对于初学者来说，我是生物信息学的新手，尤其是编程方面的新手，但我已经构建了一个脚本，它将通过所谓的 VCF 文件(仅包含个人，一个 clumn = 一个个人)，并使用搜索字符串找出每个变体(系)个体是纯合子还是杂合子。

此脚本至少在小的子集上有效，但我知道它将所有内容都存储在内存中。我想在非常大的压缩文件(甚至整个基因组)上执行此操作，但我不知道如何将此脚本转换为逐行执行所有操作的脚本(因为我想计算整列我只是不看看如何解决)。

因此每个个体的输出是 5 个事物(总变异数、纯合子数、杂合子数以及纯​​合子和杂合子的比例)。请看下面的代码:

#!usr/bin/env python
import re
import gzip

subset_cols = 'subset_cols_chr18.vcf.gz'
#nuc_div = 'nuc_div_chr18.txt'

gz_infile = gzip.GzipFile(subset_cols, "r")  
#gz_outfile = gzip.GzipFile(nuc_div, "w") 

# make a dictionary of the header line for easy retrieval of elements later on

headers = gz_infile.readline().rstrip().split('\t')             
print headers                                                   

column_dict = {}                                        
for header in headers:
        column_dict[header] = []                        
for line in gz_infile:                                     
        columns = line.rstrip().split('\t')             
        for i in range(len(columns)):                   
                c_header=headers[i]                     
                column_dict[c_header].append(columns[i])
#print column_dict

for key in column_dict:                         
        number_homozygotes = 0          
        number_heterozygotes = 0        

        for values in column_dict[key]: 
                SearchStr = '(\d)/(\d):\d+,\d+:\d+:\d+:\d+,\d+,\d+'     
#this search string contains the regexp (this regexp was tested)
                Result = re.search(SearchStr,values)                    
                if Result:
#here, it will skip the missing genoytypes ./.
                        variant_one = int(Result.group(1))              
                        variant_two = int(Result.group(2))              

                        if variant_one == 0 and variant_two == 0:
                                continue
                        elif variant_one == variant_two:                  
#count +1 in case variant one and two are equal (so 0/0, 1/1, etc.)
                                number_homozygotes += 1
                        elif variant_one != variant_two:
#count +1 in case variant one is not equal to variant two (so 1/0, 0/1, etc.)
                                number_heterozygotes += 1

        print "%s homozygotes %s" % (number_homozygotes, key) 
        print "%s heterozygotes %s" % (number_heterozygotes,key)

        variants = number_homozygotes + number_heterozygotes
        print "%s variants" % variants

        prop_homozygotes = (1.0*number_homozygotes/variants)*100
        prop_heterozygotes = (1.0*number_heterozygotes/variants)*100

        print "%s %% homozygous %s" % (prop_homozygotes, key)
        print "%s %% heterozygous %s" % (prop_heterozygotes, key)

非常感谢任何帮助，这样我就可以继续研究大型数据集， 谢谢:)

顺便说一句，VCF 文件看起来像这样: INDIVIDUAL_1 INDIVIDUAL_2 INDIVIDUAL_3 0/0:9,0:9:24:0,24,221 1/0:5,4:9:25:25,0,26 1/1:0,13:13:33:347,33,0

这是带有个人 ID 名称的标题行(我总共有 33 个带有更复杂 ID 标签的个人，我在这里进行了简化)然后我有很多具有相同特定模式的信息行。我只对带斜杠的第一部分感兴趣，因此对常规表达式感兴趣。

最佳答案

披露:我全职从事 Hail 项目。

你好!欢迎来到编程和生物信息学领域!

amirouche 正确地识别出您需要某种“流媒体”或 “逐行”算法处理太大而无法放入 RAM 的数据 你的机器。不幸的是，如果你仅限于没有库的 python，你 必须手动分 block 文件并处理 VCF 的解析。

Hail project 是供科学家使用的免费开源工具 遗传数据太大而无法放入 RAM 一直到太大而无法放入一个 机(即数十 TB 的压缩 VCF 数据)。冰雹可以利用 一台机器上的所有内核或机器云上的所有内核。冰雹 在 Mac OS X 和大多数 GNU/Linux 上运行。冰雹暴露了一个统计 遗传学领域特定语言，使您的问题更短 express 。

最简单的答案

将您的 Python 代码最忠实地翻译成 Hail 是这样的:

