iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences
Metrics: PDF 1177 views | HTML 2363 views | ?
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
Wei Chen, email: firstname.lastname@example.org
Hao Lin, email: email@example.com
Kuo-Chen Chou, email: firstname.lastname@example.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
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, by which users can easily get their desired results without the need to go through the mathematical details.
All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 License.