title: Updated Rice Kinase Database RKD 2.0: enabling transcriptome and functional analysis of rice kinase genes.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Graduate School of Biotechnology & Crop Biotech Institute, Kyung Hee University, Yongin, 446-701, Republic of Korea.
authors.forename: Anil Kumar Nalini
authors.initials: AKN
authors.lastname: Chandran
authors.affiliation: Graduate School of Biotechnology & Crop Biotech Institute, Kyung Hee University, Yongin, 446-701, Republic of Korea.
authors.forename: Yo-Han
authors.initials: YH
authors.lastname: Yoo
authors.affiliation: China Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute, Zhengzhou, 450001, China.
authors.forename: Peijian
authors.initials: P
authors.lastname: Cao
authors.affiliation: School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi, 110067, India.
authors.forename: Rita
authors.initials: R
authors.lastname: Sharma
authors.affiliation: School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067, India.
authors.forename: Manoj
authors.initials: M
authors.lastname: Sharma
authors.affiliation: Appalachian Fruit Research Station, United States Department of Agriculture - Agricultural Research Service, 2217 Wiltshire Road, Kearneysville, WV, 25442, USA.
authors.forename: Christopher
authors.initials: C
authors.lastname: Dardick
authors.affiliation: Department of Plant Pathology and the Genome Center, University of California, Davis, CA, 95616, USA. pcronald@ucdavis.edu.|The Joint Bioenergy Institute, Emeryville, CA, 95616, USA. pcronald@ucdavis.edu.
authors.forename: Pamela C
authors.initials: PC
authors.lastname: Ronald
authors.affiliation: Graduate School of Biotechnology & Crop Biotech Institute, Kyung Hee University, Yongin, 446-701, Republic of Korea. khjung2010@khu.ac.kr.
authors.forename: Ki-Hong
authors.initials: KH
authors.lastname: Jung
keywords: Functional redundancy; Meta-analysis; Mutant analysis; Pearson correlation coefficient; Phylogenomics; Rice kinase database; Transcriptome