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Choudhury S, Bajiya N, Patiyal S, Raghava GPS. MRSLpred-a hybrid approach for predicting multi-label subcellular localization of mRNA at the genome scale. FRONTIERS IN BIOINFORMATICS 2024; 4:1341479. [PMID: 38379813 PMCID: PMC10877048 DOI: 10.3389/fbinf.2024.1341479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 01/15/2024] [Indexed: 02/22/2024] Open
Abstract
In the past, several methods have been developed for predicting the single-label subcellular localization of messenger RNA (mRNA). However, only limited methods are designed to predict the multi-label subcellular localization of mRNA. Furthermore, the existing methods are slow and cannot be implemented at a transcriptome scale. In this study, a fast and reliable method has been developed for predicting the multi-label subcellular localization of mRNA that can be implemented at a genome scale. Machine learning-based methods have been developed using mRNA sequence composition, where the XGBoost-based classifier achieved an average area under the receiver operator characteristic (AUROC) of 0.709 (0.668-0.732). In addition to alignment-free methods, we developed alignment-based methods using motif search techniques. Finally, a hybrid technique that combines the XGBoost model and the motif-based approach has been developed, achieving an average AUROC of 0.742 (0.708-0.816). Our method-MRSLpred-outperforms the existing state-of-the-art classifier in terms of performance and computation efficiency. A publicly accessible webserver and a standalone tool have been developed to facilitate researchers (webserver: https://webs.iiitd.edu.in/raghava/mrslpred/).
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Affiliation(s)
| | | | | | - Gajendra P. S. Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
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Zeng M, Wu Y, Li Y, Yin R, Lu C, Duan J, Li M. LncLocFormer: a Transformer-based deep learning model for multi-label lncRNA subcellular localization prediction by using localization-specific attention mechanism. Bioinformatics 2023; 39:btad752. [PMID: 38109668 PMCID: PMC10749772 DOI: 10.1093/bioinformatics/btad752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 11/13/2023] [Accepted: 12/17/2023] [Indexed: 12/20/2023] Open
Abstract
MOTIVATION There is mounting evidence that the subcellular localization of lncRNAs can provide valuable insights into their biological functions. In the real world of transcriptomes, lncRNAs are usually localized in multiple subcellular localizations. Furthermore, lncRNAs have specific localization patterns for different subcellular localizations. Although several computational methods have been developed to predict the subcellular localization of lncRNAs, few of them are designed for lncRNAs that have multiple subcellular localizations, and none of them take motif specificity into consideration. RESULTS In this study, we proposed a novel deep learning model, called LncLocFormer, which uses only lncRNA sequences to predict multi-label lncRNA subcellular localization. LncLocFormer utilizes eight Transformer blocks to model long-range dependencies within the lncRNA sequence and shares information across the lncRNA sequence. To exploit the relationship between different subcellular localizations and find distinct localization patterns for different subcellular localizations, LncLocFormer employs a localization-specific attention mechanism. The results demonstrate that LncLocFormer outperforms existing state-of-the-art predictors on the hold-out test set. Furthermore, we conducted a motif analysis and found LncLocFormer can capture known motifs. Ablation studies confirmed the contribution of the localization-specific attention mechanism in improving the prediction performance. AVAILABILITY AND IMPLEMENTATION The LncLocFormer web server is available at http://csuligroup.com:9000/LncLocFormer. The source code can be obtained from https://github.com/CSUBioGroup/LncLocFormer.
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Affiliation(s)
- Min Zeng
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Yifan Wu
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Yiming Li
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Rui Yin
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL 32603, United States
| | - Chengqian Lu
- School of Computer Science, Key Laboratory of Intelligent Computing and Information Processing, Xiangtan University, Xiangtan, Hunan 411105, China
| | - Junwen Duan
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
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Wang J, Horlacher M, Cheng L, Winther O. RNA trafficking and subcellular localization-a review of mechanisms, experimental and predictive methodologies. Brief Bioinform 2023; 24:bbad249. [PMID: 37466130 PMCID: PMC10516376 DOI: 10.1093/bib/bbad249] [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: 03/17/2023] [Revised: 05/30/2023] [Accepted: 06/16/2023] [Indexed: 07/20/2023] Open
Abstract
RNA localization is essential for regulating spatial translation, where RNAs are trafficked to their target locations via various biological mechanisms. In this review, we discuss RNA localization in the context of molecular mechanisms, experimental techniques and machine learning-based prediction tools. Three main types of molecular mechanisms that control the localization of RNA to distinct cellular compartments are reviewed, including directed transport, protection from mRNA degradation, as well as diffusion and local entrapment. Advances in experimental methods, both image and sequence based, provide substantial data resources, which allow for the design of powerful machine learning models to predict RNA localizations. We review the publicly available predictive tools to serve as a guide for users and inspire developers to build more effective prediction models. Finally, we provide an overview of multimodal learning, which may provide a new avenue for the prediction of RNA localization.
