title: Robust Estimation of Evolutionary Distances with Information Theory.
abstract: Methods for measuring genetic distances in phylogenetics are known to be sensitive to the evolutionary model assumed. However, there is a lack of established methodology to accommodate the trade-off between incorporating sufficient biological reality and avoiding model overfitting. In addition, as traditional methods measure distances based on the observed number of substitutions, their tend to underestimate distances between diverged sequences due to backward and parallel substitutions. Various techniques were proposed to correct this, but they lack the robustness against sequences that are distantly related and of unequal base frequencies. In this article, we present a novel genetic distance estimate based on information theory that overcomes the above two hurdles. Instead of examining the observed number of substitutions, this method estimates genetic distances using Shannon's mutual information. This naturally provides an effective framework for balancing model complexity and goodness of fit. Our distance estimate is shown to be approximately linear to elapsed time and hence is less sensitive to the divergence of sequence data and compositional biased sequences. Using extensive simulation data, we show that our method 1) consistently reconstructs more accurate phylogeny topologies than existing methods, 2) is robust in extreme conditions such as diverged phylogenies, unequal base frequencies data, and heterogeneous mutation patterns, and 3) scales well with large phylogenies.
authors.affiliation: Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia Clayton School of Information Technology, Monash University, Clayton, VIC, Australia m.cao1@uq.edu.au m.boden@uq.edu.au.
authors.forename: Minh Duc
authors.initials: MD
authors.lastname: Cao
authors.affiliation: Clayton School of Information Technology, Monash University, Clayton, VIC, Australia.
authors.forename: Lloyd
authors.initials: L
authors.lastname: Allison
authors.affiliation: Clayton School of Information Technology, Monash University, Clayton, VIC, Australia.
authors.forename: Trevor I
authors.initials: TI
authors.lastname: Dix
authors.affiliation: Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD, Australia m.cao1@uq.edu.au m.boden@uq.edu.au.
authors.forename: Mikael
authors.initials: M
authors.lastname: Bodén
mesh_terms: D000465:Algorithms; D001482:Base Composition; D005075:Biological Evolution; D003198:Computer Simulation; D019143:Evolution, Molecular; D014644:Genetic Variation; D007257:Information Theory; D008957:Models, Genetic; D010802:Phylogeny; D017421:Sequence Analysis / Q000379:methods*
keywords: genetic distance.; information theory