Copy number variation (CNV) contributes in phenotypically relevant ways to the genetic variability of many organisms. Cost-effective genomewide methods for identifying copy number variation are necessary to elucidate the contribution that these structural variants make to the genomes of model organisms. We have developed a novel approach for the identification of copy number variation by next generation sequencing. As a proof of concept our method has been applied to map the deletions of three Drosophila deficiency strains. We demonstrate that low sequence coverage is sufficient for identifying and mapping large deletions at kilobase resolution, suggesting that data generated from high-throughput sequencing experiments are sufficient for simultaneously analyzing many strains. Genomic DNA from two Drosophila deficiency stocks was barcoded and sequenced in multiplex, and the breakpoints associated with each deletion were successfully identified. The approach we describe is immediately applicable to the systematic exploration of copy number variation in model organisms and humans. S TRUCTURAL variation is known to contribute extensively to the genetic variability of humans, mammals, and many model organisms. One class of structural variant, termed copy number variation (CNV), includes deletions, duplications, insertions, and genomic rearrangements which affect the number of occurrences of a specific DNA sequence present in the genome . CNV is known to occur extensively in the Drosophila genome with functionally significant consequences (Bridges 1936;Dopman and Hartl 2007;Tibshirani and Wang 2008;Zhou et al. 2008). In one study of 15 Drosophila strains, as many as 10% of genes were observed to harbor CNVs (Emerson et al. 2008). Cryptic CNVs that affect the phenotype observed in a model organism have the potential to confound research on multiple levels. For example, a recent report indicates that terminal deletions on chromosome (chr) 2L are frequent among deficiency kit stocks with mutations on the second chromosome and that the associated deletion of lgl has distorted the results of several previous studies (Roegiers et al. 2009). Despite widespread existence of CNV, the biological consequences of this phenomenon remain largely unexplored due to the lack of efficient tools for detection and characterization.Until recently, comparative genomic hybridization with whole-genome tiling arrays (array-CGH) was the primary method for characterizing CNVs (Carter 2007); however, several limitations for this platform reduce its efficacy and efficiency. First, cross-hybridization and reliance on intensity scores lead to data that are difficult to interpret. Second, custom array design and optimization is labor intensive and costly. Third, array-CGH methods can only detect CNV, not other complex rearrangements such as balanced translocations and inversions. Finally, the overall cost of array-CGH methods is relatively high, particularly when high-resolution, whole-genome tiling arrays are employed.Direct sequencing usin...