GATE is a model-based, open source tool for chromatin states prediction based on time-course Epigenetic marks data.

Description [ top ]

GATE proposes a dynamic analysis to chromatin states based on time-course epigenetic marks data. It uses a combinatory Finite Mixture model nested with HMM to model the time course marks data in which each single hidden markov model describes the hidden states for a region set across different time points. It also takes advantage of Poisson distribution to use real epigenetic counts for chromatin states prediction.

 

 

Parameter inference is implemented by a nesting EM algorithm in which parameters from hidden markov model is inferred by classic Baum-Welch Algorithm. The relative algorithm is compiled by C and runs on R platform. It is easy to use and can return several kinds of results that users my concern.

Reference for GATE and supplementary materials for the reference is shown in  "Reference" below.