miRGate: a curated database of human, mouse and rat miRNA-mRNA targets

Abstract

MicroRNAs (miRNAs) are small non-coding elements involved in the post-transcriptional down-regulation of gene expression through base pairing with messenger RNAs (mRNAs). Through this mechanism, several miRNA-mRNA pairs have been described as critical in the regulation of multiple cellular processes, including early embryonic development and pathological conditions. Many of these pairs (such as miR-15 b/BCL2 in apoptosis or BART-6/BCL6 in diffuse large B-cell lymphomas) were experimentally discovered and/or computationally predicted. Available tools for target prediction are usually based on sequence matching, thermodynamics and conservation, among other approaches. Nevertheless, the main issue on miRNA-mRNA pair prediction is the little overlapping results among different prediction methods, or even with experimentally validated pairs lists, despite the fact that all rely on similar principles. To circumvent this problem, we have developed miRGate, a database containing novel computational predicted miRNA-mRNA pairs that are calculated using well-established algorithms. In addition, it includes an updated and complete dataset of sequences for both miRNA and mRNAs 3'-Untranslated region from human (including human viruses), mouse and rat, as well as experimentally validated data from four well-known databases. The underlying methodology of miRGate has been successfully applied to independent datasets providing predictions that were convincingly validated by functional assays. miRGate is an open resource available at http://mirgate.bioinfo.cnio.es. For programmatic access, we have provided a representational state transfer web service application programming interface that allows accessing the database at http://mirgate.bioinfo.cnio.es/API/ Database URL: http://mirgate.bioinfo.cnio.es

Figure 1.

Distribution of known 3′-UTR sizes for human, mouse and rat. The statistical mode for human (142 bp), mouse (131 bp) and rat (122 bp). The average of these three values, which is ∼130 bp, was used from unknown 3′-UTRS.

Figure 2.

Venn diagram to represent the overlap between OncomirDB, Tarbase, miRTarBase and miRecords, four databases that compile experimentally validated miRNA–mRNA targets through article classification.

Figure 3.

ROC curve illustrating the performance of miRGate and each individual method separately, over four datasets of validated targets: OncomirDB, miRecords, Tarbase and miRTarBase. The AUC obtained for each method is: microtar: 0.528, RNAHybrid: 0.609, miRanda: 0.632, TargetScan: 0.638, Pita: 0.548 and miRGate: 0.704.

Figure 4.

Integration of miRGate predictions versus downloadable predictions from each individual method (only available for miRanda, Targetscan and Pita) over validated targets. The best resulting datasets where selected for each method: miRanda (purple): good scores and conserved targets (AUC: 0.599). Targetscan (blue): conserved targets (AUC: 0.560) and Pita (light green): top scores (AUC: 0.630). miRGate (red, AUC: 0.704).

Figure 5.

Accuracy achieved when validated databases are distributed according to a reliable criterion. OncomirDB, AUC of 0.769, based on manually curation (high reliability), miRecords, AUC of 0.727, as a partially curated database (medium reliability) and miRTarBase and Tarbase, AUC of 0.699, relying on text mining techniques (lower reliability).