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.
We are happy to share an interview Richa gave for the portal “Research in Germany”. In this interview Richa gave insights about her research in Germany, and what she likes about living here.read more
New publication about “Accurate machine learning-based germination detection, prediction and quality assessment of three grain crops” just got published in Plant Methods.
Our proposed machine learning-based method can help to speed up the assessment of seed germination experiments for different seed cultivars. It has lower error rates and a higher performance compared to conventional and manual methods, leading to more accurate germination indices and quality assessments of seeds.read more
New publication about “Network-guided search for genetic heterogeneity between gene pairs” just got published in Bioinformatics: https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btaa581/5861532
We propose a novel method for finding pairs of interacting genes that are, upon combination, associated with a phenotype of interest under a model of genetic heterogeneity. We guide the interaction search using biological prior knowledge in the form of protein-protein interaction networks. Our method controls type I error and outperforms state-of-the-art methods with respect to statistical power. Additionally, we find novel associations for multiple A. thaliana phenotypes, and for a study of rare variants in migraine patients.read more