Dataset Information

RGmatch: matching genomic regions to proximal genes in omics data integration.

ABSTRACT: 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.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.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.

SUBMITTER: Furio-Tari P

PROVIDER: S-EPMC5133492 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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RGmatch: matching genomic regions to proximal genes in omics data integration.

Furió-Tarí Pedro P Conesa Ana A Tarazona Sonia S

BMC bioinformatics 20161122 Suppl 15

<h4>Background</h4>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.<h4>Results</h4>In this work we review the tools that are publicly available for making region-to-gene as ...[more]

PMID: 28185573

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Project description:BackgroundRapid computational and technological developments made large amounts of omics data available in different biological levels. It is becoming clear that simultaneous data analysis methods are needed for better interpretation and understanding of the underlying systems biology. Different methods have been proposed for this task, among them Partial Least Squares (PLS) related methods. To also deal with orthogonal variation, systematic variation in the data unrelated to one another, we consider the Two-way Orthogonal PLS (O2PLS): an integrative data analysis method which is capable of modeling systematic variation, while providing more parsimonious models aiding interpretation.ResultsA simulation study to assess the performance of O2PLS showed positive results in both low and higher dimensions. More noise (50 % of the data) only affected the systematic part estimates. A data analysis was conducted using data on metabolomics and transcriptomics from a large Finnish cohort (DILGOM). A previous sequential study, using the same data, showed significant correlations between the Lipo-Leukocyte (LL) module and lipoprotein metabolites. The O2PLS results were in agreement with these findings, identifying almost the same set of co-varying variables. Moreover, our integrative approach identified other associative genes and metabolites, while taking into account systematic variation in the data. Including orthogonal components enhanced overall fit, but the orthogonal variation was difficult to interpret.ConclusionsSimulations showed that the O2PLS estimates were close to the true parameters in both low and higher dimensions. In the presence of more noise (50 %), the orthogonal part estimates could not distinguish well between joint and unique variation. The joint estimates were not systematically affected. Simultaneous analysis with O2PLS on metabolome and transcriptome data showed that the LL module, together with VLDL and HDL metabolites, were important for the metabolomic and transcriptomic relation. This is in agreement with an earlier study. In addition more gene expression and metabolites are identified being important for the joint covariation.

Dataset Information

RGmatch: matching genomic regions to proximal genes in omics data integration.

Publications

RGmatch: matching genomic regions to proximal genes in omics data integration.

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