miEAA 2.0: integrating multi-species microRNA enrichment analysis and workflow management systems

Abstract

Gene set enrichment analysis has become one of the most frequently used applications in molecular biology research. Originally developed for gene sets, the same statistical principles are now available for all omics types. In 2016, we published the miRNA enrichment analysis and annotation tool (miEAA) for human precursor and mature miRNAs. Here, we present miEAA 2.0, supporting miRNA input from ten frequently investigated organisms. To facilitate inclusion of miEAA in workflow systems, we implemented an Application Programming Interface (API). Users can perform miRNA set enrichment analysis using either the web-interface, a dedicated Python package, or custom remote clients. Moreover, the number of category sets was raised by an order of magnitude. We implemented novel categories like annotation confidence level or localisation in biological compartments. In combination with the miRBase miRNA-version and miRNA-to-precursor converters, miEAA supports research settings where older releases of miRBase are in use. The web server also offers novel comprehensive visualizations such as heatmaps and running sum curves with background distributions. We demonstrate the new features with case studies for human kidney cancer, a biomarker study on Parkinson's disease from the PPMI cohort, and a mouse model for breast cancer. The tool is freely accessible at: https://www.ccb.uni-saarland.de/mieaa2.

Figure 1.

miEAA workflow and exemplary results. (A) Each miRNA/precursor enrichment analysis consists of at most five steps. First, users should select whether they want to perform enrichment on precursors or miRNAs. Second, the enrichment algorithm, i.e. either ORA or GSEA must be selected. Next, the desired test set can be defined either through a textbox or a file upload. The fourth step only appears for ORAs where custom background reference sets can be inserted or uploaded. This is optional since miEAA provides pre-computed reference sets for all categories. Lastly, the set of categories and databases as well as statistical parameters should be selected. (B) Typical result view for an ORA. Users can sort, select, filter, and export the obtained enrichment results interactively. Moreover, several visualizations of the results are provided for each run, such as the precursor/miRNA to category heatmap and the category word cloud.

Figure 2.

Web server visualisation of case study results. (A) Category (x-axis) to precursor (y-axis) heatmap with −log₁₀-scaled enrichment P-values for the first case study. (B) GSEA plot with simulated background distributions (green to orange lines) and actual depletion for breast cancer (dark blue line) observed during evaluation of the second case study.