Bioinformatics pipelines for targeted resequencing and whole-exome sequencing of human and mouse genomes: a virtual appliance approach for instant deployment
- Li, J; Doyle, MA; Saeed, I; Wong, SQ; Mar, V; Goode, DL; Caramia, F; Doig, K; Ryland, GL; Thompson, ER; Hunter, SM; Halgamuge, SK; Ellul, J; Dobrovic, A; Campbell, IG; Papenfuss, AT; McArthur, GA; Tothill, RW;
Publication Year 2014, Volume 9, Issue #4, Page e95217
- Journal Title
- PLoS One
- Publication Type
- Journal Article
- Targeted resequencing by massively parallel sequencing has become an effective and affordable way to survey small to large portions of the genome for genetic variation. Despite the rapid development in open source software for analysis of such data, the practical implementation of these tools through construction of sequencing analysis pipelines still remains a challenging and laborious activity, and a major hurdle for many small research and clinical laboratories. We developed TREVA (Targeted REsequencing Virtual Appliance), making pre-built pipelines immediately available as a virtual appliance. Based on virtual machine technologies, TREVA is a solution for rapid and efficient deployment of complex bioinformatics pipelines to laboratories of all sizes, enabling reproducible results. The analyses that are supported in TREVA include: somatic and germline single-nucleotide and insertion/deletion variant calling, copy number analysis, and cohort-based analyses such as pathway and significantly mutated genes analyses. TREVA is flexible and easy to use, and can be customised by Linux-based extensions if required. TREVA can also be deployed on the cloud (cloud computing), enabling instant access without investment overheads for additional hardware. TREVA is available at http://bioinformatics.petermac.org/treva/.
- Public Library of Science
- WEHI Research Division(s)
- Publisher's Version
- Open Access at Publisher's Site
- Rights Notice
- Copyright: © 2014 Li et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Creation Date: 2014-06-02 11:08:27Last Modified: 0001-01-01 12:00:00