def _process_data(self, raw, limit=None):
LOG.info("Processing Data from %s", raw)
if self.test_mode:
graph = self.testgraph
else:
graph = self.graph
model = Model(graph)
geno = Genotype(graph)
# Add the taxon as a class
taxon_id = self.globaltt['Mus musculus']
model.addClassToGraph(taxon_id, None)
# with open(raw, 'r', encoding="utf8") as csvfile:
col = self.files['all']['columns']
with gzip.open(raw, 'rt') as csvfile:
reader = csv.reader(csvfile, delimiter=',', quotechar='\"')
row = next(reader) # presumed header
if not self.check_fileheader(col, row):
pass
for row in reader:
# | head -1 | tr ',' '\n' | sed "s|\(.*\)|# \1 = row[col.index('\1')]|g"
marker_accession_id = row[col.index('marker_accession_id')].strip()
marker_symbol = row[col.index('marker_symbol')].strip()
phenotyping_center = row[col.index('phenotyping_center')].strip()
colony_raw = row[col.index('colony_id')].strip()
sex = row[col.index('sex')].strip()
zygosity = row[col.index('zygosity')].strip()
allele_accession_id = row[col.index('allele_accession_id')].strip()
allele_symbol = row[col.index('allele_symbol')].strip()
# allele_name = row[col.index('allele_name')]
strain_accession_id = row[col.index('strain_accession_id')].strip()
strain_name = row[col.index('strain_name')].strip()
# project_name = row[col.index('project_name')]
project_fullname = row[col.index('project_fullname')].strip()
pipeline_name = row[col.index('pipeline_name')].strip()
pipeline_stable_id = row[col.index('pipeline_stable_id')].strip()
procedure_stable_id = row[col.index('procedure_stable_id')].strip()
procedure_name = row[col.index('procedure_name')].strip()
parameter_stable_id = row[col.index('parameter_stable_id')].strip()
parameter_name = row[col.index('parameter_name')].strip()
# top_level_mp_term_id = row[col.index('top_level_mp_term_id')]
# top_level_mp_term_name = row[col.index('top_level_mp_term_name')]
mp_term_id = row[col.index('mp_term_id')].strip()
mp_term_name = row[col.index('mp_term_name')].strip()
p_value = row[col.index('p_value')].strip()
percentage_change = row[col.index('percentage_change')].strip()
effect_size = row[col.index('effect_size')].strip()
statistical_method = row[col.index('statistical_method')].strip()
resource_name = row[col.index('resource_name')].strip()
if self.test_mode and marker_accession_id not in self.gene_ids:
continue
# ##### cleanup some of the identifiers ######
zygosity = zygosity.strip()
zygosity_id = self.resolve(zygosity)
if zygosity_id == zygosity:
LOG.warning(
"Zygosity '%s' unmapped. detting to indeterminate", zygosity)
zygosity_id = self.globaltt['indeterminate']
# colony ids sometimes have <> in them, spaces,
# or other non-alphanumerics and break our system;
# replace these with underscores
colony_id = '_:' + re.sub(r'\W+', '_', colony_raw)
if not re.match(r'MGI', allele_accession_id):
allele_accession_id = '_:IMPC-'+re.sub(
r':', '', allele_accession_id)
if re.search(r'EUROCURATE', strain_accession_id):
# the eurocurate links don't resolve at IMPC
# TODO blank nodes do not maintain identifiers
strain_accession_id = '_:' + strain_accession_id
elif not re.match(r'MGI', strain_accession_id):
LOG.info(
"Found a strange strain accession...%s", strain_accession_id)
strain_accession_id = 'IMPC:'+strain_accession_id
######################
# first, add the marker and variant to the graph as with MGI,
# the allele is the variant locus. IF the marker is not known,
# we will call it a sequence alteration. otherwise,
# we will create a BNode for the sequence alteration.
sequence_alteration_id = variant_locus_id = None
variant_locus_name = sequence_alteration_name = None
# extract out what's within the <> to get the symbol
if re.match(r'.*<.*>', allele_symbol):
sequence_alteration_name = re.match(
r'.*<(.*)>', allele_symbol)
if sequence_alteration_name is not None:
sequence_alteration_name = sequence_alteration_name.group(1)
else:
sequence_alteration_name = allele_symbol
if marker_accession_id is not None and marker_accession_id == '':
LOG.warning("Marker unspecified on row %d", reader.line_num)
marker_accession_id = None
if marker_accession_id is not None:
variant_locus_id = allele_accession_id
variant_locus_name = allele_symbol
variant_locus_type = self.globaltt['variant_locus']
geno.addGene(
marker_accession_id, marker_symbol, self.globaltt['gene'])
geno.addAllele(
variant_locus_id, variant_locus_name, variant_locus_type, None)
geno.addAlleleOfGene(variant_locus_id, marker_accession_id)
# TAG bnode
sequence_alteration_id = '_:seqalt' + re.sub(
r':', '', allele_accession_id)
geno.addSequenceAlterationToVariantLocus(
sequence_alteration_id, variant_locus_id)
else:
sequence_alteration_id = allele_accession_id
# IMPC contains targeted mutations with either gene traps,
# knockouts, insertion/intragenic deletions.
# but I don't really know what the SeqAlt is here,
# so I don't add it.
geno.addSequenceAlteration(
sequence_alteration_id, sequence_alteration_name)
# ############# BUILD THE COLONY #############
# First, let's describe the colony that the animals come from
# The Colony ID refers to the ES cell clone
# used to generate a mouse strain.
# Terry sez: we use this clone ID to track
# ES cell -> mouse strain -> mouse phenotyping.
# The same ES clone maybe used at multiple centers,
# so we have to concatenate the two to have a unique ID.
# some useful reading about generating mice from ES cells:
# http://ki.mit.edu/sbc/escell/services/details
# here, we'll make a genotype
# that derives from an ES cell with a given allele.
# the strain is not really attached to the colony.
# the colony/clone is reflective of the allele, with unknown zygosity
stem_cell_class = self.globaltt['embryonic stem cell line']
if colony_id is None:
print(colony_raw, stem_cell_class, "\nline:\t", reader.line_num)
model.addIndividualToGraph(colony_id, colony_raw, stem_cell_class)
# vslc of the colony has unknown zygosity
# note that we will define the allele
# (and it's relationship to the marker, etc.) later
# FIXME is it really necessary to create this vslc
# when we always know it's unknown zygosity?
vslc_colony = '_:'+re.sub(
r':', '', allele_accession_id + self.globaltt['indeterminate'])
vslc_colony_label = allele_symbol + '/<?>'
# for ease of reading, we make the colony genotype variables.
# in the future, it might be desired to keep the vslcs
colony_genotype_id = vslc_colony
colony_genotype_label = vslc_colony_label
geno.addGenotype(colony_genotype_id, colony_genotype_label)
geno.addParts(
allele_accession_id, colony_genotype_id,
self.globaltt['has_variant_part'])
geno.addPartsToVSLC(
vslc_colony, allele_accession_id, None,
self.globaltt['indeterminate'], self.globaltt['has_variant_part'])
graph.addTriple(
colony_id, self.globaltt['has_genotype'], colony_genotype_id)
# ########## BUILD THE ANNOTATED GENOTYPE ##########
# now, we'll build the genotype of the individual that derives
# from the colony/clone genotype that is attached to
# phenotype = colony_id + strain + zygosity + sex
# (and is derived from a colony)
# this is a sex-agnostic genotype
genotype_id = self.make_id(
(colony_id + phenotyping_center + zygosity + strain_accession_id))
geno.addSequenceDerivesFrom(genotype_id, colony_id)
# build the VSLC of the sex-agnostic genotype
# based on the zygosity
allele1_id = allele_accession_id
allele2_id = allele2_rel = None
allele1_label = allele_symbol
allele2_label = '<?>'
# Making VSLC labels from the various parts,
# can change later if desired.
if zygosity == 'heterozygote':
allele2_label = re.sub(r'<.*', '<+>', allele1_label)
allele2_id = None
elif zygosity == 'homozygote':
allele2_label = allele1_label
allele2_id = allele1_id
allele2_rel = self.globaltt['has_variant_part']
elif zygosity == 'hemizygote':
allele2_label = re.sub(r'<.*', '<0>', allele1_label)
allele2_id = None
elif zygosity == 'not_applicable':
allele2_label = re.sub(r'<.*', '<?>', allele1_label)
allele2_id = None
else:
LOG.warning("found unknown zygosity %s", zygosity)
break
vslc_name = '/'.join((allele1_label, allele2_label))
# Add the VSLC
vslc_id = '-'.join(
(marker_accession_id, allele_accession_id, zygosity))
vslc_id = re.sub(r':', '', vslc_id)
vslc_id = '_:'+vslc_id
model.addIndividualToGraph(
vslc_id, vslc_name,
self.globaltt['variant single locus complement'])
geno.addPartsToVSLC(
vslc_id, allele1_id, allele2_id, zygosity_id,
self.globaltt['has_variant_part'], allele2_rel)
# add vslc to genotype
geno.addVSLCtoParent(vslc_id, genotype_id)
# note that the vslc is also the gvc
model.addType(vslc_id, self.globaltt['genomic_variation_complement'])
# Add the genomic background
# create the genomic background id and name
if strain_accession_id != '':
genomic_background_id = strain_accession_id
else:
genomic_background_id = None
genotype_name = vslc_name
if genomic_background_id is not None:
geno.addGenotype(
genomic_background_id, strain_name,
self.globaltt['genomic_background'])
# make a phenotyping-center-specific strain
# to use as the background
pheno_center_strain_label = strain_name + '-' + phenotyping_center \
+ '-' + colony_raw
pheno_center_strain_id = '-'.join((
re.sub(r':', '', genomic_background_id),
re.sub(r'\s', '_', phenotyping_center),
re.sub(r'\W+', '', colony_raw)))
if not re.match(r'^_', pheno_center_strain_id):
# Tag bnode
pheno_center_strain_id = '_:' + pheno_center_strain_id
geno.addGenotype(
pheno_center_strain_id, pheno_center_strain_label,
self.globaltt['genomic_background'])
geno.addSequenceDerivesFrom(
pheno_center_strain_id, genomic_background_id)
