Omics-/Data-based Bioinformatics

Director: Prof. Dr. Björn Usadel

Whole genomes and transcriptomes of various economically relevant plant species obtained from sequencing results in large amounts of data that have to be processed and analysed. The group develops algorithms and tools and implements machine learning to investigate and visualize big data. 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.

The group focuses on five research areas:

Bioinformatics

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

Databases and Data Science

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

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.

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.

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.

Research Projects in the Usadel group

Here you find an overview on our third-party-funded research projects

Third-party funded research projects

News and awards in the group Omics-/Data-based Bioinformatics

Omics, Data Analysis and Integration

Applied Machine Learning

Applied Machine Learning

Databases and Data Science

Bioinformatics

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

third-party-funding

third-party-funding

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. In addition, adaptation mechanisms of the plant to environmental stresse are used to modify plants and their metabolism or their gene expression in a targeted manner.