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

Bing Qiao, Chiyuan Li, Victoria W. Allen, Mimi Shirasu-Hiza, Sheyum Syed

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

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.

Original languageEnglish (US)
Article numbere34497
JournaleLife
Volume7
DOIs
StatePublished - Feb 27 2018

Keywords

  • D. melanogaster
  • Drosophila
  • circadian rhythm
  • computational biology
  • ethogram
  • grooming
  • neuroscience
  • period
  • systems biology
  • two-process

ASJC Scopus subject areas

  • Neuroscience(all)
  • Immunology and Microbiology(all)
  • Biochemistry, Genetics and Molecular Biology(all)

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