WorMachine: machine learning-based phenotypic analysis tool for worms

New Software for phenotypic high-throughput analysis of C.Elegans Microscope Images
Published in Ecology & Evolution
WorMachine: machine learning-based phenotypic analysis tool for worms

Share this post

Choose a social network to share with, or copy the shortened URL to share elsewhere

This is a representation of how your post may appear on social media. The actual post will vary between social networks

A link to the paper in BMC Biology can be found here.

Also, we created a short demo video for the software:

Caenorhabditis elegans nematodes are powerful model organisms, yet quantification of visible phenotypes is still often labor-intensive, biased, and error-prone. Particularly, evolution research requires numerous samples, which make manual analysis extremely strenuous. 

Hence, we developed WorMachine, a MATLAB-based image analysis software that utilized cutting-edge techniques from deep & machine learning, and consists of three steps:

  1. Automated identification of C. elegans worms in microscope images.
  2. Extraction of morphological features and quantification of fluorescent signals
  3. Machine learning techniques for high-level analysis.

We examined the power of WorMachine using five separate representative assays: supervised classification of binary-sex phenotype, scoring continuous-sexual phenotypes, quantifying the effects of two different RNA interference treatments, and measuring intracellular protein aggregation.

WorMachine is suitable for analysis of a variety of evolutionary questions and provides an accurate and reproducible analysis tool for measuring diverse phenotypes. It serves as a “quick and easy,” convenient, high-throughput, and automated solution for nematode research. We hope you find it helpful for your research needs, and are happy to be of service for any questions or comments.

Please sign in or register for FREE

If you are a registered user on Research Communities by Springer Nature, please sign in