Sampling Strategies for Experimentally Mapping Molecular Fitness Landscapes Using High-Throughput Methods

Exhaustive experimental mapping of genotype-phenotype fitness landscapes is crucial for revealing evolutionary pathways and understanding key processes in evolution. Due to limitations on experimental throughput, high-throughput mapping of the vast space of possible evolutionary pathways is extremely challenging. The recent emergence of multiplexed assays of variant effect (MAVEs) present new solutions to this problem by enabling hundreds of thousands of evolutionary pathways to be tested in pooled genetic screens. However, without an understanding of the necessary sampling requirements for MAVE screens, generating vast genotype-phenotype fitness landscapes remains a major challenge. Chen, Liu, and colleagues address the sampling problem by proposing calculations and simulation methods for determining minimum sampling requirements for sampling all possible variants within the MAVE experiments.

This sampling procedure enables researchers to strike a balance between the depth of genome mutation analysis and the breath of site coverage. Additionally, by creating two four-codon combinatorial libraries of mutations, Chen, Liu, and colleagues validates their proposed approach, thus facilitating the usage of MAVE experiments for future research.

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