ChromHMM(1)

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.