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云端基因组学
book

云端基因组学

by Geraldine A. Van der Auwera, Brian D. O’Connor
April 2022
Beginner to intermediate
486 pages
10h 22m
Chinese
China Electric Power Press Ltd.
Content preview from 云端基因组学
GATK
最佳实践发现体细胞变异
211
像是
5
,而染色体
12
的拷贝数则为
3
,并且我们无法真正猜出其拷贝率,因为变异
太多,我们无法充分掌握该基因组的平均倍数。
正常细胞系
癌细胞系HCC1954
7
-
6
:用光谱核型分析技术为每对染色体上色,可看到不同染色体片段被扩增或缺失(左
右面板用色并不一致)
不过相比短变异分析,拷贝数分析对我们之前所列举的一些难点并不敏感,因为我
们不太关心确切序列。然而,该方法对影响序列覆盖分布均匀性的技术偏差更敏感。
这两点对分析设计的影响是:有没有一个与之配对的正常样本不太重要,更重要的
是要有一个
PoN
,而且组成该
PoN
的正常样本的技术特点与肿瘤样本密切匹配。不
过,需注意的是拷贝数
PoN
完全不同于短变异分析所用
PoN
。我们下节介绍细节。
7.3.1
仅有肿瘤样本的分析工作流概览
体细胞拷贝数分析,其标准工作流的主要步骤有四个(见图
7-7
)。首先,我们为
案例样本采集覆盖度统计数据以及
PoN
所用正常样本。案例样本暂且放一放,我们
先创建一个拷贝数
PoN
,捕获在外显子组或基因组观测的覆盖度的系统性偏度。接着,
我们使用该信息标准化我们为案例样本所采集的覆盖度数据,消除技术噪音,揭露
数据中实际拷贝率的差异。最后,我们运行片段连接算法,识别具有相同拷贝率的
序列的连续片段,对拷贝率与均值差别较大的片段,输出
CNA
识别结果。我们可
以通过绘制图表,在不同阶段可视化数据,以便于我们理解该流程。
你将再次在你虚拟机
GATK
容器做这一工作,因此请确认一切都已开启,可正常运行。
一定要切到
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Publisher Resources

ISBN: 9787519864422