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      EWAS Atlas: a curated knowledgebase of epigenome-wide association studies

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          Abstract

          Epigenome-Wide Association Study (EWAS) has become increasingly significant in identifying the associations between epigenetic variations and different biological traits. In this study, we develop EWAS Atlas ( http://bigd.big.ac.cn/ewas), a curated knowledgebase of EWAS that provides a comprehensive collection of EWAS knowledge. Unlike extant data-oriented epigenetic resources, EWAS Atlas features manual curation of EWAS knowledge from extensive publications. In the current implementation, EWAS Atlas focuses on DNA methylation—one of the key epigenetic marks; it integrates a large number of 329 172 high-quality EWAS associations, involving 112 tissues/cell lines and covering 305 traits, 1830 cohorts and 390 ontology entities, which are completely based on manual curation from 649 studies reported in 401 publications. In addition, it is equipped with a powerful trait enrichment analysis tool, which is capable of profiling trait-trait and trait-epigenome relationships. Future developments include regular curation of recent EWAS publications, incorporation of more epigenetic marks and possible integration of EWAS with GWAS. Collectively, EWAS Atlas is dedicated to the curation, integration and standardization of EWAS knowledge and has the great potential to help researchers dissect molecular mechanisms of epigenetic modifications associated with biological traits.

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          NCBI GEO: archive for functional genomics data sets—update

          Tanya Barrett, Stephen Wilhite, Pierre Ledoux … (2012)
          The Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) is an international public repository for high-throughput microarray and next-generation sequence functional genomic data sets submitted by the research community. The resource supports archiving of raw data, processed data and metadata which are indexed, cross-linked and searchable. All data are freely available for download in a variety of formats. GEO also provides several web-based tools and strategies to assist users to query, analyse and visualize data. This article reports current status and recent database developments, including the release of GEO2R, an R-based web application that helps users analyse GEO data.
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            DNA methylation age of human tissues and cell types

            Steve Horvath, Ilias Lagkouvardos (2014)
            Background It is not yet known whether DNA methylation levels can be used to accurately predict age across a broad spectrum of human tissues and cell types, nor whether the resulting age prediction is a biologically meaningful measure. Results I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance. Conclusions I propose that DNA methylation age measures the cumulative effect of an epigenetic maintenance system. This novel epigenetic clock can be used to address a host of questions in developmental biology, cancer and aging research.
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              GENCODE: The reference human genome annotation for The ENCODE Project

              Jennifer Harrow, Adam Frankish, Jose M Gonzalez … (2012)
              The GENCODE Consortium aims to identify all gene features in the human genome using a combination of computational analysis, manual annotation, and experimental validation. Since the first public release of this annotation data set, few new protein-coding loci have been added, yet the number of alternative splicing transcripts annotated has steadily increased. The GENCODE 7 release contains 20,687 protein-coding and 9640 long noncoding RNA loci and has 33,977 coding transcripts not represented in UCSC genes and RefSeq. It also has the most comprehensive annotation of long noncoding RNA (lncRNA) loci publicly available with the predominant transcript form consisting of two exons. We have examined the completeness of the transcript annotation and found that 35% of transcriptional start sites are supported by CAGE clusters and 62% of protein-coding genes have annotated polyA sites. Over one-third of GENCODE protein-coding genes are supported by peptide hits derived from mass spectrometry spectra submitted to Peptide Atlas. New models derived from the Illumina Body Map 2.0 RNA-seq data identify 3689 new loci not currently in GENCODE, of which 3127 consist of two exon models indicating that they are possibly unannotated long noncoding loci. GENCODE 7 is publicly available from gencodegenes.org and via the Ensembl and UCSC Genome Browsers.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Nucleic Acids Res
                Journal ID (iso-abbrev): Nucleic Acids Res
                Journal ID (publisher-id): nar
                Title: Nucleic Acids Research
                Publisher: Oxford University Press
                ISSN (Print): 0305-1048
                ISSN (Electronic): 1362-4962
                Publication date (Print): 08 January 2019
                Publication date (Electronic): 26 October 2018
                Publication date PMC-release: 26 October 2018
                Volume: 47
                Issue: Database issue , Database issue
                Pages: D983-D988
                Affiliations
                [1 ]BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
                [2 ]CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
                [3 ]College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
                [4 ]School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
                [5 ]CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China
                Author notes
                To whom correspondence should be addressed. Zhang Zhang: Tel: +86 10 84097261; Fax: +86 10 84097720; Email: zhangzhang@ 123456big.ac.cn . Correspondence may also be addressed to Yiming Bao. Email: baoym@ 123456big.ac.cn

                The authors wish it to be known that, in their opinion, these authors should be regarded as Joint First Authors.

                Article
                Publisher ID: gky1027
                DOI: 10.1093/nar/gky1027
                PMC ID: 6324068
                PubMed ID: 30364969
                SO-VID: 852540a1-5070-46bc-b9f3-6d846b66ce54
                Copyright © © The Author(s) 2018. Published by Oxford University Press on behalf of Nucleic Acids Research.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                Date accepted : 17 October 2018
                Date revision received : 26 September 2018
                Date received : 12 August 2018
                Page count
                Pages: 6
                Funding
                Funded by: Strategic Priority Research Program of the Chinese Academy of Sciences 10.13039/501100002367
                Award ID: XDA19050302
                Award ID: XDB13040500
                Funded by: National Key Research Program of China
                Award ID: 2017YFC0907502
                Funded by: National Programs for High Technology Research and Development (863 Program)
                Award ID: 2015AA020108
                Award ID: 2012AA020409
                Funded by: National Natural Science Foundation of China 10.13039/501100001809
                Award ID: 31200978
                Funded by: 13th Five-year Informatization Plan of Chinese Academy of Sciences
                Award ID: XXH13505-05
                Funded by: International Partnership Program of the Chinese Academy of Sciences
                Award ID: 153F11KYSB20160008
                Categories
                Subject: Database Issue

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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