Development of Bioimage Informatics Tools for Spatial Expression Datasets

University Of Calif-Lawrenc Berkeley Lab, Berkeley CA

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Abstract

DESCRIPTION (provided by applicant): One of the critical tools for probing gene function is imaging spatial expression with confocal or higher resolution microscopy, yielding a pattern of when and where a particular gene is expressed. As a result of the increased availability of such spatial expression patterns and the increased importance of high throughput imaging, there is a need for general purpose tools for managing and analyzing spatial gene expression datasets for research and systems biology. We propose to develop and make available to the general community a suite of tools for managing and analyzing large-scale spatial gene expression datasets. For our large Drosophila spatial expression dataset, we have developed an image based virtual representation that has been practical for visualization, data mining and analysis, while being compact enough to distribute and store large expression datasets. Moreover, this representation allows for analyzing image based data without specialized image processing knowledge. We propose to enhance these tools by adding an integrated data management and analysis platform to a popular scientific image processing program, ImageJ. This ImageJ workbench will be complementary to existing web-interfaces and will enable biologists to perform additional analyses and bioinformatics researchers to extend the software by writing their own custom modules. We will expand the applicability of our virtual embryo representations to complex patterns found at later stages of development and extend microscope automation software to generate virtual representations from automatically acquired images. Finally, we plan to extend the capabilities of the software to process images from other model organisms and develop methods for cross species comparisons. The proposed toolkit will provide a strong foundation for integration of gene expression data with regulatory and gene sequences to promote research for discovering networks of regulatory interactions.