Genome-scale identification of cell-wall related genes in Arabidopsis based on co-expression network analysis
- Equal contributors
1 Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, and Institute of Bioinformatics, Athens, GA, USA
2 BESC BioEerngy Science Center, University of Georgia, Athens, GA, USA
3 Key Lab for Molecular Enzymology and Engineering of the Ministry of Education, Jilin University, Changchun, China
4 Biotechnology Research Centre, Jilin Academy of Agricultural Sciences (JAAS), Changchun, China
5 College of Computer Science and Technology, Jilin University, Changchun, China
BMC Plant Biology 2012, 12:138 doi:10.1186/1471-2229-12-138Published: 9 August 2012
Identification of the novel genes relevant to plant cell-wall (PCW) synthesis represents a highly important and challenging problem. Although substantial efforts have been invested into studying this problem, the vast majority of the PCW related genes remain unknown.
Here we present a computational study focused on identification of the novel PCW genes in Arabidopsis based on the co-expression analyses of transcriptomic data collected under 351 conditions, using a bi-clustering technique. Our analysis identified 217 highly co-expressed gene clusters (modules) under some experimental conditions, each containing at least one gene annotated as PCW related according to the Purdue Cell Wall Gene Families database. These co-expression modules cover 349 known/annotated PCW genes and 2,438 new candidates. For each candidate gene, we annotated the specific PCW synthesis stages in which it is involved and predicted the detailed function. In addition, for the co-expressed genes in each module, we predicted and analyzed their cis regulatory motifs in the promoters using our motif discovery pipeline, providing strong evidence that the genes in each co-expression module are transcriptionally co-regulated. From the all co-expression modules, we infer that 108 modules are related to four major PCW synthesis components, using three complementary methods.
We believe our approach and data presented here will be useful for further identification and characterization of PCW genes. All the predicted PCW genes, co-expression modules, motifs and their annotations are available at a web-based database: http://csbl.bmb.uga.edu/publications/materials/shanwang/CWRPdb/index.html webcite.