def parse(self, limit=None):
if limit is not None:
logger.info("Only parsing first %s rows of each file", limit)
if self.version_num is None:
import os
logger.info("Figuring out version num for files")
# probe the raw directory for the WSnumber on
# the "letter.WS###" file.
# this is the only one that we keep the version number on
files = os.listdir(self.rawdir)
letter_file = next(f for f in files if re.match(r'letter', f))
vernum = re.search(r'(WS\d+)', letter_file)
self.update_wsnum_in_files(vernum.group(1))
logger.info("Parsing files...")
if self.testOnly:
self.testMode = True
if self.testMode:
g = self.testgraph
else:
g = self.graph
self.nobnodes = True # FIXME
# to hold any label for a given id
self.id_label_map = {}
# to hold the mappings between genotype and background
self.genotype_backgrounds = {}
self.extrinsic_id_to_enviro_id_hash = {}
# to hold the genes variant due to a seq alt
self.variant_loci_genes = {}
# to hold the parts of an environment
self.environment_hash = {}
self.wildtype_genotypes = []
# stores the rnai_reagent to gene targets
self.rnai_gene_map = {}
self.process_gene_ids(limit)
# self.process_gene_desc(limit) #TEC imput file is mia 2016-Mar-03
self.process_allele_phenotype(limit)
self.process_rnai_phenotypes(limit)
self.process_pub_xrefs(limit)
self.process_feature_loc(limit)
self.process_disease_association(limit)
# TODO add this when when complete
# self.process_gene_interaction(limit)
logger.info("Finished parsing.")
self.load_bindings()
gu = GraphUtils(curie_map.get())
gu.loadAllProperties(g)
gu.loadObjectProperties(g, Genotype.object_properties)
logger.info("Found %d nodes in graph", len(self.graph))
logger.info("Found %d nodes in testgraph", len(self.testgraph))
return
def _process_genes(self, taxid, limit=None):
gu = GraphUtils(curie_map.get())
if self.testMode:
g = self.testgraph
else:
g = self.graph
geno = Genotype(g)
raw = '/'.join((self.rawdir, self.files[taxid]['file']))
line_counter = 0
logger.info("Processing Ensembl genes for tax %s", taxid)
with open(raw, 'r', encoding="utf8") as csvfile:
filereader = csv.reader(csvfile, delimiter='\t')
for row in filereader:
if len(row) < 4:
logger.error("Data error for file %s", raw)
return
(ensembl_gene_id, external_gene_name, description,
gene_biotype, entrezgene) = row[0:5]
# in the case of human genes, we also get the hgnc id,
# and is the last col
if taxid == '9606':
hgnc_id = row[5]
else:
hgnc_id = None
if self.testMode and entrezgene != '' \
and int(entrezgene) not in self.gene_ids:
continue
line_counter += 1
gene_id = 'ENSEMBL:'+ensembl_gene_id
if description == '':
description = None
gene_type_id = self._get_gene_type(gene_biotype)
gene_type_id = None
gu.addClassToGraph(
g, gene_id, external_gene_name, gene_type_id, description)
if entrezgene != '':
gu.addEquivalentClass(g, gene_id, 'NCBIGene:'+entrezgene)
if hgnc_id is not None and hgnc_id != '':
gu.addEquivalentClass(g, gene_id, hgnc_id)
geno.addTaxon('NCBITaxon:'+taxid, gene_id)
if not self.testMode \
and limit is not None and line_counter > limit:
break
gu.loadProperties(g, Feature.object_properties, gu.OBJPROP)
gu.loadProperties(g, Feature.data_properties, gu.DATAPROP)
gu.loadProperties(g, Genotype.object_properties, gu.OBJPROP)
gu.loadAllProperties(g)
return
def _process_orthologs(self, raw, limit=None):
"""
This method maps orthologs for a species to the KEGG orthology classes.
Triples created:
<gene_id> is a class
<orthology_class_id> is a class
<assoc_id> has subject <gene_id>
<assoc_id> has object <orthology_class_id>
:param limit:
:return:
"""
logger.info("Processing orthologs")
if self.testMode:
g = self.testgraph
else:
g = self.graph
line_counter = 0
gu = GraphUtils(curie_map.get())
gu.loadAllProperties(g)
with open(raw, 'r', encoding="iso-8859-1") as csvfile:
filereader = csv.reader(csvfile, delimiter='\t', quotechar='\"')
for row in filereader:
line_counter += 1
(gene_id, orthology_class_id) = row
orthology_class_id = 'KEGG:'+orthology_class_id.strip()
gene_id = 'KEGG:'+gene_id.strip()
# note that the panther_id references a group of orthologs,
# and is not 1:1 with the rest
# add the KO id as a gene-family grouping class
OrthologyAssoc(
self.name, gene_id, None).add_gene_family_to_graph(
g, orthology_class_id)
# add gene and orthology class to graph;
# assume labels will be taken care of elsewhere
gu.addClassToGraph(g, gene_id, None)
gu.addClassToGraph(g, orthology_class_id, None)
if not self.testMode and \
limit is not None and line_counter > limit:
break
logger.info("Done with orthologs")
return
def parse(self, limit=None):
"""
MPD data is delivered in four separate csv files and one xml file,
which we process iteratively and write out as
one large graph.
:param limit:
:return:
"""
if limit is not None:
logger.info("Only parsing first %s rows fo each file", str(limit))
logger.info("Parsing files...")
if self.testOnly:
self.testMode = True
g = self.testgraph
self.geno = Genotype(self.testgraph)
else:
g = self.graph
self._process_straininfo(limit)
# the following will provide us the hash-lookups
# These must be processed in a specific order
# mapping between assays and ontology terms
self._process_ontology_mappings_file(limit)
# this is the metadata about the measurements
self._process_measurements_file(limit)
# get all the measurements per strain
self._process_strainmeans_file(limit)
# The following will use the hash populated above
# to lookup the ids when filling in the graph
self._fill_provenance_graph(limit)
logger.info("Finished parsing.")
self.load_bindings()
gu = GraphUtils(curie_map.get())
gu.loadAllProperties(g)
gu.loadProperties(g, G2PAssoc.object_properties, GraphUtils.OBJPROP)
gu.loadProperties(g, G2PAssoc.datatype_properties, GraphUtils.OBJPROP)
gu.loadProperties(
g, G2PAssoc.annotation_properties, GraphUtils.ANNOTPROP)
logger.info("Found %d nodes", len(self.graph))
return
def parse(self, limit=None):
if limit is not None:
logger.info("Only parsing first %s rows of each file", limit)
logger.info("Parsing files...")
if self.testOnly:
self.testMode = True
if self.testMode:
g = self.testgraph
else:
g = self.graph
self.nobnodes = True # FIXME
# build the id map for mapping uniprot ids to genes
uniprot_entrez_id_map = self.get_uniprot_entrez_id_map()
for s in self.files:
if s in ['go-references', 'id-map']:
continue
if not self.testMode and int(s) not in self.tax_ids:
continue
file = '/'.join((self.rawdir, self.files.get(s)['file']))
self.process_gaf(file, limit, uniprot_entrez_id_map)
logger.info("Finished parsing.")
self.load_bindings()
gu = GraphUtils(curie_map.get())
gu.loadAllProperties(g)
gu.loadObjectProperties(g, Genotype.object_properties)
logger.info("Found %d nodes in graph", len(self.graph))
logger.info("Found %d nodes in testgraph", len(self.testgraph))
return
def _process_all(self, limit):
"""
This takes the list of omim identifiers from the omim.txt.Z file,
and iteratively queries the omim api for the json-formatted data.
This will create OMIM classes, with the label, definition, and some synonyms.
If an entry is "removed", it is added as a deprecated class.
If an entry is "moved", it is deprecated and consider annotations are added.
Additionally, we extract:
*phenotypicSeries ids as superclasses
*equivalent ids for Orphanet and UMLS
If set to testMode, it will write only those items in the test_ids to the testgraph.
:param limit:
:return:
"""
omimids = self._get_omim_ids() # store the set of omim identifiers
omimparams = {
'format': 'json',
'include': 'all',
}
# you will need to add the API key into the conf.json file, like:
# keys : { 'omim' : '<your api key here>' }
omimparams.update({'apiKey': config.get_config()['keys']['omim']})
# http://api.omim.org/api/entry?mimNumber=100100&include=all
if self.testMode:
g = self.testgraph
else:
g = self.graph
gu = GraphUtils(curie_map.get())
it = 0 # for counting
# note that you can only do request batches of 20
# see info about "Limits" at http://omim.org/help/api
groupsize = 20
if not self.testMode and limit is not None:
# just in case the limit is larger than the number of records, max it out
max = min((limit, omimids.__len__()))
else:
max = omimids.__len__()
# max = 10 #for testing
# TODO write the json to local files - make the assumption that downloads within 24 hrs are the same
# now, loop through the omim numbers and pull the records as json docs
while it < max:
end = min((max, it+groupsize))
# iterate through the omim ids list, and fetch from the OMIM api in batches of 20
if self.testMode:
intersect = list(set([str(i) for i in self.test_ids]) & set(omimids[it:end]))
if len(intersect) > 0: # some of the test ids are in the omimids
logger.info("found test ids: %s", intersect)
omimparams.update({'mimNumber': ','.join(intersect)})
else:
it += groupsize
continue
else:
omimparams.update({'mimNumber': ','.join(omimids[it:end])})
p = urllib.parse.urlencode(omimparams)
url = '/'.join((self.OMIM_API, 'entry'))+'?%s' % p
logger.info('fetching: %s', '/'.join((self.OMIM_API, 'entry'))+'?%s' % p)
# ### if you want to test a specific entry number, uncomment the following code block
# if ('101600' in omimids[it:end]): #104000
# print("FOUND IT in",omimids[it:end])
# else:
# #testing very specific record
# it+=groupsize
# continue
# ### end code block for testing
# print ('fetching:',(',').join(omimids[it:end]))
# print('url:',url)
d = urllib.request.urlopen(url)
resp = d.read().decode()
request_time = datetime.now()
it += groupsize
myjson = json.loads(resp)
entries = myjson['omim']['entryList']
geno = Genotype(g)
# add genome and taxon
tax_num = '9606'
tax_id = 'NCBITaxon:9606'
tax_label = 'Human'
geno.addGenome(tax_id, str(tax_num)) # tax label can get added elsewhere
gu.addClassToGraph(g, tax_id, None) # label added elsewhere
for e in entries:
# 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(';', label)) > 1:
abbrev = (re.split(';', label)[1].strip())
newlabel = self._cleanup_label(label)
description = self._get_description(e['entry'])
omimid = 'OMIM:'+str(omimnum)
if e['entry']['status'] == 'removed':
gu.addDeprecatedClass(g, omimid)
else:
omimtype = self._get_omimtype(e['entry'])
# this uses our cleaned-up label
gu.addClassToGraph(g, omimid, newlabel, omimtype)
# add the original OMIM label as a synonym
gu.addSynonym(g, omimid, label)
# add the alternate labels and includes as synonyms
for l in other_labels:
gu.addSynonym(g, omimid, l)
# for OMIM, we're adding the description as a definition
gu.addDefinition(g, omimid, description)
if abbrev is not None:
gu.addSynonym(g, omimid, abbrev)
# if this is a genetic locus (but not sequenced) then add the chrom loc info
if omimtype == Genotype.genoparts['biological_region']:
if 'geneMapExists' in e['entry'] and e['entry']['geneMapExists']:
genemap = e['entry']['geneMap']
if 'cytoLocation' in genemap:
cytoloc = genemap['cytoLocation']
# parse the cytoloc. add this omim thing as a subsequence of the cytofeature
# 18p11.3-p11.2
# for now, just take the first one
# 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
cytoloc = cytoloc.split('-')[0]
f = Feature(omimid, None, None)
if 'chromosome' in genemap:
chrom = makeChromID(str(genemap['chromosome']), tax_num, 'CHR')
geno.addChromosomeClass(str(genemap['chromosome']), tax_id, tax_label)
loc = makeChromID(cytoloc, tax_num, 'CHR')
gu.addClassToGraph(g, loc, cytoloc) # this is the chr band
f.addSubsequenceOfFeature(g, loc)
f.addFeatureToGraph(g)
pass
# 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('and', str(e['entry']['movedTo'])):
# split the movedTo entry on 'and'
newids = re.split('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())
gu.addDeprecatedClass(g, omimid, fixedids)
self._get_phenotypicseries_parents(e['entry'], g)
self._get_mappedids(e['entry'], g)
self._get_pubs(e['entry'], g)
self._get_process_allelic_variants(e['entry'], g)
### end iterating over batch of entries
# can't have more than 4 req per sec,
# so wait the remaining time, if necessary
dt = datetime.now() - request_time
rem = 0.25 - dt.total_seconds()
if rem > 0:
logger.info("waiting %d sec", rem)
time.sleep(rem/1000)
gu.loadAllProperties(g)
return
def process_catalog(self, limit=None):
"""
:param limit:
:return:
"""
raw = '/'.join((self.rawdir, self.files['catalog']['file']))
logger.info("Processing Data from %s", raw)
gu = GraphUtils(curie_map.get())
if self.testMode: # set the graph to build
g = self.testgraph
else:
g = self.graph
line_counter = 0
geno = Genotype(g)
gu.loadProperties(g, geno.object_properties, gu.OBJPROP)
gu.loadAllProperties(g)
tax_id = 'NCBITaxon:9606' # hardcode
genome_version = 'GRCh38' # hardcode
# build a hashmap of genomic location to identifiers,
# to try to get the equivalences
loc_to_id_hash = {}
with open(raw, 'r', encoding="iso-8859-1") as csvfile:
filereader = csv.reader(csvfile, delimiter='\t', quotechar='\"')
next(filereader, None) # skip the header row
for row in filereader:
if not row:
pass
else:
line_counter += 1
(date_added_to_catalog, pubmed_num, first_author, pub_date,
journal, link, study_name, disease_or_trait,
initial_sample_description, replicate_sample_description,
region, chrom_num, chrom_pos, reported_gene_nums,
mapped_gene, upstream_gene_num, downstream_gene_num,
snp_gene_nums, upstream_gene_distance,
downstream_gene_distance, strongest_snp_risk_allele, snps,
merged, snp_id_current, context, intergenic_flag,
risk_allele_frequency, pvalue, pvalue_mlog, pvalue_text,
or_or_beta, confidence_interval_95,
platform_with_snps_passing_qc, cnv_flag, mapped_trait,
mapped_trait_uri) = row
intersect = \
list(set([str(i) for i in self.test_ids['gene']]) &
set(re.split(r',', snp_gene_nums)))
# skip if no matches found in test set
if self.testMode and len(intersect) == 0:
