Munro, Jacob E and Dunwoodie, Sally L and Giannoulatou, Eleni (2017) SVPV: a structural variant prediction viewer for paired-end sequencing datasets. Bioinformatics, 33 (13). pp.2032-2033. ISSN 1367-4811 (OA)
Full text not available from this repository.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.
e.giannoulatou@victorchang.edu.au.
Supplementary information. Supplementary data are available at Bioinformatics online.
| Item Type: | Article |
|---|---|
| Additional Information: | This article is available for free from the publisher's website. Please click on the link above to access. |
| Subjects: | R Medicine > R Medicine (General) |
| Depositing User: | Repository Administrator |
| Date Deposited: | 28 Mar 2017 00:49 |
| Last Modified: | 05 Dec 2018 00:22 |
| URI: | https://eprints.victorchang.edu.au/id/eprint/574 |
Actions (login required)
 |
View Item |