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Research and education in the Doctoral School AgriGenomics is build around four research areas that form the pillars of agricultural genomics:
Bio-informatics and statistics
Focuses on the handling and correct analysis of large genomics data sets by: statistical data analysis of complex high-dimensional data with resampling-based approaches, probabilistic modelling of high throughput data and software development, as well as the analysis and interpretation of high-throughput next-generation sequencing data in the context of genome de novo sequencing, variation analysis, epigenetic analysis and expression profiling.
Genomics of diversity and selection including phenomics
Focuses on the usage of genomic data in breeding such as: the design and improvement of breeding programs using molecular markers, crossbreeding, genomic selection, marker-assisted introgression breeding, and maintenance of population genetic diversity and improving phenotyping methods. Phenome assessment will reveal how genetic and environmental impact influence physical and biochemical traits.
Focuses on the usage of genomic data in the localization and description of gene functions and their interactions involving: mapping of genes/QTL with agronomic relevance, detection and development of molecular markers ideally perfect markers, cloning, deletion and overexpression of agronomically relevant genes in pathogens, plants and animals, generation and characterization of transgenic plants and animals, gene editing, identifying gene networks and the disclosure of gene functions in relation to relevant traits.
Epigenomics, Metabolomics, Proteomics
Focuses on in depth understanding and functional description of epigenetic modifications and their relation with the phenotype, and on the study of the biochemical composition and processes of the organisms under investigation and their response to external stimuli on protein expression and on the primary and secondary metabolic profiles.