A robust benchmark for detection of germline large deletions and insertions

By Justin M. Zook, Nancy F. Hansen, Nathan D. Olson, Lesley M. Chapman, James C. Mullikin, Chunlin Xiao, S. Sherry, Sergey Koren, Adam M. Phillippy, Paul C. Boutros and 40 more. Nature Biotechnology, 2020

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New technologies and analysis methods are enabling genomic structural variants (SVs) to be detected with ever-increasing accuracy, resolution and comprehensiveness. To help translate these methods to routine research and clinical practice, we developed a sequence-resolved benchmark set for identification of both false-negative and false-positive germline large insertions and deletions. 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 Consortium integrated 19 sequence-resolved variant calling methods from diverse technologies. The final benchmark set contains 12,745 isolated, sequence-resolved insertion (7,281) and deletion (5,464) calls ≥50 base pairs (bp). The Tier 1 benchmark regions, for which any extra calls are putative false positives, cover 2.51 Gbp and 5,262 insertions and 4,095 deletions supported by ≥1 diploid assembly. We demonstrate that the benchmark set reliably identifies false negatives and false positives in high-quality SV callsets from short-, linked- and long-read sequencing and optical mapping. Detection of structural variants in the human genome is facilitated by a benchmark set of large deletions and insertions.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/01deab52bdc571f57a65e9ec6670636da151da88

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Citations in the corpus

Best practices for variant calling in clinical sequencing (2020, background): “Even so, multiple “gold standard” SV callsets such as GIAB [99], HS1011 [102], and HuRef [103] have been published which employ orthogonal sequencing technologies to define reference callsets comprising thousands of structural variants.”
New strategies to improve minimap2 alignment accuracy (2021, neutral): “3 (Li et al., 2018) for HG002 assemblies (AC: GCA 018852605.1 and GCA 018852615.1) and compared to the GIAB truth (Zook et al., 2020) using “truvari -r 2000 -s 1000 -”
Detection of mosaic and population-level structural variants with Sniffles2 (2024, positive): “We assessed the performance of Sniffles2 (version 2.2) with respect to Sniffles 28 (version 1.12), cuteSV 47 (version 1.0.11), PBSV 48 (version 2.6.2) and SVIM 49 (version 1.4.2) using Truvari 50 (version 2.1) and the Genome in a Bottle (GIAB) recommended parameters 51 .”
Pangenomics enables genotyping known structural variants in 5,202 diverse genomes (2021, methodology): “Hence, the SVs genotyped in this study match the original SVs discovered in long-read sequencing studies ( 1 , 22 , 28 ) in terms of number, size distribution, and sequence context.”
HiCanu: accurate assembly of segmental duplications, satellites, and allelic variants from high-fidelity long reads (2020, methodology): “HG002 and parent Illumina data are available from GIAB (Zook et al. 2016) at GitHub (https://github.com/genome-in-a-bottle/ giab_data_indexes), we only used the 2×250 data sets.”