Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/28261
Title: Computational Identification of Novel MicroRNAs Using Intrinsic RNA Folding Measures
Authors: NG KWANG LOONG STANLEY
Keywords: classification, intrinsic RNA folding measures, microRNAs, precursor microRNAs, pseudo hairpins, secondary structure, support vector machine
Issue Date: 1-Oct-2008
Source: NG KWANG LOONG STANLEY (2008-10-01). Computational Identification of Novel MicroRNAs Using Intrinsic RNA Folding Measures. ScholarBank@NUS Repository.
Abstract: MicroRNAs (miRNAs) are small endogenous non-coding RNAs participating in diverse cellular and physiological processes by post-transcriptionally suppressing the target genes. Motivated by the incomplete knowledge on the number of miRNAs present in the genomes of vertebrates, worms, plants, and even viruses, an in-depth statistical study was conducted to elucidate the unique hairpin folding of an entire precursor miRNA (pre-miR). A new de novo Support Vector Machine classifier miPred was developed for identifying pre-miRs without relying on phylogenetic conservation information, while able to handle arbitrary secondary structures. It achieved significantly higher sensitivity and specificity than existing (quasi) de novo predictors. Two novel miRNAs dre-miR-N1 and dre-miR-N2 identified by miPred in the brain and gonads of juvenile and adult zebrafish, were validated experimentally as bona fide through Northern Blot, and were found to be localized in the adult ovary and testis via frozen section in situ.
URI: http://scholarbank.nus.edu.sg/handle/10635/28261
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