A Statistical Guide to the Design of Deep Mutational Scanning Experiments.

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

SUBMITTER: Matuszewski S 

PROVIDER: S-EPMC5012406 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature