diff --git a/ChromHMM/index.html b/ChromHMM/index.html new file mode 100644 index 00000000..60e42adb --- /dev/null +++ b/ChromHMM/index.html @@ -0,0 +1,104 @@ + + + + +ChromHMM: Chromatin state discovery and +characterization + + + +

ChromHMM: Chromatin state discovery and +characterization

+
+ +
+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. +
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+
  • ChromHMM software +v1.25 (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).
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  • A liftover of the hg19 annotations to hg38 for the 127 Reference Epigenomes (Roadmap Epigenomics) is available here.
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  • 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.
    + + + + +
  • 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 +
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  • ChromHMM is released under a GPL 3 license.
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  • ChromHMM source code is available on GitHub here.
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  • 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). + +