A comprehensive benchmarking of WGS-based structural variant callers

Author:

Sarwal Varuni,Niehus Sebastian,Ayyala Ram,Chang Sei,Lu Angela,Darci-Maher Nicholas,Littman Russell,Chhugani Karishma,Soylev Arda,Comarova Zoia,Wesel Emily,Castellanos Jacqueline,Chikka Rahul,Distler Margaret G.,Eskin Eleazar,Flint Jonathan,Mangul SergheiORCID

Abstract

AbstractAdvances 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.

Publisher

Cold Spring Harbor Laboratory

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