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Whole genomes and transcriptomes of various economically relevant plant species are studies by using cutting-edge sequencing technologies, resulting in large amounts of data that have to be processed and analysed. To accomplish this, the group develops algorithms, tools, and implements machine learning to investigate and visualize big data. With these approaches, complex data can be interpreted and subsequently utilised more efficiently. Finally, to facilitate public data access, the group designs and hosts databases with user-friendly interfaces. Combinations of theoretical and experimental research are used to identify and validate novel candidate genes involved in, improved yield and plant robustness against environmental stressors and in plant biosynthetic pathways. IBG-4 under direction of Prof. Björn Usadel is divided in five research areas:



In the research area bioinformatics, algorithms are developed which support the analysis and evaluation of sequencing.

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Databases and Data Science

Researching genomes and transcriptomes generates large and complex data sets for which a well ordered data infrastructure is required.

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Applied Machine Learning

A computer-assisted, untargeted screening for patterns and correlations in data sets helps to unravel new relationships, and to visualise and interpret complex data.

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Omics, Data Analysis and Integration

In order to interpret quantitative and qualitative differences in genes (genomics, transcriptomics) or metabolites (metabolomics), it is necessary to assign functions and classifications to the target genes or metabolites. The research area of omics, data analysis and integration makes this possible via algorithms and the creation of ontologies.

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Quantitative Bioeconomy

The aim of the research area Quantitative Bioeconomy is to establish or optimize the utilisation of plants and agricultural or horticultural plant residuals for extraction of plant compounds for bioeconomy.

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