Comparing the utility of in vivo transposon mutagenesis approaches in yeast species to infer gene essentiality
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ORIGINAL ARTICLE
Comparing the utility of in vivo transposon mutagenesis approaches in yeast species to infer gene essentiality Anton Levitan1,2 · Andrew N. Gale3 · Emma K. Dallon3 · Darby W. Kozan3 · Kyle W. Cunningham3 · Roded Sharan4 · Judith Berman1 Received: 26 June 2020 / Revised: 26 June 2020 / Accepted: 8 July 2020 © The Author(s) 2020
Abstract In vivo transposon mutagenesis, coupled with deep sequencing, enables large-scale genome-wide mutant screens for genes essential in different growth conditions. We analyzed six large-scale studies performed on haploid strains of three yeast species (Saccharomyces cerevisiae, Schizosaccaromyces pombe, and Candida albicans), each mutagenized with two of three different heterologous transposons (AcDs, Hermes, and PiggyBac). Using a machine-learning approach, we evaluated the ability of the data to predict gene essentiality. Important data features included sufficient numbers and distribution of independent insertion events. All transposons showed some bias in insertion site preference because of jackpot events, and preferences for specific insertion sequences and short-distance vs long-distance insertions. For PiggyBac, a stringent target sequence limited the ability to predict essentiality in genes with few or no target sequences. The machine learning approach also robustly predicted gene function in less well-studied species by leveraging cross-species orthologs. Finally, comparisons of isogenic diploid versus haploid S. cerevisiae isolates identified several genes that are haplo-insufficient, while most essential genes, as expected, were recessive. We provide recommendations for the choice of transposons and the inference of gene essentiality in genome-wide studies of eukaryotic haploid microbes such as yeasts, including species that have been less amenable to classical genetic studies. Keywords Genomics · Yeasts · Bioinformatics · Machine learning · High-throughput
Introduction
Communicated by M. Kupiec. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00294-020-01096-6) contains supplementary material, which is available to authorized users. * Judith Berman [email protected] 1
School of Molecular Microbiology and Biotechnology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
2
Edmond J. Safra Center for Bioinformatics, Tel Aviv University, Tel Aviv, Israel
3
Department of Biology, Johns Hopkins University, Baltimore, MD, USA
4
Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
Work with model yeasts such as Saccharomyces cerevisiae and Schizosaccharomyces pombe has pioneered the combination of genotype/phenotype comparisons at a genomic scale. Work on these yeasts, which have had genome sequences available for more than 20 years (Goffeau et al. 1996; Wood et al. 2002), along with facile gene replacement protocols, has relied heavily on comprehensive collections of deletion mutants (Giaever et al. 2002; Kim et al. 2010) for high-throughput dissecti
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