Analysis of next-generation genomic data in cancer: accomplishments and challenges

被引：95

作者：

Ding, Li ^{[1
]}

Wendl, Michael C. ^{[1
]}

Koboldt, Daniel C. ^{[1
]}

Mardis, Elaine R. ^{[1
]}

机构：

[1] Washington Univ, Sch Med, Genome Ctr, Dept Genet, St Louis, MO 63108 USA

来源：

HUMAN MOLECULAR GENETICS | 2010年 / 19卷

基金：

美国国家卫生研究院;

关键词：

PATTERN GROWTH APPROACH; SOMATIC MUTATIONS; HUMAN BREAST; SEQUENCE; PATHWAYS; REARRANGEMENTS; SIGNATURES; VARIANTS; GENE; READ;

D O I：

10.1093/hmg/ddq391

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

The application of next-generation sequencing technology has produced a transformation in cancer genomics, generating large data sets that can be analyzed in different ways to answer a multitude of questions about the genomic alterations associated with the disease. Analytical approaches can discover focused mutations such as substitutions and small insertion/deletions, large structural alterations and copy number events. As our capacity to produce such data for multiple cancers of the same type is improving, so are the demands to analyze multiple tumor genomes simultaneously growing. For example, pathway-based analyses that provide the full mutational impact on cellular protein networks and correlation analyses aimed at revealing causal relationships between genomic alterations and clinical presentations are both enabled. As the repertoire of data grows to include mRNA-seq, non-coding RNA-seq and methylation for multiple genomes, our challenge will be to intelligently integrate data types and genomes to produce a coherent picture of the genetic basis of cancer.

引用

页码：R188 / R196

页数：9

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