344 lines
11 KiB
Python
Executable File
344 lines
11 KiB
Python
Executable File
#!/usr/bin/env python3
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# -*- coding: UTF-8 -*-
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"""
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Created on: 2021-10-09
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@author: cjs
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# 用途：处理遗传性并发症信息
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# 版本：0.0.3
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# 0.0.2 2021-11-09 输出文件1，如果没有阳性结果，输出文件大小为0kb，表头也不写
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# 0.0.3 2022-01-28 160和650基因信息用不同的文件
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# 0.0.3 2022-01-28 日志中记录参数列表以空格合并
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# 0.0.4 2022-12-06 增加pro.txt，检测到的基因原始信息，临床意义包含1、2、3
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# 0.0.4 2022-12-06 1.txt修改文件名为:hereditary.txt,临床意义包含1、2、3;2.txt修改为:hereditary.risk.txt
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# 0.0.4 2022-12-06 风险等级"高风险"修改为"偏高"
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# 0.0.5 2023-03-06 增加624项目
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# 最后编辑日期: 2022-01-28
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"""
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from glob import glob
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from collections import defaultdict
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import datetime
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import traceback
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import os
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import sys
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# 全局参数
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Output_dir = r''
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P_Name = r''
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Project = r''
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Panle_Txt = 'r'
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# 脚本运行的参数
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Exe_Path = r''
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GLog = None
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StartTime = r''
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# 配置文件
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Gene_Panle = {'160gene': '160_panel.hereditary.txt',
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'624gene': '624_panel.hereditary.txt',
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'650gene': '650_panel.hereditary.txt'}
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Gene_TD = {'160gene': 'gene_dd.160.txt',
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'624gene': 'gene_dd.624.txt',
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'650gene': 'gene_dd.650.txt'}
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Min_Rate = 0.10 # 胚系突变默认最低突变频率
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# 基因常见转录本信息
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Ref_Txt = 'oncokbgene.txt'
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Gene_NM = {}
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def usage(df_exe):
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"""使用方法."""
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print("Usage:")
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print("%s -o Output_dir -p Project [--n P_Name ]" % df_exe)
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sys.exit(0)
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def Get_Argvs():
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"""获取环境变量."""
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global Output_dir
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global P_Name
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global Project
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global Panle_Txt
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global Exe_Path
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global StartTime
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argvs = sys.argv
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file_real = os.path.realpath(argvs[0])
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Exe_Path = os.path.dirname(file_real)
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bin_name = os.path.basename(file_real)
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StartTime = datetime.datetime.now()
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ymd = StartTime.__format__('%Y%m%d_%H%M%S')
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# print(argvs)
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if len(argvs) < 5:
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print('参数列表数量不对, %s' % argvs)
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usage(bin_name)
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else:
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# 参数解析
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opt_normal = ''
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argv_index = 1
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for argv in argvs[1:]:
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argv_index += 1
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argv = argv.upper()
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# 必选参数获取
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if argv == '-O':
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Output_dir = os.path.realpath(argvs[argv_index])
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# 新建日志
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if not os.path.exists(Output_dir):
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print('项目路径不存在: %s' % Output_dir)
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usage(bin_name)
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elif argv == '-P':
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Project = argvs[argv_index]
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Panle_Txt = Gene_Panle.get(Project, '')
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# 可选参数获得
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elif argv == '--N':
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opt_normal = argvs[argv_index]
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# 核对参数解析结果
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if Output_dir == '':
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print('未能解析出项目路径')
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usage(bin_name)
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elif Panle_Txt == '':
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print('-p:%s, 指定的项目类型不正确' % Project)
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usage(bin_name)
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# 解析noraml
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mut_Path = os.path.join(Output_dir, 'mutation')
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ff_txt = ''
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txt_base = ''
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ft_txts = []
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if opt_normal == '':
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ft_txts = glob('%s/*Germline*filtered.txt' % (mut_Path))
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else:
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ft_txts = glob('%s/*%s*Germline*filtered.txt' % (
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mut_Path, opt_normal))
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if len(ft_txts) == 0:
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if opt_normal == '':
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print('%s,项目路径未发现filtered.txt文件' % Output_dir)
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else:
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print('项目路径未发现名称为:%s的相关filtered.txt文件' % opt_normal)
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usage(bin_name)
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else:
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# 理论只能找到1个相关文件，只取列表中的第一个文件
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ff_txt = ft_txts[0]
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txt_base = os.path.basename(ff_txt)
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print('使用的txt文件:\n%s' % txt_base)
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txt_name = txt_base.split('.')[0]
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if opt_normal == '':
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P_Name = txt_name
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else:
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P_Name = opt_normal
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# 日志
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hd_path = os.path.join(Output_dir, 'hereditary')
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if not os.path.exists(hd_path):
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os.makedirs(hd_path)
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return ff_txt
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def Preocess_NM():
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"""读取基因的常见转录本信息."""
