Python Genotype.addTaxon方法代码示例

# 需要导入模块: from dipper.models.Genotype import Genotype [as 别名]
# 或者: from dipper.models.Genotype.Genotype import addTaxon [as 别名]
    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

# 需要导入模块: from dipper.models.Genotype import Genotype [as 别名]
# 或者: from dipper.models.Genotype.Genotype import addTaxon [as 别名]
    def process_gene_ids(self, limit):
        raw = '/'.join((self.rawdir, self.files['gene_ids']['file']))

        if self.testMode:
            g = self.testgraph
        else:
            g = self.graph

        model = Model(g)
        logger.info("Processing: %s", self.files['gene_ids']['file'])
        line_counter = 0
        geno = Genotype(g)
        with gzip.open(raw, 'rb') as csvfile:
            filereader = csv.reader(
                io.TextIOWrapper(csvfile, newline=""), delimiter=',',
                quotechar='\"')
            for row in filereader:
                line_counter += 1
                (taxon_num,
                 gene_num,
                 gene_symbol,
                 gene_synonym,
                 live,
                 gene_type) = row
                # 6239,WBGene00000001,aap-1,Y110A7A.10,Live,protein_coding_gene

                if self.testMode and gene_num not in self.test_ids['gene']:
                    continue

                taxon_id = 'NCBITaxon:'+taxon_num
                gene_id = 'WormBase:'+gene_num
                if gene_symbol == '':
                    gene_symbol = gene_synonym
                if gene_symbol == '':
                    gene_symbol = None
                model.addClassToGraph(
                    gene_id, gene_symbol, Genotype.genoparts['gene'])
                if live == 'Dead':
                    model.addDeprecatedClass(gene_id)
                geno.addTaxon(taxon_id, gene_id)
                if gene_synonym != '' and gene_synonym is not None:
                    model.addSynonym(gene_id, gene_synonym)

                if not self.testMode \
                        and limit is not None and line_counter > limit:
                    break

        return

# 需要导入模块: from dipper.models.Genotype import Genotype [as 别名]
# 或者: from dipper.models.Genotype.Genotype import addTaxon [as 别名]
    def _process_qtls_genomic_location(
            self, raw, txid, build_id, build_label, common_name, limit=None):
        """
        This method

        Triples created:

        :param limit:
        :return:
        """
        if self.test_mode:
            graph = self.testgraph
        else:
            graph = self.graph
        model = Model(graph)
        line_counter = 0
        geno = Genotype(graph)
        # assume that chrs get added to the genome elsewhere

        taxon_curie = 'NCBITaxon:' + txid
        eco_id = self.globaltt['quantitative trait analysis evidence']
        LOG.info("Processing QTL locations for %s from %s", taxon_curie, raw)
        with gzip.open(raw, 'rt', encoding='ISO-8859-1') as tsvfile:
            reader = csv.reader(tsvfile, delimiter="\t")
            for row in reader:
                line_counter += 1
                if re.match(r'^#', ' '.join(row)):
                    continue

                (chromosome, qtl_source, qtl_type, start_bp, stop_bp, frame, strand,
                 score, attr) = row
                example = '''
Chr.Z   Animal QTLdb    Production_QTL  33954873      34023581...
QTL_ID=2242;Name="Spleen percentage";Abbrev="SPLP";PUBMED_ID=17012160;trait_ID=2234;
trait="Spleen percentage";breed="leghorn";"FlankMarkers=ADL0022";VTO_name="spleen mass";
MO_name="spleen weight to body weight ratio";Map_Type="Linkage";Model="Mendelian";
Test_Base="Chromosome-wise";Significance="Significant";P-value="<0.05";F-Stat="5.52";
Variance="2.94";Dominance_Effect="-0.002";Additive_Effect="0.01
                '''
                str(example)
                # make dictionary of attributes
                # keys are:
                # QTL_ID,Name,Abbrev,PUBMED_ID,trait_ID,trait,FlankMarkers,
                # VTO_name,Map_Type,Significance,P-value,Model,
                # Test_Base,Variance, Bayes-value,PTO_name,gene_IDsrc,peak_cM,
                # CMO_name,gene_ID,F-Stat,LOD-score,Additive_Effect,
                # Dominance_Effect,Likelihood_Ratio,LS-means,Breed,
                # trait (duplicate with Name),Variance,Bayes-value,
                # F-Stat,LOD-score,Additive_Effect,Dominance_Effect,
                # Likelihood_Ratio,LS-means

