A robust benchmark for detection of germline large deletions and insertions


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.

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Fig. 1: Pairwise comparison of sequence-resolved SV callsets obtained from multiple technologies and SV callers for SVs ≥50 bp from HG002.
Fig. 2: Process to integrate SV callsets and diploid assemblies from different technologies and analysis methods and form the benchmark set.
Fig. 3: Size distributions of deletions and insertions in the benchmark set.
Fig. 4: Support for benchmark SVs by long reads, short reads and optical mapping.
Fig. 5: Summary of manual curation of putative false positives and false negatives when benchmarking short and long reads against the v0.6 benchmark set.
Fig. 6: Inverse cumulative distribution showing the number of discovery methods that supported each SV.
Fig. 7: Fraction of SVs for each number of discovery callsets that estimated exactly matching sequence changes.

Data availability

Raw sequence data were previously published in Scientific Data (https://doi.org/10.1038/sdata.2016.25) and deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive with the accession codes SRX847862 to SRX848317, SRX1388732 to SRX1388743, SRX852933, SRX5527202, SRX5327410 and SRX1033793 to SRX1033798. 10× Genomics Chromium bam files used are available at ftp://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data/AshkenazimTrio/analysis/10XGenomics_ChromiumGenome_LongRanger2.2_Supernova2.0.1_04122018/. The data used in this paper and other data sets for these genomes are available at ftp://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data/ and in the NCBI BioProject PRJNA200694.

The v0.6 SV benchmark set (only compare to variants in the Tier 1 vcf inside the Tier 1 bed with the FILTER ‘PASS’) for HG002 on GRCh37 is available in dbVar accession nstd175 and at ftp://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data/AshkenazimTrio/analysis/NIST_SVs_Integration_v0.6/.

Input SV callsets, assemblies and other analyses for this trio are available at ftp://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/data/AshkenazimTrio/analysis/.

Code availability

Scripts for integrating candidate structural variants to form the benchmark set in this paper are available in a GitHub repository at https://github.com/jzook/genome-data-integration/tree/master/StructuralVariants/NISTv0.6. This repository includes Jupyter notebooks for the comparisons to HGSVC, GRC, vg, paragraph and Bionano. Publicly available software used to generate input callsets is described in the Methods.


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We thank many GIAB Consortium Analysis Team members for helpful discussions about the design of this benchmark. We thank J. Monlong and G. Hickey for sharing genotypes for HG002 from vg and paragraph. We thank T. Hefferon at NIH/NCBI for assistance with the dbVar submission. Certain commercial equipment, instruments or materials are identified to specify adequately experimental conditions or reported results. Such identification does not imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the equipment, instruments or materials identified are necessarily the best available for the purpose. C.X. and S.S. were supported by the Intramural Research Program of the National Library of Medicine, National Institutes of Health. N.F.H., J.C.M., S.K. and A.M.P. were supported by the Intramural Research Program of the National Human Genome Research Institute, National Institutes of Health. J.M.Z. and N.D.O. were supported by the National Institute of Standards and Technology and an interagency agreement with the Food and Drug Administration. C.E.M. acknowledges the XSEDE Supercomputing Resources, STARR I13-0052 and NIH R01AI151059.

