Research Centre of Bioinformatics
About us
The 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.
Computational Genomics
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.
Biomedical String Analysis
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.
Applied Mathematical Biology
The 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.
Teaching Activities
The focus includes teaching at BSc, MSc and PhD level in computational sciences with a focus on computational genomics, bioinformatics, mathematical modelling, biostatistics, programming and algorithms for molecular biology.
Team Bioinformatics
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Head, Fachstellenleiterin Applied Computational ...
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Programme Director, MSc specialisation in Applied ...
Projects
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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 ...
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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 ...
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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 ...
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Positive selection detection of genome from Ralstonia bacterium
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Bio-SODA – Enabling Complex, Semantic Queries to Bioinformatics Databases through Intuitive Searching over Data (SNSF NRP 75 "Big Data")
One of the major promises of Big Data lies in the simultaneous mining of multiple sources of data. This is particularly important in life sciences, where different and complementary data are scattered across multiple resources. To overcome this issue, the use of RDF/semantic web technology is emerging, but querying ...
Publications
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Lardos, Andreas; Aghaebrahimian, Ahmad; Koroleva, Anna; Sidorova, Julia; Wolfram, Evelyn; Anisimova, Maria; Gil, Manuel,
2022.
Frontiers in Bioinformatics.
2(827207).
Available from: https://doi.org/10.3389/fbinf.2022.827207
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Nguyen, Huu-Giao; Lundström, Oxana; Blank, Annika; Dawson, Heather; Lugli, Alessandro; Anisimova, Maria; Zlobec, Inti,
2021.
Modern Pathology.
35, pp. 240-248.
Available from: https://doi.org/10.1038/s41379-021-00894-8
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Delucchi, Matteo; Näf, Paulina; Bliven, Spencer; Anisimova, Maria,
2021.
Frontiers in Bioinformatics.
1(691865).
Available from: https://doi.org/10.3389/fbinf.2021.691865
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Verbiest, Max; Delucchi, Matteo; Bilgin Sonay, Tugce; Anisimova, Maria,
2021.
Frontiers in Bioinformatics.
1(685844).
Available from: https://doi.org/10.3389/fbinf.2021.685844
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Pecerska, Julija; Kühnert, Denise; Meehan, Conor J.; Coscollá, Mireia; de Jong, Bouke C.; Gagneux, Sebastien; Stadler, Tanja,
2021.
Quantifying transmission fitness costs of multi-drug resistant tuberculosis.
Epidemics.
36(100471).
Available from: https://doi.org/10.1016/j.epidem.2021.100471