TY - JOUR T1 - A comprehensive benchmarking of WGS-based structural variant callers JF - bioRxiv DO - 10.1101/2020.04.16.045120 SP - 2020.04.16.045120 AU - Varuni Sarwal AU - Sebastian Niehus AU - Ram Ayyala AU - Sei Chang AU - Angela Lu AU - Nicholas Darci-Maher AU - Russell Littman AU - Emily Wesel AU - Jacqueline Castellanos AU - Rahul Chikka AU - Margaret G. Distler AU - Eleazar Eskin AU - Jonathan Flint AU - Serghei Mangul Y1 - 2020/01/01 UR - http://biorxiv.org/content/early/2020/04/18/2020.04.16.045120.abstract N2 - Advances in whole genome sequencing promise to enable the accurate and comprehensive structural variant (SV) discovery. Dissecting SVs from whole genome sequencing (WGS) data presents a substantial number of challenges and a plethora of SV-detection methods have been developed. Currently, there is a paucity of evidence which investigators can use to select appropriate SV-detection tools. In this paper, we evaluated the performance of SV-detection tools using a comprehensive PCR-confirmed gold standard set of SVs. In contrast to the previous benchmarking studies, our gold standard dataset included a complete set of SVs allowing us to report both precision and sensitivity rates of SV-detection methods. Our study investigates the ability of the methods to detect deletions, thus providing an optimistic estimate of SV detection performance, as the SV-detection methods that fail to detect deletions are likely to miss more complex SVs. We found that SV-detection tools varied widely in their performance, with several methods providing a good balance between sensitivity and precision. Additionally, we have determined the SV callers best suited for low and ultra-low pass sequencing data.Competing Interest StatementThe authors have declared no competing interest. ER -