NAME

       ChromHMM - Learning and analysis chromatin states using a multivariate Hidden Markov Model

SYNOPSIS

       java -Xmx[GB]g -jar /usr/share/java/chromhmm.jar [options]

DESCRIPTION

       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.

OPTIONS

       LearnModel
              Takes a set of binarized data files, learns chromatin state models,  and  by  default  produces  a
              segmentation,  generates  browser  output  with  default settings, and calls OverlapEnrichment and
              NeighborhoodEnrichments with default settings for the specified genome assembly. A  webpage  is  a
              created with links to all the files and images created.

       BinarizeBed
              Converts  a  set  of  bed  files of aligned reads into binarized data files for model learning and
              optionally prints the intermediate signal files.

       BinarizeBam
              Converts a set of bam files of aligned reads into binarized data  files  for  model  learning  and
              optionally prints the intermediate signal files.

       BinarizeSignal
              Converts a set of signal files into binarized files.

       MakeSegmentation
              Takes a learned model and binarized data and outputs a segmentation.

       MakeBrowserFiles
              Can convert segmentation files into a browser viewable format.

       OverlapEnrichment
              Shows the enrichment of each state of a segmentation for a set of external data.

       NeighborhoodEnrichment
              Shows the enrichment of each state relative to a set of anchor positions.

       CompareModels
              Can  compare  models  with  different  numbers  of  states  in  terms  of  correlation in emission
              parameters.

       Reorder
              Allows reordering the states of the model, the columns of the emission  matrix,  or  adding  state
              labels.

       EvalSubset
              Can be used to evaluate the extent to which a subset of marks can recover a segmentation using the
              full set of marks.

       StatePruning
              Can be used to prune states from a model in order to initialize models when using the  non-default
              two pass approach.

SEE ALSO

       http://compbio.mit.edu/ChromHMM/

AUTHOR

       ChromHMM was written by Jason Ernst.