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

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ABSTRACT

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.

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A schematic overview of diHMM.(a) Shown is the underlying graphic model for diHMM with two levels of hidden states corresponding to the domain level (represented by rectangles) and nucleosome level (represented by squares), respectively. Multidimensional input ChIP-seq data are represented by circles. Arrows indicate the conditional dependence structure of diHMM. Nucleosome-level state transitions are dependent on the domain-level state at the end but not the initial position. The emission probability is conditionally independent of the domain-level state given the nucleosome-level state (see methods and Supplementary Fig. 1 for additional details). (b) Genome tracks displaying diHMM state calls in H1 cells for domain- and nucleosome-level states, and nine histone marks in the HOXB cluster region in chromosome 17. Grey box is expanded in c and shows a region of ∼8 kb. In the domain-level track black bars indicate transitions between different domains.
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f1: A schematic overview of diHMM.(a) Shown is the underlying graphic model for diHMM with two levels of hidden states corresponding to the domain level (represented by rectangles) and nucleosome level (represented by squares), respectively. Multidimensional input ChIP-seq data are represented by circles. Arrows indicate the conditional dependence structure of diHMM. Nucleosome-level state transitions are dependent on the domain-level state at the end but not the initial position. The emission probability is conditionally independent of the domain-level state given the nucleosome-level state (see methods and Supplementary Fig. 1 for additional details). (b) Genome tracks displaying diHMM state calls in H1 cells for domain- and nucleosome-level states, and nine histone marks in the HOXB cluster region in chromosome 17. Grey box is expanded in c and shows a region of ∼8 kb. In the domain-level track black bars indicate transitions between different domains.

Mentions: diHMM differs from existing methods in that it uses a hierarchical hidden Markov model framework, where each level of hidden states corresponds to a distinct length-scale (Fig. 1). It can be used to analyse any number of levels of chromatin states (Methods). diHMM takes multiple ChIP-seq (chromatin immunoprecipitation with sequencing) data as input, and outputs a genome-wide segmentation of the genome into functionally annotated, multilevel chromatin states, each corresponding to a specific length scale.


Multi-scale chromatin state annotation using a hierarchical hidden Markov model
A schematic overview of diHMM.(a) Shown is the underlying graphic model for diHMM with two levels of hidden states corresponding to the domain level (represented by rectangles) and nucleosome level (represented by squares), respectively. Multidimensional input ChIP-seq data are represented by circles. Arrows indicate the conditional dependence structure of diHMM. Nucleosome-level state transitions are dependent on the domain-level state at the end but not the initial position. The emission probability is conditionally independent of the domain-level state given the nucleosome-level state (see methods and Supplementary Fig. 1 for additional details). (b) Genome tracks displaying diHMM state calls in H1 cells for domain- and nucleosome-level states, and nine histone marks in the HOXB cluster region in chromosome 17. Grey box is expanded in c and shows a region of ∼8 kb. In the domain-level track black bars indicate transitions between different domains.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5385569&req=5

f1: A schematic overview of diHMM.(a) Shown is the underlying graphic model for diHMM with two levels of hidden states corresponding to the domain level (represented by rectangles) and nucleosome level (represented by squares), respectively. Multidimensional input ChIP-seq data are represented by circles. Arrows indicate the conditional dependence structure of diHMM. Nucleosome-level state transitions are dependent on the domain-level state at the end but not the initial position. The emission probability is conditionally independent of the domain-level state given the nucleosome-level state (see methods and Supplementary Fig. 1 for additional details). (b) Genome tracks displaying diHMM state calls in H1 cells for domain- and nucleosome-level states, and nine histone marks in the HOXB cluster region in chromosome 17. Grey box is expanded in c and shows a region of ∼8 kb. In the domain-level track black bars indicate transitions between different domains.
Mentions: diHMM differs from existing methods in that it uses a hierarchical hidden Markov model framework, where each level of hidden states corresponds to a distinct length-scale (Fig. 1). It can be used to analyse any number of levels of chromatin states (Methods). diHMM takes multiple ChIP-seq (chromatin immunoprecipitation with sequencing) data as input, and outputs a genome-wide segmentation of the genome into functionally annotated, multilevel chromatin states, each corresponding to a specific length scale.

View Article: PubMed Central - PubMed

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus