Abstract
BackgroundAnalysis of gene expression from different species is a powerful way to identify evolutionarily conserved transcriptional responses. However, due to evolutionary events such as gene duplication, there is no one-to-one correspondence between genes from different species which makes comparison of their expression profiles complex.ResultsIn this paper we describe a new method for cross-species meta-analysis of gene expression. The method takes the homology structure between compared species into account and can therefore compare expression data from genes with any number of orthologs and paralogs. A simulation study shows that the proposed method results in a substantial increase in statistical power compared to previously suggested procedures. As a proof of concept, we analyzed microarray data from heat stress experiments performed in eight species and identified several well-known evolutionarily conserved transcriptional responses. The method was also applied to gene expression profiles from five studies of estrogen exposed fish and both known and potentially novel responses were identified.ConclusionsThe method described in this paper will further increase the potential and reliability of meta-analysis of gene expression profiles from evolutionarily distant species. The method has been implemented in R and is freely available at http://bioinformatics.math.chalmers.se/Xspecies/.
Highlights
Analysis of gene expression from different species is a powerful way to identify evolutionarily conserved transcriptional responses
If a biological process is evolutionarily conserved between two species, it is likely that the transcriptional responses associated with that process share similarities
A novel method for cross-species analysis of gene expression Assume that a number of large-scale gene expression experiments have been performed in a set of species investigating an evolutionarily conserved transcriptional response
Summary
Analysis of gene expression from different species is a powerful way to identify evolutionarily conserved transcriptional responses. Cross-species meta-analysis has proven useful in biogeronotology where evolutionarily conserved agerelated gene expression responses have been identified based on data from several species, including the fruit fly Drosophila melanogaster and the worm Caenorhabditis elegans [16,17]. Another example is ecotoxicology, where changes of molecular biomarkers are used to detect toxic effects and to monitor populations and ecosystem health[18]. Metaanalysis of gene expression profiles from multiple species provides a powerful tool for identification and evaluation of biomarkers [19,20]
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