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_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 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
# gu = GraphUtils(curie_map.get()) # TODO unused
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, self.nobnodes)
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))
if self.nobnodes:
vl_id = ':'+vl_id
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(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)
r = Reference(ref)
if re.search(r'Person', ref):
r.setType(r.ref_types['person'])
# also add
# inferred from background scientific knowledge
assoc.add_evidence('ECO:0000001')
r.addRefToGraph(g)
assoc.add_source(ref)
assoc.add_association_to_graph(g)
# finish looping through all alleles
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_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_data(self, raw, 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:
"""
logger.info("Processing Data from %s", raw)
gu = GraphUtils(curie_map.get())
if self.testMode: # set the graph to build
g = self.testgraph
else:
g = self.graph
line_counter = 0
geno = Genotype(g)
du = DipperUtil()
gu.loadProperties(g, geno.object_properties, gu.OBJPROP)
gu.loadAllProperties(g)
with open(raw, 'r', encoding="iso-8859-1") as csvfile:
filereader = csv.reader(csvfile, delimiter=',', quotechar='\"')
next(filereader, None) # skip the header row
for row in filereader:
if not row:
pass
else:
line_counter += 1
(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
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.strip()
# Map the cell/sample type
cell_type = self._map_cell_type(sample_type)
# Make a cell line label
line_label = \
collection.partition(' ')[0]+'-'+catalog_id.strip()
# Map the repository/collection
repository = self._map_collection(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'
if self.nobnodes:
patient_id = ':'+patient_id
if family_id != '':
patient_id = \
'-'.join((patient_id, family_id, family_member))
else:
# make an anonymous patient
patient_id = '-'.join((patient_id, catalog_id.strip()))
# 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.
short_desc = (description.split(';')[0]).capitalize()
if affected == 'Yes':
affected = 'affected'
elif affected == 'No':
affected = 'unaffected'
gender = gender.lower()
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 = 'CLO:0000031'
gu.addIndividualToGraph(
g, cell_line_id, line_label, cell_line_reagent_id)
# add the equivalent id == dna_ref
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
gu.addIndividualToGraph(
g, equiv_cell_line, None, cell_line_reagent_id)
gu.addSameIndividual(g, 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
gu.addMember(g, repository, cell_line_id)
if cat_remark != '':
gu.addDescription(g, 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(
# g,age_id,age,self.terms['age'])
# gu.addTriple(
# g,age_id,self.properties['has_measurement'],age,
# True)
# ############# BUILD THE PATIENT #############
# Add the patient ID as an individual.
gu.addPerson(g, 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._map_race(race)
# if mapped_race is not None:
# gu.addTriple(
# g,patient_id,self.terms['race'],mapped_race)
# gu.addSubclass(
# g,self.terms['ethnic_group'],mapped_race)
# ############# BUILD THE FAMILY #############
# Add triples for family_id, if present.
if family_id != '':
family_comp_id = 'CoriellFamily:'+family_id
family_label = \
' '.join(('Family of proband with', short_desc))
# Add the family ID as a named individual
gu.addIndividualToGraph(
g, family_comp_id, family_label,
geno.genoparts['family'])
# Add the patient as a member of the family
gu.addMemberOf(g, 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!
if species is None or species == '':
species = 'H**o sapiens'
taxon = self._map_species(species)
# if there's a dbSNP id,
# this is actually the individual's genotype
genotype_id = None
genotype_label = None
if dbsnp_id != '':
genotype_id = 'dbSNPIndividual:'+dbsnp_id.strip()
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 = du.remove_control_characters(karyotype)
karyotype_id = None
if karyotype.strip() != '':
karyotype_id = \
'_'+re.sub('MONARCH:', '', self.make_id(karyotype))
if self.nobnodes:
karyotype_id = ':'+karyotype_id
# add karyotype as karyotype_variation_complement
gu.addIndividualToGraph(
g, karyotype_id, karyotype,
geno.genoparts['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 c in karyo_chrs:
chr_id = makeChromID(c, taxon, 'CHR')
# add an anonymous sequence feature,
# each located on chr
karyotype_feature_id = '-'.join((karyotype_id, c))
karyotype_feature_label = \
'some karyotype alteration on chr'+str(c)
f = Feature(
karyotype_feature_id, karyotype_feature_label,
geno.genoparts['sequence_alteration'])
f.addFeatureStartLocation(None, chr_id)
f.addFeatureToGraph(g)
f.loadAllProperties(g)
geno.addParts(
karyotype_feature_id, karyotype_id,
geno.object_properties['has_alternate_part'])
if gene != '':
vl = gene+'('+mutation+')'
# fix the variant_id so it's always in the same order
vids = variant_id.split(';')
variant_id = ';'.join(sorted(list(set(vids))))
if karyotype.strip() != '' \
and not self._is_normal_karyotype(karyotype):
mutation = mutation.strip()
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
if gvc_id is not None and gvc_id != karyotype_id \
and self.nobnodes:
