Format

Send to

Choose Destination
Syst Biol. 2016 Jan;65(1):82-97. doi: 10.1093/sysbio/syv068. Epub 2015 Sep 30.

Fast Dating Using Least-Squares Criteria and Algorithms.

Author information

1
Institut de Biologie Computationnelle, LIRMM, UMR 5506 CNRS - Université de Montpellier, France;
2
Institut de Biologie Computationnelle, LIRMM, UMR 5506 CNRS - Université de Montpellier, France; IGBMC (Institut de Génétique et de Biologie Moléculaire et Cellulaire), INSERM, U596, CNRS, UMR7104, Université de Strasbourg, Illkirch, France;
3
Institute of Evolutionary Biology, University of Edinburgh, Ashworth Laboratories, Edinburgh, UK.
4
Institut de Biologie Computationnelle, LIRMM, UMR 5506 CNRS - Université de Montpellier, France; gascuel@lirmm.fr.

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 immunodeficiency 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 precedence 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-Fitch method, 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. An Online Appendix, providing additional algorithm descriptions, tables, and figures can be found in the Supplementary Material available on Dryad at http://dx.doi.org/10.5061/dryad.968t3.

KEYWORDS:

Active-set method; algorithms; computer simulations; dating; influenza (H1N1); least-squares; linear algebra; molecular clock; serial data; substitution rate estimation; temporal precedence constraints; viruses

PMID:
26424727
PMCID:
PMC4678253
DOI:
10.1093/sysbio/syv068
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Silverchair Information Systems Icon for PubMed Central
Loading ...
Support Center