Numerous large clinical cohorts comprising thousands of individuals are being analyzed by exome sequencing. Until now, exome sequencing data analysis has yielded primarily single-nucleotide variants, but in a recent issue of Genome Research, Krumm et al. describe a new algorithm that allows the identification copy number variants from these data sets. Their approach makes use of singular-value decomposition to remove noise resulting from both nonuniformity in exome capture reactions and systematic biases between batches of samples. The authors benchmark their method on two sets of samples for which both exome and single-nucleotide polymorphism (SNP) microarray data were available to call copy number variants. On eight HapMap samples, exome analysis identified >68% of the 32 variants called by microarray approaches, whereas on data from a cohort of autism spectrum disorder patients, 94% of the 124 calls made by the authors' method were concordant with results from either array analysis or quantitative PCR. This method could be applied to several recently published data sets, such as a study of DNA sequence variants in drug target genes (Science advance online publication, doi:10.1126/science.1217876, 17 May 2012) or the exomes of 2,440 individuals of European and African ancestry (Science advance online publication, doi:10.1126/science.1219240, 17 May 2012; Genome Research advance online publication, doi:10.1101/gr.138115.112, 14 May 2012).