PRESa2i: incremental decision trees for prediction of Adenosine to Inosine RNA editing sites

RNA editing is a very crucial cellular process affecting protein encoding and is sometimes correlated with the cause of fatal diseases, such as cancer. Thus knowledge about RNA editing sites in a RNA sequence is very important. Adenosine to Inosine (A-to-I) is the most common of the RNA editing events. In this paper,we present PRESa2i, a computation prediction tool for identification of A-to-I RNA editing sites in given RNA sequences. PRESa2i uses a simple, yet effective set of sequence based features generated from RNA sequences and a novel feature selection technique. It uses an incremental decision tree algorithm as the classification algorithm. On a standard benchmark dataset and independent set, it achieves 86.48% accuracy and 90.67% sensitivity and significantly outperforms state-of-the-art methods. We have also implemented a web application based on PRESa2i and made it available freely at: http://brl.uiu.ac.bd/presa2i/index.php. The materials for this paper are also available to use from: https://github.com/swakkhar/RNA-Editing/.

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