HomeHome

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

Bing Qiao; Chiyuan Li; Victoria Wing Allen; Mimi M. Shirasu-Hiza; Sheyum Syed

Title:
Automated analysis of long-term grooming behavior in Drosophila using a k-nearest neighbors classifier
Author(s):
Qiao, Bing
Li, Chiyuan
Allen, Victoria Wing
Shirasu-Hiza, Mimi M.
Syed, Sheyum
Date:
Type:
Articles
Department(s):
Genetics and Development
Volume:
7
Persistent URL:
Book/Journal Title:
eLife
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.
Subject(s):
Drosophila
Neurosciences
Systems biology
Circadian rhythms
Grooming behavior in animals
Publisher DOI:
https://doi.org/10.7554/eLife.34497
Item views
13
Metadata:
text | xml
Suggested Citation:
Bing Qiao, Chiyuan Li, Victoria Wing Allen, Mimi M. Shirasu-Hiza, Sheyum Syed, , Automated analysis of long-term grooming behavior in Drosophila using a k-nearest neighbors classifier, Columbia University Academic Commons, .

Columbia University Libraries | Policies | FAQ