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Zhong B, Dai Y, Chen L, Xu X, Lan Y, Deng L, Ren L, Luo N, Ning L. ncRS: A resource of non-coding RNAs in sepsis. Comput Biol Med 2024; 172:108256. [PMID: 38489989 DOI: 10.1016/j.compbiomed.2024.108256] [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] [Received: 11/18/2023] [Revised: 02/10/2024] [Accepted: 03/06/2024] [Indexed: 03/17/2024]
Abstract
Sepsis, a life-threatening condition triggered by the body's response to infection, presents a significant global healthcare challenge characterized by disarrayed host responses, widespread inflammation, organ impairment, and heightened mortality rates. This study introduces the ncRS database (http://www.ncrdb.cn), a meticulously curated repository housing 1144 experimentally validated non-coding RNAs (ncRNAs) intricately linked with sepsis. ncRS offers comprehensive RNA data, exhaustive experimental insights, and integrated annotations from diverse databases. This resource empowers researchers and clinicians to decipher ncRNAs' roles in sepsis pathogenesis, potentially identifying vital biomarkers for early diagnosis and prognosis, thus facilitating personalized treatments.
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Affiliation(s)
- Baocai Zhong
- School of Computer and Software, Chengdu Neusoft University, Chengdu, China
| | - Yongfang Dai
- School of Computer and Software, Chengdu Neusoft University, Chengdu, China
| | - Li Chen
- School of Computer and Software, Chengdu Neusoft University, Chengdu, China.
| | - Xinying Xu
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Yuxi Lan
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Leyao Deng
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Liping Ren
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Nanchao Luo
- School of Computer Science and Technology, A Ba Teachers University, Wenchuan, China.
| | - Lin Ning
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China; Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China.
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2
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He S, Ye X, Dou L, Sakurai T. FIAMol-AB: A feature fusion and attention-based deep learning method for enhanced antibiotic discovery. Comput Biol Med 2024; 168:107762. [PMID: 38056212 DOI: 10.1016/j.compbiomed.2023.107762] [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] [Received: 07/29/2023] [Revised: 10/31/2023] [Accepted: 11/21/2023] [Indexed: 12/08/2023]
Abstract
Antibiotic resistance continues to be a growing concern for global health, accentuating the need for novel antibiotic discoveries. Traditional methodologies in this field have relied heavily on extensive experimental screening, which is often time-consuming and costly. Contrastly, computer-assisted drug screening offers rapid, cost-effective solutions. In this work, we propose FIAMol-AB, a deep learning model that combines graph neural networks, text convolutional networks and molecular fingerprint techniques. This method also combines an attention mechanism to fuse multiple forms of information within the model. The experiments show that FIAMol-AB may offer potential advantages in antibiotic discovery tasks over some existing methods. We conducted some analysis based on our model's results, which help highlight the potential significance of certain features in the model's predictive performance. Compared to different models, ours demonstrate promising results, indicating potential robustness and versatility. This suggests that by integrating multi-view information and attention mechanisms, FIAMol-AB might better learn complex molecular structures, potentially improving the precision and efficiency of antibiotic discovery. We hope our FIAMol-AB can be used as a useful method in the ongoing fight against antibiotic resistance.
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Affiliation(s)
- Shida He
- Department of Computer Science, University of Tsukuba, Tsukuba, Ibaraki, 305-8577, Japan
| | - Xiucai Ye
- Department of Computer Science, University of Tsukuba, Tsukuba, Ibaraki, 305-8577, Japan.
