ArticleScientific reports2024
RiceProteomeDB (RPDB): a user-friendly database for proteomics data storage, retrieval, and analysis.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 5 citations in OpenAlex.
- Recent Advances and Application of Machine Learning for Protein-Protein Interaction Prediction in Rice: Challenges and Future Perspectives.Proteomes · 2025Review
- Quantitative proteomic analysis deciphers mechanisms of sheath blight resistance in novel rice landrace against Rhizoctonia solani.Scientific reports · 2025Article
- The Rice Online Expression Profiles Array Database Version 2 (ROADv2): An Interactive Atlas for Rice Functional Genomics.Rice (New York, N.Y.) · 2024Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rice, feeding a significant portion of the world, poses unique proteomic challenges critical to agricultural research and global food security. The complexity of the rice proteome, influenced by various genetic and environmental factors, demands specialized analytical approaches for effective study. The central challenges in rice proteomics lie in developing custom methods suited to the unique aspects of rice biology. These include data preprocessing, method selection, and result validation, all of which are essential for advancing rice research. Our aim is to decode these proteomic intricacies to facilitate breakthroughs in strain improvement, disease resistance, and yield optimization, all vital for combating global food insecurity. To achieve this, we have created the RiceProteomeDB (RPDB), a React + Django database, offering a streamlined and comprehensive platform for the analysis of rice proteomics data. RiceProteomeDB (RPDB) simplifies proteomics data management and analysis. It offers features for data organization, preprocessing, method selection, result validation, and data sharing. Researchers can access processed rice proteomics data, conduct analyses, and explore experimental conditions. The user-friendly web interface enhances navigation and interaction. RPDB fosters collaboration by enabling data sharing and proper acknowledgment of sources, contributing to proteomics research and knowledge dissemination. Availability and implementation: Web application: http://riceproteome.plantprofile.net/ . The web application's source code, user's manual, and sample data: https://github.com/dongu7610/Riceproteome .
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Registered trials
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