<i>enrichMiR</i> predicts functionally relevant microRNAs based on target collections
Michael Soutschek, Tomás Germade, Pierre‐Luc Germain, Gerhard Schratt
Abstract
MicroRNAs (miRNAs) are small non-coding RNAs that are among the main post-transcriptional regulators of gene expression. A number of data collections and prediction tools have gathered putative or confirmed targets of these regulators. It is often useful, for discovery and validation, to harness such collections to perform target enrichment analysis in given transcriptional signatures or gene-sets in order to predict involved miRNAs. While several methods have been proposed to this end, a flexible and user-friendly interface for such analyses using various approaches and collections is lacking. enrichMiR (https://ethz-ins.org/enrichMiR/) addresses this gap by enabling users to perform a series of enrichment tests, based on several target collections, to rank miRNAs according to their likely involvement in the control of a given transcriptional signature or gene-set. enrichMiR results can furthermore be visualised through interactive and publication-ready plots. To guide the choice of the appropriate analysis method, we benchmarked various tests across a panel of experiments involving the perturbation of known miRNAs. Finally, we showcase enrichMiR functionalities in a pair of use cases.