title: Automated annotation of developmental stages of Drosophila embryos in images containing spatial patterns of expression.
abstract: BACKGROUND
  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.
  
  
  METHODS
  The software package is availablefor download at: http://www.public.asu.edu/*jye02/Software/Fly-Project/.
authors.affiliation: School of Computing, Informatics, and Decision Systems Engineering, Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA, National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China, School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA and Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia.
authors.forename: Lei
authors.initials: L
authors.lastname: Yuan
authors.forename: Cheng
authors.initials: C
authors.lastname: Pan
authors.forename: Shuiwang
authors.initials: S
authors.lastname: Ji
authors.forename: Michael
authors.initials: M
authors.lastname: McCutchan
authors.forename: Zhi-Hua
authors.initials: ZH
authors.lastname: Zhou
authors.forename: Stuart J
authors.initials: SJ
authors.lastname: Newfeld
authors.forename: Sudhir
authors.initials: S
authors.lastname: Kumar
authors.forename: Jieping
authors.initials: J
authors.lastname: Ye
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology; D029721:Drosophila Proteins / Q000235:genetics*; D004331:Drosophila melanogaster / Q000196:embryology / Q000235:genetics*; D004625:Embryo, Nonmammalian / Q000166:cytology* / Q000378:metabolism; D047108:Embryonic Development / Q000235:genetics; D020869:Gene Expression Profiling; D018507:Gene Expression Regulation, Developmental; D007091:Image Processing, Computer-Assisted; D010363:Pattern Recognition, Automated
chemical_list: D029721:Drosophila Proteins