Bioinformatics
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
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.
Software and Methods developed by the group can be found and downloaded at our Lab News
Dominik receives the excellent teaching award
Recently, the student council of the TUM Campus Straubing (TUMCS) for Biotechnology and Sustainability awarded the prize for excellent teaching for the first time to Prof. Dr. Dominik Grimm. A “Tree of Teaching” behind the new “Sustainable Chemistry” teaching and research building facing the Danube is dedicated to him. The student council had already chosen Prof. Grimm as the winner last year, and now the students congratulated him with a sign next to a newly planted silver willow after completion of the new building and the adjacent green space.
New Journal Paper about Machine Learning for Horticultural Sales Predictions
We published an original research paper on a comparative study with several Machine Learning and classical forecasting algorithms to predict horticultural sales.
Jonathan joins the Team as PhD Student
Jonathan joins the team as PhD students. He will work on novel machine learning and reinforcement learning problems within the project “Reinforcement Learning for Automated Flowsheet Synthesis of Steady-State Processes”, funded by the German Research Foundation (DFG).
