Discovery and characterization of chromatin states for systematic annotation of the human genome

By Jason Ernst, Manolis Kellis. Nature Biotechnology, 2010

1,100 citations recorded by the source corpus.

A plethora of epigenetic modifications have been described in the human genome and shown to play diverse roles in gene regulation, cellular differentiation and the onset of disease. Although individual modifications have been linked to the activity levels of various genetic functional elements, their combinatorial patterns are still unresolved and their potential for systematic de novo genome annotation remains untapped. Here, we use a multivariate Hidden Markov Model to reveal 'chromatin states' in human T cells, based on recurrent and spatially coherent combinations of chromatin marks. We define 51 distinct chromatin states, including promoter-associated, transcription-associated, active intergenic, large-scale repressed and repeat-associated states. Each chromatin state shows specific enrichments in functional annotations, sequence motifs and specific experimentally observed characteristics, suggesting distinct biological roles. This approach provides a complementary functional annotation of the human genome that reveals the genome-wide locations of diverse classes of epigenetic function.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/54e14736e6d6b411530181042f3a39ba85abe1da

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Citations in the corpus

Global Quantitative Modeling of Chromatin Factor Interactions (2014, background): “probabilistic models, and these models have offered insights into association between chromatin states and function [8–11].”
Charting histone modifications and the functional organization of mammalian genomes (2011, background): “Two recent studies have presented innovative approaches for integrating genome-wide chromatin map”
Integrative annotation of chromatin elements from ENCODE data (2012, neutral): “For the second phase of ENCODE, two research groups within the consortium independently developed chromatin state annotation algorithms, ChromHMM (13,14) and Segway (15).”
Comprehensive analysis of the chromatin landscape in Drosophila (2010, background): “PREs,90%ofwhichalsodisplaynarrowerpeaksofH3K4me2enrichment (SupplementaryFig.20).”
metilene: fast and sensitive calling of differentially methylated regions from bisulfite sequencing data (2016, methodology): “We used ENCODE RRBS data (ENCODE: ENCSR000DFT) and an ENCODE chromatin state segmentation, both of GM12878, for data simulations.”
Sequence and chromatin determinants of cell-type–specific transcription factor binding (2012, methodology): “Several integrative methods have combined PSSMs and chromatin data in probabilistic models (Ernst et al. 2010; Won et al. 2010; Pique-Regi et al. 2011) or identified chromatin states representative of promoters and enhancers that are enriched for known TF binding sites (Ernst and Kellis 2010).”
Chromatin-state discovery and genome annotation with ChromHMM (2017, methodology): “…chromatin states such as a state associated with Zinc finger genes (despite the diffuse signal of its associated marks and small genome coverage) 3,7,12 and a putative bivalent promoter state 67 that are often missed by other methods that directly model signal levels, even when applied on the…”
DEEP: a general computational framework for predicting enhancers (2014, background): “All the above-mentioned data sets are well studied and annotation maps for them also exist (29).”
Master Transcription Factors and Mediator Establish Super-Enhancers at Key Cell Identity Genes (2013, neutral): “There are, however, algorithms that are designed to identify large domains (Ernst and Kellis, 2010; Filion et al., 2010; Hon et al., 2008; Thurman et al., 2012), and the algorithm we describe here should be useful for further discovery of super-enhancers and other large domains.”
Sequential regulatory activity prediction across chromosomes with convolutional neural networks (2017, methodology): “It offers a finer resolution view than the popular ChromHMM method, which studies these data at 200 bp resolution (Ernst and Kellis 2010).”
ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia (2012, background): “These endeavors typically use sophisticated machine learningmethods (Ernst and Kellis 2010; Ernst et al. 2011; A Mortazavi, S Pepke, G Marinov, and B Wold, in prep.) with complex and varying sensitivity to ChIP strength; and such efforts can be very sensitive to data quality.”
Heterogeneous substitution systems revisited (2016, neutral): “To accommodate the explosive growth of access user equipments (UEs), next-generation networks must enhance capacity through network densification and aggressive frequency reuse [2].”