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Using dms_tools, the data can be analyzed to infer the “preference” of each site for each amino acid in the visualization, letter heights are proportional to the preference for that amino acid. Deep sequencing is used to count mutations in a sample of the variants present pre- and post-selection. The mutant library is introduced into cells or viruses and subjected to a functional selection that enriches beneficial mutations and depletes deleterious ones.
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(A) A gene is mutagenized to create a library that contains all single codon mutations. New techniques to create comprehensive codon-mutant libraries of genes make it possible to profile all amino-acid mutations, while new techniques for targeted mutagenesis of mammalian genomes enable deep mutational scanning to be applied across the biological spectrum from viruses and bacteria to human cells. , the technique has been applied to a wide range of genes, both to measure mutational tolerance given a single selection pressure as in Figure 1A, or to identify mutations that have different effects under alternative selections as in Figure 1B. Since the original description of deep mutational scanning by Fowler et al. Deep sequencing of the variants pre- and post-selection provides information about the functional impacts of mutations. A gene is mutagenized, and the library of resulting variants is introduced into cells or viruses, which are then subjected to an experimental selection that enriches for functional variants and depletes non-functional ones. Figure 1 shows a schematic of deep mutational scanning. Conclusionsĭms_tools implements a statistically principled approach for the analysis and subsequent visualization of deep mutational scanning data.ĭeep mutational scanning is a high-throughput experimental technique to assess the impacts of mutations on a protein-coding gene. The preferences and their changes can be intuitively visualized with sequence-logo-style plots created using an extension to weblogo. Using dms_tools, one can infer the preference of each site for each amino acid given a single selection pressure, or assess the extent to which these preferences change under different selection pressures. I show that dms_tools yields more accurate inferences on simulated data than simply calculating ratios of counts pre- and post-selection.
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I describe a software package, dms_tools, to infer the impacts of mutations from deep mutational scanning data using a likelihood-based treatment of the mutation counts. The impacts of mutations must be inferred from changes in their counts after selection.
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Deep mutational scanning is a technique to estimate the impacts of mutations on a gene by using deep sequencing to count mutations in a library of variants before and after imposing a functional selection.