Automated analysis of long-term grooming behavior in Drosophila using a k-nearest neighbors classifier

Abstract
Data availability
Article and author information
Metrics

Abstract

Despite being pervasive, the control of programmed grooming is poorly understood. We addressed this gap by developing a high-throughput platform that allows long-term detection of grooming in Drosophila melanogaster. In our method, a k-nearest neighbors algorithm automatically classifies fly behavior and finds grooming events with over 90% accuracy in diverse genotypes. Our data show that flies spend ~13% of their waking time grooming, driven largely by two major internal programs. One of these programs regulates the timing of grooming and involves the core circadian clock components cycle, clock, and period. The second program regulates the duration of grooming and, while dependent on cycle and clock, appears to be independent of period. This emerging dual control model in which one program controls timing and another controls duration, resembles the two-process regulatory model of sleep. Together, our quantitative approach presents the opportunity for further dissection of mechanisms controlling long-term grooming in Drosophila.

Data availability

The following data sets were generated

1. Qiao B
2. Li C
3. Allen VW
4. Shirasu-Hiza MM
5. Syed S
(2018) Automated analysis of internally programmed grooming behavior in
Available at Dryad Digital Repository under a CC0 Public Domain Dedication.

https://doi.org/10.5061/dryad.94082

Article and author information

Author details

Bing Qiao

Department of Physics, University of Miami, Coral Gables, United States

Competing interests
The authors declare that no competing interests exist.
Chiyuan Li

Department of Physics, University of Miami, Coral Gables, United States

Competing interests
The authors declare that no competing interests exist.
Victoria W Allen

Department of Genetics and Development, Columbia University, New York, United States

Competing interests
The authors declare that no competing interests exist.
Mimi M Shirasu-Hiza

Department of Genetics and Development, Columbia University, New York, United States

Competing interests
The authors declare that no competing interests exist.
Sheyum Syed

Department of Physics, University of Miami, Coral Gables, United States

For correspondence
s.syed@miami.edu

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-4642-6678

Funding

National Science Foundation (IOS-1656603)

Sheyum Syed

National Institutes of Health (R01GM105775)

Mimi M Shirasu-Hiza

National Institutes of Health (R01AG045842)

Mimi M Shirasu-Hiza

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.