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Brief Funct Genomics. 2013 Sep;12(5):457-67. doi: 10.1093/bfgp/elt003. Epub 2013 Feb 12.

Biostatistical approaches for the reconstruction of gene co-expression networks based on transcriptomic data.

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  • 1Statistical Department, Universidad Nacional de Colombia, Ciudad Universitaria. Cra 30 No 45-03, Colombia. llopezk@unal.edu.co.


Techniques in molecular biology have permitted the gathering of an extremely large amount of information relating organisms and their genes. The current challenge is assigning a putative function to thousands of genes that have been detected in different organisms. One of the most informative types of genomic data to achieve a better knowledge of protein function is gene expression data. Based on gene expression data and assuming that genes involved in the same function should have a similar or correlated expression pattern, a function can be attributed to those genes with unknown functions when they appear to be linked in a gene co-expression network (GCN). Several tools for the construction of GCNs have been proposed and applied to plant gene expression data. Here, we review recent methodologies used for plant gene expression data and compare the results, advantages and disadvantages in order to help researchers in their choice of a method for the construction of GCNs.


gene co-expression networks; gene functional prediction; microarray datasets; plants; transcriptomics

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