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YeastMate: neural network-assisted segmentation of mating and budding events in Saccharomyces cerevisiae

2022; Oxford University Press; Volume: 38; Issue: 9 Linguagem: Inglês

10.1093/bioinformatics/btac107

1367-4811

David M. Bunk, Julian Moriasy, Felix Thoma, Christopher Jakubke, Christof Osman, David Hörl,

Fungal and yeast genetics research

Abstract 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.