RT Journal Article
SR Electronic
T1 A robust benchmark for germline structural variant detection
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 664623
DO 10.1101/664623
A1 Justin M. Zook
A1 Nancy F. Hansen
A1 Nathan D. Olson
A1 Lesley M. Chapman
A1 James C. Mullikin
A1 Chunlin Xiao
A1 Stephen Sherry
A1 Sergey Koren
A1 Adam M. Phillippy
A1 Paul C. Boutros
A1 Sayed Mohammad E. Sahraeian
A1 Vincent Huang
A1 Alexandre Rouette
A1 Noah Alexander
A1 Christopher E. Mason
A1 Iman Hajirasouliha
A1 Camir Ricketts
A1 Joyce Lee
A1 Rick Tearle
A1 Ian T. Fiddes
A1 Alvaro Martinez Barrio
A1 Jeremiah Wala
A1 Andrew Carroll
A1 Noushin Ghaffari
A1 Oscar L. Rodriguez
A1 Ali Bashir
A1 Shaun Jackman
A1 John J Farrell
A1 Aaron M Wenger
A1 Can Alkan
A1 Arda Soylev
A1 Michael C. Schatz
A1 Shilpa Garg
A1 George Church
A1 Tobias Marschall
A1 Ken Chen
A1 Xian Fan
A1 Adam C. English
A1 Jeffrey A. Rosenfeld
A1 Weichen Zhou
A1 Ryan E. Mills
A1 Jay M. Sage
A1 Jennifer R. Davis
A1 Michael D. Kaiser
A1 John S. Oliver
A1 Anthony P. Catalano
A1 Mark JP Chaisson
A1 Noah Spies
A1 Fritz J. Sedlazeck
A1 Marc Salit
A1 the Genome in a Bottle Consortium
YR 2019
UL http://biorxiv.org/content/early/2019/07/18/664623.abstract
AB New technologies and analysis methods are enabling genomic structural variants (SVs) to be detected with ever-increasing accuracy, resolution, and comprehensiveness. Translating these methods to routine research and clinical practice requires robust benchmark sets. We developed the first benchmark set for identification of both false negative and false positive germline SVs, which complements recent efforts emphasizing increasingly comprehensive characterization of SVs. To create this benchmark for a broadly consented son in a Personal Genome Project trio with broadly available cells and DNA, the Genome in a Bottle (GIAB) Consortium integrated 19 sequence-resolved variant calling methods, both alignment- and de novo assembly-based, from short-, linked-, and long-read sequencing, as well as optical and electronic mapping. The final benchmark set contains 12745 isolated, sequence-resolved insertion and deletion calls ≥50 base pairs (bp) discovered by at least 2 technologies or 5 callsets, genotyped as heterozygous or homozygous variants by long reads. The Tier 1 benchmark regions, for which any extra calls are putative false positives, cover 2.66 Gbp and 9641 SVs supported by at least one diploid assembly. Support for SVs was assessed using svviz with short-, linked-, and long-read sequence data. In general, there was strong support from multiple technologies for the benchmark SVs, with 90 % of the Tier 1 SVs having support in reads from more than one technology. The Mendelian genotype error rate was 0.3 %, and genotype concordance with manual curation was >98.7 %. We demonstrate the utility of the benchmark set by showing it reliably identifies both false negatives and false positives in high-quality SV callsets from short-, linked-, and long-read sequencing and optical mapping.