@article {85, title = {svtools: population-scale analysis of structural variation.}, journal = {Bioinformatics}, volume = {35}, year = {2019}, month = {2019 Nov 01}, pages = {4782-4787}, abstract = {

SUMMARY: Large-scale human genetics studies are now employing whole genome sequencing with the goal of conducting comprehensive trait mapping analyses of all forms of genome variation. However, methods for structural variation (SV) analysis have lagged far behind those for smaller scale variants, and there is an urgent need to develop more efficient tools that scale to the size of human populations. Here, we present a fast and highly scalable software toolkit (svtools) and cloud-based pipeline for assembling high quality SV maps-including deletions, duplications, mobile element insertions, inversions and other rearrangements-in many thousands of human genomes. We show that this pipeline achieves similar variant detection performance to established per-sample methods (e.g. LUMPY), while providing fast and affordable joint analysis at the scale of >=100~000 genomes. These tools will help enable the next generation of human genetics studies.

AVAILABILITY AND IMPLEMENTATION: svtools is implemented in Python and freely available (MIT) from https://github.com/hall-lab/svtools.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btz492}, author = {Larson, David E and Abel, Haley J and Chiang, Colby and Badve, Abhijit and Das, Indraniel and Eldred, James M and Layer, Ryan M and Hall, Ira M} } @article {20, title = {SVScore: an impact prediction tool for structural variation.}, journal = {Bioinformatics}, volume = {33}, year = {2017}, month = {2017 Apr 01}, pages = {1083-1085}, 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.

}, keywords = {Gene Frequency, Genomic Structural Variation, Genomics, Humans, Polymorphism, Single Nucleotide, Sequence Deletion, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btw789}, author = {Ganel, Liron and Abel, Haley J and Hall, Ira M} }