# Making genotype labels from the various parts,
# can change later if desired.
# since the genotype is reflective of the place
# it got made, should put that in to disambiguate
genotype_name = \
genotype_name + ' [' + pheno_center_strain_label + ']'
geno.addGenomicBackgroundToGenotype(
pheno_center_strain_id, genotype_id)
geno.addTaxon(taxon_id, pheno_center_strain_id)
# this is redundant, but i'll keep in in for now
geno.addSequenceDerivesFrom(genotype_id, colony_id)
geno.addGenotype(genotype_id, genotype_name)
# Make the sex-qualified genotype,
# which is what the phenotype is associated with
sex_qualified_genotype_id = \
self.make_id((
colony_id + phenotyping_center + zygosity +
strain_accession_id + sex))
sex_qualified_genotype_label = genotype_name + ' (' + sex + ')'
sq_type_id = self.resolve(sex, False)
if sq_type_id == sex:
sq_type_id = self.globaltt['intrinsic_genotype']
LOG.warning(
"Unknown sex qualifier %s, adding as intrinsic_genotype",
sex)
geno.addGenotype(
sex_qualified_genotype_id, sex_qualified_genotype_label, sq_type_id)
geno.addParts(
genotype_id, sex_qualified_genotype_id,
self.globaltt['has_variant_part'])
if genomic_background_id is not None and genomic_background_id != '':
# Add the taxon to the genomic_background_id
geno.addTaxon(taxon_id, genomic_background_id)
else:
# add it as the genomic background
geno.addTaxon(taxon_id, genotype_id)
# ############# BUILD THE G2P ASSOC #############
# from an old email dated July 23 2014:
# Phenotypes associations are made to
# imits colony_id+center+zygosity+gender
# sometimes phenotype ids are missing. (about 711 early 2020)
if mp_term_id is None or mp_term_id == '':
LOG.warning(
"No phenotype id specified for row %d", reader.line_num)
continue
# hard coded ECO code
eco_id = self.globaltt['mutant phenotype evidence']
# the association comes as a result of a g2p from
# a procedure in a pipeline at a center and parameter tested
assoc = G2PAssoc(
graph, self.name, sex_qualified_genotype_id, mp_term_id)
assoc.add_evidence(eco_id)
# assoc.set_score(float(p_value))
# TODO add evidence instance using
# pipeline_stable_id +
# procedure_stable_id +
# parameter_stable_id
assoc.add_association_to_graph()
assoc_id = assoc.get_association_id()
model._addSexSpecificity(assoc_id, self.resolve(sex))
# add a free-text description
try:
description = ' '.join((
mp_term_name, 'phenotype determined by', phenotyping_center,
'in an', procedure_name, 'assay where', parameter_name.strip(),
'was measured with an effect_size of',
str(round(float(effect_size), 5)),
'(p =', "{:.4e}".format(float(p_value)), ').'))
except ValueError:
description = ' '.join((
mp_term_name, 'phenotype determined by', phenotyping_center,
'in an', procedure_name, 'assay where', parameter_name.strip(),
'was measured with an effect_size of', str(effect_size),
'(p =', "{0}".format(p_value), ').'))
study_bnode = self._add_study_provenance(
phenotyping_center, colony_raw, project_fullname, pipeline_name,
pipeline_stable_id, procedure_stable_id, procedure_name,
parameter_stable_id, parameter_name, statistical_method,
resource_name)
evidence_line_bnode = self._add_evidence(
assoc_id, eco_id, p_value, percentage_change, effect_size,
study_bnode)
self._add_assertion_provenance(assoc_id, evidence_line_bnode)
model.addDescription(evidence_line_bnode, description)
# resource_id = resource_name
# assoc.addSource(graph, assoc_id, resource_id)
if not self.test_mode and limit is not None and reader.line_num > limit:
break
def _process_phenotype_data(self, limit):
"""
NOTE: If a Strain carries more than one mutation,
then each Mutation description,
i.e., the set: (
Mutation Type - Chromosome - Gene Symbol -
Gene Name - Allele Symbol - Allele Name)
will require a separate line.
Note that MMRRC curates phenotypes to alleles,
even though they distribute only one file with the
phenotypes appearing to be associated with a strain.
So, here we process the allele-to-phenotype relationships separately
from the strain-to-allele relationships.
:param limit:
:return:
"""
src_key = 'catalog'
if self.test_mode:
graph = self.testgraph
else:
graph = self.graph
model = Model(graph)
fname = '/'.join((self.rawdir, self.files[src_key]['file']))
self.strain_hash = {}
self.id_label_hash = {}
genes_with_no_ids = set()
stem_cell_class = self.globaltt['stem cell']
mouse_taxon = self.globaltt['Mus musculus']
geno = Genotype(graph)
with open(fname, 'r', encoding="utf8") as csvfile:
reader = csv.reader(csvfile, delimiter=',', quotechar='\"')
# First line is header not date/version info. This changed recently,
# apparently as of Sep 2019. Also, 3rd line is no longer blank.
row = [x.strip() for x in next(reader)] # messy messy
col = self.files['catalog']['columns']
strain_missing_allele = [] # to count the ones w/insufficent info
if not self.check_fileheader(col, row):
pass
for row in reader:
strain_id = row[col.index('STRAIN/STOCK_ID')].strip()
strain_label = row[col.index('STRAIN/STOCK_DESIGNATION')]
# strain_type_symbol = row[col.index('STRAIN_TYPE')]
strain_state = row[col.index('STATE')]
mgi_allele_id = row[col.index(
'MGI_ALLELE_ACCESSION_ID')].strip()
mgi_allele_symbol = row[col.index('ALLELE_SYMBOL')]
# mgi_allele_name = row[col.index('ALLELE_NAME')]
# mutation_type = row[col.index('MUTATION_TYPE')]
# chrom = row[col.index('CHROMOSOME')]
mgi_gene_id = row[col.index('MGI_GENE_ACCESSION_ID')].strip()
mgi_gene_symbol = row[col.index('GENE_SYMBOL')].strip()
mgi_gene_name = row[col.index('GENE_NAME')]
# sds_url = row[col.index('SDS_URL')]
# accepted_date = row[col.index('ACCEPTED_DATE')]
mpt_ids = row[col.index('MPT_IDS')].strip()
pubmed_nums = row[col.index('PUBMED_IDS')].strip()
research_areas = row[col.index('RESEARCH_AREAS')].strip()
if self.test_mode and (strain_id not in self.test_ids) \
or mgi_gene_name == 'withdrawn':
continue
# strip off stuff after the dash -
# is the holding center important?
# MMRRC:00001-UNC --> MMRRC:00001
strain_id = re.sub(r'-\w+$', '', strain_id)
self.id_label_hash[strain_id] = strain_label
# get the variant or gene to save for later building of
# the genotype
if strain_id not in self.strain_hash:
self.strain_hash[strain_id] = {
'variants': set(),
'genes': set()
}
# flag bad ones
if mgi_allele_id[:4] != 'MGI:' and mgi_allele_id != '':
LOG.error("Erroneous MGI allele id: %s", mgi_allele_id)
if mgi_allele_id[:3] == 'MG:':
mgi_allele_id = 'MGI:' + mgi_allele_id[3:]
else:
mgi_allele_id = ''
if mgi_allele_id != '':
self.strain_hash[strain_id]['variants'].add(mgi_allele_id)
self.id_label_hash[mgi_allele_id] = mgi_allele_symbol
# use the following if needing to add the sequence alteration types
# var_type = self.localtt[mutation_type]
# make a sequence alteration for this variant locus,
# and link the variation type to it
# sa_id = '_'+re.sub(r':','',mgi_allele_id)+'SA'
# if self.nobnodes:
# sa_id = ':'+sa_id
# gu.addIndividualToGraph(g, sa_id, None, var_type)
# geno.addSequenceAlterationToVariantLocus(sa_id, mgi_allele_id)
# scrub out any spaces, fix known issues
mgi_gene_id = re.sub(r'\s+', '', mgi_gene_id)
if mgi_gene_id == 'NULL':
mgi_gene_id = ''
elif mgi_gene_id[:7] == 'GeneID:':
mgi_gene_id = 'NCBIGene:' + mgi_gene_id[7:]
if mgi_gene_id != '':
try:
[curie, localid] = mgi_gene_id.split(':')
except ValueError as verror:
LOG.warning(
"Problem parsing mgi_gene_id %s from file %s: %s",
mgi_gene_id, fname, verror)
if curie not in ['MGI', 'NCBIGene']:
LOG.info("MGI Gene id not recognized: %s", mgi_gene_id)
self.strain_hash[strain_id]['genes'].add(mgi_gene_id)
self.id_label_hash[mgi_gene_id] = mgi_gene_symbol
# catch some errors - too many. report summary at the end
# some things have gene labels, but no identifiers - report
if mgi_gene_symbol != '' and mgi_gene_id == '':
# LOG.error(
# "Gene label with no MGI identifier for strain %s: %s",
# strain_id, mgi_gene_symbol)
genes_with_no_ids.add(mgi_gene_symbol)
# make a temp id for genes that aren't identified ... err wow.
# tmp_gene_id = '_' + mgi_gene_symbol
# self.id_label_hash[tmp_gene_id.strip()] = mgi_gene_symbol
# self.strain_hash[strain_id]['genes'].add(tmp_gene_id)
# split apart the mp ids
# ataxia [MP:0001393] ,hypoactivity [MP:0001402] ...
# mpt_ids are a comma delimited list
# labels with MP terms following in brackets
phenotype_ids = []
if mpt_ids != '':
for lb_mp in mpt_ids.split(r','):
lb_mp = lb_mp.strip()
if lb_mp[-1:] == ']' and lb_mp[-12:-8] == '[MP:':
phenotype_ids.append(lb_mp[-11:-2])
# pubmed ids are space delimited
pubmed_ids = []
if pubmed_nums != '':
for pm_num in re.split(r'\s+', pubmed_nums):
pmid = 'PMID:' + pm_num.strip()
pubmed_ids.append(pmid)
ref = Reference(graph, pmid,
self.globaltt['journal article'])
ref.addRefToGraph()
# https://www.mmrrc.org/catalog/sds.php?mmrrc_id=00001
# is a good example of 4 genotype parts
model.addClassToGraph(mouse_taxon, None)
if research_areas == '':
research_areas = None
else:
research_areas = 'Research Areas: ' + research_areas
strain_type = mouse_taxon
if strain_state == 'ES':
strain_type = stem_cell_class
model.addIndividualToGraph( # an inst of mouse??
