title: Software for the analysis and visualization of deep mutational scanning data.
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.
authors.affiliation: Division of Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, 98109, WA, USA. jbloom@fredhutch.org.
authors.forename: Jesse D
authors.initials: JD
authors.lastname: Bloom
mesh_terms: D000595:Amino Acid Sequence; D019295:Computational Biology; D003196:Computer Graphics; D004252:DNA Mutational Analysis / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D008969:Molecular Sequence Data; D009154:Mutation / Q000235:genetics*; D011506:Proteins / Q000737:chemistry / Q000235:genetics*; D012641:Selection, Genetic; D012984:Software
chemical_list: D011506:Proteins