复杂肿瘤全基因组异常的检测难题得以解决

青草

研究组提出一种基于全局参数化隐Markov模型的新算法，通过期望最大化方法对模型进行训练和参数估计，从而有效解决了利用新一代测序技术检测复杂肿瘤全基因组异常的国际性难题。

5月20日，中国科学技术大学信息科学与技术学院副教授李骜研究组以原创性论文（Original paper）的形式，在Bioinformatics上在线发表了最新研究成果。论文的第一作者是博士生余振华。

基因组异常是多种恶性肿瘤的标志性特征，在肿瘤发病机理、临床诊断和治疗等研究中具有极为重要的作用。传统的肿瘤基因组异常检测技术存在着通量低、分辨率差等问题，随着近年来大规模平行测序实验技术的快速兴起，新一代测序凭借其在通量和分辨率方面的独特优势，目前已经成为癌症基因组学研究中最流行的实验手段。但由于肿瘤本身的复杂性，从新一代测序数据中准确检测基因组异常仍面临着正常细胞掺杂和污染、肿瘤基因组非整倍体性等棘手问题。
该研究组提出一种基于全局参数化隐Markov模型的新算法，通过期望最大化方法对模型进行训练和参数估计，从而有效解决了利用新一代测序技术检测复杂肿瘤全基因组异常的国际性难题。
审稿人认为，该算法具有令人关注的特性并为解决上述问题提供了极佳的思路。

原文摘要：
CLImAT: accurate detection of copy number alteration and loss of heterozygosity in impure and aneuploid tumor samples using whole-genome sequencing data
Motivation: Whole-genome sequencing of tumor samples has been demonstrated as an efficient approach for comprehensive analysis of genomic aberrations in cancer genome. Critical issues such as tumor impurity and aneuploidy, GC-content and mappability bias have been reported to complicate identification of copy number alteration and loss of heterozygosity in complex tumor samples. Therefore efficient computational methods are required to address these issues.
Results: We introduce CLImAT, a bioinformatics tool for identification of genomic aberrations from tumor samples using whole-genome sequencing data. Without requiring a matched normal sample, CLImAT takes integrated analysis of read depth and allelic frequency and provides extensive data processing procedures including GC-content and mappability correction of read depth and quantile normalization of B allele frequency. CLImAT accurately identifies copy number alteration and loss of heterozygosity even for highly impure tumor samples with aneuploidy. We evaluate CLImAT on both simulated and real DNA sequencing data to demonstrate its ability to infer tumor impurity and ploidy and identify genomic aberrations in complex tumor samples.

来源：中科院

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