diHMM v0.1 beta
:: DESCRIPTION
diHMM (Hierarchical Hidden Markov Model) is a novel computational method for finding chromatin states at multiple scales. The model takes as input a multidimensional set of histone modifications for several cell types and classifies the genome into a preselected number of nucleosome-level and domain-level hidden states.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
- Linux/Windows/MacOsX
- Matlab
:: DOWNLOAD
:: MORE INFORMATION
Citation
Marco E, Meuleman W, Huang J, Glass K, Pinello L, Wang J, Kellis M, Yuan GC.
Multi-scale chromatin state annotation using a hierarchical hidden Markov model.
Nature Communications. 2017 Apr 7;8:15011.