Automated annotation of developmental stages of Drosophila embryos in images containing spatial patterns of expression.

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 availablefor download at: http://www.public.asu.edu/*jye02/Software/Fly-Project/.

Exploring spatial patterns of gene expression from fruit fly embryogenesis on the iPhone.

UNLABELLED: Mobile technologies provide unique opportunities for ubiquitous distribution of scientific information through user-friendly interfaces. Therefore, we have developed a new FlyExpress mobile application that makes available a growing collection (>100 000) of standardized in situ hybridization images containing spatial patterns of gene expression from Drosophila melanogaster (fruit fly) embryogenesis. Using this application, scientists can visualize and compare expression patterns of >4000 developmentally relevant genes. The FlyExpress app displays the expression patterns of the selected gene for different visual projections (e.g. lateral) and displays them according to their developmental stages, which shows a gene's progression of spatial expression over developmental time. Ultimately, we envision the use of FlyExpress app in the laboratory where scientists may wish to immediately conduct a visual comparison of a known expression pattern with the one observed on the bench top or to display expression patterns of interest during scientific discussions at large. AVAILABILITY: Search "FlyExpress" on the Apple iTunes store.

Quantitative analysis of the Drosophila segmentation regulatory network using pattern generating potentials.

Cis-regulatory modules that drive precise spatial-temporal patterns of gene expression are central to the process of metazoan development. We describe a new computational strategy to annotate genomic sequences based on their "pattern generating potential" and to produce quantitative descriptions of transcriptional regulatory networks at the level of individual protein-module interactions. We use this approach to convert the qualitative understanding of interactions that regulate Drosophila segmentation into a network model in which a confidence value is associated with each transcription factor-module interaction. Sequence information from multiple Drosophila species is integrated with transcription factor binding specificities to determine conserved binding site frequencies across the genome. These binding site profiles are combined with transcription factor expression information to create a model to predict module activity patterns. This model is used to scan genomic sequences for the potential to generate all or part of the expression pattern of a nearby gene, obtained from available gene expression databases. Interactions between individual transcription factors and modules are inferred by a statistical method to quantify a factor's contribution to the module's pattern generating potential. We use these pattern generating potentials to systematically describe the location and function of known and novel cis-regulatory modules in the segmentation network, identifying many examples of modules predicted to have overlapping expression activities. Surprisingly, conserved transcription factor binding site frequencies were as effective as experimental measurements of occupancy in predicting module expression patterns or factor-module interactions. Thus, unlike previous module prediction methods, this method predicts not only the location of modules but also their spatial activity pattern and the factors that directly determine this pattern. As databases of transcription factor specificities and in vivo gene expression patterns grow, analysis of pattern generating potentials provides a general method to decode transcriptional regulatory sequences and networks.

Comparison of embryonic expression within multigene families using the FlyExpress discovery platform reveals more spatial than temporal divergence.

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: The application of FlyExpress to understanding the process by which new genes acquire novel functions is just one of a myriad of ways in which it can contribute to our understanding of developmental and evolutionary biology. This resource has many other potential applications, limited only by the investigator's imagination.

FlyExpress: visual mining of spatiotemporal patterns for genes and publications in Drosophila embryogenesis.

SUMMARY: Images containing spatial expression patterns illuminate the roles of different genes during embryogenesis. In order to generate initial clues to regulatory interactions, biologists frequently need to know the set of genes expressed at the same time at specific locations in a developing embryo, as well as related research publications. However, text-based mining of image annotations and research articles cannot produce all relevant results, because the primary data are images that exist as graphical objects. We have developed a unique knowledge base (FlyExpress) to facilitate visual mining of images from Drosophila melanogaster embryogenesis. By clicking on specific locations in pictures of fly embryos from different stages of development and different visual projections, users can produce a list of genes and publications instantly. In FlyExpress, each queryable embryo picture is a heat-map that captures the expression patterns of more than 4500 genes and more than 2600 published articles. In addition, one can view spatial patterns for particular genes over time as well as find other genes with similar expression patterns at a given developmental stage. Therefore, FlyExpress is a unique tool for mining spatiotemporal expression patterns in a format readily accessible to the scientific community. AVAILABILITY: http://www.flyexpress.net CONTACT: s.kumar@asu.edu.