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
Ulzega, Simone; Albert, Carlo,
1st Swiss “Workshop on Machine Learning for Environmental and Geosciences” (MLEG2019), Dübendorf, 16-17 January 2019.
Ulzega, Simone; Albert, Carlo,
PASC19 (Platform for Advanced Scientific Computing), ETH Zurich, 12-14.06.2019.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Available from: https://doi.org/10.21256/zhaw-3225
Available from: https://doi.org/10.1007/s00701-018-3712-8
Life in Numbers 4, ZHAW, Waedenswil, 4 October 2018.
8th World Congress of Biomechanics, WCB2018, Dublin, Ireland, 8-12 July 2018.
List of current projects
Researchers from the School of Life Sciences and Facility Management (LSFM) initiated a platform to promote interdisciplinary research in the field of health. The School of LSFM supports this initiative to increase visibility of all health-research related activities in teaching, R&D, continued education, and services. The platform has the ...
BISTOM - Bayesian Inference with Stochastic Models
In essentially all applied sciences, data-driven modeling heavily relies on a sound calibration of model parameters to measured data for making probabilistic predictions. Bayesian statistics is a consistent framework for parameter inference where knowledge about model parameters is expressed through probability distributions and updated using ...