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Title:   Author  item
                                            Bickhart, Derekitem
                                            Hutchison, Janaitem
                                            XU, LINGYANG - University Of Marylanditem
                                            SCHNABEL, ROBERT - University Of Missouriitem
                                            TAYLOR, JEREMY - University Of Marylanditem
                                            REECY, JAMES - Iowa State Universityitem
                                            Schroeder, Steven

                                                         - *Steve*item
                                            Van Tassell, Curtis

                                                         - *Curt*item
                                            Sonstegard, Taditem
                                            Liu, Ge

                                                         - *George*
  Submitted to: Bioinformatics
                                    Publication Type: Peer Reviewed Journal
                                    Publication Acceptance Date: 2/9/2015
                                    Publication  Date: 6/26/2015
                                    Publication URL: http://handle.nal.usda.gov/10113/61483Citation: Bickhart, D.M., Hutchison, J.L., Xu, L., Schnabel, R.D., Taylor, J.F., Reecy, J.M., Schroeder, S.G., Van Tassell, C.P., Sonstegard, T.S., Liu, G. 2015. RAPTR-SV: a hybrid method for the detection of structural variants. Bioinformatics. 31(13):2084-2090.Interpretive Summary: Structural Variants, or large deletions and duplications of the genome of an individual, are likely linked to many diseases and productive traits in animals and humans but these variants are difficult to detect using modern DNA sequencing technologies. We have written a program, RAPTR-SV, that detects these variants from commonly used sequencing technologies without making a large number of false positive variant calls. Tests of RAPTR-SV on simulations and real data have revealed it to perform better than another program that uses a similar algorithm.
                                Technical Abstract: Motivation: Identification of Structural Variants (SV) in sequence data results in a large number of false positive calls using existing software, which overburdens subsequent validation.

Results: Simulations using RAPTR-SV and another software package that uses a similar algorithm for SV detection revealed that RAPTR-SV had superior sensitivity and precision, as it recovered 66.4% of simulated tandem duplications with a precision of 99.2%. When compared with calls made by other software on available datasets from the 1000 genomes project, RAPTR-SV showed superior sensitivity for tandem duplications, as it identified two-fold more duplications than a similar algorithm, while making approximately 85% fewer duplication predictions. 

Availability and Implementation: RAPTR-SV is written in Java and uses new features in the collections framework in the latest release of the Java version 8 language specifications. A compiled version of the software, instructions for usage and test results files are available on the GitHub repository page: https://github.com/njdbickhart/RAPTR-SV.


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