Related Experiment Video
Updated: Oct 3, 2025
Determination of the Mating Efficiency of Haploids in Saccharomyces cerevisiae
Published on: December 2, 2022
YeastMate: neural network-assisted segmentation of mating and budding events in Saccharomyces cerevisiae
David Bunk1, Julian Moriasy1, Felix Thoma1
1Faculty of Biology, Ludwig-Maximilians-Universität München, 82152 Planegg-Martinsried, Germany.
Summary:
Here, we introduce YeastMate, a user-friendly deep learning-based application for automated detection and segmentation of Saccharomyces cerevisiae cells and their mating and budding events in microscopy images. We build upon Mask R-CNN with a custom segmentation head for the subclassification of mother and daughter cells during lifecycle transitions. YeastMate can be used directly as a Python library or through a standalone application with a graphical user interface (GUI) and a Fiji plugin as easy-to-use frontends.
Availability And Implementation:
The source code for YeastMate is freely available at https://github.com/hoerlteam/YeastMate under the MIT license. We offer installers for our software stack for Windows, macOS and Linux. A detailed user guide is available at https://yeastmate.readthedocs.io.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
