Optimizing complex phenotypes through model-guided multiplex genome engineering
Goodman, Daniel B.
Lajoie, Marc J.
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CitationKuznetsov, Gleb, Daniel B. Goodman, Gabriel T. Filsinger, Matthieu Landon, Nadin Rohland, John Aach, Marc J. Lajoie, and George M. Church. 2017. “Optimizing complex phenotypes through model-guided multiplex genome engineering.” Genome Biology 18 (1): 100. doi:10.1186/s13059-017-1217-z. http://dx.doi.org/10.1186/s13059-017-1217-z.
AbstractWe 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.
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