Software for the analysis and visualization of deep mutational scanning data

BMC Bioinformatics

Jesse D Bloom¹ 

[1] Division of Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle 98109, WA, USA

关键词: Amino-acid preferences;    Sequence logo;    Deep mutational scanning;   

Others  :  1232559
DOI  :  10.1186/s12859-015-0590-4

 received in 2015-01-09, accepted in 2015-04-22,  发布年份 2015

【 摘 要 】

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.

【 授权许可】

   
2015 Bloom; licensee BioMed Central.

【 图 表 】

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