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Targeted next-generation sequencing (NGS) to capture genes or regions of interest has proven to be a cost-effective alternative to whole genome sequencing (WGS), particularly for cancer research and clinical cancer care. In this context, biologically or clinically relevant selected genes/regions are sequenced to several hundred-fold coverage. However, current algorithms and tools for detecting large structural variants (SVs), such as translocations, fail to achieve either high specificity or sensitivity, due to the fact that most available methods were designed for WGS data, and thus do not take advantage of the reduced size and higher coverage of targeted sequencing to improve SV calling. We developed a novel method, BreaKmer, to detect indels, rearrangements, and translocations from single sample targeted genomic reads. The algorithm extracts mis-mapped single or paired-end NGS reads. From these reads, hypothesized to contain SV breakpoints, contigs are built with a kmer strategy: reads are broken into k-length substrings or kmers, and those occurring in the reference are filtered. The remaining kmers represent sequences containing any sequence variant from the reference, ranging from single nucleotides to larger variants. Contigs are assembled from reads containing sample specific kmers. SVs are called based on alignment of the contigs to the reference sequence. With paired-end (PE) reads, discordantly mapped paired reads are extracted and coupled with SV calls that are made. To demonstrate BreaKmer, we analyzed NGS data from 166 samples enriched using 3 different capture panels (ranging from 305-504 genes). Our dataset contained 25 cancer specimens with known translocation events verified by orthogonal clinical methods and a negative control set of 141 ‘normal’ samples with no known SVs. The samples represented DNA extracted from FFPE, fresh frozen, blood and cell lines. Among 25 samples, 15 had additional replicate samples. Mean target coverage over all the samples was on average 150x. Specimens were barcoded at library preparation and pooled, followed by hybrid-capture targeting cancer genes and sequenced 2x100bp PE. All translocation events from the 25 test samples and their replicates (46) were detected by BreaKmer. An additional 19 translocations were detected among all the samples and their replicates, while no translocation calls were made for the 141 negative control samples. Our novel kmer strategy to detect SVs displayed high sensitivity and specificity. We reliably detected rearrangements of ALK, BCL2-IGH, BCR-ABL1 from lung adenocarcinoma, B-cell lymphoma, and chronic myeloid leukemia samples, respectively; indicating real clinical utility of this algorithm. In addition, our tool effectively detects other SV types - such as indels in FLT3 and KIT, among other genes. Our algorithm thus serves a pressing need for improved SV detection in targeted NGS data, particularly in precision cancer medicine. Citation Format: Ryan P. Abo, Elizabeth P. Garcia, Matthew Ducar, Ravali Adusumilli, Marc Breneiser, Vanessa Rojas-Rudilla, Lynette M. Sholl, Neal I. Lindeman, Matthew L. Meyerson, William C. Hahn, Paul Van Hummelen, Laura E. MacConaill. BreaKmer: Detection of structural rearrangements in targeted next-generation sequencing data using kmers. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5321. doi:10.1158/1538-7445.AM2014-5321