gvc_id = ':'+gvc_id
# add the karyotype to the gvc.
# use reference if normal karyotype
karyo_rel = geno.object_properties['has_alternate_part']
if self._is_normal_karyotype(karyotype):
karyo_rel = \
geno.object_properties['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 v in variant_id.split(';'):
# handle omim-style and odd var ids
# like 610661.p.R401X
m = re.match(r'(\d+)\.+(.*)', v.strip())
if m is not None and len(m.groups()) == 2:
(locus_num, var_num) = m.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 o in omim_map:
# gene_id = 'OMIM:' + o # TODO unused
vslc_id = \
'_' + '-'.join(
[o + '.' + a for a in omim_map.get(o)])
if self.nobnodes:
vslc_id = ':'+vslc_id
vslc_label = vl
# we don't really know the zygosity of
# the alleles at all.
# so the vslcs are just a pot of them
gu.addIndividualToGraph(
g, vslc_id, vslc_label,
geno.genoparts[
'variant_single_locus_complement'])
for v in omim_map.get(o):
# this is actually a sequence alt
allele1_id = 'OMIM:'+o+'.'+v
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,
geno.zygosity['indeterminate'],
geno.object_properties[
'has_alternate_part'])
if vslc_id != gvc_id:
geno.addVSLCtoParent(vslc_id, gvc_id)
if affected == 'unaffected':
# let's just say that this person is wildtype
gu.addType(g, patient_id, geno.genoparts['wildtype'])
elif genotype_id is None:
# make an anonymous genotype id
genotype_id = '_geno'+catalog_id.strip()
if self.nobnodes:
genotype_id = ':'+genotype_id
# add the gvc
if gvc_id is not None:
gu.addIndividualToGraph(
g, gvc_id, gvc_label,
geno.genoparts['genomic_variation_complement'])
# add the gvc to the genotype
if genotype_id is not None:
if affected == 'unaffected':
rel = \
geno.object_properties[
'has_reference_part']
else:
rel = \
geno.object_properties[
'has_alternate_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))
else:
genotype_label = karyotype
if genotype_id is None:
genotype_id = karyotype_id
else:
geno.addParts(
karyotype_id, genotype_id,
geno.object_properties[
'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,
geno.genoparts['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
gu.addTriple(
g, patient_id,
geno.properties['has_genotype'], genotype_id)
else:
geno.addTaxon(taxon, patient_id)
# TODO: Add sex/gender (as part of the karyotype?)
# ############# DEAL WITH THE DISEASES #############
# we associate the disease to the patient
if affected == 'affected':
if omim_number != '':
for d in omim_number.split(';'):
if d is not None and d != '':
# if the omim number is in omim_map,
# then it is a gene not a pheno
if d not in omim_map:
disease_id = 'OMIM:'+d.strip()
# assume the label is taken care of
gu.addClassToGraph(g, disease_id, None)
# add the association:
# the patient has the disease
assoc = G2PAssoc(
self.name, patient_id, disease_id)
assoc.add_association_to_graph(g)
