作物基因表型关联综合平台。

IF 3.5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY NPJ Systems Biology and Applications Pub Date : 2024-02-12 DOI:10.1038/s41540-024-00343-7
Yujia Gao, Qian Zhou, Jiaxin Luo, Chuan Xia, Youhua Zhang, Zhenyu Yue
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引用次数: 0

摘要

随着农作物大规模生物学数据的日益增多,人们迫切需要一个多功能平台来充分挖掘和利用这些数据,促进现代分子育种。我们推出的 Crop-GPA ( https://crop-gpa.aielab.net ) 是一个功能全面的作物基因表型关联数据开源平台。当前的 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(GPA)信息。GPA-BERT 和 GPA-GCN 这两个计算工具是专门开发并集成到 Crop-GPA 中的,有助于从生物作物文献中自动提取基因-表型关联,并根据已知关联预测未知关系。通过使用实例,我们展示了我们的平台如何帮助探索作物基因与表型之间的复杂关联。总之,Crop-GPA 是一种有价值的多功能资源,可帮助作物研究界深入了解感兴趣的生物机制。
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Crop-GPA: an integrated platform of crop gene-phenotype associations.

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.

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来源期刊
NPJ Systems Biology and Applications
NPJ Systems Biology and Applications Mathematics-Applied Mathematics
CiteScore
5.80
自引率
0.00%
发文量
46
审稿时长
8 weeks
期刊介绍: npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.
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