Provided by: chromhmm_1.17+dfsg-1_all bug

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.