gene_num not in self.test_ids['gene']:
continue
# TODO add NOT phenotypes
if is_not == 'NOT':
continue
eco_id = None
if eco_symbol == 'IMP':
eco_id = 'ECO:0000015'
elif eco_symbol.strip() != '':
logger.warning("Encountered an ECO code we don't have: %s",
eco_symbol)
# according to the GOA spec, persons are not allowed to be
# in the reference column, therefore they the variant and
# persons are swapped between the reference and with column.
# we unswitch them here.
temp_var = temp_ref = None
if re.search(r'WBVar|WBRNAi', ref):
temp_var = ref
# move the paper from the with column into the ref
if re.search(r'WBPerson', with_or_from):
temp_ref = with_or_from
if temp_var is not None or temp_ref is not None:
with_or_from = temp_var
ref = temp_ref
allele_list = re.split(r'\|', with_or_from)
if len(allele_list) == 0:
logger.error(
"Missing alleles from phenotype assoc at line %d",
line_counter)
continue
else:
for a in allele_list:
allele_num = re.sub(r'WB:', '', a.strip())
allele_id = 'WormBase:' + allele_num
gene_id = 'WormBase:' + gene_num
if re.search(r'WBRNAi', allele_id):
# make the reagent-targeted gene,
# & annotate that instead of the RNAi item directly
rnai_num = re.sub(r'WormBase:', '', allele_id)
rnai_id = allele_id
rtg_id = self.make_reagent_targeted_gene_id(
gene_num, rnai_num)
geno.addReagentTargetedGene(
rnai_id, 'WormBase:' + gene_num, rtg_id)
geno.addGeneTargetingReagent(
rnai_id, None, geno.genoparts['RNAi_reagent'],
gene_id)
allele_id = rtg_id
elif re.search(r'WBVar', allele_id):
# this may become deprecated by using wormmine
# make the allele to gene relationship
# the WBVars are really sequence alterations
# the public name will come from elsewhere
geno.addSequenceAlteration(allele_id, None)
vl_id = '_:' + '-'.join((gene_num, allele_num))
geno.addSequenceAlterationToVariantLocus(
allele_id, vl_id)
geno.addAlleleOfGene(vl_id, gene_id)
else:
logger.warning(
"Some kind of allele I don't recognize: %s",
allele_num)
continue
assoc = G2PAssoc(g, self.name, allele_id, phenotype_id)
if eco_id is not None:
assoc.add_evidence(eco_id)
if ref is not None and ref != '':
ref = re.sub(r'(WB:|WB_REF:)', 'WormBase:', ref)
reference = Reference(g, ref)
if re.search(r'Person', ref):
reference.setType(
reference.ref_types['person'])
# also add
# inferred from background scientific knowledge
assoc.add_evidence('ECO:0000001')
reference.addRefToGraph()
assoc.add_source(ref)
assoc.add_association_to_graph()
# finish looping through all alleles
if not self.testMode \
and limit is not None and line_counter > limit:
break
return
def _get_process_allelic_variants(self, entry, g):
model = Model(g)
reference = Reference(g)
geno = Genotype(g)
if entry is not None:
# to hold the entry-specific publication mentions
# for the allelic variants
publist = {}
entry_num = entry['mimNumber']
# process the ref list just to get the pmids
ref_to_pmid = self._get_pubs(entry, g)
if 'allelicVariantList' in entry:
allelicVariantList = entry['allelicVariantList']
for al in allelicVariantList:
al_num = al['allelicVariant']['number']
al_id = 'OMIM:'+str(entry_num)+'.'+str(al_num).zfill(4)
al_label = None
al_description = None
if al['allelicVariant']['status'] == 'live':
publist[al_id] = set()
if 'mutations' in al['allelicVariant']:
al_label = al['allelicVariant']['mutations']
if 'text' in al['allelicVariant']:
al_description = al['allelicVariant']['text']
m = re.findall(r'\{(\d+)\:', al_description)
