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Keywords {🔍}

regions, binding, pubmed, cell, genome, article, google, scholar, types, drms, trfs, data, trf, signals, enhancers, cas, chromatin, human, gene, regulatory, figure, methods, genes, elements, peaks, hot, histone, lines, prms, hkme, central, dna, encode, potential, additional, sites, active, bins, file, features, transcripts, expression, dataset, line, experiments, pairs, general, set, nature, transcription,

Topics {✒️}

article  google scholar subtle cell-type-specific patterns long-range dna interactions call transcription-related factors long-range gene regulation article number r48 ten-fold cross-validation procedure ctcf-mediated chromatin interactions detecting lineage-specific selection full size image vivo protein-dna interactions tissue-specific human enhancers article download pdf mapping dna-protein interactions cis-regulatory module predictions human protein-encoding transcriptomes protein-dna binding depends promote steady-state binding c-fos families dimerize promoter-proximal regulatory modules gene-distal regulatory modules predicting cis-regulatory modules typical chip-seq experiment long-range interactions cell-specific regulatory modules 100 transcription-related factors human transcription factors event region-specific trf drm-target transcript pairs explore functional aspects transcription factor binding paired-end tag sequencing sequence-specific trf binding drm-target transcript pair chip-seq binding peaks single chip-seq experiment dna binding proteins long-range regulation /cgi-bin/hgliftover] kheradpour gata1-occupied dna segments full access transcription factors involved potential drm-target transcripts predicts developmental state 'assisted loading' mechanism cell-specific chromatin features protein-dna interactions enhancer blocking activity transcription end sites privacy choices/manage cookies

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         headline:Classification of human genomic regions based on experimentally determined binding sites of more than 100 transcription-related factors
         description:Transcription factors function by binding different classes of regulatory elements. The Encyclopedia of DNA Elements (ENCODE) project has recently produced binding data for more than 100 transcription factors from about 500 ChIP-seq experiments in multiple cell types. While this large amount of data creates a valuable resource, it is nonetheless overwhelmingly complex and simultaneously incomplete since it covers only a small fraction of all human transcription factors. As part of the consortium effort in providing a concise abstraction of the data for facilitating various types of downstream analyses, we constructed statistical models that capture the genomic features of three paired types of regions by machine-learning methods: firstly, regions with active or inactive binding; secondly, those with extremely high or low degrees of co-binding, termed HOT and LOT regions; and finally, regulatory modules proximal or distal to genes. From the distal regulatory modules, we developed computational pipelines to identify potential enhancers, many of which were validated experimentally. We further associated the predicted enhancers with potential target transcripts and the transcription factors involved. For HOT regions, we found a significant fraction of transcription factor binding without clear sequence motifs and showed that this observation could be related to strong DNA accessibility of these regions. Overall, the three pairs of regions exhibit intricate differences in chromosomal locations, chromatin features, factors that bind them, and cell-type specificity. Our machine learning approach enables us to identify features potentially general to all transcription factors, including those not included in the data.
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      description:Transcription factors function by binding different classes of regulatory elements. The Encyclopedia of DNA Elements (ENCODE) project has recently produced binding data for more than 100 transcription factors from about 500 ChIP-seq experiments in multiple cell types. While this large amount of data creates a valuable resource, it is nonetheless overwhelmingly complex and simultaneously incomplete since it covers only a small fraction of all human transcription factors. As part of the consortium effort in providing a concise abstraction of the data for facilitating various types of downstream analyses, we constructed statistical models that capture the genomic features of three paired types of regions by machine-learning methods: firstly, regions with active or inactive binding; secondly, those with extremely high or low degrees of co-binding, termed HOT and LOT regions; and finally, regulatory modules proximal or distal to genes. From the distal regulatory modules, we developed computational pipelines to identify potential enhancers, many of which were validated experimentally. We further associated the predicted enhancers with potential target transcripts and the transcription factors involved. For HOT regions, we found a significant fraction of transcription factor binding without clear sequence motifs and showed that this observation could be related to strong DNA accessibility of these regions. Overall, the three pairs of regions exhibit intricate differences in chromosomal locations, chromatin features, factors that bind them, and cell-type specificity. Our machine learning approach enables us to identify features potentially general to all transcription factors, including those not included in the data.
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         Bioinformatics
         Evolutionary Biology
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      name:Ewan Birney
      affiliation:
            name:Wellcome Trust Genome Campus
            address:
               name:European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, UK
               type:PostalAddress
            type:Organization
      name:Peter Bickel
      affiliation:
            name:University of California at Berkeley
            address:
               name:Department Statistics, University of California at Berkeley, USA
               type:PostalAddress
            type:Organization
      name:Michael Snyder
      affiliation:
            name:Stanford University School of Medicine
            address:
               name:Department of Genetics, Stanford University School of Medicine, Stanford, USA
               type:PostalAddress
            type:Organization
      name:Mark Gerstein
      affiliation:
            name:Yale University
            address:
               name:Program in Computational Biology and Bioinformatics, Yale University, New Haven, USA
               type:PostalAddress
            type:Organization
            name:Yale University
            address:
               name:Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, USA
               type:PostalAddress
            type:Organization
            name:Yale University
            address:
               name:Department of Computer Science, Yale University, New Haven, USA
               type:PostalAddress
            type:Organization
      email:[email protected]
PostalAddress:
      name:Program in Computational Biology and Bioinformatics, Yale University, New Haven, USA
      name:Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, USA
      name:Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong
      name:Hong Kong Bioinformatics Centre, The Chinese University of Hong Kong, Shatin, Hong Kong
      name:CUHK-BGI Innovation Institute of Trans-omics, The Chinese University of Hong Kong, Shatin, Hong Kong
      name:Program in Computational Biology and Bioinformatics, Yale University, New Haven, USA
      name:Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, USA
      name:Program in Computational Biology and Bioinformatics, Yale University, New Haven, USA
      name:Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, USA
      name:Department Statistics, University of California at Berkeley, USA
      name:Program in Computational Biology and Bioinformatics, Yale University, New Haven, USA
      name:Department of Computer Science, Stanford University, Stanford, USA
      name:Program in Computational Biology and Bioinformatics, Yale University, New Haven, USA
      name:Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, USA
      name:European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, UK
      name:Department Statistics, University of California at Berkeley, USA
      name:Department of Genetics, Stanford University School of Medicine, Stanford, USA
      name:Program in Computational Biology and Bioinformatics, Yale University, New Haven, USA
      name:Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, USA
      name:Department of Computer Science, Yale University, New Haven, USA

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