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Grimm Lab Sub-Menu


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.


AraPheno is a central and public repository of high-quality Arabidopsis thaliana phenotypes.

Software & Methods

Software and Methods developed by the group can be found and downloaded at our Github repository.

Lab News

New Publication: Automated synthesis of steady-state continuous processes using reinforcement learning

New Publication:  Automated synthesis of steady-state continuous processes using reinforcement learning

Automated flowsheet synthesis is an important field in computer-aided process engineering. The present work demonstrates how reinforcement learning can be used for automated flowsheet synthesis without any heuristics or prior knowledge of conceptual design. The environment consists of a steady-state flowsheet simulator that contains all physical knowledge. An agent is trained to take discrete actions and sequentially build up flowsheets that solve a given process problem. A novel method named SynGameZero is developed to ensure good exploration schemes in the complex problem. Therein, flowsheet synthesis is modelled as a game of two competing players. The agent plays this game against itself during training and consists of an artificial neural network and a tree search for forward planning. The method is applied successfully to a reaction-distillation process in a quaternary system.

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New Book Chapter: Design and Analysis of RNA Sequencing Data

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:

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Professorship Bioinformatics

Petersgasse 18
94315 Straubing


Prof. Dr. Dominik Grimm

Phone: +49 (0) 9421 187-230
Fax: +49 (0) 9421 187-285

Team Assistent

Ingrid Meindl

Phone: +49 (0) 9421 187-271
Fax: +49 (0) 9421 187-285