Predicting Tissue-Specific Enhancers in the Human Genome

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Determining how transcriptional regulatory signals areencoded in vertebrate genomes is essential for understanding the originsof multi-cellular complexity; yet the genetic code of vertebrate generegulation remains poorly understood. In an attempt to elucidate thiscode, we synergistically combined genome-wide gene expression profiling,vertebrate genome comparisons, and transcription factor binding siteanalysis to define sequence signatures characteristic of candidatetissue-specific enhancers in the human genome. We applied this strategyto microarray-based gene expression profiles from 79 human tissues andidentified 7,187 candidate enhancers that defined their flanking geneexpression, the majority of which were located outside of knownpromoters. We cross-validated this method for its ability to de novopredict tissue-specific ... continued below

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Pennacchio, Len A.; Loots, Gabriela G.; Nobrega, Marcelo A. & Ovcharenko, Ivan July 1, 2006.

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Determining how transcriptional regulatory signals areencoded in vertebrate genomes is essential for understanding the originsof multi-cellular complexity; yet the genetic code of vertebrate generegulation remains poorly understood. In an attempt to elucidate thiscode, we synergistically combined genome-wide gene expression profiling,vertebrate genome comparisons, and transcription factor binding siteanalysis to define sequence signatures characteristic of candidatetissue-specific enhancers in the human genome. We applied this strategyto microarray-based gene expression profiles from 79 human tissues andidentified 7,187 candidate enhancers that defined their flanking geneexpression, the majority of which were located outside of knownpromoters. We cross-validated this method for its ability to de novopredict tissue-specific gene expression and confirmed its reliability in57 of the 79 available human tissues, with an average precision inenhancer recognition ranging from 32 percent to 63 percent, and asensitivity of 47 percent. We used the sequence signatures identified bythis approach to assign tissue-specific predictions to ~;328,000human-mouse conserved noncoding elements in the human genome. Byoverlapping these genome-wide predictions with a large in vivo dataset ofenhancers validated in transgenic mice, we confirmed our results with a28 percent sensitivity and 50 percent precision. These results indicatethe power of combining complementary genomic datasets as an initialcomputational foray into the global view of tissue-specific generegulation in vertebrates.

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  • Journal Name: Nature Biotechnology; Journal Volume: 17; Journal Issue: 2; Related Information: Journal Publication Date: 02/2007

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  • Report No.: LBNL--61287
  • Grant Number: DE-AC02-05CH11231
  • Grant Number: NIHHL066681
  • Office of Scientific & Technical Information Report Number: 919377
  • Archival Resource Key: ark:/67531/metadc889041

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  • July 1, 2006

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  • Sept. 22, 2016, 2:13 a.m.

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  • Sept. 30, 2016, 2:30 p.m.

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Pennacchio, Len A.; Loots, Gabriela G.; Nobrega, Marcelo A. & Ovcharenko, Ivan. Predicting Tissue-Specific Enhancers in the Human Genome, article, July 1, 2006; Berkeley, California. (digital.library.unt.edu/ark:/67531/metadc889041/: accessed August 19, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.