                # deal with poorly formed attributes
                if re.search(r'"FlankMarkers";', attr):
                    attr = re.sub(r'FlankMarkers;', '', attr)
                attr_items = re.sub(r'"', '', attr).split(";")
                bad_attrs = set()
                for attributes in attr_items:
                    if not re.search(r'=', attributes):
                        # remove this attribute from the list
                        bad_attrs.add(attributes)

                attr_set = set(attr_items) - bad_attrs
                attribute_dict = dict(item.split("=") for item in attr_set)

                qtl_num = attribute_dict.get('QTL_ID')
                if self.test_mode and int(qtl_num) not in self.test_ids:
                    continue
                # make association between QTL and trait based on taxon

                qtl_id = common_name + 'QTL:' + str(qtl_num)
                model.addIndividualToGraph(qtl_id, None, self.globaltt['QTL'])
                geno.addTaxon(taxon_curie, qtl_id)

                #
                trait_id = 'AQTLTrait:' + attribute_dict.get('trait_ID')

                # if pub is in attributes, add it to the association
                pub_id = None
                if 'PUBMED_ID' in attribute_dict.keys():
                    pub_id = attribute_dict.get('PUBMED_ID')
                    if re.match(r'ISU.*', pub_id):
                        pub_id = 'AQTLPub:' + pub_id.strip()
                        reference = Reference(graph, pub_id)
                    else:
                        pub_id = 'PMID:' + pub_id.strip()
                        reference = Reference(
                            graph, pub_id, self.globaltt['journal article'])
                    reference.addRefToGraph()

                # Add QTL to graph
                assoc = G2PAssoc(
                    graph, self.name, qtl_id, trait_id,
                    self.globaltt['is marker for'])
                assoc.add_evidence(eco_id)
                assoc.add_source(pub_id)
                if 'P-value' in attribute_dict.keys():
                    scr = re.sub(r'<', '', attribute_dict.get('P-value'))
                    if ',' in scr:
                        scr = re.sub(r',', '.', scr)
                    if scr.isnumeric():
#.........这里部分代码省略.........

# 需要导入模块: from dipper.models.Genotype import Genotype [as 别名]
# 或者: from dipper.models.Genotype.Genotype import addTaxon [as 别名]

#.........这里部分代码省略.........
                # ucsc_id = row[col.index('ucsc_id')]
                # ena = row[col.index('ena')]
                # refseq_accession = row[col.index('refseq_accession')]
                # ccds_id = row[col.index('ccds_id')]
                # uniprot_ids = row[col.index('uniprot_ids')]
                pubmed_ids = row[col.index('pubmed_id')].strip()  # pipe seperated!
                # mgd_id = row[col.index('mgd_id')]
                # rgd_id = row[col.index('rgd_id')]
                # lsdb = row[col.index('lsdb')]
                # cosmic = row[col.index('cosmic')]
                omim_ids = row[col.index('omim_id')].strip()  # pipe seperated!
                # mirbase = row[col.index('mirbase')]
                # homeodb = row[col.index('homeodb')]
                # snornabase = row[col.index('snornabase')]
                # bioparadigms_slc = row[col.index('bioparadigms_slc')]
                # orphanet = row[col.index('orphanet')]
                # pseudogene.org = row[col.index('pseudogene.org')]
                # horde_id = row[col.index('horde_id')]
                # merops = row[col.index('merops')]
                # imgt = row[col.index('imgt')]
                # iuphar = row[col.index('iuphar')]
                # kznf_gene_catalog = row[col.index('kznf_gene_catalog')]
                # mamit_trnadb = row[col.index('mamit-trnadb')]
                # cd = row[col.index('cd')]
                # lncrnadb = row[col.index('lncrnadb')]
                # enzyme_id = row[col.index('enzyme_id')]
                # intermediate_filament_db = row[col.index('intermediate_filament_db')]
                # rna_central_ids = row[col.index('rna_central_ids')]
                # lncipedia = row[col.index('lncipedia')]
                # gtrnadb = row[col.index('gtrnadb')]

                if self.test_mode and entrez_id != '' and \
                        entrez_id not in self.gene_ids:
                    continue

                if name == '':
                    name = None

                if locus_type == 'withdrawn':
                    model.addDeprecatedClass(hgnc_id)
                else:
                    gene_type_id = self.resolve(locus_type, False)  # withdrawn -> None?
                    if gene_type_id != locus_type:
                        model.addClassToGraph(hgnc_id, symbol, gene_type_id, name)
                    model.makeLeader(hgnc_id)

                if entrez_id != '':
                    model.addEquivalentClass(hgnc_id, 'NCBIGene:' + entrez_id)

                if ensembl_gene_id != '':
                    model.addEquivalentClass(hgnc_id, 'ENSEMBL:' + ensembl_gene_id)

                for omim_id in omim_ids.split('|'):
                    if omim_id in self.omim_replaced:
                        repl = self.omim_replaced[omim_id]
                        LOG.warning('%s is replaced with %s', omim_id, repl)
                        for omim in repl:
                            if self.omim_type[omim] == self.globaltt['gene']:
                                omim_id = omim

                    if omim_id in self.omim_type and \
                            self.omim_type[omim_id] == self.globaltt['gene']:
                        model.addEquivalentClass(hgnc_id, 'OMIM:' + omim_id)

                geno.addTaxon(self.hs_txid, hgnc_id)

