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Software for the analysis and visualization of deep mutational scanning data

By Jesse Bloom

Posted 10 Jan 2015
bioRxiv DOI: 10.1101/013623 (published DOI: 10.1186/s12859-015-0590-4)

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.

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