RAPTR-SV: a hybrid method for the detection of structural variants.

Bioinformatics

Animal Genomics and Improvement Laboratory, ARS, USDA, Beltsville, MD 20705, USA, Department of Animal and Avian Sciences, University of Maryland, College Park, MD 20705, USA, Division of Animal Sciences, University of Missouri, Columbia, MO 65211, USA and Department of Animal Science, Iowa State University, Ames, IA 50011, USA.

Published: July 2015

AI Article Synopsis

  • Existing software for identifying structural variants (SVs) in sequence data often leads to many false positives, complicating validation processes.
  • RAPTR-SV outperformed other algorithms like Delly and LUMPY in simulations, recovering 66.4% of tandem duplications with impressive precision (99.2%) and identifying twice as many duplications with fewer overall predictions.
  • RAPTR-SV is a Java-based software available on GitHub, providing easy access with usage instructions and test results.

Article Abstract

Motivation: Identification of structural variants (SVs) 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 other, similar algorithms 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 Delly and LUMPY on available datasets from the 1000 genomes project, RAPTR-SV showed superior sensitivity for tandem duplications, as it identified 2-fold more duplications than Delly, while making ∼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.

Contact: derek.bickhart@ars.usda.gov.

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Source
http://dx.doi.org/10.1093/bioinformatics/btv086DOI Listing

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