                # add pubs as "is about"
                for pubmed_id in pubmed_ids.split('|'):
                    graph.addTriple(
                        'PMID:' + pubmed_id, self.globaltt['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_match = chr_pattern.match(location)
                if chr_match is not None and len(chr_match.groups()) > 0:
                    chrom = chr_match.group(1)
                    chrom_id = makeChromID(chrom, self.hs_txid, 'CHR')
                    band_match = band_pattern.search(location)
                    feat = Feature(graph, 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
                        band_id = makeChromID(band, self.hs_txid, 'CHR')
                        model.addClassToGraph(band_id, None)
                        feat.addSubsequenceOfFeature(band_id)
                    else:
                        model.addClassToGraph(chrom_id, None)
                        feat.addSubsequenceOfFeature(chrom_id)

                if not self.test_mode and limit is not None and \
                        filereader.line_num > limit:
                    break

# 需要导入模块: from dipper.models.Genotype import Genotype [as 别名]
# 或者: from dipper.models.Genotype.Genotype import addTaxon [as 别名]
    def _process_haplotype(
            self, hap_id, hap_label, chrom_num, chrom_pos, context,
            risk_allele_frequency, mapped_gene, so_ontology):
        tax_id = 'NCBITaxon:9606'

        if self.testMode:
            g = self.testgraph
        else:
            g = self.graph
        geno = Genotype(g)
        model = Model(g)
        # add the feature to the graph
        hap_description = None
        if risk_allele_frequency != '' and \
                risk_allele_frequency != 'NR':
            hap_description = \
                str(risk_allele_frequency) + \
                ' [risk allele frequency]'

        model.addIndividualToGraph(hap_id, hap_label.strip(),
                                   Feature.types['haplotype'], hap_description)
        geno.addTaxon(tax_id, hap_id)

        snp_labels = re.split(r';\s?', hap_label)
        chrom_nums = re.split(r';\s?', chrom_num)
        chrom_positions = re.split(r';\s?', chrom_pos)
        context_list = re.split(r';\s?', context)
        mapped_genes = re.split(r';\s?', mapped_gene)
        snp_curies = list()

        for index, snp in enumerate(snp_labels):
            snp_curie, snp_type = self._get_curie_and_type_from_id(snp)
            if snp_type is None:
                # make blank node
                snp_curie = self.make_id(snp, "_")

            g.addTriple(hap_id, geno.object_properties['has_variant_part'],
                        snp_curie)
            snp_curies.append(snp_curie)

        # courtesy http://stackoverflow.com/a/16720915
        length = len(snp_labels)
        if not all(len(lst) == length
                   for lst in [chrom_nums, chrom_positions, context_list]):
            logger.warn(
                "Unexpected data field for haplotype {} \n "
                "will not add snp details".format(hap_label))
            return

        variant_in_gene_count = 0
        for index, snp_curie in enumerate(snp_curies):
            self._add_snp_to_graph(
                snp_curie, snp_labels[index], chrom_nums[index],
                chrom_positions[index], context_list[index])

            if len(mapped_genes) == len(snp_labels):

                so_class = self._map_variant_type(context_list[index])

                if so_class is None:
                    raise ValueError("Unknown SO class {} in haplotype {}"
                                     .format(context_list[index], hap_label))
                so_query = """
                    SELECT ?variant_label
                    WHERE {{
                        {0} rdfs:subClassOf+ SO:0001564 ;
                            rdfs:label ?variant_label .
                    }}
                """.format(so_class)

                query_result = so_ontology.query(so_query)
                if len(list(query_result)) > 0:
                    gene_id = DipperUtil.get_ncbi_id_from_symbol(
                        mapped_genes[index])
                    if gene_id is not None:
                        geno.addAffectedLocus(snp_curie, gene_id)
                        geno.addAffectedLocus(hap_id, gene_id)
                        variant_in_gene_count += 1

                if context_list[index] == 'upstream_gene_variant':
                    gene_id = DipperUtil.get_ncbi_id_from_symbol(
                        mapped_genes[index])
                    if gene_id is not None:
                        g.addTriple(
                            snp_curie,
                            Feature.object_properties[
                                'upstream_of_sequence_of'],
                            gene_id)
                elif context_list[index] == 'downstream_gene_variant':
                    gene_id = DipperUtil.get_ncbi_id_from_symbol(
                        mapped_genes[index])
                    if gene_id is not None:
                        g.addTriple(
                            snp_curie,
                            Feature.object_properties[
                                'downstream_of_sequence_of'],
                            gene_id)
            else:
                logger.warn("More mapped genes than snps, "
                            "cannot disambiguate for {}".format(hap_label))
#.........这里部分代码省略.........

