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… Click to show full abstract
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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