continue
# 06-May-2015 25917933 Zai CC 20-Nov-2014 J Psychiatr Res http://europepmc.org/abstract/MED/25917933
# A genome-wide association study of suicide severity scores in bipolar disorder.
# Suicide in bipolar disorder
# 959 European ancestry individuals NA
# 10p11.22 10 32704340 C10orf68, CCDC7, ITGB1 CCDC7
# rs7079041-A rs7079041 0 7079041 intron 0 2E-6 5.698970
if chrom_num != '' and chrom_pos != '':
loc = 'chr'+str(chrom_num)+':'+str(chrom_pos)
if loc not in loc_to_id_hash:
loc_to_id_hash[loc] = set()
else:
loc = None
if re.search(r' x ', strongest_snp_risk_allele) \
or re.search(r',', strongest_snp_risk_allele):
# TODO deal with haplotypes
logger.warning(
"We can't deal with haplotypes yet: %s",
strongest_snp_risk_allele)
continue
elif re.match(r'rs', strongest_snp_risk_allele):
rs_id = 'dbSNP:'+strongest_snp_risk_allele.strip()
# remove the alteration
elif re.match(r'kgp', strongest_snp_risk_allele):
# FIXME this isn't correct
rs_id = 'dbSNP:'+strongest_snp_risk_allele.strip()
# http://www.1000genomes.org/faq/what-are-kgp-identifiers
# for some information
# They were created by Illumina for their genotyping
# platform before some variants identified during the
# pilot phase of the project had been assigned
# rs numbers.
elif re.match(r'chr', strongest_snp_risk_allele):
# like: chr10:106180121-G
rs_id = ':gwas-' + \
re.sub(
r':', '-', strongest_snp_risk_allele.strip())
elif strongest_snp_risk_allele.strip() == '':
# logger.debug(
# "No strongest SNP risk allele for %s:\n%s",
# pubmed_num, str(row))
# FIXME still consider adding in the EFO terms
# for what the study measured?
continue
else:
logger.warning(
"There's a snp id i can't manage: %s",
strongest_snp_risk_allele)
continue
alteration = re.search(r'-(.*)$', rs_id)
if alteration is not None \
and re.match(r'[ATGC]', alteration.group(1)):
# add variation to snp
pass # TODO
rs_id = re.sub(r'-.*$', '', rs_id).strip()
if loc is not None:
loc_to_id_hash[loc].add(rs_id)
pubmed_id = 'PMID:'+pubmed_num
r = Reference(
pubmed_id, Reference.ref_types['journal_article'])
r.addRefToGraph(g)
# create the chromosome
chrom_id = makeChromID(chrom_num, genome_version, 'CHR')
# add the feature to the graph
snp_description = None
if risk_allele_frequency != '' and \
risk_allele_frequency != 'NR':
snp_description = \
str(risk_allele_frequency) + \
' [risk allele frequency]'
f = Feature(
rs_id, strongest_snp_risk_allele.strip(),
Feature.types[r'SNP'], snp_description)
if chrom_num != '' and chrom_pos != '':
f.addFeatureStartLocation(chrom_pos, chrom_id)
f.addFeatureEndLocation(chrom_pos, chrom_id)
f.addFeatureToGraph(g)
f.addTaxonToFeature(g, tax_id)
# TODO consider adding allele frequency as property;
# but would need background info to do that
# also want to add other descriptive info about
# the variant from the context
for c in re.split(r';', context):
cid = self._map_variant_type(c.strip())
if cid is not None:
gu.addType(g, rs_id, cid)
# add deprecation information
if merged == 1 and str(snp_id_current.strip()) != '':
# get the current rs_id
current_rs_id = 'dbSNP:'
if not re.match(r'rs', snp_id_current):
current_rs_id += 'rs'
if loc is not None:
loc_to_id_hash[loc].append(current_rs_id)
current_rs_id += str(snp_id_current)
gu.addDeprecatedIndividual(g, rs_id, current_rs_id)
# TODO check on this
# should we add the annotations to the current
# or orig?
gu.makeLeader(g, current_rs_id)
else:
gu.makeLeader(g, rs_id)
# add the feature as a sequence alteration
# affecting various genes
# note that intronic variations don't necessarily list
# the genes such as for rs10448080 FIXME
if snp_gene_nums != '':
for s in re.split(r',', snp_gene_nums):
s = s.strip()
# still have to test for this,
# because sometimes there's a leading comma
if s != '':
gene_id = 'NCBIGene:'+s
geno.addAlleleOfGene(rs_id, gene_id)
# add the up and downstream genes if they are available
if upstream_gene_num != '':
downstream_gene_id = 'NCBIGene:'+downstream_gene_num
gu.addTriple(
g, rs_id,
Feature.object_properties[
r'upstream_of_sequence_of'],
downstream_gene_id)
if downstream_gene_num != '':
upstream_gene_id = 'NCBIGene:'+upstream_gene_num
gu.addTriple(
g, rs_id,
Feature.object_properties[
'downstream_of_sequence_of'],
upstream_gene_id)
description = 'A study of ' + disease_or_trait + \
' in ' + initial_sample_description
if replicate_sample_description != '':
description = \
' '.join(
(description, 'with',
replicate_sample_description))
if platform_with_snps_passing_qc != '':
description = ' '.join(
(description, 'on platform',
platform_with_snps_passing_qc))
description = ' '.join((description, '(p='+pvalue+')'))
# make associations to the EFO terms; there can be >1
if mapped_trait_uri.strip() != '':
for t in re.split(r',', mapped_trait_uri):
t = t.strip()
cu = CurieUtil(curie_map.get())
tid = cu.get_curie(t)
assoc = G2PAssoc(
self.name, rs_id, tid,
gu.object_properties['contributes_to'])
assoc.add_source(pubmed_id)
# combinatorial evidence
# used in automatic assertion
eco_id = 'ECO:0000213'
assoc.add_evidence(eco_id)
# assoc.set_description(description)
# FIXME score should get added to provenance/study
# assoc.set_score(pvalue)
assoc.add_association_to_graph(g)
if not self.testMode and\
(limit is not None and line_counter > limit):
break
Assoc(self.name).load_all_properties(g)
# loop through the location hash,
# and make all snps at that location equivalent
for l in loc_to_id_hash:
snp_ids = loc_to_id_hash[l]
if len(snp_ids) > 1:
logger.info("%s has >1 snp id: %s", l, str(snp_ids))
return
def _process_diseasegene(self, limit):
"""
:param limit:
:return:
"""
if self.testMode:
g = self.testgraph
else:
g = self.graph
line_counter = 0
geno = Genotype(g)
gu = GraphUtils(curie_map.get())
myfile = "/".join((self.rawdir, self.files["disease-gene"]["file"]))
for event, elem in ET.iterparse(myfile):
if elem.tag == "Disorder":
# get the element name and id
# id = elem.get('id') # some internal identifier
disorder_num = elem.find("OrphaNumber").text
disorder_id = "Orphanet:" + str(disorder_num)
if self.testMode and disorder_id not in config.get_config()["test_ids"]["disease"]:
continue
disorder_label = elem.find("Name").text
# make a hash of internal gene id to type for later lookup
gene_iid_to_type = {}
gene_list = elem.find("GeneList")
for gene in gene_list.findall("Gene"):
gene_iid = gene.get("id")
gene_type = gene.find("GeneType").get("id")
gene_iid_to_type[gene_iid] = gene_type
gu.addClassToGraph(g, disorder_id, disorder_label) # assuming that these are in the ontology
assoc_list = elem.find("DisorderGeneAssociationList")
for a in assoc_list.findall("DisorderGeneAssociation"):
gene_iid = a.find(".//Gene").get("id")
gene_name = a.find(".//Gene/Name").text
gene_symbol = a.find(".//Gene/Symbol").text
gene_num = a.find("./Gene/OrphaNumber").text
gene_id = "Orphanet:" + str(gene_num)
gene_type_id = self._map_gene_type_id(gene_iid_to_type[gene_iid])
gu.addClassToGraph(g, gene_id, gene_symbol, gene_type_id, gene_name)
syn_list = a.find("./Gene/SynonymList")
if int(syn_list.get("count")) > 0:
for s in syn_list.findall("./Synonym"):
gu.addSynonym(g, gene_id, s.text)
dgtype = a.find("DisorderGeneAssociationType").get("id")
rel_id = self._map_rel_id(dgtype)
dg_label = a.find("./DisorderGeneAssociationType/Name").text
if rel_id is None:
logger.warn(
"Cannot map association type (%s) to RO for association (%s | %s). Skipping.",
dg_label,
disorder_label,
gene_symbol,
)
continue
alt_locus_id = "_" + gene_num + "-" + disorder_num + "VL"
alt_label = " ".join(
("some variant of", gene_symbol.strip(), "that is a", dg_label.lower(), disorder_label)
)
if self.nobnodes:
alt_locus_id = ":" + alt_locus_id
gu.addIndividualToGraph(g, alt_locus_id, alt_label, geno.genoparts["variant_locus"])
geno.addAlleleOfGene(alt_locus_id, gene_id)
# consider typing the gain/loss-of-function variants like:
# http://sequenceontology.org/browser/current_svn/term/SO:0002054
# http://sequenceontology.org/browser/current_svn/term/SO:0002053
# use "assessed" status to issue an evidence code
# FIXME I think that these codes are sub-optimal
status_code = a.find("DisorderGeneAssociationStatus").get("id")
eco_id = "ECO:0000323" # imported automatically asserted information used in automatic assertion
if status_code == "17991": # Assessed # TODO are these internal ids stable between releases?
eco_id = "ECO:0000322" # imported manually asserted information used in automatic assertion
# Non-traceable author statement ECO_0000034
# imported information in automatic assertion ECO_0000313
assoc = G2PAssoc(self.name, alt_locus_id, disorder_id, rel_id)
assoc.add_evidence(eco_id)
assoc.add_association_to_graph(g)
rlist = a.find("./Gene/ExternalReferenceList")
eqid = None
for r in rlist.findall("ExternalReference"):
if r.find("Source").text == "Ensembl":
eqid = "ENSEMBL:" + r.find("Reference").text
elif r.find("Source").text == "HGNC":
eqid = "HGNC:" + r.find("Reference").text
elif r.find("Source").text == "OMIM":
eqid = "OMIM:" + r.find("Reference").text
else:
pass # skip the others for now
if eqid is not None:
gu.addClassToGraph(g, eqid, None)
gu.addEquivalentClass(g, gene_id, eqid)
pass
elem.clear() # discard the element
if self.testMode and limit is not None and line_counter > limit:
return
gu.loadProperties(g, G2PAssoc.annotation_properties, G2PAssoc.ANNOTPROP)
gu.loadProperties(g, G2PAssoc.datatype_properties, G2PAssoc.DATAPROP)
gu.loadProperties(g, G2PAssoc.object_properties, G2PAssoc.OBJECTPROP)
gu.loadAllProperties(g)
return
def _get_gene_info(self, limit):
"""
Currently loops through the gene_info file and creates the genes as classes, typed with SO. It will add their
label, any alternate labels as synonyms, alternate ids as equivlaent classes. HPRDs get added as
protein products. The chromosome and chr band get added as blank node regions, and the gene is faldo:located
on the chr band.
