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

Genomics, Proteomics & Bioinformatics 2024年第3期22卷 29-41页

作者： Fei Qi Junjie Chen Yue Chen Jianfeng Sun Yiting Lin Zipeng Chen philipp kapranov State Key Laboratory of Cellular Stress Biology School of Life SciencesFaculty of Medicine and Life SciencesXiamen UniversityXiamen 361102China Institute of Genomics School of MedicineHuaqiao UniversityXiamen 361021China Botnar Research Centre University of OxfordOxfordOX37LDUnited Kingdom

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.

关键词： RNA secondary structure RNA secondary structure prediction Ribozyme Deep learning Pseudoknot

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Traditional Chinese Medicine Treatment, Gua Sha, can Induce Subtle Molecular Changes in Gene Expression

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Biomedical and Environmental Sciences 2023年第5期36卷 441-451页

作者： QI Fei CAI Ye CHEN Jun Jie CHEN Chun Li HAN Xue Er XIA Qiu kapranov philipp Institute of Genomics School of MedicineHuaqiao UniversityXiamen 361021FujianChina Traditional Chinese Medicine Hospital of Huian Quanzhou 36200FujianChina

Objective Here, we explored molecular changes that could potentially mediate healing effects of Gua Sha — a method employed by the Chinese traditional medicine with proven track records of safe and efficient applications dating back to ancient times as well as support from randomized controlled trials performed by modern medical studies — yet remaining almost entirely unexplored by the modern-day high-throughput methods of the-omics *** We investigated transcriptome changes occurring shortly after Gua Sha treatment in the whole blood of healthy volunteers using bulk RNA-seq analysis. We applied various analytical tools to identify genes with consistent expression changes in multiple individuals in response to Gua Sha and their *** We found that while the changes were very subtle and individual-specific, we could identify consistent upregulation of three histone genes. Further analysis of the potential regulatory networks of these histone genes revealed the enrichment of functions involved in the immune response and *** The significance of these results in the context of potential effects of Gua Sha and the next steps in exploring the molecular mechanisms of action of this technique are discussed.

关键词： Gua Sha Transcriptomics Histone Long non-coding RNAs Chinese traditional medicine Immune system Inflammation

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