Multi-scale chromatin state annotation using a hierarchical hidden Markov model

Multi-scale chromatin state annotation using a hierarchical hidden Markov model

Chromatin-state analysis is widely applied in the studies of development and diseases. However, existing methods operate at a single length scale, and therefore cannot distinguish large domains from isolated elements of the same type. To overcome this limitation, we present a hierarchical hidden Mar...

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Bibliographic Details
Main Authors: Huang, Jialiang (Author), Glass, Kimberly (Author), Pinello, Luca (Author), Yuan, Guo-Cheng (Author), Marco Rubio, Eugenio (Contributor), Meuleman, Wouter (Contributor), Wang, Jianrong (Contributor), Kellis, Manolis (Contributor)
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
Format: Article
Language:English
Published: Nature Publishing Group, 2017-06-22T14:24:57Z.
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