Applied Computational Genomics
Summary & introduction
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.
Projects & services
- 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.
⇒ Get more information about our current project supported by the SNF
List of our publications
Computer Methods in Biomechanics and Biomedical Engineering : Imaging & Visualization.
Available from: https://doi.org/10.21256/zhaw-19849
Watanabe, Kazuhiro; Anzai, Hitomi; Juchler, Norman; Hirsch, Sven; Ohta, Makoto,
International Mechanical Engineering Congress and Exposition : Volume 3 - biomedical and biotechnology engineering.
2019 International Mechanical Engineering Congress and Exposition, IMECE2019, Salt Lake City, Utah, USA, 11-14 November 2019.
The American Society of Mechanical Engineers.
Available from: https://doi.org/10.1115/IMECE2019-11125
Detmer, Felicitas J.; Hadad, Sara; Chung, Bong Jae; Mut, Fernando; Slawski, Martin; Juchler, Norman; Kurtcuoglu, Vartan; Hirsch, Sven; Bijlenga, Philippe; Uchiyama, Yuya; Fujimura, Soichiro; Yamamoto, Makoto; Murayama, Yuichi; Takao, Hiroyuki; Koivisto, Timo; Frösen, Juhana; Cebral, Juan R.,
Available from: https://doi.org/10.21256/zhaw-19549
Ulzega, Simone; Albert, Carlo,
1st Swiss “Workshop on Machine Learning for Environmental and Geosciences” (MLEG2019), Dübendorf, 16-17 January 2019.
Ulzega, Simone; Albert, Carlo,
4th Solar Dynamo Thinkshop, Rome, Italy, 25 - 26 November 2019.
List of current projects
Bayes network analysis for data-driven decision support in hospital catering
Constantly rising costs in the healthcare sector require economic action without compromising the quality of care. Hospital catering is cost-intensive, but also very significant for patient satisfaction. In addition to purely economic optimization, numerous qualitative factors such as sustainability and employee satisfaction are also of key ...
Data mining in neurological medicine
Restless legs syndrome (RLS, Willis-Ekbom disease) is a neurological movement disorder characterised by motor and sensory symptoms, such as the uncontrollable need to move the legs (and sometimes also the arms). Such need is associated with an unpleasant and disturbing sensation in the lower limbs that typically worsens during rest and that can be ...