Beyond the Identification of Transcribed Sequences:
Functional, Evolutionary and Expression Analysis
12th International Workshop
October 25-28, 2002
Washington, DC


List of Abstracts * Speakers * Organizers * Authors * Original Announcement


BEST: A Computational Approach for Comparing Gene Expression Patterns from Early Stages of Drosophila melanogaster Development

Sudhir Kumar
Center for Evolutionary & Functional Genomics, Tempe, AZ 85287-1501 USA
Telephone: 480-727-6949
Fax: 480-965-2519
Email: s.kumar@asu.edu

Embryonic gene expression patterns are an indispensable part of modern developmental biology. Currently, investigators must visually inspect numerous images containing embryonic expression patterns to identify spatially similar patterns for inferring potential genetic interactions. The lack of a computational approach to identify pattern similarities is an impediment to advancement in developmental biology research because of the rapidly increasing amount of available embryonic gene expression data. Therefore, we have developed a computational approach to automatically compare the expression patterns contained in images of early stage Drosophila melanogaster embryos (prior to the beginning of germ band elongation); similarities and differences in gene expression patterns in these early stages have extensive developmental effects. Here we describe (a) the Basic Expression Search Tool (BEST) to retrieve best matching expression patterns for a given query expression pattern and (b) a computational device for gene interaction inference using gene expression pattern images and information on their genotypes and probes. The usage and impact of BEST for gene expression patterns is akin to that of the BLAST search for finding similar sequences. Analysis of a prototype collection of Drosophila gene expression pattern images is presented to demonstrate the utility of these methods in identifying biologically meaningful matches and inferring gene interactions by direct image content analysis. These Computational Developmental Biology methodologies are likely to make the great wealth of embryonic gene expression pattern data easily accessible and accelerate the discovery of developmental networks.



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