SVScore: an impact prediction tool for structural variation.

TitleSVScore: an impact prediction tool for structural variation.
Publication TypeJournal Article
Year of Publication2017
AuthorsGanel, L, Abel, HJ, Hall, IM
Corporate AuthorsFinMetSeq Consortium
JournalBioinformatics
Volume33
Issue7
Pagination1083-1085
Date Published2017 04 01
ISSN1367-4811
KeywordsGene Frequency, Genomic Structural Variation, Genomics, Humans, Polymorphism, Single Nucleotide, Sequence Deletion, Software
Abstract

Summary: Here we present SVScore, a tool for in silico structural variation (SV) impact prediction. SVScore aggregates per-base single nucleotide polymorphism (SNP) pathogenicity scores across relevant genomic intervals for each SV in a manner that considers variant type, gene features and positional uncertainty. We show that the allele frequency spectrum of high-scoring SVs is strongly skewed toward lower frequencies, suggesting that they are under purifying selection, and that SVScore identifies deleterious variants more effectively than alternative methods. Notably, our results also suggest that duplications are under surprisingly strong selection relative to deletions, and that there are a similar number of strongly pathogenic SVs and SNPs in the human population.

Availability and Implementation: SVScore is implemented in Perl and available freely at {{ http://www.github.com/lganel/SVScore }} for use under the MIT license.

Contact: ihall@wustl.edu.

Supplementary information: Supplementary data are available at Bioinformatics online.

DOI10.1093/bioinformatics/btw789
PubMed ID28031184
PubMed Central IDPMC5408916
Grant ListU54 HG003079 / HG / NHGRI NIH HHS / United States
UM1 HG008853 / HG / NHGRI NIH HHS / United States