strain_id, strain_label, strain_type, research_areas)
model.makeLeader(strain_id)
# phenotypes are associated with the alleles
for pid in phenotype_ids:
# assume the phenotype label is in some ontology
model.addClassToGraph(pid, None)
if mgi_allele_id is not None and mgi_allele_id != '':
assoc = G2PAssoc(graph, self.name, mgi_allele_id, pid,
self.globaltt['has phenotype'])
for p in pubmed_ids:
assoc.add_source(p)
assoc.add_association_to_graph()
else:
# too chatty here. report aggregate
# LOG.info("Phenotypes and no allele for %s", strain_id)
strain_missing_allele.append(strain_id)
if not self.test_mode and (limit is not None
and reader.line_num > limit):
break
# report misses
if strain_missing_allele:
LOG.info("Phenotypes and no allele for %i strains",
len(strain_missing_allele))
# now that we've collected all of the variant information, build it
# we don't know their zygosities
for s in self.strain_hash:
h = self.strain_hash.get(s)
variants = h['variants']
genes = h['genes']
vl_set = set()
# make variant loci for each gene
if variants:
for var in variants:
vl_id = var.strip()
vl_symbol = self.id_label_hash[vl_id]
geno.addAllele(vl_id, vl_symbol,
self.globaltt['variant_locus'])
vl_set.add(vl_id)
if len(variants) == 1 and len(genes) == 1:
for gene in genes:
geno.addAlleleOfGene(vl_id, gene)
else:
geno.addAllele(vl_id, vl_symbol)
else: # len(vars) == 0
# it's just anonymous variants in some gene
for gene in genes:
vl_id = '_:' + re.sub(r':', '', gene) + '-VL'
vl_symbol = self.id_label_hash[gene] + '<?>'
self.id_label_hash[vl_id] = vl_symbol
geno.addAllele(vl_id, vl_symbol,
self.globaltt['variant_locus'])
geno.addGene(gene, self.id_label_hash[gene])
geno.addAlleleOfGene(vl_id, gene)
vl_set.add(vl_id)
# make the vslcs
vl_list = sorted(vl_set)
vslc_list = []
for vl in vl_list:
# for unknown zygosity
vslc_id = re.sub(r'^_', '', vl) + 'U'
vslc_id = re.sub(r':', '', vslc_id)
vslc_id = '_:' + vslc_id
vslc_label = self.id_label_hash[vl] + '/?'
self.id_label_hash[vslc_id] = vslc_label
vslc_list.append(vslc_id)
geno.addPartsToVSLC(vslc_id, vl, None,
self.globaltt['indeterminate'],
self.globaltt['has_variant_part'],
None)
model.addIndividualToGraph(
vslc_id, vslc_label,
self.globaltt['variant single locus complement'])
if vslc_list:
if len(vslc_list) > 1:
gvc_id = '-'.join(vslc_list)
gvc_id = re.sub(r'_|:', '', gvc_id)
gvc_id = '_:' + gvc_id
gvc_label = '; '.join(self.id_label_hash[v]
for v in vslc_list)
model.addIndividualToGraph(
gvc_id, gvc_label,
self.globaltt['genomic_variation_complement'])
for vslc_id in vslc_list:
geno.addVSLCtoParent(vslc_id, gvc_id)
else:
# the GVC == VSLC, so don't have to make an extra piece
gvc_id = vslc_list.pop()
gvc_label = self.id_label_hash[gvc_id]
genotype_label = gvc_label + ' [n.s.]'
bkgd_id = re.sub(
r':', '', '-'.join(
(self.globaltt['unspecified_genomic_background'],
s)))
genotype_id = '-'.join((gvc_id, bkgd_id))
bkgd_id = '_:' + bkgd_id
geno.addTaxon(mouse_taxon, bkgd_id)
geno.addGenomicBackground(
bkgd_id, 'unspecified (' + s + ')',
self.globaltt['unspecified_genomic_background'],
"A placeholder for the unspecified genetic background for "
+ s)
geno.addGenomicBackgroundToGenotype(
bkgd_id, genotype_id,
self.globaltt['unspecified_genomic_background'])
geno.addParts(gvc_id, genotype_id,
self.globaltt['has_variant_part'])
geno.addGenotype(genotype_id, genotype_label)
graph.addTriple(s, self.globaltt['has_genotype'],
genotype_id)
else:
# LOG.debug(
# "Strain %s is not making a proper genotype.", s)
pass
LOG.warning(
"The following gene symbols did not list identifiers: %s",
str(sorted(list(genes_with_no_ids))))
LOG.error('%i symbols given are missing their gene identifiers',
len(genes_with_no_ids))
return
def _process_phenotype_data(self, limit):
"""
NOTE: If a Strain carries more than one mutation,
then each Mutation description,
i.e., the set: (
Mutation Type - Chromosome - Gene Symbol -
Gene Name - Allele Symbol - Allele Name)
will require a separate line.
Note that MMRRC curates phenotypes to alleles,
even though they distribute only one file with the
phenotypes appearing to be associated with a strain.
So, here we process the allele-to-phenotype relationships separately
from the strain-to-allele relationships.
:param limit:
:return:
"""
if self.testMode:
g = self.testgraph
else:
g = self.graph
line_counter = 0
gu = GraphUtils(curie_map.get())
fname = '/'.join((self.rawdir, self.files['catalog']['file']))
self.strain_hash = {}
self.id_label_hash = {}
genes_with_no_ids = set()
stem_cell_class = 'CL:0000034'
mouse_taxon = 'NCBITaxon:10090'
geno = Genotype(g)
with open(fname, 'r', encoding="utf8") as csvfile:
filereader = csv.reader(csvfile, delimiter=',', quotechar='\"')
for row in filereader:
line_counter += 1
# skip the first 3 lines which are header, etc.
if line_counter < 4:
continue
(strain_id, strain_label, strain_type_symbol, strain_state,
mgi_allele_id, mgi_allele_symbol, mgi_allele_name,
mutation_type, chrom, mgi_gene_id, mgi_gene_symbol,
mgi_gene_name, sds_url, accepted_date, mp_ids, pubmed_nums,
research_areas) = row
if self.testMode and (strain_id not in self.test_ids):
continue
# strip off stuff after the dash -
# is the holding center important?
# MMRRC:00001-UNC --> MMRRC:00001
strain_id = re.sub(r'-\w+$', '', strain_id)
self.id_label_hash[strain_id] = strain_label
# get the variant or gene to save for later building of
# the genotype
if strain_id not in self.strain_hash:
self.strain_hash[strain_id] = {'variants': set(),
'genes': set()}
# clean up the bad one
if mgi_allele_id == 'multiple mutation':
logger.error("Erroneous gene id: %s", mgi_allele_id)
mgi_allele_id = ''
if mgi_allele_id != '':
self.strain_hash[strain_id]['variants'].add(mgi_allele_id)
self.id_label_hash[mgi_allele_id] = mgi_allele_symbol
# use the following if needing to add the
# sequence alteration types
# var_type =
# self._get_variant_type_from_abbrev(mutation_type)
# make a sequence alteration for this variant locus,
# and link the variation type to it
# sa_id = '_'+re.sub(r':','',mgi_allele_id)+'SA'
# if self.nobnodes:
# sa_id = ':'+sa_id
# gu.addIndividualToGraph(g, sa_id, None, var_type)
# geno.addSequenceAlterationToVariantLocus(sa_id,
# mgi_allele_id)
# scrub out any spaces
mgi_gene_id = re.sub(r'\s+', '', mgi_gene_id)
if mgi_gene_id.strip() != '':
if re.match(r'Gene\s*ID:', mgi_gene_id, re.I):
mgi_gene_id = re.sub(r'Gene\s*ID:\s*', 'NCBIGene:',
mgi_gene_id)
elif not re.match(r'MGI', mgi_gene_id):
logger.info("Gene id not recognized: %s", mgi_gene_id)
if re.match(r'\d+$', mgi_gene_id):
# assume that if it's all numbers, then it's MGI
mgi_gene_id = 'MGI:'+str(mgi_gene_id)
logger.info("Assuming numerics are MGI.")
self.strain_hash[strain_id]['genes'].add(mgi_gene_id)
self.id_label_hash[mgi_gene_id] = mgi_gene_symbol
# catch some errors -
# some things have gene labels, but no identifiers - report
if mgi_gene_symbol.strip() != '' and mgi_gene_id == '':
logger.error(
"Gene label with no identifier for strain %s: %s",
strain_id, mgi_gene_symbol)
genes_with_no_ids.add(mgi_gene_symbol.strip())
# make a temp id for genes that aren't identified
# tmp_gene_id = '_'+mgi_gene_symbol
# self.id_label_hash[tmp_gene_id] = mgi_gene_symbol
# self.strain_hash[strain_id]['genes'].add(tmp_gene_id)
# split apart the mp ids
# ataxia [MP:0001393] ,hypoactivity [MP:0001402] ...
# mp_ids are now a comma delimited list
# with MP terms in brackets
phenotype_ids = []
if mp_ids != '':
for i in re.split(r',', mp_ids):
i = i.strip()
mps = re.search(r'\[(.*)\]', i)
if mps is not None:
mp_id = mps.group(1).strip()
phenotype_ids.append(mp_id)
# pubmed ids are space delimited
pubmed_ids = []
if pubmed_nums.strip() != '':
for i in re.split(r'\s+', pubmed_nums):
pmid = 'PMID:'+i.strip()
pubmed_ids.append(pmid)
r = Reference(pmid,
Reference.ref_types['journal_article'])
r.addRefToGraph(g)
# https://www.mmrrc.org/catalog/sds.php?mmrrc_id=00001
# is a good example of 4 genotype parts
gu.addClassToGraph(g, mouse_taxon, None)
if research_areas.strip() == '':
research_areas = None
else:
research_areas = 'Research Areas: '+research_areas
strain_type = mouse_taxon
if strain_state == 'ES':
strain_type = stem_cell_class
gu.addIndividualToGraph(
g, strain_id, strain_label, strain_type,
research_areas) # an inst of mouse??
