Abstract
Background Adenine auxotrophy is a commonly used non-selective genetic marker in yeast research. It allows investigators to easily visualize and quantify various genetic and epigenetic events by simply reading out colony color. However, manual counting of large numbers of colonies is extremely time-consuming, difficult to reproduce and possibly inaccurate.
Results Using cutting-edge neural networks, we have developed a fully automated pipeline for colony segmentation and classification, which speeds up white/red colony quantification 100-fold over manual counting by an experienced researcher.
Conclusions Our approach uses readily available training data and can be smoothly integrated into existing protocols, vastly speeding up screening assays and increasing the statistical power of experiments that employ adenine auxotrophy.
Footnotes
Co-authors: Lea Duempelmann: lea.duempelmann{at}fmi.ch Yukiko Shimada: Yukiko.Shimada{at}fmi.ch
Postal address of submitting author: Marc Bühler, Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66, 4058 Basel Switzerland
ABBREVIATIONS
- SGDR
- stochastic gradient descent with restarts
- SGD
- stochastic gradient descent
- MRI
- magnetic resonance imaging
- S. pombe
- Schizosaccharomyces pombe
- Paf1C
- polymerase associated factor 1 complex
- siRNA
- small interfering RNA
- ade6si3
- ade6+ epiallele marked with histone H3 lysine 9 tri-methylation and siRNAs
- NCCR
- national centers of competence in research
- FMI
- Friedrich Miescher institute for biomedical research
- MTA
- material transfer agreement
- UBMTA
- uniform biological material transfer agreement