Fast Dating using Least Squares

Olivier Gascuel 1, 2
1 MAB - Méthodes et Algorithmes pour la Bioinformatique
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Phylogenies provide a useful way to understand the evolutionary history of genetic samples, and data sets with more than a thousand taxa are becoming increasingly common, notably with viruses (e.g., human immunodefi- ciency virus (HIV)). Dating ancestral events is one of the first, essential goals with such data. However, current sophisticated probabilistic approaches struggle to handle data sets of this size. Here,we present very fast dating algorithms, based on a Gaussian model closely related to the Langley-Fitch molecular-clock model. We show that this model is robust to uncorrelated violations of the molecular clock. Our algorithms apply to serial data, where the tips of the tree have been sampled through times. They estimate the substitution rate and the dates of all ancestral nodes. When the input tree is unrooted, they can provide an estimate for the root position, thus representing a new, practical alternative to the standard rooting methods (e.g., midpoint). Our algorithms exploit the tree (recursive) structure of the problem at hand, and the close relationships between least squares and linear algebra. We distinguish between an unconstrained setting and the case where the temporal prece- dence constraint (i.e., an ancestral node must be older that its daughter nodes) is accounted for. With rooted trees, the former is solved using linear algebra in linear computing time (i.e., proportional to the number of taxa), while the resolution of the latter, constrained setting, is based on an active-set method that runs in nearly linear time. With unrooted trees the computing time becomes (nearly) quadratic (i.e., proportional to the square of the number of taxa). In all cases, very large input trees (¿10,000 taxa) can easily be processed and transformed into time-scaled trees. We compare these algorithms to standard methods (root-to-tip, r8s version of Langley-Fitchmethod, and BEAST). Using simulated data, we show that their estimation accuracy is similar to that of the most sophisticated methods, while their computing time is much faster. We apply these algorithms on a large data set comprising 1194 strains of Influenza virus from the pdm09 H1N1 Human pandemic. Again the results show that these algorithms provide a very fast alternative with results similar to those of other computer programs. These algorithms are implemented in the LSD software (least-squares dating), which can be downloaded from http://www.atgc-montpellier.fr/LSD/, along with all our data sets and detailed results.
Type de document :
Communication dans un congrès
Mathematical Models for Epidemiology and Phylogenetics, May 2016, Lille, France. 2016, 〈http://math.univ-lille1.fr/~tran/journees_epidemio2016.html〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01397427
Contributeur : Isabelle Gouat <>
Soumis le : mardi 15 novembre 2016 - 18:48:16
Dernière modification le : jeudi 11 janvier 2018 - 06:26:13

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Olivier Gascuel. Fast Dating using Least Squares. Mathematical Models for Epidemiology and Phylogenetics, May 2016, Lille, France. 2016, 〈http://math.univ-lille1.fr/~tran/journees_epidemio2016.html〉. 〈lirmm-01397427〉

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