Job tasks: The evolutionary history of molecules is described by a tree structure called phylogeny, which is inferred from genomic sequences. Phylogenies are used for testing biological hypotheses with applications ranging from medicine to ecology. Phylogeny inference usually relies on an inferred alignment of homologous sequences, which, in turn, relies on a guide-tree reflecting their ancestral relationships. To improve the reliability of phylogenetic analyses, we develop methods to address this apparent circularity. A recent breakthrough in our team enables such joint inferences using a likelihood-based method based on a single residue insertion-deletion process. Building upon this work, we aim to develop a new maximum likelihood method for scalable joint inference of the complete evolutionary history (alignment, tree, ancestral sequences) under more realistic models that allow multiple residue insertions and deletions. An integral part of the research is to apply the methods to real data, notably, Swiss HIV Cohort. The project is funded by the Swiss National Science Foundation.
The positions are based at the Institute of Computational Life Sciences, Zurich University of Applied Sciences (ZHAW Wädenswil). The PhD student will also be enrolled at the University of Zurich. The research group is also part of Swiss Institute of Bioinformatics, which provides additional training and networking opportunities.
A selection of relevant articles:
1. Maiolo M, Zhang X, Gil M, Anisimova M. "Progressive multiple sequence alignment with indel evolution" BMC Bioinformatics. 2018. 19(1) : 331. doi : 10.1186/s12859-018-2357-1.
2. Pečerska, J., Gil, M. and Anisimova, M. “Joint alignment and tree inference” bioRxiv, 2021. pp.2021-09. doi : 10.1101/2021.09.28.462230.
3. Jowkar, G., Pečerska, J., Maiolo, M., Gil, M., & Anisimova, M. “ARPIP : Ancestral sequence Reconstruction with insertions and deletions under the Poisson Indel Process” Systematic biology. 2022. syac050-syac050. doi : 10.1093/sysbio/syac050
Profile requirements:
* Strong background in computational science, algorithms, programming, statistics, stochastic modeling or similar;
* Working knowledge of C++ and/or Rust;
* Some knowledge of phylogenetics and molecular evolution is an advantage.
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