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Review
. 2010 Oct 15;19(R2):R188-96.
doi: 10.1093/hmg/ddq391. Epub 2010 Sep 15.

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

Affiliations
Review

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

Li Ding et al. Hum Mol Genet. .

Abstract

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.

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Figures

Figure 1.
Figure 1.
Analysis of 430 genes mutated across seven cancer genomes with DAVID (http://david.abcc.ncifcrf.gov/). (A) Number of mutated genes by GO terms of gene function. (B) Enriched biological processes and/or pathways among mutated genes according to DAVID clustering.
Figure 2.
Figure 2.
Landscape of cancer genomics analyses. NGS data will be generated for hundreds of tumors from all major cancer types in the near future. The integrated analysis of DNA, RNA and methylation sequencing data will help elucidate all relevant genetic changes in cancers.

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