# 需要导入模块: from dipper.models.Genotype import Genotype [as 别名]
# 或者: from dipper.models.Genotype.Genotype import addTaxon [as 别名]
    def _process_haplotype(
            self, hap_id, hap_label, chrom_num, chrom_pos, context,
            risk_allele_frequency, mapped_gene, so_ontology):

        if self.test_mode:
            graph = self.testgraph
        else:
            graph = self.graph
        geno = Genotype(graph)
        model = Model(graph)
        # add the feature to the graph
        hap_description = None
        if risk_allele_frequency != '' and risk_allele_frequency != 'NR':
            hap_description = str(risk_allele_frequency) + ' [risk allele frequency]'

        model.addIndividualToGraph(
            hap_id, hap_label.strip(), self.globaltt['haplotype'], hap_description)
        geno.addTaxon(self.globaltt["Homo sapiens"], hap_id)

        snp_labels = re.split(r';\s?', hap_label)
        chrom_nums = re.split(r';\s?', chrom_num)
        chrom_positions = re.split(r';\s?', chrom_pos)
        context_list = re.split(r';\s?', context)
        mapped_genes = re.split(r';\s?', mapped_gene)
        snp_curies = list()

        for index, snp in enumerate(snp_labels):
            snp_curie, snp_type = self._get_curie_and_type_from_id(snp)
            if snp_type is None:
                # make blank node
                snp_curie = self.make_id(snp, "_")

            graph.addTriple(hap_id, self.globaltt['has_variant_part'], snp_curie)
            snp_curies.append(snp_curie)

        # courtesy http://stackoverflow.com/a/16720915
        length = len(snp_labels)
        if not all(len(lst) == length
                   for lst in [chrom_nums, chrom_positions, context_list]):
            LOG.warning(
                "Unexpected data field for haplotype %s \n "
                "will not add snp details", hap_label)
            return

        variant_in_gene_count = 0
        for index, snp_curie in enumerate(snp_curies):
            self._add_snp_to_graph(
                snp_curie, snp_labels[index], chrom_nums[index],
                chrom_positions[index], context_list[index])

            if len(mapped_genes) == len(snp_labels):
                so_class = self.resolve(context_list[index])
                # removed the '+' for recursive  one-or-more rdfs:subClassOf  paths
                # just so it did not return an empty graph
                so_query = """
SELECT ?variant_label
    WHERE {{
        {0} rdfs:subClassOf {1} ;
        rdfs:label ?variant_label .
    }}
                """.format(so_class, self.globaltt['gene_variant'])

                query_result = so_ontology.query(so_query)

                if len(list(query_result)) == 1:
                    gene_id = DipperUtil.get_ncbi_id_from_symbol(mapped_genes[index])

                    if gene_id is not None:
                        geno.addAffectedLocus(snp_curie, gene_id)
                        geno.addAffectedLocus(hap_id, gene_id)
                        variant_in_gene_count += 1

                gene_id = DipperUtil.get_ncbi_id_from_symbol(mapped_genes[index])
                if gene_id is not None:
                    graph.addTriple(
                        snp_curie, self.resolve(context_list[index]), gene_id)

            else:
                LOG.warning(
                    "More mapped genes than snps, cannot disambiguate for %s",
                    hap_label)

        # Seperate in case we want to apply a different relation
        # If not this is redundant with triples added above
        if len(mapped_genes) == variant_in_gene_count and len(set(mapped_genes)) == 1:
            gene_id = DipperUtil.get_ncbi_id_from_symbol(mapped_genes[0])
            geno.addAffectedLocus(hap_id, gene_id)

        return

# 需要导入模块: from dipper.models.Genotype import Genotype [as 别名]
# 或者: from dipper.models.Genotype.Genotype import addTaxon [as 别名]

#.........这里部分代码省略.........
                        limit is not None and reader.line_num > limit):
                    break

            # now that we've collected all of the variant information, build it
            # we don't know their zygosities
            for s in self.strain_hash:
                h = self.strain_hash.get(s)
                variants = h['variants']
                genes = h['genes']
                vl_set = set()
                # make variant loci for each gene
                if len(variants) > 0:
                    for var in variants:
                        vl_id = var.strip()
                        vl_symbol = self.id_label_hash[vl_id]
                        geno.addAllele(
                            vl_id, vl_symbol, self.globaltt['variant_locus'])
                        vl_set.add(vl_id)
                        if len(variants) == 1 and len(genes) == 1:
                            for gene in genes:
                                geno.addAlleleOfGene(vl_id, gene)
                        else:
                            geno.addAllele(vl_id, vl_symbol)
                else:  # len(vars) == 0
                    # it's just anonymous variants in some gene
                    for gene in genes:
                        vl_id = '_:' + re.sub(r':', '', gene) + '-VL'
                        vl_symbol = self.id_label_hash[gene]+'<?>'
                        self.id_label_hash[vl_id] = vl_symbol
                        geno.addAllele(
                            vl_id, vl_symbol, self.globaltt['variant_locus'])
                        geno.addGene(gene, self.id_label_hash[gene])
                        geno.addAlleleOfGene(vl_id, gene)
                        vl_set.add(vl_id)

