Molecular Phylogeny

Molecular phylogenetics aims at reconstructing the evolutionary history of organisms from present (or recent) molecular data (mostly DNA and RNA sequences). Combined with other data such as spatial, temporal, phenotypic, etc…, these methods allow to infer about the biological processes that occurs in the past such as population dynamics, movements, etc… Applied to sequence of infectious diseases agents, this allows better understanding of the origin, the transmission intensity and the spread of infectious diseases in space and time. Combined with clinical data, phylogenies can also help understanding the determinant (pathogen vs host) of disease traits such as severity.

    • Definition and properties of phylogenetic trees
    • Sequences alignment and cleaning
    • Models of molecular evolution
    • Phylogenetic reconstruction with distance and parsimony methods
    • Phylogenetic reconstruction with maximum likelihood methods
    • Phylogenetic reconstruction with Bayesian methods
    • Phylogenetic correlation, phylodynamics and phylogeography
    • Positive, negative and neutral molecular evolution


  • Maria Anisimova
    Maria Anisimova is lecturer at the Zurich University of Applied Sciences (ZHAW). Since 2014 she leads the Applied Computational Genomics Team (ACGT) and is the group leader at the Swiss Institute of Bioinformatics. She worked as senior researcher at the ETH Zurich (2007-2014) and as postdoc with Ziheng Yang (2005-2007) and Olivier Gascuel (2003-2005). Her PhD focused on methods for detecting positive selection and adaptive evolution in protein-coding genes (2000-2003, with Ziheng Yang, UCL, UK). Maria edited the book “Evolutionary Genomics: Statistical and computational methods” in 2 volumes (published in 2012 by Springer). Her group developed the CodonPhyML package for the maximum likelihood phylogeny inference with codon models; the Python library TRAL for detecting and analysing tandem repeats in genomic sequences ; and the PrographMSA package for fast phylogeny-aware graph-based alignment for difficult genomics sequences.
    Joëlle Barido-Sottani
    Joëlle Barido-Sottani has been a PhD student in the Computational Evolution group at ETH Zurich since October 2014. She obtained an MSc degree in Computational Biology and Bioinformatics from ETH Zurich and University of Zurich in 2014. Her current research focuses on developing a multi-states birth-death model with an unknown number of states, and implementing it in BEAST2. She is generally interested in the mathematical and computational modelling of complex evolutionary processes, particularly when different populations with different evolutionary characteristics are involved.
    Olivier Gascuel
    Olivier Gascuel studied mathematics and completed a PhD in computer science. He started working on bioinformatics by the end of the 1980’s, at the very beginning of the genomic era and of the rapid development of interactions between mathematicians, computer scientists and molecular biologists. His early interests were in sequence analysis and protein structure prediction, using machine learning approaches. Since the mid-1990’s, Olivier Gascuel has concentrated on evolution and phylogenetics, with particular focus on the mathematical and computational tools and concepts. He recently became the head of the new Center for Bioinformatics, Biostatistics and Integrative Biology (C3BI) of the Pasteur Institute at Paris, and turned part of his activities toward pathogens and epidemiology. He is an associate editor of Systematic Biology and belongs to the editorial board of several bioinformatics journals. He has published more than 150 papers and book chapters, and authored several widely used computer programs in phylogenetics and bioinformatics such as BioNJ and PhyML.
    David Rasmussen
    David Rasmussen is currently a postdoctoral fellow at ETH Zurich working with Dr. Tanja Stadler on developing new phylogenetic methods for infectious disease epidemiology. He received his PhD in 2014 from Duke University, where he became interested in combing epidemiological modeling with phylogenetic methods, a field that has become known as phylodynamics. As a postdoc, he has continued to work on developing and applying phylodynamic approaches to human pathogens such as dengue, Ebola and influenza. He is currently most interested in using phylogenetics to study the epidemic dynamics of HIV and the transmission networks through which the virus spreads.