Abstract
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/.
Highlights
Targeted resequencing (TR) by massively parallel sequencing, which includes whole-exome sequencing (WES), is a wellestablished and cost-effective means to analyse specific regions of a genome
We have proposed a novel solution to the problem of pipeline construction for TR/WES data analysis using a virtual appliance (TREVA), which requires minimal effort on the management and configuration of the underlying hardware and software systems
This allows TREVA to be transferrable to multiple laboratories or research institutions, enabling them to reproducibly run complex analysis pipelines with ease
Summary
Targeted resequencing (TR) by massively parallel sequencing, which includes whole-exome sequencing (WES), is a wellestablished and cost-effective means to analyse specific regions of a genome. Coupled with the popularity of TR is the deluge of bioinformatics tools that have been developed to analyse sequence data, with over 570 tools published within a span of only 2 years [5]. CGATools/MutSig) for conducting pathway analysis; and, TREAT [14] and VarSifter [15] for annotation and visualization. Some of these methods are tailored to TR data Projects/fastqc) and htSeqTools [6] for assessing the quality of short-read data; BWA [7] and Bowtie2 [8] for sequence alignment; MuTect [9] and GATK [10] for detecting singlenucleotide variations; CONTRA [11] and ExomeCNV [12] for identifying copy number aberrations; Genome MuSiC [13] and MutSig
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