Graduate School of Biotechnology & Crop Biotech Institute, Kyung Hee University, Yongin, 446-701, Republic of Korea.
China Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute, Zhengzhou, 450001, China.
Rice (N Y). 2016 Dec;9(1):40. doi: 10.1186/s12284-016-0106-5. Epub 2016 Aug 19.
Protein kinases catalyze the transfer of a phosphate moiety from a phosphate donor to the substrate molecule, thus playing critical roles in cell signaling and metabolism. Although plant genomes contain more than 1000 genes that encode kinases, knowledge is limited about the function of each of these kinases. A major obstacle that hinders progress towards kinase characterization is functional redundancy. To address this challenge, we previously developed the rice kinase database (RKD) that integrated omics-scale data within a phylogenetics context.
An updated version of rice kinase database (RKD) that contains metadata derived from NCBI GEO expression datasets has been developed. RKD 2.0 facilitates in-depth transcriptomic analyses of kinase-encoding genes in diverse rice tissues and in response to biotic and abiotic stresses and hormone treatments. We identified 261 kinases specifically expressed in particular tissues, 130 that are significantly up- regulated in response to biotic stress, 296 in response to abiotic stress, and 260 in response to hormones. Based on this update and Pearson correlation coefficient (PCC) analysis, we estimated that 19 out of 26 genes characterized through loss-of-function studies confer dominant functions. These were selected because they either had paralogous members with PCC values of <0.5 or had no paralog.
Compared with the previous version of RKD, RKD 2.0 enables more effective estimations of functional redundancy or dominance because it uses comprehensive expression profiles rather than individual profiles. The integrated analysis of RKD with PCC establishes a single platform for researchers to select rice kinases for functional analyses.
蛋白激酶催化磷酸基团从磷酸供体转移到底物分子上，因此在细胞信号转导和代谢中起着关键作用。尽管植物基因组包含 1000 多个编码激酶的基因，但对这些激酶中的每一个的功能了解有限。阻碍激酶特征描述进展的一个主要障碍是功能冗余。为了解决这个挑战，我们之前开发了水稻激酶数据库（RKD），该数据库将组学规模的数据整合在系统发育背景下。
开发了一个包含从 NCBI GEO 表达数据集衍生的元数据的水稻激酶数据库（RKD）的更新版本。RKD 2.0 促进了在不同水稻组织和对生物和非生物胁迫以及激素处理的响应中对激酶编码基因的深入转录组分析。我们鉴定了 261 种在特定组织中特异性表达的激酶、130 种对生物胁迫有显著上调的激酶、296 种对非生物胁迫有上调的激酶和 260 种对激素有上调的激酶。基于此更新和 Pearson 相关系数（PCC）分析，我们估计通过功能丧失研究表征的 26 个基因中的 19 个赋予显性功能。选择这些基因是因为它们要么具有 PCC 值<0.5 的旁系同源物，要么没有旁系同源物。
与之前的 RKD 版本相比，RKD 2.0 能够更有效地估计功能冗余或主导性，因为它使用综合表达谱而不是单个谱。将 RKD 与 PCC 进行综合分析为研究人员提供了一个用于选择水稻激酶进行功能分析的单一平台。
