A Classification Method for RNA Splicing Regulatory Elements
1 Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7365, USA
2 School of Computer Science and Technology, Anhui University, Hefei 230039, China
3 Department of Endocrinology, Anhui Province Hospital, Hefei 230001, China
Abstract：The sequence classification methods have broad application in various bioinformatics areas such as the identification of regulatory elements of transcription and the prediction of protein structure. Here we presented a new classification method to analyze short sequences based on their sequential features, and used this method to study RNA splicing regulatory elements. This method extracted the sequential features from the known spicing regulatory elements, and developed a scoring system to evaluate how possible a given short sequence can regulate RNA splicing. This method was compared with some other methods through applying to a set of exonic splicing enhancer (ESE) and silencer (ESS) octamers. The average prediction accuracy of this sequential feature-based method for three kinds of computation validation experiments reached about 93% and the transparent predictive structure of the method helps to interpret the biological mechanism. This paper shows a new method for biology series’ data analysis and presents a new way for the study of regulatory sequences that control gene expression.