Sc-TUSV-Ext:单细胞克隆谱系推断从单核苷酸变异,拷贝数改变,和结构变异。

IF 1.4 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS
Nishat Anjum Bristy, Xuecong Fu, Russell Schwartz
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引用次数: 0

摘要

克隆谱系推断(“肿瘤系统发育”)已经成为理解体细胞进化过程的关键工具,这些过程是癌症发展的基础,并且越来越被认为是正常组织生长和衰老的一部分。从单细胞序列数据推断克隆谱系树为揭示体细胞进化过程提供了前所未有的细节。然而,大多数这样的工具都是基于在体细胞进化中观察到的突变事件类型及其发展过程的相当有限的模型。目前的工作旨在通过更全面地利用肿瘤进化的分子变异类型范围来增强单细胞谱系重建工具的功能和多功能性。我们介绍了Sc-TUSV-ext,这是一种基于整数线性规划的肿瘤系统发育重建方法,首次将单核苷酸变异、拷贝数改变和结构变异整合到单细胞DNA测序数据的克隆谱系重建中。我们在合成数据中表明,相对于仅考虑变异类型子集的先前方法,考虑这些变异类型共同导致克隆谱系重建的准确性提高。我们进一步证明了真实数据在解决存在多种变异类型的克隆进化中的有效性,为更全面地了解各种形式的体细胞突变如何共同影响组织发育提供了一条途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sc-TUSV-Ext: Single-Cell Clonal Lineage Inference from Single Nucleotide Variants, Copy Number Alterations, and Structural Variants.

Clonal lineage inference ("tumor phylogenetics") has become a crucial tool for making sense of somatic evolution processes that underlie cancer development and are increasingly recognized as part of normal tissue growth and aging. The inference of clonal lineage trees from single-cell sequence data offers particular promise for revealing processes of somatic evolution in unprecedented detail. However, most such tools are based on fairly restrictive models of the types of mutation events observed in somatic evolution and of the processes by which they develop. The present work seeks to enhance the power and versatility of tools for single-cell lineage reconstruction by making more comprehensive use of the range of molecular variant types by which tumors evolve. We introduce Sc-TUSV-ext, an integer linear programming-based tumor phylogeny reconstruction method that, for the first time, integrates single nucleotide variants, copy number alterations, and structural variations into clonal lineage reconstruction from single-cell DNA sequencing data. We show on synthetic data that accounting for these variant types collectively leads to improved accuracy in clonal lineage reconstruction relative to prior methods that consider only subsets of the variant types. We further demonstrate the effectiveness of real data in resolving clonal evolution in the presence of multiple variant types, providing a path toward more comprehensive insight into how various forms of somatic mutability collectively shape tissue development.

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来源期刊
Journal of Computational Biology
Journal of Computational Biology 生物-计算机:跨学科应用
CiteScore
3.60
自引率
5.90%
发文量
113
审稿时长
6-12 weeks
期刊介绍: Journal of Computational Biology is the leading peer-reviewed journal in computational biology and bioinformatics, publishing in-depth statistical, mathematical, and computational analysis of methods, as well as their practical impact. Available only online, this is an essential journal for scientists and students who want to keep abreast of developments in bioinformatics. Journal of Computational Biology coverage includes: -Genomics -Mathematical modeling and simulation -Distributed and parallel biological computing -Designing biological databases -Pattern matching and pattern detection -Linking disparate databases and data -New tools for computational biology -Relational and object-oriented database technology for bioinformatics -Biological expert system design and use -Reasoning by analogy, hypothesis formation, and testing by machine -Management of biological databases
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