Profiling a Caenorhabditis elegans behavioral parametric dataset with a supervised K-means clustering algorithm identifies genetic networks regulating locomotion

Defining genetic networks underlying animal behavior in a high throughput manner is an important but challenging task that has not yet been achieved for any organism. Using Caenorhabditis elegans, we collected quantitative parametric data related to various aspects of locomotion from wild type and 31 mutant worm strains with single mutations in genes functioning in sensory reception, neurotransmission, G-protein signaling, neuromuscular control or other facets of motor regulation. We applied unsupervised and constrained K-means clustering algorithms to the data and found that the genes that clustered together due to the behavioral similarity of their mutants encoded proteins in the same signaling networks. This approach provides a framework to identify genes and genetic networks underlying worm neuromotor function in a high-throughput manner. A publicly accessible database harboring the visual and quantitative behavioral data collected in this study adds valuable information to the rapidly growing C. elegans databanks that can be employed in a similar context.

► We setup a publicly accessible Caenorhabditis elegans behavioral parametric and visual dataset.
► Profiling this dataset identifies genetic signaling networks regulating locomotion.
► Our work provides a framework to study worm locomotion in high throughput manner.
► This worm visual and parametric behavioral data provides a novel research resource.