publist[al_id] = set(m)
geno.addAllele(
al_id, al_label, geno.genoparts['variant_locus'],
al_description)
geno.addAlleleOfGene(
al_id, 'OMIM:'+str(entry_num),
geno.object_properties[
'is_sequence_variant_instance_of'])
for r in publist[al_id]:
pmid = ref_to_pmid[int(r)]
g.addTriple(
pmid, model.object_properties['is_about'],
al_id)
# look up the pubmed id in the list of references
if 'dbSnps' in al['allelicVariant']:
dbsnp_ids = \
re.split(r',', al['allelicVariant']['dbSnps'])
for dnum in dbsnp_ids:
did = 'dbSNP:'+dnum.strip()
model.addIndividualToGraph(did, None)
model.addSameIndividual(al_id, did)
if 'clinvarAccessions' in al['allelicVariant']:
# clinvarAccessions triple semicolon delimited
# each >1 like RCV000020059;;;
rcv_ids = \
re.split(
r';;;',
al['allelicVariant']['clinvarAccessions'])
rcv_ids = [
(re.match(r'(RCV\d+);*', r)).group(1)
for r in rcv_ids]
for rnum in rcv_ids:
rid = 'ClinVar:'+rnum
model.addXref(al_id, rid)
reference.addPage(
al_id, "http://omim.org/entry/" +
str(entry_num)+"#" + str(al_num).zfill(4))
elif re.search(
r'moved', al['allelicVariant']['status']):
# for both 'moved' and 'removed'
moved_ids = None
if 'movedTo' in al['allelicVariant']:
moved_id = 'OMIM:'+al['allelicVariant']['movedTo']
moved_ids = [moved_id]
model.addDeprecatedIndividual(al_id, moved_ids)
else:
logger.error('Uncaught alleleic variant status %s',
al['allelicVariant']['status'])
# end loop allelicVariantList
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 process_allele_phenotype(self, limit=None):
"""
This file compactly lists variant to phenotype associations,
such that in a single row, there may be >1 variant listed
per phenotype and paper. This indicates that each variant is
individually assocated with the given phenotype,
as listed in 1+ papers.
(Not that the combination of variants is producing the phenotype.)
:param limit:
:return:
"""
src_key = 'allele_pheno'
raw = '/'.join((self.rawdir, self.files[src_key]['file']))
col = self.files[src_key]['columns']
graph = self.graph
model = Model(self.graph)
LOG.info("Processing Allele phenotype associations")
geno = Genotype(graph)
with open(raw, 'r') as csvfile:
reader = csv.reader(csvfile, delimiter='\t', quotechar='\"')
row = next(reader)
if row[0] != '!gaf-version: 2.0':
LOG.error('Not a vlaid gaf v2.0 formatted file: %s', raw)
# raise
for row in reader:
if row[0][0] == '!':
continue
# db = row[col.index('DB')]
gene_num = row[col.index('DB Object ID')]
# gene_symbol = row[col.index('DB Object Symbol')]
is_not = row[col.index('Qualifier')]
phenotype_id = row[col.index('GO ID')]
ref = row[col.index('DB:Reference (|DB:Reference)')].strip()
eco_symbol = row[col.index('Evidence Code')]
with_or_from = row[col.index('With (or) From')]
# aspect = row[col.index('Aspect')]
# gene_name = row[col.index('DB Object Name')]
# gene_synonym = row[col.index('DB Object Synonym (|Synonym)')]
# gene_class = row[col.index('DB Object Type')]
# taxon = row[col.index('Taxon(|taxon)')]
# date = row[col.index('Date')]
# assigned_by = row[col.index('Assigned By')]
# blank = row[col.index('Annotation Extension')]
# blank2 = row[col.index('Gene Product Form ID')]
# TODO add NOT phenotypes
if is_not == 'NOT':
continue
eco_symbol = eco_symbol.strip()
eco_curie = None
if eco_symbol.strip() != '' and eco_symbol in self.gaf_eco:
eco_curie = self.gaf_eco[eco_symbol]
else:
LOG.warning(
'Evidence code %s is not found in the (gaf) gaf_eco',
eco_symbol)
