ViVar: a comprehensive platform for the analysis and visualization of structural genomic variation

PLoS One. 2014 Dec 12;9(12):e113800. doi: 10.1371/journal.pone.0113800. eCollection 2014.

Abstract

Structural genomic variations play an important role in human disease and phenotypic diversity. With the rise of high-throughput sequencing tools, mate-pair/paired-end/single-read sequencing has become an important technique for the detection and exploration of structural variation. Several analysis tools exist to handle different parts and aspects of such sequencing based structural variation analyses pipelines. A comprehensive analysis platform to handle all steps, from processing the sequencing data, to the discovery and visualization of structural variants, is missing. The ViVar platform is built to handle the discovery of structural variants, from Depth Of Coverage analysis, aberrant read pair clustering to split read analysis. ViVar provides you with powerful visualization options, enables easy reporting of results and better usability and data management. The platform facilitates the processing, analysis and visualization, of structural variation based on massive parallel sequencing data, enabling the rapid identification of disease loci or genes. ViVar allows you to scale your analysis with your work load over multiple (cloud) servers, has user access control to keep your data safe and is easy expandable as analysis techniques advance. URL: https://www.cmgg.be/vivar/

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computational Biology / methods*
  • Genome, Human
  • Genomic Structural Variation*
  • Humans
  • Internet
  • Karyotype

Grants and funding

Sarah Vergult is supported by a postdoctoral grant from the Special Research Fund (BOF) from Ghent University. Katleen De Preter is supported by a postdoctoral grant from the Research Foundation - Flanders (FWO). This article presents research results of the Belgian program of Interuniversity Poles of attraction initiated by the Belgian State, Prime Minister's Office, Science Policy Programming (IUAP). The authors would like to acknowledge the N2N (Nucleotide 2 Networks) Multidisciplinary Research Partnership funded by the Special Research Fund of Ghent University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.