svtools: population-scale analysis of structural variation

Author:

Larson David E12ORCID,Abel Haley J12,Chiang Colby1,Badve Abhijit1,Das Indraniel1,Eldred James M1,Layer Ryan M34,Hall Ira M125

Affiliation:

1. McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA

2. Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA

3. Biofrontiers Institute, University of Colorado, Boulder, CO 80309, USA

4. Department of Computer Science, University of Colorado, Boulder, CO 80309, USA

5. Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA

Abstract

Abstract Summary Large-scale human genetics studies are now employing whole genome sequencing with the goal of conducting comprehensive trait mapping analyses of all forms of genome variation. However, methods for structural variation (SV) analysis have lagged far behind those for smaller scale variants, and there is an urgent need to develop more efficient tools that scale to the size of human populations. Here, we present a fast and highly scalable software toolkit (svtools) and cloud-based pipeline for assembling high quality SV maps—including deletions, duplications, mobile element insertions, inversions and other rearrangements—in many thousands of human genomes. We show that this pipeline achieves similar variant detection performance to established per-sample methods (e.g. LUMPY), while providing fast and affordable joint analysis at the scale of ≥100 000 genomes. These tools will help enable the next generation of human genetics studies. Availability and implementation svtools is implemented in Python and freely available (MIT) from https://github.com/hall-lab/svtools. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

National Institutes of Health

National Human Genome Research Institute

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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