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

IF 2.2 4区 生物学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY
Yeast Pub Date : 2024-07-01 Epub Date: 2024-06-08 DOI:10.1002/yea.3965
Balint Szücs, Raghavendra Selvan, Michael Lisby
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引用次数: 0

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.

利用深度学习对 S. cerevisiae tetrads 进行高通量分类。
减数分裂交叉对大多数有性生殖生物的染色体正常分离和进化起着至关重要的作用。利用置于基因组内确定间隔的链接孢子自主荧光标记,可以直观地观察到酿酒酵母四分体中的减数分裂重组。为了实现分析自动化,我们开发了基于深度学习的图像识别和分类管道,用于高通量四分体检测和减数分裂交叉分类。作为概念验证,我们分析了野生型和选定基因敲除突变体的大量图像数据集,以量化交叉频率、干扰、染色体错分离和基因转换事件。这种基于深度学习的方法有望加速发现参与酿酒葡萄孢减数分裂重组的新基因,如控制交叉频率和干扰的潜在因素。
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来源期刊
Yeast
Yeast 生物-生化与分子生物学
CiteScore
5.30
自引率
3.80%
发文量
55
审稿时长
3 months
期刊介绍: Yeast publishes original articles and reviews on the most significant developments of research with unicellular fungi, including innovative methods of broad applicability. It is essential reading for those wishing to keep up to date with this rapidly moving field of yeast biology. Topics covered include: biochemistry and molecular biology; biodiversity and taxonomy; biotechnology; cell and developmental biology; ecology and evolution; genetics and genomics; metabolism and physiology; pathobiology; synthetic and systems biology; tools and resources
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