                # make the vslcs
                vl_list = sorted(vl_set)
                vslc_list = []
                for vl in vl_list:
                    # for unknown zygosity
                    vslc_id = re.sub(r'^_', '', vl)+'U'
                    vslc_id = re.sub(r':', '', vslc_id)
                    vslc_id = '_:' + vslc_id
                    vslc_label = self.id_label_hash[vl] + '/?'
                    self.id_label_hash[vslc_id] = vslc_label
                    vslc_list.append(vslc_id)
                    geno.addPartsToVSLC(
                        vslc_id, vl, None, self.globaltt['indeterminate'],
                        self.globaltt['has_variant_part'], None)
                    model.addIndividualToGraph(
                        vslc_id, vslc_label,
                        self.globaltt['variant single locus complement'])
                if len(vslc_list) > 0:
                    if len(vslc_list) > 1:
                        gvc_id = '-'.join(vslc_list)
                        gvc_id = re.sub(r'_|:', '', gvc_id)
                        gvc_id = '_:'+gvc_id
                        gvc_label = '; '.join(self.id_label_hash[v] for v in vslc_list)
                        model.addIndividualToGraph(
                            gvc_id, gvc_label,
                            self.globaltt['genomic_variation_complement'])
                        for vslc_id in vslc_list:
                            geno.addVSLCtoParent(vslc_id, gvc_id)
                    else:
                        # the GVC == VSLC, so don't have to make an extra piece
                        gvc_id = vslc_list.pop()
                        gvc_label = self.id_label_hash[gvc_id]

                    genotype_label = gvc_label + ' [n.s.]'
                    bkgd_id = re.sub(
                        r':', '', '-'.join((
                            self.globaltt['unspecified_genomic_background'], s)))
                    genotype_id = '-'.join((gvc_id, bkgd_id))
                    bkgd_id = '_:' + bkgd_id
                    geno.addTaxon(mouse_taxon, bkgd_id)
                    geno.addGenomicBackground(
                        bkgd_id, 'unspecified (' + s + ')',
                        self.globaltt['unspecified_genomic_background'],
                        "A placeholder for the unspecified genetic background for " + s)
                    geno.addGenomicBackgroundToGenotype(
                        bkgd_id, genotype_id,
                        self.globaltt['unspecified_genomic_background'])
                    geno.addParts(
                        gvc_id, genotype_id, self.globaltt['has_variant_part'])
                    geno.addGenotype(genotype_id, genotype_label)
                    graph.addTriple(
                        s, self.globaltt['has_genotype'], genotype_id)
                else:
                    # LOG.debug(
                    #   "Strain %s is not making a proper genotype.", s)
                    pass

            LOG.warning(
                "The following gene symbols did not list identifiers: %s",
                str(sorted(list(genes_with_no_ids))))
            LOG.error(
                '%i symbols given are missing their gene identifiers',
                len(genes_with_no_ids))

        return

# 需要导入模块: from dipper.models.Genotype import Genotype [as 别名]
# 或者: from dipper.models.Genotype.Genotype import addTaxon [as 别名]
    def add_orthologs_by_gene_group(self, graph, gene_ids):
        """
        This will get orthologies between human and other vertebrate genomes
        based on the gene_group annotation pipeline from NCBI.
        More information 9can be learned here:
        http://www.ncbi.nlm.nih.gov/news/03-13-2014-gene-provides-orthologs-regions/
        The method for associations is described in
        [PMCID:3882889](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3882889/)
        == [PMID:24063302](http://www.ncbi.nlm.nih.gov/pubmed/24063302/).
        Because these are only between human and vertebrate genomes,
        they will certainly miss out on very distant orthologies,
        and should not be considered complete.

        We do not run this within the NCBI parser itself;
        rather it is a convenience function for others parsers to call.

        :param graph:
        :param gene_ids:  Gene ids to fetch the orthology
        :return:

        """

        logger.info("getting gene groups")
        line_counter = 0
        f = '/'.join((self.rawdir, self.files['gene_group']['file']))
        found_counter = 0
        # because many of the orthologous groups are grouped by human gene,
        # we need to do this by generating two-way hash

