Chapter

Research in Computational Molecular Biology

Volume 4453 of the series Lecture Notes in Computer Science pp 533-547

Comparative Analysis of Spatial Patterns of Gene Expression in Drosophila melanogaster Imaginal Discs

  • Cyrus L. HarmonAffiliated withDepartment of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720
  • , Parvez AhammadAffiliated withDepartment of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA 94720
  • , Ann HammondsAffiliated withDepartment of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720
  • , Richard WeiszmannAffiliated withDepartment of Genome Sciences, Lawrence Berkeley National Laboratory, Berkeley, CA 94720
  • , Susan E. CelnikerAffiliated withDepartment of Genome Sciences, Lawrence Berkeley National Laboratory, Berkeley, CA 94720
  • , S. Shankar SastryAffiliated withDepartment of Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA 94720
  • , Gerald M. RubinAffiliated withDepartment of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720

* Final gross prices may vary according to local VAT.

Get Access

Abstract

Determining the precise spatial extent of expression of genes across different tissues, along with knowledge of the biochemical function of the genes is critical for understanding the roles of various genes in the development of metazoan organisms. To address this problem, we have developed high-throughput methods for generating images of gene expression in Drosophila melanogaster imaginal discs and for the automated analysis of these images. Our method automatically learns tissue shapes from a small number of manually segmented training examples and automatically aligns, extracts and scores new images, which are analyzed to generate gene expression maps for each gene. We have developed a reverse lookup procedure that enables us to identify genes that have spatial expression patterns most similar to a given gene of interest. Our methods enable us to cluster both the genes and the pixels that of the maps, thereby identifying sets of genes that have similar patterns, and regions of the tissues of interest that have similar gene expression profiles across a large number of genes.

Primary keyphrases: Genomic imaging, Gene expression analysis,Clustering.

Secondary keyphrases: Microarray data analysis, Imaginal discs.