Expanding the computational toolbox for mining cancer genomes

Nat Rev Genet. 2014 Aug;15(8):556-70. doi: 10.1038/nrg3767. Epub 2014 Jul 8.

Abstract

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.

Publication types

  • Research Support, N.I.H., Extramural
  • Review

MeSH terms

  • Animals
  • Data Mining / methods*
  • Genomics / methods*
  • High-Throughput Nucleotide Sequencing
  • Humans
  • Mutation
  • Neoplasms / genetics*
  • Neoplasms / metabolism
  • Signal Transduction
  • Software