Cross-species comparison using expression data

Abstract

Molecular evolution, which is classically assessed by the comparison of individual proteins or genes between species, can now be studied by comparing coexpressed functional groups of genes. This approach, which better reflects the functional constraints on the evolution of organisms, can exploit the large amount of data generated by overall, genome-wide expression analyses. To optimize cross-species comparison, particular caution must be used in the selection of expression data, using, for example, the most related experimental conditions between species. In addition, defining gene pairs having interspecies correspondence is also a critical step that can create misleading relations between genes and that could benefit from international annotation efforts like the Gene Ontology (GO) Consortium. In this chapter, we describe methodologies based on global approaches or gene-centered methods that can be used to answer precise biological questions. Finally, through a set of examples, we show that expression profile comparison between species can help to discover functional annotation for unknown genes and improve orthology links between organisms. © 2007 Humana Press Inc.