def _transform_entry(self, e, graph):
g = graph
model = Model(g)
geno = Genotype(graph)
tax_num = '9606'
tax_id = 'NCBITaxon:9606'
tax_label = 'Human'
build_num = "GRCh38"
build_id = "NCBIGenome:"+build_num
# get the numbers, labels, and descriptions
omimnum = e['entry']['mimNumber']
titles = e['entry']['titles']
label = titles['preferredTitle']
other_labels = []
if 'alternativeTitles' in titles:
other_labels += self._get_alt_labels(titles['alternativeTitles'])
if 'includedTitles' in titles:
other_labels += self._get_alt_labels(titles['includedTitles'])
# add synonyms of alternate labels
# preferredTitle": "PFEIFFER SYNDROME",
# "alternativeTitles":
# "ACROCEPHALOSYNDACTYLY, TYPE V; ACS5;;\nACS V;;\nNOACK SYNDROME",
# "includedTitles":
# "CRANIOFACIAL-SKELETAL-DERMATOLOGIC DYSPLASIA, INCLUDED"
# remove the abbreviation (comes after the ;) from the preferredTitle,
# and add it as a synonym
abbrev = None
if len(re.split(r';', label)) > 1:
abbrev = (re.split(r';', label)[1].strip())
newlabel = self._cleanup_label(label)
description = self._get_description(e['entry'])
omimid = 'OMIM:'+str(omimnum)
if e['entry']['status'] == 'removed':
model.addDeprecatedClass(omimid)
else:
omimtype = self._get_omimtype(e['entry'])
nodelabel = newlabel
# this uses our cleaned-up label
if omimtype == Genotype.genoparts['heritable_phenotypic_marker']:
if abbrev is not None:
nodelabel = abbrev
# in this special case,
# make it a disease by not declaring it as a gene/marker
model.addClassToGraph(omimid, nodelabel, None, newlabel)
elif omimtype == Genotype.genoparts['gene']:
if abbrev is not None:
nodelabel = abbrev
model.addClassToGraph(omimid, nodelabel, omimtype, newlabel)
else:
model.addClassToGraph(omimid, newlabel, omimtype)
# add the original screaming-caps OMIM label as a synonym
model.addSynonym(omimid, label)
# add the alternate labels and includes as synonyms
for l in other_labels:
model.addSynonym(omimid, l, 'OIO:hasRelatedSynonym')
# for OMIM, we're adding the description as a definition
model.addDefinition(omimid, description)
if abbrev is not None:
model.addSynonym(omimid, abbrev, 'OIO:hasRelatedSynonym')
# if this is a genetic locus (but not sequenced)
# then add the chrom loc info
# but add it to the ncbi gene identifier,
# not to the omim id (we reserve the omim id to be the phenotype)
feature_id = None
feature_label = None
if 'geneMapExists' in e['entry'] and e['entry']['geneMapExists']:
genemap = e['entry']['geneMap']
is_gene = False
if omimtype == \
Genotype.genoparts['heritable_phenotypic_marker']:
# get the ncbigene ids
ncbifeature = self._get_mapped_gene_ids(e['entry'], g)
if len(ncbifeature) == 1:
feature_id = 'NCBIGene:'+str(ncbifeature[0])
# add this feature as a cause for the omim disease
# TODO SHOULD I EVEN DO THIS HERE?
assoc = G2PAssoc(g, self.name, feature_id, omimid)
assoc.add_association_to_graph()
elif len(ncbifeature) > 1:
logger.info(
"Its ambiguous when %s maps to >1 gene id: %s",
omimid, str(ncbifeature))
else: # no ncbi feature, make an anonymous one
feature_id = self._make_anonymous_feature(str(omimnum))
feature_label = abbrev
elif omimtype == Genotype.genoparts['gene']:
feature_id = omimid
is_gene = True
else:
# 158900 falls into this category
feature_id = self._make_anonymous_feature(str(omimnum))
if abbrev is not None:
feature_label = abbrev
omimtype = \
Genotype.genoparts[
'heritable_phenotypic_marker']
if feature_id is not None:
if 'comments' in genemap:
# add a comment to this feature
comment = genemap['comments']
if comment.strip() != '':
model.addDescription(feature_id, comment)
if 'cytoLocation' in genemap:
cytoloc = genemap['cytoLocation']
# parse the cytoloc.
# add this omim thing as
# a subsequence of the cytofeature
# 18p11.3-p11.2
# FIXME
# add the other end of the range,
# but not sure how to do that
# not sure if saying subsequence of feature
# is the right relationship
f = Feature(g, feature_id, feature_label, omimtype)
if 'chromosomeSymbol' in genemap:
chrom_num = str(genemap['chromosomeSymbol'])
chrom = makeChromID(chrom_num, tax_num, 'CHR')
geno.addChromosomeClass(
chrom_num, tax_id, tax_label)
# add the positional information, if available
fstart = fend = -1
if 'chromosomeLocationStart' in genemap:
fstart = genemap['chromosomeLocationStart']
if 'chromosomeLocationEnd' in genemap:
fend = genemap['chromosomeLocationEnd']
if fstart >= 0:
# make the build-specific chromosome
chrom_in_build = makeChromID(chrom_num,
build_num,
'MONARCH')
# then, add the chromosome instance
# (from the given build)
geno.addChromosomeInstance(
chrom_num, build_id, build_num, chrom)
if omimtype == \
Genotype.genoparts[
'heritable_phenotypic_marker']:
postypes = [Feature.types['FuzzyPosition']]
else:
postypes = None
# NOTE that no strand information
# is available in the API
f.addFeatureStartLocation(
fstart, chrom_in_build, None, postypes)
if fend >= 0:
f.addFeatureEndLocation(
fend, chrom_in_build, None, postypes)
if fstart > fend:
logger.info(
"start>end (%d>%d) for %s",
fstart, fend, omimid)
# add the cytogenic location too
# for now, just take the first one
cytoloc = cytoloc.split('-')[0]
loc = makeChromID(cytoloc, tax_num, 'CHR')
model.addClassToGraph(loc, None)
f.addSubsequenceOfFeature(loc)
f.addFeatureToGraph(True, None, is_gene)
# end adding causative genes/features
# check if moved, if so,
# make it deprecated and
# replaced consider class to the other thing(s)
# some entries have been moved to multiple other entries and
# use the joining raw word "and"
# 612479 is movedto: "603075 and 603029" OR
# others use a comma-delimited list, like:
# 610402 is movedto: "609122,300870"
if e['entry']['status'] == 'moved':
if re.search(r'and', str(e['entry']['movedTo'])):
# split the movedTo entry on 'and'
newids = re.split(r'and', str(e['entry']['movedTo']))
elif len(str(e['entry']['movedTo']).split(',')) > 0:
# split on the comma
newids = str(e['entry']['movedTo']).split(',')
else:
# make a list of one
newids = [str(e['entry']['movedTo'])]
# cleanup whitespace and add OMIM prefix to numeric portion
fixedids = []
for i in newids:
fixedids.append('OMIM:'+i.strip())
model.addDeprecatedClass(omimid, fixedids)
self._get_phenotypicseries_parents(e['entry'], g)
self._get_mappedids(e['entry'], g)
self._get_mapped_gene_ids(e['entry'], g)
self._get_pubs(e['entry'], g)
self._get_process_allelic_variants(e['entry'], g) # temp gag
return
def _get_variants(self, limit):
"""
Currently loops through the variant_summary file.
:param limit:
:return:
"""
if self.testMode:
g = self.testgraph
else:
g = self.graph
model = Model(g)
geno = Genotype(g)
f = Feature(g, None, None, None)
# add the taxon and the genome
tax_num = '9606' # HARDCODE
tax_id = 'NCBITaxon:' + tax_num
tax_label = 'Human'
model.addClassToGraph(tax_id, None)
geno.addGenome(tax_id, tax_label) # label gets added elsewhere
# not unzipping the file
logger.info("Processing Variant records")
line_counter = 0
myfile = '/'.join((self.rawdir, self.files['variant_summary']['file']))
with gzip.open(myfile, 'rb') as f:
for line in f:
# skip comments
line = line.decode().strip()
if re.match(r'^#', line):
continue
# AlleleID integer value as stored in the AlleleID field in ClinVar (//Measure/@ID in the XML)
# Type character, the type of variation
# Name character, the preferred name for the variation
# GeneID integer, GeneID in NCBI's Gene database
# GeneSymbol character, comma-separated list of GeneIDs overlapping the variation
# ClinicalSignificance character, comma-separated list of values of clinical significance reported for this variation
# for the mapping between the terms listed here and the integers in the .VCF files, see
# http://www.ncbi.nlm.nih.gov/clinvar/docs/clinsig/
# RS# (dbSNP) integer, rs# in dbSNP
# nsv (dbVar) character, the NSV identifier for the region in dbVar
# RCVaccession character, list of RCV accessions that report this variant
# TestedInGTR character, Y/N for Yes/No if there is a test registered as specific to this variation in the NIH Genetic Testing Registry (GTR)
# PhenotypeIDs character, list of db names and identifiers for phenotype(s) reported for this variant
# Origin character, list of all allelic origins for this variation
# Assembly character, name of the assembly on which locations are based
# Chromosome character, chromosomal location
# Start integer, starting location, in pter->qter orientation
# Stop integer, end location, in pter->qter orientation
# Cytogenetic character, ISCN band
# ReviewStatus character, highest review status for reporting this measure. For the key to the terms,
# and their relationship to the star graphics ClinVar displays on its web pages,
# see http://www.ncbi.nlm.nih.gov/clinvar/docs/variation_report/#interpretation
# HGVS(c.) character, RefSeq cDNA-based HGVS expression
# HGVS(p.) character, RefSeq protein-based HGVS expression
# NumberSubmitters integer, number of submissions with this variant
# LastEvaluated datetime, the latest time any submitter reported clinical significance
# Guidelines character, ACMG only right now, for the reporting of incidental variation in a Gene
# (NOTE: if ACMG, not a specific to the allele but to the Gene)
# OtherIDs character, list of other identifiers or sources of information about this variant
# VariantID integer, the value used to build the URL for the current default report,
# e.g. http://www.ncbi.nlm.nih.gov/clinvar/variation/1756/
#
# a crude check that there's an expected number of cols.
# if not, error out because something changed.
num_cols = len(line.split('\t'))
expected_numcols = 29
if num_cols != expected_numcols:
logger.error(
"Unexpected number of columns in raw file " +
"(%d actual vs %d expected)", num_cols,
expected_numcols)
(allele_num, allele_type, allele_name, gene_num, gene_symbol,
clinical_significance, dbsnp_num, dbvar_num, rcv_nums,
tested_in_gtr, phenotype_ids, origin, assembly, chr, start,
stop, cytogenetic_loc, review_status, hgvs_c, hgvs_p,
number_of_submitters, last_eval, guidelines, other_ids,
variant_num, reference_allele, alternate_allele, categories,
ChromosomeAccession) = 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
line_counter += 1
pheno_list = []
if phenotype_ids != '-':
# trim any leading/trailing semicolons/commas
phenotype_ids = re.sub(r'^[;,]', '', phenotype_ids)
phenotype_ids = re.sub(r'[;,]$', '', phenotype_ids)
pheno_list = re.split(r'[,;]', phenotype_ids)
if self.testMode:
# get intersection of test disease ids
# and these phenotype_ids
intersect = \
list(
set([str(i)
for i in self.disease_ids]) & set(pheno_list))
if int(gene_num) not in self.gene_ids and\
int(variant_num) not in self.variant_ids and\
len(intersect) < 1:
continue
# TODO may need to switch on assembly to create correct
# assembly/build identifiers
build_id = ':'.join(('NCBIGenome', assembly))
# make the reference genome build
geno.addReferenceGenome(build_id, assembly, tax_id)
allele_type_id = self._map_type_of_allele(allele_type)
bandinbuild_id = None
if str(chr) == '':
# check cytogenic location
if str(cytogenetic_loc).strip() != '':
# use cytogenic location to get the apx location
# oddly, they still put an assembly number even when
# there's no numeric location
if not re.search(r'-', str(cytogenetic_loc)):
band_id = makeChromID(
re.split(r'-', str(cytogenetic_loc)), tax_num,
'CHR')
geno.addChromosomeInstance(cytogenetic_loc,
build_id, assembly,
band_id)
bandinbuild_id = makeChromID(
re.split(r'-', str(cytogenetic_loc)), assembly,
'MONARCH')
else:
# can't deal with ranges yet
pass
else:
# add the human chromosome class to the graph,
# and add the build-specific version of it
chr_id = makeChromID(str(chr), tax_num, 'CHR')
geno.addChromosomeClass(str(chr), tax_id, tax_label)
geno.addChromosomeInstance(str(chr), build_id, assembly,
chr_id)
chrinbuild_id = makeChromID(str(chr), assembly, 'MONARCH')
seqalt_id = ':'.join(('ClinVarVariant', variant_num))
gene_id = None
# they use -1 to indicate unknown gene
if str(gene_num) != '-1' and str(gene_num) != 'more than 10':
if re.match(r'^Gene:', gene_num):
gene_num = "NCBI" + gene_num
else:
gene_id = ':'.join(('NCBIGene', str(gene_num)))
# FIXME there are some "variants" that are actually haplotypes
# probably will get taken care of when we switch to processing
# the xml for example, variant_num = 38562
# but there's no way to tell if it's a haplotype
# in the csv data so the dbsnp or dbvar
# should probably be primary,
# and the variant num be the vslc,
# with each of the dbsnps being added to it
# TODO clinical significance needs to be mapped to
# a list of terms
# first, make the variant:
f = Feature(seqalt_id, allele_name, allele_type_id)
if start != '-' and start.strip() != '':
f.addFeatureStartLocation(start, chrinbuild_id)
if stop != '-' and stop.strip() != '':
f.addFeatureEndLocation(stop, chrinbuild_id)
f.addFeatureToGraph()
f.addTaxonToFeature(tax_id)
# make the ClinVarVariant the clique leader
model.makeLeader(seqalt_id)
if bandinbuild_id is not None:
f.addSubsequenceOfFeature(bandinbuild_id)
# CHECK - this makes the assumption that there is
# only one affected chromosome per variant what happens with
# chromosomal rearrangement variants?
# shouldn't both chromosomes be here?
# add the hgvs as synonyms
if hgvs_c != '-' and hgvs_c.strip() != '':
model.addSynonym(seqalt_id, hgvs_c)
if hgvs_p != '-' and hgvs_p.strip() != '':
model.addSynonym(seqalt_id, hgvs_p)
# add the dbsnp and dbvar ids as equivalent
if dbsnp_num != '-' and int(dbsnp_num) != -1:
dbsnp_id = 'dbSNP:rs' + str(dbsnp_num)
model.addIndividualToGraph(dbsnp_id, None)
model.addSameIndividual(seqalt_id, dbsnp_id)
if dbvar_num != '-':
dbvar_id = 'dbVar:' + dbvar_num
model.addIndividualToGraph(dbvar_id, None)
model.addSameIndividual(seqalt_id, dbvar_id)
