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.

体内转座子诱变与深度测序相结合，可以对不同生长条件下的重要基因进行大规模的全基因组突变体筛选。我们分析了对三种酵母（酿酒酵母，裂殖酵母和白色念珠菌）的单倍体菌株进行的六项大规模研究，每种均被三种不同异源转座子（AcDs，Hermes和PiggyBac中的两种）诱变）。使用机器学习方法，我们评估了数据预测基因重要性的能力。重要的数据功能包括独立插入事件的足够数量和分布。由于头奖事件，所有转座子在插入位点偏好上都表现出一些偏见，而特定插入序列以及短距离和长距离插入的偏好也有所不同。对于PiggyBac而言，严格的目标序列限制了预测具有很少或没有目标序列的基因中必需性的能力。机器学习方法还通过利用跨物种的直系同源物，稳健地预测了研究较少的物种中的基因功能。最后，等基因二倍体与单倍体酿酒酵母的比较分离株鉴定出几个单倍基因不足的基因，而大多数必需基因如预期的那样是隐性的。我们为真核单倍体微生物（例如酵母）的全基因组研究提供转座子的选择和基因重要性推断的建议，包括那些不适合经典遗传研究的物种。