Using machine vision to analyze and classify Caenorhabditis elegans behavioral phenotypes quantitatively.

Mutants with abnormal patterns of locomotion, also known as uncoordinated (Unc) mutants, have facilitated the genetic dissection of many important aspects of nervous system function and development in the nematode Caenorhabditis elegans. Although a large number of distinct classes of Unc mutants can be distinguished by an experienced observer, precise quantitative definitions of these classes have not been available. Here we describe a new approach for using automatically-acquired image data to quantify the locomotion patterns of wild-type and mutant worms.