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
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AI for colorectal cancer: towards the improved CMS classification and interpretability
Access to large complex biomedical data today allows scientists to take full advantage of AI-driven approaches in a variety of applications with high societal impact. One such application is precision medicine, which is gradually becoming reality for some cancers. Unfortunately, for colorectal…
expired, 11/2022 - 11/2023
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Digital Tools for Codon Optimization
Exploring biotech's potential for negative emissions technologies
expired, 01/2021 - 12/2022
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Trans-omic approach to colorectal cancer: an integrative computational and clinical perspective
Colorectal Cancer (CRC) is an important cause of cancer-related mortality world-wide. The Consensus Molecular Subtypes represent the first comprehensive molecular classification with clinical implications, but many aspects are still missing. We use a transomic approach to improve the stratification,…
expired, 10/2020 - 10/2025
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Dynamik Knowledge Platform
expired, 10/2020 - 12/2021
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Data mining in neurological medicine
Pattern recognition algorithms
expired, 09/2019 - 04/2020
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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…
expired, 08/2019 - 07/2021
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Repeat protein Function, Refinement, Annotation and Classification of Topologies (REFRACT)
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…
expired, 04/2019 - 04/2025
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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…
expired, 03/2019 - 03/2023
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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…
expired, 05/2018 - 01/2023
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Positive selection detection of genome from Ralstonia bacterium
expired, 09/2017 - 04/2018
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Bio-SODA – Enabling Complex, Semantic Queries to Bioinformatics Databases through Intuitive Searching over Data (SNSF)
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…
expired, 04/2017 - 03/2021
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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…
expired, 01/2017 - 12/2018
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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…
expired, 10/2016 - 02/2020
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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…
expired, 02/2015 - 09/2018
Publications
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Anisimova, Maria,
2015.
Disentangling tandem repeats, their evolution and roles in genomic sequences.
In:
EES Seminar Series LMU, München, Deutschland, 2015.
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Zoller, Stefan; Boskova, Veronika; Anisimova, Maria,
2015.
Maximum-likelihood tree estimation using codon substitution models with multiple partitions.
Molecular Biology and Evolution.
32(8), pp. 2208-2216.
Available from: https://doi.org/10.21256/zhaw-3882
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Gil, Manuel; Anisimova, Maria,
2015.
Methodologies for phylogenetic inference.
In:
Encyclopedia of life sciences.
Hoboken:
Wiley.
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Anisimova, Maria,
2015.
Next generation comparative phylogenomics.
In:
StarOmics Symposium, Geneva, 2015.
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Anisimova, Maria; Pečerska, Jūlija; Schaper, Elke,
2015.
Statistical approaches to detecting and analyzing tandem repeats in genomic sequences.
Frontiers in Bioengineering and Biotechnology.
3(31).
Available from: https://doi.org/10.21256/zhaw-3883