# TODO - not sure if this is right... add as xref?
# the rcv is like the combo of the phenotype with the variant
if rcv_nums != '-':
for rcv_num in re.split(r';', rcv_nums):
rcv_id = 'ClinVar:' + rcv_num
model.addIndividualToGraph(rcv_id, None)
model.addXref(seqalt_id, rcv_id)
if gene_id is not None:
# add the gene
model.addClassToGraph(gene_id, gene_symbol)
# make a variant locus
vl_id = '_' + gene_num + '-' + variant_num
if self.nobnodes:
vl_id = ':' + vl_id
vl_label = allele_name
model.addIndividualToGraph(vl_id, vl_label,
geno.genoparts['variant_locus'])
geno.addSequenceAlterationToVariantLocus(seqalt_id, vl_id)
geno.addAlleleOfGene(vl_id, gene_id)
else:
# some basic reporting
gmatch = re.search(r'\(\w+\)', allele_name)
if gmatch is not None and len(gmatch.groups()) > 0:
logger.info(
"Gene found in allele label, but no id provided: %s",
gmatch.group(1))
elif re.match(r'more than 10', gene_symbol):
logger.info(
"More than 10 genes found; "
"need to process XML to fetch (variant=%d)",
int(variant_num))
else:
logger.info("No gene listed for variant %d",
int(variant_num))
# parse the list of "phenotypes" which are diseases.
# add them as an association
# ;GeneReviews:NBK1440,MedGen:C0392514,OMIM:235200,SNOMED CT:35400008;MedGen:C3280096,OMIM:614193;MedGen:CN034317,OMIM:612635;MedGen:CN169374
# the list is both semicolon delimited and comma delimited,
# but i don't know why! some are bad, like:
# Orphanet:ORPHA ORPHA319705,SNOMED CT:49049000
if phenotype_ids != '-':
for phenotype in pheno_list:
m = re.match(r"(Orphanet:ORPHA(?:\s*ORPHA)?)",
phenotype)
if m is not None and len(m.groups()) > 0:
phenotype = re.sub(m.group(1), 'Orphanet:',
phenotype.strip())
elif re.match(r'ORPHA:\d+', phenotype):
phenotype = re.sub(r'^ORPHA', 'Orphanet',
phenotype.strip())
elif re.match(r'Human Phenotype Ontology', phenotype):
phenotype = re.sub(r'^Human Phenotype Ontology',
'', phenotype.strip())
elif re.match(r'SNOMED CT:\s?', phenotype):
phenotype = re.sub(r'SNOMED CT:\s?', 'SNOMED:',
phenotype.strip())
elif re.match(r'^Gene:', phenotype):
continue
assoc = G2PAssoc(g, self.name, seqalt_id,
phenotype.strip())
assoc.add_association_to_graph()
if other_ids != '-':
id_list = other_ids.split(',')
# process the "other ids" ex:
# CFTR2:F508del,HGMD:CD890142,OMIM Allelic Variant:602421.0001
# TODO make more xrefs
for xrefid in id_list:
prefix = xrefid.split(':')[0].strip()
if prefix == 'OMIM Allelic Variant':
xrefid = 'OMIM:' + xrefid.split(':')[1]
model.addIndividualToGraph(xrefid, None)
model.addSameIndividual(seqalt_id, xrefid)
elif prefix == 'HGMD':
model.addIndividualToGraph(xrefid, None)
model.addSameIndividual(seqalt_id, xrefid)
elif prefix == 'dbVar' \
and dbvar_num == xrefid.split(':')[1].strip():
pass # skip over this one
elif re.search(r'\s', prefix):
pass
# logger.debug(
# 'xref prefix has a space: %s', xrefid)
else:
# should be a good clean prefix
# note that HGMD variants are in here as Xrefs
# because we can't resolve URIs for them
# logger.info("Adding xref: %s", xrefid)
# gu.addXref(g, seqalt_id, xrefid)
# logger.info("xref prefix to add: %s", xrefid)
pass
if not self.testMode and limit is not None \
and line_counter > limit:
break
logger.info("Finished parsing variants")
return
def _process_qtls_genomic_location(
self, raw, 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)
line_counter = 0
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")
for row in reader:
line_counter += 1
if re.match(r'^#', ' '.join(row)):
continue
(chromosome, qtl_source, qtl_type, start_bp, stop_bp, frame, strand,
score, attr) = row
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 line_counter > 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)
return
def _get_chrbands(self, limit, taxon):
"""
:param limit:
:return:
"""
model = Model(self.graph)
# TODO PYLINT figure out what limit was for and why it is unused
line_counter = 0
myfile = '/'.join((self.rawdir, self.files[taxon]['file']))
logger.info("Processing Chr bands from FILE: %s", myfile)
geno = Genotype(self.graph)
monochrom = Monochrom(self.graph_type, self.are_bnodes_skized)
# used to hold band definitions for a chr
# in order to compute extent of encompasing bands
mybands = {}
# build the organism's genome from the taxon
genome_label = self.files[taxon]['genome_label']
taxon_id = 'NCBITaxon:'+taxon
# add the taxon as a class. adding the class label elsewhere
model.addClassToGraph(taxon_id, None)
model.addSynonym(taxon_id, genome_label)
geno.addGenome(taxon_id, genome_label)
# add the build and the taxon it's in
build_num = self.files[taxon]['build_num']
build_id = 'UCSC:'+build_num
geno.addReferenceGenome(build_id, build_num, taxon_id)
# process the bands
with gzip.open(myfile, 'rb') as f:
for line in f:
# skip comments
line = line.decode().strip()
if re.match('^#', line):
continue
# chr13 4500000 10000000 p12 stalk
(scaffold, start, stop, band_num, rtype) = line.split('\t')
line_counter += 1
# NOTE some less-finished genomes have
# placed and unplaced scaffolds
# * Placed scaffolds:
# the scaffolds have been placed within a chromosome.
# * Unlocalized scaffolds:
# although the chromosome within which the scaffold occurs
# is known, the scaffold's position or orientation
# is not known.
# * Unplaced scaffolds:
# it is not known which chromosome the scaffold belongs to
#
# find out if the thing is a full on chromosome, or a scaffold:
# ex: unlocalized scaffold: chr10_KL568008v1_random
# ex: unplaced scaffold: chrUn_AABR07022428v1
placed_scaffold_pattern = r'(chr(?:\d+|X|Y|Z|W|M))'
unlocalized_scaffold_pattern = \
placed_scaffold_pattern+r'_(\w+)_random'
unplaced_scaffold_pattern = r'chr(Un(?:_\w+)?)'
m = re.match(placed_scaffold_pattern+r'$', scaffold)
if m is not None and len(m.groups()) == 1:
# the chromosome is the first match of the pattern
chrom_num = m.group(1)
else:
# skip over anything that isn't a placed_scaffold
# at the class level
logger.info("Found non-placed chromosome %s", scaffold)
chrom_num = None
m_chr_unloc = re.match(unlocalized_scaffold_pattern, scaffold)
m_chr_unplaced = re.match(unplaced_scaffold_pattern, scaffold)
scaffold_num = None
if m:
pass
elif m_chr_unloc is not None and\
len(m_chr_unloc.groups()) == 2:
chrom_num = m_chr_unloc.group(1)
scaffold_num = chrom_num+'_'+m_chr_unloc.group(2)
elif m_chr_unplaced is not None and\
len(m_chr_unplaced.groups()) == 1:
scaffold_num = m_chr_unplaced.group(1)
else:
logger.error(
"There's a chr pattern that we aren't matching: %s",
scaffold)
if chrom_num is not None:
# the chrom class (generic) id
chrom_class_id = makeChromID(chrom_num, taxon, 'CHR')
# first, add the chromosome class (in the taxon)
geno.addChromosomeClass(
chrom_num, taxon_id, self.files[taxon]['genome_label'])
# then, add the chromosome instance (from the given build)
geno.addChromosomeInstance(chrom_num, build_id, build_num,
chrom_class_id)
# add the chr to the hashmap of coordinates for this build
# the chromosome coordinate space is itself
if chrom_num not in mybands.keys():
mybands[chrom_num] = {
'min': 0,
'max': int(stop),
'chr': chrom_num,
'ref': build_id,
'parent': None,
'stain': None,
'type': Feature.types['chromosome']}
if scaffold_num is not None:
# this will put the coordinates of the scaffold
# in the scaffold-space and make sure that the scaffold
# is part of the correct parent.
# if chrom_num is None,
# then it will attach it to the genome,
# just like a reg chrom
mybands[scaffold_num] = {
'min': start,
'max': stop,
'chr': scaffold_num,
'ref': build_id,
'parent': chrom_num,
'stain': None,
'type': Feature.types['assembly_component'],
'synonym': scaffold}
if band_num is not None and band_num.strip() != '':
# add the specific band
mybands[chrom_num+band_num] = {'min': start,
'max': stop,
'chr': chrom_num,
'ref': build_id,
'parent': None,
'stain': None,
'type': None}
# add the staining intensity of the band
if re.match(r'g(neg|pos|var)', rtype):
mybands[chrom_num+band_num]['stain'] = \
Feature.types.get(rtype)
# get the parent bands, and make them unique
parents = list(
monochrom.make_parent_bands(band_num, set()))
# alphabetical sort will put them in smallest to biggest,
# so we reverse
parents.sort(reverse=True)
# print('parents of',chrom,band,':',parents)
if len(parents) > 0:
mybands[chrom_num+band_num]['parent'] = \
chrom_num+parents[0]
else:
# TODO PYLINT why is 'parent'
# a list() a couple of lines up and a set() here?
parents = set()
# loop through the parents and add them to the hash
# add the parents to the graph, in hierarchical order
# TODO PYLINT Consider using enumerate
# instead of iterating with range and len
for i in range(len(parents)):
rti = getChrPartTypeByNotation(parents[i])
pnum = chrom_num+parents[i]
sta = int(start)
sto = int(stop)
if pnum not in mybands.keys():
# add the parental band to the hash
b = {'min': min(sta, sto),
'max': max(sta, sto),
'chr': chrom_num,
'ref': build_id,
'parent': None,
'stain': None,
'type': rti}
mybands[pnum] = b
else:
# band already in the hash means it's a grouping band
# need to update the min/max coords
b = mybands.get(pnum)
b['min'] = min(sta, sto, b['min'])
b['max'] = max(sta, sto, b['max'])
mybands[pnum] = b
# also, set the max for the chrom
c = mybands.get(chrom_num)
c['max'] = max(sta, sto, c['max'])
mybands[chrom_num] = c
# add the parent relationships to each
if i < len(parents) - 1:
mybands[pnum]['parent'] = chrom_num+parents[i+1]
else:
# add the last one (p or q usually)
# as attached to the chromosome
mybands[pnum]['parent'] = chrom_num
f.close() # end looping through file
# loop through the hash and add the bands to the graph
for b in mybands.keys():
myband = mybands.get(b)
band_class_id = makeChromID(b, taxon, 'CHR')
band_class_label = makeChromLabel(b, genome_label)
band_build_id = makeChromID(b, build_num, 'MONARCH')
band_build_label = makeChromLabel(b, build_num)
# the build-specific chrom
chrom_in_build_id = makeChromID(
myband['chr'], build_num, 'MONARCH')
# if it's != part, then add the class
if myband['type'] != Feature.types['assembly_component']:
model.addClassToGraph(band_class_id,
band_class_label, myband['type'])
bfeature = Feature(self.graph, band_build_id, band_build_label,
band_class_id)
else:
bfeature = Feature(self.graph, band_build_id, band_build_label,
myband['type'])
if 'synonym' in myband:
model.addSynonym(band_build_id, myband['synonym'])
if myband['parent'] is None:
if myband['type'] == Feature.types['assembly_component']:
# since we likely don't know the chr,
# add it as a part of the build
geno.addParts(band_build_id, build_id)
elif myband['type'] == Feature.types['assembly_component']:
# geno.addParts(band_build_id, chrom_in_build_id)
parent_chrom_in_build = makeChromID(myband['parent'],
build_num, 'MONARCH')
bfeature.addSubsequenceOfFeature(parent_chrom_in_build)
# add the band as a feature
# (which also instantiates the owl:Individual)
bfeature.addFeatureStartLocation(myband['min'], chrom_in_build_id)
bfeature.addFeatureEndLocation(myband['max'], chrom_in_build_id)
if 'stain' in myband and myband['stain'] is not None:
# TODO 'has_staining_intensity' being dropped by MB
bfeature.addFeatureProperty(
Feature.properties['has_staining_intensity'],
myband['stain'])
# type the band as a faldo:Region directly (add_region=False)
# bfeature.setNoBNodes(self.nobnodes)
# to come when we merge in ZFIN.py
bfeature.addFeatureToGraph(False)
return
def _process_QTLs_genomic_location(self, raw, taxon_id, build_id, build_label, limit=None):
"""
This method
Triples created:
:param limit:
:return:
"""
if self.testMode:
g = self.testgraph
else:
g = self.graph
gu = GraphUtils(curie_map.get())
line_counter = 0
geno = Genotype(g)
genome_id = geno.makeGenomeID(taxon_id) # assume that chrs get added to the genome elsewhere
eco_id = "ECO:0000061" # Quantitative Trait Analysis Evidence
with gzip.open(raw, 'rt', encoding='ISO-8859-1') as tsvfile:
reader = csv.reader(tsvfile, delimiter="\t")
for row in reader:
line_counter += 1
if re.match('^#', ' '.join(row)):
continue
(chromosome, qtl_source, qtl_type, start_bp, stop_bp, frame, strand, score, attr) = row
# 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";
# CMO_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"
# 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('"FlankMarkers";', attr):
attr = re.sub('"FlankMarkers";', '', attr)
attr_items = re.sub('"', '', attr).split(";")
bad_attr_flag = False
for a in attr_items:
if not re.search('=', a):
bad_attr_flag = True
if bad_attr_flag:
logger.error("Poorly formed data on line %d:\n %s", line_counter, '\t'.join(row))
continue
attribute_dict = dict(item.split("=") for item in re.sub('"', '', attr).split(";"))
qtl_num = attribute_dict.get('QTL_ID')
if self.testMode and int(qtl_num) not in self.test_ids:
continue
# make association between QTL and trait
qtl_id = 'AQTL:' + str(qtl_num)
gu.addIndividualToGraph(g, qtl_id, None, geno.genoparts['QTL'])
geno.addTaxon(taxon_id, 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('ISU.*', pub_id):
pub_id = 'AQTLPub:' + pub_id.strip()
p = Reference(pub_id)
else:
pub_id = 'PMID:' + pub_id.strip()
p = Reference(pub_id, Reference.ref_types['journal_article'])
p.addRefToGraph(g)
# Add QTL to graph
assoc = G2PAssoc(self.name, qtl_id, trait_id, gu.object_properties['is_marker_for'])
assoc.add_evidence(eco_id)
assoc.add_source(pub_id)
if 'P-value' in attribute_dict.keys():
score = float(re.sub('<', '', attribute_dict.get('P-value')))
assoc.set_score(score)
assoc.add_association_to_graph(g)
# TODO make association to breed (which means making QTL feature in Breed background)
# get location of QTL
chromosome = re.sub('Chr\.', '', chromosome)
chrom_id = makeChromID(chromosome, taxon_id, 'CHR')
chrom_in_build_id = makeChromID(chromosome, build_id, 'MONARCH')
geno.addChromosomeInstance(chromosome, build_id, build_label, chrom_id)
qtl_feature = Feature(qtl_id, None, geno.genoparts['QTL'])
if start_bp == '':
start_bp = None
qtl_feature.addFeatureStartLocation(start_bp, chrom_in_build_id, strand,
[Feature.types['FuzzyPosition']])
if stop_bp == '':
stop_bp = None
qtl_feature.addFeatureEndLocation(stop_bp, chrom_in_build_id, strand,
[Feature.types['FuzzyPosition']])
qtl_feature.addTaxonToFeature(g, taxon_id)
qtl_feature.addFeatureToGraph(g)
if not self.testMode and limit is not None and line_counter > limit:
break
logger.info("Done with QTL genomic mappings for %s", taxon_id)
return
def _process_qtls_genetic_location(
self, raw, txid, common_name, limit=None):
"""
This function processes
Triples created:
:param limit:
:return:
"""
aql_curie = self.files[common_name + '_cm']['curie']
if self.test_mode:
graph = self.testgraph
else:
graph = self.graph
line_counter = 0
geno = Genotype(graph)
model = Model(graph)
eco_id = self.globaltt['quantitative trait analysis evidence']
taxon_curie = 'NCBITaxon:' + txid
LOG.info("Processing genetic location for %s from %s", taxon_curie, raw)
with open(raw, 'r', encoding="iso-8859-1") as csvfile:
filereader = csv.reader(csvfile, delimiter='\t', quotechar='\"')
for row in filereader:
line_counter += 1
(qtl_id,
qtl_symbol,
trait_name,
assotype,
empty,
chromosome,
position_cm,
range_cm,
flankmark_a2,
flankmark_a1,
peak_mark,
flankmark_b1,
flankmark_b2,
exp_id,
model_id,
test_base,
sig_level,
lod_score,
ls_mean,
p_values,
f_statistics,
variance,
bayes_value,
likelihood_ratio,
trait_id, dom_effect,
add_effect,
pubmed_id,
gene_id,
gene_id_src,
gene_id_type,
empty2) = row
if self.test_mode and int(qtl_id) not in self.test_ids:
continue
qtl_id = common_name + 'QTL:' + qtl_id.strip()
trait_id = ':'.join((aql_curie, trait_id.strip()))
# Add QTL to graph
feature = Feature(graph, qtl_id, qtl_symbol, self.globaltt['QTL'])
feature.addTaxonToFeature(taxon_curie)
# deal with the chromosome
chrom_id = makeChromID(chromosome, taxon_curie, 'CHR')
# add a version of the chromosome which is defined as
# the genetic map
build_id = 'MONARCH:'+common_name.strip()+'-linkage'
build_label = common_name+' genetic map'
geno.addReferenceGenome(build_id, build_label, taxon_curie)
chrom_in_build_id = makeChromID(chromosome, build_id, 'MONARCH')
geno.addChromosomeInstance(
chromosome, build_id, build_label, chrom_id)
start = stop = None
# range_cm sometimes ends in "(Mb)" (i.e pig 2016 Nov)
range_mb = re.split(r'\(', range_cm)
if range_mb is not None:
range_cm = range_mb[0]
if re.search(r'[0-9].*-.*[0-9]', range_cm):
range_parts = re.split(r'-', range_cm)
# check for poorly formed ranges
if len(range_parts) == 2 and\
range_parts[0] != '' and range_parts[1] != '':
(start, stop) = [
int(float(x.strip())) for x in re.split(r'-', range_cm)]
else:
LOG.info(
"A cM range we can't handle for QTL %s: %s",
qtl_id, range_cm)
elif position_cm != '':
match = re.match(r'([0-9]*\.[0-9]*)', position_cm)
if match is not None:
position_cm = match.group()
start = stop = int(float(position_cm))
# FIXME remove converion to int for start/stop
# when schema can handle floats add in the genetic location
# based on the range
feature.addFeatureStartLocation(
start, chrom_in_build_id, None,
[self.globaltt['FuzzyPosition']])
feature.addFeatureEndLocation(
stop, chrom_in_build_id, None,
[self.globaltt['FuzzyPosition']])
feature.addFeatureToGraph()
