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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.



Reference and Acknowledgement [ top ]

Yu PF, Xiao S, Xin XY, Song CX, Huang W, McDee D, Tanaka T, Wang T, He C, Zhong S. 2013. Spatiotemporal clustering of epigenome reveals rules of dynamic gene regulation. Genome Research. Feb;23(2):352-64.

[Supplemental text: Algorithm details]



Contact [ top ]

Contact Pengfei Yu (yu68 AT illinois DOT edu) for any problem or comment about this program.


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