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Journal of Nucleic Acids
Volume 2012 (2012), Article ID 652979, 10 pages
http://dx.doi.org/10.1155/2012/652979
Research Article

Plant MicroRNA Prediction by Supervised Machine Learning Using C5.0 Decision Trees

Division of Plant Sciences, Research School of Biology, College of Medicine, Biology & Environment, The Australian National University, Canberra, ACT 0200, Australia

Received 6 July 2012; Revised 10 September 2012; Accepted 17 September 2012

Academic Editor: Thomas Litman

Copyright © 2012 Philip H. Williams et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

MicroRNAs (miRNAs) are nonprotein coding RNAs between 20 and 22 nucleotides long that attenuate protein production. Different types of sequence data are being investigated for novel miRNAs, including genomic and transcriptomic sequences. A variety of machine learning methods have successfully predicted miRNA precursors, mature miRNAs, and other nonprotein coding sequences. MirTools, mirDeep2, and miRanalyzer require “read count” to be included with the input sequences, which restricts their use to deep-sequencing data. Our aim was to train a predictor using a cross-section of different species to accurately predict miRNAs outside the training set. We wanted a system that did not require read-count for prediction and could therefore be applied to short sequences extracted from genomic, EST, or RNA-seq sources. A miRNA-predictive decision-tree model has been developed by supervised machine learning. It only requires that the corresponding genome or transcriptome is available within a sequence window that includes the precursor candidate so that the required sequence features can be collected. Some of the most critical features for training the predictor are the miRNA:miRNA duplex energy and the number of mismatches in the duplex. We present a cross-species plant miRNA predictor with 84.08% sensitivity and 98.53% specificity based on rigorous testing by leave-one-out validation.