# this line is a model of this disease
# TODO abstract out model into
# it's own association class?
gu.addTriple(
g, cell_line_id,
gu.properties['model_of'],
disease_id)
else:
logger.info(
'removing %s from disease list ' +
'since it is a gene', d)
# ############# ADD PUBLICATIONS #############
if pubmed_ids != '':
for s in pubmed_ids.split(';'):
pubmed_id = 'PMID:'+s.strip()
ref = Reference(pubmed_id)
ref.setType(Reference.ref_types['journal_article'])
ref.addRefToGraph(g)
gu.addTriple(
g, pubmed_id, gu.properties['mentions'],
cell_line_id)
if not self.testMode \
and (limit is not None and line_counter > limit):
break
Assoc(self.name).load_all_properties(g)
return
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_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 _process_phenotype_tab(self, raw, limit):
if self.testMode:
g = self.testgraph
else:
g = self.graph
line_counter = 0
gu = GraphUtils(curie_map.get())
with open(raw, 'r', encoding="utf8") as csvfile:
filereader = csv.reader(csvfile, delimiter='\t', quotechar='\"')
for row in filereader:
line_counter += 1
(db, num, name, qual, pheno_id, publist, eco, onset, freq, w, asp, syn, date, curator) = row
disease_id = db + ":" + str(num)
if self.testMode and disease_id.strip() not in config.get_config()['test_ids']['disease']:
continue
# logger.info('adding %s', disease_id)
gu.addClassToGraph(g, disease_id, None)
gu.addClassToGraph(g, pheno_id, None)
eco_id = self._map_evidence_to_codes(eco)
gu.addClassToGraph(g, eco_id, None)
if onset is not None and onset.strip() != '':
gu.addClassToGraph(g, onset, None)
# we want to do things differently depending on the aspect of the annotation
if asp == 'O' or asp == 'M': # organ abnormality or mortality
assoc = D2PAssoc(self.name, disease_id, pheno_id, onset, freq)
elif asp == 'I': # inheritance patterns for the whole disease
assoc = DispositionAssoc(self.name, disease_id, pheno_id)
elif asp == 'C': # clinical course / onset
assoc = DispositionAssoc(self.name, disease_id, pheno_id)
else:
logger.error("I don't know what this aspect is:", asp)
assoc.add_evidence(eco_id)
publist = publist.split(';')
# blow these apart if there is a list of pubs
for pub in publist:
pub = pub.strip()
if pub != '':
# if re.match('http://www.ncbi.nlm.nih.gov/bookshelf/br\.fcgi\?book=gene', pub):
# #http://www.ncbi.nlm.nih.gov/bookshelf/br.fcgi?book=gene&part=ced
# m = re.search('part\=(\w+)', pub)
# pub_id = 'GeneReviews:'+m.group(1)
# elif re.search('http://www.orpha.net/consor/cgi-bin/OC_Exp\.php\?lng\=en\&Expert\=', pub):
# m = re.search('Expert=(\d+)', pub)
# pub_id = 'Orphanet:'+m.group(1)
if not re.match('http', pub):
r = Reference(pub)
if re.match('PMID', pub):
r.setType(Reference.ref_types['journal_article'])
r.addRefToGraph(g)
# TODO add curator
assoc.add_source(pub)
assoc.add_association_to_graph(g)
if not self.testMode and limit is not None and line_counter > limit:
break
Assoc(None).load_all_properties(g)
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: %f.2%% of %i benifited from the 1/4 day id mapping download",
uniprot_per, uniprot_tot)
return
def process_gaf(self, file, limit, id_map=None):
if self.testMode:
g = self.testgraph
else:
g = self.graph
model = Model(g)
geno = Genotype(g)
logger.info("Processing Gene Associations from %s", file)
line_counter = 0
if 7955 in self.tax_ids:
zfin = ZFIN(self.graph_type, self.are_bnodes_skized)
elif 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
(db, 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 (db == '' 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 == ''):
logger.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
db = self.clean_db_prefix(db)
uniprotid = None
gene_id = None
if db == 'UniProtKB':
mapped_ids = id_map.get(gene_num)
if id_map is not None and mapped_ids is not None:
if len(mapped_ids) == 1:
gene_id = mapped_ids[0]
uniprotid = ':'.join((db, gene_num))
gene_num = re.sub(r'\w+\:', '', gene_id)
elif len(mapped_ids) > 1:
# logger.warning(
# "Skipping gene id mapped for >1 gene %s -> %s",
# gene_num, str(mapped_ids))
continue
else:
continue
elif db == 'MGI':
gene_num = re.sub(r'MGI:', '', gene_num)
gene_id = ':'.join((db, gene_num))
gene_id = re.sub(r'MGI\:MGI\:', 'MGI:', gene_id)
else:
gene_id = ':'.join((db, gene_num))
if self.testMode \
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 s in re.split(r'\|', gene_synonym):
model.addSynonym(gene_id, s.strip())
if re.search(r'\|', taxon):
# TODO add annotations with >1 taxon
logger.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(g, self.name)
assoc.set_subject(gene_id)
assoc.set_object(go_id)
eco_id = self.map_go_evidence_code_to_eco(eco_symbol)
if eco_id is not None:
assoc.add_evidence(eco_id)
refs = re.split(r'\|', ref)
for r in refs:
r = r.strip()
if r != '':
prefix = re.split(r':', r)[0]
r = re.sub(prefix, self.clean_db_prefix(prefix), r)
r = re.sub(r'MGI\:MGI\:', 'MGI:', r)
ref = Reference(g, r)
if re.match(r'PMID', r):
ref_type = Reference.ref_types['journal_article']
ref.setType(ref_type)
ref.addRefToGraph()
assoc.add_source(r)
