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Yang S, Kim SH, Yang E, Kang M, Joo JY. Molecular insights into regulatory RNAs in the cellular machinery. Exp Mol Med 2024; 56:1235-1249. [PMID: 38871819 PMCID: PMC11263585 DOI: 10.1038/s12276-024-01239-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/27/2024] [Accepted: 03/05/2024] [Indexed: 06/15/2024] Open
Abstract
It is apparent that various functional units within the cellular machinery are derived from RNAs. The evolution of sequencing techniques has resulted in significant insights into approaches for transcriptome studies. Organisms utilize RNA to govern cellular systems, and a heterogeneous class of RNAs is involved in regulatory functions. In particular, regulatory RNAs are increasingly recognized to participate in intricately functioning machinery across almost all levels of biological systems. These systems include those mediating chromatin arrangement, transcription, suborganelle stabilization, and posttranscriptional modifications. Any class of RNA exhibiting regulatory activity can be termed a class of regulatory RNA and is typically represented by noncoding RNAs, which constitute a substantial portion of the genome. These RNAs function based on the principle of structural changes through cis and/or trans regulation to facilitate mutual RNA‒RNA, RNA‒DNA, and RNA‒protein interactions. It has not been clearly elucidated whether regulatory RNAs identified through deep sequencing actually function in the anticipated mechanisms. This review addresses the dominant properties of regulatory RNAs at various layers of the cellular machinery and covers regulatory activities, structural dynamics, modifications, associated molecules, and further challenges related to therapeutics and deep learning.
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Affiliation(s)
- Sumin Yang
- Department of Pharmacy, College of Pharmacy, Hanyang University, Ansan, Gyeonggi-do, 15588, Republic of Korea
| | - Sung-Hyun Kim
- Department of Pharmacy, College of Pharmacy, Hanyang University, Ansan, Gyeonggi-do, 15588, Republic of Korea
| | - Eunjeong Yang
- Department of Pharmacy, College of Pharmacy, Hanyang University, Ansan, Gyeonggi-do, 15588, Republic of Korea
| | - Mingon Kang
- Department of Computer Science, University of Nevada, Las Vegas, NV, 89154, USA
| | - Jae-Yeol Joo
- Department of Pharmacy, College of Pharmacy, Hanyang University, Ansan, Gyeonggi-do, 15588, Republic of Korea.
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Avila Santos AP, de Almeida BLS, Bonidia RP, Stadler PF, Stefanic P, Mandic-Mulec I, Rocha U, Sanches DS, de Carvalho ACPLF. BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification. RNA Biol 2024; 21:1-12. [PMID: 38528797 DOI: 10.1080/15476286.2024.2329451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/23/2024] [Indexed: 03/27/2024] Open
Abstract
The accurate classification of non-coding RNA (ncRNA) sequences is pivotal for advanced non-coding genome annotation and analysis, a fundamental aspect of genomics that facilitates understanding of ncRNA functions and regulatory mechanisms in various biological processes. While traditional machine learning approaches have been employed for distinguishing ncRNA, these often necessitate extensive feature engineering. Recently, deep learning algorithms have provided advancements in ncRNA classification. This study presents BioDeepFuse, a hybrid deep learning framework integrating convolutional neural networks (CNN) or bidirectional long short-term memory (BiLSTM) networks with handcrafted features for enhanced accuracy. This framework employs a combination of k-mer one-hot, k-mer dictionary, and feature extraction techniques for input representation. Extracted features, when embedded into the deep network, enable optimal utilization of spatial and sequential nuances of ncRNA sequences. Using benchmark datasets and real-world RNA samples from bacterial organisms, we evaluated the performance of BioDeepFuse. Results exhibited high accuracy in ncRNA classification, underscoring the robustness of our tool in addressing complex ncRNA sequence data challenges. The effective melding of CNN or BiLSTM with external features heralds promising directions for future research, particularly in refining ncRNA classifiers and deepening insights into ncRNAs in cellular processes and disease manifestations. In addition to its original application in the context of bacterial organisms, the methodologies and techniques integrated into our framework can potentially render BioDeepFuse effective in various and broader domains.
