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


METHODS
SVPV is available at GitHub ( https://github.com/VCCRI/SVPV/ ).


BACKGROUND
e.giannoulatou@victorchang.edu.au.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Victor Chang Cardiac Research Institute, Sydney, NSW 2010, Australia.
Jacob E
JE
Munro
Victor Chang Cardiac Research Institute, Sydney, NSW 2010, Australia.|St Vincent's Clinical School.|School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW 2052, Australia.
Sally L
SL
Dunwoodie
Victor Chang Cardiac Research Institute, Sydney, NSW 2010, Australia.|St Vincent's Clinical School.
Eleni
E
Giannoulatou
D000465:Algorithms; D005787:Gene Frequency; D015894:Genome, Human; D056914:Genomic Structural Variation; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D058977:Molecular Sequence Annotation; D012984:Software; D000073336:Whole Genome Sequencing / Q000379:methods*