hiHMM: Bayesian non-parametric joint inference of chromatin state maps

Motivation: Genome-wide mapping of chromatin states is essential for defining regulatory elements and inferring their activities in eukaryotic genomes. A number of hidden Markov model (HMM)-based methods have been developed to infer chromatin state maps from genome-wide histone modification data for...

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Detalles Bibliográficos
Autores principales: Sohn, Kyung-Ah, Ho, Joshua W. K., Djordjevic, Djordje, Jeong, Hyun-hwan, Park, Peter J., Kim, Ju Han
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2015
Materias:
Original Papers
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4481846/
https://www.ncbi.nlm.nih.gov/pubmed/25725496
http://dx.doi.org/10.1093/bioinformatics/btv117

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4481846/
https://www.ncbi.nlm.nih.gov/pubmed/25725496
http://dx.doi.org/10.1093/bioinformatics/btv117

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