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      Systematic analysis of chromatin state dynamics in nine human cell types

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          Abstract

          Chromatin profiling has emerged as a powerful means for genome annotation and detection of regulatory activity. Here we map nine chromatin marks across nine cell types to systematically characterize regulatory elements, their cell type-specificities, and their functional interactions. Focusing on cell type-specific patterns of promoters and enhancers, we define multi-cell activity profiles for chromatin state, gene expression, regulatory motif enrichment, and regulator expression. We use correlations between these profiles to link enhancers to putative target genes, and predict the cell type-specific activators and repressors that modulate them. The resulting annotations and regulatory predictions have implications for interpreting genome-wide association studies. Top-scoring disease SNPs are frequently positioned within enhancer elements specifically active in relevant cell types, and in some cases affect a motif instance for a predicted regulator, thus proposing a mechanism for the association. Our study presents a general framework for deciphering cis-regulatory connections and their roles in disease.

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          Most cited references36

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Direct multiplexed measurement of gene expression with color-coded probe pairs.

            We describe a technology, the NanoString nCounter gene expression system, which captures and counts individual mRNA transcripts. Advantages over existing platforms include direct measurement of mRNA expression levels without enzymatic reactions or bias, sensitivity coupled with high multiplex capability, and digital readout. Experiments performed on 509 human genes yielded a replicate correlation coefficient of 0.999, a detection limit between 0.1 fM and 0.5 fM, and a linear dynamic range of over 500-fold. Comparison of the NanoString nCounter gene expression system with microarrays and TaqMan PCR demonstrated that the nCounter system is more sensitive than microarrays and similar in sensitivity to real-time PCR. Finally, a comparison of transcript levels for 21 genes across seven samples measured by the nCounter system and SYBR Green real-time PCR demonstrated similar patterns of gene expression at all transcript levels.
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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 .

                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                0028-0836
                1476-4687
                15 February 2011
                23 March 2011
                5 May 2011
                5 November 2011
                : 473
                : 7345
                : 43-49
                Affiliations
                [1 ]Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
                [2 ]MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, Massachusetts, USA
                [3 ]Howard Hughes Medical Institute, Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
                [4 ]Center for Systems Biology and Center for Cancer Research, Massachusetts General Hospital, Boston, Massachusetts, USA
                Author notes

                Author contributions

                JE conducted chromatin state analysis. JE and PK conducted regulatory motif analysis. JE and LW conducted GWAS SNP analysis. TM, NS and TD implemented the ChIP-seq data processing pipeline. CE, XZ, LW, RI, MC and MK(1) developed the experimental pipeline and conducted experiments. MK(2) envisioned and directed the computational analysis. BB envisioned the experimental approach and oversaw the work. JE, MK and BB and wrote the paper.

                Author information

                Sequencing and expression data has been deposited into the Gene Expression Omnibus under accession GSE26386. Reprints and permissions information is available at www.nature.com/reprints.

                [* ]Correspondence should be addressed to M.K. ( manoli@ 123456mit.edu ).
                Article
                nihpa270546
                10.1038/nature09906
                3088773
                21441907
                f973103b-280d-4cdb-96a4-501ff62f5fc2

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                History
                Funding
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: U54 HG004570-04 ||HG
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: U54 HG004570-03S1 ||HG
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: U54 HG004570-03 ||HG
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: U54 HG004570-02 ||HG
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: U54 HG004570-01 ||HG
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