Visually Exploring,Analyzing,and Relating Gene Expression and in vivo DNA Binding Data.

详细信息   

• 作者：Huang ; Min-Yu.
• 学历：Doctor
• 年：2012
• 导师：Hamann, Bernd,eadvisorBiggin, Mark D.ecommittee memberJoy, Kenneth I.ecommittee member
• 毕业院校：University of California
• Department：Computer Science
• ISBN：9781267658692
• CBH：3540605
• Country：USA
• 语种：English
• FileSize：21551073
• Pages：142

文摘

The Berkeley Drosophila Transcription Network Project has developed a suite of methods that support quantitative, computational analysis of 3D gene expression patterns with cellular resolution in early Drosophila embryos. The visualization tool PointCloudXplore was developed to support exploration of the relationships between different genes expression. Here I describe my improvements to PointCloudXplore , which helped the development of the registration techniques for VirtualEmbryos. I also describe using existing visualization techniques to illustrate cell movements that occur along side changes in expression patterns. Defining gene expression patterns is an essential step for modeling gene interrelationships. To address this challenge, I developed an integrated, interactive approach based on ridge-detection and compared it to thresholding and edge-detection methods. This approach can be further improved by user interaction and additional post-processing steps. Most analyses of in vivo DNA binding data have focused on qualitative descriptions of whether genomic regions are bound or not. There is increasing evidence, however, that factors bind in a highly overlapping manner to the same genomic regions and that it is quantitative differences in occupancy on these commonly bound regions that are the critical determinants of the different biological specificity of factors. I developed a visualization framework to allow the user to interactively analyze and explore the quantitative differences between transcription factors and the genomic regions that they bind to. I describe this framework and provide a discussion of biological examples. The 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. I proposed a straightforward approach that makes use of the average expression driven by multiple of cis-control regions within a binding strength cohort to visually relate gene expression and in vivo DNA binding data. 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 defines whether the regulation is positive or negative.