De novo SVM classification of precursor microRNAs from genomic pseudo hairpins using global and intrinsic folding measures

Abstract

MicroRNAs (miRNAs) are small ncRNAs participating in diverse cellular and physiological processes through the post-transcriptional gene regulatory pathway. Critically associated with the miRNAs biogenesis, the hairpin structure is a necessary feature for the computational classification of novel precursor miRNAs (pre-miRs). Though many of the abundant genomic inverted repeats (pseudo hairpins) can be filtered computationally, novel species-specific pre-miRs are likely to remain elusive. miPred is a de novo Support Vector Machine (SVM) classifier for identifying pre-miRs without relying on phylogenetic conservation. To achieve significantly higher sensitivity and specificity than existing (quasi) de novo predictors, it employs a Gaussian Radial Basis Function kernel (RBF) as a similarity measure for 29 global and intrinsic hairpin folding attributes. They characterize a pre-miR at the dinucleotide sequence, hairpin folding, non-linear statistical thermodynamics and topological levels. Trained on 200 human pre-miRs and 400 pseudo hairpins, miPred achieves 93.50% (5-fold cross-validation accuracy) and 0.9833 (ROC score). Tested on the remaining 123 human pre-miRs and 246 pseudo hairpins, it reports 84.55% (sensitivity), 97.97% (specificity) and 93.50% (accuracy). Validated onto 1918 pre-miRs across 40 non-human species and 3836 pseudo hairpins, it yields 87.65% (92.08%), 97.75% (97.42%) and 94.38% (95.64%) for the mean (overall) sensitivity, specificity and accuracy. Notably, A.mellifera, A.geoffroyi, C.familiaris, E.Barr, H. Simplex virus, H.cytomegalovirus, O.aries, P.patens, R.lymphocryptovirus, Simian virus and Z.mays are unambiguously classified with 100.00% (sensitivity) and >93.75% (specificity). Data sets, raw statistical results and source codes are available at http://web.bii.a-star.edu.sg/~stanley/Publications