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Related Experiment Video

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

Balint Szücs1,2, Raghavendra Selvan3,4, Michael Lisby1,2

  • 1Section for Functional Genomics, Department of Biology, University of Copenhagen, Copenhagen, Denmark.

Yeast (Chichester, England)
|June 8, 2024
PubMed
Summary

We developed an automated deep learning pipeline to analyze meiotic recombination in yeast. This method efficiently quantifies crossover frequency and chromosome segregation, accelerating the discovery of genes regulating these essential processes.

Keywords:
convolutional neural networksdeep learninggene conversioninterferencemeiotic recombinationnondisjunctiontetrads

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Area of Science:

  • Genetics and Molecular Biology
  • Cell Biology
  • Bioinformatics

Background:

  • Meiotic crossovers are crucial for accurate chromosome segregation and evolution in sexually reproducing organisms.
  • Analyzing meiotic recombination in Saccharomyces cerevisiae typically requires tetrad dissection, a labor-intensive process.
  • Visualizing meiotic events using fluorescent markers in yeast tetrads offers an alternative to dissection.

Purpose of the Study:

  • To develop and validate a deep learning-based pipeline for automated high-throughput analysis of meiotic recombination in Saccharomyces cerevisiae.
  • To quantify key meiotic events, including crossover frequency, interference, chromosome missegregation, and gene conversion.
  • To demonstrate the potential of this automated method for accelerating the discovery of genes involved in meiotic regulation.

Main Methods:

  • Development of a deep learning pipeline for image recognition and classification of yeast tetrads.
  • Application of the pipeline to analyze a large dataset of images from wild-type and gene knock-out yeast strains.
  • Quantification of meiotic crossover frequency, interference, chromosome missegregation, and gene conversion events.

Main Results:

  • The deep learning pipeline successfully detected and classified meiotic events in yeast tetrads.
  • The method allowed for high-throughput quantification of crossover frequency and interference.
  • Analysis of gene knock-out mutants revealed alterations in meiotic parameters, highlighting the pipeline's utility.

Conclusions:

  • The developed deep learning pipeline provides an efficient and automated method for analyzing meiotic recombination in Saccharomyces cerevisiae.
  • This approach significantly accelerates the study of meiotic processes and the identification of regulatory genes.
  • The tool has broad potential for advancing research in chromosome segregation and evolutionary genetics.