The NIH Roadmap Epigenomics Mapping Consortium

By Bradley E. Bernstein, John A. Stamatoyannopoulos, J. Costello, Bing Ren, Aleksandar Milosavljevic, Alexander Meissner, Manolis Kellis, Marco A. Marra, A. Beaudet, Joseph R. Ecker and 5 more. Nature Biotechnology, 2010

1,887 citations recorded by the source corpus.

The NIH Roadmap Epigenomics Mapping Consortium aims to produce a public resource of epigenomic maps for stem cells and primary ex vivo tissues selected to represent the normal counterparts of tissues and organ systems frequently involved in human disease.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/09b25d21d3105670b31d597d6d0544a21306bc0a

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Citations in the corpus

Ensembl regulation resources (2016, methodology): “Both Reduced Representation and Whole Genome Bisulfite Sequencing (RRBS/WGBS) data (16, 25, 26, 40) are provided via the database, characterising 44 cell lines.”
Global Quantitative Modeling of Chromatin Factor Interactions (2014, background): “…epigenome mapping projects take the strategy of characterizing a large number of chromatin factors for only a few cell types while characterizing a much smaller subset for other cell types [2,33], and it is costly to perform extensive chromatin profiling in new cell types or under new conditions.”
DEEP: a general computational framework for predicting enhancers (2014, background): “Projects, such as the ENCODE (9) and the NIH Epigenome Roadmap (10), released libraries of histone modification marks in human genome, whereas the FANTOM5 project (11) released CAGE-based transcription start sites (TSSs) in different cell types and tissues and enabled for the comprehensive identific”
Predicting enhancer-promoter interaction from genomic sequence with deep neural networks (2016, methodology): “Note that, since we are interested in studying the sub-sequences that are learned by SPEID, in this section, we used a randomly initialized model (i.e., not initialized with motifs from JASPAR).”
ChIPBase v2.0: decoding transcriptional regulatory networks of non-coding RNAs and protein-coding genes from ChIP-seq data (2016, methodology): “All our peak datasets were curated from NCBI GEO database (15), ENCODE project (16), modENCODE project (17,18) and NIH Roadmap Epigenomics Project (19) (Tables 1 and 2).”
DNA methylation age of human tissues and cell types (2013, background): “The epigenomic landscape varies markedly across tissue types [16-18] and many age-related changes depend on tissue type [8,19].”
Ensembl 2017 (2016, methodology): “The increase is largely based on datasets from the IHEC member projects BLUEPRINT (23) and Roadmap Epigenomics (24), which were uniformly annotated using the Ensembl Regulatory Build methodology (25).”
Colocalization of GWAS and eQTL Signals Detects Target Genes (2016, methodology): “It is worth mentioning that we can use epigenomic data (e.g., NIH Roadmap Epigenomics [41]) to detect relevant tissues as an orthogonal analysis compared to using eCAVIAR.”