# according to the GOA spec, persons are not allowed to be
# in the reference column, therefore they the variant and
# persons are swapped between the reference and with column.
# we unswitch them here.
temp_var = temp_ref = None
if re.search(r'WBVar|WBRNAi', ref):
temp_var = ref
# move the paper from the with column into the ref
if re.search(r'WBPerson', with_or_from):
temp_ref = with_or_from
if temp_var is not None or temp_ref is not None:
with_or_from = temp_var
ref = temp_ref
allele_list = re.split(r'\|', with_or_from)
if len(allele_list) == 0:
LOG.error(
"Missing alleles from phenotype assoc at line %d",
reader.line_num)
continue
else:
for allele in allele_list:
allele_num = re.sub(r'WB:', '', allele.strip())
allele_id = 'WormBase:' + allele_num
gene_id = 'WormBase:' + gene_num
if re.search(r'WBRNAi', allele_id):
# @kshefchek - removing this blank node
# in favor of simpler modeling
# make the WormBase:WBRNAi* id
# a self.globaltt['reagent_targeted_gene'], and attach
# phenotype to this ID
# Previous model - make a bnode reagent-targeted gene,
# & annotate that instead of the RNAi item directly
# rnai_num = re.sub(r'WormBase:', '', allele_id)
# rnai_id = allele_id
# rtg_id = self.make_reagent_targeted_gene_id(
# gene_num, rnai_num)
# geno.addReagentTargetedGene(
# rnai_id, 'WormBase:' + gene_num, rtg_id)
# allele_id = rtg_id
# Could type the IRI as both the reagant and reagant
# targeted gene but not sure if this needed
# geno.addGeneTargetingReagent(
# allele_id, None, self.globaltt['RNAi_reagent'], gene_id)
model.addIndividualToGraph(
allele_id, None,
self.globaltt['reagent_targeted_gene'])
self.graph.addTriple(
allele_id,
self.globaltt['is_expression_variant_of'],
gene_id)
elif re.search(r'WBVar', allele_id):
# this may become deprecated by using wormmine
# make the allele to gene relationship
# the WBVars are really sequence alterations
# the public name will come from elsewhere
# @kshefchek - removing this blank node
# in favor of simpler modeling, treat variant
# like an allele
# vl_id = '_:'+'-'.join((gene_num, allele_num))
# geno.addSequenceAlterationToVariantLocus(
# allele_id, vl_id)
# geno.addAlleleOfGene(vl_id, gene_id)
geno.addSequenceAlteration(allele_id, None)
geno.addAlleleOfGene(allele_id, gene_id)
else:
LOG.warning(
"Some kind of allele I don't recognize: %s",
allele_num)
continue
assoc = G2PAssoc(graph, self.name, allele_id,
phenotype_id)
if eco_curie is not None:
assoc.add_evidence(eco_curie)
if ref is not None and ref != '':
ref = re.sub(r'(WB:|WB_REF:)', 'WormBase:', ref)
reference = Reference(graph, ref)
if re.search(r'Person', ref):
reference.setType(self.globaltt['person'])
assoc.add_evidence(self.globaltt[
'inference from background scientific knowledge']
)
reference.addRefToGraph()
assoc.add_source(ref)
assoc.add_association_to_graph()
# finish looping through all alleles
if limit is not None and reader.line_num > limit:
break
def _process_allele_phenotype(self, limit):
"""
Make allele to phenotype associations using derived_pheno_class
and derived_pheno_manifest cvterm in the flybase db, an example entry is:
FBal0257663 @FBcv0000351:lethal@ | @FBcv0000308:female limited@,
with @FBal0130657:Scer\GAL4<up>dome-PG14</up>@
The first term is the phenotype, and all follow up terms are qualifiers,
self.globaltt['has_qualifier'])
Our previous approach was to use the genotype id associated with
FBal0257663/FBal0130657 , however, this required us to create blank
nodes and was considered unnecessarily granular
Note that sometimes identifiers do not exist for a term, eg
@:heat sensitive | tetracycline conditional@
derived_pheno_class - FBcv terms, these are phenotypes
derived_pheno_manifest - Anatomy terms FBbt, we currently
