miRmap: Comprehensive prediction of microRNA target repression strength

Abstract

MicroRNAs, or miRNAs, post-transcriptionally repress the expression of protein-coding genes. The human genome encodes over 1000 miRNA genes that collectively target the majority of messenger RNAs (mRNAs). Base pairing of the so-called miRNA ‘seed’ region with mRNAs identifies many thousands of putative targets. Evaluating the strength of the resulting mRNA repression remains challenging, but is essential for a biologically informative ranking of potential miRNA targets. To address these challenges, predictors may use thermodynamic, evolutionary, probabilistic or sequence-based features. We developed an open-source software library, miRmap, which for the first time comprehensively covers all four approaches using 11 predictor features, 3 of which are novel. This allowed us to examine feature correlations and to compare their predictive power in an unbiased way using high-throughput experimental data from immunopurification, transcriptomics, proteomics and polysome fractionation experiments. Overall, target site accessibility appears to be the most predictive feature. Our novel feature based on PhyloP, which evaluates the significance of negative selection, is the best performing predictor in the evolutionary category. We combined all the features into an integrated model that almost doubles the predictive power of TargetScan. miRmap is freely available from http://cegg.unige.ch/mirmap.

Repression of Arginase-2 Expression in Dendritic Cells by MicroRNA-155 Is Critical for Promoting T Cell Proliferation

“…Well-established miRNA binding site prediction criteria were used to generate a list of potential targets of miR155 ( Fig. 1A) (43). The list of putative targets was compared with the set of mRNAs that exhibited elevated expression in activated BM-cDCs and BM-pDCs from miR155 2/2 mice.…”

“…Arg2 mRNA was analyzed for the presence of potential miR155 binding sites by computational approaches relying on well-established criteria for identifying miRNA target sites (43). A likely miR155 binding site was identified in the 39-UTR of mouse Arg2 mRNA (Fig.…”

Integrating Heterogeneous omics Data via Statistical Inference and Learning Techniques

“…Information about canonical pathways, protein-protein, protein-DNA as well as predicted miRNA-gene interactions can nowadays be found in large scale databases [62][63][64][65][66][67]. Based on these resources molecular networks can be reconstructed and employed for mapping statistics (e.g.…”

Cupid: simultaneous reconstruction of microRNA-target and ceRNA networks

“…We first compared Cupid Step I performance to that of several published algorithms, including TargetScan (Lewis et al 2005), miRanda (John et al 2004), PITA (Kertesz et al 2007), DIANAmicroT-CDS (Reczko et al 2012), ElMMo (Gaidatzis et al 2007), miRmap (Vejnar and Zdobnov 2012), mirSVR (Betel et al 2010), RepTar (Elefant et al 2011), rna22 (Miranda et al 2006), RNAhybrid (Rehmsmeier et al 2004), and TargetSpy (Sturm et al 2010) by analyzing the overlap of their predictions with experimentally assessed AGO crosslink-centered regions (CCR) in HEK293 cells (Hafner et al 2010). We tested both the accuracy of binding-site scores ( Fig.…”