title: Automatic detection of rate change in large data sets with an unsupervised approach: the case of influenza viruses.
abstract: Influenza viruses evolve at such a high rate that vaccine recommendations need to be changed, but not quite on a regular basis. This observation suggests that the rate of evolution of these viruses is not constant through time, which begs the question as to when such rate changes occur, if they do so independently of the host in which they circulate and (or) independently of their subtype. To address these outstanding questions, we introduce a novel heuristics, Mclust*, that is based on a two-tier clustering approach in a phylogenetic context to estimate (i) absolute rates of evolution and (ii) when rate change occurs. We employ the novel approach to compare the two influenza surface proteins, hemagglutinin and neuraminidase, that circulated in avian, human, and swine hosts between 1960 and 2014 in two subtypes: H3N2 and H1N1. We show that the algorithm performs well in most conditions, accounting for phylogenetic uncertainty by means of bootstrapping and scales up to analyze very large data sets. Our results show that our approach is robust to the time-dependent artifact of rate estimation, and confirm pervasive punctuated evolution across hosts and subtypes. As such, the novel approach can potentially detect when vaccine composition needs to be updated.
authors.affiliation: a Department of Biology, Centre for Advanced Research in Environmental Genomics, University of Ottawa, Ottawa, ON K1N 6N5, Canada.
authors.forename: Kasandra
authors.initials: K
authors.lastname: Labonté
authors.affiliation: a Department of Biology, Centre for Advanced Research in Environmental Genomics, University of Ottawa, Ottawa, ON K1N 6N5, Canada.|b Department of Mathematics and Statistics, University of Ottawa, Ottawa, ON K1N 6N5, Canada.
authors.forename: Stéphane
authors.initials: S
authors.lastname: Aris-Brosou
mesh_terms: D000465:Algorithms; D000818:Animals; D001717:Birds; D016000:Cluster Analysis; D019295:Computational Biology; D066264:Datasets as Topic; D019143:Evolution, Molecular; D019267:Hemagglutinin Glycoproteins, Influenza Virus / Q000235:genetics*; D006801:Humans; D053118:Influenza A Virus, H1N1 Subtype / Q000235:genetics*; D053122:Influenza A Virus, H3N2 Subtype / Q000235:genetics*; D000069550:Machine Learning; D015233:Models, Statistical; D009439:Neuraminidase / Q000235:genetics*; D010802:Phylogeny; D017423:Sequence Analysis, RNA; D013552:Swine
chemical_list: D019267:Hemagglutinin Glycoproteins, Influenza Virus; C483468:hemagglutinin, human influenza A virus; D009439:Neuraminidase
keywords: apprentissage automatique; hemagglutinin; hémagglutinine; influenza; machine learning; neuraminidase