Research Papers:

iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition

Wang-Ren Qiu, Shi-Yu Jiang, Zhao-Chun Xu, Xuan Xiao _ and Kuo-Chen Chou

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Oncotarget. 2017; 8:41178-41188. https://doi.org/10.18632/oncotarget.17104

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Wang-Ren Qiu1,2,3, Shi-Yu Jiang2, Zhao-Chun Xu2, Xuan Xiao2,3 and Kuo-Chen Chou3,4,5

1Department of Computer Science and Bond Life Science Center, University of Missouri, Columbia, MO, USA

2Computer Department, Jingdezhen Ceramic Institute, Jingdezhen, China

3Gordon Life Science Institute, Boston, MA, USA

4Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China

5Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia

Correspondence to:

Xuan Xiao, email: [email protected]

Keywords: RNA 5-methylcytosine sites, pseudo dinucleotide composition, physical-chemical property matrix, auto/cross-covariance, web-server

Received: January 18, 2017     Accepted: March 15, 2017     Published: April 17, 2017


Occurring at cytosine (C) of RNA, 5-methylcytosine (m5C) is an important post-transcriptional modification (PTCM). The modification plays significant roles in biological processes by regulating RNA metabolism in both eukaryotes and prokaryotes. It may also, however, cause cancers and other major diseases. Given an uncharacterized RNA sequence that contains many C residues, can we identify which one of them can be of m5C modification, and which one cannot? It is no doubt a crucial problem, particularly with the explosive growth of RNA sequences in the postgenomic age. Unfortunately, so far no user-friendly web-server whatsoever has been developed to address such a problem. To meet the increasingly high demand from most experimental scientists working in the area of drug development, we have developed a new predictor called iRNAm5C-PseDNC by incorporating ten types of physical-chemical properties into pseudo dinucleotide composition via the auto/cross-covariance approach. Rigorous jackknife tests show that its anticipated accuracy is quite high. For most experimental scientists’ convenience, a user-friendly web-server for the predictor has been provided at http://www.jci-bioinfo.cn/iRNAm5C-PseDNC along with a step-by-step user guide, by which users can easily obtain their desired results without the need to go through the complicated mathematical equations involved. It has not escaped our notice that the approach presented here can also be used to deal with many other problems in genome analysis.

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