Comparing the utility of in vivo transposon mutagenesis approaches in yeast species to infer gene essentiality

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 pomb...

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Bibliographic Details
Published in:	Current genetics Vol. 66; no. 6; pp. 1117 - 1134
Main Authors:	Levitan, Anton, Gale, Andrew N., Dallon, Emma K., Kozan, Darby W., Cunningham, Kyle W., Sharan, Roded, Berman, Judith
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01-12-2020 Springer Nature B.V
Subjects:	Biochemistry Biomedical and Life Sciences Cell Biology Diploids Gene sequencing Genes Genetic screening Genomes Growth conditions Insertion Insertion sequences Learning algorithms Life Sciences Machine learning Microbial Genetics and Genomics Microbiology Mutagenesis Original Original Article Plant Sciences Proteomics Saccharomyces cerevisiae Species Transposon mutagenesis Transposons Yeast Yeasts High-throughput Yeasts Bioinformatics Genomics Machine learning
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Summary:	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.
Bibliography:	Communicated by M. Kupiec.
ISSN:	0172-8083 1432-0983
DOI:	10.1007/s00294-020-01096-6