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A model-based high throughput method for fecundity estimation in fruit fly studies.

Authors
  • Ng'oma, Enoch1
  • King, Elizabeth G1
  • Middleton, Kevin M2
  • 1 a Division of Biological Sciences , University of Missouri , Columbia , MO , USA.
  • 2 b Department of Pathology and Anatomical Sciences , University of Missouri School of Medicine , Columbia , MO , USA.
Type
Published Article
Journal
Fly
Publisher
Landes Bioscience
Publication Date
Jan 01, 2018
Volume
12
Issue
3-4
Pages
183–190
Identifiers
DOI: 10.1080/19336934.2018.1562267
PMID: 30580661
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

The ability to quantify fecundity is critically important to a wide range of experimental applications, particularly in widely-used model organisms such as Drosophila melanogaster. However, the standard method of manually counting eggs is time consuming and limits the feasibility of large-scale experiments. We develop a predictive model to automate the counting of eggs from images of eggs removed from the media surface and washed onto dark filter paper. Our method uses the simple relationship between the white area in an image and the number of eggs present to create a predictive model that performs well even at high egg densities where clumping can complicate the individual identification of eggs. A cross-validation approach demonstrates our method performs well, with a correlation between predicted and manually counted values of 0.88. We show how this method can be applied to a large data set where egg densities vary widely.

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