(BioMed Central, 2017) Kuznetsov, Gleb; Goodman, Daniel B.; Filsinger, Gabriel; Landon, Matthieu; Rohland, Nadin; Aach, John; Lajoie, Marc J.; Church, George
We present a method for identifying genomic modifications that optimize a complex phenotype through multiplex genome engineering and predictive modeling. We apply our method to identify six single nucleotide mutations that recover 59% of the fitness defect exhibited by the 63-codon E. coli strain C321.∆A. By introducing targeted combinations of changes in multiplex we generate rich genotypic and phenotypic diversity and characterize clones using whole-genome sequencing and doubling time measurements. Regularized multivariate linear regression accurately quantifies individual allelic effects and overcomes bias from hitchhiking mutations and context-dependence of genome editing efficiency that would confound other strategies. Electronic supplementary material The online version of this article (doi:10.1186/s13059-017-1217-z) contains supplementary material, which is available to authorized users.
