miRmap: Comprehensive prediction of microRNA target repression strength

By Charles E. Vejnar, E. Zdobnov. Nucleic Acids Research, 2012

377 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1a29eb237018d35edadb88a502953a11f11eb759

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Citations in the corpus

mirDIP 4.1—integrative database of human microRNA target predictions (2017, neutral): “miRGator 2008 2013 yes yes (51) MirMAP 2012 2013 yes yes yes yes (52)”
Predicting effective microRNA target sites in mammalian mRNAs (2015, background): “…quantitative models of targeting efficacy ( Grimson et al., 2007 ; Nielsen et al., 2007 ; Wang and El Naqa, 2008 ; Betel et al., 2010 ; Liu et al., 2010 ; Garcia et al., 2011 ; Wen et al., 2011 ; Reczko et al., 2012 ; Vejnar and Zdobnov, 2012 ; Marin et al., 2013 ; Gumienny and Zavolan, 2015 ).”