gu.makeLeader(g, strain_id)
# phenotypes are associated with the alleles
for pid in phenotype_ids:
# assume the phenotype label is in the ontology
gu.addClassToGraph(g, pid, None)
if mgi_allele_id is not None and mgi_allele_id != '':
assoc = G2PAssoc(self.name, mgi_allele_id, pid,
gu.object_properties['has_phenotype'])
for p in pubmed_ids:
assoc.add_source(p)
assoc.add_association_to_graph(g)
else:
logger.info("Phenotypes and no allele for %s",
strain_id)
if not self.testMode and (
limit is not None and line_counter > limit):
break
# now that we've collected all of the variant information, build it
# we don't know their zygosities
for s in self.strain_hash:
h = self.strain_hash.get(s)
variants = h['variants']
genes = h['genes']
vl_set = set()
# make variant loci for each gene
if len(variants) > 0:
for v in variants:
vl_id = v
vl_symbol = self.id_label_hash[vl_id]
geno.addAllele(vl_id, vl_symbol,
geno.genoparts['variant_locus'])
vl_set.add(vl_id)
if len(variants) == 1 and len(genes) == 1:
for gene in genes:
geno.addAlleleOfGene(vl_id, gene)
else:
geno.addAllele(vl_id, vl_symbol)
else: # len(vars) == 0
# it's just anonymous variants in some gene
for gene in genes:
vl_id = '_'+gene+'-VL'
vl_id = re.sub(r':', '', vl_id)
if self.nobnodes:
vl_id = ':'+vl_id
vl_symbol = self.id_label_hash[gene]+'<?>'
self.id_label_hash[vl_id] = vl_symbol
geno.addAllele(vl_id, vl_symbol,
geno.genoparts['variant_locus'])
geno.addGene(gene, self.id_label_hash[gene])
geno.addAlleleOfGene(vl_id, gene)
vl_set.add(vl_id)
# make the vslcs
vl_list = sorted(vl_set)
vslc_list = []
for vl in vl_list:
# for unknown zygosity
vslc_id = '_'+re.sub(r'^_', '', vl)+'U'
vslc_id = re.sub(r':', '', vslc_id)
if self.nobnodes:
vslc_id = ':' + vslc_id
vslc_label = self.id_label_hash[vl] + '/?'
self.id_label_hash[vslc_id] = vslc_label
vslc_list.append(vslc_id)
geno.addPartsToVSLC(
vslc_id, vl, None, geno.zygosity['indeterminate'],
geno.object_properties['has_alternate_part'], None)
gu.addIndividualToGraph(
g, vslc_id, vslc_label,
geno.genoparts['variant_single_locus_complement'])
if len(vslc_list) > 0:
if len(vslc_list) > 1:
gvc_id = '-'.join(vslc_list)
gvc_id = re.sub(r':', '', gvc_id)
if self.nobnodes:
gvc_id = ':'+gvc_id
gvc_label = \
'; '.join(self.id_label_hash[v] for v in vslc_list)
gu.addIndividualToGraph(
g, gvc_id, gvc_label,
geno.genoparts['genomic_variation_complement'])
for vslc_id in vslc_list:
geno.addVSLCtoParent(vslc_id, gvc_id)
else:
# the GVC == VSLC, so don't have to make an extra piece
gvc_id = vslc_list.pop()
gvc_label = self.id_label_hash[gvc_id]
genotype_label = gvc_label + ' [n.s.]'
bkgd_id = \
'_' + re.sub(r':', '', '-'.join(
(geno.genoparts['unspecified_genomic_background'],
s)))
genotype_id = '-'.join((gvc_id, bkgd_id))
if self.nobnodes:
bkgd_id = ':'+bkgd_id
geno.addTaxon(mouse_taxon, bkgd_id)
geno.addGenomicBackground(
bkgd_id, 'unspecified ('+s+')',
geno.genoparts['unspecified_genomic_background'],
"A placeholder for the " +
"unspecified genetic background for "+s)
geno.addGenomicBackgroundToGenotype(
bkgd_id, genotype_id,
geno.genoparts['unspecified_genomic_background'])
geno.addParts(
gvc_id, genotype_id,
geno.object_properties['has_alternate_part'])
geno.addGenotype(genotype_id, genotype_label)
gu.addTriple(
g, s, geno.object_properties['has_genotype'],
genotype_id)
else:
# logger.debug(
# "Strain %s is not making a proper genotype.", s)
pass
gu.loadProperties(
g, G2PAssoc.object_properties, G2PAssoc.OBJECTPROP)
gu.loadProperties(
g, G2PAssoc.datatype_properties, G2PAssoc.DATAPROP)
gu.loadProperties(
g, G2PAssoc.annotation_properties, G2PAssoc.ANNOTPROP)
gu.loadAllProperties(g)
logger.warning(
"The following gene symbols did not list identifiers: %s",
str(sorted(list(genes_with_no_ids))))
return
def _process_data(self, raw, limit=None):
logger.info("Processing Data from %s", raw)
gu = GraphUtils(curie_map.get())
if self.testMode:
g = self.testgraph
else:
g = self.graph
geno = Genotype(g)
line_counter = 0
gu.loadAllProperties(g)
gu.loadObjectProperties(g, geno.object_properties)
# Add the taxon as a class
taxon_id = 'NCBITaxon:10090' # map to Mus musculus
gu.addClassToGraph(g, taxon_id, None)
# with open(raw, 'r', encoding="utf8") as csvfile:
with gzip.open(raw, 'rt') as csvfile:
filereader = csv.reader(csvfile, delimiter=',', quotechar='\"')
next(filereader, None) # skip the header row
for row in filereader:
line_counter += 1
(marker_accession_id, marker_symbol, phenotyping_center,
colony, sex, zygosity, allele_accession_id, allele_symbol,
allele_name, strain_accession_id, strain_name, project_name,
project_fullname, pipeline_name, pipeline_stable_id,
procedure_stable_id, procedure_name, parameter_stable_id,
parameter_name, top_level_mp_term_id, top_level_mp_term_name,
mp_term_id, mp_term_name, p_value, percentage_change,
effect_size, statistical_method, resource_name) = row
if self.testMode and marker_accession_id not in self.test_ids:
continue
# ##### cleanup some of the identifiers ######
zygosity_id = self._map_zygosity(zygosity)
# colony ids sometimes have <> in them, spaces,
# or other non-alphanumerics and break our system;
# replace these with underscores
colony_id = '_'+re.sub(r'\W+', '_', colony)
if self.nobnodes:
colony_id = ':'+colony_id
if not re.match(r'MGI', allele_accession_id):
allele_accession_id = \
'_IMPC-'+re.sub(r':', '', allele_accession_id)
if self.nobnodes:
allele_accession_id = ':'+allele_accession_id
if re.search(r'EUROCURATE', strain_accession_id):
# the eurocurate links don't resolve at IMPC
strain_accession_id = '_'+strain_accession_id
if self.nobnodes:
strain_accession_id = ':'+strain_accession_id
elif not re.match(r'MGI', strain_accession_id):
logger.info(
"Found a strange strain accession...%s",
strain_accession_id)
strain_accession_id = 'IMPC:'+strain_accession_id
######################
# first, add the marker and variant to the graph as with MGI,
# the allele is the variant locus. IF the marker is not known,
# we will call it a sequence alteration. otherwise,
# we will create a BNode for the sequence alteration.
sequence_alteration_id = variant_locus_id = None
variant_locus_name = sequence_alteration_name = None
# extract out what's within the <> to get the symbol
if re.match(r'.*<.*>', allele_symbol):
sequence_alteration_name = \
re.match(r'.*<(.*)>', allele_symbol).group(1)
else:
sequence_alteration_name = allele_symbol
if marker_accession_id is not None and \
marker_accession_id == '':
logger.warning(
"Marker unspecified on row %d", line_counter)
marker_accession_id = None
if marker_accession_id is not None:
variant_locus_id = allele_accession_id
variant_locus_name = allele_symbol
variant_locus_type = geno.genoparts['variant_locus']
geno.addGene(marker_accession_id, marker_symbol,
geno.genoparts['gene'])
geno.addAllele(variant_locus_id, variant_locus_name,
variant_locus_type, None)
geno.addAlleleOfGene(variant_locus_id, marker_accession_id)
sequence_alteration_id = \
'_seqalt'+re.sub(r':', '', allele_accession_id)
if self.nobnodes:
sequence_alteration_id = ':'+sequence_alteration_id
geno.addSequenceAlterationToVariantLocus(
sequence_alteration_id, variant_locus_id)
else:
sequence_alteration_id = allele_accession_id
# IMPC contains targeted mutations with either gene traps,
# knockouts, insertion/intragenic deletions.
# but I don't really know what the SeqAlt is here,
# so I don't add it.
geno.addSequenceAlteration(sequence_alteration_id,
sequence_alteration_name)
# ############# BUILD THE COLONY #############
# First, let's describe the colony that the animals come from
# The Colony ID refers to the ES cell clone
# used to generate a mouse strain.
# Terry sez: we use this clone ID to track
# ES cell -> mouse strain -> mouse phenotyping.
# The same ES clone maybe used at multiple centers,
# so we have to concatenate the two to have a unique ID.
# some useful reading about generating mice from ES cells:
# http://ki.mit.edu/sbc/escell/services/details
# here, we'll make a genotype
# that derives from an ES cell with a given allele.
# the strain is not really attached to the colony.
# the colony/clone is reflective of the allele,
# with unknown zygosity
stem_cell_class = 'ERO:0002002'
gu.addIndividualToGraph(g, colony_id, colony, stem_cell_class)
# vslc of the colony has unknown zygosity
# note that we will define the allele
# (and it's relationship to the marker, etc.) later
# FIXME is it really necessary to create this vslc
# when we always know it's unknown zygosity?
vslc_colony = \
'_'+allele_accession_id+geno.zygosity['indeterminate']
vslc_colony = re.sub(r':', '', vslc_colony)
if self.nobnodes:
vslc_colony = ':'+vslc_colony
vslc_colony_label = allele_symbol+'/<?>'