        # group_id => orthologs
        # ortholog id => group
        # this will be the fastest approach, though not memory-efficient.
        geno = Genotype(graph)
        model = Model(graph)
        group_to_orthology = {}
        gene_to_group = {}
        gene_to_taxon = {}

        with gzip.open(f, 'rb') as csvfile:
            filereader = csv.reader(
                io.TextIOWrapper(csvfile, newline=""),
                delimiter='\t',
                quotechar='\"')

            for row in filereader:
                # skip comment lines
                if re.match(r'\#', ''.join(row)):
                    continue
                line_counter += 1
                (tax_a, gene_a, rel, tax_b, gene_b) = row

                if rel != 'Ortholog':
                    continue

                if gene_a not in group_to_orthology:
                    group_to_orthology[gene_a] = set()
                group_to_orthology[gene_a].add(gene_b)

                if gene_b not in gene_to_group:
                    gene_to_group[gene_b] = set()
                gene_to_group[gene_b].add(gene_a)

                gene_to_taxon[gene_a] = tax_a
                gene_to_taxon[gene_b] = tax_b

                # also add the group lead as a member of the group
                group_to_orthology[gene_a].add(gene_a)

            # end loop through gene_group file
        logger.debug("Finished hashing gene groups")
        logger.debug("Making orthology associations")
        for gid in gene_ids:
            gene_num = re.sub(r'NCBIGene:', '', gid)
            group_nums = gene_to_group.get(gene_num)
            if group_nums is not None:
                for group_num in group_nums:
                    orthologs = group_to_orthology.get(group_num)
                    if orthologs is not None:
                        for o in orthologs:
                            oid = 'NCBIGene:'+str(o)
                            model.addClassToGraph(
                                oid, None, Genotype.genoparts['gene'])
                            otaxid = 'NCBITaxon:'+str(gene_to_taxon[o])
                            geno.addTaxon(otaxid, oid)
                            assoc = OrthologyAssoc(graph, self.name, gid, oid)
                            assoc.add_source('PMID:24063302')
                            assoc.add_association_to_graph()
                            # todo get gene label for orthologs -
                            # this could get expensive
                            found_counter += 1

            # finish loop through annotated genes
        logger.info(
            "Made %d orthology relationships for %d genes",
            found_counter, len(gene_ids))
        return

# 需要导入模块: from dipper.models.Genotype import Genotype [as 别名]
# 或者: from dipper.models.Genotype.Genotype import addTaxon [as 别名]

#.........这里部分代码省略.........
                    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

# 需要导入模块: from dipper.models.Genotype import Genotype [as 别名]
# 或者: from dipper.models.Genotype.Genotype import addTaxon [as 别名]

#.........这里部分代码省略.........
                # 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

# 需要导入模块: from dipper.models.Genotype import Genotype [as 别名]
# 或者: from dipper.models.Genotype.Genotype import addTaxon [as 别名]

#.........这里部分代码省略.........
                if dbase == 'UniProtKB':
                    if id_map is not None and gene_num in id_map:
                        gene_id = id_map[gene_num]
                        uniprotid = ':'.join((dbase, gene_num))
                        (dbase, gene_num) = gene_id.split(':')
                        uniprot_hit += 1
                    else:
                        # LOG.warning(
                        #   "UniProt id %s  is without a 1:1 mapping to entrez/ensembl",
                        #    gene_num)
                        uniprot_miss += 1
                        continue
                else:
                    gene_num = gene_num.split(':')[-1]  # last
                    gene_id = ':'.join((dbase, gene_num))

                if self.test_mode and not(
                        re.match(r'NCBIGene', gene_id) and
                        int(gene_num) in self.test_ids):
                    continue

                model.addClassToGraph(gene_id, gene_symbol)
                if gene_name != '':
                    model.addDescription(gene_id, gene_name)
                if gene_synonym != '':
                    for syn in re.split(r'\|', gene_synonym):
                        model.addSynonym(gene_id, syn.strip())
                if re.search(r'\|', taxon):
                    # TODO add annotations with >1 taxon
                    LOG.info(
                        ">1 taxon (%s) on line %d.  skipping", taxon, line_counter)
                else:
                    tax_id = re.sub(r'taxon:', 'NCBITaxon:', taxon)
                    geno.addTaxon(tax_id, gene_id)

                assoc = Assoc(graph, self.name)
                assoc.set_subject(gene_id)
                assoc.set_object(go_id)

                try:
                    eco_id = eco_map[eco_symbol]
                    assoc.add_evidence(eco_id)
                except KeyError:
                    LOG.error("Evidence code (%s) not mapped", eco_symbol)

                refs = re.split(r'\|', ref)
                for ref in refs:
                    ref = ref.strip()
                    if ref != '':
                        prefix = ref.split(':')[0]  # sidestep 'MGI:MGI:'
                        if prefix in self.localtt:
                            prefix = self.localtt[prefix]
                        ref = ':'.join((prefix, ref.split(':')[-1]))
                        refg = Reference(graph, ref)
                        if prefix == 'PMID':
                            ref_type = self.globaltt['journal article']
                            refg.setType(ref_type)
                        refg.addRefToGraph()
                        assoc.add_source(ref)

                # TODO add the source of the annotations from assigned by?

                rel = self.resolve(aspect, mandatory=False)
                if rel is not None and aspect == rel:
                    if aspect == 'F' and re.search(r'contributes_to', qualifier):
                        assoc.set_relationship(self.globaltt['contributes to'])

