Abstract
Motivation: Functional interpretation of miRNA expression data is currently done in a three step procedure: select differentially expressed miRNAs, find their target genes, and carry out gene set overrepresentation analysis. Nevertheless, major limitations of this approach have already been described at the gene level, while some newer arise in the miRNA scenario.Here, we propose an enhanced methodology that builds on the well-established gene set analysis paradigm. Evidence for differential expression at the miRNA level is transferred to a gene differential inhibition score which is easily interpretable in terms of gene sets or pathways. Such transferred indexes account for the additive effect of several miRNAs targeting the same gene, and also incorporate cancellation effects between cases and controls. Together, these two desirable characteristics allow for more accurate modeling of regulatory processes.Results: We analyze high-throughput sequencing data from 20 different cancer types and provide exhaustive reports of gene and Gene Ontology-term deregulation by miRNA action.Availability and Implementation: The proposed methodology was implemented in the Bioconductor library mdgsa. http://bioconductor.org/packages/mdgsa. For the purpose of reproducibility all of the scripts are available at https://github.com/dmontaner-papers/gsa4mirnaContact: david.montaner@gmail.comSupplementary information: Supplementary data are available at Bioinformatics online.
Highlights
MicroRNAs are small non-coding RNA molecules which participate in post-transcriptional gene regulation (He and Hannon, 2004)
Differential expression analysis was carried out for each cancer type using edgeR followed by P-value correction to control the false discovery rate (Benjamini and Hochberg, 1995)
We have introduced a novel approach to the functional interpretation of miRNA studies which is primarily designed to unravel the effects of differential miRNA expression on groups of genes or pathways
Summary
MicroRNAs (miRNAs) are small non-coding RNA molecules which participate in post-transcriptional gene regulation (He and Hannon, 2004) They bind to target mRNAs with partial complementarity, causing translational repression or target degradation (Wei et al, 2013). Aberrant miRNAs expression has been reported to be linked to disease (Jiang et al, 2009) and so many genomic experiments are being conducted with the aim of clarifying the relationship between miRNA levels and phenotype These experiments generally use microarrays or high-throughput sequencing to record miRNA expression between different biological conditions, followed by differential-expression analysis to evaluate the association of each miRNA to phenotype. Despite being less instinctive or intuitive, this approach has been shown to reduce the effect of biased database information This two-step paradigm, known as over representation analysis (ORA), VC The Author 2016.
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