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Chen W, Feng P, Song X, Lv H, Lin H. iRNA-m7G: Identifying N 7-methylguanosine Sites by Fusing Multiple Features. MOLECULAR THERAPY. NUCLEIC ACIDS 2019; 18:269-274. [PMID: 31581051 PMCID: PMC6796804 DOI: 10.1016/j.omtn.2019.08.022] [Citation(s) in RCA: 80] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 08/07/2019] [Accepted: 08/19/2019] [Indexed: 11/18/2022]
Abstract
As an essential post-transcriptional modification, N7-methylguanosine (m7G) regulates nearly every step of the life cycle of mRNA. Accurate identification of the m7G site in the transcriptome will provide insights into its biological functions and mechanisms. Although the m7G-methylated RNA immunoprecipitation sequencing (MeRIP-seq) method has been proposed in this regard, it is still cost-ineffective for detecting the m7G site. Therefore, it is urgent to develop new methods to identify the m7G site. In this work, we developed the first computational predictor called iRNA-m7G to identify m7G sites in the human transcriptome. The feature fusion strategy was used to integrate both sequence- and structure-based features. In the jackknife test, iRNA-m7G obtained an accuracy of 89.88%. The superiority of iRNA-m7G for identifying m7G sites was also demonstrated by comparing with other methods. We hope that iRNA-m7G can become a useful tool to identify m7G sites. A user-friendly web server for iRNA-m7G is freely accessible at http://lin-group.cn/server/iRNA-m7G/.
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Affiliation(s)
- Wei Chen
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611730, China; Center for Genomics and Computational Biology, School of Life Sciences, North China University of Science and Technology, Tangshan 063000, China.
| | - Pengmian Feng
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611730, China
| | - Xiaoming Song
- Center for Genomics and Computational Biology, School of Life Sciences, North China University of Science and Technology, Tangshan 063000, China
| | - Hao Lv
- Key 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 610054, China
| | - Hao Lin
- Key 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 610054, China.
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Lai HY, Zhang ZY, Su ZD, Su W, Ding H, Chen W, Lin H. iProEP: A Computational Predictor for Predicting Promoter. MOLECULAR THERAPY. NUCLEIC ACIDS 2019; 17:337-346. [PMID: 31299595 PMCID: PMC6616480 DOI: 10.1016/j.omtn.2019.05.028] [Citation(s) in RCA: 110] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2019] [Revised: 05/18/2019] [Accepted: 05/19/2019] [Indexed: 11/29/2022]
Abstract
Promoter is a fundamental DNA element located around the transcription start site (TSS) and could regulate gene transcription. Promoter recognition is of great significance in determining transcription units, studying gene structure, analyzing gene regulation mechanisms, and annotating gene functional information. Many models have already been proposed to predict promoters. However, the performances of these methods still need to be improved. In this work, we combined pseudo k-tuple nucleotide composition (PseKNC) with position-correlation scoring function (PCSF) to formulate promoter sequences of Homo sapiens (H. sapiens), Drosophila melanogaster (D. melanogaster), Caenorhabditis elegans (C. elegans), Bacillus subtilis (B. subtilis), and Escherichia coli (E. coli). Minimum Redundancy Maximum Relevance (mRMR) algorithm and increment feature selection strategy were then adopted to find out optimal feature subsets. Support vector machine (SVM) was used to distinguish between promoters and non-promoters. In the 10-fold cross-validation test, accuracies of 93.3%, 93.9%, 95.7%, 95.2%, and 93.1% were obtained for H. sapiens, D. melanogaster, C. elegans, B. subtilis, and E. coli, with the areas under receiver operating curves (AUCs) of 0.974, 0.975, 0.981, 0.988, and 0.976, respectively. Comparative results demonstrated that our method outperforms existing methods for identifying promoters. An online web server was established that can be freely accessed (http://lin-group.cn/server/iProEP/).
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Affiliation(s)
- Hong-Yan Lai
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhao-Yue Zhang
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhen-Dong Su
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Wei Su
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Hui Ding
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Wei Chen
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China; Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611730, China; Center for Genomics and Computational Biology, School of Life Sciences, North China University of Science and Technology, Tangshan 063000, China.
| | - Hao Lin
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China.
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