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

[This corrects the article DOI: 10.1155/2012/652979.].

microRNA profiling of root tissues and root forming explant cultures in Medicago truncatula.

Plant root architecture is regulated by the initiation and modulation of cell division in regions containing pluripotent stem cells known as meristems. In roots, meristems are formed early in embryogenesis, in the case of the root apical meristem (RAM), and during organogenesis at the site of lateral root or, in legumes, nodule formation. Root meristems can also be generated in vitro from leaf explants cultures supplemented with auxin. microRNAs (miRNAs) have emerged as regulators of many key biological functions in plants including root development. To identify key miRNAs involved in root meristem formation in Medicago truncatula, we used deep sequencing to compare miRNA populations. Comparisons were made between: (1) the root tip (RT), containing the RAM and the elongation zone (EZ) tissue and (2) root forming callus (RFC) and non-root forming callus (NRFC). We identified 83 previously reported miRNAs, 24 new to M. truncatula, in 44 families. For the first time in M. truncatula, members of conserved miRNA families miR165, miR181 and miR397 were found. Bioinformatic analysis identified 38 potential novel miRNAs. Selected miRNAs and targets were validated using Taqman miRNA assays and 5' RACE. Many miRNAs were differentially expressed between tissues, particularly RFC and NRFC. Target prediction revealed a number of miRNAs to target genes previously shown to be differentially expressed between RT and EZ or RFC and NRFC and important in root development. Additionally, we predict the miRNA/target relationships for miR397 and miR160 to be conserved in M. truncatula. Amongst the predictions, were AUXIN RESPONSE FACTOR 10, targeted by miR160 and a LACCASE-like gene, targeted by miR397, both are miRNA/target pairings conserved in other species.

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

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.