ChromHMM: automating chromatin-state discovery and characterization (original) (raw)

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

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To the Editor:

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.

References

  1. Day, N., Hemmaplardh, A., Thurman, R.E., Stamatoyannopoulos, J.A. & Noble, W.S. Bioinformatics 23, 1424–1426 (2007).
    Article CAS Google Scholar
  2. Ernst, J. & Kellis, M. Nat. Biotechnol. 28, 817–825 (2010).
    Article CAS Google Scholar
  3. Ernst, J. et al. Nature 473, 43–49 (2011).
    Article CAS Google Scholar
  4. Filion, G.J. et al. Cell 143, 212–224 (2010).
    Article CAS Google Scholar
  5. Roy, S. et al. Science 330, 1787–1797 (2010).
    Article CAS Google Scholar
  6. Kent, W.J. et al. Genome Res. 12, 996–1006 (2002).
    Article CAS Google Scholar

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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

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  1. Jason Ernst
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  2. Manolis Kellis
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Correspondence toManolis Kellis.

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The authors declare no competing financial interests.

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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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