High-throughput classification of S. cerevisiae tetrads using deep learning

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Standard

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

Harvard

Szücs, B, Selvan, R & Lisby, M 2024, 'High-throughput classification of S. cerevisiae tetrads using deep learning', Yeast, vol. 41, pp. 423-436. https://doi.org/10.1002/yea.3965

APA

Szücs, B., Selvan, R., & Lisby, M. (2024). High-throughput classification of S. cerevisiae tetrads using deep learning. Yeast, 41, 423-436. https://doi.org/10.1002/yea.3965

Vancouver

Szücs B, Selvan R, Lisby M. High-throughput classification of S. cerevisiae tetrads using deep learning. Yeast. 2024;41:423-436. https://doi.org/10.1002/yea.3965

Author

Szücs, Balint ; Selvan, Raghavendra ; Lisby, Michael. / High-throughput classification of S. cerevisiae tetrads using deep learning. In: Yeast. 2024 ; Vol. 41. pp. 423-436.

Bibtex

@article{2ca050c0832a41a1ad9db5d53035c4ce,

title = "High-throughput classification of S. cerevisiae tetrads using deep learning",

abstract = "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.",

keywords = "convolutional neural networks, deep learning, gene conversion, interference, meiotic recombination, nondisjunction, tetrads",

author = "Balint Sz{\"u}cs and Raghavendra Selvan and Michael Lisby",

note = "Publisher Copyright: {\textcopyright} 2024 The Author(s). Yeast published by John Wiley & Sons Ltd.",

year = "2024",

doi = "10.1002/yea.3965",

language = "English",

volume = "41",

pages = "423--436",

journal = "Yeast",

issn = "0749-503X",

publisher = "JohnWiley & Sons Ltd",

}

RIS

TY - JOUR

T1 - High-throughput classification of S. cerevisiae tetrads using deep learning

AU - Szücs, Balint

AU - Selvan, Raghavendra

AU - Lisby, Michael

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