. Project title: Bayesian methods and graphical approaches to improve our understanding of molecular evolution using mutation maps. . Principal investigator: Stephane Guindon (CNRS) . Associate investigators: Nicolas Galtier (CNRS), Fabio Pardi (CNRS), Anne-Muriel Chifolleau (UM), Francois Chevenet (IRD) & Anne-Laure Banuls (IRD). . Host institution: CNRS-University of Montpellier, France. . Funding: NUMEV (Digital and Hardware Solutions, Environmental and Organic Life Modeling). . Net salary: ~2100 euros/month. . Starting date: no later than April 2017.
A 18 month postdoc position in statistical phylogenetics is available at the University of Montpellier (UM), France. The successful candidate will join the “Methods and Algorithms in Bioinformatics” group and work in close collaboration with other Montpellier-based research teams (see above for more information about the personnel involved).
The project relies on Bayesian sampling techniques that map mutations on phylogenies [1]. These approaches are very relevant from a biological [2,3] and a computational [4] perspective. However, we believe that their full potential has not been reached yet. In particular, applying novel data exploration techniques to visualize mutation maps should help us improve our understanding of the fine scale mechanisms governing molecular evolution.
The postdoc candidate will thus develop original information visualization methods that will provide relevant summaries of the mutational process. The proposed techniques will also detect episodes of evolution that are not well predicted by our Markov models of evolution. Applications of these new visualization techniques are manifold. Adaptive evolution and biased gene conversion are associated with peculiar substitution patterns that mutation maps could potentially help identify. Similarly, errors in sequence alignment and/or improper orthology relationships are expected to generate atypical maps. Lastly, preliminary analyses of Mycobacterium tuberculosis genomes demonstrated how mutation maps can help recover the series of mutations leading to antibiotic resistance. The successful candidate will further develop one or several of these research leads.
The ideal candidate for the proposed project will have a PhD in statistical phylogenetics or population genetics, although pure statisticians and physicists with strong interest in molecular evolution should also apply. Good skills in data analysis with modern tools and programming languages (R and/or Python and/or Java or C/C++) are essential.
Please send CVs and inquiries to Stephane Guindon (guindon@lirmm.fr).
Bibliography:
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[1] Nielsen, R. Mapping mutations on phylogenies. Systematic biology. 51. 729-739. 2002.
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[2] Dutheil J, Pupko T, Jean-Marie A, Galtier N. A model-based approach for detecting co-evolving positions in a molecule. Molecular biology and evolution. 22. 1919-1928. 2005.
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[3] Dutheil J, Galtier N. Detecting groups of co-evolving positions in a molecule: a clustering approach. BMC evolutionary biology. 7. 2007.
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[4] Rodrigue N., Philippe H., Lartillot N. Uniformization for sampling realizations of Markov processes: applications to Bayesian implementations of codon substitution models. Bioinformatics. 24. 2007.