# sometimes there's a peak marker, like a rsid.
# we want to add that as a variant of the gene,
# and xref it to the qtl.
dbsnp_id = None
if peak_mark != '' and peak_mark != '.' and \
re.match(r'rs', peak_mark.strip()):
dbsnp_id = 'dbSNP:'+peak_mark.strip()
model.addIndividualToGraph(
dbsnp_id, None,
self.globaltt['sequence_alteration'])
model.addXref(qtl_id, dbsnp_id)
gene_id = gene_id.replace('uncharacterized ', '').strip()
if gene_id is not None and gene_id != '' and gene_id != '.'\
and re.fullmatch(r'[^ ]*', gene_id) is not None:
# we assume if no src is provided and gene_id is an integer,
# then it is an NCBI gene ... (okay, lets crank that back a notch)
if gene_id_src == '' and gene_id.isdigit() and \
gene_id in self.gene_info:
# LOG.info(
# 'Warm & Fuzzy saying %s is a NCBI gene for %s',
# gene_id, common_name)
gene_id_src = 'NCBIgene'
elif gene_id_src == '' and gene_id.isdigit():
LOG.warning(
'Cold & Prickely saying %s is a NCBI gene for %s',
gene_id, common_name)
gene_id_src = 'NCBIgene'
elif gene_id_src == '':
LOG.error(
' "%s" is a NOT NCBI gene for %s', gene_id, common_name)
gene_id_src = None
if gene_id_src == 'NCBIgene':
gene_id = 'NCBIGene:' + gene_id
# we will expect that these will get labels elsewhere
geno.addGene(gene_id, None)
# FIXME what is the right relationship here?
geno.addAffectedLocus(qtl_id, gene_id)
if dbsnp_id is not None:
# add the rsid as a seq alt of the gene_id
vl_id = '_:' + re.sub(
r':', '', gene_id) + '-' + peak_mark.strip()
geno.addSequenceAlterationToVariantLocus(
dbsnp_id, vl_id)
geno.addAffectedLocus(vl_id, gene_id)
# add the trait
model.addClassToGraph(trait_id, trait_name)
# Add publication
reference = None
if re.match(r'ISU.*', pubmed_id):
pub_id = 'AQTLPub:'+pubmed_id.strip()
reference = Reference(graph, pub_id)
elif pubmed_id != '':
pub_id = 'PMID:' + pubmed_id.strip()
reference = Reference(
graph, pub_id, self.globaltt['journal article'])
if reference is not None:
reference.addRefToGraph()
# make the association to the QTL
assoc = G2PAssoc(
graph, self.name, qtl_id, trait_id, self.globaltt['is marker for'])
assoc.add_evidence(eco_id)
assoc.add_source(pub_id)
# create a description from the contents of the file
# desc = ''
# assoc.addDescription(g, assoc_id, desc)
# TODO add exp_id as evidence
# if exp_id != '':
# exp_id = 'AQTLExp:'+exp_id
# gu.addIndividualToGraph(g, exp_id, None, eco_id)
if p_values != '':
scr = re.sub(r'<', '', p_values)
scr = re.sub(r',', '.', scr) # international notation
if scr.isnumeric():
score = float(scr)
assoc.set_score(score) # todo add score type
# TODO add LOD score?
assoc.add_association_to_graph()
# make the association to the dbsnp_id, if found
if dbsnp_id is not None:
# make the association to the dbsnp_id
assoc = G2PAssoc(
graph, self.name, dbsnp_id, trait_id,
self.globaltt['is marker for'])
assoc.add_evidence(eco_id)
assoc.add_source(pub_id)
# create a description from the contents of the file
# desc = ''
# assoc.addDescription(g, assoc_id, desc)
# TODO add exp_id
# if exp_id != '':
# exp_id = 'AQTLExp:'+exp_id
# gu.addIndividualToGraph(g, exp_id, None, eco_id)
if p_values != '':
scr = re.sub(r'<', '', p_values)
scr = re.sub(r',', '.', scr)
if scr.isnumeric():
score = float(scr)
assoc.set_score(score) # todo add score type
# TODO add LOD score?
assoc.add_association_to_graph()
if not self.test_mode and limit is not None and line_counter > limit:
break
LOG.info("Done with QTL genetic info")
return
def _transform_entry(self, ent, graph):
self.graph = graph
model = Model(graph)
geno = Genotype(graph)
tax_label = 'H**o sapiens'
tax_id = self.globaltt[tax_label]
build_num = "GRCh38"
asm_curie = ':'.join(('NCBIAssembly', build_num))
# get the numbers, labels, and descriptions
omim_num = str(ent['entry']['mimNumber'])
titles = ent['entry']['titles']
label = titles['preferredTitle']
other_labels = []
if 'alternativeTitles' in titles:
other_labels += self._get_alt_labels(titles['alternativeTitles'])
if 'includedTitles' in titles:
other_labels += self._get_alt_labels(titles['includedTitles'])
# remove the abbreviation (comes after the ;) from the preferredTitle,
abbrev = None
lab_lst = label.split(';')
if len(lab_lst) > 1:
abbrev = lab_lst[1].strip()
newlabel = self._cleanup_label(label)
omim_curie = 'OMIM:' + omim_num
omimtype = self.omim_type[omim_num]
nodelabel = newlabel
# this uses our cleaned-up label
if omimtype == self.globaltt['heritable_phenotypic_marker']:
if abbrev is not None:
nodelabel = abbrev
# in this special case,
# make it a disease by not declaring it as a gene/marker
# ??? and if abbrev is None?
model.addClassToGraph(omim_curie, nodelabel, description=newlabel)
# class_type=self.globaltt['disease or disorder'],
elif omimtype in [
self.globaltt['gene'], self.globaltt['has_affected_feature']
]:
omimtype = self.globaltt['gene']
if abbrev is not None:
nodelabel = abbrev
# omim is subclass_of gene (provide type term)
model.addClassToGraph(omim_curie, nodelabel, self.globaltt['gene'],
newlabel)
else:
# omim is NOT subclass_of D|P|or ?...
model.addClassToGraph(omim_curie, newlabel)
# KS: commenting out, we will get disease descriptions
# from MONDO, and gene descriptions from the mygene API
# if this is a genetic locus (not sequenced) then
# add the chrom loc info to the ncbi gene identifier,
# not to the omim id (we reserve the omim id to be the phenotype)
#################################################################
# the above makes no sense to me. (TEC)
# For Monarch, OMIM is authoritative for disease / phenotype
# if they say a phenotype is associated with a locus
# that is what dipper should report.
# OMIM is not authoritative for NCBI gene locations, locus or otherwise.
# and dipper should not be reporting gene locations via OMIM.
feature_id = None
feature_label = None
if 'geneMapExists' in ent['entry'] and ent['entry']['geneMapExists']:
genemap = ent['entry']['geneMap']
is_gene = False
if omimtype == self.globaltt['heritable_phenotypic_marker']:
# get the ncbigene ids
ncbifeature = self._get_mapped_gene_ids(ent['entry'], graph)
if len(ncbifeature) == 1:
feature_id = 'NCBIGene:' + str(ncbifeature[0])
# add this feature as a cause for the omim disease
# TODO SHOULD I EVEN DO THIS HERE?
assoc = G2PAssoc(graph, self.name, feature_id, omim_curie)
assoc.add_association_to_graph()
else:
LOG.info(
"Its ambiguous when %s maps to not one gene id: %s",
omim_curie, str(ncbifeature))
elif omimtype in [
self.globaltt['gene'],
self.globaltt['has_affected_feature']
]:
feature_id = omim_curie
is_gene = True
omimtype = self.globaltt['gene']
else:
# 158900 falls into this category
feature_id = self._make_anonymous_feature(omim_num)
if abbrev is not None:
feature_label = abbrev
omimtype = self.globaltt['heritable_phenotypic_marker']
if feature_id is not None:
if 'comments' in genemap:
# add a comment to this feature
comment = genemap['comments']
if comment.strip() != '':
model.addDescription(feature_id, comment)
if 'cytoLocation' in genemap:
cytoloc = genemap['cytoLocation']
# parse the cytoloc.
# add this omim thing as
# a subsequence of the cytofeature
# 18p11.3-p11.2
# FIXME
# add the other end of the range,
# but not sure how to do that
# not sure if saying subsequence of feature
# is the right relationship
feat = Feature(graph, feature_id, feature_label, omimtype)
if 'chromosomeSymbol' in genemap:
chrom_num = str(genemap['chromosomeSymbol'])
chrom = makeChromID(chrom_num, tax_id, 'CHR')
geno.addChromosomeClass(chrom_num,
self.globaltt['H**o sapiens'],
tax_label)
# add the positional information, if available
fstart = fend = -1
if 'chromosomeLocationStart' in genemap:
fstart = genemap['chromosomeLocationStart']
if 'chromosomeLocationEnd' in genemap:
fend = genemap['chromosomeLocationEnd']
if fstart >= 0:
# make the build-specific chromosome
chrom_in_build = makeChromID(
chrom_num, build_num, 'MONARCH')
# then, add the chromosome instance
# (from the given build)
geno.addChromosomeInstance(chrom_num, asm_curie,
build_num, chrom)
if omimtype == self.globaltt[
'heritable_phenotypic_marker']:
postypes = [self.globaltt['FuzzyPosition']]
else:
postypes = None
# NOTE that no strand information
# is available in the API
feat.addFeatureStartLocation(
fstart, chrom_in_build, None, postypes)
if fend >= 0:
feat.addFeatureEndLocation(
fend, chrom_in_build, None, postypes)
if fstart > fend:
LOG.info("start>end (%d>%d) for %s", fstart,
fend, omim_curie)
# add the cytogenic location too
# for now, just take the first one
cytoloc = cytoloc.split('-')[0]
loc = makeChromID(cytoloc, tax_id, 'CHR')
model.addClassToGraph(loc, None)
feat.addSubsequenceOfFeature(loc)
feat.addFeatureToGraph(True, None, is_gene)
# end adding causative genes/features
if ent['entry']['status'] in ['moved', 'removed']:
LOG.warning('UNEXPECTED! not expecting obsolete record %s',
omim_curie)
self._get_phenotypicseries_parents(ent['entry'], graph)
self._get_mappedids(ent['entry'], graph)
self._get_mapped_gene_ids(ent['entry'], graph)
self._get_pubs(ent['entry'], graph)
self._get_process_allelic_variants(ent['entry'], graph)
def _get_variants(self, limit):
"""
Currently loops through the variant_summary file.