Author information




J.M.Z. contributed project design, manuscript writing, generating SV input callsets and integrating SV calls. N.D.O. contributed SV integration and figures. L.M.C. contributed benchmark evaluation. N.F.H. contributed SV callsets, benchmark evaluation, SV integration and manuscript editing. J.C.M. contributed SV callsets and SV integration. C.X. contributed data management, SV callsets, benchmark evaluation and manuscript editing. S.S. contributed data management and SV callsets. S.K. contributed de novo assembilies. A.M.P. contributed de novo assemblies. P.C.B. contributed manuscript writing, SV callsets and benchmark evaluation. S.M.E.S. contributed SV input callsets, benchmark evaluation and manuscript editing. V.H. contributed SV callsets and benchmark evaluation. A.R. contributed SV callsets and benchmark evaluation. N.A. contributed benchmark evaluation. C.E.M. contributed project design, manuscript editing and benchmark evaluation. I.H. contributed project design, manuscript editing and SV callsets. C.R. contributed SV callsets. J.L. contributed SV callsets and benchmark evaluation. R.T. contributed provision and interpretation of Complete Genomics data and formats. I.T.F. contributed SV callsets, benchmark evaluation and de novo assemblies. A.M.B. contributed SV callsets, benchmark evaluation and de novo assemblies. J.W. contributed SV callsets. A.C. contributed SV callsets and benchmark evaluation. N.G. contributed genome assembly of the Ashkenazi trio, DISCOVER de novo and manuscript editing. O.L.R. contributed SV callsets and de novo assemblies. A.B. contributed SV callsets and de novo assemblies. S.J. contributed de novo assembilies. J.J.F. contributed SV callsets. A.M.W. contributed SV callsets and benchmark evaluation. C.A. contributed SV callsets. A.S. contributed SV callsets. M.C.S. contributed project design and manuscript editing. S.G. contributed integrative phasing short variant calls. G.C. contributed integrative phasing short variant calls. T.M. contributed haplotype phasing. K.C. contributed SV callsets. X.F. contributed SV callsets. A.C.E. contributed SV callsets, benchmark evaluations and SV integration. J.A.R. contributed SV callsets and project design. W.Z. contributed SV callsets. R.E.M. contributed SV callsets. J.M.S. contributed data collection, SV callsets and benchmark evaluation. J.R.D. contributed data collection, SV callsets and benchmark evaluation. M.D.K. contributed SV callsets, benchmark evaluation and SV-Verify development. J.S.O. contributed SV callsets and benchmark evaluation. A.P.C. contributed data collection. N.S. contributed SV integration (svviz2 development). M.J.P.C. contributed SV callsets. F.J.S. contributed SV callsets, manuscript editing and SV integration. M.S. contributed project design and manuscript writing.

Corresponding author

Correspondence to Justin M. Zook.

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Competing interests

A.M.W. is an employee and shareholder of Pacific Biosciences. A.M.B. and I.T.F. are employees and shareholders of 10× Genomics. G.M.C. is the founder and holds leadership positions of many companies described at http://arep.med.harvard.edu/gmc/tech.html. F.J.S. has received sponsored travel from Oxford Nanopore and Pacific Biosciences and received a 2018 sequencing grant from Pacific Biosciences. J.L. is an employee and shareholder of Bionano Genomics. A.C. is an employee of Google and is a former employee of DNAnexus. J.M.S., J.R.D., M.D.K., J.S.O. and A.P.C. are employees of Nabsys 2.0. A.C.E. is an employee and shareholder of Spiral Genetics. S.M.E.S. is an employee of Roche.

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Extended data

Extended Data Fig. 1 Number of long reads supporting the SV allele vs. the reference allele in the benchmark set.

Variants are colored by heterozygous (blue) and homozygous (dark orange) genotype, and are stratified into deletions and insertions, and into SVs overlapping and not overlapping tandem repeats longer than 100 bp in the reference.

Extended Data Fig. 2 Mendelian contingency table for sites with consensus genotypes from svviz in the son, father, and mother.

SVs in boxes highlighted in red violate the expected Mendelian inheritance pattern. Variants on chromosomes X and Y are excluded.

Extended Data Fig. 3 Comparison of false negative rates for the union of all long read-based SV discovery methods, the union of all short read-based discovery methods, and paired-end and mate-pair short read genotyping of known SVs.

Variants are stratified into deletions (top) and insertions (bottom), and into SVs overlapping (right) and not overlapping (left) tandem repeats longer than 100 bp in the reference. SVs are also stratified by size into 50 bp to 99 bp, 100 bp to 299 bp, 300 bp to 999 bp, and ≥1000 bp.

Extended Data Fig. 4 Known limitations of the v0.6 benchmark.

It is important to understand the limitations of any benchmark, such as the limitations below for v0.6, when interpreting the resulting performance metrics.

Supplementary information

Supplementary Information

Supplementary Notes 1–4.

Reporting Summary

Supplementary Table 1

Variant callsets used to develop the benchmark (‘discovery’) and to evaluate the benchmark’s reliability in identifying false positives and false negatives (‘evaluation’).

Supplementary Table 2

Detailed results from manual curation of putative false positives and false negatives from evaluation of benchmark set and of deletions not in v0.6 that were in the population-based gnomAD-SV v2.1 callset that were homozygous reference in less than 5% of individuals of European ancestry, and at least 1,000 Europeans had the variant.

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Zook, J.M., Hansen, N.F., Olson, N.D. et al. A robust benchmark for detection of germline large deletions and insertions. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0538-8

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