title: Identification of positive selection in genes is greatly improved by using experimentally informed site-specific models.
abstract: Sites of positive selection are identified by comparing observed evolutionary patterns to those expected under a null model for evolution in the absence of such selection. For protein-coding genes, the most common null model is that nonsynonymous and synonymous mutations fix at equal rates; this unrealistic model has limited power to detect many interesting forms of selection.
  
  
  
  I describe a new approach that uses a null model based on experimental measurements of a gene's site-specific amino-acid preferences generated by deep mutational scanning in the lab. This null model makes it possible to identify both diversifying selection for repeated amino-acid change and differential selection for mutations to amino acids that are unexpected given the measurements made in the lab. I show that this approach identifies sites of adaptive substitutions in four genes (lactamase, Gal4, influenza nucleoprotein, and influenza hemagglutinin) far better than a comparable method that simply compares the rates of nonsynonymous and synonymous substitutions.
  
  
  
  As rapid increases in biological data enable increasingly nuanced descriptions of the constraints on individual protein sites, approaches like the one here can improve our ability to identify many interesting forms of selection in natural sequences.
  
  
  
  This article was reviewed by Sebastian Maurer-Stroh, Olivier Tenaillon, and Tal Pupko. All three reviewers are members of the Biology Direct editorial board.
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.identifier: 0000-0003-1267-3408
authors.initials: JD
authors.lastname: Bloom
mesh_terms: D000596:Amino Acids / Q000737:chemistry; D004268:DNA-Binding Proteins / Q000737:chemistry / Q000235:genetics; D019143:Evolution, Molecular; D019267:Hemagglutinin Glycoproteins, Influenza Virus / Q000737:chemistry / Q000235:genetics; D008957:Models, Genetic; D010802:Phylogeny; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry / Q000235:genetics; D012641:Selection, Genetic; D014157:Transcription Factors / Q000737:chemistry / Q000235:genetics; D014758:Viral Core Proteins / Q000737:chemistry / Q000235:genetics; D001618:beta-Lactamases / Q000737:chemistry / Q000235:genetics
chemical_list: D000596:Amino Acids; D004268:DNA-Binding Proteins; C052130:GAL4 protein, S cerevisiae; D019267:Hemagglutinin Glycoproteins, Influenza Virus; D029701:Saccharomyces cerevisiae Proteins; D014157:Transcription Factors; D014758:Viral Core Proteins; D001618:beta-Lactamases
keywords: Deep mutational scanning; Diversifying selection; Phylogenetics; Substitution model; dN/dS