Computational analysis of cancer genome sequencing data

被引：70

作者：

Cortes-Ciriano, Isidro ^{[1
]}

Gulhan, Doga C. ^{[2
]}

Lee, Jake June-Koo ^{[2
]}

Melloni, Giorgio E. M. ^{[2
]}

Park, Peter J. ^{[2
]}

机构：

[1] European Bioinformat Inst, European Mol Biol Lab, Wellcome Genome Campus, Hinxton, England

[2] Harvard Med Sch, Dept Biomed Informat, Boston, MA 02115 USA

来源：

NATURE REVIEWS GENETICS | 2022年 / 23卷 / 05期

关键词：

COPY-NUMBER ANALYSIS; STRUCTURAL-VARIATION; SOMATIC MUTATION; CHROMATIN ORGANIZATION; TUMOR HETEROGENEITY; EVOLUTION; DNA; LANDSCAPE; VARIANTS; ARTIFACTS;

D O I：

10.1038/s41576-021-00431-y

中图分类号：

Q3 [遗传学];

学科分类号：

071007 ; 090102 ;

摘要：

Distilling biologically meaningful information from cancer genome sequencing data requires comprehensive identification of somatic alterations using rigorous computational methods. As the amount and complexity of sequencing data have increased, so has the number of tools for analysing them. Here, we describe the main steps involved in the bioinformatic analysis of cancer genomes, review key algorithmic developments and highlight popular tools and emerging technologies. These tools include those that identify point mutations, copy number alterations, structural variations and mutational signatures in cancer genomes. We also discuss issues in experimental design, the strengths and limitations of sequencing modalities and methodological challenges for the future. In this Review the authors provide an overview of key algorithmic developments, popular tools and emerging technologies used in the bioinformatic analysis of genomes. They also describe how such analysis can identify point mutations, copy number alterations, structural variations and mutational signatures in cancer genomes.

引用

页码：298 / 314

页数：17

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