bioRxivEvolutionary Biology
Saturated Transposon Analysis in Yeast as a One-step Method to Quantify the Fitness Effects of Gene Disruptions on a Genome-Wide Scale
Abstract
Transposon insertion site sequencing (TIS) is an extremely powerful tool that has greatly advanced our knowledge of functional genomics. However, studies using TIS often focus on gene essentiality and neglect possibly interesting but subtle differences in the importance of genes for fitness. As shown by other studies, expanding of the analysis of TIS data to allow a quantitative estimate of fitness has important application in genetics and evolutionary biology. Here, we present a method to estimate the fitness of gene disruption mutants on a quantitative level using data obtained from a TIS screen developed for the yeast Saccharomyces cerevisiae called SATAY. We show that using the average read count per transposon insertion site provides a metric for fitness that is robust across biological and technical replicate experiments. Importantly, the ability to resolve differences between gene disruption mutants with low fitness depends crucially on the inclusion of insertion sites that are not observed in the sequencing data to estimate the mean. While our method provides reproducible results between replicate SATAY datasets, the obtained fitness distribution differs substantially from those obtained using other techniques. It remains unclear whether these inconsistencies are due to biological or technical differences between the methods. Finally, we give suggestions for modifications of the SATAY procedure that could improve the resolution of the fitness estimates. Our analysis indicates that increasing the sequencing depth does very little to reduce the uncertainty in the estimates, while replacing the PCR amplification with methods that avoid or reduce the number of amplification cycles will likely be most effective in reducing noise.
- organism: yeast
- assay: whole-genome sequencing
- research focus: gene regulatory network inference
- research focus: variant effect prediction
- research focus: benchmarking
- approach: experimental assay
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