High-throughput classification of S. cerevisiae tetrads using deep learning
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Meiotic crossovers play a vital role in proper chromosome segregation and evolution of most sexually reproducing organisms. Meiotic recombination can be visually observed in Saccharomyces cerevisiae tetrads using linked spore-autonomous fluorescent markers placed at defined intervals within the genome, which allows for analysis of meiotic segregation without the need for tetrad dissection. To automate the analysis, we developed a deep learning-based image recognition and classification pipeline for high-throughput tetrad detection and meiotic crossover classification. As a proof of concept, we analyzed a large image data set from wild-type and selected gene knock-out mutants to quantify crossover frequency, interference, chromosome missegregation, and gene conversion events. The deep learning-based method has the potential to accelerate the discovery of new genes involved in meiotic recombination in S. cerevisiae such as the underlying factors controlling crossover frequency and interference.
Originalsprog | Engelsk |
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Tidsskrift | Yeast |
Vol/bind | 41 |
Sider (fra-til) | 423-436 |
Antal sider | 14 |
ISSN | 0749-503X |
DOI | |
Status | Udgivet - 2024 |
Bibliografisk note
Publisher Copyright:
© 2024 The Author(s). Yeast published by John Wiley & Sons Ltd.
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