Format

Send to

Choose Destination
Genome Res. 2018 Apr;28(4):581-591. doi: 10.1101/gr.221028.117. Epub 2018 Mar 13.

SvABA: genome-wide detection of structural variants and indels by local assembly.

Author information

1
The Broad Institute of Harvard and MIT, Cambridge, Massachusetts 02142, USA.
2
Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA.
3
Bioinformatics and Integrative Genomics, Harvard University, Cambridge, Massachusetts 02138, USA.
4
Harvard Medical School, Boston, Massachusetts 02115, USA.
5
Seven Bridges Genomics, Cambridge, Massachusetts 02142, USA.
6
Cancer Genome Project, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, United Kingdom.
7
The Finsen Laboratory, Rigshospitalet, University of Copenhagen, DK-2200 Copenhagen, Denmark.
8
Tri-Institutional PhD Program in Computational Biology and Medicine, New York, New York 10065, USA.
9
New York Genome Center, New York, New York 10013, USA.
10
Department of Haematology, University of Cambridge, Cambridge CB2 2XY, United Kingdom.
11
Department of Pathology and Cancer Center, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.
12
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA.
13
Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts 02115, USA.
14
Department 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.

PMID:
29535149
PMCID:
PMC5880247
DOI:
10.1101/gr.221028.117
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for HighWire Icon for PubMed Central
Loading ...
Support Center