Background: 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. The impacts of mutations must be inferred from changes in their counts after selection. Results: 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. I show that dms_tools yields more accurate inferences on simulated data than simply calculating ratios of counts pre- and post-selection. 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. The preferences and their changes can be intuitively visualized with sequence-logo-style plots created using an extension to weblogo. Conclusions: dms_tools implements a statistically principled approach for the analysis and subsequent visualization of deep mutational scanning data.
CITATION STYLE
Bloom, J. D. (2015). Software for the analysis and visualization of deep mutational scanning data. BMC Bioinformatics, 16(1). https://doi.org/10.1186/s12859-015-0590-4
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