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Affiliation(s)
- Anderson P Avila Santos
- Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, Brazil
- Department of Applied Microbial Ecology, Helmholtz Centre for Environmental Research - UFZ GmbH, Leipzig, Saxony, Germany
| | - Breno L S de Almeida
- Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, Brazil
| | - Robson P Bonidia
- Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, Brazil
- Department of Computer Science, Federal University of Technology - Paraná, UTFPR, Cornélio Procópio, Brazil
| | - Peter F Stadler
- Department of Computer Science and Interdisciplinary Center of Bioinformatics, University of Leipzig, Leipzig, Saxony, Germany
| | - Polonca Stefanic
- Department of Food Science and Technology, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Ines Mandic-Mulec
- Department of Food Science and Technology, Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Ulisses Rocha
- Department of Applied Microbial Ecology, Helmholtz Centre for Environmental Research - UFZ GmbH, Leipzig, Saxony, Germany
| | - Danilo S Sanches
- Department of Computer Science, Federal University of Technology - Paraná, UTFPR, Cornélio Procópio, Brazil
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Ruivinho C, Gama-Carvalho M. Small non-coding RNAs encoded by RNA viruses: old controversies and new lessons from the COVID-19 pandemic. Front Genet 2023; 14:1216890. [PMID: 37415603 PMCID: PMC10322155 DOI: 10.3389/fgene.2023.1216890] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 06/07/2023] [Indexed: 07/08/2023] Open
Abstract
The recurring outbreaks caused by emerging RNA viruses have fostered an increased interest in the research of the mechanisms that regulate viral life cycles and the pathological outcomes associated with infections. Although interactions at the protein level are well-studied, interactions mediated by RNA molecules are less explored. RNA viruses can encode small non-coding RNAs molecules (sncRNAs), including viral miRNAs (v-miRNAs), that play important roles in modulating host immune responses and viral replication by targeting viral or host transcripts. Starting from the analysis of public databases compiling the known repertoire of viral ncRNA molecules and the evolution of publications and research interests on this topic in the wake of the COVID-19 pandemic, we provide an updated view on the current knowledge on viral sncRNAs, with a focus on v-miRNAs encoded by RNA viruses, and their mechanisms of action. We also discuss the potential of these molecules as diagnostic and prognostic biomarkers for viral infections and the development of antiviral therapies targeting v-miRNAs. This review emphasizes the importance of continued research efforts to characterize sncRNAs encoded by RNA viruses, identifies the most relevant pitfalls in the study of these molecules, and highlights the paradigm changes that have occurred in the last few years regarding their biogenesis, prevalence and functional relevance in the context of host-pathogen interactions.
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Dunkel H, Wehrmann H, Jensen LR, Kuss AW, Simm S. MncR: Late Integration Machine Learning Model for Classification of ncRNA Classes Using Sequence and Structural Encoding. Int J Mol Sci 2023; 24:8884. [PMID: 37240230 PMCID: PMC10218863 DOI: 10.3390/ijms24108884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 05/11/2023] [Accepted: 05/13/2023] [Indexed: 05/28/2023] Open
Abstract
Non-coding RNA (ncRNA) classes take over important housekeeping and regulatory functions and are quite heterogeneous in terms of length, sequence conservation and secondary structure. High-throughput sequencing reveals that the expressed novel ncRNAs and their classification are important to understand cell regulation and identify potential diagnostic and therapeutic biomarkers. To improve the classification of ncRNAs, we investigated different approaches of utilizing primary sequences and secondary structures as well as the late integration of both using machine learning models, including different neural network architectures. As input, we used the newest version of RNAcentral, focusing on six ncRNA classes, including lncRNA, rRNA, tRNA, miRNA, snRNA and snoRNA. The late integration of graph-encoded structural features and primary sequences in our MncR classifier achieved an overall accuracy of >97%, which could not be increased by more fine-grained subclassification. In comparison to the actual best-performing tool ncRDense, we had a minimal increase of 0.5% in all four overlapping ncRNA classes on a similar test set of sequences. In summary, MncR is not only more accurate than current ncRNA prediction tools but also allows the prediction of long ncRNA classes (lncRNAs, certain rRNAs) up to 12.000 nts and is trained on a more diverse ncRNA dataset retrieved from RNAcentral.
