High-throughput classification of S. cerevisiae tetrads using deep learning
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High-throughput classification of S. cerevisiae tetrads using deep learning. / Szücs, Balint; Selvan, Raghavendra; Lisby, Michael.
In: Yeast, Vol. 41, 2024, p. 423-436.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - High-throughput classification of S. cerevisiae tetrads using deep learning
AU - Szücs, Balint
AU - Selvan, Raghavendra
AU - Lisby, Michael
N1 - Publisher Copyright: © 2024 The Author(s). Yeast published by John Wiley & Sons Ltd.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - convolutional neural networks
KW - deep learning
KW - gene conversion
KW - interference
KW - meiotic recombination
KW - nondisjunction
KW - tetrads
U2 - 10.1002/yea.3965
DO - 10.1002/yea.3965
M3 - Journal article
C2 - 38850080
AN - SCOPUS:85195608604
VL - 41
SP - 423
EP - 436
JO - Yeast
JF - Yeast
SN - 0749-503X
ER -
ID: 395087548