# 需要导入模块: from dipper.models.Genotype import Genotype [as 别名]
# 或者: from dipper.models.Genotype.Genotype import addTaxon [as 别名]

#.........这里部分代码省略.........
                # add embryonic onset
                assoc = D2PAssoc(self.name, omia_id, disease_id)
                assoc.add_association_to_graph(self.g)
                disease_id = None
        else:
            logger.info(
                "No disease superclass defined for %s:  %s",
                omia_id, group_name)
            # default to general disease  FIXME this may not be desired
            disease_id = 'DOID:4'

        if group_summary == '':
            group_summary = None
        if group_name == '':
            group_name = None

        self.gu.addClassToGraph(
            self.g, omia_id, group_name, disease_id, group_summary)

        self.label_hash[omia_id] = group_name

        return

    def _process_gene_row(self, row):
        if self.testMode and row['gene_id'] not in self.test_ids['gene']:
            return
        gene_id = 'NCBIGene:'+str(row['gene_id'])
        self.id_hash['gene'][row['gene_id']] = gene_id
        gene_label = row['symbol']
        self.label_hash[gene_id] = gene_label
        tax_id = 'NCBITaxon:'+str(row['gb_species_id'])
        gene_type_id = NCBIGene.map_type_of_gene(row['gene_type'])
        self.gu.addClassToGraph(self.g, gene_id, gene_label, gene_type_id)
        self.geno.addTaxon(tax_id, gene_id)

        return

    def _process_article_breed_row(self, row):
        # article_id, breed_id, added_by
        # don't bother putting these into the test... too many!

        # and int(row['breed_id']) not in self.test_ids['breed']:
        if self.testMode:
            return

        article_id = self.id_hash['article'].get(row['article_id'])
        breed_id = self.id_hash['breed'].get(row['breed_id'])

        # there's some missing data (article=6038).  in that case skip
        if article_id is not None:
            self.gu.addTriple(
                self.g, article_id, self.gu.object_properties['is_about'],
                breed_id)
        else:
            logger.warning("Missing article key %s", str(row['article_id']))

        return

    def _process_article_phene_row(self, row):
        """
        Linking articles to species-specific phenes.

        :param row:
        :return:
        """
        # article_id, phene_id, added_by

# 需要导入模块: from dipper.models.Genotype import Genotype [as 别名]
# 或者: from dipper.models.Genotype.Genotype import addTaxon [as 别名]
    def _process_genes(self, limit=None):

        if self.testMode:
            g = self.testgraph
        else:
            g = self.graph

        geno = Genotype(g)
        model = Model(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='\"')
            # curl -s ftp://ftp.ebi.ac.uk/pub/databases/genenames/new/tsv/hgnc_complete_set.txt | head -1 | tr '\t' '\n' | grep -n  .
            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,
                 rna_central_ids) = 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)
                model.addClassToGraph(hgnc_id, symbol, gene_type_id, name)
                if locus_type == 'withdrawn':
                    model.addDeprecatedClass(hgnc_id)
                else:
                    model.makeLeader(hgnc_id)
                if entrez_id != '':
                    model.addEquivalentClass(
                        hgnc_id, 'NCBIGene:' + entrez_id)
                if ensembl_gene_id != '':
                    model.addEquivalentClass(
                        hgnc_id, 'ENSEMBL:' + ensembl_gene_id)
                if omim_id != '' and "|" not in omim_id:
                    omim_curie = 'OMIM:' + omim_id
                    if not DipperUtil.is_omim_disease(omim_curie):
                        model.addEquivalentClass(hgnc_id, omim_curie)

                geno.addTaxon('NCBITaxon:9606', hgnc_id)

                # add pubs as "is about"
                if pubmed_id != '':
#.........这里部分代码省略.........

# 需要导入模块: from dipper.models.Genotype import Genotype [as 别名]
# 或者: from dipper.models.Genotype.Genotype import addTaxon [as 别名]
    def _process_genes(self, taxid, limit=None):
        if self.testMode:
            g = self.testgraph
        else:
            g = self.graph

        model = Model(g)
        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:
                    raise ValueError("Data error for file %s", raw)
                (ensembl_gene_id, external_gene_name,
                 description, gene_biotype, entrezgene,
                 peptide_id, uniprot_swissprot) = row[0:7]