:param limit:
:return:
"""
if self.testMode:
g = self.testgraph
else:
g = self.graph
model = Model(g)
geno = Genotype(g)
f = Feature(g, None, None, None)
# add the taxon and the genome
tax_num = '9606' # HARDCODE
tax_id = 'NCBITaxon:'+tax_num
tax_label = 'Human'
model.addClassToGraph(tax_id, None)
geno.addGenome(tax_id, tax_label) # label gets added elsewhere
# not unzipping the file
logger.info("Processing Variant records")
line_counter = 0
myfile = '/'.join((self.rawdir, self.files['variant_summary']['file']))
with gzip.open(myfile, 'rb') as f:
for line in f:
# skip comments
line = line.decode().strip()
if re.match(r'^#', line):
continue
# AlleleID integer value as stored in the AlleleID field in ClinVar (//Measure/@ID in the XML)
# Type character, the type of variation
# Name character, the preferred name for the variation
# GeneID integer, GeneID in NCBI's Gene database
# GeneSymbol character, comma-separated list of GeneIDs overlapping the variation
# ClinicalSignificance character, comma-separated list of values of clinical significance reported for this variation
# for the mapping between the terms listed here and the integers in the .VCF files, see
# http://www.ncbi.nlm.nih.gov/clinvar/docs/clinsig/
# RS# (dbSNP) integer, rs# in dbSNP
# nsv (dbVar) character, the NSV identifier for the region in dbVar
# RCVaccession character, list of RCV accessions that report this variant
# TestedInGTR character, Y/N for Yes/No if there is a test registered as specific to this variation in the NIH Genetic Testing Registry (GTR)
# PhenotypeIDs character, list of db names and identifiers for phenotype(s) reported for this variant
# Origin character, list of all allelic origins for this variation
# Assembly character, name of the assembly on which locations are based
# Chromosome character, chromosomal location
# Start integer, starting location, in pter->qter orientation
# Stop integer, end location, in pter->qter orientation
# Cytogenetic character, ISCN band
# ReviewStatus character, highest review status for reporting this measure. For the key to the terms,
# and their relationship to the star graphics ClinVar displays on its web pages,
# see http://www.ncbi.nlm.nih.gov/clinvar/docs/variation_report/#interpretation
# HGVS(c.) character, RefSeq cDNA-based HGVS expression
# HGVS(p.) character, RefSeq protein-based HGVS expression
# NumberSubmitters integer, number of submissions with this variant
# LastEvaluated datetime, the latest time any submitter reported clinical significance
# Guidelines character, ACMG only right now, for the reporting of incidental variation in a Gene
# (NOTE: if ACMG, not a specific to the allele but to the Gene)
# OtherIDs character, list of other identifiers or sources of information about this variant
# VariantID integer, the value used to build the URL for the current default report,
# e.g. http://www.ncbi.nlm.nih.gov/clinvar/variation/1756/
#
# a crude check that there's an expected number of cols.
# if not, error out because something changed.
num_cols = len(line.split('\t'))
expected_numcols = 29
if num_cols != expected_numcols:
logger.error(
"Unexpected number of columns in raw file " +
"(%d actual vs %d expected)",
num_cols, expected_numcols)
(allele_num, allele_type, allele_name, gene_num, gene_symbol,
clinical_significance, dbsnp_num, dbvar_num, rcv_nums,
tested_in_gtr, phenotype_ids, origin, assembly, chr, start,
stop, cytogenetic_loc, review_status, hgvs_c, hgvs_p,
number_of_submitters, last_eval, guidelines, other_ids,
variant_num, reference_allele, alternate_allele, categories,
ChromosomeAccession) = 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
line_counter += 1
pheno_list = []
if phenotype_ids != '-':
# trim any leading/trailing semicolons/commas
phenotype_ids = re.sub(r'^[;,]', '', phenotype_ids)
phenotype_ids = re.sub(r'[;,]$', '', phenotype_ids)
pheno_list = re.split(r'[,;]', phenotype_ids)
if self.testMode:
# get intersection of test disease ids
# and these phenotype_ids
intersect = \
list(
set([str(i)
for i in self.disease_ids]) & set(pheno_list))
if int(gene_num) not in self.gene_ids and\
int(variant_num) not in self.variant_ids and\
len(intersect) < 1:
continue
# TODO may need to switch on assembly to create correct
# assembly/build identifiers
build_id = ':'.join(('NCBIGenome', assembly))
# make the reference genome build
geno.addReferenceGenome(build_id, assembly, tax_id)
allele_type_id = self._map_type_of_allele(allele_type)
bandinbuild_id = None
if str(chr) == '':
# check cytogenic location
if str(cytogenetic_loc).strip() != '':
# use cytogenic location to get the apx location
# oddly, they still put an assembly number even when
# there's no numeric location
if not re.search(r'-', str(cytogenetic_loc)):
band_id = makeChromID(
re.split(r'-', str(cytogenetic_loc)),
tax_num, 'CHR')
geno.addChromosomeInstance(
cytogenetic_loc, build_id, assembly, band_id)
bandinbuild_id = makeChromID(
re.split(r'-', str(cytogenetic_loc)),
assembly, 'MONARCH')
else:
# can't deal with ranges yet
pass
else:
# add the human chromosome class to the graph,
# and add the build-specific version of it
chr_id = makeChromID(str(chr), tax_num, 'CHR')
geno.addChromosomeClass(str(chr), tax_id, tax_label)
geno.addChromosomeInstance(
str(chr), build_id, assembly, chr_id)
chrinbuild_id = makeChromID(str(chr), assembly, 'MONARCH')
seqalt_id = ':'.join(('ClinVarVariant', variant_num))
gene_id = None
# they use -1 to indicate unknown gene
if str(gene_num) != '-1' and str(gene_num) != 'more than 10':
if re.match(r'^Gene:', gene_num):
gene_num = "NCBI" + gene_num
else:
gene_id = ':'.join(('NCBIGene', str(gene_num)))
# FIXME there are some "variants" that are actually haplotypes
# probably will get taken care of when we switch to processing
# the xml for example, variant_num = 38562
# but there's no way to tell if it's a haplotype
# in the csv data so the dbsnp or dbvar
# should probably be primary,
# and the variant num be the vslc,
# with each of the dbsnps being added to it
# TODO clinical significance needs to be mapped to
# a list of terms
# first, make the variant:
f = Feature(seqalt_id, allele_name, allele_type_id)
if start != '-' and start.strip() != '':
f.addFeatureStartLocation(start, chrinbuild_id)
if stop != '-' and stop.strip() != '':
f.addFeatureEndLocation(stop, chrinbuild_id)
f.addFeatureToGraph()
f.addTaxonToFeature(tax_id)
# make the ClinVarVariant the clique leader
model.makeLeader(seqalt_id)
if bandinbuild_id is not None:
f.addSubsequenceOfFeature(bandinbuild_id)
# CHECK - this makes the assumption that there is
# only one affected chromosome per variant what happens with
# chromosomal rearrangement variants?
# shouldn't both chromosomes be here?
# add the hgvs as synonyms
if hgvs_c != '-' and hgvs_c.strip() != '':
model.addSynonym(seqalt_id, hgvs_c)
if hgvs_p != '-' and hgvs_p.strip() != '':
model.addSynonym(seqalt_id, hgvs_p)
# add the dbsnp and dbvar ids as equivalent
if dbsnp_num != '-' and int(dbsnp_num) != -1:
dbsnp_id = 'dbSNP:rs'+str(dbsnp_num)
model.addIndividualToGraph(dbsnp_id, None)
model.addSameIndividual(seqalt_id, dbsnp_id)
if dbvar_num != '-':
dbvar_id = 'dbVar:'+dbvar_num
model.addIndividualToGraph(dbvar_id, None)
model.addSameIndividual(seqalt_id, dbvar_id)
# TODO - not sure if this is right... add as xref?
# the rcv is like the combo of the phenotype with the variant
if rcv_nums != '-':
for rcv_num in re.split(r';', rcv_nums):
rcv_id = 'ClinVar:' + rcv_num
model.addIndividualToGraph(rcv_id, None)
model.addXref(seqalt_id, rcv_id)
if gene_id is not None:
# add the gene
model.addClassToGraph(gene_id, gene_symbol)
# make a variant locus
vl_id = '_'+gene_num+'-'+variant_num
if self.nobnodes:
vl_id = ':'+vl_id
vl_label = allele_name
model.addIndividualToGraph(
vl_id, vl_label, geno.genoparts['variant_locus'])
geno.addSequenceAlterationToVariantLocus(seqalt_id, vl_id)
geno.addAlleleOfGene(vl_id, gene_id)
else:
# some basic reporting
gmatch = re.search(r'\(\w+\)', allele_name)
if gmatch is not None and len(gmatch.groups()) > 0:
logger.info(
"Gene found in allele label, but no id provided: %s",
gmatch.group(1))
elif re.match(r'more than 10', gene_symbol):
logger.info(
"More than 10 genes found; "
"need to process XML to fetch (variant=%d)",
int(variant_num))
else:
logger.info(
"No gene listed for variant %d",
int(variant_num))
# parse the list of "phenotypes" which are diseases.
# add them as an association
# ;GeneReviews:NBK1440,MedGen:C0392514,OMIM:235200,SNOMED CT:35400008;MedGen:C3280096,OMIM:614193;MedGen:CN034317,OMIM:612635;MedGen:CN169374
# the list is both semicolon delimited and comma delimited,
# but i don't know why! some are bad, like:
# Orphanet:ORPHA ORPHA319705,SNOMED CT:49049000
if phenotype_ids != '-':
for phenotype in pheno_list:
m = re.match(
r"(Orphanet:ORPHA(?:\s*ORPHA)?)", phenotype)
if m is not None and len(m.groups()) > 0:
phenotype = re.sub(
m.group(1), 'Orphanet:', phenotype.strip())
elif re.match(r'ORPHA:\d+', phenotype):
phenotype = re.sub(
r'^ORPHA', 'Orphanet', phenotype.strip())
elif re.match(r'Human Phenotype Ontology', phenotype):
phenotype = re.sub(
r'^Human Phenotype Ontology', '',
phenotype.strip())
elif re.match(r'SNOMED CT:\s?', phenotype):
phenotype = re.sub(
r'SNOMED CT:\s?', 'SNOMED:', phenotype.strip())
elif re.match(r'^Gene:', phenotype):
continue
assoc = G2PAssoc(
g, self.name, seqalt_id, phenotype.strip())
assoc.add_association_to_graph()
if other_ids != '-':
id_list = other_ids.split(',')
# process the "other ids" ex:
# CFTR2:F508del,HGMD:CD890142,OMIM Allelic Variant:602421.0001
# TODO make more xrefs
for xrefid in id_list:
prefix = xrefid.split(':')[0].strip()
if prefix == 'OMIM Allelic Variant':
xrefid = 'OMIM:'+xrefid.split(':')[1]
model.addIndividualToGraph(xrefid, None)
model.addSameIndividual(seqalt_id, xrefid)
elif prefix == 'HGMD':
model.addIndividualToGraph(xrefid, None)
model.addSameIndividual(seqalt_id, xrefid)
elif prefix == 'dbVar' \
and dbvar_num == xrefid.split(':')[1].strip():
pass # skip over this one
elif re.search(r'\s', prefix):
pass
# logger.debug(
# 'xref prefix has a space: %s', xrefid)
else:
# should be a good clean prefix
# note that HGMD variants are in here as Xrefs
# because we can't resolve URIs for them
# logger.info("Adding xref: %s", xrefid)
# gu.addXref(g, seqalt_id, xrefid)
# logger.info("xref prefix to add: %s", xrefid)
pass
if not self.testMode and limit is not None \
and line_counter > limit:
break
logger.info("Finished parsing variants")
return
def process_feature_loc(self, limit):
src_key = 'feature_loc'
raw = '/'.join((self.rawdir, self.files[src_key]['file']))
graph = self.graph
model = Model(graph)
geno = Genotype(graph)
LOG.info("Processing: %s", self.files[src_key]['file'])
strain_to_variant_map = {}
build_num = self.version_num
build_id = 'WormBase:' + build_num
col = self.files[src_key]['columns']
with gzip.open(raw, 'rb') as csvfile:
reader = csv.reader(io.TextIOWrapper(csvfile, newline=""),
delimiter='\t',
quotechar='\"')
for row in reader:
if re.match(r'\#', ''.join(row)):
continue
chrom = row[col.index('seqid')]
# db = row[col.index('source')]
feature_type_label = row[col.index('type')]
start = row[col.index('start')]
# end = row[col.index('end')]
# score = row[col.index('score')]
strand = row[col.index('strand')]
# phase = row[col.index('phase')]
attributes = row[col.index('attributes')]
'''
I interpolated_pmap_position gene 1 559768 . . . ID=gmap:spe-13;gmap=spe-13;status=uncloned;Note=-21.3602 cM (+/- 1.84 cM)
I WormBase gene 3747 3909 . - . ID=Gene:WBGene00023193;Name=WBGene00023193;interpolated_map_position=-21.9064;sequence_name=Y74C9A.6;biotype=snoRNA;Alias=Y74C9A.6
I absolute_pmap_position gene 4119 10230 . . . ID=gmap:homt-1;gmap=homt-1;status=cloned;Note=-21.8252 cM (+/- 0.00 cM)
'''
# dbs = re.split(
# r' ', 'assembly_component expressed_sequence_match Coding_transcript Genomic_canonical Non_coding_transcript Orfeome Promoterome Pseudogene RNAi_primary RNAi_secondary Reference Transposon Transposon_CDS cDNA_for_RNAi miRanda ncRNA operon polyA_signal_sequence polyA_site snlRNA')
#
# if db not in dbs:
# continue
if feature_type_label not in [
'gene', 'point_mutation', 'deletion', 'RNAi_reagent',
'duplication', 'enhancer', 'binding_site',
'biological_region', 'complex_substitution',
'substitution', 'insertion', 'inverted_repeat'
]:
# note biological_regions include balancers
# other options here: promoter, regulatory_region, reagent
continue
attribute_dict = {}
if attributes != '':
attributes.replace('"', '')
attribute_dict = dict(
tuple(atv.split('=')) for atv in attributes.split(";"))
fid = flabel = desc = None
if 'ID' in attribute_dict:
fid = attribute_dict['ID']
if re.search(r'WB(Gene|Var|sf)', fid):
fid = re.sub(r'^\w+:WB', 'WormBase:WB', fid)
elif re.match(r'(gmap|landmark)', fid):
continue
else:
LOG.info('other identifier %s', fid)
fid = None
elif 'variation' in attribute_dict:
fid = 'WormBase:' + attribute_dict['variation']
flabel = attribute_dict.get('public_name')
sub = attribute_dict.get('substitution')
ins = attribute_dict.get('insertion')
# if it's a variation:
# variation=WBVar00604246;public_name=gk320600;strain=VC20384;substitution=C/T
desc = ''
if sub is not None:
desc = 'substitution=' + sub
if ins is not None:
desc = 'insertion=' + ins
# keep track of the strains with this variation,
# for later processing
strain_list = attribute_dict.get('strain')
if strain_list is not None:
for strn in strain_list.split(','):
strn = strn.strip()
if strn not in strain_to_variant_map:
strain_to_variant_map[strn] = set()
strain_to_variant_map[strn].add(fid)
# if feature_type_label == 'RNAi_reagent':
# Target=WBRNAi00096030 1 4942
# this will tell us where the RNAi is actually binding
# target = attribute_dict.get('Target') # TODO unused
# rnai_num = re.split(r' ', target)[0] # TODO unused
# it will be the reagent-targeted-gene that has a position,
# (i think)
# TODO finish the RNAi binding location
name = attribute_dict.get('Name')
polymorphism = attribute_dict.get('polymorphism')
if fid is None:
if name is not None and re.match(r'WBsf', name):
fid = 'WormBase:' + name
name = None
else:
continue
# these really aren't that interesting
if polymorphism is not None:
continue
if name is not None and not re.search(name, fid):
if flabel is None:
flabel = name
else:
model.addSynonym(fid, name)
if desc is not None and desc != '':
model.addDescription(fid, desc)
alias = attribute_dict.get('Alias')
biotype = attribute_dict.get('biotype')
note = attribute_dict.get('Note')
other_name = attribute_dict.get('other_name')
for n in [alias, other_name]:
if n is not None:
model.addSynonym(fid, other_name)
if feature_type_label == 'gene':
ftype_id = self.resolve(biotype)
else:
# so far, they all come with SO label syntax. resolve if need be.
ftype_id = self.globaltt[feature_type_label]
chr_id = makeChromID(chrom, build_id, 'CHR')
geno.addChromosomeInstance(chrom, build_id, build_num)
feature = Feature(graph, fid, flabel, ftype_id)
feature.addFeatureStartLocation(start, chr_id, strand)
feature.addFeatureEndLocation(start, chr_id, strand)
feature_is_class = False
if feature_type_label == 'gene':
feature_is_class = True
feature.addFeatureToGraph(True, None, feature_is_class)
if note is not None and note != '':
model.addDescription(fid, note)
if limit is not None and reader.line_num > limit:
break
# RNAi reagents:
'''
I RNAi_primary RNAi_reagent 4184 10232 . + . Target=WBRNAi00001601 1 6049 +;laboratory=YK;history_name=SA:yk326e10
I RNAi_primary RNAi_reagent 4223 10147 . + . Target=WBRNAi00033465 1 5925 +;laboratory=SV;history_name=MV_SV:mv_G_YK5052
I RNAi_primary RNAi_reagent 5693 9391 . + . Target=WBRNAi00066135 1 3699 +;laboratory=CH
'''
# TODO TF binding sites and network:
'''
def _add_variant_cdna_variant_assoc_to_graph(self, row):
"""
Generates relationships between variants and cDNA variants
given a row of data
:param iterable: row of data, see add_variant_info_to_graph()
docstring for expected structure.