/path/to/hail importvcf -f YOUR_FILE.vcf.gz \
  annotatesamples expr -c \
    'sa.nCalled = gs.filter(g => g.isCalled).count(),
     sa.nHom = gs.filter(g => g.isHomRef || g.isHomVar).count(),
     sa.nHet = gs.filter(g => g.isHet).count()'
  annotatesamples expr -c \
    'sa.pHom =  sa.nHom / sa.nCalled,
     sa.pHet =  sa.nHet / sa.nCalled' \
  exportsamples -c 'sample = s, sa.*' -o sampleInfo.tsv

我在我的双核笔记本电脑上运行了一个 2.0GB 文件的上述命令:

# ls -alh profile225.vcf.bgz
-rw-r--r--  1 dking  1594166068   2.0G Aug 25 15:43 profile225.vcf.bgz
# ../hail/build/install/hail/bin/hail importvcf -f profile225.vcf.bgz \
  annotatesamples expr -c \
    'sa.nCalled = gs.filter(g => g.isCalled).count(),
     sa.nHom = gs.filter(g => g.isHomRef || g.isHomVar).count(),
     sa.nHet = gs.filter(g => g.isHet).count()' \
  annotatesamples expr -c \
    'sa.pHom =  sa.nHom / sa.nCalled,
     sa.pHet =  sa.nHet / sa.nCalled' \
  exportsamples -c 'sample = s, sa.*' -o sampleInfo.tsv
hail: info: running: importvcf -f profile225.vcf.bgz
[Stage 0:=======================================================> (63 + 2) / 65]hail: info: Coerced sorted dataset
hail: info: running: annotatesamples expr -c 'sa.nCalled = gs.filter(g => g.isCalled).count(),
     sa.nHom = gs.filter(g => g.isHomRef || g.isHomVar).count(),
     sa.nHet = gs.filter(g => g.isHet).count()'
[Stage 1:========================================================>(64 + 1) / 65]hail: info: running: annotatesamples expr -c 'sa.pHom =  sa.nHom / sa.nCalled,
     sa.pHet =  sa.nHet / sa.nCalled'
hail: info: running: exportsamples -c 'sample = s, sa.*' -o sampleInfo.tsv
hail: info: while importing:
    file:/Users/dking/projects/hail-data/profile225.vcf.bgz  import clean
hail: info: timing:
  importvcf: 34.211s
  annotatesamples expr: 6m52.4s
  annotatesamples expr: 21.399ms
  exportsamples: 121.786ms
  total: 7m26.8s
# head sampleInfo.tsv 
sample  pHomRef pHet    nHom    nHet    nCalled
HG00096 9.49219e-01 5.07815e-02 212325  11359   223684
HG00097 9.28419e-01 7.15807e-02 214035  16502   230537
HG00099 9.27182e-01 7.28184e-02 211619  16620   228239
HG00100 9.19605e-01 8.03948e-02 214554  18757   233311
HG00101 9.28714e-01 7.12865e-02 214283  16448   230731
HG00102 9.24274e-01 7.57260e-02 212095  17377   229472
HG00103 9.36543e-01 6.34566e-02 209944  14225   224169
HG00105 9.29944e-01 7.00564e-02 214153  16133   230286
HG00106 9.25831e-01 7.41687e-02 213805  17128   230933

哇! 7 分钟 2GB，太慢了!不幸的是，这是因为 VCF 不是一种很好的数据分析格式!

优化存储格式

让我们转换成 Hail 优化的存储格式，一个 VDS，然后重新运行命令:

# ../hail/build/install/hail/bin/hail importvcf -f profile225.vcf.bgz write -o profile225.vds
hail: info: running: importvcf -f profile225.vcf.bgz
[Stage 0:========================================================>(64 + 1) / 65]hail: info: Coerced sorted dataset
hail: info: running: write -o profile225.vds
[Stage 1:>                                                         (0 + 4) / 65]
[Stage 1:========================================================>(64 + 1) / 65]
# ../hail/build/install/hail/bin/hail read -i profile225.vds \
       annotatesamples expr -c \
         'sa.nCalled = gs.filter(g => g.isCalled).count(),
          sa.nHom = gs.filter(g => g.isHomRef || g.isHomVar).count(),
          sa.nHet = gs.filter(g => g.isHet).count()' \
       annotatesamples expr -c \
         'sa.pHom =  sa.nHom / sa.nCalled,
          sa.pHet =  sa.nHet / sa.nCalled' \
       exportsamples -c 'sample = s, sa.*' -o sampleInfo.tsv
hail: info: running: read -i profile225.vds
[Stage 1:>                                                          (0 + 0) / 4]SLF4J: Failed to load class "org.slf4j.impl.StaticLoggerBinder".
SLF4J: Defaulting to no-operation (NOP) logger implementation
SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.
[Stage 1:============================================>              (3 + 1) / 4]hail: info: running: annotatesamples expr -c 'sa.nCalled = gs.filter(g => g.isCalled).count(),
         sa.nHom = gs.filter(g => g.isHomRef || g.isHomVar).count(),
         sa.nHet = gs.filter(g => g.isHet).count()'
[Stage 2:========================================================>(64 + 1) / 65]hail: info: running: annotatesamples expr -c 'sa.pHom =  sa.nHom / sa.nCalled,
         sa.pHet =  sa.nHet / sa.nCalled'
hail: info: running: exportsamples -c 'sample = s, sa.*' -o sampleInfo.tsv
hail: info: timing:
  read: 2.969s
  annotatesamples expr: 1m20.4s
  annotatesamples expr: 21.868ms
  exportsamples: 151.829ms
  total: 1m23.5s