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Affiliation(s)
- Jun Wang
- Bioinformatics Centre, Department of Biology, University of Copenhagen, København Ø 2100, Denmark
| | - Marc Horlacher
- Computational Health Center, Helmholtz Center, Munich, Germany
| | - Lixin Cheng
- Shenzhen People’s Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen 518020, China
| | - Ole Winther
- Bioinformatics Centre, Department of Biology, University of Copenhagen, København Ø 2100, Denmark
- Center for Genomic Medicine, Rigshospitalet (Copenhagen University Hospital), Copenhagen 2100, Denmark
- Section for Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby 2800, Denmark
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Androvic P, Schifferer M, Perez Anderson K, Cantuti-Castelvetri L, Jiang H, Ji H, Liu L, Gouna G, Berghoff SA, Besson-Girard S, Knoferle J, Simons M, Gokce O. Spatial Transcriptomics-correlated Electron Microscopy maps transcriptional and ultrastructural responses to brain injury. Nat Commun 2023; 14:4115. [PMID: 37433806 DOI: 10.1038/s41467-023-39447-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 06/14/2023] [Indexed: 07/13/2023] Open
Abstract
Understanding the complexity of cellular function within a tissue necessitates the combination of multiple phenotypic readouts. Here, we developed a method that links spatially-resolved gene expression of single cells with their ultrastructural morphology by integrating multiplexed error-robust fluorescence in situ hybridization (MERFISH) and large area volume electron microscopy (EM) on adjacent tissue sections. Using this method, we characterized in situ ultrastructural and transcriptional responses of glial cells and infiltrating T-cells after demyelinating brain injury in male mice. We identified a population of lipid-loaded "foamy" microglia located in the center of remyelinating lesion, as well as rare interferon-responsive microglia, oligodendrocytes, and astrocytes that co-localized with T-cells. We validated our findings using immunocytochemistry and lipid staining-coupled single-cell RNA sequencing. Finally, by integrating these datasets, we detected correlations between full-transcriptome gene expression and ultrastructural features of microglia. Our results offer an integrative view of the spatial, ultrastructural, and transcriptional reorganization of single cells after demyelinating brain injury.
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Affiliation(s)
- Peter Androvic
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Martina Schifferer
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
| | - Katrin Perez Anderson
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Ludovico Cantuti-Castelvetri
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany
| | - Hanyi Jiang
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
| | - Hao Ji
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Lu Liu
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Garyfallia Gouna
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany
| | - Stefan A Berghoff
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany
| | - Simon Besson-Girard
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Johanna Knoferle
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany
- Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Mikael Simons
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany
| | - Ozgun Gokce
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany.
- Munich Cluster of Systems Neurology (SyNergy), Munich, Germany.
- Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany.
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Zhang C, Zhang R, Liang C, Deng Y, Li Z, Deng Y, Tang BZ. Charge-elimination strategy for constructing RNA-selective fluorescent probe undisturbed by mitochondria. Biomaterials 2022; 291:121915. [DOI: 10.1016/j.biomaterials.2022.121915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 11/04/2022] [Accepted: 11/13/2022] [Indexed: 11/18/2022]
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Nakai K, Wei L. Recent Advances in the Prediction of Subcellular Localization of Proteins and Related Topics. FRONTIERS IN BIOINFORMATICS 2022; 2:910531. [PMID: 36304291 PMCID: PMC9580943 DOI: 10.3389/fbinf.2022.910531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 04/25/2022] [Indexed: 11/13/2022] Open
Abstract
Prediction of subcellular localization of proteins from their amino acid sequences has a long history in bioinformatics and is still actively developing, incorporating the latest advances in machine learning and proteomics. Notably, deep learning-based methods for natural language processing have made great contributions. Here, we review recent advances in the field as well as its related fields, such as subcellular proteomics and the prediction/recognition of subcellular localization from image data.
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Affiliation(s)
- Kenta Nakai
- Institute of Medical Science, The University of Tokyo, Minato-Ku, Japan
- *Correspondence: Kenta Nakai,
| | - Leyi Wei
- School of Software, Shandong University, Jinan, China
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