Expanding the computational toolbox for mining cancer genomes

被引：150

作者：

Ding, Li ^{[1
,2
,3
,4
]}

Wendl, Michael C. ^{[1
,3
,5
]}

McMichael, Joshua F. ^{[1
]}

Raphael, Benjamin J. ^{[6
,7
]}

机构：

[1] Washington Univ, Genome Inst, St Louis, MO 63108 USA

[2] Washington Univ, Dept Med, St Louis, MO 63110 USA

[3] Washington Univ, Dept Genet, St Louis, MO 63110 USA

[4] Washington Univ, Siteman Canc Ctr, St Louis, MO 63110 USA

[5] Washington Univ, Dept Math, St Louis, MO 63130 USA

[6] Brown Univ, Dept Comp Sci, Providence, RI 02912 USA

[7] Brown Univ, Ctr Computat Mol Biol, Providence, RI 02912 USA

来源：

NATURE REVIEWS GENETICS | 2014年 / 15卷 / 08期

关键词：

SINGLE NUCLEOTIDE POLYMORPHISMS; SOMATIC POINT MUTATIONS; PATTERN GROWTH APPROACH; COPY-NUMBER ALTERATION; PAIRED-END; STRUCTURAL VARIATION; FUNCTIONAL IMPACT; IDENTIFYING RECURRENT; INTERACTION DATABASE; PROBABILISTIC MODEL;

D O I：

10.1038/nrg3767

中图分类号：

Q3 [遗传学];

学科分类号：

071007 [遗传学];

摘要：

High-throughput DNA sequencing has revolutionized the study of cancer genomics with numerous discoveries that are relevant to cancer diagnosis and treatment. The latest sequencing and analysis methods have successfully identified somatic alterations, including single-nucleotide variants, insertions and deletions, copy-number aberrations, structural variants and gene fusions. Additional computational techniques have proved useful for defining the mutations, genes and molecular networks that drive diverse cancer phenotypes and that determine clonal architectures in tumour samples. Collectively, these tools have advanced the study of genomic, transcriptomic and epigenomic alterations in cancer, and their association to clinical properties. Here, we review cancer genomics software and the insights that have been gained from their application.

引用

页码：556 / 570

页数：15

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