April 2022
Beginner to intermediate
486 pages
10h 22m
Chinese
Content preview from 云端基因组学
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用
GATK
最佳实践发现体细胞变异
｜
203
大不同。两者各自的算法，请见
GATK
网站
HaplotypeCaller
和
Mutect2
文 档（
https://
oreil/ly/BIts1
）。现在，只要记住
Mutect2
同样重比对读段即可，因此我们若想将读
段数据作为后续变异审核过程的一部分，我们就需要
bamout
文件。分析师识别体细
胞变异经常这么做（相对而言，它比种系变异识别更常见），我们设置
Mutect2
命令，
使其自动生成
bamout
文件。
7.2.4
估计样本交叉污染
如之前我们所概括的，体细胞突变的识别工作与种系突变识别相比，受低级噪音的
影响更严重。样本之间的少量污染（研究者可能想不到它们竟如此常见）就能迷惑
识别工具，导致很多假变异被划归到真变异。一个等位基因出现在数据，是因为其
生物组织发生真突变，还是因为我们样本受他人
DNA
污染，而这个人的种系恰好
拥有该等位基因。这两种情况我们确实没有直接方法来区分。
我们无法直接纠正这类污染，但是可以估计它对任意给定样本的影响有多大。我们
可以据此标记我们观测到的任意突变识别结果，如果其等位基因分数等于或小于污
染率。虽然我们并不认为这些识别结果必然为假，从而过滤它们，但我们下阶段分析，
势必会对这些识别结果多一份怀疑。
为估计肿瘤样本中的污染率，我们首先识别正常样本的纯合变异位点，选择待研究
的肿瘤位点，接着根据这些位点的支持参考等位基因的读段量，计算肿瘤样本含多
少污染。我们的思路是：因为这些位点应该是纯合变异，所以肿瘤样本中出现的参
考读段是一次污染事故造成的，它们来自其他人的样本。 ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.