Evaluating Performance of Different RNA Secondary Structure Prediction Programs Using Self-cleaving Ribozymes

作     者：Fei Qi Junjie Chen Yue Chen Jianfeng Sun Yiting Lin Zipeng Chen Philipp Kapranov 

作者机构：State Key Laboratory of Cellular Stress BiologySchool of Life SciencesFaculty of Medicine and Life SciencesXiamen UniversityXiamen 361102China Institute of GenomicsSchool of MedicineHuaqiao UniversityXiamen 361021China Botnar Research CentreUniversity of OxfordOxfordOX37LDUnited Kingdom 

出 版 物：《Genomics, Proteomics & Bioinformatics》 (基因组蛋白质组与生物信息学报（英文版）)

年 卷 期：2024年第22卷第3期

页      面：29-41页

核心收录：

学科分类：0710[理学-生物学] 07[理学] 071007[理学-遗传学] 

基　　金：supported by the National Natural Science Foundation of China(Grant No.32000462 to Fei Qi,Grant No.32170619 to Philipp Kapranov and Grant No.32201055 to Yue Chen) the Research Fund for International Senior Scientists from the National Natural Science Foundation of China(Grant No.32150710525 to Philipp Kapranov) the Natural Science Foundation of Fujian Province,China(Grant No.2020J02006 to Philipp Kapranov) the Scientific Research Funds of Huaqiao University,China(Grant No.22BS114 to Fei Qi,Grant No.21BS127 to Yue Chen,and Grant No.15BS101 to Philipp Kapranov) 

主　　题：RNA secondary structure RNA secondary structure prediction Ribozyme Deep learning Pseudoknot 

摘      要：Accurate identification of the correct,biologically relevant RNA structures is critical to understanding various aspects of RNA biology since proper folding represents the key to the functionality of all types of RNA molecules and plays pivotal roles in many essential biological ***,a plethora of approaches have been developed to predict,identify,or solve RNA structures based on various computational,molecular,genetic,chemical,or physicochemical *** computational approaches hold distinct advantages over all other strategies in terms of the ease of implementation,time,speed,cost,and throughput,but they strongly underperform in terms of accuracy that significantly limits their broader ***,the advantages of these methods led to a steady development of multiple in silico RNA secondary structure prediction approaches including recent deep learning-based ***,we compared the accuracy of predictions of biologically relevant secondary structures of dozens of self-cleaving ribozyme sequences using seven in silico RNA folding prediction tools with tasks of varying *** found that while many programs performed well in relatively simple tasks,their performance varied significantly in more complex RNA folding ***,in general,a modern deep learning method outperformed the other programs in the complex tasks in predicting the RNA secondary structures,at least based on the specific class of sequences tested,suggesting that it may represent the future of RNA structure prediction algorithms.