Integrative analysis of 111 reference human epigenomes

By R. Consortium, Anshul B Kundaje, W. Meuleman, Jason Ernst, M. Bilenky, Angela Yen, A. Moussavi, Pouya Kheradpour, Zhizhuo Zhang, Jianrong Wang and 86 more. Nature, 2015

6,046 citations recorded by the source corpus.

The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/fd89721f007f62d52cbc3ac329aa5e36a6e0f8d9

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In Molecular Biology

Citations in the corpus

Predicting enhancer-promoter interaction from genomic sequence with deep neural networks (2016, methodology): “Cellline specific active enhancers and promoters were identified using annotations from the ENCODE Project [28] and Roadmap Epigenomics Project [29].”
A review on machine learning principles for multi-view biological data integration (2016, methodology): “Generation of type-2 multi-omics data requires collaborative efforts within a big consortium such as the Cancer Genome Atlas [2], the Encyclopedia of DNA Elements Consortium [3], the Roadmap Epigenomics Project [4] and the Genotype-Tissue Expression Project [5].”
ReMap 2018: an updated atlas of regulatory regions from an integrative analysis of DNA-binding ChIP-seq experiments (2017, methodology): “The NIH Roadmap Epigenomics Mapping Consortium (19)”
Genome-wide analysis identifies 12 loci influencing human reproductive behavior (2016, neutral): “RegulomeDB integrates results from the Roadmap Epigenomics 26 and ENCODE 86 projects.”
Landscape of multi-nucleotide variants in 125,748 human exomes and 15,708 genomes (2020, methodology): “Thirteen functional annotations were collected from Finucane et al. 41 as a bed ﬁ le (which originates from database, such as ENCODE, Roadmap 61 and UCSC genome browser 62 .)”
Base-resolution models of transcription factor binding reveal soft motif syntax (2021, neutral): “(3) Experimental control data are used as an auxiliary input (PAtCh-CAP for ChIP-nexus data59).”
A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies (2017, positive): “The EpiDISH algorithm and validation in blood using flow-cytometry We collected DHS and Illumina 450k DNA methylation data from the NIH Epigenomics Roadmap [17] and ENCODE [22, 23], as well as from other Illumina 450k studies profiling individual cell-types [24, 29] and normal tissues [26, 35] (Tabl”
Genome-wide enhancer maps link risk variants to disease genes (2021, methodology): “…chromatin marks (H3K27ac, H3K9ac, H3K4me1, H3K4me2 and DNase I hypersensitivity) across 56 biosamples, and these correlation links were then used to make predictions for the predicted enhancers (regions with the ‘7Enh’ ChromHMM state) in 127 biosamples from the Roadmap Epigenome Atlas 25,45 .”