| | - Lijun Dou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland, OH, 44106, USA
| | - Tetsuya Sakurai
- Department of Computer Science, University of Tsukuba, Tsukuba, Ibaraki, 305-8577, Japan
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3
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Momanyi BM, Zhou YW, Grace-Mercure BK, Temesgen SA, Basharat A, Ning L, Tang L, Gao H, Lin H, Tang H. SAGESDA: Multi-GraphSAGE networks for predicting SnoRNA-disease associations. Curr Res Struct Biol 2023; 7:100122. [PMID: 38188542 PMCID: PMC10771890 DOI: 10.1016/j.crstbi.2023.100122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Revised: 11/30/2023] [Accepted: 12/24/2023] [Indexed: 01/09/2024] Open
Abstract
Over the years, extensive research has highlighted the functional roles of small nucleolar RNAs in various biological processes associated with the development of complex human diseases. Therefore, understanding the existing relationships between different snoRNAs and diseases is crucial for advancing disease diagnosis and treatment. However, classical biological experiments for identifying snoRNA-disease associations are expensive and time-consuming. Therefore, there is an urgent need for cost-effective computational techniques that can enhance the efficiency and accuracy of prediction. While several computational models have already been proposed, many suffer from limitations and suboptimal performance. In this study, we introduced a novel Graph Neural Network-based (GNN) classification model, called SAGESDA, which is implemented through the GraphSAGE architecture with attention for the prediction of snoRNA-disease associations. The classifier leverages local neighbouring nodes in a heterogeneous network to generate new node embeddings through message passing. The mini-batch gradient descent technique was applied to divide the graph into smaller sub-graphs, which enhances the model's accuracy, speed and scalability. With these advancements, SAGESDA attained an area under the receiver operating characteristic (ROC) curve (AUC) of 0.92 using the standard dot product classifier, surpassing previous related studies. This notable performance demonstrates that SAGESDA is a promising model for predicting unknown snoRNA-disease associations with high accuracy. The SAGESDA implementation details can be obtained from https://github.com/momanyibiffon/SAGESDA.git.
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Affiliation(s)
- Biffon Manyura Momanyi
- School of Computer Science and Engineering, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yu-Wei Zhou
- School of Health Care Technology, Chengdu Neusoft University, Chengdu, China
| | - Bakanina Kissanga Grace-Mercure
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Sebu Aboma Temesgen
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Ahmad Basharat
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Lin Ning
- School of Health Care Technology, Chengdu Neusoft University, Chengdu, China
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Lixia Tang
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Hui Gao
- School of Computer Science and Engineering, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Lin
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Hua Tang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, 646000, China
- Basic Medicine Research Innovation Center for Cardiometabolic Diseases, Ministry of Education, Luzhou, 646000, China
- Central Nervous System Drug Key Laboratory of Sichuan Province, Luzhou, 646000, China
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4
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Chen J, Lin J, Hu Y, Ye M, Yao L, Wu L, Zhang W, Wang M, Deng T, Guo F, Huang Y, Zhu B, Wang D. RNADisease v4.0: an updated resource of RNA-associated diseases, providing RNA-disease analysis, enrichment and prediction. Nucleic Acids Res 2022; 51:D1397-D1404. [PMID: 36134718 PMCID: PMC9825423 DOI: 10.1093/nar/gkac814] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 09/06/2022] [Accepted: 09/09/2022] [Indexed: 02/06/2023] Open
Abstract
Numerous studies have shown that RNA plays an important role in the occurrence and development of diseases, and RNA-disease associations are not limited to noncoding RNAs in mammals but also exist for protein-coding RNAs. Furthermore, RNA-associated diseases are found across species including plants and nonmammals. To better analyze diseases at the RNA level and facilitate researchers in exploring the pathogenic mechanism of diseases, we decided to update and change MNDR v3.0 to RNADisease v4.0, a repository for RNA-disease association (http://www.rnadisease.org/ or http://www.rna-society.org/mndr/). Compared to the previous version, new features include: (i) expanded data sources and categories of species, RNA types, and diseases; (ii) the addition of a comprehensive analysis of RNAs from thousands of high-throughput sequencing data of cancer samples and normal samples; (iii) the addition of an RNA-disease enrichment tool and (iv) the addition of four RNA-disease prediction tools. In summary, RNADisease v4.0 provides a comprehensive and concise data resource of RNA-disease associations which contains a total of 3 428 058 RNA-disease entries covering 18 RNA types, 117 species and 4090 diseases to meet the needs of biological research and lay the foundation for future therapeutic applications of diseases.