:param limit:
:return:
"""
gu = GraphUtils(curie_map.get())
if self.testMode:
g = self.testgraph
else:
g = self.graph
geno = Genotype(g)
# not unzipping the file
logger.info("Processing Gene records")
line_counter = 0
myfile = '/'.join((self.rawdir, self.files['gene_info']['file']))
logger.info("FILE: %s", myfile)
# Add taxa and genome classes for those in our filter
for tax_num in self.tax_ids:
tax_id = ':'.join(('NCBITaxon', str(tax_num)))
geno.addGenome(tax_id, str(tax_num)) # tax label can get added elsewhere
gu.addClassToGraph(g, tax_id, None) # label added elsewhere
with gzip.open(myfile, 'rb') as f:
for line in f:
# skip comments
line = line.decode().strip()
if re.match('^#', line):
continue
(tax_num, gene_num, symbol, locustag,
synonyms, xrefs, chr, map_loc, desc,
gtype, authority_symbol, name,
nomenclature_status, other_designations, modification_date) = line.split('\t')
##### set filter=None in init if you don't want to have a filter
#if self.filter is not None:
# if ((self.filter == 'taxids' and (int(tax_num) not in self.tax_ids))
# or (self.filter == 'geneids' and (int(gene_num) not in self.gene_ids))):
# continue
##### end filter
if self.testMode and int(gene_num) not in self.gene_ids:
continue
if int(tax_num) not in self.tax_ids:
continue
line_counter += 1
gene_id = ':'.join(('NCBIGene', gene_num))
tax_id = ':'.join(('NCBITaxon', tax_num))
gene_type_id = self._map_type_of_gene(gtype)
if symbol == 'NEWENTRY':
label = None
else:
label = symbol
# TODO might have to figure out if things aren't genes, and make them individuals
gu.addClassToGraph(g, gene_id, label, gene_type_id, desc)
# we have to do special things here for genes, because they're classes not individuals
# f = Feature(gene_id,label,gene_type_id,desc)
if name != '-':
gu.addSynonym(g, gene_id, name)
if synonyms.strip() != '-':
for s in synonyms.split('|'):
gu.addSynonym(g, gene_id, s.strip(), Assoc.annotation_properties['hasRelatedSynonym'])
if other_designations.strip() != '-':
for s in other_designations.split('|'):
gu.addSynonym(g, gene_id, s.strip(), Assoc.annotation_properties['hasRelatedSynonym'])
# deal with the xrefs
# MIM:614444|HGNC:HGNC:16851|Ensembl:ENSG00000136828|HPRD:11479|Vega:OTTHUMG00000020696
if xrefs.strip() != '-':
for r in xrefs.strip().split('|'):
fixedr = self._cleanup_id(r)
if fixedr is not None and fixedr.strip() != '':
if re.match('HPRD', fixedr):
# proteins are not == genes.
gu.addTriple(g, gene_id, self.properties['has_gene_product'], fixedr)
else:
# skip some of these for now
if fixedr.split(':')[0] not in ['Vega', 'IMGT/GENE-DB']:
gu.addEquivalentClass(g, gene_id, fixedr)
# edge cases of id | symbol | chr | map_loc:
# 263 AMD1P2 X|Y with Xq28 and Yq12
# 438 ASMT X|Y with Xp22.3 or Yp11.3 # in PAR
# 419 ART3 4 with 4q21.1|4p15.1-p14 # no idea why there's two bands listed - possibly 2 assemblies
# 28227 PPP2R3B X|Y Xp22.33; Yp11.3 # in PAR
# 619538 OMS 10|19|3 10q26.3;19q13.42-q13.43;3p25.3 #this is of "unknown" type == susceptibility
# 101928066 LOC101928066 1|Un - # unlocated scaffold
# 11435 Chrna1 2 2 C3|2 43.76 cM # mouse --> 2C3
# 11548 Adra1b 11 11 B1.1|11 25.81 cM # mouse --> 11B1.1
# 11717 Ampd3 7 7 57.85 cM|7 E2-E3 # mouse
# 14421 B4galnt1 10 10 D3|10 74.5 cM # mouse
# 323212 wu:fb92e12 19|20 - # fish
# 323368 ints10 6|18 - # fish
# 323666 wu:fc06e02 11|23 - # fish
# feel that the chr placement can't be trusted in this table when there is > 1 listed
# with the exception of human X|Y, i will only take those that align to one chr
# FIXME remove the chr mapping below when we pull in the genomic coords
if str(chr) != '-' and str(chr) != '':
if re.search('\|', str(chr)) and str(chr) not in ['X|Y','X; Y']:
# this means that there's uncertainty in the mapping. skip it
# TODO we'll need to figure out how to deal with >1 loc mapping
logger.info('%s is non-uniquely mapped to %s. Skipping for now.', gene_id, str(chr))
continue
# X|Y Xp22.33;Yp11.3
# if (not re.match('(\d+|(MT)|[XY]|(Un)$',str(chr).strip())):
# print('odd chr=',str(chr))
if str(chr) == 'X; Y':
chr = 'X|Y' # rewrite the PAR regions for processing
# do this in a loop to allow PAR regions like X|Y
for c in re.split('\|',str(chr)) :
geno.addChromosomeClass(c, tax_id, None) # assume that the chromosome label will get added elsewhere
mychrom = makeChromID(c, tax_num, 'CHR')
mychrom_syn = makeChromLabel(c, tax_num) # temporarily use the taxnum for the disambiguating label
gu.addSynonym(g, mychrom, mychrom_syn)
band_match = re.match('[0-9A-Z]+[pq](\d+)?(\.\d+)?$', map_loc)
if band_match is not None and len(band_match.groups()) > 0:
# if tax_num != '9606':
# continue
# this matches the regular kind of chrs, so make that kind of band
# not sure why this matches? chrX|Y or 10090chr12|Un"
# TODO we probably need a different regex per organism
# the maploc_id already has the numeric chromosome in it, strip it first
bid = re.sub('^'+c, '', map_loc)
maploc_id = makeChromID(c+bid, tax_num, 'CHR') # the generic location (no coordinates)
# print(map_loc,'-->',bid,'-->',maploc_id)
band = Feature(maploc_id, None, None) # Assume it's type will be added elsewhere
band.addFeatureToGraph(g)
# add the band as the containing feature
gu.addTriple(g, gene_id, Feature.object_properties['is_subsequence_of'], maploc_id)
else:
# TODO handle these cases
# examples are: 15q11-q22, Xp21.2-p11.23, 15q22-qter, 10q11.1-q24,
## 12p13.3-p13.2|12p13-p12, 1p13.3|1p21.3-p13.1, 12cen-q21, 22q13.3|22q13.3
logger.debug('not regular band pattern for %s: %s', gene_id, map_loc)
# add the gene as a subsequence of the chromosome
gu.addTriple(g, gene_id, Feature.object_properties['is_subsequence_of'], mychrom)
geno.addTaxon(tax_id, gene_id)
if not self.testMode and limit is not None and line_counter > limit:
break
gu.loadProperties(g, Feature.object_properties, gu.OBJPROP)
gu.loadProperties(g, Feature.data_properties, gu.DATAPROP)
gu.loadProperties(g, Genotype.object_properties, gu.OBJPROP)
gu.loadAllProperties(g)
return
def _process_data(self, raw, limit=None):
"""
This function will process the data files from Coriell.
We make the assumption that any alleles listed are variants
(alternates to w.t.)
Triples: (examples)
:NIGMSrepository a CLO_0000008 #repository
label : NIGMS Human Genetic Cell Repository
foaf:page https://catalog.coriell.org/0/sections/collections/NIGMS/?SsId=8
line_id a CL_0000057, #fibroblast line
derives_from patient_id
part_of :NIGMSrepository
RO:model_of OMIM:disease_id
patient id a foaf:person,
label: "fibroblast from patient 12345 with disease X"
member_of family_id #what is the right thing here?
SIO:race EFO:caucasian #subclass of EFO:0001799
in_taxon NCBITaxon:9606
dc:description Literal(remark)
RO:has_phenotype OMIM:disease_id
GENO:has_genotype genotype_id
family_id a owl:NamedIndividual
foaf:page "https://catalog.coriell.org/0/Sections/BrowseCatalog/FamilyTypeSubDetail.aspx?PgId=402&fam=2104&coll=GM"
genotype_id a intrinsic_genotype
GENO:has_alternate_part allelic_variant_id
we don't necessarily know much about the genotype,
other than the allelic variant. also there's the sex here
pub_id mentions cell_line_id
:param raw:
:param limit:
:return:
"""
logger.info("Processing Data from %s", raw)
gu = GraphUtils(curie_map.get())
if self.testMode: # set the graph to build
g = self.testgraph
else:
g = self.graph
line_counter = 0
geno = Genotype(g)
du = DipperUtil()
gu.loadProperties(g, geno.object_properties, gu.OBJPROP)
gu.loadAllProperties(g)
with open(raw, 'r', encoding="iso-8859-1") as csvfile:
filereader = csv.reader(csvfile, delimiter=',', quotechar='\"')
next(filereader, None) # skip the header row
for row in filereader:
if not row:
pass
else:
line_counter += 1
(catalog_id, description, omim_number, sample_type,
cell_line_available, dna_in_stock, dna_ref, gender, age,
race, ethnicity, affected, karyotype, relprob, mutation,
gene, family_id, collection, url, cat_remark, pubmed_ids,
family_member, variant_id, dbsnp_id, species) = row
# example:
# GM00003,HURLER SYNDROME,607014,Fibroblast,Yes,No,,Female,26 YR,Caucasian,,,,
# parent,,,39,NIGMS Human Genetic Cell Repository,
# http://ccr.coriell.org/Sections/Search/Sample_Detail.aspx?Ref=GM00003,
# 46;XX; clinically normal mother of a child with Hurler syndrome; proband not in Repository,,
# 2,,18343,H**o sapiens
if self.testMode and catalog_id not in self.test_lines:
# skip rows not in our test lines, when in test mode
continue
# ########### BUILD REQUIRED VARIABLES ###########
# Make the cell line ID
cell_line_id = 'Coriell:'+catalog_id.strip()
# Map the cell/sample type
cell_type = self._map_cell_type(sample_type)
# Make a cell line label
line_label = \
collection.partition(' ')[0]+'-'+catalog_id.strip()
# Map the repository/collection
repository = self._map_collection(collection)
# patients are uniquely identified by one of:
# dbsnp id (which is == an individual haplotype)
# family id + family member (if present) OR
# probands are usually family member zero
# cell line id
# since some patients have >1 cell line derived from them,
# we must make sure that the genotype is attached to
# the patient, and can be inferred to the cell line
# examples of repeated patients are:
# famid=1159, member=1; fam=152,member=1
# Make the patient ID
# make an anonymous patient
patient_id = '_person'
if self.nobnodes:
patient_id = ':'+patient_id
if family_id != '':
patient_id = \
'-'.join((patient_id, family_id, family_member))
else:
# make an anonymous patient
patient_id = '-'.join((patient_id, catalog_id.strip()))
# properties of the individual patients: sex, family id,
# member/relproband, description descriptions are
# really long and ugly SCREAMING text, so need to clean up
# the control cases are so odd with this labeling scheme;
# but we'll deal with it as-is for now.
short_desc = (description.split(';')[0]).capitalize()
if affected == 'Yes':
affected = 'affected'
elif affected == 'No':
affected = 'unaffected'
gender = gender.lower()
patient_label = ' '.join((affected, gender, relprob))
if relprob == 'proband':
patient_label = \
' '.join(
(patient_label.strip(), 'with', short_desc))
else:
patient_label = \
' '.join(
(patient_label.strip(), 'of proband with',
short_desc))
# ############# BUILD THE CELL LINE #############
# Adding the cell line as a typed individual.
cell_line_reagent_id = 'CLO:0000031'
gu.addIndividualToGraph(
g, cell_line_id, line_label, cell_line_reagent_id)
# add the equivalent id == dna_ref
if dna_ref != '' and dna_ref != catalog_id:
equiv_cell_line = 'Coriell:'+dna_ref
# some of the equivalent ids are not defined
# in the source data; so add them
gu.addIndividualToGraph(
g, equiv_cell_line, None, cell_line_reagent_id)
gu.addSameIndividual(g, cell_line_id, equiv_cell_line)
# Cell line derives from patient
geno.addDerivesFrom(cell_line_id, patient_id)
geno.addDerivesFrom(cell_line_id, cell_type)
# Cell line a member of repository
gu.addMember(g, repository, cell_line_id)
if cat_remark != '':
gu.addDescription(g, cell_line_id, cat_remark)