# for ease of reading, we make the colony genotype variables.
# in the future, it might be desired to keep the vslcs
colony_genotype_id = vslc_colony
colony_genotype_label = vslc_colony_label
geno.addGenotype(colony_genotype_id, colony_genotype_label)
geno.addParts(allele_accession_id, colony_genotype_id,
geno.object_properties['has_alternate_part'])
geno.addPartsToVSLC(
vslc_colony, allele_accession_id, None,
geno.zygosity['indeterminate'],
geno.object_properties['has_alternate_part'])
gu.addTriple(
g, colony_id,
geno.object_properties['has_genotype'],
colony_genotype_id)
# ########## BUILD THE ANNOTATED GENOTYPE ##########
# now, we'll build the genotype of the individual that derives
# from the colony/clone genotype that is attached to
# phenotype = colony_id + strain + zygosity + sex
# (and is derived from a colony)
# this is a sex-agnostic genotype
genotype_id = \
self.make_id(
(colony_id + phenotyping_center + zygosity +
strain_accession_id))
geno.addSequenceDerivesFrom(genotype_id, colony_id)
# build the VSLC of the sex-agnostic genotype
# based on the zygosity
allele1_id = allele_accession_id
allele2_id = allele2_rel = None
allele1_label = allele_symbol
allele2_label = '<?>'
# Making VSLC labels from the various parts,
# can change later if desired.
if zygosity == 'heterozygote':
allele2_label = re.sub(r'<.*', '<+>', allele1_label)
allele2_id = None
elif zygosity == 'homozygote':
allele2_label = allele1_label
allele2_id = allele1_id
allele2_rel = geno.object_properties['has_alternate_part']
elif zygosity == 'hemizygote':
allele2_label = re.sub(r'<.*', '<0>', allele1_label)
allele2_id = None
elif zygosity == 'not_applicable':
allele2_label = re.sub(r'<.*', '<?>', allele1_label)
allele2_id = None
else:
logger.warning("found unknown zygosity %s", zygosity)
break
vslc_name = '/'.join((allele1_label, allele2_label))
# Add the VSLC
vslc_id = '_' + '-'.join((marker_accession_id,
allele_accession_id, zygosity))
vslc_id = re.sub(r':', '', vslc_id)
if self.nobnodes:
vslc_id = ':'+vslc_id
gu.addIndividualToGraph(
g, vslc_id, vslc_name,
geno.genoparts['variant_single_locus_complement'])
geno.addPartsToVSLC(
vslc_id, allele1_id, allele2_id, zygosity_id,
geno.object_properties['has_alternate_part'],
allele2_rel)
# add vslc to genotype
geno.addVSLCtoParent(vslc_id, genotype_id)
# note that the vslc is also the gvc
gu.addType(
g, vslc_id,
Genotype.genoparts['genomic_variation_complement'])
# Add the genomic background
# create the genomic background id and name
if strain_accession_id != '':
genomic_background_id = strain_accession_id
else:
genomic_background_id = None
genotype_name = vslc_name
if genomic_background_id is not None:
geno.addGenotype(
genomic_background_id, strain_name,
geno.genoparts['genomic_background'])
# make a phenotyping-center-specific strain
# to use as the background
pheno_center_strain_label = \
strain_name + '/' + phenotyping_center
pheno_center_strain_id = \
'-'.join((re.sub(r':', '', genomic_background_id),
re.sub(r'\s', '_', phenotyping_center)))
if not re.match(r'^_', pheno_center_strain_id):
pheno_center_strain_id = '_'+pheno_center_strain_id
if self.nobnodes:
pheno_center_strain_id = ':'+pheno_center_strain_id
geno.addGenotype(pheno_center_strain_id,
pheno_center_strain_label,
geno.genoparts['genomic_background'])
geno.addSequenceDerivesFrom(pheno_center_strain_id,
genomic_background_id)
# Making genotype labels from the various parts,
# can change later if desired.
# since the genotype is reflective of the place
# it got made, should put that in to disambiguate
genotype_name = \
genotype_name+' ['+pheno_center_strain_label+']'
geno.addGenomicBackgroundToGenotype(
pheno_center_strain_id, genotype_id)
geno.addTaxon(pheno_center_strain_id, taxon_id)
# this is redundant, but i'll keep in in for now
geno.addSequenceDerivesFrom(genotype_id, colony_id)
genotype_name += '['+colony+']'
geno.addGenotype(genotype_id, genotype_name)
# Make the sex-qualified genotype,
# which is what the phenotype is associated with
sex_qualified_genotype_id = \
self.make_id(
(colony_id + phenotyping_center + zygosity +
strain_accession_id+sex))
sex_qualified_genotype_label = genotype_name+' ('+sex+')'
if sex == 'male':
sq_type_id = geno.genoparts['male_genotype']
elif sex == 'female':
sq_type_id = geno.genoparts['female_genotype']
else:
sq_type_id = geno.genoparts['sex_qualified_genotype']
geno.addGenotype(
sex_qualified_genotype_id,
sex_qualified_genotype_label, sq_type_id)
geno.addParts(
genotype_id, sex_qualified_genotype_id,
geno.object_properties['has_alternate_part'])
if genomic_background_id is not None and \
genomic_background_id != '':
# Add the taxon to the genomic_background_id
geno.addTaxon(taxon_id, genomic_background_id)
else:
# add it as the genomic background
geno.addTaxon(taxon_id, genotype_id)
# ############# BUILD THE G2P ASSOC #############
# from an old email dated July 23 2014:
# Phenotypes associations are made to
# imits colony_id+center+zygosity+gender
phenotype_id = mp_term_id
# it seems that sometimes phenotype ids are missing.
# indicate here
if phenotype_id is None or phenotype_id == '':
logger.warning(
"No phenotype id specified for row %d: %s",
line_counter, str(row))
continue
# experimental_phenotypic_evidence This was used in ZFIN
eco_id = "ECO:0000059"
# the association comes as a result of a g2p from
# a procedure in a pipeline at a center and parameter tested
assoc = G2PAssoc(self.name, sex_qualified_genotype_id,
phenotype_id)
assoc.add_evidence(eco_id)
# assoc.set_score(float(p_value))
# TODO add evidence instance using
# pipeline_stable_id +
# procedure_stable_id +
# parameter_stable_id
assoc.add_association_to_graph(g)
assoc_id = assoc.get_association_id()
# add a free-text description
description = \
' '.join((mp_term_name, 'phenotype determined by',
phenotyping_center, 'in an',
procedure_name, 'assay where',
parameter_name.strip(),
'was measured with an effect_size of',
str(round(float(effect_size), 5)),
'(p =', "{:.4e}".format(float(p_value)), ').'))
gu.addDescription(g, assoc_id, description)
# TODO add provenance information
# resource_id = resource_name
# assoc.addSource(g, assoc_id, resource_id)
if not self.testMode and \
limit is not None and line_counter > limit:
break
gu.loadProperties(g, G2PAssoc.object_properties, gu.OBJPROP)
gu.loadProperties(g, G2PAssoc.annotation_properties, gu.ANNOTPROP)
gu.loadProperties(g, G2PAssoc.datatype_properties, gu.DATAPROP)
return
def _process_phenotype_data(self, limit):
"""
NOTE: If a Strain carries more than one mutation,
then each Mutation description,
i.e., the set: (
Mutation Type - Chromosome - Gene Symbol -
Gene Name - Allele Symbol - Allele Name)
will require a separate line.
Note that MMRRC curates phenotypes to alleles,
even though they distribute only one file with the
phenotypes appearing to be associated with a strain.
So, here we process the allele-to-phenotype relationships separately
from the strain-to-allele relationships.
:param limit:
:return:
"""
if self.testMode:
g = self.testgraph
else:
g = self.graph
model = Model(g)
line_counter = 0
fname = '/'.join((self.rawdir, self.files['catalog']['file']))
self.strain_hash = {}
self.id_label_hash = {}
genes_with_no_ids = set()
stem_cell_class = 'CL:0000034'
mouse_taxon = 'NCBITaxon:10090'
geno = Genotype(g)
with open(fname, 'r', encoding="utf8") as csvfile:
filereader = csv.reader(csvfile, delimiter=',', quotechar='\"')
for row in filereader:
line_counter += 1
# skip the first 3 lines which are header, etc.
if line_counter < 4:
continue
(strain_id, strain_label, strain_type_symbol, strain_state,
mgi_allele_id, mgi_allele_symbol, mgi_allele_name,
mutation_type, chrom, mgi_gene_id, mgi_gene_symbol,
mgi_gene_name, sds_url, accepted_date, mp_ids, pubmed_nums,
research_areas) = row
if self.testMode and (strain_id not in self.test_ids) \
or mgi_gene_name == 'withdrawn':
continue
# strip off stuff after the dash -
# is the holding center important?
# MMRRC:00001-UNC --> MMRRC:00001
strain_id = re.sub(r'-\w+$', '', strain_id)
self.id_label_hash[strain_id] = strain_label
# get the variant or gene to save for later building of
# the genotype
if strain_id not in self.strain_hash:
self.strain_hash[strain_id] = {
'variants': set(),
'genes': set()
}
# clean up the bad one
if mgi_allele_id == 'multiple mutation':
logger.error("Erroneous gene id: %s", mgi_allele_id)
mgi_allele_id = ''
if mgi_allele_id != '':
self.strain_hash[strain_id]['variants'].add(mgi_allele_id)
self.id_label_hash[mgi_allele_id] = mgi_allele_symbol
# use the following if needing to add the
# sequence alteration types
# var_type =
# self._get_variant_type_from_abbrev(mutation_type)
# make a sequence alteration for this variant locus,
# and link the variation type to it
# sa_id = '_'+re.sub(r':','',mgi_allele_id)+'SA'
# if self.nobnodes:
# sa_id = ':'+sa_id
# gu.addIndividualToGraph(g, sa_id, None, var_type)
# geno.addSequenceAlterationToVariantLocus(sa_id,
# mgi_allele_id)
# scrub out any spaces
mgi_gene_id = re.sub(r'\s+', '', mgi_gene_id)
if mgi_gene_id.strip() != '':
if re.match(r'Gene\s*ID:', mgi_gene_id, re.I):
mgi_gene_id = re.sub(r'Gene\s*ID:\s*', 'NCBIGene:',
mgi_gene_id)
elif not re.match(r'MGI', mgi_gene_id):
logger.info("Gene id not recognized: %s", mgi_gene_id)
if re.match(r'\d+$', mgi_gene_id):
# assume that if it's all numbers, then it's MGI
mgi_gene_id = 'MGI:' + str(mgi_gene_id)
logger.info("Assuming numerics are MGI.")
self.strain_hash[strain_id]['genes'].add(mgi_gene_id)
self.id_label_hash[mgi_gene_id] = mgi_gene_symbol
# catch some errors -
# some things have gene labels, but no identifiers - report
if mgi_gene_symbol.strip() != '' and mgi_gene_id == '':
logger.error(
"Gene label with no identifier for strain %s: %s",
strain_id, mgi_gene_symbol)
genes_with_no_ids.add(mgi_gene_symbol.strip())
# make a temp id for genes that aren't identified
# tmp_gene_id = '_'+mgi_gene_symbol
# self.id_label_hash[tmp_gene_id] = mgi_gene_symbol
# self.strain_hash[strain_id]['genes'].add(tmp_gene_id)
# split apart the mp ids
# ataxia [MP:0001393] ,hypoactivity [MP:0001402] ...
# mp_ids are now a comma delimited list
# with MP terms in brackets
phenotype_ids = []
if mp_ids != '':
for i in re.split(r',', mp_ids):
i = i.strip()
mps = re.search(r'\[(.*)\]', i)
if mps is not None:
mp_id = mps.group(1).strip()
phenotype_ids.append(mp_id)
# pubmed ids are space delimited
pubmed_ids = []
if pubmed_nums.strip() != '':
for i in re.split(r'\s+', pubmed_nums):
pmid = 'PMID:' + i.strip()
pubmed_ids.append(pmid)
r = Reference(g, pmid,
Reference.ref_types['journal_article'])
r.addRefToGraph()
# https://www.mmrrc.org/catalog/sds.php?mmrrc_id=00001
# is a good example of 4 genotype parts
model.addClassToGraph(mouse_taxon, None)
if research_areas.strip() == '':
research_areas = None
else:
research_areas = 'Research Areas: ' + research_areas
strain_type = mouse_taxon
if strain_state == 'ES':
strain_type = stem_cell_class
model.addIndividualToGraph(
strain_id, strain_label, strain_type,
research_areas) # an inst of mouse??
