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Jasmine and Iris: population-scale structural variant comparison and analysis

Nature Methods volume 20, pages 408–417 (2023)Cite this article

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Abstract

The availability of long reads is revolutionizing studies of structural variants (SVs). However, because SVs vary across individuals and are discovered through imprecise read technologies and methods, they can be difficult to compare. Addressing this, we present Jasmine and Iris (https://github.com/mkirsche/Jasmine/), for fast and accurate SV refinement, comparison and population analysis. Using an SV proximity graph, Jasmine outperforms six widely used comparison methods, including reducing the rate of Mendelian discordance in trio datasets by more than fivefold, and reveals a set of high-confidence de novo SVs confirmed by multiple technologies. We also present a unified callset of 122,813 SVs and 82,379 indels from 31 samples of diverse ancestry sequenced with long reads. We genotype these variants in 1,317 samples from the 1000 Genomes Project and the Genotype-Tissue Expression project with DNA and RNA-sequencing data and assess their widespread impact on gene expression, including within medically relevant genes.

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Fig. 1: Structural variant inference pipeline.
Fig. 2: Mendelian discordance in the HG002 Ashkenazim trio.
Fig. 3: Structural variant inference across sequencing technologies in HG002.
Fig. 4: De novo variant discovery in HG002.
Fig. 5: Population-scale inference from public datasets.
Fig. 6: Functional impact of structural variants from Jasmine.

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Data availability

The sequencing data used in this study are available from the publications listed in Supplementary Table 1 and Supplementary Table 2. All variant calls and associations are available at http://data.schatz-lab.org/jasmine/.

Code availability

The Jasmine and Iris code and documentation are available open source at https://github.com/mkirsche/Jasmine/ and https://github.com/mkirsche/Iris/. The versions used in the paper are archived in Zenodo for Jasmine62 and Iris63. These methods are also available in Bioconda and Galaxy to simplify use on the command line or within the Galaxy graphical user interface. The versions of all software packages used in the manuscript are described in Supplementary Table 3.

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Acknowledgements

We thank F. Sedlazeck and M. Alonge for helpful discussions. This work was supported, in part, by National Science Foundation grants DBI-1350041 (to M.C.S.), IOS-1732253 (to M.C.S.) and IOS-1758800 (to M.C.S.) and National Institutes of Health grants NCI U01CA253481 (to M.C.S.), NCI U24CA231877 (to M.C.S.), NHGRI U41HG006620 (to M.C.S.), NHGRI U24HG010263 (to M.C.S.), NIH R03CA272952 (to M.C.S.) and NIGMS R35GM139580 (to A.B.). This work was also supported in part by the Mark Foundation for Cancer Research award 19-033-ASP (to M.C.S.) and a Microsoft Research Fellows award (to A.B.). Part of this research project was conducted using computational resources at the Maryland Advanced Research Computing Center (MARCC). We also thank the investigators and the patient donors from the Human Pangenome Reference Consortium, GTEx, and 1000 Genomes for making their data available.

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Authors and Affiliations

  1. Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA

    Melanie Kirsche, Gautam Prabhu, Rachel Sherman, Bohan Ni, Sergey Aganezov & Michael C. Schatz

  2. Department of Biology, Johns Hopkins University, Baltimore, MD, USA

    Gautam Prabhu & Michael C. Schatz

  3. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

    Alexis Battle

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Contributions

M.K. was the principal author of the Jasmine and Iris software, and led most of the presented analyses. G.P. contributed to the genotyping and eQTL analysis of the 1000 Genomes cohort. R.S. contributed to the genotyping of the 1000 Genomes cohort. B.N. led the genotyping and eQTL analysis of the GTEx cohort. A.B. assisted in the analysis of the GTEx cohort. S.A. helped design the software methods and the overall research strategy. M.C.S. oversaw all aspects of the research and analysis. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Sergey Aganezov or Michael C. Schatz.

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Competing interests

S.A. has become an employee at Oxford Nanopore. R.S. has become an employee at Illumina. M.K. has become an employee at Variant Bio.

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Nature Methods thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Lin Tang, in collaboration with the Nature Methods team. Peer reviewer reports are available.

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Supplementary Tables 1–3, Figs. 1–47 and Notes 1 and 2.

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Kirsche, M., Prabhu, G., Sherman, R. et al. Jasmine and Iris: population-scale structural variant comparison and analysis. Nat Methods 20, 408–417 (2023). https://doi.org/10.1038/s41592-022-01753-3

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