# Cell age_at_sampling
# TODO add the age nodes when modeled properly in #78
# if (age != ''):
# this would give a BNode that is an instance of Age.
# but i don't know how to connect
# the age node to the cell line? we need to ask @mbrush
# age_id = '_'+re.sub('\s+','_',age)
# gu.addIndividualToGraph(
# g,age_id,age,self.terms['age'])
# gu.addTriple(
# g,age_id,self.properties['has_measurement'],age,
# True)
# ############# BUILD THE PATIENT #############
# Add the patient ID as an individual.
gu.addPerson(g, patient_id, patient_label)
# TODO map relationship to proband as a class
# (what ontology?)
# Add race of patient
# FIXME: Adjust for subcategories based on ethnicity field
# EDIT: There are 743 different entries for ethnicity...
# Too many to map?
# Add ethnicity as literal in addition to the mapped race?
# Adjust the ethnicity txt (if using)
# to initial capitalization to remove ALLCAPS
# TODO race should go into the individual's background
# and abstracted out to the Genotype class punting for now.
# if race != '':
# mapped_race = self._map_race(race)
# if mapped_race is not None:
# gu.addTriple(
# g,patient_id,self.terms['race'],mapped_race)
# gu.addSubclass(
# g,self.terms['ethnic_group'],mapped_race)
# ############# BUILD THE FAMILY #############
# Add triples for family_id, if present.
if family_id != '':
family_comp_id = 'CoriellFamily:'+family_id
family_label = \
' '.join(('Family of proband with', short_desc))
# Add the family ID as a named individual
gu.addIndividualToGraph(
g, family_comp_id, family_label,
geno.genoparts['family'])
# Add the patient as a member of the family
gu.addMemberOf(g, patient_id, family_comp_id)
# ############# BUILD THE GENOTYPE #############
# the important things to pay attention to here are:
# karyotype = chr rearrangements (somatic?)
# mutation = protein-level mutation as a label,
# often from omim
# gene = gene symbol - TODO get id
# variant_id = omim variant ids (; delimited)
# dbsnp_id = snp individual ids = full genotype?
# note GM00633 is a good example of chromosomal variation
# - do we have enough to capture this?
# GM00325 has both abnormal karyotype and variation
# make an assumption that if the taxon is blank,
# that it is human!
if species is None or species == '':
species = 'H**o sapiens'
taxon = self._map_species(species)
# if there's a dbSNP id,
# this is actually the individual's genotype
genotype_id = None
genotype_label = None
if dbsnp_id != '':
genotype_id = 'dbSNPIndividual:'+dbsnp_id.strip()
omim_map = {}
gvc_id = None
# some of the karyotypes are encoded
# with terrible hidden codes. remove them here
# i've seen a <98> character
karyotype = du.remove_control_characters(karyotype)
karyotype_id = None
if karyotype.strip() != '':
karyotype_id = \
'_'+re.sub('MONARCH:', '', self.make_id(karyotype))
if self.nobnodes:
karyotype_id = ':'+karyotype_id
# add karyotype as karyotype_variation_complement
gu.addIndividualToGraph(
g, karyotype_id, karyotype,
geno.genoparts['karyotype_variation_complement'])
# TODO break down the karyotype into parts
# and map into GENO. depends on #77
# place the karyotype in a location(s).
karyo_chrs = \
self._get_affected_chromosomes_from_karyotype(
karyotype)
for c in karyo_chrs:
chr_id = makeChromID(c, taxon, 'CHR')
# add an anonymous sequence feature,
# each located on chr
karyotype_feature_id = '-'.join((karyotype_id, c))
karyotype_feature_label = \
'some karyotype alteration on chr'+str(c)
f = Feature(
karyotype_feature_id, karyotype_feature_label,
geno.genoparts['sequence_alteration'])
f.addFeatureStartLocation(None, chr_id)
f.addFeatureToGraph(g)
f.loadAllProperties(g)
geno.addParts(
karyotype_feature_id, karyotype_id,
geno.object_properties['has_alternate_part'])
if gene != '':
vl = gene+'('+mutation+')'
# fix the variant_id so it's always in the same order
vids = variant_id.split(';')
variant_id = ';'.join(sorted(list(set(vids))))
if karyotype.strip() != '' \
and not self._is_normal_karyotype(karyotype):
mutation = mutation.strip()
gvc_id = karyotype_id
if variant_id != '':
gvc_id = '_' + variant_id.replace(';', '-') + '-' \
+ re.sub(r'\w*:', '', karyotype_id)
if mutation.strip() != '':
gvc_label = '; '.join((vl, karyotype))
else:
gvc_label = karyotype
elif variant_id.strip() != '':
gvc_id = '_' + variant_id.replace(';', '-')
gvc_label = vl
else:
# wildtype?
pass
if gvc_id is not None and gvc_id != karyotype_id \
and self.nobnodes:
gvc_id = ':'+gvc_id
# add the karyotype to the gvc.
# use reference if normal karyotype
karyo_rel = geno.object_properties['has_alternate_part']
if self._is_normal_karyotype(karyotype):
karyo_rel = \
geno.object_properties['has_reference_part']
if karyotype_id is not None \
and not self._is_normal_karyotype(karyotype) \
and gvc_id is not None and karyotype_id != gvc_id:
geno.addParts(karyotype_id, gvc_id, karyo_rel)
if variant_id.strip() != '':
# split the variants & add them as part of the genotype
# we don't necessarily know their zygosity,
# just that they are part of the genotype variant ids
# are from OMIM, so prefix as such we assume that the
# sequence alts will be defined in OMIM not here
# TODO sort the variant_id list, if the omim prefix is
# the same, then assume it's the locus make a hashmap
# of the omim id to variant id list;
# then build the genotype hashmap is also useful for
# removing the "genes" from the list of "phenotypes"
# will hold gene/locus id to variant list
omim_map = {}
locus_num = None
for v in variant_id.split(';'):
# handle omim-style and odd var ids
# like 610661.p.R401X
m = re.match(r'(\d+)\.+(.*)', v.strip())
if m is not None and len(m.groups()) == 2:
(locus_num, var_num) = m.groups()
if locus_num is not None \
and locus_num not in omim_map:
omim_map[locus_num] = [var_num]
else:
omim_map[locus_num] += [var_num]
for o in omim_map:
# gene_id = 'OMIM:' + o # TODO unused
vslc_id = \
'_' + '-'.join(
[o + '.' + a for a in omim_map.get(o)])
if self.nobnodes:
vslc_id = ':'+vslc_id
vslc_label = vl
# we don't really know the zygosity of
# the alleles at all.
# so the vslcs are just a pot of them
gu.addIndividualToGraph(
g, vslc_id, vslc_label,
geno.genoparts[
'variant_single_locus_complement'])
for v in omim_map.get(o):
# this is actually a sequence alt
allele1_id = 'OMIM:'+o+'.'+v
geno.addSequenceAlteration(allele1_id, None)
# assume that the sa -> var_loc -> gene
# is taken care of in OMIM
geno.addPartsToVSLC(
vslc_id, allele1_id, None,
geno.zygosity['indeterminate'],
geno.object_properties[
'has_alternate_part'])
if vslc_id != gvc_id:
geno.addVSLCtoParent(vslc_id, gvc_id)
if affected == 'unaffected':
# let's just say that this person is wildtype
gu.addType(g, patient_id, geno.genoparts['wildtype'])
elif genotype_id is None:
# make an anonymous genotype id
genotype_id = '_geno'+catalog_id.strip()
if self.nobnodes:
genotype_id = ':'+genotype_id
# add the gvc
if gvc_id is not None:
gu.addIndividualToGraph(
g, gvc_id, gvc_label,
geno.genoparts['genomic_variation_complement'])
# add the gvc to the genotype
if genotype_id is not None:
if affected == 'unaffected':
rel = \
geno.object_properties[
'has_reference_part']
else:
rel = \
geno.object_properties[
'has_alternate_part']
geno.addParts(gvc_id, genotype_id, rel)
if karyotype_id is not None \
and self._is_normal_karyotype(karyotype):
if gvc_label is not None and gvc_label != '':
genotype_label = \
'; '.join((gvc_label, karyotype))
else:
genotype_label = karyotype
if genotype_id is None:
genotype_id = karyotype_id
else:
geno.addParts(
karyotype_id, genotype_id,
geno.object_properties[
'has_reference_part'])
else:
genotype_label = gvc_label
# use the catalog id as the background
genotype_label += ' ['+catalog_id.strip()+']'
if genotype_id is not None and gvc_id is not None:
# only add the genotype if it has some parts
geno.addGenotype(
genotype_id, genotype_label,
geno.genoparts['intrinsic_genotype'])
geno.addTaxon(taxon, genotype_id)
# add that the patient has the genotype
# TODO check if the genotype belongs to
# the cell line or to the patient
gu.addTriple(
g, patient_id,
geno.properties['has_genotype'], genotype_id)
else:
geno.addTaxon(taxon, patient_id)
# TODO: Add sex/gender (as part of the karyotype?)
# ############# DEAL WITH THE DISEASES #############
# we associate the disease to the patient
if affected == 'affected':
if omim_number != '':
for d in omim_number.split(';'):
if d is not None and d != '':
# if the omim number is in omim_map,
# then it is a gene not a pheno
if d not in omim_map:
disease_id = 'OMIM:'+d.strip()
# assume the label is taken care of
gu.addClassToGraph(g, disease_id, None)
# add the association:
# the patient has the disease
assoc = G2PAssoc(
self.name, patient_id, disease_id)
assoc.add_association_to_graph(g)
# this line is a model of this disease
# TODO abstract out model into
# it's own association class?
gu.addTriple(
g, cell_line_id,
gu.properties['model_of'],
disease_id)
else:
logger.info(
'removing %s from disease list ' +
'since it is a gene', d)
# ############# ADD PUBLICATIONS #############
if pubmed_ids != '':
for s in pubmed_ids.split(';'):
pubmed_id = 'PMID:'+s.strip()
ref = Reference(pubmed_id)
ref.setType(Reference.ref_types['journal_article'])
ref.addRefToGraph(g)
gu.addTriple(
g, pubmed_id, gu.properties['mentions'],
cell_line_id)
if not self.testMode \
and (limit is not None and line_counter > limit):
break
Assoc(self.name).load_all_properties(g)
return
class Monochrom(Source):
"""
This class will leverage the GENO ontology and modeling patterns to build
an ontology of chromosomes for any species. These classes represent major
structural pieces of Chromosomes which are often universally referenced,
using physical properties/observations that remain constant over different
genome builds (such as banding patterns and arms). The idea is to create a
scaffold upon which we can hang build-specific chromosomal coordinates,
and reason across them.
In general, this will take the cytogenic bands files from UCSC, and create
missing grouping classes, in order to build the partonomy from a very
specific chromosomal band up through the chromosome itself and enable
overlap and containment queries. We use RO:subsequence_of as our
relationship between nested chromosomal parts. For example,
13q21.31 ==> 13q21.31, 13q21.3, 13q21, 13q2, 13q, 13
At the moment, this only computes the bands for
Human, Mouse, Zebrafish, and Rat
but will be expanding in the future as needed.
Because this is a universal framework to represent the chromosomal
structure of any species, we must mint identifiers for each chromosome
and part. We differentiate species by first creating a species-specific
genome, then for each species-specific chromosome we include the NCBI taxon
number together with the chromosome number, like:
```<species number>chr<num><band>```. For 13q21.31, this would be
9606chr13q21.31.
We then create triples for a given band like:
<pre>
CHR:9606chr1p36.33 rdf[type] SO:chromosome_band
CHR:9606chr1p36 subsequence_of :9606chr1p36.3
</pre>
where any band in the file is an instance of a chr_band
(or a more specific type), is a subsequence of it's containing region.
We determine the containing regions of the band by parsing the band-string;
since each alphanumeric is a significant "place", we can split it with the
shorter strings being parents of the longer string
Since this is small, and we have not limited other items in our test set to
a small region, we simply use the whole graph (genome)
for testing purposes, and copy the main graph to the test graph.
Since this Dipper class is building an ONTOLOGY,
rather than instance-level data, we must also include domain and range
constraints, and other owl-isms.
TODO: any species by commandline argument
We are currently mapping these to the **CHR idspace**,
but this is NOT YET APPROVED and is subject to change.
