April 2022
Beginner to intermediate
486 pages
10h 22m
Chinese
Content preview from 云端基因组学
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442
｜
第
14
章
原访问
受限数据
合成数据
其技术和生物学特征
与原数据等价
图
14
-
4
：我们用合成数据集替换无法公开的真实数据集，新数据集模仿原数据集的特征
14.2.1
总体方法论
使用合成基因组数据这种想法并不新鲜，且与原方法差别不是很大；研究者采用合
成数据已有一段时间。例如，
ICGC-TCGA DREAM Mutation
挑战赛就使用合成数据。
该挑战赛由一系列反复举行的竞赛组成，主办方提供的合成数据含特定已知变异。
要求参赛者开发分析方法，以高准确率和特异性识别其中的突变。
多个程序都能生成竞赛用途的合成序列数据；实际上，一些程序研究者开发它们的
部分原因正是为该类竞赛提供数据。生成这类数据的基本原理是根据参考基因组序
列，仿造读段，并将其输出为标准
FASTQ
或
BAM
文件。这些工具往往接受一个存
放变异识别结果的
VCF
文件作为次要输入，根据该文件修改数据模拟算法，使其所
得序列数据支持
VCF
输入文件所含变异。此外，还有一些程序能够为已存在的序列
数据引入（或插入）变异。你为这种工具提供一组变异，它将修改一段读段，使其
支持期望的变异识别结果。
实际上，在早先一轮的头脑风暴中，我们思考过编辑千人基因组项目真实样本，使
其包含我们感兴趣的变异，并将该数据集剩余样本留作
control
样本。该方法可以避
开生成合成数据这一步。然而我们最初的测试表明低覆盖度（
Low Coverage
）数据
集的外显子组数据的质量不够好，无法满足我们的目的。当时，我们在第
13
章介绍
的高覆盖度（
High Coverage
）数据集尚不可用，而且因为该数据是用 ...
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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.