Research Centre for Bioinformatics
About us
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.
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.
Group leader: Prof. Dr. Maria Anisimova
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.
Group leader: Dr. Manuel Gil | Learn more about the research group Biomedical String Analysis
Applied Mathematical Biology
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
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.
Projects
As part of the reorganization of the research database, the previous lists of research projects are no longer available. Die Zukunft geht in Richtung Volltextsuche und Filterung, um bestmögliche Suchergebnisse für unsere Besucher:innen zur Verfügung zu stellen.
In the meantime, you can easily find the projects via text search using the following link: «To the new search in the project database»
Publications
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Silov, Serhii; Zaburannyi, Nestor; Anisimova, Maria; Ostash, Bohdan,
2020.
The use of the rare TTA codon in Streptomyces genes : significance of the codon context?.
Indian Journal of Microbiology.
61(1), pp. 24-30.
Available from: https://doi.org/10.1007/s12088-020-00902-6
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Ostash, Bohdan; Anisimova, Maria,
2020.
Visualizing codon usage within and across genomes : concepts and tools
.
In:
Srinivasa, K. G.; Siddesh, G. M.; Manisekhar, S. R., eds.,
Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications.
Singapore:
Springer.
pp. 213-288.
Algorithms for Intelligent Systems.
Available from: https://doi.org/10.1007/978-981-15-2445-5_13
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Tørresen, Ole K; Star, Bastiaan; Mier, Pablo; Andrade-Navarro, Miguel A; Bateman, Alex; Jarnot, Patryk; Gruca, Aleksandra; Grynberg, Marcin; Kajava, Andrey V; Promponas, Vasilis J; Anisimova, Maria; Jakobsen, Kjetill S; Linke, Dirk,
2019.
Nucleic Acids Research.
47(21), pp. 10994-11006.
Available from: https://doi.org/10.1093/nar/gkz841
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Anisimova, Maria, ed.,
2019.
Evolutionary genomics : statistical and computational methods.
Second Edition.
New York:
Humana.
Methods in Molecular Biology.
ISBN 978-1-4939-9073-3.
Available from: https://doi.org/10.1007/978-1-4939-9074-0
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Kosiol, Carolin; Anisimova, Maria,
2019.
.
In:
Anisimova, Maria, ed.,
Evolutionary genomics : statistical and computational methods.
Humana.
pp. 373-397.
Methods in Molecular Biology.
Available from: https://doi.org/10.1007/978-1-4939-9074-0_12