"""
files = {
'9606': {
'file': '9606cytoBand.txt.gz',
'url': MCDL + '/hg19/database/cytoBand.txt.gz',
'build_num': 'hg19',
'genome_label': 'Human'
},
'10090': {
'file': '10090cytoBand.txt.gz',
'url': MCDL + '/mm10/database/cytoBandIdeo.txt.gz',
'build_num': 'mm10',
'genome_label': 'Mouse'
},
# Note that there are no bands, arms or staining components
# for the following genomes at the moment
'7955': {
'file': '7955cytoBand.txt.gz',
'url': MCDL + '/danRer10/database/cytoBandIdeo.txt.gz',
'build_num': 'danRer10',
'genome_label': 'Zebrafish'
},
'10116': {
'file': '10116cytoBand.txt.gz',
'url': MCDL + '/rn6/database/cytoBandIdeo.txt.gz',
'build_num': 'rn6',
'genome_label': 'Rat'
},
'9913': {
'file': 'bosTau7cytoBand.txt.gz',
'url': MCDL + '/bosTau7/database/cytoBandIdeo.txt.gz',
'build_num': 'bosTau7',
'genome_label': 'cow'
},
'9031': {
'file': 'galGal4cytoBand.txt.gz',
'url': MCDL + '/galGal4/database/cytoBandIdeo.txt.gz',
'build_num': 'galGal4',
'genome_label': 'chicken'
},
'9823': {
'file': 'susScr3cytoBand.txt.gz',
'url': MCDL + '/susScr3/database/cytoBandIdeo.txt.gz',
'build_num': 'susScr3',
'genome_label': 'pig'
},
'9940': {
'file': 'oviAri3cytoBand.txt.gz',
'url': MCDL + '/oviAri3/database/cytoBandIdeo.txt.gz',
'build_num': 'oviAri3',
'genome_label': 'sheep'
},
'9796': {
'file': 'equCab2cytoBand.txt.gz',
'url': MCDL + '/equCab2/database/cytoBandIdeo.txt.gz',
'build_num': 'equCab2',
'genome_label': 'horse'
},
}
region_type_map = {
'acen': Feature.types['centromere'],
'gvar': Feature.types['chromosome_band'],
'stalk': Feature.types['chromosome_band'],
'gneg': Feature.types['chromosome_band'],
'gpos100': Feature.types['chromosome_band'],
'gpos25': Feature.types['chromosome_band'],
'gpos33': Feature.types['chromosome_band'],
'gpos50': Feature.types['chromosome_band'],
'gpos66': Feature.types['chromosome_band'],
'gpos75': Feature.types['chromosome_band'],
'chromosome': Feature.types['chromosome'],
'chromosome_arm': Feature.types['chromosome_arm'],
'chromosome_band': Feature.types['chromosome_band'],
'chromosome_part': Feature.types['chromosome_part']
}
def __init__(self, tax_ids=None):
super().__init__('monochrom')
self.tax_ids = tax_ids
self.load_bindings()
self.gu = GraphUtils(curie_map.get())
# Defaults
if self.tax_ids is None:
self.tax_ids = [
9606, 10090, 7955, 10116, 9913, 9031, 9823, 9940, 9796]
self._check_tax_ids()
# TODO add license
self.dataset = Dataset(
'monochrom', 'Monarch Chromosome Ontology',
'http://monarchinitiative.org', None,
'http://creativecommons.org/licenses/by/4.0/')
return
def fetch(self, is_dl_forced=False):
self.get_files(is_dl_forced)
return
def parse(self, limit=None):
if limit is not None:
logger.info("Only parsing first %d rows", limit)
logger.info("Parsing files...")
if self.testOnly:
self.testMode = True
for taxon in self.tax_ids:
self._get_chrbands(limit, str(taxon))
self.load_core_bindings()
self.load_bindings()
# using the full graph as the test here
self.testgraph = self.graph
logger.info("Found %d nodes", len(self.graph))
logger.info("Done parsing files.")
return
def _get_chrbands(self, limit, taxon):
"""
For the given taxon, it will fetch the chr band file.
We will not deal with the coordinate information with this parser.
Here, we only are concerned with building the partonomy.
:param limit:
:return:
"""
line_counter = 0
myfile = '/'.join((self.rawdir, self.files[taxon]['file']))
logger.info("Processing Chr bands from FILE: %s", myfile)
geno = Genotype(self.graph)
# 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
self.gu.addClassToGraph(self.graph, taxon_id, None)
self.gu.addSynonym(self.graph, taxon_id, genome_label)
self.gu.loadObjectProperties(self.graph, Feature.object_properties)
genome_id = geno.makeGenomeID(taxon_id)
geno.addGenome(taxon_id, genome_label)
self.gu.addOWLPropertyClassRestriction(
self.graph, genome_id, Genotype.object_properties['in_taxon'],
taxon_id)
with gzip.open(myfile, 'rb') as f:
for line in f:
# skip comments
line = line.decode().strip()
if re.match(r'^#', line):
continue
# chr13 4500000 10000000 p12 stalk
(chrom, start, stop, band, rtype) = line.split('\t')
line_counter += 1
# NOTE
# some less-finished genomes have placed and unplaced scaffolds
# * Placed scaffolds:
# Scaffold has an oriented location within a chromosome.
# * Unlocalized scaffolds:
# scaffold 's chromosome is known,
# scaffold's position, orientation or both 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|MT|M)'
# TODO unused
# unlocalized_scaffold_pattern = \
# placed_scaffold_pattern + r'_(\w+)_random'
# unplaced_scaffold_pattern = r'chrUn_(\w+)'
m = re.match(placed_scaffold_pattern+r'$', chrom)
if m is not None and len(m.groups()) == 1:
# the chromosome is the first match of the pattern
# ch = m.group(1) # TODO unused
pass
else:
# let's skip over anything that isn't a placed_scaffold
# at the class level
logger.info("Skipping non-placed chromosome %s", chrom)
continue
# the chrom class, taxon as the reference
cclassid = makeChromID(chrom, taxon, 'CHR')
# add the chromosome as a class
geno.addChromosomeClass(chrom, taxon_id, genome_label)
self.gu.addOWLPropertyClassRestriction(
self.graph, cclassid,
self.gu.object_properties['member_of'], genome_id)
# add the band(region) as a class
maplocclass_id = cclassid+band
maplocclass_label = makeChromLabel(chrom+band, genome_label)
if band is not None and band.strip() != '':
region_type_id = self.map_type_of_region(rtype)
self.gu.addClassToGraph(
self.graph, maplocclass_id, maplocclass_label,
region_type_id)
else:
region_type_id = Feature.types['chromosome']
# add the staining intensity of the band
if re.match(r'g(neg|pos|var)', rtype):
if region_type_id in [
Feature.types['chromosome_band'],
Feature.types['chromosome_subband']]:
stain_type = Feature.types.get(rtype)
if stain_type is not None:
self.gu.addOWLPropertyClassRestriction(
self.graph, maplocclass_id,
Feature.properties['has_staining_intensity'],
Feature.types.get(rtype))
else:
# usually happens if it's a chromosome because
# they don't actually have banding info
logger.info("feature type %s != chr band",
region_type_id)
else:
logger.warning('staining type not found: %s', rtype)
# get the parent bands, and make them unique
parents = list(self.make_parent_bands(band, set()))
# alphabetical sort will put them in smallest to biggest
parents.sort(reverse=True)
# print("PARENTS of",maplocclass_id,"=",parents)
# add the parents to the graph, in hierarchical order
# TODO this is somewhat inefficient due to
# re-adding upper-level nodes when iterating over the file
# TODO PYLINT Consider using enumerate
# instead of iterating with range and len
for i in range(len(parents)):
pclassid = cclassid+parents[i] # class chr parts
pclass_label = \
makeChromLabel(chrom+parents[i], genome_label)
rti = getChrPartTypeByNotation(parents[i])
self.gu.addClassToGraph(
self.graph, pclassid, pclass_label, rti)
# for canonical chromosomes,
# then the subbands are subsequences of the full band
# add the subsequence stuff as restrictions
if i < len(parents) - 1:
pid = cclassid+parents[i+1] # the instance
self.gu.addOWLPropertyClassRestriction(
self.graph, pclassid,
Feature.object_properties['is_subsequence_of'],
pid)
self.gu.addOWLPropertyClassRestriction(
self.graph, pid,
Feature.object_properties['has_subsequence'],
pclassid)
else:
# add the last one (p or q usually)
# as attached to the chromosome
self.gu.addOWLPropertyClassRestriction(
self.graph, pclassid,
Feature.object_properties['is_subsequence_of'],
cclassid)
self.gu.addOWLPropertyClassRestriction(
self.graph, cclassid,
Feature.object_properties['has_subsequence'],
pclassid)
# connect the band here to the first one in the parent list
if len(parents) > 0:
self.gu.addOWLPropertyClassRestriction(
self.graph, maplocclass_id,
Feature.object_properties['is_subsequence_of'],
cclassid+parents[0])
self.gu.addOWLPropertyClassRestriction(
self.graph, cclassid+parents[0],
Feature.object_properties['has_subsequence'],
maplocclass_id)
if limit is not None and line_counter > limit:
break
self.gu.loadAllProperties(self.graph)
# TODO figure out the staining intensities for the encompassing bands
return
def make_parent_bands(self, band, child_bands):
"""
this will determine the grouping bands that it belongs to, recursively
13q21.31 ==> 13, 13q, 13q2, 13q21, 13q21.3, 13q21.31
:param band:
:param child_bands:
:return:
"""
m = re.match(r'([pq][A-H\d]+(?:\.\d+)?)', band)
if len(band) > 0:
if m:
p = str(band[0:len(band)-1])
p = re.sub(r'\.$', '', p)
if p is not None:
child_bands.add(p)
self.make_parent_bands(p, child_bands)
else:
child_bands = set()
return child_bands
def map_type_of_region(self, regiontype):
"""
Note that "stalk" refers to the short arm of acrocentric chromosomes
chr13,14,15,21,22 for human.
:param regiontype:
:return:
"""
so_id = Feature.types['chromosome_part']
if regiontype in self.region_type_map.keys():
so_id = self.region_type_map.get(regiontype)
else:
logger.warning(
"Unmapped code %s. Defaulting to chr_part 'SO:0000830'.",
regiontype)
return so_id
def _check_tax_ids(self):
for taxon in self.tax_ids:
if str(taxon) not in self.files:
raise Exception("Taxon " + str(taxon) +
" not supported by source Monochrom")
def getTestSuite(self):
# import unittest
# from tests.test_ucscbands import UCSCBandsTestCase
test_suite = None
# test_suite = \
# unittest.TestLoader().loadTestsFromTestCase(UCSCBandsTestCase)
return test_suite
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_kegg_disease2gene(self, limit=None):
"""
This method creates an association between diseases and their associated genes.
We are being conservative here, and only processing those diseases for which there
is no mapping to OMIM.
Triples created:
<alternate_locus> is an Individual
<alternate_locus> has type <variant_locus>
<alternate_locus> is an allele of <gene_id>
<assoc_id> has subject <disease_id>
<assoc_id> has object <gene_id>
:param limit:
:return:
"""
logger.info("Processing KEGG disease to gene")
if self.testMode:
g = self.testgraph
else:
g = self.graph
line_counter = 0
geno = Genotype(g)
gu = GraphUtils(curie_map.get())
rel = gu.object_properties['is_marker_for']
gu.loadAllProperties(g)
noomimset = set()
raw = '/'.join((self.rawdir, self.files['disease_gene']['file']))
with open(raw, 'r', encoding="iso-8859-1") as csvfile:
filereader = csv.reader(csvfile, delimiter='\t', quotechar='\"')
for row in filereader:
line_counter += 1
(gene_id, disease_id) = row
if self.testMode and gene_id not in self.test_ids['genes']:
continue
gene_id = 'KEGG-'+gene_id.strip()
disease_id = 'KEGG-'+disease_id.strip()
# only add diseases for which there is no omim id and not a grouping class
if disease_id not in self.kegg_disease_hash:
# add as a class
disease_label = None
if disease_id in self.label_hash:
disease_label = self.label_hash[disease_id]
if re.search('includ', str(disease_label)):
# they use 'including' when it's a grouping class
logger.info("Skipping this association because it's a grouping class: %s", disease_label)
continue
gu.addClassToGraph(g, disease_id, disease_label, 'DOID:4') # type this disease_id as a disease
noomimset.add(disease_id)
alt_locus_id = self._make_variant_locus_id(gene_id, disease_id)
alt_label = self.label_hash[alt_locus_id]
gu.addIndividualToGraph(g, alt_locus_id, alt_label, geno.genoparts['variant_locus'])
geno.addAlleleOfGene(alt_locus_id, gene_id)
# Add the disease to gene relationship.
assoc = G2PAssoc(self.name, alt_locus_id, disease_id, rel)
assoc.load_all_properties(g)
assoc.add_association_to_graph(g)
if (not self.testMode) and (limit is not None and line_counter > limit):
break
logger.info("Done with KEGG disease to gene")
logger.info("Found %d diseases with no omim id", len(noomimset))
return
def _process_diseasegene(self, limit):
"""
:param limit:
:return:
"""
if self.testMode:
g = self.testgraph
else:
g = self.graph
line_counter = 0
geno = Genotype(g)
gu = GraphUtils(curie_map.get())
myfile = '/'.join((self.rawdir, self.files['disease-gene']['file']))
