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

  • 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.

    Guy Baele

    Guy Baele obtained his master’s degree in computer science in 2003 and his PhD degree in bioinformatics from the University of Ghent, Belgium, in 2008. Having worked in the field of evolutionary robotics at the Flemish Institute for Biotechnology for 2 years after obtaining his PhD, he is now working in infectious disease research in the Evolutionary and Computational Virology group within the Rega Institute / KU Leuven, focusing on developing statistical and computational approaches to perform accurate analyses of virus data . A large part of his work in recent years has focused on the development of model selection approaches and of time-heterogeneous codon models. These approaches are also widely applicable outside of the field of infectious diseases, for example in plant and yeast research. He is currently working towards accelerating analyses that contain a large amount of partitions, by exploiting the inherent parallelism in modern-day hardware.

    Maria Anisimova

    Since 2014 Maria 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.

    Veronika Boskova
    Veronika Boskova is a PhD student in the Computational Evolution group at ETH Zurich since October 2013. She obtained an MSc degree in Computational Biology and Bioinformatics from ETH Zurich and University of Zurich in 2013 and an MSc degree in Cancer Genomics and Developmental Biology from Utrecht University in 2011. Her current research focuses on developing algorithms and models for efficient analysis of the datasets with duplicate sequences and implementation of these models within BEAST2 software. She is generally interested in the phylogenetics and the phylodynamics, the spread and causes of epidemics/diseases and the copy number variation.