SVPV: a structural variant prediction viewer for paired-end sequencing datasets

Jacob E Munro; Sally L Dunwoodie; Eleni Giannoulatou

doi:10.1093/bioinformatics/btx117

SVPV: a structural variant prediction viewer for paired-end sequencing datasets

Bioinformatics. 2017 Jul 1;33(13):2032-2033. doi: 10.1093/bioinformatics/btx117.

Authors

Jacob E Munro¹, Sally L Dunwoodie^{1

2

3}, Eleni Giannoulatou^{1

2}

Affiliations

¹ Victor Chang Cardiac Research Institute, Sydney, NSW 2010, Australia.
² St Vincent's Clinical School.
³ School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW 2052, Australia.

PMID: 28334120
DOI: 10.1093/bioinformatics/btx117

Abstract

Motivation: A wide range of algorithms exist for the prediction of structural variants (SVs) from paired-end whole genome sequencing (WGS) alignments. It is essential for the purpose of quality control to be able to visualize, compare and contrast the data underlying the predictions across multiple different algorithms.

Results: We provide the structural variant prediction viewer, a tool which presents a visual summary of the most relevant features for SV prediction from WGS data. SV calls from multiple prediction algorithms may be visualized together, along with annotation of population allele frequencies from reference SV datasets. Gene annotations may also be included. The application is capable of running in a Graphical User Interface (GUI) mode for visualizing SVs one by one, or in batch mode for processing many SVs serially.

Availability and implementation: SVPV is available at GitHub ( https://github.com/VCCRI/SVPV/ ).

Contact: [email protected].

Supplementary information: Supplementary data are available at Bioinformatics online.

MeSH terms

Algorithms
Gene Frequency
Genome, Human*
Genomic Structural Variation*
High-Throughput Nucleotide Sequencing / methods*
Humans
Molecular Sequence Annotation
Software*
Whole Genome Sequencing / methods*