make phenotype IRI equivalents that end up in UPheno, but
this is being developed and updated, see
https://github.com/monarch-initiative/dipper/issues/770
Adds triples to self.graph
:param limit: number of rows to process
:return: None
"""
model = Model(self.graph)
src_key = 'allele_phenotype'
raw = '/'.join((self.rawdir, self.queries[src_key]['file']))
LOG.info("processing allele phenotype associations")
col = self.queries[src_key]['columns']
transgenic_alleles = self._get_foreign_transgenic_alleles()
# flybase terms - terms we prefix with FlyBase:
fly_prefixes = ['FBal', 'FBti', 'FBab', 'FBba', 'FBtp']
# a alphanumeric id followed by a colon then
# any character but a colon bordered by @s
term_regex = re.compile(r'@([\w]*):([^:@]*)@')
id_regex = re.compile(r'([a-zA-Z]+)(\d+)')
with open(raw, 'r') as tsvfile:
reader = csv.reader(tsvfile, delimiter='\t')
row = next(reader) # headers
self.check_fileheader(col, row)
for row in reader:
allele_id = row[col.index('allele_id')]
pheno_desc = row[col.index('pheno_desc')]
pheno_type = row[col.index('pheno_type')]
pub_id = row[col.index('pub_id')]
pub_title = row[col.index('pub_title')]
pmid_id = row[col.index('pmid_id')]
# Don't get phenotypes for transgenic alleles
if allele_id in transgenic_alleles:
continue
allele_curie = 'FlyBase:' + allele_id
terms = re.findall(term_regex, pheno_desc)
if not terms:
LOG.warning('Could not @terms@ in description: %s',
pheno_desc)
continue
term_ids, term_labels = zip(*terms)
id_match = re.match(id_regex, term_ids[0])
if id_match is not None:
prefix, reference = id_match.group(1, 2)
else:
raise ValueError("Could not parse id {}".format(
term_ids[0]))
# derived_pheno_class should all start with a FBcv term
if pheno_type == 'derived_pheno_class' and prefix != 'FBcv':
LOG.warning(
'derived_pheno_class does not '
'start with FBcv: %s', pheno_desc)
continue
# Create phenotype curie
if pheno_type == 'derived_pheno_class':
phenotype_curie = prefix + ':' + reference
elif pheno_type == 'derived_pheno_manifest':
# These are not proper FBcv phenotype terms
# but rather anatomical entities, go terms, sometimes free text
# skip parsing for now
continue
else:
raise ValueError(
"Unexpected phenotype type: {}".format(pheno_type))
if pmid_id:
ref_curie = 'PMID:' + pmid_id
else:
ref_curie = 'FlyBase:' + pub_id
reference = Reference(self.graph, ref_curie)
reference.setTitle(pub_title)
reference.addRefToGraph()
assoc = G2PAssoc(self.graph, self.name, allele_curie,
phenotype_curie,
self.globaltt['has phenotype'])
assoc.add_source(ref_curie)
# Associations need to be disambiguated via their qualifiers
# see http://flybase.org/reports/FBal0207398 as an example
assoc.set_association_id(
assoc.make_association_id(self.name, allele_curie,
self.globaltt['has phenotype'],
phenotype_curie, term_ids[1:]))
assoc.add_association_to_graph()
assoc_id = assoc.get_association_id()
# add the rest as qualifiers
for term in term_ids[1:]:
if term:
# FBal, GO, FBti, FBab ...
id_match = re.match(id_regex, term)
if id_match is not None:
prefix, reference = id_match.group(1, 2)
if prefix in fly_prefixes:
term_curie = 'FlyBase:' + term
else:
term_curie = prefix + ':' + reference
else:
raise ValueError(
"Could not parse id {}".format(term))
else:
# There is not an id for a term,
# eg @:heat sensitive | tetracycline conditional@
continue
self.graph.addTriple(assoc_id,
self.globaltt['has_qualifier'],
term_curie)
if limit is not None and reader.line_num > limit:
break
def _get_gene2pubmed(self, limit):
"""
Loops through the gene2pubmed file and adds a simple triple to say
that a given publication is_about a gene.
Publications are added as NamedIndividuals.
These are filtered on the taxon.