# TODO add the source of the annotations from assigned by?
aspect_rel_map = {
'P': model.object_properties['involved_in'], # involved in
'F': model.object_properties['enables'], # enables
'C': model.object_properties['part_of'] # part of
}
if aspect not in aspect_rel_map:
logger.error("Aspect not recognized: %s", aspect)
rel = aspect_rel_map.get(aspect)
if aspect == 'F' and re.search(r'contributes_to', qualifier):
rel = model.object_properties['contributes_to']
assoc.set_relationship(rel)
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
assoc.add_association_to_graph()
# 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):
logger.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:
# Redefinition of assoc type from
# dipper.models.assoc.Association.Assoc to
# dipper.models.assoc.G2PAssoc.G2PAssoc
assoc = G2PAssoc(g, 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(
g, self.name, targeted_gene_id, phenotypeid)
else:
assoc = G2PAssoc(g, self.name, i, phenotypeid)
for r in refs:
r = r.strip()
if r != '':
prefix = re.split(r':', r)[0]
r = re.sub(
prefix, self.clean_db_prefix(prefix), r)
r = re.sub(r'MGI\:MGI\:', 'MGI:', r)
assoc.add_source(r)
# experimental phenotypic evidence
assoc.add_evidence("ECO:0000059")
assoc.add_association_to_graph()
# TODO should the G2PAssoc be
# the evidence for the GO assoc?
if not self.testMode and \
limit is not None and line_counter > limit:
break
return
def process_gaf(self, file, limit, id_map=None):
if self.testMode:
g = self.testgraph
else:
g = self.graph
model = Model(g)
geno = Genotype(g)
logger.info("Processing Gene Associations from %s", file)
line_counter = 0
if 7955 in self.tax_ids:
zfin = ZFIN(self.graph_type, self.are_bnodes_skized)
elif 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
(db, 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 (db == '' 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 == ''):
logger.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
db = self.clean_db_prefix(db)
uniprotid = None
gene_id = None
if db == 'UniProtKB':
mapped_ids = id_map.get(gene_num)
if id_map is not None and mapped_ids is not None:
if len(mapped_ids) == 1:
gene_id = mapped_ids[0]
uniprotid = ':'.join((db, gene_num))
gene_num = re.sub(r'\w+\:', '', gene_id)
elif len(mapped_ids) > 1:
# logger.warning(
# "Skipping gene id mapped for >1 gene %s -> %s",
# gene_num, str(mapped_ids))
continue
else:
continue
elif db == 'MGI':
gene_num = re.sub(r'MGI:', '', gene_num)
gene_id = ':'.join((db, gene_num))
gene_id = re.sub(r'MGI\:MGI\:', 'MGI:', gene_id)
else:
gene_id = ':'.join((db, gene_num))
if self.testMode \
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 s in re.split(r'\|', gene_synonym):
model.addSynonym(gene_id, s.strip())
if re.search(r'\|', taxon):
# TODO add annotations with >1 taxon
logger.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(g, self.name)
assoc.set_subject(gene_id)
assoc.set_object(go_id)
eco_id = self.map_go_evidence_code_to_eco(eco_symbol)
if eco_id is not None:
assoc.add_evidence(eco_id)
refs = re.split(r'\|', ref)
for r in refs:
r = r.strip()
if r != '':
prefix = re.split(r':', r)[0]
r = re.sub(prefix, self.clean_db_prefix(prefix), r)
r = re.sub(r'MGI\:MGI\:', 'MGI:', r)
ref = Reference(g, r)
if re.match(r'PMID', r):
ref_type = Reference.ref_types['journal_article']
ref.setType(ref_type)
ref.addRefToGraph()
assoc.add_source(r)
# TODO add the source of the annotations from assigned by?
aspect_rel_map = {
'P': model.object_properties['involved_in'], # involved in
'F': model.object_properties['enables'], # enables
'C': model.object_properties['part_of'] # part of
}
if aspect not in aspect_rel_map:
logger.error("Aspect not recognized: %s", aspect)
rel = aspect_rel_map.get(aspect)
if aspect == 'F' and re.search(r'contributes_to', qualifier):
rel = model.object_properties['contributes_to']
assoc.set_relationship(rel)
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
assoc.add_association_to_graph()
# 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):
logger.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:
# Redefinition of assoc type from
# dipper.models.assoc.Association.Assoc to
# dipper.models.assoc.G2PAssoc.G2PAssoc
assoc = G2PAssoc(g, 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(g, self.name, targeted_gene_id,
phenotypeid)
else:
assoc = G2PAssoc(g, self.name, i, phenotypeid)
for r in refs:
r = r.strip()
if r != '':
prefix = re.split(r':', r)[0]
r = re.sub(prefix,
self.clean_db_prefix(prefix), r)
r = re.sub(r'MGI\:MGI\:', 'MGI:', r)
assoc.add_source(r)
# experimental phenotypic evidence
assoc.add_evidence("ECO:0000059")
assoc.add_association_to_graph()
# TODO should the G2PAssoc be
# the evidence for the GO assoc?
if not self.testMode and \
limit is not None and line_counter > limit:
break
return