model.makeLeader(strain_id)
# phenotypes are associated with the alleles
for pid in phenotype_ids:
# assume the phenotype label is in the ontology
model.addClassToGraph(pid, None)
if mgi_allele_id is not None and mgi_allele_id != '':
assoc = G2PAssoc(
g, self.name, mgi_allele_id, pid,
model.object_properties['has_phenotype'])
for p in pubmed_ids:
assoc.add_source(p)
assoc.add_association_to_graph()
else:
logger.info("Phenotypes and no allele for %s",
strain_id)
if not self.testMode and (limit is not None
and line_counter > limit):
break
# now that we've collected all of the variant information, build it
# we don't know their zygosities
for s in self.strain_hash:
h = self.strain_hash.get(s)
variants = h['variants']
genes = h['genes']
vl_set = set()
# make variant loci for each gene
if len(variants) > 0:
for v in variants:
vl_id = v
vl_symbol = self.id_label_hash[vl_id]
geno.addAllele(vl_id, vl_symbol,
geno.genoparts['variant_locus'])
vl_set.add(vl_id)
if len(variants) == 1 and len(genes) == 1:
for gene in genes:
geno.addAlleleOfGene(vl_id, gene)
else:
geno.addAllele(vl_id, vl_symbol)
else: # len(vars) == 0
# it's just anonymous variants in some gene
for gene in genes:
vl_id = '_:' + re.sub(r':', '', gene) + '-VL'
vl_symbol = self.id_label_hash[gene] + '<?>'
self.id_label_hash[vl_id] = vl_symbol
geno.addAllele(vl_id, vl_symbol,
geno.genoparts['variant_locus'])
geno.addGene(gene, self.id_label_hash[gene])
geno.addAlleleOfGene(vl_id, gene)
vl_set.add(vl_id)
# make the vslcs
vl_list = sorted(vl_set)
vslc_list = []
for vl in vl_list:
# for unknown zygosity
vslc_id = re.sub(r'^_', '', vl) + 'U'
vslc_id = re.sub(r':', '', vslc_id)
vslc_id = '_:' + vslc_id
vslc_label = self.id_label_hash[vl] + '/?'
self.id_label_hash[vslc_id] = vslc_label
vslc_list.append(vslc_id)
geno.addPartsToVSLC(
vslc_id, vl, None, geno.zygosity['indeterminate'],
geno.object_properties['has_alternate_part'], None)
model.addIndividualToGraph(
vslc_id, vslc_label,
geno.genoparts['variant_single_locus_complement'])
if len(vslc_list) > 0:
if len(vslc_list) > 1:
gvc_id = '-'.join(vslc_list)
gvc_id = re.sub(r'_|:', '', gvc_id)
gvc_id = '_:' + gvc_id
gvc_label = \
'; '.join(self.id_label_hash[v] for v in vslc_list)
model.addIndividualToGraph(
gvc_id, gvc_label,
geno.genoparts['genomic_variation_complement'])
for vslc_id in vslc_list:
geno.addVSLCtoParent(vslc_id, gvc_id)
else:
# the GVC == VSLC, so don't have to make an extra piece
gvc_id = vslc_list.pop()
gvc_label = self.id_label_hash[gvc_id]
genotype_label = gvc_label + ' [n.s.]'
bkgd_id = \
re.sub(r':', '', '-'.join(
(geno.genoparts['unspecified_genomic_background'],
s)))
genotype_id = '-'.join((gvc_id, bkgd_id))
bkgd_id = '_:' + bkgd_id
geno.addTaxon(mouse_taxon, bkgd_id)
geno.addGenomicBackground(
bkgd_id, 'unspecified (' + s + ')',
geno.genoparts['unspecified_genomic_background'],
"A placeholder for the " +
"unspecified genetic background for " + s)
geno.addGenomicBackgroundToGenotype(
bkgd_id, genotype_id,
geno.genoparts['unspecified_genomic_background'])
geno.addParts(gvc_id, genotype_id,
geno.object_properties['has_alternate_part'])
geno.addGenotype(genotype_id, genotype_label)
g.addTriple(s, geno.object_properties['has_genotype'],
genotype_id)
else:
# logger.debug(
# "Strain %s is not making a proper genotype.", s)
pass
logger.warning(
"The following gene symbols did not list identifiers: %s",
str(sorted(list(genes_with_no_ids))))
return
def _process_data(self, raw, limit=None):
logger.info("Processing Data from %s", raw)
if self.testMode:
g = self.testgraph
else:
g = self.graph
model = Model(g)
geno = Genotype(g)
line_counter = 0
impc_map = self.open_and_parse_yaml(self.map_files['impc_map'])
impress_map = json.loads(
self.fetch_from_url(
self.map_files['impress_map']).read().decode('utf-8'))
# Add the taxon as a class
taxon_id = 'NCBITaxon:10090' # map to Mus musculus
model.addClassToGraph(taxon_id, None)
# with open(raw, 'r', encoding="utf8") as csvfile:
with gzip.open(raw, 'rt') as csvfile:
filereader = csv.reader(csvfile, delimiter=',', quotechar='\"')
next(filereader, None) # skip the header row
for row in filereader:
line_counter += 1
(marker_accession_id, marker_symbol, phenotyping_center,
colony, sex, zygosity, allele_accession_id, allele_symbol,
allele_name, strain_accession_id, strain_name, project_name,
project_fullname, pipeline_name, pipeline_stable_id,
procedure_stable_id, procedure_name, parameter_stable_id,
parameter_name, top_level_mp_term_id, top_level_mp_term_name,
mp_term_id, mp_term_name, p_value, percentage_change,
effect_size, statistical_method, resource_name) = row
if self.testMode and marker_accession_id not in self.test_ids:
continue
# ##### cleanup some of the identifiers ######
zygosity_id = self._map_zygosity(zygosity)
# colony ids sometimes have <> in them, spaces,
# or other non-alphanumerics and break our system;
# replace these with underscores
colony_id = '_:' + re.sub(r'\W+', '_', colony)
if not re.match(r'MGI', allele_accession_id):
allele_accession_id = \
'_:IMPC-'+re.sub(r':', '', allele_accession_id)
if re.search(r'EUROCURATE', strain_accession_id):
# the eurocurate links don't resolve at IMPC
strain_accession_id = '_:' + strain_accession_id
elif not re.match(r'MGI', strain_accession_id):
logger.info("Found a strange strain accession...%s",
strain_accession_id)
strain_accession_id = 'IMPC:' + strain_accession_id
######################
# first, add the marker and variant to the graph as with MGI,
# the allele is the variant locus. IF the marker is not known,
# we will call it a sequence alteration. otherwise,
# we will create a BNode for the sequence alteration.
sequence_alteration_id = variant_locus_id = None
variant_locus_name = sequence_alteration_name = None
# extract out what's within the <> to get the symbol
if re.match(r'.*<.*>', allele_symbol):
sequence_alteration_name = \
re.match(r'.*<(.*)>', allele_symbol).group(1)
else:
sequence_alteration_name = allele_symbol
if marker_accession_id is not None and \
marker_accession_id == '':
logger.warning("Marker unspecified on row %d",
line_counter)
marker_accession_id = None
if marker_accession_id is not None:
variant_locus_id = allele_accession_id
variant_locus_name = allele_symbol
variant_locus_type = geno.genoparts['variant_locus']
geno.addGene(marker_accession_id, marker_symbol,
geno.genoparts['gene'])
geno.addAllele(variant_locus_id, variant_locus_name,
variant_locus_type, None)
geno.addAlleleOfGene(variant_locus_id, marker_accession_id)
sequence_alteration_id = \
'_:seqalt'+re.sub(r':', '', allele_accession_id)
geno.addSequenceAlterationToVariantLocus(
sequence_alteration_id, variant_locus_id)
else:
sequence_alteration_id = allele_accession_id
# IMPC contains targeted mutations with either gene traps,
# knockouts, insertion/intragenic deletions.
# but I don't really know what the SeqAlt is here,
# so I don't add it.
geno.addSequenceAlteration(sequence_alteration_id,
sequence_alteration_name)
# ############# BUILD THE COLONY #############
# First, let's describe the colony that the animals come from
# The Colony ID refers to the ES cell clone
# used to generate a mouse strain.
# Terry sez: we use this clone ID to track
# ES cell -> mouse strain -> mouse phenotyping.
# The same ES clone maybe used at multiple centers,
# so we have to concatenate the two to have a unique ID.
# some useful reading about generating mice from ES cells:
# http://ki.mit.edu/sbc/escell/services/details
# here, we'll make a genotype
# that derives from an ES cell with a given allele.
# the strain is not really attached to the colony.
# the colony/clone is reflective of the allele,
# with unknown zygosity
stem_cell_class = 'ERO:0002002'
model.addIndividualToGraph(colony_id, colony, stem_cell_class)
# vslc of the colony has unknown zygosity
# note that we will define the allele
# (and it's relationship to the marker, etc.) later
# FIXME is it really necessary to create this vslc
# when we always know it's unknown zygosity?
vslc_colony = \
'_:'+re.sub(r':', '', allele_accession_id+geno.zygosity['indeterminate'])
vslc_colony_label = allele_symbol + '/<?>'