                # in the case of human genes, we also get the hgnc id,
                # and is the last col
                if taxid == '9606':
                    hgnc_id = row[7]
                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
                peptide_curie = 'ENSEMBL:{}'.format(peptide_id)
                uniprot_curie = 'UniProtKB:{}'.format(uniprot_swissprot)
                entrez_curie = 'NCBIGene:{}'.format(entrezgene)

                if description == '':
                    description = None
                # gene_type_id = self._get_gene_type(gene_biotype)
                gene_type_id = None
                model.addClassToGraph(
                    gene_id, external_gene_name, gene_type_id, description)
                model.addIndividualToGraph(peptide_curie, None, self._get_gene_type("polypeptide"))
                model.addIndividualToGraph(uniprot_curie, None, self._get_gene_type("polypeptide"))

                if entrezgene != '':
                    model.addEquivalentClass(gene_id, entrez_curie)
                if hgnc_id is not None and hgnc_id != '':
                    model.addEquivalentClass(gene_id, hgnc_id)
                geno.addTaxon('NCBITaxon:'+taxid, gene_id)
                if peptide_id != '':
                    geno.addGeneProduct(gene_id, peptide_curie)
                    if uniprot_swissprot != '':
                        geno.addGeneProduct(gene_id, uniprot_curie)
                        model.addXref(peptide_curie, uniprot_curie)

                if not self.testMode \
                        and limit is not None and line_counter > limit:
                    break

        return

# 需要导入模块: from dipper.models.Genotype import Genotype [as 别名]
# 或者: from dipper.models.Genotype.Genotype import addTaxon [as 别名]
    def process_gaf(self, file, limit, id_map=None):

        if self.testMode:
            g = self.testgraph
        else:
            g = self.graph

        model = Model(g)
        geno = Genotype(g)
        logger.info("Processing Gene Associations from %s", file)
        line_counter = 0

        if 7955 in self.tax_ids:
            zfin = ZFIN(self.graph_type, self.are_bnodes_skized)
        elif 6239 in self.tax_ids:
            wbase = WormBase(self.graph_type, self.are_bnodes_skized)

        with gzip.open(file, 'rb') as csvfile:
            filereader = csv.reader(io.TextIOWrapper(csvfile, newline=""),
                                    delimiter='\t', quotechar='\"')
            for row in filereader:
                line_counter += 1
                # comments start with exclamation
                if re.match(r'!', ''.join(row)):
                    continue
                (db, gene_num, gene_symbol, qualifier, go_id, ref, eco_symbol,
                 with_or_from, aspect, gene_name, gene_synonym, object_type,
                 taxon, date, assigned_by, annotation_extension,
                 gene_product_form_id) = row

                # test for required fields
                if (db == '' or gene_num == '' or gene_symbol == '' or
                        go_id == '' or ref == '' or eco_symbol == '' or
                        aspect == '' or object_type == '' or taxon == '' or
                        date == '' or assigned_by == ''):
                    logger.error(
                        "Missing required part of annotation " +
                        "on row %d:\n"+'\t'.join(row),
                        line_counter)
                    continue

                # deal with qualifier NOT, contributes_to, colocalizes_with
                if re.search(r'NOT', qualifier):
                    continue

                db = self.clean_db_prefix(db)
                uniprotid = None
                gene_id = None
                if db == 'UniProtKB':
                    mapped_ids = id_map.get(gene_num)
                    if id_map is not None and mapped_ids is not None:
                        if len(mapped_ids) == 1:
                            gene_id = mapped_ids[0]
                            uniprotid = ':'.join((db, gene_num))
                            gene_num = re.sub(r'\w+\:', '', gene_id)
                        elif len(mapped_ids) > 1:
                            # logger.warning(
                            #   "Skipping gene id mapped for >1 gene %s -> %s",
                            #    gene_num, str(mapped_ids))
                            continue
                    else:
                        continue
                elif db == 'MGI':
                    gene_num = re.sub(r'MGI:', '', gene_num)
                    gene_id = ':'.join((db, gene_num))
                    gene_id = re.sub(r'MGI\:MGI\:', 'MGI:', gene_id)
                else:
                    gene_id = ':'.join((db, gene_num))

                if self.testMode \
                        and not(
                            re.match(r'NCBIGene', gene_id) and
                            int(gene_num) in self.test_ids):
                    continue

                model.addClassToGraph(gene_id, gene_symbol)
                if gene_name != '':
                    model.addDescription(gene_id, gene_name)
                if gene_synonym != '':
                    for s in re.split(r'\|', gene_synonym):
                        model.addSynonym(gene_id, s.strip())
                if re.search(r'\|', taxon):
                    # TODO add annotations with >1 taxon
                    logger.info(">1 taxon (%s) on line %d.  skipping", taxon,
                                line_counter)
                else:
                    tax_id = re.sub(r'taxon:', 'NCBITaxon:', taxon)
                    geno.addTaxon(tax_id, gene_id)

                assoc = Assoc(g, self.name)

                assoc.set_subject(gene_id)
                assoc.set_object(go_id)

                eco_id = self.map_go_evidence_code_to_eco(eco_symbol)
                if eco_id is not None:
                    assoc.add_evidence(eco_id)

                refs = re.split(r'\|', ref)
                for r in refs:
#.........这里部分代码省略.........