Computational Challenges in miRNA Target Predictions: To Be or Not to Be a True Target?
Table 2
Algorithms and software tools specifically developed for functional interpretation of miRNA expression data, inference of miRNA gene regulation from mRNA trascriptomic profiles, combination of parallel mRNA and miRNA expression data.
Series of Microsoft Excel macros that compute an enrichment statistic for over-representation of predicted microRNA targets within the analyzed gene set. The software supports PicTar, TargetScan, and miRanda prediction algorithms.
Integration of different analysis results of the same data, each represented by a ranked list of entities. The algorithm finds the optimal list combining all the input ones. This system can be applied to the output lists of different microRNA target predictors as well as to different differentially expressed gene lists.
Using a Bayesian learning network, the algorithm accounts for patterns of mRNA gene expression using miRNA expression data and a set of predicted miRNA targets. A smaller set of high-confidence functional miRNA targets then obtained from the data using the algorithm.
This method infers the level of microRNA expression starting from the gene expression profile and a gene target prediction. It is similar to GSEA for the analysis of gene expression. Every microRNA has an enrichment score based on the differential expression of its targets, weighted by a binding energy matrix.
