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:

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Omics, Data Analysis and Integration

The research group Omics, Data Analysis and Integration investigates molecular mechanisms of plant phenotype emergence by means of large-scale data integration on the genomic, transcriptomic, metabolomic, and phenomic levels. We apply machine learning and graph theory to big data to discover new gene functions, and interactions between genes, metabolites and phenotypes.

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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. 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.

Research Projects in the Usadel group

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

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Third-party funded research projects

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

Bioinformatics

Bioinformatics

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

Databases and Data Science

Databases and Data Science

Omics, Data Analysis and Integration

Omics, Data Analysis and Integration

The research group Omics, Data Analysis and Integration investigates molecular mechanisms of plant phenotype emergence by means of large-scale data integration on the genomic, transcriptomic, metabolomic, and phenomic levels. We apply machine learning and graph theory to big data to discover new gene functions, and interactions between genes, metabolites and phenotypes.

Quantitative Bioeconomy

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.

Applied Machine Learning

Applied Machine Learning

Applied Machine Learning

third-party-funding

third-party-funding

third-party-funding

Last Modified: 03.05.2024