Protein subcellular localization prediction tools

Protein subcellular localization prediction tools

2024 | Maryam Gillani, Gianluca Pollastri
Protein subcellular localization prediction is a critical area in bioinformatics, with significant implications for understanding cellular functions and protein interactions. This paper reviews recent advancements in subcellular localization prediction tools, focusing on Eukaryotic, Prokaryotic, and Virus-based categories. The review highlights the strengths and weaknesses of various prediction methods, including sequence-based, annotation/knowledge/homology-based, and structure-based approaches. It also discusses the challenges and research gaps in the field, such as the limitations of homology-based methods for unannotated proteins and the complexity of ab-initio predictions. The article provides a comprehensive taxonomy of prediction tools, categorized by their target organisms and methods, to help researchers choose the most suitable tools for their needs. Additionally, it offers a detailed analysis of the latest prediction tools, their algorithms, and performance metrics, making it a valuable resource for both researchers and practitioners in the field.Protein subcellular localization prediction is a critical area in bioinformatics, with significant implications for understanding cellular functions and protein interactions. This paper reviews recent advancements in subcellular localization prediction tools, focusing on Eukaryotic, Prokaryotic, and Virus-based categories. The review highlights the strengths and weaknesses of various prediction methods, including sequence-based, annotation/knowledge/homology-based, and structure-based approaches. It also discusses the challenges and research gaps in the field, such as the limitations of homology-based methods for unannotated proteins and the complexity of ab-initio predictions. The article provides a comprehensive taxonomy of prediction tools, categorized by their target organisms and methods, to help researchers choose the most suitable tools for their needs. Additionally, it offers a detailed analysis of the latest prediction tools, their algorithms, and performance metrics, making it a valuable resource for both researchers and practitioners in the field.
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