Only applicable for structure 2.
:return None
"""
gu = GraphUtils(curie_map.get())
geno = Genotype(self.graph)
is_literal = True
(variant_key, variant_label, amino_acid_variant, amino_acid_position,
transcript_id, transcript_priority, protein_variant_type,
functional_impact, stop_gain_loss, transcript_gene,
protein_variant_source, variant_gene, bp_pos, variant_cdna,
cosmic_id, db_snp_id, genome_pos_start, genome_pos_end, ref_base,
variant_base, primary_transcript_exons,
primary_transcript_variant_sub_types, variant_type, chromosome,
genome_build, build_version, build_date) = row
variant_id = self.make_cgd_id('variant{0}'.format(variant_key))
# Add gene
self._add_variant_gene_relationship(variant_id, variant_gene)
# Transcript reference for nucleotide position
transcript_curie = self._make_transcript_curie(transcript_id)
# Make region IDs
cdna_region_id = ":_{0}Region".format(transcript_curie)
chrom_region_id = ":_{0}{1}Region-{2}-{3}".format(genome_build,
chromosome,
genome_pos_start,
genome_pos_end)
# Add the genome build
genome_label = "Human"
build_id = "UCSC:{0}".format(genome_build)
taxon_id = 'NCBITaxon:9606'
geno.addGenome(taxon_id, genome_label)
geno.addReferenceGenome(build_id, genome_build, taxon_id)
# Add chromosome
chrom_class_id = makeChromID(chromosome, '9606', 'CHR') # the chrom class (generic) id
chrom_instance_id = makeChromID(chromosome, build_id, 'MONARCH')
# first, add the chromosome class (in the taxon)
geno.addChromosomeClass(chromosome, taxon_id, 'Human')
# then, add the chromosome instance (from the given build)
geno.addChromosomeInstance(chromosome, build_id, genome_build, chrom_class_id)
# Add variant coordinates in reference to chromosome
self._add_feature_with_coords(variant_id,genome_pos_start,
genome_pos_end, chrom_instance_id, chrom_region_id)
# Add mutation coordinates in reference to gene
self._add_feature_with_coords(variant_id, bp_pos,
bp_pos, transcript_curie, cdna_region_id)
# Add nucleotide mutation
gu.addTriple(self.graph, variant_id,
geno.properties['reference_nucleotide'],
ref_base, is_literal)
gu.addTriple(self.graph, variant_id,
geno.properties['altered_nucleotide'],
variant_base, is_literal)
"""
Here we update any internal cgd variant IDS with a cosmic ID
or dbSNP ID. Alternatively we could do this using sql rather
than a sparql update which may be safer
"""
# Add SNP xrefs
if cosmic_id is not None:
cosmic_id_list = cosmic_id.split(', ')
cosmic_curie_list = []
for c_id in cosmic_id_list:
cosmic_curie = re.sub(r'COSM(\d+)', r'COSMIC:\1', c_id)
cosmic_curie_list.append(cosmic_curie)
gu.addIndividualToGraph(self.graph, cosmic_curie, c_id,
geno.genoparts['missense_variant'])
# If there are multiple ids set them equivalent to the first
for curie in cosmic_curie_list[1:]:
gu.addSameIndividual(self.graph, cosmic_curie_list[0], curie)
self._replace_entity(self.graph, variant_id, cosmic_curie_list[0], self.bindings)
if db_snp_id is not None:
db_snp_curie = re.sub(r'rs(\d+)', r'dbSNP:\1', db_snp_id)
gu.addIndividualToGraph(self.graph, db_snp_curie, db_snp_id,
geno.genoparts['missense_variant'])
if cosmic_id is None:
self._replace_entity(self.graph, variant_id, db_snp_curie, self.bindings)
else:
cosmic_id_list = cosmic_id.split(', ')
for c_id in cosmic_id_list:
cosmic_curie = re.sub(r'COSM(\d+)', r'COSMIC:\1', c_id)
gu.addSameIndividual(self.graph, cosmic_curie, db_snp_curie)
return
def _process_qtls_genetic_location(
self, raw, txid, common_name, limit=None):
"""
This function processes
Triples created:
:param limit:
:return:
"""
if self.testMode:
graph = self.testgraph
else:
graph = self.graph
line_counter = 0
geno = Genotype(graph)
model = Model(graph)
eco_id = self.globaltt['quantitative trait analysis evidence']
taxon_curie = 'NCBITaxon:' + txid
LOG.info("Processing genetic location for %s from %s", taxon_curie, raw)
with open(raw, 'r', encoding="iso-8859-1") as csvfile:
filereader = csv.reader(csvfile, delimiter='\t', quotechar='\"')
for row in filereader:
line_counter += 1
(qtl_id,
qtl_symbol,
trait_name,
assotype,
empty,
chromosome,
position_cm,
range_cm,
flankmark_a2,
flankmark_a1,
peak_mark,
flankmark_b1,
flankmark_b2,
exp_id,
model_id,
test_base,
sig_level,
lod_score,
ls_mean,
p_values,
f_statistics,
variance,
bayes_value,
likelihood_ratio,
trait_id, dom_effect,
add_effect,
pubmed_id,
gene_id,
gene_id_src,
gene_id_type,
empty2) = row
if self.testMode and int(qtl_id) not in self.test_ids:
continue
qtl_id = common_name + 'QTL:' + qtl_id.strip()
trait_id = 'AQTLTrait:' + trait_id.strip()
# Add QTL to graph
feature = Feature(graph, qtl_id, qtl_symbol, self.globaltt['QTL'])
feature.addTaxonToFeature(taxon_curie)
# deal with the chromosome
chrom_id = makeChromID(chromosome, taxon_curie, 'CHR')
# add a version of the chromosome which is defined as
# the genetic map
build_id = 'MONARCH:'+common_name.strip()+'-linkage'
build_label = common_name+' genetic map'
geno.addReferenceGenome(build_id, build_label, taxon_curie)
chrom_in_build_id = makeChromID(chromosome, build_id, 'MONARCH')
geno.addChromosomeInstance(
chromosome, build_id, build_label, chrom_id)
start = stop = None
# range_cm sometimes ends in "(Mb)" (i.e pig 2016 Nov)
range_mb = re.split(r'\(', range_cm)
if range_mb is not None:
range_cm = range_mb[0]
if re.search(r'[0-9].*-.*[0-9]', range_cm):
range_parts = re.split(r'-', range_cm)
# check for poorly formed ranges
if len(range_parts) == 2 and\
range_parts[0] != '' and range_parts[1] != '':
(start, stop) = [
int(float(x.strip())) for x in re.split(r'-', range_cm)]
else:
LOG.info(
"A cM range we can't handle for QTL %s: %s",
qtl_id, range_cm)
elif position_cm != '':
match = re.match(r'([0-9]*\.[0-9]*)', position_cm)
if match is not None:
position_cm = match.group()
start = stop = int(float(position_cm))
# FIXME remove converion to int for start/stop
# when schema can handle floats add in the genetic location
# based on the range
feature.addFeatureStartLocation(
start, chrom_in_build_id, None,
[self.globaltt['FuzzyPosition']])
feature.addFeatureEndLocation(
stop, chrom_in_build_id, None,
[self.globaltt['FuzzyPosition']])
feature.addFeatureToGraph()
# sometimes there's a peak marker, like a rsid.
# we want to add that as a variant of the gene,
# and xref it to the qtl.
dbsnp_id = None
if peak_mark != '' and peak_mark != '.' and \
re.match(r'rs', peak_mark.strip()):
dbsnp_id = 'dbSNP:'+peak_mark.strip()
model.addIndividualToGraph(
dbsnp_id, None,
self.globaltt['sequence_alteration'])
model.addXref(qtl_id, dbsnp_id)
gene_id = gene_id.replace('uncharacterized ', '').strip()
if gene_id is not None and gene_id != '' and gene_id != '.'\
and re.fullmatch(r'[^ ]*', gene_id) is not None:
# we assume if no src is provided and gene_id is an integer,
# then it is an NCBI gene ... (okay, lets crank that back a notch)
if gene_id_src == '' and gene_id.isdigit() and \
gene_id in self.gene_info:
# LOG.info(
# 'Warm & Fuzzy saying %s is a NCBI gene for %s',
# gene_id, common_name)
gene_id_src = 'NCBIgene'
elif gene_id_src == '' and gene_id.isdigit():
LOG.warning(
'Cold & Prickely saying %s is a NCBI gene for %s',
gene_id, common_name)
gene_id_src = 'NCBIgene'
elif gene_id_src == '':
LOG.error(
' "%s" is a NOT NCBI gene for %s', gene_id, common_name)
gene_id_src = None
if gene_id_src == 'NCBIgene':
gene_id = 'NCBIGene:' + gene_id
# we will expect that these will get labels elsewhere
geno.addGene(gene_id, None)
# FIXME what is the right relationship here?
geno.addAffectedLocus(qtl_id, gene_id)
if dbsnp_id is not None:
# add the rsid as a seq alt of the gene_id
vl_id = '_:' + re.sub(
r':', '', gene_id) + '-' + peak_mark.strip()
geno.addSequenceAlterationToVariantLocus(
dbsnp_id, vl_id)
geno.addAffectedLocus(vl_id, gene_id)
# add the trait
model.addClassToGraph(trait_id, trait_name)
# Add publication
reference = None
if re.match(r'ISU.*', pubmed_id):
pub_id = 'AQTLPub:'+pubmed_id.strip()
reference = Reference(graph, pub_id)
elif pubmed_id != '':
pub_id = 'PMID:' + pubmed_id.strip()
reference = Reference(
graph, pub_id, self.globaltt['journal article'])
if reference is not None:
reference.addRefToGraph()
# make the association to the QTL
assoc = G2PAssoc(
graph, self.name, qtl_id, trait_id, self.globaltt['is marker for'])
assoc.add_evidence(eco_id)
assoc.add_source(pub_id)
# create a description from the contents of the file
# desc = ''
# assoc.addDescription(g, assoc_id, desc)
# TODO add exp_id as evidence
# if exp_id != '':
# exp_id = 'AQTLExp:'+exp_id
# gu.addIndividualToGraph(g, exp_id, None, eco_id)
if p_values != '':
scr = re.sub(r'<', '', p_values)
scr = re.sub(r',', '.', scr) # international notation
if scr.isnumeric():
score = float(scr)
assoc.set_score(score) # todo add score type
# TODO add LOD score?
assoc.add_association_to_graph()
# make the association to the dbsnp_id, if found
if dbsnp_id is not None:
# make the association to the dbsnp_id
assoc = G2PAssoc(
graph, self.name, dbsnp_id, trait_id,
self.globaltt['is marker for'])
assoc.add_evidence(eco_id)
assoc.add_source(pub_id)
# create a description from the contents of the file
# desc = ''
# assoc.addDescription(g, assoc_id, desc)
# TODO add exp_id
# if exp_id != '':
# exp_id = 'AQTLExp:'+exp_id
# gu.addIndividualToGraph(g, exp_id, None, eco_id)
if p_values != '':
scr = re.sub(r'<', '', p_values)
scr = re.sub(r',', '.', scr)
if scr.isnumeric():
score = float(scr)
assoc.set_score(score) # todo add score type
# TODO add LOD score?
assoc.add_association_to_graph()
if not self.testMode and limit is not None and line_counter > limit:
break
LOG.info("Done with QTL genetic info")
return
def _get_chrbands(self, limit, taxon):
"""
:param limit:
:return:
"""
model = Model(self.graph)
# TODO PYLINT figure out what limit was for and why it is unused
line_counter = 0
myfile = '/'.join((self.rawdir, self.files[taxon]['file']))
logger.info("Processing Chr bands from FILE: %s", myfile)
geno = Genotype(self.graph)
monochrom = Monochrom(self.graph_type, self.are_bnodes_skized)
# used to hold band definitions for a chr
# in order to compute extent of encompasing bands
mybands = {}
# build the organism's genome from the taxon
genome_label = self.files[taxon]['genome_label']
taxon_id = 'NCBITaxon:' + taxon
# add the taxon as a class. adding the class label elsewhere
model.addClassToGraph(taxon_id, None)
model.addSynonym(taxon_id, genome_label)
geno.addGenome(taxon_id, genome_label)
# add the build and the taxon it's in
build_num = self.files[taxon]['build_num']
build_id = 'UCSC:' + build_num
geno.addReferenceGenome(build_id, build_num, taxon_id)
# process the bands
with gzip.open(myfile, 'rb') as f:
for line in f:
# skip comments
line = line.decode().strip()
if re.match('^#', line):
continue
# chr13 4500000 10000000 p12 stalk
(scaffold, start, stop, band_num, rtype) = line.split('\t')
line_counter += 1
# NOTE some less-finished genomes have
# placed and unplaced scaffolds
# * Placed scaffolds:
# the scaffolds have been placed within a chromosome.
# * Unlocalized scaffolds:
# although the chromosome within which the scaffold occurs
# is known, the scaffold's position or orientation
# is not known.
# * Unplaced scaffolds:
# it is not known which chromosome the scaffold belongs to
#
# find out if the thing is a full on chromosome, or a scaffold:
# ex: unlocalized scaffold: chr10_KL568008v1_random
# ex: unplaced scaffold: chrUn_AABR07022428v1
placed_scaffold_pattern = r'(chr(?:\d+|X|Y|Z|W|M))'
unlocalized_scaffold_pattern = placed_scaffold_pattern + r'_(\w+)_random'
unplaced_scaffold_pattern = r'chr(Un(?:_\w+)?)'
