Lab of Systems Neuroscience, D-HEST Institute for Neuroscience, ETH Zürich, Switzerland.
Neuroscience Center Zurich, ETH Zurich and University of Zurich, Switzerland.
Nucleic Acids Res. 2022 Jul 5;50(W1):W280-W289. doi: 10.1093/nar/gkac395.
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.
MicroRNAs (miRNAs) 是一类小型的非编码 RNA，是基因表达的主要转录后调控因子之一。许多数据集合和预测工具已经收集了这些调节剂的假定或已确认的靶标。在给定的转录特征或基因集中，利用这些集合进行靶基因富集分析，以预测相关的 miRNAs，这对于发现和验证通常是有用的。虽然已经提出了几种方法来实现这一目标，但缺乏一种灵活且用户友好的界面，以便使用各种方法和集合进行此类分析。enrichMiR（https://ethz-ins.org/enrichMiR/）通过允许用户根据几个靶基因集合执行一系列富集测试，根据它们可能参与控制给定的转录特征或基因集的程度对 miRNAs 进行排名，从而解决了这一差距。enrichMiR 的结果还可以通过交互式和可发布的图形进行可视化。为了指导适当分析方法的选择，我们在涉及已知 miRNAs 扰动的一系列实验中对各种测试进行了基准测试。最后，我们在两个用例中展示了 enrichMiR 的功能。
