BioEnergy Research

, Volume 9, Issue 1, pp 172–180 | Cite as

Genome-Scale Identification of Cell-Wall-Related Genes in Switchgrass through Comparative Genomics and Computational Analyses of Transcriptomic Data

  • Xin Chen
  • Qin Ma
  • Xiaolan Rao
  • Yuhong Tang
  • Yan Wang
  • Gaoyang Li
  • Chi Zhang
  • Xizeng Mao
  • Richard A. Dixon
  • Ying XuEmail author


Large numbers of plant cell-wall (CW)-related genes have been identified or predicted in several plant genomes such as Arabidopsis thaliana, Oryza sativa (rice), and Zea mays (maize), as results of intensive studies of these organisms in the past 2 decades. However, no such gene list has been identified in switchgrass (Panicum virgatum), a key bioenergy crop. Here, we present a computational study for prediction of CW genes in switchgrass using a two-step procedure: (i) homology mapping of all annotated CW genes in the fore-mentioned species to switchgrass, giving rise to a total of 991 genes, and (ii) candidate prediction of CW genes based on switchgrass genes co-expressed with the 991 genes under a large number of experimental conditions. Specifically, our co-expression analyses using the 991 genes as seeds led to the identification of 104 large clusters of co-expressed genes, each referred to as a co-expression module (CEM), covering 830 of the 991 genes plus 823 additional genes that are strongly co-expressed with some of the 104 CEMs. These 1653 genes represent our prediction of CW genes in switchgrass, 112 of which are homologous to predicted CW genes in Arabidopsis. Functional inference of these genes is conducted to derive the possible functional relations among these predicted CW genes. Overall, these data may offer a highly useful information source for cell-wall biologists of switchgrass as well as plants in general.


Switchgrass Plant cell wall Homology mapping Co-expression analysis 



This work was supported in part by the National Science Foundation (DEB-0830024 and DBI-0542119) and the DOE BioEnergy Science Center grant (DE-PS02-06ER64304), which is supported by the Office of Biological and Environmental Research in the Department of Energy Office of Science. This work was also supported in part by the Agriculture Experiment Station and the Biochemical Spatiotemporal Network Resource Center (3SP680) of South Dakota State University.

XC and QM participated in the coordination of the paper, carried out or participated all the analyses of transcriptomic data and the comparative genomics framework, and drafted the manuscript; XM participated in framework design. YT provided the transcriptomic data along with relevant data details, XR offered biology guidance in co-expression analysis, and YW and GL proved the TF prediction results. CZ designed the network analysis part. RAD reviewed and edited the paper and assisted in interpretation of data, and YX conceived the study, participated in its design and coordination, and revised the manuscript. All authors read and approved the final manuscript.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.College of Computer Science and Technology, and School of Public HealthJilin UniversityChangchunChina
  2. 2.Computational Systems Biology Lab, Department of Biochemistry and Molecular Biology and Institute of BioinformaticsUniversity of GeorgiaAthensUSA
  3. 3.US Department of EnergyBioEnergy Science Center (BESC)Oak RidgeUSA
  4. 4.Department of Biological SciencesUniversity of North TexasDentonUSA
  5. 5.Plant Biology DivisionThe Samuel Roberts Noble FoundationArdmoreUSA
  6. 6.Department of Plant ScienceSouth Dakota State UniversityBrookingsUSA
  7. 7.Institute of Applied Cancer CenterMD Anderson Cancer CenterHoustonUSA
  8. 8.A110 Life Science buildingUniversity of GeorgiaAthensUSA

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