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.