Detecting Individual Sites Subject to Episodic Diversifying Selection

The imprint of natural selection on protein coding genes is often difficult to identify because selection is frequently transient or episodic, i.e. it affects only a subset of lineages. Existing computational techniques, which are designed to identify sites subject to pervasive selection, may fail to recognize sites where selection is episodic: a large proportion of positively selected sites. We present a mixed effects model of evolution (MEME) that is capable of identifying instances of both episodic and pervasive positive selection at the level of an individual site. Using empirical and simulated data, we demonstrate the superior performance of MEME over older models under a broad range of scenarios. We find…

• NS
  National Science Foundation

  Award: 0714991
• EC
  European Commission
• NR
  National Research Foundation
• NI
  National Institutes of Health

  Awards: AI36214, AI74621, AI43638, GM093939, AI57167, AI47745
• CF
  Center for AIDS Research, University of Washington
• UO
  University of California, San Diego
• NI
  National Institute of General Medical Sciences

  Award: GM093939
• NI
  National Institute of Allergy and Infectious Diseases

  Awards: AI43638, AI36214