# for ease of reading, we make the colony genotype variables.
# in the future, it might be desired to keep the vslcs
colony_genotype_id = vslc_colony
colony_genotype_label = vslc_colony_label
geno.addGenotype(colony_genotype_id, colony_genotype_label)
geno.addParts(allele_accession_id, colony_genotype_id,
geno.object_properties['has_alternate_part'])
geno.addPartsToVSLC(
vslc_colony, allele_accession_id, None,
geno.zygosity['indeterminate'],
geno.object_properties['has_alternate_part'])
g.addTriple(colony_id, geno.object_properties['has_genotype'],
colony_genotype_id)
# ########## BUILD THE ANNOTATED GENOTYPE ##########
# now, we'll build the genotype of the individual that derives
# from the colony/clone genotype that is attached to
# phenotype = colony_id + strain + zygosity + sex
# (and is derived from a colony)
# this is a sex-agnostic genotype
genotype_id = \
self.make_id(
(colony_id + phenotyping_center + zygosity +
strain_accession_id))
geno.addSequenceDerivesFrom(genotype_id, colony_id)
# build the VSLC of the sex-agnostic genotype
# based on the zygosity
allele1_id = allele_accession_id
allele2_id = allele2_rel = None
allele1_label = allele_symbol
allele2_label = '<?>'
# Making VSLC labels from the various parts,
# can change later if desired.
if zygosity == 'heterozygote':
allele2_label = re.sub(r'<.*', '<+>', allele1_label)
allele2_id = None
elif zygosity == 'homozygote':
allele2_label = allele1_label
allele2_id = allele1_id
allele2_rel = geno.object_properties['has_alternate_part']
elif zygosity == 'hemizygote':
allele2_label = re.sub(r'<.*', '<0>', allele1_label)
allele2_id = None
elif zygosity == 'not_applicable':
allele2_label = re.sub(r'<.*', '<?>', allele1_label)
allele2_id = None
else:
logger.warning("found unknown zygosity %s", zygosity)
break
vslc_name = '/'.join((allele1_label, allele2_label))
# Add the VSLC
vslc_id = '-'.join(
(marker_accession_id, allele_accession_id, zygosity))
vslc_id = re.sub(r':', '', vslc_id)
vslc_id = '_:' + vslc_id
model.addIndividualToGraph(
vslc_id, vslc_name,
geno.genoparts['variant_single_locus_complement'])
geno.addPartsToVSLC(
vslc_id, allele1_id, allele2_id, zygosity_id,
geno.object_properties['has_alternate_part'], allele2_rel)
# add vslc to genotype
geno.addVSLCtoParent(vslc_id, genotype_id)
# note that the vslc is also the gvc
model.addType(
vslc_id,
Genotype.genoparts['genomic_variation_complement'])
# Add the genomic background
# create the genomic background id and name
if strain_accession_id != '':
genomic_background_id = strain_accession_id
else:
genomic_background_id = None
genotype_name = vslc_name
if genomic_background_id is not None:
geno.addGenotype(genomic_background_id, strain_name,
geno.genoparts['genomic_background'])
# make a phenotyping-center-specific strain
# to use as the background
pheno_center_strain_label = \
strain_name + '-' + phenotyping_center + '-' + colony
pheno_center_strain_id = \
'-'.join((re.sub(r':', '', genomic_background_id),
re.sub(r'\s', '_', phenotyping_center),
re.sub(r'\W+', '', colony)))
if not re.match(r'^_', pheno_center_strain_id):
pheno_center_strain_id = '_:' + pheno_center_strain_id
geno.addGenotype(pheno_center_strain_id,
pheno_center_strain_label,
geno.genoparts['genomic_background'])
geno.addSequenceDerivesFrom(pheno_center_strain_id,
genomic_background_id)
# Making genotype labels from the various parts,
# can change later if desired.
# since the genotype is reflective of the place
# it got made, should put that in to disambiguate
genotype_name = \
genotype_name+' ['+pheno_center_strain_label+']'
geno.addGenomicBackgroundToGenotype(
pheno_center_strain_id, genotype_id)
geno.addTaxon(taxon_id, pheno_center_strain_id)
# this is redundant, but i'll keep in in for now
geno.addSequenceDerivesFrom(genotype_id, colony_id)
geno.addGenotype(genotype_id, genotype_name)
# Make the sex-qualified genotype,
# which is what the phenotype is associated with
sex_qualified_genotype_id = \
self.make_id(
(colony_id + phenotyping_center + zygosity +
strain_accession_id+sex))
sex_qualified_genotype_label = genotype_name + ' (' + sex + ')'
if sex == 'male':
sq_type_id = geno.genoparts['male_genotype']
elif sex == 'female':
sq_type_id = geno.genoparts['female_genotype']
else:
sq_type_id = geno.genoparts['sex_qualified_genotype']
geno.addGenotype(sex_qualified_genotype_id,
sex_qualified_genotype_label, sq_type_id)
geno.addParts(genotype_id, sex_qualified_genotype_id,
geno.object_properties['has_alternate_part'])
if genomic_background_id is not None and \
genomic_background_id != '':
# Add the taxon to the genomic_background_id
geno.addTaxon(taxon_id, genomic_background_id)
else:
# add it as the genomic background
geno.addTaxon(taxon_id, genotype_id)
# ############# BUILD THE G2P ASSOC #############
# from an old email dated July 23 2014:
# Phenotypes associations are made to
# imits colony_id+center+zygosity+gender
phenotype_id = mp_term_id
# it seems that sometimes phenotype ids are missing.
# indicate here
if phenotype_id is None or phenotype_id == '':
logger.warning("No phenotype id specified for row %d: %s",
line_counter, str(row))
continue
# hard coded ECO code
eco_id = "ECO:0000015"
# the association comes as a result of a g2p from
# a procedure in a pipeline at a center and parameter tested
assoc = G2PAssoc(g, self.name, sex_qualified_genotype_id,
phenotype_id)
assoc.add_evidence(eco_id)
# assoc.set_score(float(p_value))
# TODO add evidence instance using
# pipeline_stable_id +
# procedure_stable_id +
# parameter_stable_id
assoc.add_association_to_graph()
assoc_id = assoc.get_association_id()
# add a free-text description
try:
description = \
' '.join((mp_term_name, 'phenotype determined by',
phenotyping_center, 'in an',
procedure_name, 'assay where',
parameter_name.strip(),
'was measured with an effect_size of',
str(round(float(effect_size), 5)),
'(p =', "{:.4e}".format(float(p_value)), ').'))
except ValueError:
description = \
' '.join((mp_term_name, 'phenotype determined by',
phenotyping_center, 'in an',
procedure_name, 'assay where',
parameter_name.strip(),
'was measured with an effect_size of',
str(effect_size),
'(p =', "{0}".format(p_value), ').'))
study_bnode = \
self._add_study_provenance(
impc_map, impress_map, phenotyping_center, colony,
project_fullname, pipeline_name, pipeline_stable_id,
procedure_stable_id, procedure_name,
parameter_stable_id, parameter_name,
statistical_method, resource_name)
evidence_line_bnode = \
self._add_evidence(
assoc_id, eco_id, impc_map, p_value, percentage_change,
effect_size, study_bnode)
self._add_assertion_provenance(assoc_id, evidence_line_bnode,
impc_map)
model.addDescription(evidence_line_bnode, description)
# resource_id = resource_name
# assoc.addSource(g, assoc_id, resource_id)
if not self.testMode and \
limit is not None and line_counter > limit:
break
return
def _process_phenotype_data(self, limit):
"""
NOTE: If a Strain carries more than one mutation,
then each Mutation description,
i.e., the set: (
Mutation Type - Chromosome - Gene Symbol -
Gene Name - Allele Symbol - Allele Name)
will require a separate line.
Note that MMRRC curates phenotypes to alleles,
even though they distribute only one file with the
phenotypes appearing to be associated with a strain.
So, here we process the allele-to-phenotype relationships separately
from the strain-to-allele relationships.
:param limit:
:return:
"""
src_key = 'catalog'
if self.test_mode:
graph = self.testgraph
else:
graph = self.graph
model = Model(graph)
fname = '/'.join((self.rawdir, self.files[src_key]['file']))
self.strain_hash = {}
self.id_label_hash = {}
genes_with_no_ids = set()
stem_cell_class = self.globaltt['stem cell']
mouse_taxon = self.globaltt['Mus musculus']
geno = Genotype(graph)
with open(fname, 'r', encoding="utf8") as csvfile:
reader = csv.reader(csvfile, delimiter=',', quotechar='\"')
# This MMRRC catalog data file was generated on YYYY-MM-DD
# insert or check date w/dataset
line = next(reader)
# gen_date = line[-10:]
line = next(reader)
col = self.files['catalog']['columns']
if col != line:
LOG.error(
'%s\nExpected Headers:\t%s\nRecived Headers:\t%s\n',
src_key, col, line)
LOG.info(set(col) - set(line))
line = next(reader)
if line != []:
LOG.warning('Expected third line to be blank. got "%s" instead', line)
for row in reader:
strain_id = row[col.index('STRAIN/STOCK_ID')].strip()
strain_label = row[col.index('STRAIN/STOCK_DESIGNATION')]
# strain_type_symbol = row[col.index('STRAIN_TYPE')]
strain_state = row[col.index('STATE')]
mgi_allele_id = row[col.index('MGI_ALLELE_ACCESSION_ID')].strip()
mgi_allele_symbol = row[col.index('ALLELE_SYMBOL')]
# mgi_allele_name = row[col.index('ALLELE_NAME')]
# mutation_type = row[col.index('MUTATION_TYPE')]
# chrom = row[col.index('CHROMOSOME')]
mgi_gene_id = row[col.index('MGI_GENE_ACCESSION_ID')].strip()
mgi_gene_symbol = row[col.index('GENE_SYMBOL')].strip()
mgi_gene_name = row[col.index('GENE_NAME')]
# sds_url = row[col.index('SDS_URL')]
# accepted_date = row[col.index('ACCEPTED_DATE')]
mpt_ids = row[col.index('MPT_IDS')].strip()
pubmed_nums = row[col.index('PUBMED_IDS')].strip()
research_areas = row[col.index('RESEARCH_AREAS')].strip()
if self.test_mode and (strain_id not in self.test_ids) \
or mgi_gene_name == 'withdrawn':
