Bioinformatics Advance Access published online on November 29, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm595
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Prediction of both conserved and nonconserved microRNA targets in animals
Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO 63110, USA
*To whom correspondence should be addressed. Dr. Xiaowei Wang, E-mail: xwang{at}radonc.wustl.edu
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Abstract |
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Motivation: MicroRNAs (miRNAs) are involved in many diverse biological processes and they may potentially regulate the functions of thousands of genes. However, one major issue in miRNA studies is the lack of bioinformatics programs to accurately predict miRNA targets. Animal miRNAs have limited sequence complementarity to their gene targets, which makes it challenging to build target predic-tion models with high specificity.
Results: Here we present a new miRNA target prediction program based on support vector machines (SVMs) and a large microarray training dataset. By systematically analyzing public microarray data, we have identified statistically significant features that are important to target downregulation. Heterogeneous prediction features have been non-linearly integrated in an SVM machine learning framework for the training of our target prediction model, MirTarget2. About half of the predicted miRNA target sites in human are not conserved in other organisms. Our prediction algorithm has been validated with independent experimental data for its improved performance on predicting a large number of miRNA downregulated gene targets.
Availability: All the predicted targets were imported into an online database miRDB, which is freely accessible at http://mirdb.org.
Contact: xwang{at}radonc.wustl.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
Associate Editor: Dr. Alex Bateman
Received on September 25, 2007; revised on November 3, 2007; accepted on November 27, 2007
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