Research Centre for Bioinformatics
The Research Centre for Bioinformatics focuses on the theoretical and computational aspects of modelling the molecular biology processes, genome evolution and adaptive change, as well as biomedical data representation and integration. The goal is to bring basic research and new bioinformatics methods to real-world applications, ranging, for example, from biotechnology and forensics to biomedical research and environmental applications. The research area is represented by the several research groups, each focusing on certain methods or application domains.
The research group develops computational methods for comparative and evolutionary genomics, including modelling of stochastic processes in molecular evolution. Many research projects focus on the analysis of protein-coding genes and gene families, selection, adaptation, phylodynamics and evolution, including host-pathogen interactions; applications in medical genomics, epidemiology, metagenomics and forensics. Our research includes studies of genomic repeat sequences and indel evolution with applications in cancer genomics and biotechnology, as well as studies of dynamics and evolution of viruses and other pathogens.
Group leader: Prof. Dr. Maria Anisimova
The research group is specialized in the analysis of strings (i.e. finite sequences of symbols). The research projects and applications focus on genomic data and biomedical natural language. The group develops new computational science methods and applies existing methods. This includes: mathematical modeling, computational statistics, algorithm design, discrete mathematics, machine and deep learning, natural language processing, semantic web technologies.
The research group develops and applies mathematical models and methods to address open research questions in biology. Many methods use standard calculus, differential equations, machine learning and dynamical systems theory to describe and predict biological phenomena, such as for example, the relationship between codon bias and gene expression via the concept of translational efficiency, applied to codon optimization problems. Further interests lie in the exploration of cancer-immune system interactions and their predictive power for cancer immunotherapies as well as the population genetics of the early infection-phase of partially-recombining viruses.
Group leader: Dr. Victor Garcia
Computational literature-based natural product drug discovery
Natural products such as medicinal plants and extract mixtures have successfully supported the discovery of pharmaceuticals. Medically relevant products and their properties are often found through systematic analysis of the literature. In 1980s Swanson found hidden links between pieces of knowledge in the ...
REFRACT – Repeat protein Function, Refinement, Annotation and Classification of Topologies
REFRACT is an international consortium aiming to extend our knowledge on the mechanism of tandem repeat protein (TRP) function and evolution, establishing a common classification and best practices. Starting from available state of the art computational tools and databases, it aims to drive a new level of TRP ...
The effect of programmed ribosomal frameshifting on codon usage bias
The discovery of synonymous codon usage bias (CUB) –the unequal use of codons that code for the same amino-acid– has strengthened the notion that synonymous mutations can alter the fitness of organisms. Synonymous or silent mutations are mutations in DNA that do not alter the encoded amino acid sequence. Since CUB ...
Frequentist estimation of the evolutionary history of sequences with substitutions and indels
High throughput sequencing technologies have permitted a wide range of scientists to observe an astonishing molecular diversity across all domains of life. Since all observed molecular sequences area result of a long evolutionary history, most informative inferences can be made only when analysing genomic sequences ...
Positive selection detection of genome from Ralstonia bacterium
Apsley, Abner T.; Domico, Emma R.; Verbiest, Max A.; Brogan, Carly A.; Buck, Evan R.; Burich, Andrew J.; Cardone, Kathleen M.; Stone, Wesley J.; Anisimova, Maria; Vandenbergh, David J.,
Life Science Alliance.
6(4), pp. e202201677.
Available from: https://doi.org/10.26508/lsa.202201677
Verbiest, Max; Maksimov, Mikhail; Jin, Ye; Anisimova, Maria; Gymrek, Melissa; Bilgin Sonay, Tugce,
Journal of Evolutionary Biology.
36(2), pp. 321-336.
Available from: https://doi.org/10.1111/jeb.14106
Jowkar, Gholamhossein; Pecerska, Julija; Maiolo, Massimo; Gil, Manuel; Anisimova, Maria,
72(2), pp. 307-318.
Available from: https://doi.org/10.1093/sysbio/syac050
Sima, Ana Claudia; Mendes de Farias, Tarcisio; Anisimova, Maria; Dessimoz, Christophe; Robinson-Rechavi, Marc; Zbinden, Erich; Stockinger, Kurt,
Distributed and Parallel Databases.
40(2), pp. 409-440.
Available from: https://doi.org/10.1007/s10619-022-07414-w
Lardos, Andreas; Aghaebrahimian, Ahmad; Koroleva, Anna; Sidorova, Julia; Wolfram, Evelyn; Anisimova, Maria; Gil, Manuel,
Frontiers in Bioinformatics.
Available from: https://doi.org/10.3389/fbinf.2022.827207