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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Director of Department, Bioinformatics, ...
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Programme Director, MSc specialisation in Applied ...
Projects
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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 ...
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Exploring the silent fitness landscape
Since Darwin, natural selection has been recognized as one of major biological forces shaping genetic patterns in molecular data. Detecting selection on proteins has become an indispensible part of genome studies. Remarkably selection can act not only on proteins, but also on synonymous codons translating into the ...
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Discovering evolutionary innovations by assessing variation and natural selection in protein tandem repeats
Tandem repeats (TRs) are abundant in proteomes across all kingdoms of life. Having an impressive variety of sizes, structures and functions, TRs often offer enhanced binding properties and are associated with disease and immunity related functions. While mechanisms generating protein TRs are poorly understood, ...
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Fast joint estimation of alignment and phylogeny from genomics sequences in a frequentist framework
The availability of large molecular data demands accurate and fast bioinformatics methods to analyze these data. Molecular sequences of common origin are used to infer phylogenetic trees, which help to test various biological hypotheses or to support subsequent analyses. Phylogeny inference relies on sequence ...
Publications
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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.,
2023.
A novel hypervariable variable number tandem repeat in the dopamine transporter gene (SLC6A3).
Life Science Alliance.
6(4), pp. e202201677.
Available from: https://doi.org/10.26508/lsa.202201677
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Verbiest, Max; Maksimov, Mikhail; Jin, Ye; Anisimova, Maria; Gymrek, Melissa; Bilgin Sonay, Tugce,
2022.
Journal of Evolutionary Biology.
36(2), pp. 321-336.
Available from: https://doi.org/10.1111/jeb.14106
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Jowkar, Gholamhossein; Pecerska, Julija; Maiolo, Massimo; Gil, Manuel; Anisimova, Maria,
2022.
Systematic Biology.
72(2), pp. 307-318.
Available from: https://doi.org/10.1093/sysbio/syac050
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Sima, Ana Claudia; Mendes de Farias, Tarcisio; Anisimova, Maria; Dessimoz, Christophe; Robinson-Rechavi, Marc; Zbinden, Erich; Stockinger, Kurt,
2022.
Distributed and Parallel Databases.
40(2), pp. 409-440.
Available from: https://doi.org/10.1007/s10619-022-07414-w
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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