THRONE: A New Approach for Accurate Prediction of Human RNA N7-Methylguanosine Sites |
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| Institution: | 1. Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand;2. Department of Physiology, Ajou University School of Medicine, Suwon 16499, Republic of Korea;3. Department of Molecular Science and Technology, Ajou University, Suwon 16499, Republic of Korea;4. Computational Biology and Bioinformatics Laboratory, Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Gyeonggi-do, Republic of Korea;1. Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing 100191, China;2. MOE Key Lab of Cardiovascular Sciences, Peking University, Beijing 100191, China;1. Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden;2. Science for Life Laboratory, Box 1031, 17121 Solna, Sweden;3. Evi-networks, enskild konsultföretag, Sweden;1. Institute for Genomic Medicine, Nationwide Children''s Hospital, 700 Children''s Dr, Columbus, OH 43205, USA;2. Department of Physics, The Ohio State University, 191 West Woodruff Av, Columbus, OH 43210, USA;3. Department of Physics, Department of Chemistry & Biochemistry, Division of Internal Medicine, Center for RNA Biology, The Ohio State University, 191 West Woodruff Av, Columbus, OH 43210, USA;1. College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China;2. Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, 266061, China;3. School of Data Science, Qingdao University of Science and Technology, Qingdao, 266061, China;4. College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, 266061, China;1. Department of Computer Science, Abdul Wali Khan University Mardan, 23200, KP, Pakistan;2. Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea;3. Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju, 54896, South Korea |
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| Abstract: | N7-methylguanosine (m7G) is an essential, ubiquitous, and positively charged modification at the 5′ cap of eukaryotic mRNA, modulating its export, translation, and splicing processes. Although several machine learning (ML)-based computational predictors for m7G have been developed, all utilized specific computational framework. This study is the first instance we explored four different computational frameworks and identified the best approach. Based on that we developed a novel predictor, THRONE (A three-layer ensemble predictor for identifying human RNA N7-methylguanosine sites) to accurately identify m7G sites from the human genome. THRONE employs a wide range of sequence-based features inputted to several ML classifiers and combines these models through ensemble learning. The three-step ensemble learning is as follows: 54 baseline models were constructed in the first layer and the predicted probability of m7G was considered as a new feature vector for the sequential step. Subsequently, six meta-models were created using the new feature vector and their predicted probability was yet again considered as novel features. Finally, random forest was deemed as the best super classifier learner for the final prediction using a systematic approach incorporated with novel features. Interestingly, THRONE outperformed other existing methods in the prediction of m7G sites on both cross-validation analysis and independent evaluation. The proposed method is publicly accessible at: http://thegleelab.org/THRONE/ and expects to help the scientific community identify the putative m7G sites and formulate a novel testable biological hypothesis. |
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| Keywords: | RNA N7-methylguanosine sites sequence analysis bioinformatics ensemble learning machine learning |
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