化疗修改，不在vcf中出现的从bam文件中获取深度
parent
39f51ccf2e
commit
bf1d9de10e
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@ -10,7 +10,7 @@ import pysam
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class ChemoRun:
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def __init__(self, database, probe, cancer, project, output_dir, name, vcf):
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def __init__(self, database, probe, cancer, project, output_dir, name, vcf, bam, ref):
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self.database = database
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self.probe = probe
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self.output_dir = output_dir
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@ -18,6 +18,8 @@ class ChemoRun:
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self.vcf = vcf
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self.cancer = cancer
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self.project = project
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self.bam_file = pysam.AlignmentFile(bam, "rb")
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self.ref_file = pysam.FastaFile(ref)
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@staticmethod
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def read_info(project):
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@ -62,6 +64,22 @@ class ChemoRun:
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records[(chrom, pos)] = record
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return records
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def get_position_depth(self, chr, pos):
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"""
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获取位点测序深度 >=30X
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"""
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depths = self.bam_file.count_coverage(chr, start=pos - 1, stop=pos)
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depth_sum = sum([sum(arr) for arr in depths])
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return depth_sum
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def get_ref_base(self, chr, pos):
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"""
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获取参考基因组
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"""
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base = self.ref_file.fetch(chr, pos - 1, pos)
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return base
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def parsedata(self):
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"""
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处理chemo_drug_rsid_snppos.xlsx, 并生成对应bed文件
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@ -97,20 +115,33 @@ class ChemoRun:
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for _, row in beddata.iterrows():
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chrom = row['chr']
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end = row['end']
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fliter = pd.DataFrame()
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# bed 文件在 vcf 中
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if (chrom, end) in records:
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record = records[(chrom, end)]
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ref = record.ref
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alt = record.alts[0]
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# gt = '/'.join(list(map(str, sorted(record.samples.get(record.samples.keys()[0]).get('GT')))))
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freq = record.samples.get(record.samples.keys()[-1]).get('AF')[0]
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depth = record.samples.get(record.samples.keys()[-1]).get('DP')
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if freq > 0.9:
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gt = '1/1'
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elif 0.9 >= freq > 0.1:
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gt = '0/1'
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else:
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gt = '0/0'
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# 不在vcf中 去bam文件中获取该位点的测序深度 去ref 获取碱基 没有的就为NA了
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else:
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depth = self.get_position_depth(chrom, end)
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ref = self.get_ref_base(chrom, end)
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alt = ref
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freq = 1
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gt = '0/0'
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# 测序深度小于30 排除
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if depth < 30:
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alt = 'NA'
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freq = 'NA'
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gt = 'NA'
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match_drug_rsid_data = drug_rsid_data[
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(drug_rsid_data['chr'] == chrom) &
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(drug_rsid_data['end'] == end) &
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@ -119,33 +150,22 @@ class ChemoRun:
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(drug_rsid_data['genotype'] == gt)
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]
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match_drug_rsid_data = match_drug_rsid_data.reset_index()
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if match_drug_rsid_data.empty:
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match_drug_rsid_data = pd.DataFrame([
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dict(
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ref=ref,
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alt=alt,
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genotype=gt
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)
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])
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match_drug_rsid_data['chr'] = chrom
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match_drug_rsid_data['pos'] = end
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match_drug_rsid_data['freq'] = freq
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match_drug_rsid_data['depth'] = depth
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fliter = pd.concat([fliter, match_drug_rsid_data])
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fliterdata = pd.concat([fliterdata, match_drug_rsid_data])
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if fliter.empty:
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match_drug_rsid_data = drug_rsid_data[
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(drug_rsid_data['chr'] == chrom) &
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(drug_rsid_data['end'] == end) &
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(drug_rsid_data['genotype'] == '0/0')
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]
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match_drug_rsid_data = match_drug_rsid_data.reset_index()
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match_drug_rsid_data['chr'] = chrom
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match_drug_rsid_data['pos'] = end
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match_drug_rsid_data['freq'] = '.'
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match_drug_rsid_data['depth'] = '.'
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fliter = pd.concat([fliter, match_drug_rsid_data])
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if fliter.empty:
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raise UserWarning(
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'chr: %s , end: %s 数据库未能匹配, 野生型0/0也未能匹配' % (chrom, end))
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fliterdata = pd.concat([fliterdata, fliter])
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# 生成过滤之后文件
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respath = os.path.join(self.output_dir, f'{self.name}.chemo.res.csv')
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if not fliterdata.empty:
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fliterdata['probe'] = self.probe
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respath = os.path.join(self.output_dir, f'{self.name}.chemo.res.txt')
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fliterdata.to_csv(respath, sep='\t', index=False)
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# 分类汇总 同位点，药物合并 drug.infos.txt
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@ -256,6 +276,9 @@ if __name__ == "__main__":
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parser.add_argument('-c', '--cancer', help="cancer", required=True)
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parser.add_argument('-p', '--project', help="Project", required=True)
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parser.add_argument('-o', '--output_dir', help="Output directory, default ./", default='')
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parser.add_argument('-b', '--bam', help="Bamfile for sample", required=True)
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parser.add_argument('-r', '--ref', help="ref fasta", required=True)
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args = parser.parse_args()
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chemorun = ChemoRun(args.database, args.probe, args.cancer, args.project, args.output_dir, args.name, args.vcf)
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chemorun = ChemoRun(args.database, args.probe, args.cancer, args.project, args.output_dir, args.name, args.vcf,
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args.bam, args.ref)
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chemorun.run()
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@ -166,7 +166,11 @@ workflow pipeline {
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probe=probe,
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vcf=call_mutation.germline_vcf,
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cancer=cancer,
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project=project
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project=project,
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normal=normal,
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tumor_rmdup_bam=alignment.tumor_rmdup_bam,
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normal_rmdup_bam=alignment.normal_rmdup_bam,
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ref=ref
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}
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call neoantigen.call_neoantigen as call_neoantigen {
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@ -7,12 +7,14 @@ task run_chemo {
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String vcf
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String cancer
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String project
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String bam
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String ref
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command {
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if [ ! -d ${output_dir}/chemo ];then
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mkdir ${output_dir}/chemo
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fi
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chemo.py -d $DATABASE/chemo_database.xlsx -probe ${probe} -n ${name} -v ${vcf} -o ${output_dir}/chemo -c ${cancer} -p ${project}
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chemo.py -d $DATABASE/chemo_database.xlsx -probe ${probe} -n ${name} -v ${vcf} -o ${output_dir}/chemo -c ${cancer} -p ${project} -b ${bam} -r ${ref}
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}
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output {
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@ -25,23 +27,45 @@ workflow call_chemo {
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Boolean run=true
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String name
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String? normal
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String output_dir
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String probe
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String vcf
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String cancer
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String project
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String tumor_rmdup_bam
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String? normal_rmdup_bam
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String ref
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if (run) {
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call run_chemo {
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if (defined(normal)) {
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call run_chemo as run_chemo_normal {
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input:
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name=name,
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output_dir=output_dir,
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probe=probe,
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vcf=vcf,
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cancer=cancer,
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project=project
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project=project,
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bam=normal_rmdup_bam,
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ref=ref
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}
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}
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if (!defined(normal)) {
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call run_chemo as run_chemo_tumor {
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input:
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name=name,
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output_dir=output_dir,
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probe=probe,
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vcf=vcf,
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cancer=cancer,
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project=project,
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bam=tumor_rmdup_bam,
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ref=ref
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}
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}
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}
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output {
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String chemo_res = "${output_dir}/chemo/${name}.drug.res.txt"
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