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10.1101/gr.184341.114 http://scihub22266oqcxt.onion/10.1101/gr.184341.114 C4381526!4381526 !25677182     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=25677182 &cmd=llinks): Failed to open stream: HTTP request failed! 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Joint annotation of chromatin state and chromatin conformation reveals relationships among domain types and identifies domains of cell-type-specific expression |pdf=|usr=}}{{25677182 }} gab.com Text Joint annotation of chromatin state and chromatin conformation reveals relationships among domain types and identifies domains of cell-type-specific expression JO: Genome Res http://www.kidney.de/pget.php?&tw=1&pmid=25677182 #FOAvip The genomic neighborhood of a gene influences its activity, a behavior that is attributable in part to domain-scale regulation. Previous genomic studies have identified many types of regulatory domains. However, due to the difficulty of integrating genomics data sets, the relationships among these domain types are poorly understood. Semi-automated genome annotation (SAGA) algorithms facilitate human interpretation of heterogeneous collections of genomics data by simultaneously partitioning the human genome and assigning labels to the resulting genomic segments. However, existing SAGA methods cannot integrate inherently pairwise chromatin conformation data. We developed a new computational method, called graph-based regularization (GBR), for expressing a pairwise prior that encourages certain pairs of genomic loci to receive the same label in a genome annotation. We used GBR to exploit chromatin conformation information during genome annotation by encouraging positions that are close in 3D to occupy the same type of domain. Using this approach, we produced a model of chromatin domains in eight human cell types, thereby revealing the relationships among known domain types. Through this model, we identified clusters of tightly regulated genes expressed in only a small number of cell types, which we term "specific expression domains." We found that domain boundaries marked by promoters and CTCF motifs are consistent between cell types even when domain activity changes. Finally, we showed that GBR can be used to transfer information from well-studied cell types to less well-characterized cell types during genome annotation, making it possible to produce high-quality annotations of the hundreds of cell types with limited available data. Twit Text FOAVip Joint annotation of chromatin state and chromatin conformation reveals relationships among domain types and identifies domains of cell-type-specific expression ????Genome Res http://www.kidney.de/pget.php?&tw=1&pmid=25677182 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25677182 #FOAvip English Wikipedia <ref>{{Cite pmid|25677182 }}</ref> Joint annotation of chromatin state and chromatin conformation reveals relationships among domain types and identifies domains of cell-type-specific expression #MMPMID25677182 Libbrecht MW ; Ay F ; Hoffman MM ; Gilbert DM ; Bilmes JA ; Noble WS Genome Res 2015[Apr]; 25 (4 ): 544-57 PMID25677182 show ga The genomic neighborhood of a gene influences its activity, a behavior that is attributable in part to domain-scale regulation. Previous genomic studies have identified many types of regulatory domains. However, due to the difficulty of integrating genomics data sets, the relationships among these domain types are poorly understood. Semi-automated genome annotation (SAGA) algorithms facilitate human interpretation of heterogeneous collections of genomics data by simultaneously partitioning the human genome and assigning labels to the resulting genomic segments. However, existing SAGA methods cannot integrate inherently pairwise chromatin conformation data. We developed a new computational method, called graph-based regularization (GBR), for expressing a pairwise prior that encourages certain pairs of genomic loci to receive the same label in a genome annotation. We used GBR to exploit chromatin conformation information during genome annotation by encouraging positions that are close in 3D to occupy the same type of domain. Using this approach, we produced a model of chromatin domains in eight human cell types, thereby revealing the relationships among known domain types. Through this model, we identified clusters of tightly regulated genes expressed in only a small number of cell types, which we term "specific expression domains." We found that domain boundaries marked by promoters and CTCF motifs are consistent between cell types even when domain activity changes. Finally, we showed that GBR can be used to transfer information from well-studied cell types to less well-characterized cell types during genome annotation, making it possible to produce high-quality annotations of the hundreds of cell types with limited available data. |*Molecular Conformation [MESH] |Algorithms [MESH] |Amino Acid Motifs/genetics [MESH] |Cell Line, Tumor [MESH] |Chromatin/*genetics/metabolism [MESH] |Chromosome Structures [MESH] |Computational Biology/*methods [MESH] |Genome, Human/genetics [MESH] |Genomics/*methods [MESH] |HeLa Cells [MESH] |Hep G2 Cells [MESH] |Human Umbilical Vein Endothelial Cells [MESH] |Humans [MESH] |Molecular Sequence Annotation/*methods [MESH]Joint annotation of chromatin state and chromatin conformation reveals(epmc).pdf DeepDyve Pubget Overpricing