title: Systematic identification of functional modules and cis-regulatory elements in Arabidopsis thaliana.
abstract: BACKGROUND
  Several large-scale gene co-expression networks have been constructed successfully for predicting gene functional modules and cis-regulatory elements in Arabidopsis (Arabidopsis thaliana). However, these networks are usually constructed and analyzed in an ad hoc manner. In this study, we propose a completely parameter-free and systematic method for constructing gene co-expression networks and predicting functional modules as well as cis-regulatory elements.
  
  
  RESULTS
  Our novel method consists of an automated network construction algorithm, a parameter-free procedure to predict functional modules, and a strategy for finding known cis-regulatory elements that is suitable for consensus scanning without prior knowledge of the allowed extent of degeneracy of the motif. We apply the method to study a large collection of gene expression microarray data in Arabidopsis. We estimate that our co-expression network has ~94% of accuracy, and has topological properties similar to other biological networks, such as being scale-free and having a high clustering coefficient. Remarkably, among the ~300 predicted modules whose sizes are at least 20, 88% have at least one significantly enriched functions, including a few extremely significant ones (ribosome, p < 1E-300, photosynthetic membrane, p < 1.3E-137, proteasome complex, p < 5.9E-126). In addition, we are able to predict cis-regulatory elements for 66.7% of the modules, and the association between the enriched cis-regulatory elements and the enriched functional terms can often be confirmed by the literature. Overall, our results are much more significant than those reported by several previous studies on similar data sets. Finally, we utilize the co-expression network to dissect the promoters of 19 Arabidopsis genes involved in the metabolism and signaling of the important plant hormone gibberellin, and achieved promising results that reveal interesting insight into the biosynthesis and signaling of gibberellin.
  
  
  CONCLUSIONS
  The results show that our method is highly effective in finding functional modules from real microarray data. Our application on Arabidopsis leads to the discovery of the largest number of annotated Arabidopsis functional modules in the literature. Given the high statistical significance of functional enrichment and the agreement between cis-regulatory and functional annotations, we believe our Arabidopsis gene modules can be used to predict the functions of unknown genes in Arabidopsis, and to understand the regulatory mechanisms of many genes.
authors.affiliation: Department of Computer Science, The University of Texas at San Antonio, One UTSA Circle, San Antonio, Texas 78249, USA. jruan@cs.utsa.edu
authors.forename: Jianhua
authors.initials: J
authors.lastname: Ruan
authors.forename: Joseph
authors.initials: J
authors.lastname: Perez
authors.forename: Brian
authors.initials: B
authors.lastname: Hernandez
authors.forename: Chengwei
authors.initials: C
authors.lastname: Lei
authors.forename: Garry
authors.initials: G
authors.lastname: Sunter
authors.forename: Valerie M
authors.initials: VM
authors.lastname: Sponsel
mesh_terms: D000465:Algorithms; D017360:Arabidopsis / Q000235:genetics* / Q000378:metabolism; D029681:Arabidopsis Proteins / Q000235:genetics / Q000378:metabolism; D016000:Cluster Analysis; D020869:Gene Expression Profiling; D018506:Gene Expression Regulation, Plant; D053263:Gene Regulatory Networks; D005875:Gibberellins / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis; D011401:Promoter Regions, Genetic; D015398:Signal Transduction
chemical_list: D029681:Arabidopsis Proteins; D005875:Gibberellins