Epigenome-based prediction of gene expression across species

Abstract

BackgroundCross-species studies of epigenetic regulation have great potential, yet most epige-nome mapping has focused on human, mouse, and a small number of other model organisms. Here we explore whether existing reference epigenome collections can be leveraged for analyzing other species, by extrapolation and predictive transfer of epigenome information from established model organisms to less well annotated non-model organisms.\n\nResultsWe developed a methodology for cross-species mapping of epigenome data, which we used for predicting tissue-specific gene expression across twelve mammalian and one avian species. Specifically, we trained gradient boosting classifiers to predict gene expression status from reference epigenome data in human and mouse, and we applied these classifiers to epigenome profiles that were computationally transferred between species. The resulting predictions indeed identified tissue-specific differences in gene expression in the target species, thus providing initial validation of the concept of cross-species epigenome extrapolation.\n\nConclusionsOur study establishes a workflow for cross-species epigenome mapping and epigenome-based prediction of gene expression, highlighting the future potential of using epigenome maps from reference species to annotate a potentially large number of target species.