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      A User's Guide to the Encyclopedia of DNA Elements (ENCODE)

      The ENCODE Project Consortium *

      PLoS Biology

      Public Library of Science

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The mission of the Encyclopedia of DNA Elements (ENCODE) Project is to enable the scientific and medical communities to interpret the human genome sequence and apply it to understand human biology and improve health. The ENCODE Consortium is integrating multiple technologies and approaches in a collective effort to discover and define the functional elements encoded in the human genome, including genes, transcripts, and transcriptional regulatory regions, together with their attendant chromatin states and DNA methylation patterns. In the process, standards to ensure high-quality data have been implemented, and novel algorithms have been developed to facilitate analysis. Data and derived results are made available through a freely accessible database. Here we provide an overview of the project and the resources it is generating and illustrate the application of ENCODE data to interpret the human genome.

          Author Summary

          The Encyclopedia of DNA Elements (ENCODE) Project was created to enable the scientific and medical communities to interpret the human genome sequence and to use it to understand human biology and improve health. The ENCODE Consortium, a large group of scientists from around the world, uses a variety of experimental methods to identify and describe the regions of the 3 billion base-pair human genome that are important for function. Using experimental, computational, and statistical analyses, we aimed to discover and describe genes, transcripts, and transcriptional regulatory regions, as well as DNA binding proteins that interact with regulatory regions in the genome, including transcription factors, different versions of histones and other markers, and DNA methylation patterns that define states of the genome in various cell types. The ENCODE Project has developed standards for each experiment type to ensure high-quality, reproducible data and novel algorithms to facilitate analysis. All data and derived results are made available through a freely accessible database. This article provides an overview of the complete project and the resources it is generating, as well as examples to illustrate the application of ENCODE data as a user's guide to facilitate the interpretation of the human genome.

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          Most cited references 123

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          Mapping and quantifying mammalian transcriptomes by RNA-Seq.

          We have mapped and quantified mouse transcriptomes by deeply sequencing them and recording how frequently each gene is represented in the sequence sample (RNA-Seq). This provides a digital measure of the presence and prevalence of transcripts from known and previously unknown genes. We report reference measurements composed of 41-52 million mapped 25-base-pair reads for poly(A)-selected RNA from adult mouse brain, liver and skeletal muscle tissues. We used RNA standards to quantify transcript prevalence and to test the linear range of transcript detection, which spanned five orders of magnitude. Although >90% of uniquely mapped reads fell within known exons, the remaining data suggest new and revised gene models, including changed or additional promoters, exons and 3' untranscribed regions, as well as new candidate microRNA precursors. RNA splice events, which are not readily measured by standard gene expression microarray or serial analysis of gene expression methods, were detected directly by mapping splice-crossing sequence reads. We observed 1.45 x 10(5) distinct splices, and alternative splices were prominent, with 3,500 different genes expressing one or more alternate internal splices.
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            RNA-Seq: a revolutionary tool for transcriptomics.

            RNA-Seq is a recently developed approach to transcriptome profiling that uses deep-sequencing technologies. Studies using this method have already altered our view of the extent and complexity of eukaryotic transcriptomes. RNA-Seq also provides a far more precise measurement of levels of transcripts and their isoforms than other methods. This article describes the RNA-Seq approach, the challenges associated with its application, and the advances made so far in characterizing several eukaryote transcriptomes.
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              Finding the missing heritability of complex diseases.

              Genome-wide association studies have identified hundreds of genetic variants associated with complex human diseases and traits, and have provided valuable insights into their genetic architecture. Most variants identified so far confer relatively small increments in risk, and explain only a small proportion of familial clustering, leading many to question how the remaining, 'missing' heritability can be explained. Here we examine potential sources of missing heritability and propose research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.
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                Author and article information

                Affiliations
                Adolf Butenandt Institute, Germany
                Contributors
                Role: Academic Editor
                Journal
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, USA )
                1544-9173
                1545-7885
                April 2011
                April 2011
                19 April 2011
                : 9
                : 4
                3079585
                21526222
                PBIOLOGY-D-10-00316
                10.1371/journal.pbio.1001046
                (Academic Editor)
                The ENCODE Project Consortium. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                Counts
                Pages: 21
                Categories
                Research Article
                Biology
                Computational Biology
                Genetics
                Genomics

                Life sciences

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