Translating sequence information to gene function and interaction is greatly facilitated by the growing collection of spatial and temporal gene expression patterns in the model organism, Drosophila melanogaster. These patterns are links between a gene's primary sequence and its influence on the phenotype, as their overlaps provide the initial clues to functional, genetic, or regulatory interactions. However, today's vast collection of diverse gene expression patterns, made available by way of high-throughput and individual laboratory efforts, has eclipsed the standard practice of manually inspecting the images. In order to discover spatial overlap in the expression patterns of genes on a large scale, investigators need an innovative, image- based developmental bioinformatics framework. Therefore, the objective of this project is to establish a comprehensive resource to accelerate the analysis of gene expression data in the discovery of novel links in gene interaction networks. This framework will comprise the second generation of the unique FlyExpress resource we successfully engendered in the first project period. We are responding to an urgent need for developing state-of-the-art computational methods and statistical approaches to find overlapping expression using cte novo patterns, automating image standardization, and classifying images into groups based on spatial overlaps. In addition, the content of the FlyExpress knowledge-base must evolve by adding vast numbers of images from published literature and high-throughput studies and by building easy-to-use data and information submission web tools. These proposed developments will enable investigators to effectively generate and evaluate their gene interaction hypotheses based on overlaps in expression patterns by using all relevant biological information. This system will always be freely accessible through the web, and it will remove existing impediments to cross-laboratory research endeavors. The computational algorithms, statistical methods, and bioinformatics technologies developed in this project will provide the impetus for constructing similar frameworks for organizing expression pattern data from other species. The FlyExpress system will fulfill the day-to-day needs of basic and applied researchers as well as students in many areas of molecular biology crucial in human health research, including computational genomics, molecular genetics, developmental biology, genetics, and evolution. ? ? ?

National Institute of Health (NIH)
National Human Genome Research Institute (NHGRI)
Research Project (R01)
Project #
Application #
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Bonazzi, Vivien
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Arizona State University-Tempe Campus
Organized Research Units
United States
Zip Code
Kumar, Sudhir; Konikoff, Charlotte; Sanderford, Maxwell et al. (2017) FlyExpress 7: An Integrated Discovery Platform To Study Coexpressed Genes Using in Situ Hybridization Images in Drosophila. G3 (Bethesda) 7:2791-2797
Stanley Jr, Craig E; Kulathinal, Rob J (2016) flyDIVaS: A Comparative Genomics Resource for Drosophila Divergence and Selection. G3 (Bethesda) 6:2355-63
Montiel, Ivan; Konikoff, Charlotte; Braun, Bremen et al. (2014) myFX: a turn-key software for laboratory desktops to analyze spatial patterns of gene expression in Drosophila embryos. Bioinformatics 30:1319-21
Yuan, Lei; Pan, Cheng; Ji, Shuiwang et al. (2014) Automated annotation of developmental stages of Drosophila embryos in images containing spatial patterns of expression. Bioinformatics 30:266-73
Wisotzkey, Robert G; Quijano, Janine C; Stinchfield, Michael J et al. (2014) New gene evolution in the bonus-TIF1-?/TRIM33 family impacted the architecture of the vertebrate dorsal-ventral patterning network. Mol Biol Evol 31:2309-21
Zhang, Wenlu; Feng, Daming; Li, Rongjian et al. (2013) A mesh generation and machine learning framework for Drosophila gene expression pattern image analysis. BMC Bioinformatics 14:372
Shimmi, Osamu; Newfeld, Stuart J (2013) New insights into extracellular and post-translational regulation of TGF-? family signalling pathways. J Biochem 154:11-9
Sun, Qian; Muckatira, Sherin; Yuan, Lei et al. (2013) Image-level and group-level models for Drosophila gene expression pattern annotation. BMC Bioinformatics 14:350
Chen, Jianhui; Tang, Lei; Liu, Jun et al. (2013) A convex formulation for learning a shared predictive structure from multiple tasks. IEEE Trans Pattern Anal Mach Intell 35:1025-38
Wisotzkey, Robert G; Konikoff, Charlotte E; Newfeld, Stuart J (2012) Hippo pathway phylogenetics predicts monoubiquitylation of Salvador and Merlin/Nf2. PLoS One 7:e51599

Showing the most recent 10 out of 30 publications