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.