A new computational method uses gene expression databases and transcription factor binding specificities to describe regulatory elements in the Drosophila A/P patterning network in unprecedented detail.

s.kumar@asu.edu.

Overlaps in spatial patterns of gene expression are frequently an initial clue to genetic interactions during embryonic development. However, manual inspection of images requires considerable time and resources impeding the discovery of important interactions because tens of thousands of images exist. The FlyExpress discovery platform was developed to facilitate data-driven comparative analysis of expression pattern images from Drosophila embryos. An image-based search of the BDGP and Fly-FISH datasets conducted in FlyExpress yields fewer but more precise results than text-based searching when the specific goal is to find genes with overlapping expression patterns. We also provide an example of a FlyExpress contribution to scientific discovery: an analysis of gene expression patterns for multigene family members revealed that spatial divergence is far more frequent than temporal divergence, especially after the maternal to zygotic transition. This discovery provides a new clue to molecular mechanisms whereby duplicated genes acquire novel functions.

Motivation: Drosophila melanogaster is a major model organism for investigating the function and interconnection of animal genes in the earliest stages of embryogenesis. Today, images capturing Drosophila gene expression patterns are being produced at a higher throughput than ever before. The analysis of spatial patterns of gene expression is most biologically meaningful when images from a similar time point during development are compared. Thus, the critical first step is to determine the developmental stage of an embryo. This information is also needed to observe and analyze expression changes over developmental time. Currently, developmental stages (time) of embryos in images capturing spatial expression pattern are annotated manually, which is time- and labor-intensive. Embryos are often designated into stage ranges, making the information on developmental time course. This makes downstream analyses inefficient and biological interpretations of similarities and differences in spatial expression patterns challenging, particularly when using automated tools for analyzing expression patterns of large number of images.Results: Here, we present a new computational approach to annotate developmental stage for Drosophila embryos in the gene expression images. In an analysis of 3724 images, the new approach shows high accuracy in predicting the developmental stage correctly (79%). In addition, it provides a stage score that enables one to more finely annotate each embryo so that they are divided into early and late periods of development within standard stage demarcations. Stage scores for all images containing expression patterns of the same gene enable a direct way to view expression changes over developmental time for any gene. We show that the genomewide-expression-maps generated using images from embryos in refined stages illuminate global gene activities and changes much better, and more refined stage annotations improve our ability to better interpret results when expression pattern matches are discovered between genes.Availability and implementation: The software package is available for download at: http://www.public.asu.edu/∼jye02/Software/Fly-Project/.Contact: jieping.ye@asu.eduSupplementary information: Supplementary data are available at Bioinformatics online.

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