Applied Computational Genomics
The Applied Computational Genomics group focuses on theoretical and computational aspects of modelling the process of genome evolution and adaptive change. With growing size and complexity of molecular data, we strive to keep pace providing accurate, scalable and practical computational solutions that enable a wide range of scientists to analyse patterns of evolution and natural selection in large genomic and omics data.
Our goal is to bring new bioinformatics methods to real applications ranging from biotechnology to biomedical research, ecology and agriculture.
Evolutionary analyses of selective pressures in genomic data have high potential for applications, since natural selection is a leading force in function conservation, in adaptation to emerging pathogens, new environments, and plays key role in immune and resistance systems.
We develop phylogenetic methods for protein-coding sequences that enable to evaluate selective pressure and detect adaptive instances based on genomic signatures. Our computational methods serve to generate new biological hypotheses and predictions for further experimental validation, with the ultimate goal to develop practical applications.
We embrace the interdisciplinary approach by integrating different data sources and combining methods from biological, mathematical, and computer science disciplines.
- Fast alignment and phylogeny in frequentist framework
Evolutionary thinking helps to disentangle underlying biological mechanisms shaping molecular data. Genomic sequences of common origin are routinely used to infer phylogenies, which provide test-base for biological hypotheses or support downstream analyses. Based on fast approximation algorithms, we aim to include the alignment uncertainty during phylogeny estimation in the frequentist setting. This will allow for more accurate phylogenetic inferences from vast high-throughput data.
- Biosoda - Data Integration in BioSoda, NRP75 Bigdata
This project aims at enabling sophisticated semantic queries across large, decentralized and heterogeneous databases via an intuitive interface. The system will enable scientists, without prior training, to perform powerful joint queries across resources in ways that cannot be anticipated and therefore goes far and above the query functionality of specialized knowledge bases. The project represents an interdisciplinary collaboration between information systems and bioinformatics.
- Evolution and function of genomic tandem repeats
We develop statistical phylogenetic methods for analysing tandem repeats in genomic sequences. For example, leucine rich repeats (LRRs) in plant resistance genes provide a source for adaptation to emerging pathogens, so detecting selection on LRRs can bring ideas how to improve crop resistance (Shaper and Anisimova 2014, New Phytol).
- Stochastic models for protein-coding genes
We develop methods to study effects of selection on amino acid and codon mutation patterns. These methods can help to identify drug targets and study somatic processes. Our recent antibody model captures the sui generis mechanism specific to somatic hypermutation in maturating antibodies (Mirsky et al 2015, Mol Biol Evol). This provides basis for new bioinformatics methods for antibody analysis necessary for antibody selection and synthesis in the commercial context.
- Lighthouse project (Singeria SnSF) - Trans-omic approach to colorectal cancer: an integrative computational and clinical perspective
Kosiol, Carolin; Anisimova, Maria,
Anisimova, Maria, ed.,
Evolutionary genomics : statistical and computational methods.
Methods in Molecular Biology.
Available from: https://doi.org/10.1007/978-1-4939-9074-0_12
Frontiers in Artificial Intelligence.
Available from: https://doi.org/10.3389/frai.2019.00020
Hollenstein, Lukas; Lötscher, Adrian; Luccarini, Fabian,
Simulation Notes Europe.
29(3), pp. 127-132.
Available from: https://doi.org/10.11128/sne.29.tn.10483
6th International Conference on Computational and Mathematical Biomedical Engineering (CMBE19), Sendai City, Japan, 10-12 June 2019.
Zeta Computational Resources.
Braschler, Martin; Stadelmann, Thilo; Stockinger, Kurt, eds.,
Applied data science : lessons learned for the data-driven business.
Available from: https://doi.org/10.1007/978-3-030-11821-1_17
Positive selection detection of genome from Ralstonia bacterium
Positive selection detection of genome from Ralstonia bacterium with the use of HPC cluster. Ralstonia solanacearum is an aerobic non-spore-forming, Gram-negative, plant pathogenic bacterium. R. solanacearum is soil-borne and motile with a polar flagellar tuft. It colonises the xylem, causing bacterial wilt in a ...
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 ...