Predicts N6-methyladenosine sites in RNA sequences via physical-chemical properties. It was observed via a rigorous jackknife test that, in comparison with the existing predictor for the same purpose, pRNAm-PC achieved remarkably higher success rates in both overall accuracy and stability, indicating that the new predictor will become a useful high-throughput tool for identifying methylation sites in RNA, and that the novel approach can also be used to study many other RNA-related problems and conduct genome analysis.
Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China; School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China; Information School, ZheJiang Textile and Fashion College, NingBo, China; Gordon Life Science Institute, Boston, MA, USA; Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia
pRNAm-PC funding source(s)
This work was partially supported by the National Nature Science Foundation of China (Nos. 31260273, 61261027, 31560316, 61373062), the Jiangxi Provincial Foreign Scientific and Technological Cooperation Project (No. 20120BDH80023), the Natural Science Foundation of Jiangxi Province, China (Nos. 20114BAB211013, 20122BAB211033, 20122BAB201044, 20122BAB201020) the Department of Education of JiangXi Province (GJJ12490, GJJ14640), the LuoDi plan of the Department of Education of JiangXi Province (KJLD12083), and the JiangXi Provincial Foundation for Leaders of Disciplines in Science (20113BCB22008).