大约快五倍!对于更大规模，在代表完整 VCF 的 VDS 上的 Google 云上运行相同的命令 1000 基因组计划(2535 个全基因组，压缩约 315GB)使用 328 个工作内核花费 3 分 42 秒。

使用冰雹内置

Hail 还有一个 sampleqc 命令可以计算你想要的大部分内容(和 更多!):

../hail/build/install/hail/bin/hail  read -i profile225.vds \
      sampleqc \
      annotatesamples expr -c \
        'sa.myqc.pHomRef = (sa.qc.nHomRef + sa.qc.nHomVar) / sa.qc.nCalled,
         sa.myqc.pHet= sa.qc.nHet / sa.qc.nCalled' \
      exportsamples -c 'sample = s, sa.myqc.*, nHom = sa.qc.nHomRef + sa.qc.nHomVar, nHet = sa.qc.nHet, nCalled = sa.qc.nCalled' -o sampleInfo.tsv
hail: info: running: read -i profile225.vds
[Stage 0:>                                                          (0 + 0) / 4]SLF4J: Failed to load class "org.slf4j.impl.StaticLoggerBinder".
SLF4J: Defaulting to no-operation (NOP) logger implementation
SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details.
[Stage 1:============================================>              (3 + 1) / 4]hail: info: running: sampleqc
[Stage 2:========================================================>(64 + 1) / 65]hail: info: running: annotatesamples expr -c 'sa.myqc.pHomRef = (sa.qc.nHomRef + sa.qc.nHomVar) / sa.qc.nCalled,
         sa.myqc.pHet= sa.qc.nHet / sa.qc.nCalled'
hail: info: running: exportsamples -c 'sample = s, sa.myqc.*, nHom = sa.qc.nHomRef + sa.qc.nHomVar, nHet = sa.qc.nHet, nCalled = sa.qc.nCalled' -o sampleInfo.tsv
hail: info: timing:
  read: 2.928s
  sampleqc: 1m27.0s
  annotatesamples expr: 229.653ms
  exportsamples: 353.942ms
  total: 1m30.5s

安装冰雹

安装 Hail 非常简单，我们有 help you 的文档。需要更多帮助？你可以得到 Hail 用户聊天室的实时支持，或者，如果您更喜欢论坛，Hail discourse(两者都链接到主页，不幸的是我没有 足够的声誉来创建真正的链接)。

不久的将来

在不久的将来(从今天开始不到一个月)，冰雹团队将 完成一个 Python API，它将允许您将第一个片段表达为:

result = importvcf("YOUR_FILE.vcf.gz")
  .annotatesamples('sa.nCalled = gs.filter(g => g.isCalled).count(),
                    sa.nHom = gs.filter(g => g.isHomRef || g.isHomVar).count(),
                    sa.nHet = gs.filter(g => g.isHet).count()')
  .annotatesamples('sa.pHom =  sa.nHom / sa.nCalled,
                    sa.pHet =  sa.nHet / sa.nCalled')

for (x in result.sampleannotations):
  print("Sample " + x +
        " nCalled " + x.nCalled +
        " nHom " + x.nHom +
        " nHet " + x.nHet +
        " percent Hom " + x.pHom * 100 +
        " percent Het " + x.pHet * 100)

result.sampleannotations.write("sampleInfo.tsv")

编辑:在 tsv 文件中添加了 head 的输出。

EDIT2:最新的 Hail 不需要 sampleqc 的双等位基因

EDIT3:关于扩展到具有数百个内核的云的注意事项

关于python - 如何聚合大于 RAM gzip 的 csv 文件的值？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/40528566/