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ChromHMM specifications

Information


Unique identifier OMICS_03490
Name ChromHMM
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages Java
Computer skills Advanced
Stability Stable
Maintained Yes

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Publication for ChromHMM

ChromHMM citations

 (224)
library_books

Distinct epigenetic landscapes underlie the pathobiology of pancreatic cancer subtypes

2018
Nat Commun
PMCID: 5958058
PMID: 29773832
DOI: 10.1038/s41467-018-04383-6

[…] ChromHMM was used to perform hidden Markov modeling on the five histone marks and, by default, chromatin states were analyzed at 200 bp intervals and a fold threshold of 10. The tool was used to learn […]

library_books

Comprehensive epigenetic landscape of rheumatoid arthritis fibroblast like synoviocytes

2018
Nat Commun
PMCID: 5953939
PMID: 29765031
DOI: 10.1038/s41467-018-04310-9

[…] cs technologies could provide a unique opportunity to define the global epigenomic landscape of RA, they also pose a great challenge to analyze in one integrative model. Segmentation methods, such as ChromHMM and Segway can identify functional elements but focus on histone modifications and have not incorporated other data such as DNA methylation. Furthermore, the epigenomic signals in RA and OA F […]

library_books

Prediction of enhancer promoter interactions via natural language processing

2018
BMC Genomics
PMCID: 5954283
PMID: 29764360
DOI: 10.1186/s12864-018-4459-6

[…] The majority of our datasets were adapted from TargetFinder. Promoter and enhancer regions were identified using ENCODE Segway [] and ChromHMM [] annotations for K562, GM12878, HeLa-S3, and HUVEC cell lines, and using Roadmap [] Epigenomics ChromHMM annotations for NHEK and IMR90 cell lines. Since EPIs could only happen between acti […]

library_books

High Resolution Epigenomic Atlas of Human Embryonic Craniofacial Development

2018
Cell Rep
PMCID: 5965702
PMID: 29719267
DOI: 10.1016/j.celrep.2018.03.129

[…] puted samples’ signals correlated well with their primary signals and clustered generally by mark and biological function (, ). Using the imputed craniofacial data, we then segmented the genome using ChromHMM for each embryonic sample based on previously generated models of 15, 18, and 25 states of chromatin activity (). We identified similar numbers and proportions of segments in each state in ou […]

library_books

Galactic Cosmic Radiation Induces Persistent Epigenome Alterations Relevant to Human Lung Cancer

2018
Sci Rep
PMCID: 5928241
PMID: 29712937
DOI: 10.1038/s41598-018-24755-8

[…] -wide ChIP-seq data for histone modifications, RNA polymerase occupancy, and other chromatin features, Ernst, et al. used a hidden Markov model to partition the genome into functional domains, termed ChromHMM. We analyzed the ChromHMM states and existing genome-wide datasets to evaluate the chromatin structure surrounding the irradiation-sensitive CpG sites. This analysis revealed that the 56Fe io […]

library_books

Interactions between genetic variation and cellular environment in skeletal muscle gene expression

2018
PLoS One
PMCID: 5901994
PMID: 29659628
DOI: 10.1371/journal.pone.0195788

[…] y processed cell/tissue ChIP-seq (chromatin immunoprecipitation followed by sequencing) reads from a diverse set of publicly available data [,–]. Chromatin states were learned jointly by applying the ChromHMM (v1.10) algorithm [,,] at 200 bp resolution to six data tracks (Input, H3K27ac, H3K27me3, H3K36me3, H3K4me1, H3K4me3) from each of the cell/tissue types. We selected a 13-state model, which p […]

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ChromHMM institution(s)
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA; Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA

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