Predicted Arabidopsis Interactome Resource and Gene Set Linkage Analysis: A Transcriptomic Analysis Resource
Predicted Arabidopsis Interactome Resource and Gene Set Linkage Analysis: A Transcriptomic Analysis Resource
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预测拟南芥互作组资源和基因集连锁分析：转录组分析资源
DOI：
10.1104/pp.18.00144
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发表时间：
2018
期刊：
影响因子：
7.4
通讯作者：
Xin Chen
中科院分区：
文献类型：
--
作者：
Heng Yao;Xiaoxuan Wang;Pengcheng Chen;Ling Hai;Kang Jin;Lixia Yao;Chuanzao Mao;Xin Chen
对组学数据的深入功能理解对于阐明植物生理过程的设计逻辑和有效控制植物的期望性状具有重要意义。我们提出了最新版本的预测拟南芥相互作用组资源（PAIR）和基因集连锁分析（GSLA）工具，这使得在拟南芥（拟南芥）中观察到的转录组变化（差异表达基因[DEGs]）的解释，其功能的影响，生物过程。PAIR 5.0版整合了多种形式的基因间的功能关联数据，并推断出335，301种推定的功能相互作用。GSLA依赖于这种高置信度推断的功能关联网络来扩展我们对观察到的转录组变化的功能影响的感知。然后，GSLA使用已建立的生物学概念（注释术语）解释所观察到的DEG的生物学意义，不仅描述DEG本身，还描述其潜在的功能影响。这种独特的分析能力可以帮助研究人员更深入地了解他们的实验结果，并突出未来的研究方向。我们证明了GSLA的效用与两个案例研究中，GSLA揭示了分子事件如何可能引起的生理变化，通过他们的集体功能对生物过程的影响。此外，我们发现，典型的注释丰富工具无法产生类似的见解PAIR/GSLA。PAIR版本5.0-推断的相互作用组和GSLA Web工具都可以在http://public.synergylab.cn/pair/上访问。
An advanced functional understanding of omics data is important for elucidating the design logic of physiological processes in plants and effectively controlling desired traits in plants. We present the latest versions of the Predicted Arabidopsis Interactome Resource (PAIR) and of the gene set linkage analysis (GSLA) tool, which enable the interpretation of an observed transcriptomic change (differentially expressed genes [DEGs]) in Arabidopsis (Arabidopsis thaliana) with respect to its functional impact for biological processes. PAIR version 5.0 integrates functional association data between genes in multiple forms and infers 335,301 putative functional interactions. GSLA relies on this high-confidence inferred functional association network to expand our perception of the functional impacts of an observed transcriptomic change. GSLA then interprets the biological significance of the observed DEGs using established biological concepts (annotation terms), describing not only the DEGs themselves but also their potential functional impacts. This unique analytical capability can help researchers gain deeper insights into their experimental results and highlight prospective directions for further investigation. We demonstrate the utility of GSLA with two case studies in which GSLA uncovered how molecular events may have caused physiological changes through their collective functional influence on biological processes. Furthermore, we showed that typical annotation-enrichment tools were unable to produce similar insights to PAIR/GSLA. The PAIR version 5.0-inferred interactome and GSLA Web tool both can be accessed at http://public.synergylab.cn/pair/.