mch = re.match(placed_scaffold_pattern + r'$', scaffold)
if mch is not None and len(mch.groups()) == 1:
# the chromosome is the first match of the pattern
chrom_num = mch.group(1)
else:
# skip over anything that isn't a placed_scaffold
# at the class level
logger.info("Found non-placed chromosome %s", scaffold)
chrom_num = None
m_chr_unloc = re.match(unlocalized_scaffold_pattern, scaffold)
m_chr_unplaced = re.match(unplaced_scaffold_pattern, scaffold)
scaffold_num = None
if mch:
pass
elif m_chr_unloc is not None and len(
m_chr_unloc.groups()) == 2:
chrom_num = m_chr_unloc.group(1)
scaffold_num = chrom_num + '_' + m_chr_unloc.group(2)
elif m_chr_unplaced is not None and len(
m_chr_unplaced.groups()) == 1:
scaffold_num = m_chr_unplaced.group(1)
else:
logger.error(
"There's a chr pattern that we aren't matching: %s",
scaffold)
if chrom_num is not None:
# the chrom class (generic) id
chrom_class_id = makeChromID(chrom_num, taxon, 'CHR')
# first, add the chromosome class (in the taxon)
geno.addChromosomeClass(chrom_num, taxon_id,
self.files[taxon]['genome_label'])
# then, add the chromosome instance (from the given build)
geno.addChromosomeInstance(chrom_num, build_id, build_num,
chrom_class_id)
# add the chr to the hashmap of coordinates for this build
# the chromosome coordinate space is itself
if chrom_num not in mybands.keys():
mybands[chrom_num] = {
'min': 0,
'max': int(stop),
'chr': chrom_num,
'ref': build_id,
'parent': None,
'stain': None,
'type': self.globaltt['chromosome']
}
if scaffold_num is not None:
# this will put the coordinates of the scaffold
# in the scaffold-space and make sure that the scaffold
# is part of the correct parent.
# if chrom_num is None,
# then it will attach it to the genome,
# just like a reg chrom
mybands[scaffold_num] = {
'min': start,
'max': stop,
'chr': scaffold_num,
'ref': build_id,
'parent': chrom_num,
'stain': None,
'type': self.globaltt['assembly_component'],
'synonym': scaffold
}
if band_num is not None and band_num.strip() != '':
# add the specific band
mybands[chrom_num + band_num] = {
'min': start,
'max': stop,
'chr': chrom_num,
'ref': build_id,
'parent': None,
'stain': None,
'type': None
}
# add the staining intensity of the band
if re.match(r'g(neg|pos|var)', rtype):
mybands[chrom_num +
band_num]['stain'] = self.resolve(rtype)
# get the parent bands, and make them unique
parents = list(monochrom.make_parent_bands(
band_num, set()))
# alphabetical sort will put them in smallest to biggest,
# so we reverse
parents.sort(reverse=True)
# print('parents of',chrom,band,':',parents)
if len(parents) > 0:
mybands[chrom_num +
band_num]['parent'] = chrom_num + parents[0]
else:
# TODO PYLINT why is 'parent'
# a list() a couple of lines up and a set() here?
parents = set()
# loop through the parents and add them to the hash
# add the parents to the graph, in hierarchical order
# TODO PYLINT Consider using enumerate
# instead of iterating with range and len
for i in range(len(parents)):
rti = getChrPartTypeByNotation(parents[i])
pnum = chrom_num + parents[i]
sta = int(start)
sto = int(stop)
if pnum not in mybands.keys():
# add the parental band to the hash
bnd = {
'min': min(sta, sto),
'max': max(sta, sto),
'chr': chrom_num,
'ref': build_id,
'parent': None,
'stain': None,
'type': rti
}
mybands[pnum] = bnd
else:
# band already in the hash means it's a grouping band
# need to update the min/max coords
bnd = mybands.get(pnum)
bnd['min'] = min(sta, sto, bnd['min'])
bnd['max'] = max(sta, sto, bnd['max'])
mybands[pnum] = bnd
# also, set the max for the chrom
chrom = mybands.get(chrom_num)
chrom['max'] = max(sta, sto, chrom['max'])
mybands[chrom_num] = chrom
# add the parent relationships to each
if i < len(parents) - 1:
mybands[pnum]['parent'] = chrom_num + parents[i + 1]
else:
# add the last one (p or q usually)
# as attached to the chromosome
mybands[pnum]['parent'] = chrom_num
f.close() # end looping through file
# loop through the hash and add the bands to the graph
for bnd in mybands.keys():
myband = mybands.get(bnd)
band_class_id = makeChromID(bnd, taxon, 'CHR')
band_class_label = makeChromLabel(bnd, genome_label)
band_build_id = makeChromID(bnd, build_num, 'MONARCH')
band_build_label = makeChromLabel(bnd, build_num)
# the build-specific chrom
chrom_in_build_id = makeChromID(myband['chr'], build_num,
'MONARCH')
# if it's != part, then add the class
if myband['type'] != self.globaltt['assembly_component']:
model.addClassToGraph(band_class_id, band_class_label,
myband['type'])
bfeature = Feature(self.graph, band_build_id, band_build_label,
band_class_id)
else:
bfeature = Feature(self.graph, band_build_id, band_build_label,
myband['type'])
if 'synonym' in myband:
model.addSynonym(band_build_id, myband['synonym'])
if myband['parent'] is None:
if myband['type'] == self.globaltt['assembly_component']:
# since we likely don't know the chr,
# add it as a part of the build
geno.addParts(band_build_id, build_id)
elif myband['type'] == self.globaltt['assembly_component']:
# geno.addParts(band_build_id, chrom_in_build_id)
parent_chrom_in_build = makeChromID(myband['parent'],
build_num, 'MONARCH')
bfeature.addSubsequenceOfFeature(parent_chrom_in_build)
# add the band as a feature
# (which also instantiates the owl:Individual)
bfeature.addFeatureStartLocation(myband['min'], chrom_in_build_id)
bfeature.addFeatureEndLocation(myband['max'], chrom_in_build_id)
if 'stain' in myband and myband['stain'] is not None:
bfeature.addFeatureProperty(
self.globaltt['has_sequence_attribute'], myband['stain'])
# type the band as a faldo:Region directly (add_region=False)
# bfeature.setNoBNodes(self.nobnodes)
# to come when we merge in ZFIN.py
bfeature.addFeatureToGraph(False)
return
def _process_QTLs_genomic_location(
self, raw, taxon_id, build_id, build_label, limit=None):
"""
This method
Triples created:
:param limit:
:return:
"""
if self.testMode:
g = self.testgraph
else:
g = self.graph
model = Model(g)
line_counter = 0
geno = Genotype(g)
# assume that chrs get added to the genome elsewhere
# genome_id = geno.makeGenomeID(taxon_id) # TODO unused
eco_id = "ECO:0000061" # Quantitative Trait Analysis Evidence
logger.info("Processing QTL locations for %s", taxon_id)
with gzip.open(raw, 'rt', encoding='ISO-8859-1') as tsvfile:
reader = csv.reader(tsvfile, delimiter="\t")
# bad_attr_flag = False # TODO unused
for row in reader:
line_counter += 1
if re.match(r'^#', ' '.join(row)):
continue
(chromosome, qtl_source, qtl_type, start_bp, stop_bp, frame,
strand, score, attr) = row
# 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";
# CMO_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"
# 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 a in attr_items:
if not re.search(r'=', a):
# bad_attr_flag = True # TODO unused
# remove this attribute from the list
bad_attrs.add(a)
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.testMode and int(qtl_num) not in self.test_ids:
continue
# make association between QTL and trait
qtl_id = 'AQTL:' + str(qtl_num)
model.addIndividualToGraph(qtl_id, None, geno.genoparts['QTL'])
geno.addTaxon(taxon_id, 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(g, pub_id)
else:
pub_id = 'PMID:' + pub_id.strip()
reference = Reference(
g, pub_id, Reference.ref_types['journal_article'])
reference.addRefToGraph()
# Add QTL to graph
assoc = G2PAssoc(
g, self.name, qtl_id, trait_id,
model.object_properties['is_marker_for'])
assoc.add_evidence(eco_id)
assoc.add_source(pub_id)
if 'P-value' in attribute_dict.keys():
s = re.sub(r'<', '', attribute_dict.get('P-value'))
if ',' in s:
s = re.sub(r',', '.', s)
if s.isnumeric():
score = float(s)
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_id, 'CHR')
chrom_in_build_id = \
makeChromID(chromosome, build_id, 'MONARCH')
geno.addChromosomeInstance(
chromosome, build_id, build_label, chrom_id)
qtl_feature = Feature(g, qtl_id, None, geno.genoparts['QTL'])
if start_bp == '':
start_bp = None
qtl_feature.addFeatureStartLocation(
start_bp, chrom_in_build_id, strand,
[Feature.types['FuzzyPosition']])
if stop_bp == '':
stop_bp = None
qtl_feature.addFeatureEndLocation(
stop_bp, chrom_in_build_id, strand,
[Feature.types['FuzzyPosition']])
qtl_feature.addTaxonToFeature(taxon_id)
qtl_feature.addFeatureToGraph()
if not self.testMode and \
limit is not None and line_counter > limit:
break
logger.warning("Bad attribute flags in this file")
logger.info("Done with QTL genomic mappings for %s", taxon_id)
return
def _get_chrbands(self, limit, src_key, genome_id):
"""
:param limit:
:return:
"""
tax_num = src_key
if limit is None:
limit = sys.maxsize # practical limit anyway
model = Model(self.graph)
line_num = 0
myfile = '/'.join((self.rawdir, self.files[src_key]['file']))
LOG.info("Processing Chr bands from FILE: %s", myfile)
geno = Genotype(self.graph)
monochrom = Monochrom(self.graph_type, self.are_bnodes_skized)
# used to hold band definitions for a chr
# in order to compute extent of encompasing bands
mybands = {}
# build the organism's genome from the taxon
genome_label = self.files[src_key]['genome_label']
taxon_curie = 'NCBITaxon:' + tax_num
species_name = self.globaltcid[taxon_curie] # for logging
# add the taxon as a class. adding the class label elsewhere
model.addClassToGraph(taxon_curie, None)
model.addSynonym(taxon_curie, genome_label)
geno.addGenome(taxon_curie, genome_label, genome_id)
# add the build and the taxon it's in
build_num = self.files[src_key]['build_num']
build_id = 'UCSC:' + build_num
geno.addReferenceGenome(build_id, build_num, taxon_curie)
# cat (at least) also has chr[BDAECF]... hex? must be a back cat.
if tax_num == self.localtt['Felis catus']:
placed_scaffold_regex = re.compile(
r'(chr(?:[BDAECF]\d+|X|Y|Z|W|M|))$')
else:
placed_scaffold_regex = re.compile(r'(chr(?:\d+|X|Y|Z|W|M))$')
unlocalized_scaffold_regex = re.compile(r'_(\w+)_random')
unplaced_scaffold_regex = re.compile(r'chr(Un(?:_\w+)?)')
# process the bands
col = self.files[src_key]['columns']
with gzip.open(myfile, 'rb') as binreader:
for line in binreader:
line_num += 1
# skip comments
line = line.decode().strip()
if line[0] == '#' or line_num > limit:
continue
# chr13 4500000 10000000 p12 stalk
row = line.split('\t')
scaffold = row[col.index('chrom')].strip()
start = row[col.index('chromStart')]
stop = row[col.index('chromEnd')]
band_num = row[col.index('name')].strip()
rtype = row[col.index('gieStain')]
# NOTE some less-finished genomes have
# placed and unplaced scaffolds
# * Placed scaffolds:
# the scaffolds have been placed within a chromosome.
# * Unlocalized scaffolds:
# although the chromosome within which the scaffold occurs
# is known, the scaffold's position or orientation
# is not known.
# * Unplaced scaffolds:
# it is not known which chromosome the scaffold belongs to
#
# find out if the thing is a full on chromosome, or a scaffold:
# ex: unlocalized scaffold: chr10_KL568008v1_random
# ex: unplaced scaffold: chrUn_AABR07022428v1
mch = placed_scaffold_regex.match(scaffold)
if mch is not None and len(mch.groups()) == 1:
# the chromosome is the first match of the pattern
chrom_num = mch.group(1)
else:
# skip over anything that isn't a placed_scaffold at the class level
# LOG.info("Found non-placed chromosome %s", scaffold)
chrom_num = None
m_chr_unloc = unlocalized_scaffold_regex.match(scaffold)
m_chr_unplaced = unplaced_scaffold_regex.match(scaffold)
scaffold_num = None
if mch:
pass
elif m_chr_unloc is not None and len(
m_chr_unloc.groups()) == 2:
chrom_num = m_chr_unloc.group(1)
scaffold_num = chrom_num + '_' + m_chr_unloc.group(2)
elif m_chr_unplaced is not None and len(
m_chr_unplaced.groups()) == 1:
scaffold_num = m_chr_unplaced.group(1)
# else:
# LOG.error(
# "There's a chr pattern that we aren't matching: %s", scaffold)
if chrom_num is not None:
# the chrom class (generic) id
chrom_class_id = makeChromID(chrom_num, tax_num, 'CHR')
# first, add the chromosome class (in the taxon)
geno.addChromosomeClass(
chrom_num, taxon_curie,
self.files[src_key]['genome_label'])
# then, add the chromosome instance (from the given build)
geno.addChromosomeInstance(chrom_num, build_id, build_num,
chrom_class_id)
# add the chr to the hashmap of coordinates for this build
# the chromosome coordinate space is itself
if chrom_num not in mybands.keys():
mybands[chrom_num] = {
'min': 0,
'max': int(stop),
'chr': chrom_num,
'ref': build_id,
'parent': None,
'stain': None,
'type': self.globaltt['chromosome']
}
elif scaffold_num is not None:
# this will put the coordinates of the scaffold
# in the scaffold-space and make sure that the scaffold
# is part of the correct parent.
# if chrom_num is None,
# then it will attach it to the genome,
# just like a reg chrom
mybands[scaffold_num] = {
'min': start,
'max': stop,
'chr': scaffold_num,
'ref': build_id,
'parent': chrom_num,
'stain': None,
'type': self.globaltt['assembly_component'],
'synonym': scaffold
}
else:
LOG.info('%s line %i DROPPED chromosome/scaffold %s',
species_name, line_num, scaffold)
parents = list()
# see it new types have showed up
if rtype is not None and rtype not in [
'gneg', 'gpos25', 'gpos33', 'gpos50', 'gpos66',
'gpos75', 'gpos100', 'acen', 'gvar', 'stalk'
]:
LOG.info('Unknown gieStain type "%s" in %s at %i', rtype,
src_key, line_num)
self.globaltt[rtype] # blow up
if rtype == 'acen': # hacky, revisit if ontology improves
rtype = self.localtt[rtype]
if band_num is not None and band_num != '' and \
rtype is not None and rtype != '':
# add the specific band
mybands[chrom_num + band_num] = {
'min': start,
'max': stop,
'chr': chrom_num,
'ref': build_id,
'parent': None,
'stain': None,
'type': self.globaltt[rtype],
}
# add the staining intensity of the band
# get the parent bands, and make them unique
parents = list(monochrom.make_parent_bands(
band_num, set()))
# alphabetical sort will put them in smallest to biggest,
# so we reverse
parents.sort(reverse=True)
# print('parents of',chrom,band,':',parents)
if len(parents) > 0:
mybands[chrom_num +
band_num]['parent'] = chrom_num + parents[0]
# else: # band has no parents
# loop through the parents and add them to the dict
# add the parents to the graph, in hierarchical order
# TODO PYLINT Consider using enumerate
# instead of iterating with range and len
for i in range(len(parents)):
rti = getChrPartTypeByNotation(parents[i], self.graph)
pnum = chrom_num + parents[i]
sta = int(start)
sto = int(stop)
if pnum is not None and pnum not in mybands.keys():
# add the parental band to the hash
bnd = {
'min': min(sta, sto),
'max': max(sta, sto),
'chr': chrom_num,
'ref': build_id,
'parent': None,
'stain': None,
'type': rti
}
mybands[pnum] = bnd
elif pnum is not None:
# band already in the hash means it's a grouping band
# need to update the min/max coords
bnd = mybands.get(pnum)
bnd['min'] = min(sta, sto, bnd['min'])
bnd['max'] = max(sta, sto, bnd['max'])
mybands[pnum] = bnd
# also, set the max for the chrom
chrom = mybands.get(chrom_num)
chrom['max'] = max(sta, sto, chrom['max'])
mybands[chrom_num] = chrom
else:
LOG.error("pnum is None")
# add the parent relationships to each
if i < len(parents) - 1:
mybands[pnum]['parent'] = chrom_num + parents[i + 1]
else:
# add the last one (p or q usually)
# as attached to the chromosome
mybands[pnum]['parent'] = chrom_num
binreader.close() # end looping through file
# loop through the hash and add the bands to the graph
for bnd in mybands.keys():
myband = mybands.get(bnd)
band_class_id = makeChromID(bnd, tax_num, 'CHR')
band_class_label = makeChromLabel(bnd, genome_label)
band_build_id = makeChromID(bnd, build_num, 'MONARCH')
band_build_label = makeChromLabel(bnd, build_num)
# the build-specific chrom
chrom_in_build_id = makeChromID(myband['chr'], build_num,
'MONARCH')
# if it's != part, then add the class
if myband['type'] != self.globaltt['assembly_component']:
model.addClassToGraph(band_class_id, band_class_label,
myband['type'])
bfeature = Feature(self.graph, band_build_id, band_build_label,
band_class_id)
else:
bfeature = Feature(self.graph, band_build_id, band_build_label,
myband['type'])
if 'synonym' in myband:
model.addSynonym(band_build_id, myband['synonym'])
if myband['parent'] is None:
if myband['type'] == self.globaltt['assembly_component']:
# since we likely don't know the chr,
# add it as a part of the build
geno.addParts(band_build_id, build_id)
elif myband['type'] == self.globaltt['assembly_component']:
# geno.addParts(band_build_id, chrom_in_build_id)
parent_chrom_in_build = makeChromID(myband['parent'],
build_num, 'MONARCH')
bfeature.addSubsequenceOfFeature(parent_chrom_in_build)
# add the band as a feature
# (which also instantiates the owl:Individual)
bfeature.addFeatureStartLocation(myband['min'], chrom_in_build_id)
bfeature.addFeatureEndLocation(myband['max'], chrom_in_build_id)
if 'stain' in myband and myband['stain'] is not None:
bfeature.addFeatureProperty(
self.globaltt['has_sequence_attribute'], myband['stain'])
# type the band as a faldo:Region directly (add_region=False)
# bfeature.setNoBNodes(self.nobnodes)
# to come when we merge in ZFIN.py
bfeature.addFeatureToGraph(False)
def _process_qtls_genetic_location(
self, raw, src_key, txid, common_name, limit=None):
"""
This function processes
Triples created:
:param limit:
:return:
"""
aql_curie = self.files[src_key]['curie']
common_name = common_name.strip()
if self.test_mode:
graph = self.testgraph
else:
graph = self.graph
geno = Genotype(graph)
model = Model(graph)
eco_id = self.globaltt['quantitative trait analysis evidence']
taxon_curie = 'NCBITaxon:' + txid
LOG.info("Processing genetic location for %s from %s", taxon_curie, raw)
with open(raw, 'r', encoding="iso-8859-1") as csvfile:
reader = csv.reader(csvfile, delimiter='\t', quotechar='\"')
# no header in these files, so no header checking
col = self.files[src_key]['columns']
col_len = len(col)
for row in reader:
if len(row) != col_len and ''.join(row[col_len:]) != '':
LOG.warning(
"Problem parsing %s line %i containing: \n%s\n"
"got %i cols but expected %i",
raw, reader.line_num, row, len(row), col_len)
# LOG.info(row)
continue
qtl_id = row[col.index('QTL_ID')].strip()
qtl_symbol = row[col.index('QTL_symbol')].strip()
trait_name = row[col.index('Trait_name')].strip()
# assotype = row[col.index('assotype')].strip()
chromosome = row[col.index('Chromosome')].strip()
position_cm = row[col.index('Position_cm')].strip()
range_cm = row[col.index('range_cm')].strip()
# flankmark_a2 = row[col.index('FlankMark_A2')].strip()
# flankmark_a1 = row[col.index('FlankMark_A1')].strip()
peak_mark = row[col.index('Peak_Mark')].strip()
# flankmark_b1 = row[col.index('FlankMark_B1')].strip()
# flankmark_b2 = row[col.index('FlankMark_B2')].strip()
# exp_id = row[col.index('Exp_ID')].strip()
# model_id = row[col.index('Model')].strip()
# test_base = row[col.index('testbase')].strip()
# sig_level = row[col.index('siglevel')].strip()
# lod_score = row[col.index('LOD_score')].strip()
# ls_mean = row[col.index('LS_mean')].strip()
p_values = row[col.index('P_values')].strip()
# f_statistics = row[col.index('F_Statistics')].strip()
# variance = row[col.index('VARIANCE')].strip()
# bayes_value = row[col.index('Bayes_value')].strip()
# likelihood_ratio = row[col.index('LikelihoodR')].strip()
trait_id = row[col.index('TRAIT_ID')].strip()
# dom_effect = row[col.index('Dom_effect')].strip()
# add_effect = row[col.index('Add_effect')].strip()
pubmed_id = row[col.index('PUBMED_ID')].strip()
gene_id = row[col.index('geneID')].strip()
gene_id_src = row[col.index('geneIDsrc')].strip()
# gene_id_type = row[col.index('geneIDtype')].strip()
if self.test_mode and int(qtl_id) not in self.test_ids:
continue
qtl_id = common_name + 'QTL:' + qtl_id.strip()
trait_id = ':'.join((aql_curie, trait_id.strip()))
# Add QTL to graph
feature = Feature(graph, qtl_id, qtl_symbol, self.globaltt['QTL'])
feature.addTaxonToFeature(taxon_curie)
# deal with the chromosome
chrom_id = makeChromID(chromosome, taxon_curie, 'CHR')
# add a version of the chromosome which is defined as
# the genetic map
build_id = 'MONARCH:' + common_name + '-linkage'
build_label = common_name + ' genetic map'
geno.addReferenceGenome(build_id, build_label, taxon_curie)
chrom_in_build_id = makeChromID(chromosome, build_id, 'MONARCH')
geno.addChromosomeInstance(
chromosome, build_id, build_label, chrom_id)
start = stop = None
# range_cm sometimes ends in "(Mb)" (i.e pig 2016 Nov)
range_mb = re.split(r'\(', range_cm)
if range_mb is not None:
range_cm = range_mb[0]
if re.search(r'[0-9].*-.*[0-9]', range_cm):
range_parts = re.split(r'-', range_cm)
# check for poorly formed ranges
if len(range_parts) == 2 and\
range_parts[0] != '' and range_parts[1] != '':
(start, stop) = [
int(float(x.strip())) for x in re.split(r'-', range_cm)]
else:
LOG.info(
"A cM range we can't handle for QTL %s: %s",
qtl_id, range_cm)
elif position_cm != '':
match = re.match(r'([0-9]*\.[0-9]*)', position_cm)
if match is not None:
position_cm = match.group()
start = stop = int(float(position_cm))
# FIXME remove converion to int for start/stop
# when schema can handle floats add in the genetic location
# based on the range
feature.addFeatureStartLocation(
start, chrom_in_build_id, None,
[self.globaltt['FuzzyPosition']])
feature.addFeatureEndLocation(
stop, chrom_in_build_id, None,
[self.globaltt['FuzzyPosition']])
feature.addFeatureToGraph()
# sometimes there's a peak marker, like a rsid.
# we want to add that as a variant of the gene,
# and xref it to the qtl.
dbsnp_id = None
if peak_mark != '' and peak_mark != '.' and \
re.match(r'rs', peak_mark.strip()):
dbsnp_id = 'dbSNP:' + peak_mark.strip()
model.addIndividualToGraph(
dbsnp_id, None, self.globaltt['sequence_alteration'])
model.addXref(
qtl_id, dbsnp_id, xref_category=blv.terms['SequenceVariant'])
gene_id = gene_id.replace('uncharacterized ', '').strip()
gene_id = gene_id.strip(',') # for "100157483," in pig_QTLdata.txt
if gene_id is not None and gene_id != '' and gene_id != '.'\
and re.fullmatch(r'[^ ]*', gene_id) is not None:
# we assume if no src is provided and gene_id is an integer,
# then it is an NCBI gene ... (okay, lets crank that back a notch)
if gene_id_src == '' and gene_id.isdigit() and \
gene_id in self.gene_info:
# LOG.info(
# 'Warm & Fuzzy saying %s is a NCBI gene for %s',
# gene_id, common_name)
gene_id_src = 'NCBIgene'
elif gene_id_src == '' and gene_id.isdigit():
LOG.warning(
'Cold & Prickely saying %s is a NCBI gene for %s',
gene_id, common_name)
gene_id_src = 'NCBIgene'
elif gene_id_src == '':
LOG.error(
' "%s" is a NOT NCBI gene for %s', gene_id, common_name)
gene_id_src = None
if gene_id_src == 'NCBIgene':
gene_id = 'NCBIGene:' + gene_id
# we will expect that these will get labels elsewhere
geno.addGene(gene_id, None)
# FIXME what is the right relationship here?
geno.addAffectedLocus(qtl_id, gene_id)
if dbsnp_id is not None:
# add the rsid as a seq alt of the gene_id as a bnode
vl_id = self.make_id(re.sub(
r':', '', gene_id) + '-' + peak_mark.strip(), '_')
geno.addSequenceAlterationToVariantLocus(dbsnp_id, vl_id)
geno.addAffectedLocus(vl_id, gene_id)
# add the trait
model.addClassToGraph(
trait_id,
trait_name,
class_category=blv.terms['PhenotypicFeature'])
# Add publication
reference = None
if re.match(r'ISU.*', pubmed_id):
pub_id = 'AQTLPub:' + pubmed_id.strip()
reference = Reference(graph, pub_id)
elif pubmed_id != '':
pub_id = 'PMID:' + pubmed_id.strip()
reference = Reference(
graph, pub_id, self.globaltt['journal article'])
if reference is not None:
reference.addRefToGraph()
# make the association to the QTL
assoc = G2PAssoc(
graph, self.name, qtl_id, trait_id, self.globaltt['is marker for'])
assoc.add_evidence(eco_id)
assoc.add_source(pub_id)
# create a description from the contents of the file
# desc = ''
# assoc.addDescription(g, assoc_id, desc)
# TODO add exp_id as evidence
# if exp_id != '':
# exp_id = 'AQTLExp:'+exp_id
# gu.addIndividualToGraph(g, exp_id, None, eco_id)
if p_values != '':
scr = re.sub(r'<', '', p_values)
scr = re.sub(r',', '.', scr) # international notation
if scr.isnumeric():
score = float(scr)
assoc.set_score(score) # todo add score type
# TODO add LOD score?
assoc.add_association_to_graph()
# make the association to the dbsnp_id, if found
if dbsnp_id is not None:
# make the association to the dbsnp_id
assoc = G2PAssoc(
graph, self.name, dbsnp_id, trait_id,
self.globaltt['is marker for'])
assoc.add_evidence(eco_id)
assoc.add_source(pub_id)
# create a description from the contents of the file
# desc = ''
# assoc.addDescription(g, assoc_id, desc)
# TODO add exp_id
# if exp_id != '':
# exp_id = 'AQTLExp:'+exp_id
# gu.addIndividualToGraph(g, exp_id, None, eco_id)
if p_values != '':
scr = re.sub(r'<', '', p_values)
scr = re.sub(r',', '.', scr)
if scr.isnumeric():
score = float(scr)
