Address: LIRMM CNRS UMR 5506, 860 rue de St Priest 34095 Montpellier cedex 5

I design algorithms and probabilistic models of molecular evolution. Most notably, I am developing the software package PhyML (for Phylogenetics through Maximum Likelihood) which serves as a basis to implement my research outputs. I was trained as a biologist/statistician but I am now working in the computer science department of the LIRMM in Montpellier, France. I also worked for the Department of Statistics at the University of Auckland. I am currently Associate Editor for BMC Evolutionary Biology and an elected Council member of the Society of Systematic Biologists (2013-2016).


Please contact me by email for information about current postdoc opportunities (two open positions as of Oct 2016).


Recent publications (full list on Google Scholar)

Demographic inference under the coalescent in a spatial continuum. S. Guindon, H. Guo, D. Welch. Journal of Theoretical Population Biology. 111: 43–50. 2016. We describe a method that fits a structured coalescent model assuming that individuals are scattered on a continuum rather that distributed in discrete demes. We show that the density of the population and the rate of dispersal of individuals can be inferred simultaneousy from the analysis of geo-referenced genetic sequence using this technique.

Modeling competition and dispersal in a statistical phylogeographic framework. L. Ranjard, D. Welch, M. Paturel, S. Guindon. Systematic Biology. 63:743-752. 2014. We describe a model where the probability for a species to colonize an empty island at some point during the course of evolution is the same at that of an occupied one only if species do not compete with each other. Using simulations, we show that these probabilities can indeed be estimated from geo-referenced genetic sequences.

Performance of standard and stochastic branch-site models for detecting positive selection amongst coding sequences A. Lu, S. Guindon. Molecular Biology and Evolution. 31: 484-495. 2014. We describe the performance of the stochastic branch-site model (see S. Guindon et al. PNAS. 101:12957-12962. 2004.) in terms of type-I and power for detecting positive selection in coding sequences. Results indicate that this approach is more suited than the standard branch-site model (sensu codeml) in cases where it is not known a priori which lineages may have evolved under positive selection.

From trajectories to averages: an improved description of the heterogeneity of substitution rates along lineages. S. Guindon. Systematic Biology. 62:22-34. 2013. Assuming that the evolution of the substitution rate at each position along a sequence is a realization of a (geometric) Brownian process, the rate averaged over a given time interval is approximately gamma distributed. This study shows that ignoring the stochasticity of average substitution rates leads to poor estimates of important evolutionary parameters. The proposed approach also provides an efficient implementation of the covarion model that does not require augmentation of the state space.


Academic record & appointments

2006-... CNRS researcher.
2007-2016 Lecturer in the Department of Statistics, The University of Auckland.
2003-2005 Postdoc, School of Biological Sciences, The University of Auckland.
Supervisor: Allen Rodrigo
Title: Molecular evolution of the HIV-1 genome
1999-2003 PhD student in Biology. LIRMM-CNRS, Montpellier.
Supervisor: Olivier Gascuel
Title: Approche statistique pour la reconstruction de phylogénies moléculaires (Statistical approach for building molecular phylogenies)
A copy of my thesis can be found here.
1998-1999 Master Thesis in Biology, Université Claude Bernard, Lyon.
Supervisors: Guy Perrière and Manolo Gouy
Title: Les transferts horizontaux de matériel génétique chez les procaryotes (Horizontal transfers between procaryote genomes


Former students

2008 Lin Ying Wee, PgDip, “Hepatitis B virus evolution: positive selection and substitution rates
2008 Sandunie Dineika Chandrananda, BSc Hons, “A Phylogenomic analysis of the Yeast genome
2009 Maha Ahmed Baker, 6 month internship, “Graph algorithms applied to phylogenetics
2010 Lin Ying Wee, Masters, “Detecting differences in the rates of evolution after gene duplication
2010 Naveen Joshi, 6 month internship, “Deciphering the patterns of variations of evolutionary rates along yeast genomes
2010 Samuel Pichot, 6 month internship, “Graph algorithms to untangle phylogenetic trees: application to phylogeography
2011 Eric Frichot, 6 month internship, “Stochastic models of the evolution of protein lengths
2011 Yi Lu, Masters, “Comparison of methods for detecting natural selection in coding sequences
2011 Louis Ranjard, postdoc, “Developing new methods in statistical phylogeography
2012 Marie Paturel, 6 month internship, “Testing new methods in statistical phylogeography
2013 Helen Shearman, PhD, “Statistical methods for measuring biodiversity
2013 Serg Krasnozhon, PhD, “Statistically-based graphical method for characterizing molecular evolutionary processes
2013 Spencer Enesa, Masters “Connections between the coalescent and birth-death sampling processes
2014 Kelly Lu, Masters “The coalescent with randomly fluctuating population size
2014 Hongbin Guo (BSC, Hons) “Extensions of Kingman’s coalescent