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
New Book Chapter: Design and Analysis of RNA Sequencing Data
In this chapter, we introduce the concept of RNA-Seq analyses. First, we start to provide an overview of a typical RNA-Seq experiment that includes extraction of sample RNA, enrichment, and cDNA library preparation. Next, we review tools for quality control and data pre-processing followed by a standard workflow to perform RNA-Seq analyses. For this purpose, we discuss two common RNA-Seq strategies, that is a reference-based alignment and a de novo assembly approach. We learn how to do basic downstream analyses of RNA-Seq data, including quantification of expressed genes, differential gene expression (DE) between different groups as well as functional gene analysis. Eventually, we provide a best-practice example for a reference-based RNA-Seq analysis from beginning to end, including all necessary tools and steps on GitHub: https://github.com/grimmlab/BookChapter-RNA-Seq-Analyses.
read moreOpen Position: Research Scientist (f/m/d) for the project “Glycoside Production”
We are seeking highly motivated candidates for a scientific research position as Postdoctoral Researcher, to further advance the recently launched Synthetic Biology Foundry at TUM Campus Straubing (SynBiofoundry@TUM).
read moreMaura joins the Team as PhD Student
Maura will work on novel bioinformatics and machine learning techniques to gain a better understanding of genotype-phenotype relationships.
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