iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences

// Wei Chen 1, 4 , Pengmian Feng 2 , Hui Yang 3 , Hui Ding 3 , Hao Lin 3, 4 , Kuo-Chen Chou 3, 4 1 Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan, Tangshan, China 2 Hebei Province Key Laboratory of Occupational Health and Safety for Coal Industry, School of Public Health, North China University of Science and Technology, Tangshan, China 3 Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China 4 Gordon Life Science Institute, Belmont, Massachusetts, United States of America Correspondence to: Wei Chen, email: chenweiimu@gmail.com Hao Lin, email: hlin@uestc.edu.cn Kuo-Chen Chou, email: kcchou@gordonlifescience.org Keywords: A-to-I editing, nucleotide chemical properties, nucleotide density distribution, PseKNC, web-server Received: October 04, 2016      Accepted: November 23, 2016      Published: December 01, 2016 ABSTRACT Catalyzed by adenosine deaminase (ADAR), the adenosine to inosine (A-to-I) editing in RNA is not only involved in various important biological processes, but also closely associated with a series of major diseases. Therefore, knowledge about the A-to-I editing sites in RNA is crucially important for both basic research and drug development. Given an uncharacterized RNA sequence that contains many adenosine (A) residues, can we identify which one of them can be of A-to-I editing, and which one cannot? Unfortunately, so far no computational method whatsoever has been developed to address such an important problem based on the RNA sequence information alone. To fill this empty area, we have proposed a predictor called iRNA-AI by incorporating the chemical properties of nucleotides and their sliding occurrence density distribution along a RNA sequence into the general form of pseudo nucleotide composition (PseKNC). It has been shown by the rigorous jackknife test and independent dataset test that the performance of the proposed predictor is quite promising. For the convenience of most experimental scientists, a user-friendly web-server for iRNA-AI has been established at http://lin.uestc.edu.cn/server/iRNA-AI/ , by which users can easily get their desired results without the need to go through the mathematical details.