RT Journal Article
SR Electronic
T1 SvABA: Genome-wide detection of structural variants and indels by local assembly
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 105080
DO 10.1101/105080
A1 Jeremiah Wala
A1 Pratiti Bandopadhayay
A1 Noah Greenwald
A1 Ryan O’Rourke
A1 Ted Sharpe
A1 Chip Stewart
A1 Steve Schumacher
A1 Yilong Li
A1 Joachim Weischenfeldt
A1 Xiaotong Yao
A1 Chad Nusbaum
A1 Peter Campbell
A1 Matthew Meyerson
A1 Cheng-Zhong Zhang
A1 Marcin Imielinski
A1 Rameen Beroukhim
YR 2017
UL http://biorxiv.org/content/early/2017/02/01/105080.abstract
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.