PT - JOURNAL ARTICLE AU - Jeremiah Wala AU - Pratiti Bandopadhayay AU - Noah Greenwald AU - Ryan O’Rourke AU - Ted Sharpe AU - Chip Stewart AU - Steve Schumacher AU - Yilong Li AU - Joachim Weischenfeldt AU - Xiaotong Yao AU - Chad Nusbaum AU - Peter Campbell AU - Matthew Meyerson AU - Cheng-Zhong Zhang AU - Marcin Imielinski AU - Rameen Beroukhim TI - SvABA: Genome-wide detection of structural variants and indels by local assembly AID - 10.1101/105080 DP - 2017 Jan 01 TA - bioRxiv PG - 105080 4099 - http://biorxiv.org/content/early/2017/02/01/105080.short 4100 - http://biorxiv.org/content/early/2017/02/01/105080.full AB - 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 improved detection performance for variants in the 20-300 bp range, compared with existing methods. SvABA also identifies complex somatic rearrangements with chains of short (< 1,000 bp) templated-sequence insertions copied from distant genomic regions. We applied SvABA to 344 cancer genomes from 11 cancer types, and found that 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 SVs.