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

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

Subjects

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.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Subscribe to this journal

Receive 12 print issues and online access

$259.00 per year

only $21.58 per issue

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Additional access options:

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

Download references

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).

Author information

Author notes

  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
    You can also search for this author inPubMed Google Scholar
  2. Manolis Kellis
    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toManolis Kellis.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Rights and permissions

About this article

Cite this article

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

Download citation

This article is cited by

Associated content