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      Discovery and characterization of chromatin states for systematic annotation of the human genome

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      1 , 2 , 1 , 2
      Nature biotechnology

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          Abstract

          A plethora of epigenetic modifications have been described in the human genome and shown to play diverse roles in gene regulation, cellular differentiation, and the onset of disease. While some modifications have been linked with activity levels of different functional elements, their combinatorial patterns remain unresolved, and their potential for systematic de novo genome annotation remains untapped. In this paper, we systematically discover and characterize recurrent spatially-coherent and biologically-meaningful chromatin mark combinations, or chromatin states, in human T-cells. We describe 51 distinct chromatin states, including promoter-associated, transcription-associated, active intergenic, large-scale repressed and repeat-associated states. Each chromatin state shows specific functional, experimental, conservation, annotation, and sequence-motif enrichments, revealing their distinct candidate biological roles. Overall, our work provides a complementary functional annotation of the human genome revealing the genome-wide locations of diverse classes of epigenetic functions, including previously-unsuspected chromatin states enriched in transcription end sites, distinct repeat families, and disease-SNP-associated states.

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          Most cited references31

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          STEM: a tool for the analysis of short time series gene expression data

          Background Time series microarray experiments are widely used to study dynamical biological processes. Due to the cost of microarray experiments, and also in some cases the limited availability of biological material, about 80% of microarray time series experiments are short (3–8 time points). Previously short time series gene expression data has been mainly analyzed using more general gene expression analysis tools not designed for the unique challenges and opportunities inherent in short time series gene expression data. Results We introduce the Short Time-series Expression Miner (STEM) the first software program specifically designed for the analysis of short time series microarray gene expression data. STEM implements unique methods to cluster, compare, and visualize such data. STEM also supports efficient and statistically rigorous biological interpretations of short time series data through its integration with the Gene Ontology. Conclusion The unique algorithms STEM implements to cluster and compare short time series gene expression data combined with its visualization capabilities and integration with the Gene Ontology should make STEM useful in the analysis of data from a significant portion of all microarray studies. STEM is available for download for free to academic and non-profit users at .
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            High-resolution mapping and characterization of open chromatin across the genome.

            Mapping DNase I hypersensitive (HS) sites is an accurate method of identifying the location of genetic regulatory elements, including promoters, enhancers, silencers, insulators, and locus control regions. We employed high-throughput sequencing and whole-genome tiled array strategies to identify DNase I HS sites within human primary CD4+ T cells. Combining these two technologies, we have created a comprehensive and accurate genome-wide open chromatin map. Surprisingly, only 16%-21% of the identified 94,925 DNase I HS sites are found in promoters or first exons of known genes, but nearly half of the most open sites are in these regions. In conjunction with expression, motif, and chromatin immunoprecipitation data, we find evidence of cell-type-specific characteristics, including the ability to identify transcription start sites and locations of different chromatin marks utilized in these cells. In addition, and unexpectedly, our analyses have uncovered detailed features of nucleosome structure.
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              Genome-wide analysis of mammalian promoter architecture and evolution.

              Mammalian promoters can be separated into two classes, conserved TATA box-enriched promoters, which initiate at a well-defined site, and more plastic, broad and evolvable CpG-rich promoters. We have sequenced tags corresponding to several hundred thousand transcription start sites (TSSs) in the mouse and human genomes, allowing precise analysis of the sequence architecture and evolution of distinct promoter classes. Different tissues and families of genes differentially use distinct types of promoters. Our tagging methods allow quantitative analysis of promoter usage in different tissues and show that differentially regulated alternative TSSs are a common feature in protein-coding genes and commonly generate alternative N termini. Among the TSSs, we identified new start sites associated with the majority of exons and with 3' UTRs. These data permit genome-scale identification of tissue-specific promoters and analysis of the cis-acting elements associated with them.
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                Author and article information

                Journal
                9604648
                20305
                Nat Biotechnol
                Nature biotechnology
                1087-0156
                1546-1696
                8 July 2010
                25 July 2010
                August 2010
                1 February 2011
                : 28
                : 8
                : 817-825
                Affiliations
                [1 ]MIT Computer Science and Artificial Intelligence Laboratory, 32 Vassar Street, Cambridge, Massachusetts 02139, USA
                [2 ]Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, Massachusetts 02142, USA
                Author notes
                Correspondence and requests for materials should be addressed to M.K. ( manoli@ 123456mit.edu )
                Article
                nihpa214961
                10.1038/nbt.1662
                2919626
                20657582
                fdb304b6-c6ef-4a72-8164-c5c4eacf4277

                Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

                History
                Funding
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: RC1 HG005334-02 ||HG
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: R01 HG004037-04 ||HG
                Categories
                Article

                Biotechnology
                Biotechnology

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