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global Gene_NM
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ref_txt = os.path.join(Exe_Path, 'ref', Ref_Txt)
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# ref_txt = '/home/codes/reportbase/oncokbgene.txt'
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if os.path.isfile(ref_txt):
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with open(ref_txt, 'r', encoding='utf8') as ff:
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for line in ff:
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line = line.strip()
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lns = line.split('\t')
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if len(lns) > 2 and not lns[0].startswith('#'):
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gene = lns[0]
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nm = lns[2].split('.')[0] # 去除版本号
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Gene_NM[gene] = nm
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else:
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eline = '不存在ref文件:%s' % ref_txt
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print(eline)
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# 处理txt文件
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def Process_Txt(df_txt):
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"""读取项目的txt文件."""
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Preocess_NM()
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# 处理当前项目的基因列表
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pro_genes = []
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p_txt = os.path.join(Exe_Path, 'config', Panle_Txt)
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with open(p_txt, 'r', encoding='utf-8') as ff:
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for line in ff:
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gene = line.strip()
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if not line.startswith('#') and len(gene) > 0:
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pro_genes.append(gene)
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# 处理gene与综合征、遗传疾病的关系
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gene_dd = {}
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dd_base = Gene_TD.get(Project)
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dd_full = os.path.join(Exe_Path, 'config', dd_base)
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with open(dd_full, 'r', encoding='utf-8') as ff:
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for line in ff:
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line = line.strip()
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if not line.startswith('#') and len(line) > 0:
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lns = line.split('\t')
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gene = lns[0]
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gene_ls = lns[1].split('|')
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gene_dd[gene] = gene_ls
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# 筛选条件1：临床意义为1致病的2疑似致病的
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# 筛选条件2：突变频率大于10%
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muts_dd = defaultdict(list)
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inlines = []
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with open(df_txt, 'r', encoding='utf-8') as ff:
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inlines = ff.readlines()
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head = inlines[0]
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mut_index = -1
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head_infos = head.strip().split('\t')
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try:
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mut_index = head_infos.index("Otherinfo13")
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except Exception:
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print('未发现Otherinfo13列信息')
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res3 = [head]
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if mut_index > -1:
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for line in inlines[1:]:
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lns = line.split('\t')
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if len(lns) >= 100:
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normal_info = lns[mut_index]
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mut_rate = 0
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gene = lns[7]
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gene_type = ''
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gene_p = ''
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try:
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mut_infos = normal_info.split(':')
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mut_type = mut_infos[0]
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if mut_type == '0/1':
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gene_type = '杂合'
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elif mut_type == '1/1':
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gene_type = '纯合'
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# 区别对待组织版(频率带%)和血液版(频率不带%)
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if normal_info.find(r'%') > -1:
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mut_chrs = mut_infos[5]
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mut_rate = float(mut_chrs.strip('%')) / 100
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else:
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# # 血液版区别对待1/1
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mut_chrs = mut_infos[4]
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# if normal_info.startswith('1/1'):
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# mut_chrs = mut_infos[4]
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mut_rate = float(mut_chrs.strip())
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except Exception:
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print(normal_info)
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print(traceback.format_exc())
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if gene in pro_genes:
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if mut_rate >= Min_Rate:
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# 只要在项目内的基因都收集到pre.txt
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res3.append(line)
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nm_pos = 0
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gene_nms = lns[10].split(':')
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nm_index = 0