# PYLINT complains iterparse deprecated,
# but as of py 3.4 only the optional & unsupplied parse arg is.
for event, elem in ET.iterparse(myfile):
if elem.tag == 'Disorder':
# get the element name and id, ignoreS element name
# id = elem.get('id') # some internal identifier
disorder_num = elem.find('OrphaNumber').text
disorder_id = 'Orphanet:'+str(disorder_num)
if self.testMode and \
disorder_id not in \
config.get_config()['test_ids']['disease']:
continue
disorder_label = elem.find('Name').text
# make a hash of internal gene id to type for later lookup
gene_iid_to_type = {}
gene_list = elem.find('GeneList')
for gene in gene_list.findall('Gene'):
gene_iid = gene.get('id')
gene_type = gene.find('GeneType').get('id')
gene_iid_to_type[gene_iid] = gene_type
# assuming that these are in the ontology
gu.addClassToGraph(g, disorder_id, disorder_label)
assoc_list = elem.find('DisorderGeneAssociationList')
for a in assoc_list.findall('DisorderGeneAssociation'):
gene_iid = a.find('.//Gene').get('id')
gene_name = a.find('.//Gene/Name').text
gene_symbol = a.find('.//Gene/Symbol').text
gene_num = a.find('./Gene/OrphaNumber').text
gene_id = 'Orphanet:'+str(gene_num)
gene_type_id = \
self._map_gene_type_id(gene_iid_to_type[gene_iid])
gu.addClassToGraph(
g, gene_id, gene_symbol, gene_type_id, gene_name)
syn_list = a.find('./Gene/SynonymList')
if int(syn_list.get('count')) > 0:
for s in syn_list.findall('./Synonym'):
gu.addSynonym(g, gene_id, s.text)
dgtype = a.find('DisorderGeneAssociationType').get('id')
rel_id = self._map_rel_id(dgtype)
dg_label = \
a.find('./DisorderGeneAssociationType/Name').text
if rel_id is None:
logger.warning(
"Cannot map association type (%s) to RO " +
"for association (%s | %s). Skipping.",
dg_label, disorder_label, gene_symbol)
continue
alt_locus_id = '_'+gene_num+'-'+disorder_num+'VL'
alt_label = \
' '.join(('some variant of', gene_symbol.strip(),
'that is a', dg_label.lower(),
disorder_label))
if self.nobnodes:
alt_locus_id = ':'+alt_locus_id
gu.addIndividualToGraph(g, alt_locus_id, alt_label,
geno.genoparts['variant_locus'])
geno.addAlleleOfGene(alt_locus_id, gene_id)
# consider typing the gain/loss-of-function variants like:
# http://sequenceontology.org/browser/current_svn/term/SO:0002054
# http://sequenceontology.org/browser/current_svn/term/SO:0002053
# use "assessed" status to issue an evidence code
# FIXME I think that these codes are sub-optimal
status_code = \
a.find('DisorderGeneAssociationStatus').get('id')
# imported automatically asserted information
# used in automatic assertion
eco_id = 'ECO:0000323'
# Assessed
# TODO are these internal ids stable between releases?
if status_code == '17991':
# imported manually asserted information
# used in automatic assertion
eco_id = 'ECO:0000322'
# Non-traceable author statement ECO_0000034
# imported information in automatic assertion ECO_0000313
assoc = G2PAssoc(self.name, alt_locus_id,
disorder_id, rel_id)
assoc.add_evidence(eco_id)
assoc.add_association_to_graph(g)
rlist = a.find('./Gene/ExternalReferenceList')
eqid = None
for r in rlist.findall('ExternalReference'):
if r.find('Source').text == 'Ensembl':
eqid = 'ENSEMBL:'+r.find('Reference').text
elif r.find('Source').text == 'HGNC':
eqid = 'HGNC:'+r.find('Reference').text
elif r.find('Source').text == 'OMIM':
eqid = 'OMIM:'+r.find('Reference').text
else:
pass # skip the others for now
if eqid is not None:
gu.addClassToGraph(g, eqid, None)
gu.addEquivalentClass(g, gene_id, eqid)
elem.clear() # discard the element
if self.testMode and limit is not None and line_counter > limit:
return
gu.loadProperties(
g, G2PAssoc.annotation_properties, G2PAssoc.ANNOTPROP)
gu.loadProperties(g, G2PAssoc.datatype_properties, G2PAssoc.DATAPROP)
gu.loadProperties(g, G2PAssoc.object_properties, G2PAssoc.OBJECTPROP)
gu.loadAllProperties(g)
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
class CTD(Source):
"""
The Comparative Toxicogenomics Database (CTD) includes curated data describing cross-species chemical–gene/protein
interactions and chemical– and gene–disease associations to illuminate molecular mechanisms underlying variable
susceptibility and environmentally influenced diseases.
Here, we fetch, parse, and convert data from CTD into triples, leveraging only the associations based on
DIRECT evidence (not using the inferred associations). We currently process the following associations:
* chemical-disease
* gene-pathway
* gene-disease
CTD curates relationships between genes and chemicals/diseases with marker/mechanism and/or therapeutic.
Unfortunately, we cannot disambiguate between marker (gene expression) and mechanism (causation)
for these associations. Therefore, we are left to relate these simply by "marker".
CTD also pulls in genes and pathway membership from KEGG and REACTOME. We create groups of these following
the pattern that the specific pathway is a subclass of 'cellular process' (a go process), and
the gene is "involved in" that process.
For diseases, we preferentially use OMIM identifiers when they can be used uniquely over MESH. Otherwise,
we use MESH ids.
Note that we scrub the following identifiers and their associated data:
* REACT:116125 - generic disease class
* MESH:D004283 - dog diseases
* MESH:D004195 - disease models, animal
* MESH:D030342 - genetic diseases, inborn
* MESH:D040181 - genetic dieases, x-linked
* MESH:D020022 - genetic predisposition to a disease
"""
files = {
'chemical_disease_interactions': {
'file': 'CTD_chemicals_diseases.tsv.gz',
'url': 'http://ctdbase.org/reports/CTD_chemicals_diseases.tsv.gz'
},
'gene_pathway': {
'file': 'CTD_genes_pathways.tsv.gz',
'url': 'http://ctdbase.org/reports/CTD_genes_pathways.tsv.gz'
},
'gene_disease': {
'file': 'CTD_genes_diseases.tsv.gz',
'url': 'http://ctdbase.org/reports/CTD_genes_diseases.tsv.gz'
}
}
static_files = {
'publications': {'file': 'CTD_curated_references.tsv'}
}
def __init__(self):
Source.__init__(self, 'ctd')
self.dataset = Dataset('ctd', 'CTD', 'http://ctdbase.org', None, 'http://ctdbase.org/about/legal.jsp')
if 'test_ids' not in config.get_config() or 'gene' not in config.get_config()['test_ids']:
logger.warn("not configured with gene test ids.")
self.test_geneids = []
else:
self.test_geneids = config.get_config()['test_ids']['gene']
if 'test_ids' not in config.get_config() or 'disease' not in config.get_config()['test_ids']:
logger.warn("not configured with disease test ids.")
self.test_diseaseids = []
else:
self.test_diseaseids = config.get_config()['test_ids']['disease']
self.gu = GraphUtils(curie_map.get())
return
def fetch(self, is_dl_forced=False):
"""
Override Source.fetch()
Fetches resources from CTD using the CTD.files dictionary
Args:
:param is_dl_forced (bool): Force download
Returns:
:return None
"""
self.get_files(is_dl_forced)
self._fetch_disambiguating_assoc()
# consider creating subsets of the files that only have direct annotations (not inferred)
return
def parse(self, limit=None):
"""
Override Source.parse()
Parses version and interaction information from CTD
Args:
:param limit (int, optional) limit the number of rows processed
Returns:
:return None
"""
if limit is not None:
logger.info("Only parsing first %d rows", limit)
logger.info("Parsing files...")
# pub_map = dict()
# file_path = '/'.join((self.rawdir,
# self.static_files['publications']['file']))
# if os.path.exists(file_path) is True:
# pub_map = self._parse_publication_file(
# self.static_files['publications']['file']
# )
if self.testOnly:
self.testMode = True
if self.testMode:
self.g = self.testgraph
else:
self.g = self.graph
self.geno = Genotype(self.g)
self.path = Pathway(self.g, self.nobnodes)
self._parse_ctd_file(limit, self.files['chemical_disease_interactions']['file'])
self._parse_ctd_file(limit, self.files['gene_pathway']['file'])
self._parse_ctd_file(limit, self.files['gene_disease']['file'])
self._parse_curated_chem_disease(limit)
self.gu.loadAllProperties(self.g)
# self.gu.loadProperties(self.g, self.REL_MAP, self.gu.OBJPROP)
self.load_bindings()
logger.info("Done parsing files.")
return
def _parse_ctd_file(self, limit, file):
"""
Parses files in CTD.files dictionary
Args:
:param limit (int): limit the number of rows processed
:param file (str): file name (must be defined in CTD.file)
Returns:
:return None
"""
row_count = 0
version_pattern = re.compile('^# Report created: (.+)$')
is_versioned = False
file_path = '/'.join((self.rawdir, file))
with gzip.open(file_path, 'rt') as tsvfile:
reader = csv.reader(tsvfile, delimiter="\t")
for row in reader:
# Scan the header lines until we get the version
# There is no official version sp we are using
# the upload timestamp instead
if is_versioned is False:
match = re.match(version_pattern, ' '.join(row))
if match:
version = re.sub(r'\s|:', '-', match.group(1))
# TODO convert this timestamp to a proper timestamp
self.dataset.setVersion(version)
is_versioned = True
elif re.match('^#', ' '.join(row)):
pass
else:
row_count += 1
if file == self.files['chemical_disease_interactions']['file']:
self._process_interactions(row)
elif file == self.files['gene_pathway']['file']:
self._process_pathway(row)
elif file == self.files['gene_disease']['file']:
self._process_disease2gene(row)
if not self.testMode and limit is not None and row_count >= limit:
break
return
def _process_pathway(self, row):
"""
Process row of CTD data from CTD_genes_pathways.tsv.gz
and generate triples
Args:
:param row (list): row of CTD data
Returns:
:return None
"""
self._check_list_len(row, 4)
(gene_symbol, gene_id, pathway_name, pathway_id) = row
if self.testMode and (int(gene_id) not in self.test_geneids):
return
entrez_id = 'NCBIGene:'+gene_id
if re.match('REACT:116125', pathway_id):
# skipping this one, as it is generic "Disease"
return
# convert KEGG pathway ids... KEGG:12345 --> KEGG-path:map12345
if re.match('KEGG', pathway_id):
pathway_id = re.sub('KEGG:', 'KEGG-path:map', pathway_id)
self.gu.addClassToGraph(self.graph, entrez_id, None) # just in case, add it as a class
self.path.addPathway(pathway_id, pathway_name)
self.path.addGeneToPathway(pathway_id, entrez_id)
return
def _fetch_disambiguating_assoc(self):
"""
For any of the items in the chemical-disease association file that have ambiguous association types
we fetch the disambiguated associations using the batch query API, and store these in a file.
Elsewhere, we can loop through the file and create the appropriate associations.
:return:
"""
disambig_file = '/'.join((self.rawdir, self.static_files['publications']['file']))
assoc_file = '/'.join((self.rawdir, self.files['chemical_disease_interactions']['file']))
# check if there is a local association file, and download if it's dated later than the original intxn file
if os.path.exists(disambig_file):
dfile_dt = os.stat(disambig_file)
afile_dt = os.stat(assoc_file)
if dfile_dt < afile_dt:
logger.info("Local disambiguating file date < chem-disease assoc file. Downloading...")
else:
logger.info("Local disambiguating file date > chem-disease assoc file. Skipping download.")
return
all_pubs = set()
dual_evidence = re.compile('^marker\/mechanism\|therapeutic$')
# first get all the unique publications
with gzip.open(assoc_file, 'rt') as tsvfile:
reader = csv.reader(tsvfile, delimiter="\t")
for row in reader:
if re.match('^#', ' '.join(row)):
continue
self._check_list_len(row, 10)
(chem_name, chem_id, cas_rn, disease_name, disease_id, direct_evidence,
inferred_gene_symbol, inference_score, omim_ids, pubmed_ids) = row
if direct_evidence == '' or not re.match(dual_evidence, direct_evidence):
continue
if pubmed_ids is not None and pubmed_ids != '':
all_pubs.update(set(re.split('\|', pubmed_ids)))
sorted_pubs = sorted(list(all_pubs))
# now in batches of 4000, we fetch the chemical-disease associations
batch_size = 4000
params = {
'inputType': 'reference',
'report': 'diseases_curated',
'format': 'tsv',
'action': 'Download'
}
url = 'http://ctdbase.org/tools/batchQuery.go?q'
start = 0
end = min((batch_size, len(all_pubs))) # get them in batches of 4000
with open(disambig_file, 'wb') as f:
while start < len(sorted_pubs):
params['inputTerms'] = '|'.join(sorted_pubs[start:end])
# fetch the data from url
logger.info('fetching %d (%d-%d) refs: %s', len(re.split('\|', params['inputTerms'])),
start, end, params['inputTerms'])
data = urllib.parse.urlencode(params)
encoding = 'utf-8'
binary_data = data.encode(encoding)
req = urllib.request.Request(url, binary_data)
resp = urllib.request.urlopen(req)
f.write(resp.read())
start = end
end = min((start+batch_size, len(sorted_pubs)))
return
def _process_interactions(self, row):
"""
Process row of CTD data from CTD_chemicals_diseases.tsv.gz
and generate triples.