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Affiliation(s)
| | | | | | | | | | - Le Wu
- Department of Bioinformatics, Guangdong Province Key Laboratory of Molecular Tumor Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Wenhai Zhang
- Department of Bioinformatics, Guangdong Province Key Laboratory of Molecular Tumor Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Meiyi Wang
- Department of Bioinformatics, Guangdong Province Key Laboratory of Molecular Tumor Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Tingting Deng
- Department of Bioinformatics, Guangdong Province Key Laboratory of Molecular Tumor Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Feng Guo
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Yan Huang
- Cancer Research Institute, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Bofeng Zhu
- Correspondence may also be addressed to Bofeng Zhu. Tel: +86 20 61648787; Fax: +86 20 61648787;
| | - Dong Wang
- To whom correspondence should be addressed. Tel: +86 20 61648279; Fax: +86 20 61648279;
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5
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Ning L, Cui T, Zheng B, Wang N, Luo J, Yang B, Du M, Cheng J, Dou Y, Wang D. MNDR v3.0: mammal ncRNA-disease repository with increased coverage and annotation. Nucleic Acids Res 2021; 49:D160-D164. [PMID: 32833025 PMCID: PMC7779040 DOI: 10.1093/nar/gkaa707] [Citation(s) in RCA: 85] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 08/12/2020] [Accepted: 08/14/2020] [Indexed: 02/07/2023] Open
Abstract
Many studies have indicated that non-coding RNA (ncRNA) dysfunction is closely related to numerous diseases. Recently, accumulated ncRNA-disease associations have made related databases insufficient to meet the demands of biomedical research. The constant updating of ncRNA-disease resources has become essential. Here, we have updated the mammal ncRNA-disease repository (MNDR, http://www.rna-society.org/mndr/) to version 3.0, containing more than one million entries, four-fold increment in data compared to the previous version. Experimental and predicted circRNA-disease associations have been integrated, increasing the number of categories of ncRNAs to five, and the number of mammalian species to 11. Moreover, ncRNA-disease related drug annotations and associations, as well as ncRNA subcellular localizations and interactions, were added. In addition, three ncRNA-disease (miRNA/lncRNA/circRNA) prediction tools were provided, and the website was also optimized, making it more practical and user-friendly. In summary, MNDR v3.0 will be a valuable resource for the investigation of disease mechanisms and clinical treatment strategies.
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Affiliation(s)
- Lin Ning
- Dermatology Hospital, Southern Medical University, Guangzhou 510091, China
| | - Tianyu Cui
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Boyang Zheng
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Nuo Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Jiaxin Luo
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Beilei Yang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Mengze Du
- Qingyuan People's Hospital, The Sixth Affiliated Hospital of Guangzhou Medical University, B24 Yinquan South Road, Qingyuan 511518, Guangdong Province, People's Republic of China
| | - Jun Cheng
- Affiliated Foshan Maternity & Child Healthcare Hospital, Southern Medical University (Foshan Maternity & Child Healthcare Hospital)
| | - Yiying Dou
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Dong Wang
- Dermatology Hospital, Southern Medical University, Guangzhou 510091, China
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 611731, China
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6
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Huang Y, Wang J, Zhao Y, Wang H, Liu T, Li Y, Cui T, Li W, Feng Y, Luo J, Gong J, Ning L, Zhang Y, Wang D, Zhang Y. cncRNAdb: a manually curated resource of experimentally supported RNAs with both protein-coding and noncoding function. Nucleic Acids Res 2021; 49:D65-D70. [PMID: 33010163 PMCID: PMC7778915 DOI: 10.1093/nar/gkaa791] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 08/30/2020] [Accepted: 09/11/2020] [Indexed: 12/14/2022] Open
Abstract
RNA endowed with both protein-coding and noncoding functions is referred to as 'dual-function RNA', 'binary functional RNA (bifunctional RNA)' or 'cncRNA (coding and noncoding RNA)'. Recently, an increasing number of cncRNAs have been identified, including both translated ncRNAs (ncRNAs with coding functions) and untranslated mRNAs (mRNAs with noncoding functions). However, an appropriate database for storing and organizing cncRNAs is still lacking. Here, we developed cncRNAdb, a manually curated database of experimentally supported cncRNAs, which aims to provide a resource for efficient manipulation, browsing and analysis of cncRNAs. The current version of cncRNAdb documents about 2600 manually curated entries of cncRNA functions with experimental evidence, involving more than 2,000 RNAs (including over 1300 translated ncRNAs and over 600 untranslated mRNAs) across over 20 species. In summary, we believe that cncRNAdb will help elucidate the functions and mechanisms of cncRNAs and develop new prediction methods. The database is available at http://www.rna-society.org/cncrnadb/.
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MESH Headings
- 3' Untranslated Regions
- 5' Untranslated Regions
- Animals
- Databases, Nucleic Acid/organization & administration
- Drosophila melanogaster/genetics
- Humans
- Mice
- MicroRNAs/classification
- MicroRNAs/genetics
- Pan troglodytes/genetics
- RNA, Circular/classification
- RNA, Circular/genetics
- RNA, Long Noncoding/classification
- RNA, Long Noncoding/genetics
- RNA, Messenger/classification
- RNA, Messenger/genetics
- RNA, Ribosomal/classification
- RNA, Ribosomal/genetics
- RNA, Small Interfering/classification
- RNA, Small Interfering/genetics
- RNA, Transfer/classification
- RNA, Transfer/genetics
- Software
- Zebrafish/genetics
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Affiliation(s)
- Yan Huang
- Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), Foshan 528308, China
| | - Jing Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Yue Zhao
- School of Basic Medical Sciences & Forensic Medicine, Hangzhou Medical College, Hangzhou 310053, China
| | - Huafeng Wang
- Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), Foshan 528308, China
| | - Tianyuan Liu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Yuhe Li
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Tianyu Cui
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Weiyi Li
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Yige Feng
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Jiaxin Luo
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Jiaqi Gong
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Lin Ning
- Dermatology Hospital, Southern Medical University, Guangzhou 510091, China
| | - Yong Zhang
- Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), Foshan 528308, China
| | - Dong Wang
- Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), Foshan 528308, China
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
- Dermatology Hospital, Southern Medical University, Guangzhou 510091, China
| | - Yang Zhang
- Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), Foshan 528308, China
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Rosani U, Abbadi M, Green T, Bai CM, Turolla E, Arcangeli G, Wegner KM, Venier P. Parallel analysis of miRNAs and mRNAs suggests distinct regulatory networks in Crassostrea gigas infected by Ostreid herpesvirus 1. BMC Genomics 2020; 21:620. [PMID: 32912133 PMCID: PMC7488030 DOI: 10.1186/s12864-020-07026-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 08/25/2020] [Indexed: 12/17/2022] Open
Abstract
Background Since 2008, the aquaculture production of Crassostrea gigas was heavily affected by mass mortalities associated to Ostreid herpesvirus 1 (OsHV-1) microvariants worldwide. Transcriptomic studies revealed the major antiviral pathways of the oyster immune response while other findings suggested that also small non-coding RNAs (sncRNA) such as microRNAs might act as key regulators of the oyster response against OsHV-1. To explore the explicit connection between small non-coding and protein-coding transcripts, we performed paired whole transcriptome analysis of sncRNA and messenger RNA (mRNA) in six oysters selected for different intensities of OsHV-1 infection. Results The mRNA profiles of the naturally infected oysters were mostly governed by the transcriptional activity of OsHV-1, with several differentially expressed genes mapping to the interferon, toll, apoptosis, and pro-PO pathways. In contrast, miRNA profiles suggested more complex regulatory mechanisms, with 15 differentially expressed miRNAs (DE-miRNA) pointing to a possible modulation of the host response during OsHV-1 infection. We predicted 68 interactions between DE-miRNAs and oyster 3′-UTRs, but only few of them involved antiviral genes. The sncRNA reads assigned to OsHV-1 rather resembled mRNA degradation products, suggesting the absence of genuine viral miRNAs. Conclusions We provided data describing the miRNAome during OsHV-1 infection in C. gigas. This information can be used to understand the role of miRNAs in healthy and diseased oysters, to identify new targets for functional studies and, eventually to disentangle cause and effect relationships during viral infections in marine mollusks.
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Affiliation(s)
- Umberto Rosani
- Department of Biology, University of Padova, 35121, Padova, Italy. .,Coastal Ecology Section, AWI - Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Wadden Sea Station Sylt, 25992, List, Germany.
| | - Miriam Abbadi
- Istituto Zooprofilattico delle Venezie, Legnaro, Italy
| | - Timothy Green
- Centre for Shellfish Research & Department of Fisheries and Aquaculture, Vancouver Island University, Nanaimo, BC, V9R 5S5, Canada
| | - Chang-Ming Bai
- Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao, 266071, China
| | | | | | - K Mathias Wegner
- Coastal Ecology Section, AWI - Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, Wadden Sea Station Sylt, 25992, List, Germany
| | - Paola Venier
- Department of Biology, University of Padova, 35121, Padova, Italy.
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