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Title: Visually Relating Gene Expression and in vivo DNA Binding Data

Gene expression and in vivo DNA binding data provide important information for understanding gene regulatory networks: in vivo DNA binding data indicate genomic regions where transcription factors are bound, and expression data show the output resulting from this binding. Thus, there must be functional relationships between these two types of data. While visualization and data analysis tools exist for each data type alone, there is a lack of tools that can easily explore the relationship between them. We propose an approach that uses the average expression driven by multiple of ciscontrol regions to visually relate gene expression and in vivo DNA binding data. We demonstrate the utility of this tool with examples from the network controlling early Drosophila development. The results obtained support the idea that the level of occupancy of a transcription factor on DNA strongly determines the degree to which the factor regulates a target gene, and in some cases also controls whether the regulation is positive or negative.
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Conference: IEEE International Conference on Bioinformatics and Biomedicine 2011 (IEEE BIBM 2011)
Research Org:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org:
Computational Research Division; Life Sciences Division
Country of Publication:
United States
60 APPLIED LIFE SCIENCES; 97 MATHEMATICS AND COMPUTING; Interactive Data Exploration, Gene Expression, in vivo DNA Binding Data, Visualization