YALE JOu RNAL OF BiOLOGY AND mEDiCiNE 85 (2012), pp.339-345. Copyright © 2012. FOCu S: EDu CATiNG YOu RSELF iN BiOiNFORmATiCS Systematic deciphering of c ancer Genome n etworks Bernard Fendler and Gurinder Atwal Cold Spring Harbor Laboratory, Cold Spring Harbor, New York When growth regulatory genes are damaged in a cell, it may become cancerous. Current technological advances in the last decade have allowed the characterization of the whole genome of these cells by directly or indirectly measuring DNA changes. Complementary analyses were developed to make sense of the massive amounts of data generated. A large majority of these analyses were developed to construct interaction networks between genes from, primarily, expression array data. We review the current technologies and analyses that have developed in the last decade. We further argue that as cancer genomics evolves from single gene validations to gene network inferences, new analyses must be developed for the different technological platforms. in t r o du c t io n Cancer is uncontrolled accelerated cel- lular growth and is responsible for approx- imately 13 percent of deaths worldwide [1]. Over the last half century, our understand- ing of cancer development has evolved from environmental to genetic causes [2,3]. While it is likely a combination of the two [4,5], much research in the last three decades have focused on genetic predispo- sitions with the prevailing common dis- ease/common variant hypothesis [6,7]. Namely, the disease (cancer) is driven by a common set of alleles, possibly spanning To whom all correspondence should be addressed: Bernard Fendler, Cold Spring Harbor Laboratory, One Bungtown Road, Cold Spring Harbor, NY 11724; Tele: 516-367-5085; Fax: 516-367-8380; Email: bfendler@cshl.edu; and Gurinder Atwal, Cold Spring Harbor Laboratory, One Bungtown Road, Cold Spring Harbor, NY 11724; Tele: 516-367-8462; Fax: 516-367-8380; Email: atwal@cshl.edu. †Abbreviations: SNPs, single nucleotide polymorphisms; aCGH, array comparative ge- nomic hybridization; NGS, next-generation sequencing; ODEs, ordinary differential equa- tions. Keywords: gene network, inference, microarrays, rna-seq, cancer 339