Research Papers:

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

Wei Chen, Pengmian Feng, Hui Yang, Hui Ding, Hao Lin and Kuo-Chen Chou _

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Oncotarget. 2017; 8:4208-4217. https://doi.org/10.18632/oncotarget.13758

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Wei Chen1,4, Pengmian Feng2, Hui Yang3, Hui Ding3, Hao Lin3,4, Kuo-Chen Chou3,4

1Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan, Tangshan, China

2Hebei Province Key Laboratory of Occupational Health and Safety for Coal Industry, School of Public Health, North China University of Science and Technology, Tangshan, China

3Key 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

4Gordon Life Science Institute, Belmont, Massachusetts, United States of America

Correspondence to:

Wei Chen, email: [email protected]

Hao Lin, email: [email protected]

Kuo-Chen Chou, email: [email protected]

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


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.

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