RGmatch: matching genomic regions to proximal genes in omics data integration. Issue 15 (November 2016)

• Record Type: Journal Article
• Authors: Furió-Tarí, Pedro; Conesa, Ana; Tarazona, Sonia
• Is Part Of: BMC bioinformatics. Volume 17:Issue 15(2016)
• Page Start: 1
• Page End: 10
• Publication Date: 2016-11
• Subjects: Associations -- Gene -- Genomic region -- Peak -- Omics integration -- NGS; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/s12859-016-1293-1
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract Background The integrative analysis of multiple genomics data often requires that genome coordinates-based signals have to be associated with proximal genes. The relative location of a genomic region with respect to the gene (gene area) is important for functional data interpretation; hence algorithms that match regions to genes should be able to deliver insight into this information. Results In this work we review the tools that are publicly available for making region-to-gene associations. We also present a novel method, RGmatch, a flexible and easy-to-use Python tool that computes associations either at the gene, transcript, or exon level, applying a set of rules to annotate each region-gene association with the region location within the gene. RGmatch can be applied to any organism as long as genome annotation is available. Furthermore, we qualitatively and quantitatively compare RGmatch to other tools. Conclusions RGmatch simplifies the association of a genomic region with its closest gene. At the same time, it is a powerful tool because the rules used to annotate these associations are very easy to modify according to the researcher's specific interests. Some important differences between RGmatch and other similar tools already in existence are RGmatch's flexibility, its wide range of user options, compatibility with any annotatable organism, and its comprehensive and user-friendly output.