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Epigenome-based prediction of gene expression across species

Peter Ebert, Thomas Lengauer, View ORCID ProfileChristoph Bock
doi: https://doi.org/10.1101/371146
Peter Ebert
1Max Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbrücken, Germany
2Graduate School of Computer Science, Saarland University, 66123 Saarbrücken, Germany
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Thomas Lengauer
1Max Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbrücken, Germany
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Christoph Bock
1Max Planck Institute for Informatics, Saarland Informatics Campus, 66123 Saarbrücken, Germany
3CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria
4Department of Laboratory Medicine, Medical University of Vienna, 1090 Vienna, Austria
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Abstract

Background Cross-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.

Results We 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.

Conclusions Our 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.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted July 17, 2018.
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Epigenome-based prediction of gene expression across species
Peter Ebert, Thomas Lengauer, Christoph Bock
bioRxiv 371146; doi: https://doi.org/10.1101/371146
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Epigenome-based prediction of gene expression across species
Peter Ebert, Thomas Lengauer, Christoph Bock
bioRxiv 371146; doi: https://doi.org/10.1101/371146

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