The bioinformatics lab at the Technical University of Munich, TUM Campus Straubing for Biotechnology and Sustainability is led by Prof. Dr. Dominik Grimm from the University of Applied Sciences Weihenstephan-Triesdorf.
Bioinformatics is a young interdisciplinary research area that develops computational and statistical tools to analyze, store, integrate and visualize biological and biomedical data. One of the main research areas of our group is the development and usage of novel computational tools and machine learning methods to gain a deeper understanding of the underlying genetic architecture of complex biological processes and phenotypes. In addition, we are interested in developing efficient pipelines and applications to process and analyze Next Generation Sequencing (NGS) data. Further, we develop modern cloud-based applications and databases to simplify the analysis, storage, retrieval and visualization of diverse and complex biological and biomedical data.
Software & Resources
easyGWAS is a novel web- and cloud platform for performing, analysing and comparing genome-wide association studies (GWAS).
The AraGWAS Catalog
The AraGWAS Catalog is a public and manually curated database for standardised GWAS results for Arabidopsis thaliana.
Ist ein Computer schlau?
„Ist ein Computer dumm oder schlau, was meint ihr?“ Mit dieser Frage hat Prof. Dr. Grimm, Leiter der Professur für Bioinformatik, die erste Vorlesung der Kinderuni im Jahr 2023 eröffnet. Mithilfe von Lego- Steinen und eines Roboterarms machten sich die jungen Forscher mit Prof. Grimm auf die Suche nach einer Antwort.
New StMELF Funding: AI for Weed Regulation with Robots
We are happy to receive funding from the Bavarian State Ministry of Food, Agriculture and Forestry to continue or research about developing novel machine learning techniques for weed identification in drone imagery to enable automatic weed removal due to autonomous robots on agricultural fields.
Dominik talks at the Computomics Podcast about ML for agriculture
In this episode Dominik gives us insights into CropML, a BMBF funded project. The project evaluates new machine learning techniques for more accurate plant breeding by integrating heterogeneous external factors. Different phenotype prediction models, including basic genomic selection methods to more advanced deep learning-based techniques have been compared. Learn why advanced models are the future and where the challenges are.