本文整理汇总了Python中dipper.models.Genotype.Genotype.addGeneTargetingReagent方法的典型用法代码示例。如果您正苦于以下问题：Python Genotype.addGeneTargetingReagent方法的具体用法？Python Genotype.addGeneTargetingReagent怎么用？Python Genotype.addGeneTargetingReagent使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类dipper.models.Genotype.Genotype的用法示例。
在下文中一共展示了Genotype.addGeneTargetingReagent方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: process_rnai_phenotypes
# 需要导入模块: from dipper.models.Genotype import Genotype [as 别名]
# 或者: from dipper.models.Genotype.Genotype import addGeneTargetingReagent [as 别名]
def process_rnai_phenotypes(self, limit=None):
raw = '/'.join((self.rawdir, self.files['rnai_pheno']['file']))
if self.testMode:
g = self.testgraph
else:
g = self.graph
# gu = GraphUtils(curie_map.get()) # TODO unused
logger.info("Processing RNAi phenotype associations")
line_counter = 0
geno = Genotype(g)
with open(raw, 'r') as csvfile:
filereader = csv.reader(csvfile, delimiter='\t', quotechar='\"')
for row in filereader:
line_counter += 1
(gene_num, gene_alt_symbol, phenotype_label, phenotype_id,
rnai_and_refs) = row
# WBGene00001908 F17E9.9 locomotion variant WBPhenotype:0000643 WBRNAi00025129|WBPaper00006395 WBRNAi00025631|WBPaper00006395
# WBGene00001908 F17E9.9 avoids bacterial lawn WBPhenotype:0000402 WBRNAi00095640|WBPaper00040984
# WBGene00001908 F17E9.9 RAB-11 recycling endosome localization variant WBPhenotype:0002107 WBRNAi00090830|WBPaper00041129
if self.testMode and gene_num not in self.test_ids['gene']:
continue
gene_id = 'WormBase:'+gene_num
# refs = list() # TODO unused
# the rnai_and_refs has this so that
# WBRNAi00008687|WBPaper00005654 WBRNAi00025197|WBPaper00006395 WBRNAi00045381|WBPaper00025054
# space delimited between RNAi sets;
# then each RNAi should have a paper
rnai_sets = re.split(r' ', rnai_and_refs)
for s in rnai_sets:
# get the rnai_id
(rnai_num, ref_num) = re.split(r'\|', s)
if len(re.split(r'\|', s)) > 2:
logger.warning(
"There's an unexpected number of items in %s", s)
if rnai_num not in self.rnai_gene_map:
self.rnai_gene_map[rnai_num] = set()
# to use for looking up later
self.rnai_gene_map[rnai_num].add(gene_num)
rnai_id = 'WormBase:'+rnai_num
geno.addGeneTargetingReagent(
rnai_id, None, geno.genoparts['RNAi_reagent'], gene_id)
# make the "allele" of the gene
# that is targeted by the reagent
allele_id = self.make_reagent_targeted_gene_id(
gene_num, rnai_num, self.nobnodes)
allele_label = gene_alt_symbol+'<'+rnai_num+'>'
geno.addReagentTargetedGene(
rnai_id, gene_id, allele_id, allele_label)
assoc = G2PAssoc(self.name, allele_id, phenotype_id)
assoc.add_source('WormBase:'+ref_num)
# eco_id = 'ECO:0000019' # RNAi evidence # TODO unused
assoc.add_association_to_graph(g)
if not self.testMode \
and limit is not None and line_counter > limit:
break
return示例2: process_allele_phenotype
# 需要导入模块: from dipper.models.Genotype import Genotype [as 别名]
# 或者: from dipper.models.Genotype.Genotype import addGeneTargetingReagent [as 别名]
def process_allele_phenotype(self, limit=None):
"""
This file compactly lists variant to phenotype associations,
such that in a single row, there may be >1 variant listed
per phenotype and paper. This indicates that each variant is
individually assocated with the given phenotype,
as listed in 1+ papers.
(Not that the combination of variants is producing the phenotype.)
:param limit:
:return:
"""
raw = '/'.join((self.rawdir, self.files['allele_pheno']['file']))
if self.testMode:
g = self.testgraph
else:
g = self.graph
# gu = GraphUtils(curie_map.get()) # TODO unused
logger.info("Processing Allele phenotype associations")
line_counter = 0
geno = Genotype(g)
with open(raw, 'r') as csvfile:
filereader = csv.reader(csvfile, delimiter='\t', quotechar='\"')
for row in filereader:
if re.match(r'!', ''.join(row)): # header
continue
line_counter += 1
(db, gene_num, gene_symbol, is_not, phenotype_id, ref,
eco_symbol, with_or_from, aspect, gene_name, gene_synonym,
gene_class, taxon, date, assigned_by, blank, blank2) = row
if self.testMode and gene_num not in self.test_ids['gene']:
continue
# TODO add NOT phenotypes
if is_not == 'NOT':
continue
eco_id = None
if eco_symbol == 'IMP':
eco_id = 'ECO:0000015'
elif eco_symbol.strip() != '':
logger.warning(
"Encountered an ECO code we don't have: %s",
eco_symbol)
# according to the GOA spec, persons are not allowed to be
# in the reference column, therefore they the variant and
# persons are swapped between the reference and with column.
# we unswitch them here.
temp_var = temp_ref = None
if re.search(r'WBVar|WBRNAi', ref):
temp_var = ref
# move the paper from the with column into the ref
if re.search(r'WBPerson', with_or_from):
temp_ref = with_or_from
if temp_var is not None or temp_ref is not None:
with_or_from = temp_var
ref = temp_ref
allele_list = re.split(r'\|', with_or_from)
if len(allele_list) == 0:
logger.error(
"Missing alleles from phenotype assoc at line %d",
line_counter)
continue
else:
for a in allele_list:
allele_num = re.sub(r'WB:', '', a.strip())
allele_id = 'WormBase:'+allele_num
gene_id = 'WormBase:'+gene_num
if re.search(r'WBRNAi', allele_id):
# make the reagent-targeted gene,
# & annotate that instead of the RNAi item directly
rnai_num = re.sub(r'WormBase:', '', allele_id)
rnai_id = allele_id
rtg_id = self.make_reagent_targeted_gene_id(
gene_num, rnai_num, self.nobnodes)
geno.addReagentTargetedGene(
rnai_id, 'WormBase:'+gene_num, rtg_id)
geno.addGeneTargetingReagent(
rnai_id, None, geno.genoparts['RNAi_reagent'],
gene_id)
allele_id = rtg_id
elif re.search(r'WBVar', allele_id):
# this may become deprecated by using wormmine
# make the allele to gene relationship
# the WBVars are really sequence alterations
# the public name will come from elsewhere
geno.addSequenceAlteration(allele_id, None)
vl_id = '_'+'-'.join((gene_num, allele_num))
if self.nobnodes:
vl_id = ':'+vl_id
geno.addSequenceAlterationToVariantLocus(
#.........这里部分代码省略.........