ChromHMM - Learning and analysis chromatin states using a multivariate Hidden
Markov Model
java -Xmx[GB]g -jar /usr/share/java/chromhmm.jar [
options]
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.
- 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.
http://compbio.mit.edu/ChromHMM/
ChromHMM was written by Jason Ernst.