Abstract
The analysis of gene expression data has shown that transcriptionally coordinated (co-expressed) genes are often functionally related, enabling scientists to use expression data in gene function prediction. This Focused Review discusses our original paper (Large-scale co-expression approach to dissect secondary cell wall formation across plant species, Frontiers in Plant Science 2:23). In this paper we applied cross-species analysis to co-expression networks of genes involved in cellulose biosynthesis. We showed that the co-expression networks from different species are highly similar, indicating that whole biological pathways are conserved across species. This finding has two important implications. First, the analysis can transfer gene function annotation from well-studied plants, such as Arabidopsis, to other, uncharacterized plant species. As the analysis finds genes that have similar sequence and similar expression pattern across different organisms, functionally equivalent genes can be identified. Second, since co-expression analyses are often noisy, a comparative analysis should have higher performance, as parts of co-expression networks that are conserved are more likely to be functionally relevant. In this Focused Review, we outline the comparative analysis done in the original paper and comment on the recent advances and approaches that allow comparative analyses of co-function networks. We hypothesize that in comparison to simple co-expression analysis, comparative analysis would yield more accurate gene function predictions. Finally, by combining comparative analysis with genomic information of green plants, we propose a possible composition of cellulose biosynthesis machinery during earlier stages of plant evolution.
Highlights
The functional annotation of genes is essential for understanding how biological processes are formed, organized, and how they operate
Arabidopsis thaliana cellulose synthase AtCESA1 is active in plasma membrane (Ilic et al, 2007), during cell wall formation (BP), where it has β-(1→4)-glucan synthase activity (MF)
FOR GENE FUNCTION PREDICTION Prediction methods are based on the guilt by association principle, where genes are linked by some shared characteristics, such as DNA sequence similarity, similar RNA expression levels or protein 3-D structure (Eisen et al, 1998)
Summary
The functional annotation of genes is essential for understanding how biological processes are formed, organized, and how they operate. While over 40% of the genes in Arabidopsis thaliana have at least one of the three domains experimentally revealed, less than 10% of the genes have all three domains verified (reviewed in Rhee and Mutwil, 2014). While a prediction cannot replace experimental proof of gene function, it can be very helpful in suggesting MF, BP, and CC domains of the cryptic gene. This can narrow down experiments necessary to verify function. This makes gene function prediction one of the most active areas of bioinformatics, with many different flavors of analyses being constantly developed (Radivojac et al, 2013; Rhee and Mutwil, 2014). We briefly introduce different gene function prediction methods with special focus on comparative co-expression analysis, and its applications in gene function prediction and function evolution
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