title: Statistically Consistent k-mer Methods for Phylogenetic Tree Reconstruction.
abstract: Frequencies of k-mers in sequences are sometimes used as a basis for inferring phylogenetic trees without first obtaining a multiple sequence alignment. We show that a standard approach of using the squared Euclidean distance between k-mer vectors to approximate a tree metric can be statistically inconsistent. To remedy this, we derive model-based distance corrections for orthologous sequences without gaps, which lead to consistent tree inference. The identifiability of model parameters from k-mer frequencies is also studied. Finally, we report simulations showing that the corrected distance outperforms many other k-mer methods, even when sequences are generated with an insertion and deletion process. These results have implications for multiple sequence alignment as well since k-mer methods are usually the first step in constructing a guide tree for such algorithms.
authors.affiliation: 1 Department of Mathematics and Statistics, University of Alaska Fairbanks , Fairbanks, Alaska.
authors.forename: Elizabeth S
authors.initials: ES
authors.lastname: Allman
authors.affiliation: 1 Department of Mathematics and Statistics, University of Alaska Fairbanks , Fairbanks, Alaska.
authors.forename: John A
authors.initials: JA
authors.lastname: Rhodes
authors.affiliation: 2 Department of Mathematics, North Carolina State University , Raleigh, North Carolina.
authors.forename: Seth
authors.initials: S
authors.lastname: Sullivant
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D003198:Computer Simulation; D019143:Evolution, Molecular; D008957:Models, Genetic; D010802:Phylogeny; D016415:Sequence Alignment; D017422:Sequence Analysis, DNA
keywords: alignment-free methods; k-mer; phylogenetic trees