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 Paper: Deep learning-based early weed segmentation using motion blurred UAV images of sorghum fields
New paper about “Deep learning-based early weed segmentation using motion blurred UAV images of sorghum fields”. In this work, we developed and analysed different deep learning-based architectures to accurately segment crop and weed species in UAV images of agricultural fields under challenging capturing conditions. For this propose, we created an expert-curated fully-annotated weed segmentation UAV dataset in sorghum fields. We show that our trained models have high precision in detecting general plant shapes with minor weaknesses at borders of the plants. More importantly, our method is capable in segmenting intra-row and partly occluded weeds on an individual plant basis. All code and data are publicly available on GitHub and Mendeley Data.
New Paper: Systematic analysis of the underlying genomic architecture for transcriptional–translational coupling in prokaryotes
New paper about “Systematic analysis of the underlying genomic architecture for transcriptional–translational coupling in prokaryotes”. In this work, we systematically analyzed gene cassettes from more than 1800 bacterial for the abundance of transcriptional and translational associated genes clustered in distinct gene cassettes. We identified three highly frequent cassettes containing transcriptional and translational genes. Interestingly, each of the three cassettes harbors a gene (nusG, rpsD and nusA) encoding a protein which links transcription and translation in bacteria. Furthermore, our analyses suggest an enrichment of these gene cassettes in pathogenic bacterial phyla.
Maura Present our new Paper permGWAS at the European Conference on Computational Biology (ECCB)
Maura presents our new paper on “Efficient permutation-based genome-wide association studies for normal and skewed phenotypic distributions” at the 21st European Conference on Computational Biology (ECCB), the largest bioinformatics conference in Europe.
