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Software for the analysis and visualization of deep mutational scanning data

View ORCID ProfileJesse D. Bloom
doi: https://doi.org/10.1101/013623
Jesse D. Bloom
Division of Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, 98109 Seattle, WA, USA
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Abstract

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 the widely used but statistically biased approach of 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.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted January 10, 2015.
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Software for the analysis and visualization of deep mutational scanning data
Jesse D. Bloom
bioRxiv 013623; doi: https://doi.org/10.1101/013623
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Software for the analysis and visualization of deep mutational scanning data
Jesse D. Bloom
bioRxiv 013623; doi: https://doi.org/10.1101/013623

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