Only create associations based on direct evidence (not using the inferred-via-gene),
and unambiguous relationships. (Ambiguous ones will be processed in the sister method using the
disambiguated file). There are no OMIM ids for diseases in these cases, so we associate with only
the mesh disease ids.
Args:
:param row (list): row of CTD data
Returns:
:return None
"""
self._check_list_len(row, 10)
(chem_name, chem_id, cas_rn, disease_name, disease_id, direct_evidence,
inferred_gene_symbol, inference_score, omim_ids, pubmed_ids) = row
if direct_evidence == '':
return
evidence_pattern = re.compile('^therapeutic|marker\/mechanism$')
# dual_evidence = re.compile('^marker\/mechanism\|therapeutic$')
# filter on those diseases that are mapped to omim ids in the test set
intersect = list(set(['OMIM:'+str(i) for i in omim_ids.split('|')]+[disease_id]) & set(self.test_diseaseids))
if self.testMode and len(intersect) < 1:
return
chem_id = 'MESH:'+chem_id
reference_list = self._process_pubmed_ids(pubmed_ids)
if re.match(evidence_pattern, direct_evidence):
rel_id = self._get_relationship_id(direct_evidence)
self.gu.addClassToGraph(self.g, chem_id, chem_name)
self.gu.addClassToGraph(self.g, disease_id, None)
self._make_association(chem_id, disease_id, rel_id, reference_list)
else:
# there's dual evidence, but haven't mapped the pubs
pass
# logger.debug("Dual evidence for %s (%s) and %s (%s)", chem_name, chem_id, disease_name, disease_id)
return
def _process_disease2gene(self, row):
"""
Here, we process the disease-to-gene associations.
Note that we ONLY process direct associations (not inferred through chemicals).
Furthermore, we also ONLY process "marker/mechanism" associations.
We preferentially utilize OMIM identifiers over MESH identifiers for disease/phenotype.
Therefore, if a single OMIM id is listed under the "omim_ids" list, we will choose this over any
MeSH id that might be listed as the disease_id.
If multiple OMIM ids are listed in the omim_ids column, we toss this for now. (Mostly, we are not sure
what to do with this information.)
We associate "some variant of gene X" with the phenotype, rather than the gene directly.
We also pull in the MeSH labels here (but not OMIM) to ensure that we have them (as they may not be
brought in separately).
:param row:
:return:
"""
# if self.testMode:
# g = self.testgraph
# else:
# g = self.graph
# self._check_list_len(row, 9)
# geno = Genotype(g)
# gu = GraphUtils(curie_map.get())
(gene_symbol, gene_id, disease_name, disease_id, direct_evidence,
inference_chemical_name, inference_score, omim_ids, pubmed_ids) = row
# we only want the direct associations; skipping inferred for now
if direct_evidence == '' or direct_evidence != 'marker/mechanism':
return
# scrub some of the associations... it seems odd to link human genes to the following "diseases"
diseases_to_scrub = [
'MESH:D004283', # dog diseases
'MESH:D004195', # disease models, animal
'MESH:D030342', # genetic diseases, inborn
'MESH:D040181', # genetic dieases, x-linked
'MESH:D020022' # genetic predisposition to a disease
]
if disease_id in diseases_to_scrub:
logger.info("Skipping association between NCBIGene:%s and %s", str(gene_id), disease_id)
return
intersect = list(set(['OMIM:'+str(i) for i in omim_ids.split('|')]+[disease_id]) & set(self.test_diseaseids))
if self.testMode and (int(gene_id) not in self.test_geneids or len(intersect) < 1):
return
# there are three kinds of direct evidence: (marker/mechanism | marker/mechanism|therapeutic | therapeutic)
# we are only using the "marker/mechanism" for now
# TODO what does it mean for a gene to be therapeutic for disease? a therapeutic target?
gene_id = 'NCBIGene:'+gene_id
preferred_disease_id = disease_id
if omim_ids is not None and omim_ids != '':
omim_id_list = re.split('\|', omim_ids)
# If there is only one OMIM ID for the Disease ID or in the omim_ids list,
# use the OMIM ID preferentially over any MeSH ID.
if re.match('OMIM:.*', disease_id):
if len(omim_id_list) > 1:
# the disease ID is an OMIM ID and there is more than one OMIM entry in omim_ids.
# Currently no entries satisfy this condition
pass
elif disease_id != ('OMIM:'+omim_ids):
# the disease ID is an OMIM ID and there is only one non-equiv OMIM entry in omim_ids
# we preferentially use the disease_id here
logger.warn("There may be alternate identifier for %s: %s", disease_id, omim_ids)
# TODO: What should be done with the alternate disease IDs?
else:
if len(omim_id_list) == 1:
# the disease ID is not an OMIM ID and there is only one OMIM entry in omim_ids.
preferred_disease_id = 'OMIM:'+omim_ids
elif len(omim_id_list) > 1:
# This is when the disease ID is not an OMIM ID and there is more than one OMIM entry in omim_ids.
pass
# we actually want the association between the gene and the disease to be via an alternate locus
# not the "wildtype" gene itself.
# so we make an anonymous alternate locus, and put that in the association.
alt_locus = '_'+gene_id+'-'+preferred_disease_id+'VL'
alt_locus = re.sub(':', '', alt_locus) # can't have colons in the bnodes
if self.nobnodes:
alt_locus = ':'+alt_locus
alt_label = 'some variant of '+gene_symbol+' that is '+direct_evidence+' for '+disease_name
self.gu.addIndividualToGraph(self.g, alt_locus, alt_label, self.geno.genoparts['variant_locus'])
self.gu.addClassToGraph(self.g, gene_id, None) # assume that the label gets added elsewhere
self.geno.addAlleleOfGene(alt_locus, gene_id)
# not sure if MESH is getting added separately. adding labels here for good measure
dlabel = None
if re.match('MESH', preferred_disease_id):
dlabel = disease_name
self.gu.addClassToGraph(self.g, preferred_disease_id, dlabel)
# Add the disease to gene relationship.
rel_id = self._get_relationship_id(direct_evidence)
refs = self._process_pubmed_ids(pubmed_ids)
self._make_association(alt_locus, preferred_disease_id, rel_id, refs)
return
def _make_association(self, subject_id, object_id, rel_id, pubmed_ids):
"""
Make a reified association given an array of pubmed identifiers.
Args:
:param subject_id id of the subject of the association (gene/chem)
:param object_id id of the object of the association (disease)
:param rel_id relationship id
:param pubmed_ids an array of pubmed identifiers
Returns:
:return None
"""
# TODO pass in the relevant Assoc class rather than relying on G2P
assoc = G2PAssoc(self.name, subject_id, object_id, rel_id)
if pubmed_ids is not None and len(pubmed_ids) > 0:
eco = self._get_evidence_code('TAS')
for pmid in pubmed_ids:
r = Reference(pmid, Reference.ref_types['journal_article'])
r.addRefToGraph(self.g)
assoc.add_source(pmid)
assoc.add_evidence(eco)
assoc.add_association_to_graph(self.g)
assoc.load_all_properties(self.g)
return
@staticmethod
def _process_pubmed_ids(pubmed_ids):
"""
Take a list of pubmed IDs and add PMID prefix
Args:
:param pubmed_ids - string representing publication
ids seperated by a | symbol
Returns:
:return list: Pubmed curies
"""
if pubmed_ids.strip() == '':
id_list = []
else:
id_list = pubmed_ids.split('|')
for (i, val) in enumerate(id_list):
id_list[i] = 'PMID:'+val
return id_list
@staticmethod
def _get_evidence_code(evidence):
"""
Get curie for evidence class label
Args:
:param evidence (str): evidence label
Label:
:return str: curie for evidence label from ECO
"""
eco_map = {
'TAS': 'ECO:0000033'
}
return eco_map[evidence]
@staticmethod
def _get_relationship_id(rel):
"""
Get curie from relationship property label
Args:
:param rel (str): relationship label
Returns:
:return str: curie for relationship label
"""
gu = GraphUtils(curie_map.get())
rel_map = {
'therapeutic': gu.object_properties['substance_that_treats'],
'marker/mechanism': gu.object_properties['is_marker_for'],
}
return str(rel_map[rel])
@staticmethod
def _get_class_id(cls):
"""
Fet curie from CLASS_MAP dictionary
Args:
:param cls (str): class label
Returns:
:return str: curie for class label
"""
class_map = {
'pathway': 'PW:0000001',
'signal transduction': 'GO:0007165'
}
return class_map[cls]
def _parse_curated_chem_disease(self, limit):
line_counter = 0
file_path = '/'.join((self.rawdir, self.static_files['publications']['file']))
gu = GraphUtils(curie_map.get())
with open(file_path, 'r') as tsvfile:
reader = csv.reader(tsvfile, delimiter="\t")
for row in reader:
# catch comment lines
if re.match('^#', ' '.join(row)):
continue
line_counter += 1
self._check_list_len(row, 10)
(pub_id, disease_label, disease_id, disease_cat, evidence,
chem_label, chem_id, cas_rn, gene_symbol, gene_acc) = row
rel_id = self._get_relationship_id(evidence)
chem_id = 'MESH:'+chem_id
gu.addClassToGraph(self.g, chem_id, chem_label)
gu.addClassToGraph(self.g, disease_id, None)
if pub_id != '':
pub_id = 'PMID:'+pub_id
r = Reference(pub_id, Reference.ref_types['journal_article'])
r.addRefToGraph(self.g)
else:
pub_id = None
self._make_association('MESH:'+chem_id, disease_id, rel_id, ['PMID:'+pub_id])
if not self.testMode and limit is not None and line_counter >= limit:
break
return
def getTestSuite(self):
import unittest
from tests.test_ctd import CTDTestCase
test_suite = unittest.TestLoader().loadTestsFromTestCase(CTDTestCase)
# test_suite.addTests(unittest.TestLoader().loadTestsFromTestCase(InteractionsTestCase))
return test_suite
class UCSCBands(Source):
"""
This will take the UCSC defintions of cytogenic bands and create the nested
structures to enable overlap and containment queries. We use
```Monochrom.py``` to create the OWL-classes of the chromosomal parts.
Here, we simply worry about the instance-level values for particular genome
builds.
Given a chr band definition, the nested containment structures look like:
13q21.31 ==> 13q21.31, 13q21.3, 13q21, 13q2, 13q, 13
We determine the containing regions of the band by parsing the band-string;
since each alphanumeric is a significant "place", we can split it
with the shorter strings being parents of the longer string
Here we create build-specific chroms, which are instances of the classes
produced from ```Monochrom.py```.
You can instantiate any number of builds for a genome.
We leverage the Faldo model here for region definitions,
and map each of the chromosomal parts to SO.
We differentiate the build by adding the build id to the identifier prior
to the chromosome number.
These then are instances of the species-specific chromosomal class.
The build-specific chromosomes are created like:
<pre>
<build number>chr<num><band>
with triples for a given band like:
:hg19chr1p36.33 rdf[type] SO:chromosome_band, faldo:Region, CHR:9606chr1p36.33
:hg19chr1p36.33 subsequence_of :hg19chr1p36.3
:hg19chr1p36.33 faldo:location
[ a faldo:BothStrandPosition
faldo:begin 0,
faldo:end 2300000,
faldo:reference 'hg19']
</pre>
where any band in the file is an instance of a chr_band
(or a more specific type), is a subsequence of it's containing region, \
and is located in the specified coordinates.
We do not have a separate graph for testing.