:param limit:
:return:
"""
if self.testMode:
g = self.testgraph
else:
g = self.graph
model = Model(g)
logger.info("Processing Gene records")
line_counter = 0
myfile = '/'.join((self.rawdir, self.files['gene2pubmed']['file']))
logger.info("FILE: %s", myfile)
assoc_counter = 0
with gzip.open(myfile, 'rb') as f:
for line in f:
# skip comments
line = line.decode().strip()
if re.match(r'^#', line):
continue
(tax_num, gene_num, pubmed_num) = line.split('\t')
# ## set filter=None in init if you don't want to have a filter
# if self.filter is not None:
# if ((self.filter == 'taxids' and \
# (int(tax_num) not in self.tax_ids))
# or (self.filter == 'geneids' and \
# (int(gene_num) not in self.gene_ids))):
# continue
# #### end filter
if self.testMode and int(gene_num) not in self.gene_ids:
continue
if not self.testMode and int(tax_num) not in self.tax_ids:
continue
if gene_num == '-' or pubmed_num == '-':
continue
line_counter += 1
gene_id = ':'.join(('NCBIGene', gene_num))
pubmed_id = ':'.join(('PMID', pubmed_num))
if self.class_or_indiv.get(gene_id) == 'C':
model.addClassToGraph(gene_id, None)
else:
model.addIndividualToGraph(gene_id, None)
# add the publication as a NamedIndividual
# add type publication
model.addIndividualToGraph(pubmed_id, None, None)
reference = Reference(g, pubmed_id,
Reference.ref_types['journal_article'])
reference.addRefToGraph()
g.addTriple(pubmed_id, model.object_properties['is_about'],
gene_id)
assoc_counter += 1
if not self.testMode and \
limit is not None and line_counter > limit:
break
logger.info("Processed %d pub-gene associations", assoc_counter)
return
def _process_nlx_157874_1_view(self, raw, limit=None):
"""
This table contains the Elements of Morphology data .
Note that foaf:depiction is inverse of foaf:depicts relationship.
Since it is bad form to have two definitions,
we concatenate the two into one string.
Turtle:
<eom id> a owl:Class
rdf:label Literal(eom label)
oboInOwl:has_related_synonym Literal(synonym list)
IAO:definition Literal(objective_def. subjective def)
foaf:depiction Literal(small_image_url),
Literal(large_image_url)
foaf:page Literal(page_url)
rdfs:comment Literal(long commented text)
TEC_note: URL are not literals.
:param raw:
:param limit:
:return:
"""
src_key = 'tables'
model = Model(self.graph)
col = self.resources[src_key]['columns']
with open(raw, 'r') as rawread:
reader = csv.reader(rawread, delimiter='\t', quotechar='\"')
row = next(reader)
if not self.check_fileheader(col, row):
pass
for row in reader:
# head -1 dvp.pr_nlx_157874_1|tr '\t' '\n'|
# sed "s|\(.*\)|# \1 = row[col.index('\1')]|g"
morphology_term_id = row[col.index(
'morphology_term_id')].strip()
# morphology_term_num = row[col.index('morphology_term_num')]
morphology_term_label = row[col.index(
'morphology_term_label')].strip()
morphology_term_url = row[col.index(
'morphology_term_url')].strip()
# terminology_category_label = row[
# col.index('terminology_category_label')]
# terminology_category_url = row[col.index('terminology_category_url')]
# subcategory = row[col.index('subcategory')]
objective_definition = row[col.index(
'objective_definition')].strip()
subjective_definition = row[col.index(
'subjective_definition')].strip()
comments = row[col.index('comments')].strip()
synonyms = row[col.index('synonyms')].strip()
replaces = row[col.index('replaces')].strip()
small_figure_url = row[col.index('small_figure_url')].strip()
large_figure_url = row[col.index('large_figure_url')].strip()
# e_uid = row[col.index('e_uid')]
# v_uid = row[col.index('v_uid')]
# v_uuid = row[col.index('v_uuid')]
# v_lastmodified = row[col.index('v_lastmodified')]
# v_status = row[col.index('v_status')]
# v_lastmodified_epoch = row[col.index('v_lastmodified_epoch')]
# Add morphology term to graph as a class
# with label, type, and description.
model.addClassToGraph(morphology_term_id,
morphology_term_label)
# Assemble the description text
if subjective_definition != '' and not (re.match(
r'.+\.$', subjective_definition)):
# add a trailing period.
subjective_definition = subjective_definition + '.'