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Affiliation(s)
- Heiko Dunkel
- Institute of Bioinformatics, University Medicine Greifswald, Walther-Rathenau Str. 48, 17489 Greifswald, Germany
| | - Henning Wehrmann
- Department of Biosciences, Molecular Cell Biology of Plants, Goethe University, 60438 Frankfurt am Main, Germany
| | - Lars R. Jensen
- Human Molecular Genetics Group, Department of Functional Genomics, Interfaculty Institute of Genetics and Functional Genomics, University Medicine Greifswald, 17475 Greifswald, Germany
| | - Andreas W. Kuss
- Human Molecular Genetics Group, Department of Functional Genomics, Interfaculty Institute of Genetics and Functional Genomics, University Medicine Greifswald, 17475 Greifswald, Germany
| | - Stefan Simm
- Institute of Bioinformatics, University Medicine Greifswald, Walther-Rathenau Str. 48, 17489 Greifswald, Germany
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Zhang H, Wang Y, Pan Z, Sun X, Mou M, Zhang B, Li Z, Li H, Zhu F. ncRNAInter: a novel strategy based on graph neural network to discover interactions between lncRNA and miRNA. Brief Bioinform 2022; 23:6747810. [PMID: 36198065 DOI: 10.1093/bib/bbac411] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 08/04/2022] [Accepted: 08/23/2022] [Indexed: 12/14/2022] Open
Abstract
In recent years, many studies have illustrated the significant role that non-coding RNA (ncRNA) plays in biological activities, in which lncRNA, miRNA and especially their interactions have been proved to affect many biological processes. Some in silico methods have been proposed and applied to identify novel lncRNA-miRNA interactions (LMIs), but there are still imperfections in their RNA representation and information extraction approaches, which imply there is still room for further improving their performances. Meanwhile, only a few of them are accessible at present, which limits their practical applications. The construction of a new tool for LMI prediction is thus imperative for the better understanding of their relevant biological mechanisms. This study proposed a novel method, ncRNAInter, for LMI prediction. A comprehensive strategy for RNA representation and an optimized deep learning algorithm of graph neural network were utilized in this study. ncRNAInter was robust and showed better performance of 26.7% higher Matthews correlation coefficient than existing reputable methods for human LMI prediction. In addition, ncRNAInter proved its universal applicability in dealing with LMIs from various species and successfully identified novel LMIs associated with various diseases, which further verified its effectiveness and usability. All source code and datasets are freely available at https://github.com/idrblab/ncRNAInter.
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Affiliation(s)
- Hanyu Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ziqi Pan
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xiuna Sun
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Bing Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Zhaorong Li
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Honglin Li
- School of Computer Science and Technology, East China Normal University, Shanghai 200062, China.,Shanghai Key Laboratory of New Drug Design, East China University of Science and Technology, Shanghai 200237, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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Xu D, Yuan W, Fan C, Liu B, Lu MZ, Zhang J. Opportunities and Challenges of Predictive Approaches for the Non-coding RNA in Plants. FRONTIERS IN PLANT SCIENCE 2022; 13:890663. [PMID: 35498708 PMCID: PMC9048598 DOI: 10.3389/fpls.2022.890663] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 03/28/2022] [Indexed: 06/01/2023]
Affiliation(s)
- Dong Xu
- State Key Laboratory of Subtropical Silviculture, College of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou, China
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Wenya Yuan
- State Key Laboratory of Subtropical Silviculture, College of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou, China
| | - Chunjie Fan
- State Key Laboratory of Tree Genetics and Breeding, Research Institute of Tropical Forestry, Chinese Academy of Forestry, Guangzhou, China
| | - Bobin Liu
- Jiangsu Key Laboratory for Bioresources of Saline Soils, Jiangsu Synthetic Innovation Center for Coastal Bio-agriculture, School of Wetlands, Yancheng Teachers University, Yancheng, China
| | - Meng-Zhu Lu
- State Key Laboratory of Subtropical Silviculture, College of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou, China
| | - Jin Zhang
- State Key Laboratory of Subtropical Silviculture, College of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou, China
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