Prediction of both conserved and nonconserved microRNA targets in animals.
BACKGROUND
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 prediction 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 down-regulated gene targets.


BACKGROUND
All the predicted targets were imported into an online database miRDB, which is freely accessible at http://mirdb.org.
Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO 63110, USA. xwang@radonc.wustl.edu
Xiaowei
X
Wang
Issam M
IM
El Naqa
D000818:Animals; D001185:Artificial Intelligence; D017124:Conserved Sequence / Q000235:genetics*; D019143:Evolution, Molecular; D018390:Gene Targeting / Q000379:methods*; D006801:Humans; D035683:MicroRNAs / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D016415:Sequence Alignment / Q000379:methods; D017423:Sequence Analysis, RNA / Q000379:methods*; D013045:Species Specificity
D035683:MicroRNAs