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gene_nm = Gene_NM.get(gene, '')
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if gene_nm != '':
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for nm in gene_nms:
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if nm.startswith('NM_'):
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if nm == gene_nm:
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gene_p = gene_nms[nm_index + 4]
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# 防止格式不一致，多一个基因
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gene_p = gene_p.split(',')[0]
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nm_pos = 1
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break
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else:
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# 没有发现常见转录本
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gene_p = gene_nms[-1]
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if nm_pos == 0:
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print('%s, Gene未发现常见转录本' % gene)
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temp_ls = [gene_type, gene_p]
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if lns[0] != '3':
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muts_dd[gene].append(temp_ls)
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# 写最终结果文件
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# 1
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hd_path = os.path.join(Output_dir, 'hereditary')
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txt1_base = '%s.hereditary.txt' % P_Name
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txt3_base = '%s.hereditary.pre.txt' % P_Name
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txt1_full = os.path.join(hd_path, txt1_base)
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txt3_full = os.path.join(hd_path, txt3_base)
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with open(txt1_full, 'w', encoding='utf-8') as ff:
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ff.write('基因\t遗传性肿瘤综合征\t遗传方式\t杂合/纯合\t检测结果\n')
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if len(muts_dd) > 0:
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for gene in muts_dd:
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gene_values = muts_dd[gene]
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if gene in pro_genes:
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gene_info = gene_dd.get(gene, ['', ''])[0]
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gene_types = []
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gene_ps = []
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for gene_value_ls in gene_values:
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gene_types.append(gene_value_ls[0])
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gene_ps.append(gene_value_ls[1])
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# 多个综合征分隔符
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split_pos = ';'
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gene_type = split_pos.join(gene_types)
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gene_p = split_pos.join(gene_ps)
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line = '%s\t%s\t%s' % (gene, gene_info, 'AD')
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line += '\t%s\t%s\n' % (gene_type, gene_p)
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ff.write(line)
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# 3
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with open(txt3_full, 'w', encoding='utf-8') as ff:
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if len(res3) > 1:
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ff.writelines(res3)
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# 2
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txt2_base = '%s.risk.txt' % P_Name
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txt2_full = os.path.join(hd_path, txt2_base)
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cancers_high = [] # 记录已经统计过的高风险肿瘤类型
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cancers_normal = [] # 记录已经统计过的正常风险肿瘤类型
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for gene in pro_genes:
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gene_info = gene_dd.get(gene, ['', ''])[1]
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for cancer in gene_info.split(';'):
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# 突变有高风险
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if gene in muts_dd:
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if cancer not in cancers_high:
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cancers_high.append(cancer)
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# 正常分享
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else:
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if cancer not in cancers_normal:
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cancers_normal.append(cancer)
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cancers = [] # 记录所有的肿瘤类型
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with open(txt2_full, 'w', encoding='utf-8') as ff:
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ff.write('肿瘤类型\t风险值\n')
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for gene in pro_genes:
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gene_info = gene_dd.get(gene, ['', ''])[1]
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for cancer in gene_info.split(';'):
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if cancer not in cancers:
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cancers.append(cancer)
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cancer_risk = ''
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if cancer in cancers_high:
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cancer_risk = '偏高'
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else:
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cancer_risk = '同一般人群'
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line = '%s\t%s\n' % (cancer, cancer_risk)
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ff.write(line)
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if __name__ == '__main__':
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ff_txt = Get_Argvs()
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try:
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Process_Txt(ff_txt)
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except BaseException:
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print(traceback.format_exc())
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endtime = datetime.datetime.now()
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