if objective_definition != '' and not (re.match(
r'.+\.$', objective_definition)):
# add a trailing period.
objective_definition = objective_definition + '.'
definition = ' '.join(
(objective_definition, subjective_definition))
model.addDefinition(morphology_term_id, definition)
# <term id> FOAF:depicted_by literal url
# <url> type foaf:depiction
# do we want both images?
# morphology_term_id has depiction small_figure_url
if small_figure_url != '':
model.addDepiction(morphology_term_id, small_figure_url)
# morphology_term_id has depiction large_figure_url
if large_figure_url != '':
model.addDepiction(morphology_term_id, large_figure_url)
# morphology_term_id has comment comments
if comments != '':
model.addComment(morphology_term_id, comments)
for syn in synonyms.split(';'):
model.addSynonym(morphology_term_id, syn.strip(),
self.globaltt['has_exact_synonym'])
# morphology_term_id has_related_synonym replaces (; delimited)
if replaces not in ['', synonyms]:
for syn in replaces.split(';'):
model.addSynonym(morphology_term_id, syn.strip(),
self.globaltt['has_related_synonym'])
# <morphology_term_id> <foaf:page> morphology_term_url
if morphology_term_id is not None:
reference = Reference(self.graph, morphology_term_id,
self.globaltt['web page'])
# TEC 201905:
# Not so sure we need explicit <eom_uri> <webpage> <eom_url>.
# since <eom_uri> IS the <eom_url>.
reference.addPage(morphology_term_id, morphology_term_url)
if limit is not None and reader.line_num > limit:
break
def process_allele_phenotype(self, limit=None):
"""
This file compactly lists variant to phenotype associations,
such that in a single row, there may be >1 variant listed
per phenotype and paper. This indicates that each variant is
individually assocated with the given phenotype,
as listed in 1+ papers.
(Not that the combination of variants is producing the phenotype.)
:param limit:
:return:
"""
raw = '/'.join((self.rawdir, self.files['allele_pheno']['file']))
graph = self.graph
model = Model(self.graph)
LOG.info("Processing Allele phenotype associations")
line_counter = 0
geno = Genotype(graph)
with open(raw, 'r') as csvfile:
filereader = csv.reader(csvfile, delimiter='\t', quotechar='\"')
for row in filereader:
if re.match(r'!', ''.join(row)): # header
continue
line_counter += 1
(db, gene_num, gene_symbol, is_not, phenotype_id, ref,
eco_symbol, with_or_from, aspect, gene_name, gene_synonym,
gene_class, taxon, date, assigned_by, blank, blank2) = row
# TODO add NOT phenotypes
if is_not == 'NOT':
continue
eco_symbol = eco_symbol.strip()
eco_id = None
if eco_symbol.strip() != '':
eco_id = self.resolve(eco_symbol)
# according to the GOA spec, persons are not allowed to be
# in the reference column, therefore they the variant and
# persons are swapped between the reference and with column.
# we unswitch them here.
temp_var = temp_ref = None
if re.search(r'WBVar|WBRNAi', ref):
temp_var = ref
# move the paper from the with column into the ref
if re.search(r'WBPerson', with_or_from):
temp_ref = with_or_from
if temp_var is not None or temp_ref is not None:
with_or_from = temp_var
ref = temp_ref
allele_list = re.split(r'\|', with_or_from)
if len(allele_list) == 0:
LOG.error(
"Missing alleles from phenotype assoc at line %d",
line_counter)
continue
else:
for allele in allele_list:
allele_num = re.sub(r'WB:', '', allele.strip())
allele_id = 'WormBase:' + allele_num
gene_id = 'WormBase:' + gene_num
if re.search(r'WBRNAi', allele_id):
# @kshefchek - removing this blank node
# in favor of simpler modeling
# make the WormBase:WBRNAi* id
# a self.globaltt['reagent_targeted_gene'], and attach
# phenotype to this ID
# Previous model - make a bnode reagent-targeted gene,
# & annotate that instead of the RNAi item directly
#rnai_num = re.sub(r'WormBase:', '', allele_id)
#rnai_id = allele_id
#rtg_id = self.make_reagent_targeted_gene_id(
