The ENCODE (ENCyclopedia Of DNA Elements) Project

doi:10.1126/science.1105136

Cited by 2,050 publications

(834 citation statements)

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“…The Encyclopedia of DNA Elements (ENCODE) 22 and the NIH Roadmap Epigenomics 23 projects have aimed to identify the functional elements in the human genome across the major cell types and tissues. We refer to these functional and regulatory regions as epigenomic elements, i.e., genomic properties other than the DNA sequence that describes functions, properties, or experimental values associated with specific genomic regions.…”

Section: Introductionmentioning

confidence: 99%

Polycomb repressive complex 2 epigenomic signature defines age-associated hypermethylation and gene expression changes

Dozmorov

2015

Epigenetics

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Although age-associated gene expression and methylation changes have been reported throughout the literature, the unifying epigenomic principles of aging remain poorly understood. Recent explosion in availability and resolution of functional/regulatory genome annotation data (epigenomic data), such as that provided by the ENCODE and Roadmap Epigenomics projects, provides an opportunity for the identification of epigenomic mechanisms potentially altered by age-associated differentially methylated regions (aDMRs) and regulatory signatures in the promoters of ageassociated genes (aGENs). In this study we found that aDMRs and aGENs identified in multiple independent studies share a common Polycomb Repressive Complex 2 signature marked by EZH2, SUZ12, CTCF binding sites, repressive H3K27me3, and activating H3K4me1 histone modification marks, and a "poised promoter" chromatin state. This signature is depleted in RNA Polymerase II-associated transcription factor binding sites, activating H3K79me2, H3K36me3, H3K27ac marks, and an "active promoter" chromatin state. The PRC2 signature was shown to be generally stable across cell types. When considering the directionality of methylation changes, we found the PRC2 signature to be associated with aDMRs hypermethylated with age, while hypomethylated aDMRs were associated with enhancers. In contrast, aGENs were associated with the PRC2 signature independently of the directionality of gene expression changes. In this study we demonstrate that the PRC2 signature is the common epigenomic context of genomic regions associated with hypermethylation and gene expression changes in aging.

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Section: Introductionmentioning

confidence: 99%

Polycomb repressive complex 2 epigenomic signature defines age-associated hypermethylation and gene expression changes

Dozmorov

2015

Epigenetics

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“…(Consortium, 2004). This project involves a worldwide consortium and the data generated can be accessed through public databases.…”

Section: The Encyclopaedia Of Dna Elements (Encode) Projectmentioning

confidence: 99%

“…In Table 7 we summarize some of the available on-line sources that contain datasets relevant for the respiratory research community. (Consortium, 2004). NIH: National Institutes of Health.…”

Section: Integrating Human Genome Regulatory Information With Candidamentioning

confidence: 99%

“…Currently available regulatory genome annotations have been generated in diverse sets of primary cells and immortalized cell lines (Consortium, 2004), each with its advantages and disadvantages. In the coming years annotations of un-differentiated and There are a number of immortalised cell lines which are often used in respiratory research.…”

Section: Choosing the Cell Type To Work Withmentioning

confidence: 99%

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Translating Lung Function Genome-Wide Association Study (GWAS) Findings

Kheirallah

Miller

Hall

et al. 2016

Advances in Genetics

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“…WikiPathways [2]), or large collections of experimental datasets (e.g. ENCODE project [3]). If a significant number of genes are involved in the same function then this function can be considered as more likely to be relevant to the experimental conditions related to the gene set.…”

Section: Introductionmentioning

confidence: 99%

Gene Set to Diseases (GS2D): disease enrichment analysis on human gene sets with literature data

Fontaine

Andrade‐Navarro

2016

Genomics Comput Biol

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Large sets of candidate genes derived from high-throughput biological experiments can be characterized by functional enrichment analysis. The analysis consists of comparing the functions of one gene set against that of a background gene set. Then, functions related to a significant number of genes in the gene set are expected to be relevant. Web tools offering disease enrichment analysis on gene sets are often based on gene-disease associations from manually curated or experimental data that is accurate but does not cover all diseases discussed in the literature. Using associations automatically derived from literature data could be a cost effective method to improve the coverage of diseases for enrichment analysis at comparable levels of accuracy. We have implemented a method named Gene set to Diseases, GS2D, as a web tool performing disease enrichment analysis on human protein coding gene sets. It uses an automatically built dataset of more than 63 thousand gene-disease associations defined as statistically significant co-occurrences of genes and diseases in annotations of biomedical citations from PubMed. The dataset covers more diseases for enrichment analysis than the largest comparable curated database, Comparative Toxicogenomics Database, and its performance compared favourably to similar approaches based on manually curated or experimental data. Graphical and programmatic interfaces are available at http://cbdm.uni-mainz.de/geneset2diseases.

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The ENCODE (ENCyclopedia Of DNA Elements) Project

Cited by 2,050 publications

References 23 publications

Polycomb repressive complex 2 epigenomic signature defines age-associated hypermethylation and gene expression changes

Polycomb repressive complex 2 epigenomic signature defines age-associated hypermethylation and gene expression changes

Translating Lung Function Genome-Wide Association Study (GWAS) Findings

Gene Set to Diseases (GS2D): disease enrichment analysis on human gene sets with literature data

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