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| SPECIAL TOPIC — A celebration of the 90th Anniversary of the Birth of Bolin Hao |
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A comprehensive evaluation of RNA secondary structures prediction methods |
| Xinlong Chen(陈昕龙)1, En Lou(娄恩)1, Zouchenyu Zhou(周邹辰毓)1, Ya-Lan Tan(谭雅岚)2,‡, and Zhi-Jie Tan(谭志杰)1,† |
1 Department of Physics and Key Laboratory of Artificial Micro & Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072, China;
2 School of Bioengineering and Health & Research Center of Nonlinear Science, Wuhan Textile University, Wuhan 430200, China |
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Abstract RNAs have important biological functions and the functions of RNAs are generally coupled to their structures, especially their secondary structures. In this work, we have made a comprehensive evaluation of the performances of existing top RNA secondary structure prediction methods, including five deep-learning (DL) based methods and five minimum free energy (MFE) based methods. First, we made a brief overview of these RNA secondary structure prediction methods. Afterwards, we built two rigorous test datasets consisting of RNAs with non-redundant sequences and comprehensively examined the performances of the RNA secondary structure prediction methods through classifying the RNAs into different length ranges and different types. Our examination shows that the DL-based methods generally perform better than the MFE-based methods for RNAs with long lengths and complex structures, while the MFE-based methods can achieve good performance for small RNAs and some specialized MFE-based methods can achieve good prediction accuracy for pseudoknots. Finally, we provided some insights and perspectives in modeling RNA secondary structures.
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Received: 11 June 2025
Revised: 26 June 2025
Accepted manuscript online: 02 July 2025
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PACS:
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87.10.Vg
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(Biological information)
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87.15.bg
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(Tertiary structure)
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87.14.gn
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(RNA)
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| Fund: We are grateful to Profs Shi-Jie Chen (University of Missouri) and Jian Zhang (Nanjing University) for valuable discussions. This work was supported by grants from the National Science Foundation of China (Grant Nos. 12375038 and 12075171 to ZJT, and 12205223 to YLT). |
Corresponding Authors:
Zhi-Jie Tan, Ya-Lan Tan
E-mail: zjtan@whu.edu.cn;yltan@wtu.edu.cn
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Cite this article:
Xinlong Chen(陈昕龙), En Lou(娄恩), Zouchenyu Zhou(周邹辰毓), Ya-Lan Tan(谭雅岚), and Zhi-Jie Tan(谭志杰) A comprehensive evaluation of RNA secondary structures prediction methods 2025 Chin. Phys. B 34 088710
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