title: Identifiability and inference of non-parametric rates-across-sites models on large-scale phylogenies.
abstract: Mutation rate variation across loci is well known to cause difficulties, notably identifiability issues, in the reconstruction of evolutionary trees from molecular sequences. Here we introduce a new approach for estimating general rates-across-sites models. Our results imply, in particular, that large phylogenies are typically identifiable under rate variation. We also derive sequence-length requirements for high-probability reconstruction. Our main contribution is a novel algorithm that clusters sites according to their mutation rate. Following this site clustering step, standard reconstruction techniques can be used to recover the phylogeny. Our results rely on a basic insight: that, for large trees, certain site statistics experience concentration-of-measure phenomena.
authors.affiliation: U.C. Berkeley, Berkeley, USA.
authors.forename: Elchanan
authors.initials: E
authors.lastname: Mossel
authors.forename: Sebastien
authors.initials: S
authors.lastname: Roch
mesh_terms: D000465:Algorithms; D001483:Base Sequence / Q000235:genetics; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D019143:Evolution, Molecular; D008957:Models, Genetic; D009154:Mutation; D010802:Phylogeny