Applied Computational Genomics

v.L.n.R: Maria Anisimova, Lorenzo Gatti, Victor Garcia, Manuel Gil, Spencer Bliven, Massimo Maiolo

Summary & introduction

The Applied Computational Genomics group focuses on theoretical and computational aspects of modelling the process of genome evolution and adaptive change. With growing size and complexity of molecular data, we strive to keep pace providing accurate, scalable and practical computational solutions that enable a wide range of scientists to analyse patterns of evolution and natural selection in large genomic and omics data.

Our goal is to bring new bioinformatics methods to real applications ranging from biotechnology to biomedical research, ecology and agriculture.

Evolutionary analyses of selective pressures in genomic data have high potential for applications, since natural selection is a leading force in function conservation, in adaptation to emerging pathogens, new environments, and plays key role in immune and resistance systems.

We develop phylogenetic methods for protein-coding sequences that enable to evaluate selective pressure and detect adaptive instances based on genomic signatures. Our computational methods serve to generate new biological hypotheses and predictions for further experimental validation, with the ultimate goal to develop practical applications.

We embrace the interdisciplinary approach by integrating different data sources and combining methods from biological, mathematical, and computer science disciplines.

Our team

Projects & services

  1. Evolution and function of genomic tandem repeats
    We develop statistical phylogenetic methods for analysing tandem repeats in genomic sequences. For example, leucine rich repeats (LRRs) in plant resistance genes provide a source for adaptation to emerging pathogens, so detecting selection on LRRs can bring ideas how to improve crop resistance (Shaper and Anisimova 2014, New Phytol).
  2. Stochastic models for protein-coding genes
    We develop methods to study effects of selection on amino acid and codon mutation patterns. These methods can help to identify drug targets and study somatic processes. Our recent antibody model captures the sui generis mechanism specific to somatic hypermutation in maturating antibodies (Mirsky et al 2015, Mol Biol Evol). This provides basis for new bioinformatics methods for antibody analysis necessary for antibody selection and synthesis in the commercial context.
  3. Fast alignment and phylogeny in frequentist framework
    Evolutionary thinking helps to disentangle underlying biological mechanisms shaping molecular data. Genomic sequences of common origin are routinely used to infer phylogenies, which provide test-base for biological hypotheses or support downstream analyses. Based on fast approximation algorithms, we aim to include the alignment uncertainty during phylogeny estimation in the frequentist setting. This will allow for more accurate phylogenetic inferences from vast high-throughput data.

    ⇒ Get more information about our current project supported by the SNF

Software Tools

Links / Partners