Genome-scale identification of cell-wall related genes in Arabidopsis based on co-expression network analysis
Shan Wang1, 3, 4，#, Yanbin Yin1, 2，#, Qin Ma1, 2, Xiaojia Tang1, Dongyun Hao3, 4* and Ying Xu1, 2, 5*
1Computational Systems Biology Laboratory, Department of Biochemistry and Molecular Biology, and Institute of Bioinformatics;
2BESC BioEerngy Science Center, University of Georgia, Athens, GA, USA;
3Key Lab for Molecular Enzymology and Engineering of the Ministry of Education, Jilin University, Changchun, China;
4Biotechnology Research Centre, Jilin Academy of Agricultural Sciences (JAAS), Changchun, China;
5College of Computer Science and Technology, Jilin University, Changchun, China。
*correspondence address. Tel.: 706 542 9779; Fax: 706-542-9751; Email: email@example.com
#These authors contributed equally to this work.
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 identified the novel PCW related genes in Arabidopsis based on the co-expression analyses of transcriptomic data in 351 conditions, using a bi-clustering technique. Our analysis identified 217 highly co-expressed modules under some experimental conditions, each containing at least one gene related PCW synthesis according to Purdue Cell Wall Gene Families database. These modules cover 349 known PCW related genes and 2,438 new candidates. For each candidate gene, we predicted the detailed function and its involved PCW synthesis stages. For the co-expressed genes in each module, we predicted their motifs in the promoters using ourselves developed pipeline, providing strong evidence that the genes in each co-expression module are transcriptionally co-regulated. From all modules, we inferred 108 modules 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 related genes, which are available at a web-based database.
Keywords:Plant cell wall, Arabidopsis, Co-expression network analyses, Bi-clustering, Cis regulatory motifs