Gene co-expression network reconstruction: a review on computational methods for inferring functional information from plant-based expression data
- 466 Downloads
Reconstruction of gene co-expression networks is a powerful tool for better understanding of gene function, biological processes, and complex disease mechanisms. In essence, co-expression network analysis has been widely used for understanding which genes are highly co-expressed through special biological processes or differentially expressed in various conditions. Development of high-throughput experiments has provided a large amount of genomic and transcriptomic data for model and non-model organisms. The availability of genome-wide expression data has led to the development of in silico procedures for reconstruction of gene co-expression networks. Gene co-expression networks predict unknown genes’ functions; moreover, it has been successfully applied to understand important biological processes of living organisms such as plants. In this survey, we have reviewed the algorithms, databases, and tools of gene co-expression network reconstruction, which can lead to new landscapes for further research activities. Furthermore, we explain an application of some algorithms, databases, and tools that can significantly boost our current understanding of co-expression networks in Arabidopsis thaliana as a model plant using publicly available data. The presented example shows that using co-expression networks is an efficient way to detect genes, which may involve in various critical biological processes such as defense response.
KeywordsFunctional genomics Gene network Gene co-expression network Network reconstruction algorithm Transcriptomic data Co-expressed genes
PK is supported by School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no competing interests.
- Abbas OA (2008) Comparisons between data clustering algorithms. Int Arab J Inf Technol 5(3):320–325Google Scholar
- Aoki Y, Okamura Y et al (2015) ATTED-II in 2016: a plant coexpression database towards lineage-specific coexpression. Plant Cell Physiol 57(1):pcv165Google Scholar
- Butte AJ, Kohane IS (2000) Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac Symp Biocomput 5:418–429Google Scholar
- Cai J, Chen G et al (2010) ClusterViz: a Cytoscape plugin for graph clustering and visualization. School of Information Science and Engineering, Central South University, Changsha, p 1Google Scholar
- Deihimi T, Niazi A et al (2012) Finding the undiscovered roles of genes: an approach using mutual ranking of coexpressed genes and promoter architecture-case study: dual roles of thaumatin like proteins in biotic and abiotic stresses. SpringerPlus 1:30. doi: 10.1186/2193-1801-1-30 PubMedPubMedCentralCrossRefGoogle Scholar
- Dimitrakopoulos GN, Maraziotis IA et al (2014) A clustering based method accelerating gene regulatory network reconstruction. In: Procedia Computer Science, vol 29, pp 1993–2002. doi: 10.1016/j.procs.2014.05.183
- Jaeger H (2002) Tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the “echo state network” approach, vol 159. GMD-Forschungszentrum Informationstechnik, p 48Google Scholar
- Khosravi P, Gazestani V et al (2015) Comparative analysis of co-expression networks reveals molecular changes during the cancer progression. In: World Congress on Medical Physics and Biomedical Engineering, 7–12 June 2015, Toronto, Springer, pp 1481–1487Google Scholar
- Sait K (2009) The prediction of local modular structures in a co-expression network based on gene expression data sets. Genome Inform 23:117–127Google Scholar