continue
# strip off stuff after the dash -
# is the holding center important?
# MMRRC:00001-UNC --> MMRRC:00001
strain_id = re.sub(r'-\w+$', '', strain_id)
self.id_label_hash[strain_id] = strain_label
# get the variant or gene to save for later building of
# the genotype
if strain_id not in self.strain_hash:
self.strain_hash[strain_id] = {
'variants': set(), 'genes': set()}
# flag bad ones
if mgi_allele_id[:4] != 'MGI:' and mgi_allele_id != '':
LOG.error("Erroneous MGI allele id: %s", mgi_allele_id)
if mgi_allele_id[:3] == 'MG:':
mgi_allele_id = 'MGI:' + mgi_allele_id[3:]
else:
mgi_allele_id = ''
if mgi_allele_id != '':
self.strain_hash[strain_id]['variants'].add(mgi_allele_id)
self.id_label_hash[mgi_allele_id] = mgi_allele_symbol
# use the following if needing to add the sequence alteration types
# var_type = self.localtt[mutation_type]
# make a sequence alteration for this variant locus,
# and link the variation type to it
# sa_id = '_'+re.sub(r':','',mgi_allele_id)+'SA'
# if self.nobnodes:
# sa_id = ':'+sa_id
# gu.addIndividualToGraph(g, sa_id, None, var_type)
# geno.addSequenceAlterationToVariantLocus(sa_id, mgi_allele_id)
# scrub out any spaces, fix known issues
mgi_gene_id = re.sub(r'\s+', '', mgi_gene_id)
if mgi_gene_id == 'NULL':
mgi_gene_id = ''
elif mgi_gene_id[:7] == 'GeneID:':
mgi_gene_id = 'NCBIGene:' + mgi_gene_id[7:]
if mgi_gene_id != '':
[curie, localid] = mgi_gene_id.split(':')
if curie not in ['MGI', 'NCBIGene']:
LOG.info("MGI Gene id not recognized: %s", mgi_gene_id)
self.strain_hash[strain_id]['genes'].add(mgi_gene_id)
self.id_label_hash[mgi_gene_id] = mgi_gene_symbol
# catch some errors - too many. report summary at the end
# some things have gene labels, but no identifiers - report
if mgi_gene_symbol != '' and mgi_gene_id == '':
# LOG.error(
# "Gene label with no MGI identifier for strain %s: %s",
# strain_id, mgi_gene_symbol)
genes_with_no_ids.add(mgi_gene_symbol)
# make a temp id for genes that aren't identified ... err wow.
# tmp_gene_id = '_' + mgi_gene_symbol
# self.id_label_hash[tmp_gene_id.strip()] = mgi_gene_symbol
# self.strain_hash[strain_id]['genes'].add(tmp_gene_id)
# split apart the mp ids
# ataxia [MP:0001393] ,hypoactivity [MP:0001402] ...
# mpt_ids are a comma delimited list
# labels with MP terms following in brackets
phenotype_ids = []
if mpt_ids != '':
for lb_mp in mpt_ids.split(r','):
lb_mp = lb_mp.strip()
if lb_mp[-1:] == ']' and lb_mp[-12:-8] == '[MP:':
phenotype_ids.append(lb_mp[-11:-2])
# pubmed ids are space delimited
pubmed_ids = []
if pubmed_nums != '':
for pm_num in re.split(r'\s+', pubmed_nums):
pmid = 'PMID:' + pm_num.strip()
pubmed_ids.append(pmid)
ref = Reference(graph, pmid, self.globaltt['journal article'])
ref.addRefToGraph()
# https://www.mmrrc.org/catalog/sds.php?mmrrc_id=00001
# is a good example of 4 genotype parts
model.addClassToGraph(mouse_taxon, None)
if research_areas == '':
research_areas = None
else:
research_areas = 'Research Areas: ' + research_areas
strain_type = mouse_taxon
if strain_state == 'ES':
strain_type = stem_cell_class
model.addIndividualToGraph( # an inst of mouse??
strain_id, strain_label, strain_type, research_areas)
model.makeLeader(strain_id)
# phenotypes are associated with the alleles
for pid in phenotype_ids:
# assume the phenotype label is in some ontology
model.addClassToGraph(pid, None)
if mgi_allele_id is not None and mgi_allele_id != '':
assoc = G2PAssoc(
graph, self.name, mgi_allele_id, pid,
self.globaltt['has phenotype'])
for p in pubmed_ids:
assoc.add_source(p)
assoc.add_association_to_graph()
else:
LOG.info("Phenotypes and no allele for %s", strain_id)
if not self.test_mode and (
limit is not None and reader.line_num > limit):
break
# now that we've collected all of the variant information, build it
# we don't know their zygosities
for s in self.strain_hash:
h = self.strain_hash.get(s)
variants = h['variants']
genes = h['genes']
vl_set = set()
# make variant loci for each gene
if len(variants) > 0:
for var in variants:
vl_id = var.strip()
vl_symbol = self.id_label_hash[vl_id]
geno.addAllele(
vl_id, vl_symbol, self.globaltt['variant_locus'])
vl_set.add(vl_id)
if len(variants) == 1 and len(genes) == 1:
for gene in genes:
geno.addAlleleOfGene(vl_id, gene)
else:
geno.addAllele(vl_id, vl_symbol)
else: # len(vars) == 0
# it's just anonymous variants in some gene
for gene in genes:
vl_id = '_:' + re.sub(r':', '', gene) + '-VL'
vl_symbol = self.id_label_hash[gene]+'<?>'
self.id_label_hash[vl_id] = vl_symbol
geno.addAllele(
vl_id, vl_symbol, self.globaltt['variant_locus'])
geno.addGene(gene, self.id_label_hash[gene])
geno.addAlleleOfGene(vl_id, gene)
vl_set.add(vl_id)
# make the vslcs
vl_list = sorted(vl_set)
vslc_list = []
for vl in vl_list:
# for unknown zygosity
vslc_id = re.sub(r'^_', '', vl)+'U'
vslc_id = re.sub(r':', '', vslc_id)
vslc_id = '_:' + vslc_id
vslc_label = self.id_label_hash[vl] + '/?'
self.id_label_hash[vslc_id] = vslc_label
vslc_list.append(vslc_id)
geno.addPartsToVSLC(
vslc_id, vl, None, self.globaltt['indeterminate'],
self.globaltt['has_variant_part'], None)
model.addIndividualToGraph(
vslc_id, vslc_label,
self.globaltt['variant single locus complement'])
if len(vslc_list) > 0:
if len(vslc_list) > 1:
gvc_id = '-'.join(vslc_list)
gvc_id = re.sub(r'_|:', '', gvc_id)
gvc_id = '_:'+gvc_id
gvc_label = '; '.join(self.id_label_hash[v] for v in vslc_list)
model.addIndividualToGraph(
gvc_id, gvc_label,
self.globaltt['genomic_variation_complement'])
for vslc_id in vslc_list:
geno.addVSLCtoParent(vslc_id, gvc_id)
else:
# the GVC == VSLC, so don't have to make an extra piece
gvc_id = vslc_list.pop()
gvc_label = self.id_label_hash[gvc_id]
genotype_label = gvc_label + ' [n.s.]'
bkgd_id = re.sub(
r':', '', '-'.join((
self.globaltt['unspecified_genomic_background'], s)))
genotype_id = '-'.join((gvc_id, bkgd_id))
bkgd_id = '_:' + bkgd_id
geno.addTaxon(mouse_taxon, bkgd_id)
geno.addGenomicBackground(
bkgd_id, 'unspecified (' + s + ')',
self.globaltt['unspecified_genomic_background'],
"A placeholder for the unspecified genetic background for " + s)
geno.addGenomicBackgroundToGenotype(
bkgd_id, genotype_id,
self.globaltt['unspecified_genomic_background'])
geno.addParts(
gvc_id, genotype_id, self.globaltt['has_variant_part'])
geno.addGenotype(genotype_id, genotype_label)
graph.addTriple(
s, self.globaltt['has_genotype'], genotype_id)
else:
# LOG.debug(
# "Strain %s is not making a proper genotype.", s)
pass
LOG.warning(
"The following gene symbols did not list identifiers: %s",
str(sorted(list(genes_with_no_ids))))
LOG.error(
'%i symbols given are missing their gene identifiers',
len(genes_with_no_ids))
return
class GenotypeTestCase(unittest.TestCase):
def setUp(self):
self.graph = RDFGraph()
self.curie_map = curie_map.get()
self.genotype = Genotype(self.graph)
self.cutil = CurieUtil(self.curie_map)
self.test_cat_pred = self.cutil.get_uri(blv.terms['category'])
self.test_cat_genotype_category = self.cutil.get_uri(
blv.terms['Genotype'])
self.test_cat_background_category = self.cutil.get_uri(
blv.terms['PopulationOfIndividualOrganisms'])
def tearDown(self):
self.genotype = None
def test_addGenotype(self):
cutil = CurieUtil(self.curie_map)
gid = 'MGI:5515892'
label = \
'Pmp22<Tr-2J>/Pmp22<+> [C57BL/6J-Pmp22<Tr-2J>/GrsrJ]'
self.genotype.addGenotype(gid, label)
self.assertTrue((URIRef(cutil.get_uri(gid)), RDFS['label'],
Literal(label)) in self.genotype.graph)
def test_addGenomicBackgroundToGenotype_adds_genotype(self):
"""
test that addGenomicBackgroundToGenotype() correctly assigns
subject/object category
"""
genotype_id = "GENO:0000002"
background_id = "GENO:0000002" # no idea what a good example background ID is
self.genotype.addGenomicBackgroundToGenotype(
background_id=background_id, genotype_id=genotype_id)
geno_triples = list(
self.graph.triples((URIRef(self.cutil.get_uri(genotype_id)),
URIRef(self.test_cat_pred),
URIRef(self.test_cat_genotype_category))))
def test_addGenomicBackgroundToGenotype_adds_categories(self):
"""
test that addGenomicBackgroundToGenotype() correctly assigns
subject/object category
"""
genotype_id = "GENO:0000002"
background_id = "GENO:0000002" # no idea what a good example background ID is
self.genotype.addGenomicBackgroundToGenotype(
background_id=background_id, genotype_id=genotype_id)
geno_triples = list(
self.graph.triples((URIRef(self.cutil.get_uri(genotype_id)),
URIRef(self.test_cat_pred),
URIRef(self.test_cat_genotype_category))))
self.assertEqual(
len(geno_triples), 1,
"addTriples() didn't make exactly 1 genotype category triple")
self.assertEqual(
geno_triples[0][2], URIRef(self.test_cat_genotype_category),
"addTriples() didn't assign the right genotype category")
background_triples = list(
self.graph.triples((URIRef(self.cutil.get_uri(background_id)),
URIRef(self.test_cat_pred),
URIRef(self.test_cat_background_category))))
self.assertEqual(
len(background_triples), 1,
"addTriples() didn't make exactly 1 genotype category triple")
self.assertEqual(
background_triples[0][2],
URIRef(self.test_cat_background_category),
"addTriples() didn't assign the right background category")
# does not compile
# def test_addParts(self):
# """
# """
# if part_relationship is None:
# part_relationship = self.globaltt['has_part']
# # Fail loudly if parent or child identifiers are None
# if parent_id is None:
# raise TypeError('Attempt to pass None as parent')
# elif part_id is None:
# raise TypeError('Attempt to pass None as child')
# elif part_relationship is None:
# part_relationship = self.globaltt['has_part']
#
# self.graph.addTriple(parent_id, part_relationship, part_id,
# subject_category=subject_category,
# object_category=object_category)
return