TODO: any species by commandline argument
"""
files = {
# TODO accommodate multiple builds per species
'9606': {
'file': 'hg19cytoBand.txt.gz',
'url': 'http://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/cytoBand.txt.gz',
'build_num': 'hg19',
'genome_label': 'Human'
},
'10090': {
'file': 'mm10cytoBand.txt.gz',
'url': 'http://hgdownload.cse.ucsc.edu/goldenPath/mm10/database/cytoBandIdeo.txt.gz',
'build_num': 'mm10',
'genome_label': 'Mouse'
},
# Note that there are no bands,
# arms or staining components for the species below at the moment
'7955': {
'file': 'danRer10cytoBand.txt.gz',
'url': 'http://hgdownload.cse.ucsc.edu/goldenPath/danRer10/database/cytoBandIdeo.txt.gz',
'build_num': 'danRer10',
'genome_label': 'Zebrafish'
},
'9913': {
'file': 'bosTau7cytoBand.txt.gz',
'url': 'http://hgdownload.cse.ucsc.edu/goldenPath/bosTau7/database/cytoBandIdeo.txt.gz',
'build_num': 'bosTau7',
'genome_label': 'cow'
},
'9031': {
'file': 'galGal4cytoBand.txt.gz',
'url': 'http://hgdownload.cse.ucsc.edu/goldenPath/galGal4/database/cytoBandIdeo.txt.gz',
'build_num': 'galGal4',
'genome_label': 'chicken'
},
'9823': {
'file': 'susScr3cytoBand.txt.gz',
'url': 'http://hgdownload.cse.ucsc.edu/goldenPath/susScr3/database/cytoBandIdeo.txt.gz',
'build_num': 'susScr3',
'genome_label': 'pig'
},
'9940': {
'file': 'oviAri3cytoBand.txt.gz',
'url': 'http://hgdownload.cse.ucsc.edu/goldenPath/oviAri3/database/cytoBandIdeo.txt.gz',
'build_num': 'oviAri3',
'genome_label': 'sheep'
},
'9796': {
'file': 'equCab2cytoBand.txt.gz',
'url': 'http://hgdownload.cse.ucsc.edu/goldenPath/equCab2/database/cytoBandIdeo.txt.gz',
'build_num': 'equCab2',
'genome_label': 'horse'
},
# TODO rainbow trout, 8022, when available
}
def __init__(self, tax_ids=None):
super().__init__('ucscbands')
self.tax_ids = tax_ids
self.load_bindings()
self.gu = GraphUtils(curie_map.get())
# Defaults
if self.tax_ids is None:
# self.tax_ids = [9606, 10090, 7955]
self.tax_ids = [9606, 10090, 7955, 9913, 9031, 9823, 9940, 9796]
# TODO add other species as defaults
self._check_tax_ids()
self.dataset = Dataset('ucscbands', 'UCSC Cytogenic Bands',
'http://hgdownload.cse.ucsc.edu', None,
'http://genome.ucsc.edu/license/')
# data-source specific warnings
# (will be removed when issues are cleared)
return
def fetch(self, is_dl_forced=False):
self.get_files(is_dl_forced)
return
def parse(self, limit=None):
if limit is not None:
logger.info("Only parsing first %d rows", limit)
logger.info("Parsing files...")
if self.testOnly:
self.testMode = True
for taxon in self.tax_ids:
self._get_chrbands(limit, str(taxon))
self._create_genome_builds()
self.load_core_bindings()
self.load_bindings()
# using the full graph as the test here
self.testgraph = self.graph
logger.info("Found %d nodes", len(self.graph))
logger.info("Done parsing files.")
return
def _get_chrbands(self, limit, taxon):
"""
:param limit:
:return:
"""
# 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()
# 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
self.gu.addClassToGraph(self.graph, taxon_id, None)
self.gu.addSynonym(self.graph, taxon_id, genome_label)
self.gu.loadObjectProperties(self.graph, Feature.object_properties)
self.gu.loadProperties(self.graph, Feature.data_properties,
self.gu.DATAPROP)
self.gu.loadAllProperties(self.graph)
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']:
self.gu.addClassToGraph(self.graph, band_class_id,
band_class_label, myband['type'])
bfeature = Feature(band_build_id, band_build_label,
band_class_id)
else:
bfeature = Feature(band_build_id, band_build_label,
myband['type'])
if 'synonym' in myband:
self.gu.addSynonym(self.graph, 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(self.graph,
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 TEC I recall 'has_staining_intensity' being dropped by MB
bfeature.addFeatureProperty(self.graph,
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(self.graph, False)
return
def _create_genome_builds(self):
"""
Various resources will map variations to either UCSC (hg*)
or to NCBI assemblies. Here we create the equivalences between them.
Data taken from:
https://genome.ucsc.edu/FAQ/FAQreleases.html#release1
:return:
"""
# TODO add more species
ucsc_assembly_id_map = {
"9606": {
"UCSC:hg38": "NCBIGenome:GRCh38",
"UCSC:hg19": "NCBIGenome:GRCh37",
"UCSC:hg18": "NCBIGenome:36.1",
"UCSC:hg17": "NCBIGenome:35",
"UCSC:hg16": "NCBIGenome:34",
"UCSC:hg15": "NCBIGenome:33",
},
"7955": {
"UCSC:danRer10": "NCBIGenome:GRCz10",
"UCSC:danRer7": "NCBIGenome:Zv9",
"UCSC:danRer6": "NCBIGenome:Zv8",
},
"10090": {
"UCSC:mm10": "NCBIGenome:GRCm38",
"UCSC:mm9": "NCBIGenome:37"
},
"9031": {
"UCSC:galGal4": "NCBIAssembly:317958",
},
"9913": {
"UCSC:bosTau7": "NCBIAssembly:GCF_000003205.5",
},
"9823": {
"UCSC:susScr3": "NCBIAssembly:304498",
},
"9940": {
"UCSC:oviAri3": "NCBIAssembly:GCF_000298735.1",
},
"9796": {
"UCSC:equCab2": "NCBIAssembly:GCF_000002305.2",
}
}
g = self.graph
geno = Genotype(g)
logger.info("Adding equivalent assembly identifiers")
for sp in ucsc_assembly_id_map:
tax_num = sp
tax_id = 'NCBITaxon:'+tax_num
mappings = ucsc_assembly_id_map[sp]
for i in mappings:
ucsc_id = i
ucsc_label = re.split(':', i)[1]
mapped_id = mappings[i]
mapped_label = re.split(':', mapped_id)[1]
mapped_label = 'NCBI build '+str(mapped_label)
geno.addReferenceGenome(ucsc_id, ucsc_label, tax_id)
geno.addReferenceGenome(mapped_id, mapped_label, tax_id)
self.gu.addSameIndividual(g, ucsc_id, mapped_id)
return
def _check_tax_ids(self):
for taxon in self.tax_ids:
if str(taxon) not in self.files:
raise Exception("Taxon " + str(taxon) + " not supported"
" by source UCSCBands")
def getTestSuite(self):
import unittest
from tests.test_ucscbands import UCSCBandsTestCase
test_suite = unittest.TestLoader().loadTestsFromTestCase(UCSCBandsTestCase)
return test_suite
def _process_genes(self, limit=None):
gu = GraphUtils(curie_map.get())
if self.testMode:
g = self.testgraph
else:
g = self.graph
geno = Genotype(g)
raw = '/'.join((self.rawdir, self.files['genes']['file']))
line_counter = 0
logger.info("Processing HGNC genes")
with open(raw, 'r', encoding="utf8") as csvfile:
filereader = csv.reader(csvfile, delimiter='\t', quotechar='\"')
for row in filereader:
(hgnc_id, symbol, name, locus_group, locus_type, status,
location, location_sortable, alias_symbol, alias_name,
prev_symbol, prev_name, gene_family, gene_family_id,
date_approved_reserved, date_symbol_changed,
date_name_changed, date_modified, entrez_id, ensembl_gene_id,
vega_id, ucsc_id, ena, refseq_accession, ccds_id, uniprot_ids,
pubmed_id, mgd_id, rgd_id, lsdb, cosmic, omim_id, mirbase,
homeodb, snornabase, bioparadigms_slc, orphanet,
pseudogene_org, horde_id, merops, imgt, iuphar,
kznf_gene_catalog, mamit_trnadb, cd, lncrnadb, enzyme_id,
intermediate_filament_db) = row
line_counter += 1
# skip header
if line_counter <= 1:
continue
if self.testMode and entrez_id != '' \
and int(entrez_id) not in self.gene_ids:
continue
if name == '':
name = None
gene_type_id = self._get_gene_type(locus_type)
gu.addClassToGraph(g, hgnc_id, symbol, gene_type_id, name)
if locus_type == 'withdrawn':
gu.addDeprecatedClass(g, hgnc_id)
if entrez_id != '':
gu.addEquivalentClass(
g, hgnc_id, 'NCBIGene:' + entrez_id)
if ensembl_gene_id != '':
gu.addEquivalentClass(
g, hgnc_id, 'ENSEMBL:' + ensembl_gene_id)
geno.addTaxon('NCBITaxon:9606', hgnc_id)
# add pubs as "is about"
if pubmed_id != '':
for p in re.split(r'\|', pubmed_id.strip()):
if str(p) != '':
gu.addTriple(
g, 'PMID:' + str(p.strip()),
gu.object_properties['is_about'], hgnc_id)
# add chr location
# sometimes two are listed, like: 10p11.2 or 17q25
# -- there are only 2 of these FRA10A and MPFD
# sometimes listed like "1 not on reference assembly"
# sometimes listed like 10q24.1-q24.3
# sometimes like 11q11 alternate reference locus
band = chrom = None
chr_pattern = r'(\d+|X|Y|Z|W|MT)[pq$]'
chr_match = re.match(chr_pattern, location)
if chr_match is not None and len(chr_match.groups()) > 0:
chrom = chr_match.group(1)
chrom_id = makeChromID(chrom, 'NCBITaxon:9606', 'CHR')
band_pattern = r'([pq][A-H\d]?\d?(?:\.\d+)?)'
band_match = re.search(band_pattern, location)
f = Feature(hgnc_id, None, None)
if band_match is not None and len(band_match.groups()) > 0:
band = band_match.group(1)
band = chrom + band
# add the chr band as the parent to this gene
# as a feature but assume that the band is created
# as a class with properties elsewhere in Monochrom
# TEC Monoch? Monarchdom??
band_id = makeChromID(band, 'NCBITaxon:9606', 'CHR')
gu.addClassToGraph(g, band_id, None)
f.addSubsequenceOfFeature(g, band_id)
else:
gu.addClassToGraph(g, chrom_id, None)
f.addSubsequenceOfFeature(g, chrom_id)
if not self.testMode \
and limit is not None and line_counter > limit:
break
# end loop through file
gu.loadProperties(g, Feature.object_properties, gu.OBJPROP)
gu.loadProperties(g, Feature.data_properties, gu.DATAPROP)
gu.loadProperties(g, Genotype.object_properties, gu.OBJPROP)
gu.loadAllProperties(g)
return
def _get_variants(self, limit):
"""
Currently loops through the variant_summary file.
:param limit:
:return:
"""
gu = GraphUtils(curie_map.get())
if self.testMode:
g = self.testgraph
else:
g = self.graph
geno = Genotype(g)
gu.loadAllProperties(g)
f = Feature(None, None, None)
f.loadAllProperties(g)
gu.loadAllProperties(g)
# add the taxon and the genome
tax_num = '9606' # HARDCODE
tax_id = 'NCBITaxon:'+tax_num
tax_label = 'Human'
gu.addClassToGraph(g, tax_id, None)
geno.addGenome(tax_id, None) # 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('^#', 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 = 28
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) = 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('^[;,]', '', phenotype_ids)
phenotype_ids = re.sub('[;,]$', '', phenotype_ids)
pheno_list = re.split('[,;]', 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 approximate location
# strangely, they still put an assembly number even when there's no numeric location
if not re.search('-',str(cytogenetic_loc)):
band_id = makeChromID(re.split('-',str(cytogenetic_loc)), tax_num, 'CHR')
geno.addChromosomeInstance(cytogenetic_loc, build_id, assembly, band_id)
bandinbuild_id = makeChromID(re.split('-',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
if str(gene_num) != '-1' and str(gene_num) != 'more than 10': # they use -1 to indicate unknown gene
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(g)
if bandinbuild_id is not None:
f.addSubsequenceOfFeature(g, 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() != '':
gu.addSynonym(g, seqalt_id, hgvs_c)
if hgvs_p != '-' and hgvs_p.strip() != '':
gu.addSynonym(g, 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)
gu.addIndividualToGraph(g, dbsnp_id, None)
gu.addSameIndividual(g, seqalt_id, dbsnp_id)
if dbvar_num != '-':
dbvar_id = 'dbVar:'+dbvar_num
gu.addIndividualToGraph(g, dbvar_id, None)
gu.addSameIndividual(g, 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(';',rcv_nums):
rcv_id = 'ClinVar:'+rcv_num
gu.addIndividualToGraph(g, rcv_id, None)
gu.addXref(g, seqalt_id, rcv_id)
if gene_id is not None:
# add the gene
gu.addClassToGraph(g, 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
gu.addIndividualToGraph(g, 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('\(\w+\)', 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('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 p in pheno_list:
m = re.match("(Orphanet:ORPHA(?:\s*ORPHA)?)", p)
if m is not None and len(m.groups()) > 0:
p = re.sub(m.group(1), 'Orphanet:', p.strip())
elif re.match('SNOMED CT', p):
p = re.sub('SNOMED CT', 'SNOMED', p.strip())
assoc = G2PAssoc(self.name, seqalt_id, p.strip())
assoc.add_association_to_graph(g)
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]
gu.addIndividualToGraph(g, xrefid, None)
gu.addSameIndividual(g, seqalt_id, xrefid)
elif prefix == 'HGMD':
gu.addIndividualToGraph(g, xrefid, None)
gu.addSameIndividual(g, seqalt_id, xrefid)
elif prefix == 'dbVar' and dbvar_num == xrefid.split(':')[1].strip():
pass # skip over this one
elif re.search('\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
gu.loadProperties(g, G2PAssoc.object_properties, gu.OBJPROP)
gu.loadProperties(g, G2PAssoc.annotation_properties, gu.ANNOTPROP)
gu.loadProperties(g, G2PAssoc.datatype_properties, gu.DATAPROP)
logger.info("Finished parsing variants")
return
