title: Annotating genes of known and unknown function by large-scale coexpression analysis.
abstract: 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 (Arabidopsis thaliana) using a combination of sequence similarity, domain-based, and empirical approaches. Large-scale gene expression analyses of 1,310 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 coexpressed 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 overrepresented 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 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, whereas 608 PKF and 206 PUF genes responded predominantly to specific stress treatments. The provided coexpression and differentially expressed gene data represent an important resource for guiding future functional characterization experiments of PUF and PKF genes. Finally, the public Plant Gene Expression Database (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.
authors: Department of Botany and Plant Sciences , University of California, Riverside, CA 92521, USA.
authors: Kevin
authors: K
authors: Horan
authors: Charles
authors: C
authors: Jang
authors: Julia
authors: J
authors: Bailey-Serres
authors: Ron
authors: R
authors: Mittler
authors: Christian
authors: C
authors: Shelton
authors: Jeff F
authors: JF
authors: Harper
authors: Jian-Kang
authors: JK
authors: Zhu
authors: John C
authors: JC
authors: Cushman
authors: Martin
authors: M
authors: Gollery
authors: Thomas
authors: T
authors: Girke
mesh_terms: D017360:Arabidopsis / Q000235:genetics*; D029681:Arabidopsis Proteins / Q000235:genetics*; D016000:Cluster Analysis; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D020411:Oligonucleotide Array Sequence Analysis
chemical_list: D029681:Arabidopsis Proteins