Apr 16, 2016

A statistical guide to the design of deep mutational scanning experiments

BioRxiv : the Preprint Server for Biology
Sebastian MatuszewskiClaudia Bank


The characterization of the distribution of mutational effects is a key goal in evolutionary biology. Recently developed deep-sequencing approaches allow for accurate and simultaneous estimation of the fitness effects of hundreds of engineered mutations by monitoring their relative abundance across time points in a single bulk competition. Naturally, the achievable resolution of the estimated fitness effects depends on the specific experimental setup, the organism and type of mutations studied, the sequencing technology utilized, among other factors. By means of analytical approximations and simulations, we provide guidelines for optimizing time-sampled deep-sequencing bulk competition experiments, focusing on the number of mutants, the sequencing depth, and the number of sampled time points. Our analytical results show that sampling more time points together with extending the duration of the experiment improves the achievable precision disproportionately as compared with increasing the sequencing depth, or reducing the number of competing mutants. Even if the duration of the experiment is fixed, sampling more time points and clustering these at the beginning and the end of the experiment increases experimental power, and allo...Continue Reading

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Mentioned in this Paper

Nucleic Acid Sequencing
Mathematical Formula
Deep Sequencing
Monitoring - Action

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