# gene_num, rnai_num)
#geno.addReagentTargetedGene(
# rnai_id, 'WormBase:' + gene_num, rtg_id)
# allele_id = rtg_id
# Could type the IRI as both the reagant and reagant
# targeted gene but not sure if this needed
# geno.addGeneTargetingReagent(
# allele_id, None, self.globaltt['RNAi_reagent'], gene_id)
model.addIndividualToGraph(
allele_id, None,
self.globaltt['reagent_targeted_gene'])
self.graph.addTriple(
allele_id, self.globaltt['is_expression_variant_of'],
gene_id)
elif re.search(r'WBVar', allele_id):
# this may become deprecated by using wormmine
# make the allele to gene relationship
# the WBVars are really sequence alterations
# the public name will come from elsewhere
# @kshefchek - removing this blank node
# in favor of simpler modeling, treat variant
# like an allele
#vl_id = '_:'+'-'.join((gene_num, allele_num))
#geno.addSequenceAlterationToVariantLocus(
# allele_id, vl_id)
#geno.addAlleleOfGene(vl_id, gene_id)
geno.addSequenceAlteration(allele_id, None)
geno.addAlleleOfGene(allele_id, gene_id)
else:
LOG.warning(
"Some kind of allele I don't recognize: %s", allele_num)
continue
assoc = G2PAssoc(graph, self.name, allele_id, phenotype_id)
if eco_id is not None:
assoc.add_evidence(eco_id)
if ref is not None and ref != '':
ref = re.sub(r'(WB:|WB_REF:)', 'WormBase:', ref)
reference = Reference(graph, ref)
if re.search(r'Person', ref):
reference.setType(self.globaltt['person'])
assoc.add_evidence(
self.globaltt[
'inference from background scientific knowledge'
])
reference.addRefToGraph()
assoc.add_source(ref)
assoc.add_association_to_graph()
# finish looping through all alleles
if limit is not None and line_counter > limit:
break
return
def _get_var_citations(self, limit):
# Generated weekly, the first of the week
# A tab-delimited report of citations associated with data in ClinVar,
# connected to the AlleleID, the VariationID, and either rs# from dbSNP
# or nsv in dbVar.
#
# AlleleID int value (xpath //Measure/@ID )
# VariationID ID ClinVar uses to anchor default display.
# (xpath //MeasureSet/@ID)
# rs rs identifier from dbSNP
# nsv nsv identifier from dbVar
# citation_source The source of the citation, either PubMed,
# PubMedCentral, or the NCBI Bookshelf
# citation_id The identifier used by that source
logger.info("Processing Citations for variants")
line_counter = 0
myfile = \
'/'.join((self.rawdir, self.files['variant_citations']['file']))
if self.testMode:
g = self.testgraph
else:
g = self.graph
model = Model(g)
with open(myfile, 'r', encoding="utf8") as f:
filereader = csv.reader(f, delimiter='\t', quotechar='\"')
for line in filereader:
# skip comments
line = line
if re.match(r'^#', line[0]):
continue
(allele_num, variant_num, rs_num, nsv_num, citation_source,
citation_id) = line
line_counter += 1
if self.testMode:
if int(variant_num) not in self.variant_ids:
continue
if citation_id.strip() == '':
logger.info(
"Skipping blank citation for ClinVarVariant:%s",
str(variant_num))
continue
# the citation for a variant is made to some kind of
# combination of the ids here.
# but i'm not sure which, we don't know what the
# citation is for exactly, other than the variant.
# so use mentions
var_id = 'ClinVarVariant:' + variant_num
# citation source: PubMed | PubMedCentral | citation_source
# citation id:
# format the citation id:
ref_id = None
if citation_source == 'PubMed':
ref_id = 'PMID:' + str(citation_id.replace(" ", ""))
model.makeLeader(ref_id)
elif citation_source == 'PubMedCentral':
ref_id = 'PMCID:' + str(citation_id)
if ref_id is not None:
r = Reference(self.graph, ref_id,
Reference.ref_types['journal_article'])
r.addRefToGraph()
g.addTriple(ref_id, self.properties['is_about'], var_id)
if not self.testMode \
and (limit is not None and line_counter > limit):
break
logger.info("Finished processing citations for variants")
return
