School of Information and Artificial Intelligence, Anhui Beidou Precision Agriculture Information Engineering Research Center, Anhui Agricultural University, Hefei, Anhui, 230036, China.
NPJ Syst Biol Appl. 2024 Feb 12;10(1):15. doi: 10.1038/s41540-024-00343-7.
With the increasing availability of large-scale biology data in crop plants, there is an urgent demand for a versatile platform that fully mines and utilizes the data for modern molecular breeding. We present Crop-GPA ( https://crop-gpa.aielab.net ), a comprehensive and functional open-source platform for crop gene-phenotype association data. The current Crop-GPA provides well-curated information on genes, phenotypes, and their associations (GPAs) to researchers through an intuitive interface, dynamic graphical visualizations, and efficient online tools. Two computational tools, GPA-BERT and GPA-GCN, are specifically developed and integrated into Crop-GPA, facilitating the automatic extraction of gene-phenotype associations from bio-crop literature and predicting unknown relations based on known associations. Through usage examples, we demonstrate how our platform enables the exploration of complex correlations between genes and phenotypes in crop plants. In summary, Crop-GPA serves as a valuable multi-functional resource, empowering the crop research community to gain deeper insights into the biological mechanisms of interest.
随着作物生物学大数据的日益普及，人们迫切需要一个通用的平台，以便充分挖掘和利用这些数据进行现代分子育种。我们介绍了 Crop-GPA（https://crop-gpa.aielab.net），这是一个用于作物基因-表型关联数据的全面而实用的开源平台。目前的 Crop-GPA 通过直观的界面、动态图形可视化和高效的在线工具，为研究人员提供了经过精心整理的基因、表型及其关联（GPAs）信息。两个专门开发并集成到 Crop-GPA 中的计算工具，即 GPA-BERT 和 GPA-GCN，可帮助从生物作物文献中自动提取基因-表型关联，并基于已知关联预测未知关系。通过使用示例，我们展示了我们的平台如何使研究人员能够探索作物中基因和表型之间的复杂相关性。总之，Crop-GPA 是一个有价值的多功能资源，使作物研究界能够更深入地了解感兴趣的生物学机制。
