Toward Recovering Allele-specific Cancer Genome Graphs.
Toward Recovering Allele-specific Cancer Genome Graphs.
复制标题
恢复等位基因特异性癌症基因组图。
DOI：
10.1089/cmb.2018.0022
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Ma,Jian
中科院分区：
文献类型：
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作者：
Rajaraman,Ashok;Ma,Jian
非整倍体癌症基因组中结构变异（SV）和拷贝数改变的综合分析是理解肿瘤基因组复杂性的关键。最近开发的算法Weaver可以首次估计非整倍体癌症基因组中SV的等位基因特异性拷贝数及其相互连接性。然而，一个主要的限制是，并不是所有的SV确定的韦弗是分阶段的。在这篇文章中，我们开发了一个通用的凸规划框架，该框架预测了非定相SV的互连性，并将可能存在噪声的等位基因特异性拷贝数估计作为输入。我们通过模拟数据和HeLa全基因组测序数据的应用证明，我们的方法对输入拷贝数中的噪声具有鲁棒性，并且可以预测具有高特异性的SV定相。我们发现，即使大部分输入变量是非定相的，我们的方法也可以与Weaver进行一致的预测。我们还将我们的方法应用于癌症基因组图谱（TCGA）卵巢癌全基因组测序样本，以确定Weaver未定相的SV。我们的工作为恢复更完整的等位基因特异性癌症基因组图谱提供了一个重要的新算法框架。
Integrated analysis of structural variants (SVs) and copy number alterations in aneuploid cancer genomes is key to understanding tumor genome complexity. A recently developed algorithm, Weaver, can estimate, for the first time, allele-specific copy number of SVs and their interconnectivity in aneuploid cancer genomes. However, one major limitation is that not all SVs identified by Weaver are phased. In this article, we develop a general convex programming framework that predicts the interconnectivity of unphased SVs with possibly noisy allele-specific copy number estimations as input. We demonstrated through applications to both simulated data and HeLa whole-genome sequencing data that our method is robust to the noise in the input copy numbers and can predict SV phasings with high specificity. We found that our method can make consistent predictions with Weaver even if a large proportion of the input variants are unphased. We also applied our method to The Cancer Genome Atlas (TCGA) ovarian cancer whole-genome sequencing samples to phase SVs left unphased by Weaver. Our work provides an important new algorithmic framework for recovering more complete allele-specific cancer genome graphs.