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ChromHMM: automating chromatin-state discovery and characterization

Nature Methods volume 9, pages 215–216 (2012)Cite this article

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Chromatin-state annotation using combinations of chromatin modification patterns has emerged as a powerful approach for discovering regulatory regions and their cell type–specific activity patterns and for interpreting disease-association studies1,2,3,4,5. However, the computational challenge of learning chromatin-state models from large numbers of chromatin modification datasets in multiple cell types still requires extensive bioinformatics expertise. To address this challenge, we developed ChromHMM, an automated computational system for learning chromatin states, characterizing their biological functions and correlations with large-scale functional datasets and visualizing the resulting genome-wide maps of chromatin-state annotations.

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Figure 1: Sample outputs of ChromHMM.

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Acknowledgements

We thank members of the Massachusetts Institute of Technology Computational Biology group and B.E. Bernstein for useful discussions related to this work. The work was supported by a US National Science Foundation postdoctoral fellowship 0905968 to J.E. and grants from the US National Institutes of Health (1-RC1-HG005334 to M.K. and 1 U54 HG004570 to B.E. Bernstein).

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  1. Jason Ernst

    Present address: Present address: Department of Biological Chemistry, University of California Los Angeles, Los Angeles, California, USA.,

Authors and Affiliations

  1. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Jason Ernst & Manolis Kellis

  2. Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts, USA

    Jason Ernst & Manolis Kellis

Authors
  1. Jason Ernst
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  2. Manolis Kellis
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Correspondence to Manolis Kellis.

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The authors declare no competing financial interests.

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Supplementary Figures 1–4, Supplementary Note, Supplementary Data (PDF 1388 kb)

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Ernst, J., Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat Methods 9, 215–216 (2012). https://doi.org/10.1038/nmeth.1906

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