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Improving reference epigenome catalogs by computational prediction

Nature Biotechnology volume 33, pages 354–355 (2015)Cite this article

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Bioinformatic imputation of epigenomic marks promises to supplement catalogs of experimental data.

A large international effort is under way to map the human epigenome—chemical modifications of chromatin and DNA that control cellular identity (http://ihec-epigenomes.org/). With contributions from ENCODE1, Roadmap Epigenomics2, BLUEPRINT3, DEEP (http://www.deutsches-epigenom-programm.de) and other projects, epigenome maps for at least 1,000 cell types are being generated. Barring a breakthrough in technology, however, it is unlikely that the epigenomics community will be able to measure every epigenomic modification in every sample and experimental condition of interest. In this issue, Ernst and Kellis4 describe an approach that should ease the burden on experimentalists by allowing epigenomic marks to be statistically imputed—that is, predicted—from measured data. Epigenome mapping by combining experiments for the most informative marks with bioinformatic imputation of additional marks could massively increase our ability to analyze many epigenomic marks across multiple cell types.

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Figure 1: The ChromImpute workflow.

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Authors and Affiliations

  1. Peter Ebert and Christoph Bock are at the Max Planck Institute for Informatics, Saarbrücken, Germany.,

    Peter Ebert & Christoph Bock

  2. Peter Ebert is also at the Graduate School of Computer Science, Saarland University, Saarbrücken, Germany.,

    Peter Ebert

  3. and the Department of Laboratory Medicine, Christoph Bock is also at the CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria, Medical University of Vienna, Vienna, Austria.,

    Christoph Bock

Authors
  1. Peter Ebert
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  2. Christoph Bock
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Correspondence to Christoph Bock.

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The authors declare no competing financial interests.

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Ebert, P., Bock, C. Improving reference epigenome catalogs by computational prediction. Nat Biotechnol 33, 354–355 (2015). https://doi.org/10.1038/nbt.3194

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