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

I design probabilistic models of evolution and algorithms to infer their parameters from the analysis of molecular, fossil and/or spatial data. I created and am still developping the software package PhyML (for Phylogenetics through Maximum Likelihood) which serves as a basis to implement my research outputs.

Trained as a biologist/statistician, I am working as a CNRS research scientist in the computer science department of the LIRMM in Montpellier, France. I was also lucky to work for the Department of Statistics at the University of Auckland between 2007 and 2015.


Recent publications

Accounting for spatial sampling patterns in Bayesian phylogeography S. Guindon, N. De Maio. Proceeding of the National Academy of Sciences, USA. 118(52), 2022. We propose a new statistical approach to accommodate for preferential sampling in phylogeography. This new technique distinguishes between the case where the density of samples in particular areas reflects the underlying population density and the case where it does not. Read this "research highlight" in Nature Computational Science.

Sampling bias and model choice in continuous phylogeography: getting lost on a random walk A. Kalkauskas, U. Perron, Y. Sun, N. Goldman, G. Baele, S. Guindon, N. De Maio. PLOS Computational Biology. 17 (1), e1008561. 2021. In this article, we study the impact that spatial sampling schemes have on the inference of model parameters under standard approaches in continuous phylogeography.

Rates and rocks: strengths and weaknesses of molecular dating methods. S. Guindon. Frontiers in Genetics. 11. 2020. I give here an overview of some of the technical aspects of molecular dating methods, including models of rate variation along lineages and the different ways to calibrate a dating analysis using fossil data.

Accounting for ambiguity in ancestral sequence reconstruction. A. Oliva, S. Pulicani, V. Lefort, L. Brehelin, O. Gascuel & S. Guindon. Bioinformatics. 35: 4290-4297. 2019. We propose a new criterion that better accomodates for ambiguity in ancestral (nucleotide or protein) sequence reconstruction. In particular, more than one nucleotide or amino-acid are inferred whenever multiple states have similar marginal posterior probabilities. The proposed approach is computationally efficient and does not rely on arbitrarily set tuning parameters.

Accounting for calibration uncertainty: Bayesian molecular dating as a ``doubly intractable'' problem. S. Guindon. Systematic Biology. 67: 651-661. 2018. I describe a technique for molecular dating that accomodates for uncertainty in the placement of calibration constraints as defined by an expert. The method relies on the so-called ``exchange algorithm'' for sampling from doubly intractable distributions Cover of July issue!

Older publications (click)

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.

Current students
2022 Johannes Wirtz, Walter Benjamin fellowship, “Inferring parameters of the spatial Λ-Fleming-Viot model applied to the analysis of SNP data
2022 Pauline Rocu, Masters project (6 months). Co-supervised with D. Fargette and Paul Bastide. “Testing and applying novel Bayesian approaches to the analysis of geo-referenced genetic data
2022 Brice Morhain, Masters project (4 months). Co-supervised with E. Douzery. “Detecting and analyzing ``blinking'' sites in mammalian genes.
2022 Kelian Perez, Masters project (5 months). Co-supervised with A. M. Arigon and D. Fargette. “Using Nextstrain to track the evolution of the Rice Yellow Mottle virus.
Past students (click)
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
2017 Charles Dutertre, Masters “Statistical expectations under the spatial Λ-Fleming-Viot model
2017 Adrien Oliva, research engineer project “Handling ambiguities in the reconstruction of ancestral sequences
2018 Marie Suez, postdoc project (funded by LabEX NUMEV) “Graphical displays of mutation maps
2018 Inderpreet Singh Chhabra, 2 month internship, “Using deep learning to estimate the parameters of the spatial Λ-Fleming-Viot model
2019 Manu Saraswat, 4 month internship, “Using neural networks and regression trees to estimate the parameters of the spatial Λ-Fleming-Viot model
2020 Come Morel, Masters project (6 months). Co-supervised with E. Douzery. “Comparison of models describing the variability of substitution rates during the course of evolution
2021 Marie Geoffray, Masters project (6 months). Co-supervised with D. Fargette. “Bayesian phylogeography of the RYMV virus
2021 Marine Vue, Masters project (6 months). Co-supervised with R Leblois. “Comparison of isolation-by-distance models to the ``Relaxed Random Walk'' approach on simulated data.

Academic record
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