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 that episodic selection is widespread and conclude that the number of sites experiencing positive selection may have been vastly underestimated.
Biomedical Informatics Research Division, eHealth Research and Innovation Platform, Medical Research Council, Tygerberg, South Africa.
Ben
B
Murrell
Joel O
JO
Wertheim
Sasha
S
Moola
Thomas
T
Weighill
Konrad
K
Scheffler
Sergei L
SL
Kosakovsky Pond
D000596:Amino Acids / Q000235:genetics; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D019143:Evolution, Molecular; D008962:Models, Theoretical; D016366:Open Reading Frames / Q000235:genetics*; D010802:Phylogeny; D012243:Rhodopsin / Q000235:genetics; D012641:Selection, Genetic / Q000235:genetics*; D014714:Vertebrates
D000596:Amino Acids; D012243:Rhodopsin