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| <Title>ChromHMM: Chromatin state discovery and | ||
| characterization</Title> | ||
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| <h1><center>ChromHMM: Chromatin state discovery and | ||
| characterization</center></h1> | ||
| <center> | ||
| <img src="segments.png" width=600 height=200> | ||
| </center> | ||
| 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. | ||
| <br> | ||
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| <br> | ||
| <li><a href="ChromHMM.zip" onClick="javascript: _gaq.push(['_trackPageview', '/downloads/ChromHMM.zip']);">ChromHMM software | ||
| v1.25</a> (<a href="versionlog.txt">version log</a>)<br> | ||
| <li> <a href="ChromHMM_manual.pdf">ChromHMM manual</a><br> | ||
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| <br> | ||
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| Quick instructions on running ChromHMM:<br> | ||
| 1. Install Java 1.7 or later if not already installed. <br> | ||
| 2. Unzip the file ChromHMM.zip<br> | ||
| 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:<br><br> | ||
| java -mx1600M -jar ChromHMM.jar LearnModel SAMPLEDATA_HG18 OUTPUTSAMPLE 10 hg18 | ||
| <br><br> | ||
| 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.<br> In general binarized input for the <i>LearnModel</i> command can be generated | ||
| by first running the <i>BinarizeBed</i> command on bed files with coordinates of aligned reads or the <i>BinarizeBam</i> command on | ||
| bam files with the coordinates of aligned reads.<br><br> | ||
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| <li>The ChromHMM software is described in:<br> | ||
| Ernst J, Kellis M. | ||
| <a href="http://www.nature.com/nmeth/journal/v9/n3/full/nmeth.1906.html"> | ||
| ChromHMM: automating chromatin-state discovery and characterization</a></b>. <i>Nature Methods</i>, | ||
| 9:215-216, 2012.<br> | ||
| <li> A protocols paper on using ChromHMM is available here:<br> | ||
| Ernst J, Kellis M. | ||
| <a href="https://www.nature.com/articles/nprot.2017.124">Chromatin-state discovery and genome annotation with ChromHMM</a>. | ||
| <i>Nature Protocols</i>, 12:2478-2492, 2017.<br> | ||
| <li> Here are links to some existing ChromHMM annotations in hg19 available for <a href="http://compbio.mit.edu/roadmap">127 Reference Epigenomes (Roadmap Epigenomics)</a>, | ||
| <a href="https://genome.ucsc.edu/cgi-bin/hgTrackUi?g=wgEncodeBroadHmm&db=hg19">9-ENCODE cell types (from Ernst et al, Nature 2011)</a>, and | ||
| <a href="https://genome.ucsc.edu/cgi-bin/hgTrackUi?g=wgEncodeAwgSegmentation&db=hg19">6-ENCODE cell types (from ENCODE Integrative Analysis)</a>.<br> | ||
| <li> A liftover of the hg19 annotations to hg38 for the 127 Reference Epigenomes (Roadmap Epigenomics) is available <a href="http://compbio.mit.edu/roadmap">here</a>.<br> | ||
| <li> <a href="https://github.com/ernstlab/full_stack_ChromHMM_annotations">ChromHMM annotations based on a full stack model of the Roadmap Epigenomics | ||
| data</a> providing a universal chromatin state annotation of the human genome is described in:<br> | ||
| Vu H, Ernst J. | ||
| <a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02572-z"> | ||
| Universal annotation of the human genome through integration of over a thousand epigenomic datasets.</a> | ||
| <i>Genome Biology</i>, 23:9, 2022.<br> | ||
| and <a href="https://github.com/ernstlab/mouse_fullStack_annotations">an analogous annotations for mouse based on ENCODE data</a> is also available and | ||
| described in: <br> | ||
| Vu H, Ernst J. | ||
| <a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-023-02994-x"> | ||
| Universal chromatin state annotation of the mouse genome.</a> | ||
| <i>Genome Biology</i>, 24:153, 2023.<br> | ||
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| <li>Contact Jason Ernst (jason.ernst at ucla dot edu) with any questions, | ||
| comments, or bug reports. | ||
| <li>Subscribe to a <a | ||
| href="http://groups.google.com/group/ChromHMM-announce/"> mailing | ||
| list for announcements of new versions</a> | ||
| <br> | ||
| <li>ChromHMM is released under a <a href="http://www.gnu.org/licenses/gpl-3.0.html">GPL 3 license</a>. <br> | ||
| <li>ChromHMM source code is available on GitHub <a href="https://github.com/jernst98/ChromHMM">here</a>.<br> | ||
| <li>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). | ||
| </body> | ||
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