FUBAR: A fast, unconstrained bayesian AppRoximation for inferring selection

Molecular Biology and Evolution

Volumn 30, Issue 5, 2013, Pages 1196-1205

  Murrell, Ben ^a,b,c   Moola, Sasha ^a,c   Mabona, Amandla ^a,c   Weighill, Thomas ^a   Sheward, Daniel ^c   Kosakovsky Pond, Sergei L ^d   Scheffler, Konrad ^a,d  

^a STELLENBOSCH UNIVERSITY   (South Africa)

^b SOUTH AFRICAN MEDICAL RESEARCH COUNCIL   (South Africa)

^c UNIVERSITY OF CAPE TOWN   (South Africa)

^d UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

Author keywords

approximate Bayesian inference; coding sequence evolution; evolutionary model; parallel algorithms

Indexed keywords

INFLUENZA VIRUS HEMAGGLUTININ; RHODOPSIN;

ARTICLE; ARTIODACTYLA; BAYES THEOREM; CONTROLLED STUDY; ENCEPHALITIS VIRUS; GENE SEQUENCE; MONTE CARLO METHOD; NATURAL SELECTION; NONHUMAN; PROBABILITY; PURIFYING SELECTION;

ALGORITHMS; BAYES THEOREM; MARKOV CHAINS; PHYLOGENY; SELECTION, GENETIC;

EID: 84876522258     PISSN: 07374038     EISSN: 15371719     Source Type: Journal    
DOI: 10.1093/molbev/mst030     Document Type: Article

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