Department of Bioinformatics and Computational Biology, The University of Texas M,D, Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030, USA.
BMC Bioinformatics. 2014 Sep 8;15(1):299. doi: 10.1186/1471-2105-15-299.
Recent advances in deep digital sequencing have unveiled an unprecedented degree of clonal heterogeneity within a single tumor DNA sample. Resolving such heterogeneity depends on accurate estimation of fractions of alleles that harbor somatic mutations. Unlike substitutions or small indels, structural variants such as deletions, duplications, inversions and translocations involve segments of DNAs and are potentially more accurate for allele fraction estimations. However, no systematic method exists that can support such analysis.
In this paper, we present a novel maximum-likelihood method that estimates allele fractions of structural variants integratively from various forms of alignment signals. We develop a tool, BreakDown, to estimate the allele fractions of most structural variants including medium size (from 1 kilobase to 1 megabase) deletions and duplications, and balanced inversions and translocations.
Evaluation based on both simulated and real data indicates that our method systematically enables structural variants for clonal heterogeneity analysis and can greatly enhance the characterization of genomically instable tumors.
最近的深度数字测序技术的进展揭示了单个肿瘤 DNA 样本中前所未有的克隆异质性程度。解决这种异质性取决于对携带体细胞突变的等位基因分数的准确估计。与替换或小插入缺失不同，结构变体（如缺失、重复、倒位和易位）涉及 DNA 片段，并且更有可能准确估计等位基因分数。然而，目前还没有系统的方法可以支持这种分析。
在本文中，我们提出了一种新的最大似然方法，该方法可以从各种对齐信号综合估计结构变体的等位基因分数。我们开发了一个名为 BreakDown 的工具，用于估计大多数结构变体的等位基因分数，包括中等大小（从 1 千碱基到 1 百万碱基）的缺失和重复，以及平衡的倒位和易位。
基于模拟和真实数据的评估表明，我们的方法系统地为克隆异质性分析启用了结构变体，并极大地增强了基因组不稳定肿瘤的特征描述。
