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dc.contributor.authorAbo, Ryan P.en_US
dc.contributor.authorDucar, Matthewen_US
dc.contributor.authorGarcia, Elizabeth P.en_US
dc.contributor.authorThorner, Aaron R.en_US
dc.contributor.authorRojas-Rudilla, Vanesaen_US
dc.contributor.authorLin, Lingen_US
dc.contributor.authorSholl, Lynette M.en_US
dc.contributor.authorHahn, William C.en_US
dc.contributor.authorMeyerson, Matthewen_US
dc.contributor.authorLindeman, Neal I.en_US
dc.contributor.authorVan Hummelen, Paulen_US
dc.contributor.authorMacConaill, Laura E.en_US
dc.date.accessioned2015-04-01T15:30:25Z
dc.date.issued2015en_US
dc.identifier.citationAbo, R. P., M. Ducar, E. P. Garcia, A. R. Thorner, V. Rojas-Rudilla, L. Lin, L. M. Sholl, et al. 2015. “BreaKmer: detection of structural variation in targeted massively parallel sequencing data using kmers.” Nucleic Acids Research 43 (3): e19. doi:10.1093/nar/gku1211. http://dx.doi.org/10.1093/nar/gku1211.en
dc.identifier.issn0305-1048en
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:14351290
dc.description.abstractGenomic structural variation (SV), a common hallmark of cancer, has important predictive and therapeutic implications. However, accurately detecting SV using high-throughput sequencing data remains challenging, especially for ‘targeted’ resequencing efforts. This is critically important in the clinical setting where targeted resequencing is frequently being applied to rapidly assess clinically actionable mutations in tumor biopsies in a cost-effective manner. We present BreaKmer, a novel approach that uses a ‘kmer’ strategy to assemble misaligned sequence reads for predicting insertions, deletions, inversions, tandem duplications and translocations at base-pair resolution in targeted resequencing data. Variants are predicted by realigning an assembled consensus sequence created from sequence reads that were abnormally aligned to the reference genome. Using targeted resequencing data from tumor specimens with orthogonally validated SV, non-tumor samples and whole-genome sequencing data, BreaKmer had a 97.4% overall sensitivity for known events and predicted 17 positively validated, novel variants. Relative to four publically available algorithms, BreaKmer detected SV with increased sensitivity and limited calls in non-tumor samples, key features for variant analysis of tumor specimens in both the clinical and research settings.en
dc.language.isoen_USen
dc.publisherOxford University Pressen
dc.relation.isversionofdoi:10.1093/nar/gku1211en
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC4330340/pdf/en
dash.licenseLAAen_US
dc.titleBreaKmer: detection of structural variation in targeted massively parallel sequencing data using kmersen
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden
dc.relation.journalNucleic Acids Researchen
dash.depositing.authorThorner, Aaron R.en_US
dc.date.available2015-04-01T15:30:25Z
dc.identifier.doi10.1093/nar/gku1211*
dash.authorsorderedfalse
dash.contributor.affiliatedThorner, Aaron
dash.contributor.affiliatedMeyerson, Matthew
dash.contributor.affiliatedHahn, William
dash.contributor.affiliatedMacConaill, Laura


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