SvABA: genome-wide detection of structural variants and indels by local assembly
- Jeremiah A. Wala1,2,3,4,
- Pratiti Bandopadhayay1,2,
- Noah F. Greenwald1,2,
- Ryan O'Rourke1,2,
- Ted Sharpe1,
- Chip Stewart1,
- Steve Schumacher1,2,
- Yilong Li5,6,
- Joachim Weischenfeldt7,
- Xiaotong Yao8,9,
- Chad Nusbaum1,
- Peter Campbell6,10,
- Gad Getz1,3,4,11,
- Matthew Meyerson1,2,3,4,
- Cheng-Zhong Zhang12,13,15,
- Marcin Imielinski9,14,15 and
- Rameen Beroukhim1,2,3,4,15
- 1The Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA;
- 2Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA;
- 3Bioinformatics and Integrative Genomics, Harvard University, Cambridge, Massachusetts 02138, USA;
- 4Harvard Medical School, Boston, Massachusetts 02115, USA;
- 5Seven Bridges Genomics, Cambridge, Massachusetts 02142, USA;
- 6Cancer Genome Project, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, United Kingdom;
- 7The Finsen Laboratory, Rigshospitalet, University of Copenhagen, DK-2200 Copenhagen, Denmark;
- 8Tri-Institutional PhD Program in Computational Biology and Medicine, New York, New York 10065, USA;
- 9New York Genome Center, New York, New York 10013, USA;
- 10Department of Haematology, University of Cambridge, Cambridge CB2 2XY, United Kingdom;
- 11Department of Pathology and Cancer Center, Massachusetts General Hospital, Boston, Massachusetts 02114, USA;
- 12Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA;
- 13Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts 02115, USA;
- 14Department of Pathology and Laboratory Medicine, Englander Institute for Precision Medicine, Institute for Computational Biomedicine, and Meyer Cancer Center, Weill Cornell Medicine, New York, New York 10065, USA
Abstract
Structural variants (SVs), including small insertion and deletion variants (indels), are challenging to detect through standard alignment-based variant calling methods. Sequence assembly offers a powerful approach to identifying SVs, but is difficult to apply at scale genome-wide for SV detection due to its computational complexity and the difficulty of extracting SVs from assembly contigs. We describe SvABA, an efficient and accurate method for detecting SVs from short-read sequencing data using genome-wide local assembly with low memory and computing requirements. We evaluated SvABA's performance on the NA12878 human genome and in simulated and real cancer genomes. SvABA demonstrates superior sensitivity and specificity across a large spectrum of SVs and substantially improves detection performance for variants in the 20–300 bp range, compared with existing methods. SvABA also identifies complex somatic rearrangements with chains of short (<1000 bp) templated-sequence insertions copied from distant genomic regions. We applied SvABA to 344 cancer genomes from 11 cancer types and found that short templated-sequence insertions occur in ∼4% of all somatic rearrangements. Finally, we demonstrate that SvABA can identify sites of viral integration and cancer driver alterations containing medium-sized (50–300 bp) SVs.
Footnotes
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↵15 Co-senior authors.
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.221028.117.
- Received February 1, 2017.
- Accepted February 14, 2018.
This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see http://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
