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10.1038/ncomms15011 http://scihub22266oqcxt.onion/10.1038/ncomms15011 C5385569!5385569!28387224     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Nat+Commun 2017 ; 8 (ä): ä Nephropedia Template TP {{tp|p=28387224|t=2017. Multi-scale chromatin state annotation using a hierarchical hidden Markov model |pdf=|usr=}}{{28387224}} gab.com Text Multi-scale chromatin state annotation using a hierarchical hidden Markov model JO: Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=28387224 #FOAvip 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 Markov model, diHMM, to systematically annotate chromatin states at multiple length scales. We apply diHMM to analyse a public ChIP-seq data set. diHMM not only accurately captures nucleosome-level information, but identifies domain-level states that vary in nucleosome-level state composition, spatial distribution and functionality. The domain-level states recapitulate known patterns such as super-enhancers, bivalent promoters and Polycomb repressed regions, and identify additional patterns whose biological functions are not yet characterized. By integrating chromatin-state information with gene expression and Hi-C data, we identify context-dependent functions of nucleosome-level states. Thus, diHMM provides a powerful tool for investigating the role of higher-order chromatin structure in gene regulation. Twit Text FOAVip Multi-scale chromatin state annotation using a hierarchical hidden Markov model ????Nat Commun http://www.kidney.de/pget.php?&tw=1&pmid=28387224 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28387224 #FOAvip English Wikipedia <ref>{{Cite pmid|28387224}}</ref> Multi-scale chromatin state annotation using a hierarchical hidden Markov model #MMPMID28387224 Marco E; Meuleman W; Huang J; Glass K; Pinello L; Wang J; Kellis M; Yuan GC Nat Commun 2017[]; 8 (ä): ä PMID28387224show ga 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 Markov model, diHMM, to systematically annotate chromatin states at multiple length scales. We apply diHMM to analyse a public ChIP-seq data set. diHMM not only accurately captures nucleosome-level information, but identifies domain-level states that vary in nucleosome-level state composition, spatial distribution and functionality. The domain-level states recapitulate known patterns such as super-enhancers, bivalent promoters and Polycomb repressed regions, and identify additional patterns whose biological functions are not yet characterized. By integrating chromatin-state information with gene expression and Hi-C data, we identify context-dependent functions of nucleosome-level states. Thus, diHMM provides a powerful tool for investigating the role of higher-order chromatin structure in gene regulation. äMulti-scale chromatin state annotation using a hierarchical hidden Mar(epmc).pdf DeepDyve Pubget Overpricing