ChromHMM: automating chromatin state discovery and characterization

By Jason Ernst, Manolis Kellis. Nature Methods, 2012

2,409 citations recorded by the source corpus.

Chromatin state annotation using combinations of chromatin modification patterns has emerged as a powerful approach for discovering regulatory regions and their cell type specific activity patterns, and for interpreting disease-association studies1-5. However, the computational challenge of learning chromatin state models from large numbers of chromatin modification datasets in multiple cell types still requires extensive bioinformatics expertise making it inaccessible to the wider scientific community. To address this challenge, we have developed ChromHMM, an automated computational system for learning chromatin states, characterizing their biological functions and correlations with large-scale functional datasets, and visualizing the resulting genome-wide maps of chromatin state annotations.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4ee58e4ef967461109dd35a4c3c0b2a35394c35b

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Citations in the corpus

Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates (2015, methodology): “ChromHMM segments the genome into 15 types of regions.”
DEEP: a general computational framework for predicting enhancers (2014, methodology): “ChromHMM (21) utilizes hidden Markov models (HMM) and unsupervised clustering of profiles in nine cell lines, while Segway (22) offers an alternative genomic segmentation based on a Dynamic Bayesian Network.”
iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition (2016, methodology): “The chromatin state information was annotated by ChromHMM (Ernst and Kellis, 2012; Ernst et al., 2011) with the whole genome profile of multiple histone marks, such as H3K4me1, H3K4me3, H3K27ac, etc.”
Systematic allelic analysis defines the interplay of key pathways in X chromosome inactivation (2018, methodology): “To classify gene promoter chromatin states, we employed ChromHMM 35 to integrate multiple ENCODE ChIP-seq datasets de ﬁ ning chromatin states in mESC.”
Topologically-associating domains are stable units of replication-timing regulation (2014, positive): “ChromHMM 48 was applied on pooled ChIP-seq mapped reads from replicates for each of four histone modifications (H3K4me3, H3K4me1, H3K36me3, and H3K27me3) in 15 mouse cell lines (G1E, G1E-ER4+E2, Erythroblasts, Megakaryocytes, CH12, Mel, ESbruce4, Thymus, Kidney, Small intestine, E14 Whole brain,…”
annotatr: Genomic regions in context (2016, methodology): “We use the chromatin states given by chromHMM (Ernst & Kellis, 2012) in each of 9 human cell lines.”