DNA copy number variations (CNVs) play an important role in the pathogenesis and progression of cancer and confer susceptibility to a variety of human disorders.In this study, we developed an algorithm to detect CNVs from whole-genome sequencing data and applied it to a newly sequenced glioblastoma genome with a matched control.This read-depth algorithm, called BIC-seq, can accurately and efficiently identify CNVs via minimizing the Bayesian information criterion (BIC).Using BIC-seq, we identified hundreds of CNVs as small as 40 bp in the cancer genome sequenced at 10X coverage, while we could only detect large CNVs (>15 Kb) in the array CGH profiles for the same genome.Eighty percent (14/16) of the small variants tested (110 bp to 14 Kb) were experimentally validated by quantitative PCR, demonstrating high sensitivity and true positive rate of the algorithm.We also extended the algorithm to detect recurrent CNVs in multiple samples as well as deriving error bars for breakpoints using a Gibbs sampling approach .