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A statistical guide to the design of deep mutational scanning experiments

View ORCID ProfileSebastian Matuszewski, Marcel E. Hildebrandt, Ana-Hermina Ghenu, Jeffrey D. Jensen, View ORCID ProfileClaudia Bank
doi: https://doi.org/10.1101/048892
Sebastian Matuszewski
*School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
†Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
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  • ORCID record for Sebastian Matuszewski
Marcel E. Hildebrandt
*School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
**School of Basic Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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Ana-Hermina Ghenu
‡Instituto Gulbenkian de Ciência, Oeiras,Portugal
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Jeffrey D. Jensen
*School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
†Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
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Claudia Bank
*School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
†Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
‡Instituto Gulbenkian de Ciência, Oeiras,Portugal
1Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, 2780-156 Oeiras, Portugal Email:
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  • For correspondence: evoldynamics@gmail.com
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Abstract

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, and 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 allows for efficient and precise assessment of the entire range of selection coefficients. Finally, we provide a formula for calculating the 95%-confidence interval for the measurement error estimate, which we implement as an interactive web tool. This allows for quantification of the maximum expected a priori precision of the experimental setup, as well as for a statistical threshold for determining deviations from neutrality for specific selection coefficient estimates.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted June 29, 2016.
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A statistical guide to the design of deep mutational scanning experiments
Sebastian Matuszewski, Marcel E. Hildebrandt, Ana-Hermina Ghenu, Jeffrey D. Jensen, Claudia Bank
bioRxiv 048892; doi: https://doi.org/10.1101/048892
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A statistical guide to the design of deep mutational scanning experiments
Sebastian Matuszewski, Marcel E. Hildebrandt, Ana-Hermina Ghenu, Jeffrey D. Jensen, Claudia Bank
bioRxiv 048892; doi: https://doi.org/10.1101/048892

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