{"title":"CAYSS: Package for Automatic Cytometry Analysis of Yeast Spore Segregation.","authors":"Xavier Raffoux, Matthieu Falque","doi":"10.1002/yea.3988","DOIUrl":null,"url":null,"abstract":"<p><p>Meiotic recombination is a powerful source of haplotypic diversity, and thus plays an important role in the dynamics of short-term adaptation. However, high-throughput quantitative measurement of recombination parameters is challenging because of the large size of offspring to be genotyped. One of the most efficient approaches for large-scale recombination measurement is to study the segregation of fluorescent markers in gametes. Applying this to yeast spores by flow cytometry has already been proved to be highly efficient, but manual analyses of distributions of signal intensities is time-consuming and produces nonperfectly reproducible results. Such analyses are required to identify events corresponding to spores and to assign each of them to a genotypic class depending on their fluorescence intensity. The CAYSS package automatically reproduces the manual process that we've been developing to analyze yeast recombination for years, including Maximum-Likelihood estimation of fluorescence extinction (Raffoux et al. 2018a). When comparing the results of manual versus CAYSS automatic analyses of the same cytometry data, recombination rates and interference were on average very similar, with less than 3% differences on average and strong correlations (R<sup>2</sup> > 0.9). In conclusion, as compared to manual analysis, CAYSS allows to save a lot of human time and produces totally reproducible results.</p>","PeriodicalId":23870,"journal":{"name":"Yeast","volume":" ","pages":""},"PeriodicalIF":2.2000,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Yeast","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1002/yea.3988","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Meiotic recombination is a powerful source of haplotypic diversity, and thus plays an important role in the dynamics of short-term adaptation. However, high-throughput quantitative measurement of recombination parameters is challenging because of the large size of offspring to be genotyped. One of the most efficient approaches for large-scale recombination measurement is to study the segregation of fluorescent markers in gametes. Applying this to yeast spores by flow cytometry has already been proved to be highly efficient, but manual analyses of distributions of signal intensities is time-consuming and produces nonperfectly reproducible results. Such analyses are required to identify events corresponding to spores and to assign each of them to a genotypic class depending on their fluorescence intensity. The CAYSS package automatically reproduces the manual process that we've been developing to analyze yeast recombination for years, including Maximum-Likelihood estimation of fluorescence extinction (Raffoux et al. 2018a). When comparing the results of manual versus CAYSS automatic analyses of the same cytometry data, recombination rates and interference were on average very similar, with less than 3% differences on average and strong correlations (R2 > 0.9). In conclusion, as compared to manual analysis, CAYSS allows to save a lot of human time and produces totally reproducible results.
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