assoc.set_score(score) # todo add score type
# TODO add LOD score?
assoc.add_association_to_graph()
# off by one - the following actually gives us (limit + 1) records
if not self.test_mode and limit is not None and reader.line_num > limit:
break
LOG.info("Done with QTL genetic info")
def process_feature_loc(self, limit):
raw = '/'.join((self.rawdir, self.files['feature_loc']['file']))
if self.testMode:
g = self.testgraph
else:
g = self.graph
model = Model(g)
logger.info("Processing Feature location and attributes")
line_counter = 0
geno = Genotype(g)
strain_to_variant_map = {}
build_num = self.version_num
build_id = 'WormBase:' + build_num
with gzip.open(raw, 'rb') as csvfile:
filereader = csv.reader(io.TextIOWrapper(csvfile, newline=""),
delimiter='\t',
quotechar='\"')
for row in filereader:
if re.match(r'\#', ''.join(row)):
continue
(chrom, db, feature_type_label, start, end, score, strand,
phase, attributes) = row
# I interpolated_pmap_position gene 1 559768 . . . ID=gmap:spe-13;gmap=spe-13;status=uncloned;Note=-21.3602 cM (+/- 1.84 cM)
# I WormBase gene 3747 3909 . - . ID=Gene:WBGene00023193;Name=WBGene00023193;interpolated_map_position=-21.9064;sequence_name=Y74C9A.6;biotype=snoRNA;Alias=Y74C9A.6
# I absolute_pmap_position gene 4119 10230 . . . ID=gmap:homt-1;gmap=homt-1;status=cloned;Note=-21.8252 cM (+/- 0.00 cM)
# dbs = re.split(
# r' ', 'assembly_component expressed_sequence_match Coding_transcript Genomic_canonical Non_coding_transcript Orfeome Promoterome Pseudogene RNAi_primary RNAi_secondary Reference Transposon Transposon_CDS cDNA_for_RNAi miRanda ncRNA operon polyA_signal_sequence polyA_site snlRNA')
#
# if db not in dbs:
# continue
if feature_type_label not in [
'gene', 'point_mutation', 'deletion', 'RNAi_reagent',
'duplication', 'enhancer', 'binding_site',
'biological_region', 'complex_substitution',
'substitution', 'insertion', 'inverted_repeat'
]:
# note biological_regions include balancers
# other options here: promoter, regulatory_region, reagent
continue
line_counter += 1
attribute_dict = {}
if attributes != '':
attribute_dict = dict(
item.split("=")
for item in re.sub(r'"', '', attributes).split(";"))
fid = flabel = desc = None
if 'ID' in attribute_dict:
fid = attribute_dict.get('ID')
if re.search(r'WB(Gene|Var|sf)', fid):
fid = re.sub(r'^\w+:WB', 'WormBase:WB', fid)
elif re.match(r'(gmap|landmark)', fid):
continue
else:
logger.info('other identifier %s', fid)
fid = None
elif 'variation' in attribute_dict:
fid = 'WormBase:' + attribute_dict.get('variation')
flabel = attribute_dict.get('public_name')
sub = attribute_dict.get('substitution')
ins = attribute_dict.get('insertion')
# if it's a variation:
# variation=WBVar00604246;public_name=gk320600;strain=VC20384;substitution=C/T
desc = ''
if sub is not None:
desc = 'substitution=' + sub
if ins is not None:
desc = 'insertion=' + ins
# keep track of the strains with this variation,
# for later processing
strain_list = attribute_dict.get('strain')
if strain_list is not None:
for s in re.split(r',', strain_list):
if s.strip() not in strain_to_variant_map:
strain_to_variant_map[s.strip()] = set()
strain_to_variant_map[s.strip()].add(fid)
# if feature_type_label == 'RNAi_reagent':
# Target=WBRNAi00096030 1 4942
# this will tell us where the RNAi is actually binding
# target = attribute_dict.get('Target') # TODO unused
# rnai_num = re.split(r' ', target)[0] # TODO unused
# it will be the reagent-targeted-gene that has a position,
# (i think)
# TODO finish the RNAi binding location
name = attribute_dict.get('Name')
polymorphism = attribute_dict.get('polymorphism')
if fid is None:
if name is not None and re.match(r'WBsf', name):
fid = 'WormBase:' + name
name = None
else:
continue
if self.testMode \
and re.sub(r'WormBase:', '', fid) \
not in self.test_ids['gene']+self.test_ids['allele']:
continue
# these really aren't that interesting
if polymorphism is not None:
continue
if name is not None and not re.search(name, fid):
if flabel is None:
flabel = name
else:
model.addSynonym(fid, name)
if desc is not None:
model.addDescription(fid, desc)
alias = attribute_dict.get('Alias')
biotype = attribute_dict.get('biotype')
note = attribute_dict.get('Note')
other_name = attribute_dict.get('other_name')
for n in [alias, other_name]:
if n is not None:
model.addSynonym(fid, other_name)
ftype = self.get_feature_type_by_class_and_biotype(
feature_type_label, biotype)
chr_id = makeChromID(chrom, build_id, 'CHR')
geno.addChromosomeInstance(chrom, build_id, build_num)
feature = Feature(g, fid, flabel, ftype)
feature.addFeatureStartLocation(start, chr_id, strand)
feature.addFeatureEndLocation(start, chr_id, strand)
feature_is_class = False
if feature_type_label == 'gene':
feature_is_class = True
feature.addFeatureToGraph(True, None, feature_is_class)
if note is not None:
model.addDescription(fid, note)
if not self.testMode \
and limit is not None and line_counter > limit:
break
# RNAi reagents:
# I RNAi_primary RNAi_reagent 4184 10232 . + . Target=WBRNAi00001601 1 6049 +;laboratory=YK;history_name=SA:yk326e10
# I RNAi_primary RNAi_reagent 4223 10147 . + . Target=WBRNAi00033465 1 5925 +;laboratory=SV;history_name=MV_SV:mv_G_YK5052
# I RNAi_primary RNAi_reagent 5693 9391 . + . Target=WBRNAi00066135 1 3699 +;laboratory=CH
# TODO TF bindiing sites and network:
# I TF_binding_site_region TF_binding_site 1861 2048 . + . Name=WBsf292777;tf_id=WBTranscriptionFactor000025;tf_name=DAF-16
# I TF_binding_site_region TF_binding_site 3403 4072 . + . Name=WBsf331847;tf_id=WBTranscriptionFactor000703;tf_name=DPL-1
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_feature_loc(self, limit):
raw = '/'.join((self.rawdir, self.files['feature_loc']['file']))
if self.testMode:
g = self.testgraph
else:
g = self.graph
gu = GraphUtils(curie_map.get())
logger.info("Processing Feature location and attributes")
line_counter = 0
geno = Genotype(g)
strain_to_variant_map = {}
build_num = self.version_num
build_id = 'WormBase:'+build_num
with gzip.open(raw, 'rb') as csvfile:
filereader = csv.reader(
io.TextIOWrapper(csvfile, newline=""), delimiter='\t',
quotechar='\"')
for row in filereader:
if re.match(r'\#', ''.join(row)):
continue
(chrom, db, feature_type_label, start, end, score, strand,
phase, attributes) = row
# I interpolated_pmap_position gene 1 559768 . . . ID=gmap:spe-13;gmap=spe-13;status=uncloned;Note=-21.3602 cM (+/- 1.84 cM)
# I WormBase gene 3747 3909 . - . ID=Gene:WBGene00023193;Name=WBGene00023193;interpolated_map_position=-21.9064;sequence_name=Y74C9A.6;biotype=snoRNA;Alias=Y74C9A.6
# I absolute_pmap_position gene 4119 10230 . . . ID=gmap:homt-1;gmap=homt-1;status=cloned;Note=-21.8252 cM (+/- 0.00 cM)
# dbs = re.split(
# r' ', 'assembly_component expressed_sequence_match Coding_transcript Genomic_canonical Non_coding_transcript Orfeome Promoterome Pseudogene RNAi_primary RNAi_secondary Reference Transposon Transposon_CDS cDNA_for_RNAi miRanda ncRNA operon polyA_signal_sequence polyA_site snlRNA')
#
# if db not in dbs:
# continue
if feature_type_label not in [
'gene', 'point_mutation', 'deletion', 'RNAi_reagent',
'duplication', 'enhancer', 'binding_site',
'biological_region', 'complex_substitution',
'substitution', 'insertion', 'inverted_repeat']:
# note biological_regions include balancers
# other options here: promoter, regulatory_region, reagent
continue
line_counter += 1
attribute_dict = {}
if attributes != '':
attribute_dict = dict(
item.split("=")for item in
re.sub(r'"', '', attributes).split(";"))
fid = flabel = desc = None
if 'ID' in attribute_dict:
fid = attribute_dict.get('ID')
if re.search(r'WB(Gene|Var|sf)', fid):
fid = re.sub(r'^\w+:WB', 'WormBase:WB', fid)
elif re.match(r'(gmap|landmark)', fid):
continue
else:
logger.info('other identifier %s', fid)
fid = None
elif 'variation' in attribute_dict:
fid = 'WormBase:'+attribute_dict.get('variation')
flabel = attribute_dict.get('public_name')
sub = attribute_dict.get('substitution')
ins = attribute_dict.get('insertion')
# if it's a variation:
# variation=WBVar00604246;public_name=gk320600;strain=VC20384;substitution=C/T
desc = ''
if sub is not None:
desc = 'substitution='+sub
if ins is not None:
desc = 'insertion='+ins
# keep track of the strains with this variation,
# for later processing
strain_list = attribute_dict.get('strain')
if strain_list is not None:
for s in re.split(r',', strain_list):
if s.strip() not in strain_to_variant_map:
strain_to_variant_map[s.strip()] = set()
strain_to_variant_map[s.strip()].add(fid)
# if feature_type_label == 'RNAi_reagent':
# Target=WBRNAi00096030 1 4942
# this will tell us where the RNAi is actually binding
# target = attribute_dict.get('Target') # TODO unused
# rnai_num = re.split(r' ', target)[0] # TODO unused
# it will be the reagent-targeted-gene that has a position,
# (i think)
# TODO finish the RNAi binding location
name = attribute_dict.get('Name')
polymorphism = attribute_dict.get('polymorphism')
if fid is None:
if name is not None and re.match(r'WBsf', name):
fid = 'WormBase:'+name
name = None
else:
continue
if self.testMode \
and re.sub(r'WormBase:', '', fid) \
not in self.test_ids['gene']+self.test_ids['allele']:
continue
# these really aren't that interesting
if polymorphism is not None:
continue
if name is not None and not re.search(name, fid):
if flabel is None:
flabel = name
else:
gu.addSynonym(g, fid, name)
if desc is not None:
gu.addDescription(g, fid, desc)
alias = attribute_dict.get('Alias')
biotype = attribute_dict.get('biotype')
note = attribute_dict.get('Note')
other_name = attribute_dict.get('other_name')
for n in [alias, other_name]:
if n is not None:
gu.addSynonym(g, fid, other_name)
ftype = self.get_feature_type_by_class_and_biotype(
feature_type_label, biotype)
chr_id = makeChromID(chrom, build_id, 'CHR')
geno.addChromosomeInstance(chrom, build_id, build_num)
f = Feature(fid, flabel, ftype)
f.addFeatureStartLocation(start, chr_id, strand)
f.addFeatureEndLocation(start, chr_id, strand)
feature_is_class = False
if feature_type_label == 'gene':
feature_is_class = True
f.addFeatureToGraph(g, True, None, feature_is_class)
if note is not None:
gu.addDescription(g, fid, note)
if not self.testMode \
and limit is not None and line_counter > limit:
break
# RNAi reagents:
# I RNAi_primary RNAi_reagent 4184 10232 . + . Target=WBRNAi00001601 1 6049 +;laboratory=YK;history_name=SA:yk326e10
# I RNAi_primary RNAi_reagent 4223 10147 . + . Target=WBRNAi00033465 1 5925 +;laboratory=SV;history_name=MV_SV:mv_G_YK5052
# I RNAi_primary RNAi_reagent 5693 9391 . + . Target=WBRNAi00066135 1 3699 +;laboratory=CH
# TODO TF bindiing sites and network:
# I TF_binding_site_region TF_binding_site 1861 2048 . + . Name=WBsf292777;tf_id=WBTranscriptionFactor000025;tf_name=DAF-16
# I TF_binding_site_region TF_binding_site 3403 4072 . + . Name=WBsf331847;tf_id=WBTranscriptionFactor000703;tf_name=DPL-1
return
def _process_QTLs_genetic_location(self, raw, taxon_id, common_name, limit=None):
"""
This function processes
Triples created:
:param limit:
:return:
"""
if self.testMode:
g = self.testgraph
else:
g = self.graph
line_counter = 0
geno = Genotype(g)
gu = GraphUtils(curie_map.get())
eco_id = "ECO:0000061" # Quantitative Trait Analysis Evidence
logger.info("Processing genetic location for %s", taxon_id)
with open(raw, 'r', encoding="iso-8859-1") as csvfile:
filereader = csv.reader(csvfile, delimiter='\t', quotechar='\"')
for row in filereader:
line_counter += 1
(qtl_id, qtl_symbol, trait_name, assotype, empty, chromosome, position_cm, range_cm,
flankmark_a2, flankmark_a1, peak_mark, flankmark_b1, flankmark_b2, exp_id, model, test_base,
sig_level, lod_score, ls_mean, p_values, f_statistics, variance, bayes_value, likelihood_ratio,
trait_id, dom_effect, add_effect, pubmed_id, gene_id, gene_id_src, gene_id_type, empty2) = row
if self.testMode and int(qtl_id) not in self.test_ids:
continue
qtl_id = 'AQTL:'+qtl_id
trait_id = 'AQTLTrait:'+trait_id
# Add QTL to graph
f = Feature(qtl_id, qtl_symbol, geno.genoparts['QTL'])
f.addTaxonToFeature(g, taxon_id)
# deal with the chromosome
chrom_id = makeChromID(chromosome, taxon_id, 'CHR')
# add a version of the chromosome which is defined as the genetic map
build_id = 'MONARCH:'+common_name.strip()+'-linkage'
build_label = common_name+' genetic map'
geno.addReferenceGenome(build_id, build_label, taxon_id)
chrom_in_build_id = makeChromID(chromosome, build_id, 'MONARCH')
geno.addChromosomeInstance(chromosome, build_id, build_label, chrom_id)
start = stop = None
if re.search('-', range_cm):
range_parts = re.split('-', range_cm)
# check for poorly formed ranges
if len(range_parts) == 2 and range_parts[0] != '' and range_parts[1] != '':
(start, stop) = [int(float(x.strip())) for x in re.split('-', range_cm)]
else:
logger.info("There's a cM range we can't handle for QTL %s: %s", qtl_id, range_cm)
elif position_cm != '':
start = stop = int(float(position_cm))
# FIXME remove converion to int for start/stop when schema can handle floats
# add in the genetic location based on the range
f.addFeatureStartLocation(start, chrom_in_build_id, None, [Feature.types['FuzzyPosition']])
f.addFeatureEndLocation(stop, chrom_in_build_id, None, [Feature.types['FuzzyPosition']])
f.addFeatureToGraph(g)
# sometimes there's a peak marker, like a rsid. we want to add that as a variant of the gene,
# and xref it to the qtl.
dbsnp_id = None
if peak_mark != '' and peak_mark != '.' and re.match('rs', peak_mark.strip()):
dbsnp_id = 'dbSNP:'+peak_mark.strip()
gu.addIndividualToGraph(g, dbsnp_id, None, geno.genoparts['sequence_alteration'])
gu.addXref(g, qtl_id, dbsnp_id)
if gene_id is not None and gene_id != '' and gene_id != '.':
if gene_id_src == 'NCBIgene' or gene_id_src == '': # we assume if no src is provided, it's NCBI
gene_id = 'NCBIGene:'+gene_id.strip()
geno.addGene(gene_id, None) # we will expect that these labels provided elsewhere
geno.addAlleleOfGene(qtl_id, gene_id, geno.object_properties['feature_to_gene_relation']) # FIXME what is the right relationship here?
if dbsnp_id is not None:
# add the rsid as a seq alt of the gene_id
vl_id = '_' + re.sub(':', '', gene_id) + '-' + peak_mark
if self.nobnodes:
vl_id = ':' + vl_id
geno.addSequenceAlterationToVariantLocus(dbsnp_id, vl_id)
geno.addAlleleOfGene(vl_id, gene_id)
# add the trait
gu.addClassToGraph(g, trait_id, trait_name)
# Add publication
r = None
if re.match('ISU.*', pubmed_id):
pub_id = 'AQTLPub:'+pubmed_id.strip()
r = Reference(pub_id)
elif pubmed_id != '':
pub_id = 'PMID:'+pubmed_id.strip()
r = Reference(pub_id, Reference.ref_types['journal_article'])
if r is not None:
r.addRefToGraph(g)
# make the association to the QTL
assoc = G2PAssoc(self.name, qtl_id, trait_id, gu.object_properties['is_marker_for'])
assoc.add_evidence(eco_id)
assoc.add_source(pub_id)
# create a description from the contents of the file
# desc = ''
# assoc.addDescription(g, assoc_id, desc)
# TODO add exp_id as evidence
# if exp_id != '':
# exp_id = 'AQTLExp:'+exp_id
# gu.addIndividualToGraph(g, exp_id, None, eco_id)
if p_values != '':
score = float(re.sub('<', '', p_values))
assoc.set_score(score) # todo add score type
# TODO add LOD score?
assoc.add_association_to_graph(g)
# make the association to the dbsnp_id, if found
if dbsnp_id is not None:
# make the association to the dbsnp_id
assoc = G2PAssoc(self.name, dbsnp_id, trait_id, gu.object_properties['is_marker_for'])
assoc.add_evidence(eco_id)
assoc.add_source(pub_id)
# create a description from the contents of the file
# desc = ''
# assoc.addDescription(g, assoc_id, desc)
# TODO add exp_id
# if exp_id != '':
# exp_id = 'AQTLExp:'+exp_id
# gu.addIndividualToGraph(g, exp_id, None, eco_id)
if p_values != '':
score = float(re.sub('<', '', p_values))
assoc.set_score(score) # todo add score type
# TODO add LOD score?
assoc.add_association_to_graph(g)
if not self.testMode and limit is not None and line_counter > limit:
break
logger.info("Done with QTL genetic info")
return
