Genome-Wide Co-Expression Distributions as a Metric to Prioritize Genes of Functional Importance
Uncovering the genetic architecture behind complex phenotypes involves analysing the expression patterns of a large variety of genes that interact with each in co-expression networks. A pipeline developed by CSIRO Animal Genomics Team, recently published in a special issue of Genes – Algorithms and Workflows in RNA Bioinformatics, explored the concept that correlation dynamics in co-expression networks have biological meaning. In it, they evaluate an alternative metric to group genes based on the distribution of each gene’s correlation to all the other genes under scrutiny. Each pair-wise genome-wide co-expression distribution was then assigned to one of 8 template distributions shapes varying between unimodal, bimodal, skewed, or symmetrical, representing different proportions of positive and negative correlations. By evaluating genes grouped in each distribution shape across five different datasets, our researches demonstrated there is indeed a striking additional biological signal present in the genome-wide distribution of co-expression values which would be overlooked by currently adopted approaches. This method can be applied to extract further information from transcriptomic data and help uncover the molecular mechanisms involved in the regulation of complex biological process and phenotypes.
Alexandre, P.A.; Hudson, N.J.; Lehnert, S.A.; Fortes, M.R.S.; Naval-Sánchez, M.; Nguyen, L.T.; Porto-Neto, L.R.; Reverter, A. Genome-Wide Co-Expression Distributions as a Metric to Prioritize Genes of Functional Importance. Genes 2020, 11, 1231.