Plant Physiology Preview
Published on March 19, 2008; 10.1104/pp.108.117366
OPEN ACCESS ARTICLE
Received February 3, 2008
Accepted March 10, 2008
Annotating Genes of Known and Unknown Function by Large-Scale Co-Expression Analysis
Kevin Horan , Charles Jang , Julia Bailey-Serres , Ron Mittler , Christian Shelton , Jeff F Harper , Jian-Kang Zhu , John JC Cushman , Martin Gollery , and Thomas Girke *
Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA 92521; Department of Computer Science & Engineering, University of California, Riverside, Riverside, CA 92521; Department of Biochemistry & Molecular Biology, University of Nevada, Reno, NA 89557; Department of Plant Science, Hebrew University of Jerusalem, Givat Ram, Jerusalem 91904, Israel; TimeLogic - a Division of Active Motif, Incline Village, NV 89451
* Corresponding author; email: thomas.girke{at}ucr.edu.
About 40% of the proteins encoded in eukaryotic genomes are proteins of unknown function (PUFs). Their functional characterization remains one of the main challenges in modern biology. In this study we identified the PUF encoding genes from Arabidopsis thaliana using a combination of sequence similarity, domain-based and empirical approaches. Large-scale gene expression analyses of 1310 publicly available Affymetrix chips were performed to associate the identified PUF genes with regulatory networks and biological processes of known function. To generate quality results, the study was restricted to expression sets with replicated samples. First, genome-wide clustering and gene function enrichment analysis of clusters allowed us to associate 1,541 PUF genes with tightly co-expressed genes for proteins of known function (PKFs). Over 70% of them could be assigned to more specific Biological Process annotations than the ones available in the current Gene Ontology release. The most highly over-represented functional categories in the obtained clusters were ribosome assembly, photosynthesis and cell wall pathways. Interestingly, the majority of the PUF genes appeared to be controlled by the same regulatory networks as most PKF genes, because clusters enriched in PUF genes were extremely rare. Second, large-scale analysis of differentially expressed genes (DEGs) was applied to identify a comprehensive set of abiotic stress response genes. This analysis resulted in the identification of 269 PKF and 104 PUF genes that responded to a wide variety of abiotic stresses, while 608 PKF and 206 PUF genes responded predominantly to specific stress treatments. The provided co-expression and DEG data represent an important resource for guiding future functional characterization experiments of PUF and PKF genes. Finally, the public Plant Gene Expression Database (PED, URL: http://bioweb.ucr.edu/PED) was developed as part of this project to provide efficient access and mining tools for the vast gene expression data of this study.
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