ChromHMM: Chromatin state discovery and characterization

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ChromHMM is software for learning and characterizing chromatin states. ChromHMM can integrate multiple chromatin datasets such as ChIP-seq data of various histone modifications to discover de novo the major re-occuring combinatorial and spatial patterns of marks. ChromHMM is based on a multivariate Hidden Markov Model that explicitly models the presence or absence of each chromatin mark. The resulting model can then be used to systematically annotate a genome in one or more cell types. By automatically computing state enrichments for large-scale functional and annotation datasets ChromHMM facilitates the biological characterization of each state. ChromHMM also produces files with genome-wide maps of chromatin state annotations that can be directly visualized in a genome browser.

  • ChromHMM software v1.28 (version log)
  • ChromHMM manual

    Quick instructions on running ChromHMM:
    1. Install Java 1.7 or later if not already installed.
    2. Unzip the file ChromHMM.zip
    3. To try out ChromHMM learning a 10-state model on the sample data enter from a command line in the directory with the ChromHMM.jar file the command:

    java -mx1600M -jar ChromHMM.jar LearnModel SAMPLEDATA_HG18 OUTPUTSAMPLE 10 hg18

    After termination in ~5-10 minutes a file in OUTPUTSAMPLE/webpage_10.html will be created showing output images and linking to all the output files created. If a web browser is found on the computer the webpage will automatically be opened in it.
    In general binarized input for the LearnModel command can be generated by first running the BinarizeBed command on bed files with coordinates of aligned reads or the BinarizeBam command on bam files with the coordinates of aligned reads.

  • The ChromHMM software is described in:
    Ernst J, Kellis M. ChromHMM: automating chromatin-state discovery and characterization. Nature Methods, 9:215-216, 2012.
  • A protocols paper on using ChromHMM is available here:
    Ernst J, Kellis M. Chromatin-state discovery and genome annotation with ChromHMM. Nature Protocols, 12:2478-2492, 2017.
  • Here are links to some existing ChromHMM annotations in hg19 available for 127 Reference Epigenomes (Roadmap Epigenomics), 9-ENCODE cell types (from Ernst et al, Nature 2011), and 6-ENCODE cell types (from ENCODE Integrative Analysis).
  • A liftover of the hg19 annotations to hg38 for the 127 Reference Epigenomes (Roadmap Epigenomics) is available here.
  • ChromHMM annotations based on a full stack model of the Roadmap Epigenomics data providing a universal chromatin state annotation of the human genome is described in:
    Vu H, Ernst J. Universal annotation of the human genome through integration of over a thousand epigenomic datasets. Genome Biology, 23:9, 2022.
    and an analogous annotations for mouse based on ENCODE data is also available and described in:
    Vu H, Ernst J. Universal chromatin state annotation of the mouse genome. Genome Biology, 24:153, 2023.
  • A method for gene-based chromatin annotations, ChromGene, building on ChromHMM is described in:
    Jaroszewicz A, Ernst J. ChromGene: gene-based modeling of epigenomic data. Genome Biology, 24:203, 2023.
  • Contact Jason Ernst (jason.ernst at ucla dot edu) with any questions, comments, or bug reports.
  • Subscribe to a mailing list for announcements of new versions
  • ChromHMM is released under a GPL 3 license.
  • ChromHMM source code is available on GitHub here.
  • Funding for ChromHMM provided by NSF Postdoctoral Fellowship 0905968 to JE and grants from the National Institutes of Health (NIH 1-RC1-HG005334 and NIH 1 U54 HG004570).