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BMC Bioinformatics. 2015 May 20;16:168. doi: 10.1186/s12859-015-0590-4.

Software for the analysis and visualization of deep mutational scanning data.

Author information

1
Division of Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, 98109, WA, USA. jbloom@fredhutch.org.

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 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.

PMID:
25990960
PMCID:
PMC4491876
DOI:
10.1186/s12859-015-0590-4
[Indexed for MEDLINE]
Free PMC Article

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