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- Heinrich Heine University cooperates with numerous research institutions and networks beyond the boundaries of the faculties. The HHU's affiliated institutes in particular act as a link to industry. As independent institutions, they maintain close contact with research in the faculties and participate in the training of young academics.
- One of our foci today, linking all faculties, are the Life Sciences. Cross-departmental, joint study programmes (such as Business Chemistry) are one of our major strengths.
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Recent Submissions
Data for "TopEC: Improved classification of enzyme function by a localized 3D protein descriptor and 3D Graph Neural Networks"
(N/A, 2024-08-25) van der Weg, Karel; Merdivan, Erinc; Piraud, Marie; Gohlke, Holger
Accurately annotating molecular function of enzymes remains challenging. Computational methods can aid in this and allow for high-throughput annotation. Tools available for inferring enzyme function from general sequence, fold, or evolutionary information are generally successful. However, they can lead to misclassification if for certain sequences a deviation in local structural features influences the function. Here, we present TopEC, a 3D graph neural network based on a localized 3D descriptor to learn chemical reactions of enzymes from (predicted) enzyme structures and predict Enzyme Commission (EC) classes. Using the message passing frameworks from SchNet and DimeNet++, we include distance and angle information to improve the predictive performance compared to regular 2D graph neural networks. We obtained significantly improved EC classification prediction (F-score: 0.72) to 2D GNNs, without fold bias at residue and atomic resolutions and trained networks that can classify both experimental and computationally generated enzyme structures for a vast functional space (> 800 ECs). Our model is robust to uncertainties in binding site locations and similar functions in distinct binding sites. By investigating the importance of each graph node to the predictive performance, we see that TopEC networks learn from an interplay between biochemical features and local shape-dependent features. TopEC is available as a repository, including accompanying data, on github: https://github.com/IBG4-CBCLab/TopEC.
The data in this repository is available under the CC-BY-NC-SA 4.0 license.
Training Data Sets: Streamlining mRNA-Seq Data Preprocessing and Statistical Analysis: A Rapid Protocol Empowering Insightful Exploration within a Richly Annotated Biological Context
(Biology Methods & Protocols, 2024-06-11) Mai, Hans-Jörg
mRNA-seq is a powerful tool that provides comprehensive insights into gene expression and regulation, thereby advancing our understanding of biology and contributing to various fields such as medicine and agriculture. The complexity of RNA-seq analysis for biologists arises from the challenge to combine experimental biology with technical and computational skills, underscoring the need for interdisciplinary expertise. To enable integrating bioinformatics and robust analytical frameworks for extracting meaningful insights from RNA-seq experiments and answering biological questions, I introduce here a streamlined mRNA-Seq data preprocessing pipeline. The protocol, executed mainly through sequential execution of the provided bash scripts in the Linux console, encompasses decompression, quality and adapter trimming, quality control, alignment of the reads and transcript quantification. The implementation necessitates only basic knowledge of the Linux shell, making it accessible equally to novice and bioinformatically inexperienced senior scientists. Additionally, the provided R script automatically performs basic statistical data analyses with the newly generated data in RStudio, yielding all the important tables and figures that form an excellent starting point for creating the relevant charts and/or further analyses. Thus, the here-described method is designed for easy, rapid and efficient RNA-seq data extraction, requiring minimal expertise in bioinformatics.
Petersilie et al. 2024b - Figure 4
(STAR Protocols, 2024) Petersilie, Laura; Kafitz, Karl W.; Neu, Louis A.; Heiduschka, Sonja; Le, Stephanie; Prigione, Alessandro; Rose, Christine R.
Three-dimensional brain organoids from human pluripotent stem cells are a powerful tool for studying human neural networks. This article presents a refined protocol for generating robust brain organoid slices derived from regionalized cortical organoids and grown at the air-liquid interphase. The procedures for slicing organoids and maintaining them in long-term culture are described. We then detail approaches for quality control including evaluation of cell death and cellular identity. Finally, we describe procedures for expression of a genetically-encoded nanosensor for ATP.
Petersilie et al. 2024b - Figure 2
(STAR Protocols, 2024) Petersilie, Laura; Kafitz, Karl W.; Neu, Louis A.; Heiduschka, Sonja; Le, Stephanie; Prigione, Alessandro; Rose, Christine R.
Three-dimensional brain organoids from human pluripotent stem cells are a powerful tool for studying human neural networks. This article presents a refined protocol for generating robust brain organoid slices derived from regionalized cortical organoids and grown at the air-liquid interphase. The procedures for slicing organoids and maintaining them in long-term culture are described. We then detail approaches for quality control including evaluation of cell death and cellular identity. Finally, we describe procedures for expression of a genetically-encoded nanosensor for ATP.
Petersilie et al. 2024b - Figure 5
(STAR Protocols, 2024) Petersilie, Laura; Kafitz, Karl W.; Neu, Louis A.; Heiduschka, Sonja; Le, Stephanie; Prigione, Alessandro; Rose, Christine R.
Three-dimensional brain organoids from human pluripotent stem cells are a powerful tool for studying human neural networks. This article presents a refined protocol for generating robust brain organoid slices derived from regionalized cortical organoids and grown at the air-liquid interphase. The procedures for slicing organoids and maintaining them in long-term culture are described. We then detail approaches for quality control including evaluation of cell death and cellular identity. Finally, we describe procedures for expression of a genetically-encoded nanosensor for ATP.
Petersilie et al. 2024b - Figure 3
(STAR Protocols, 2024) Petersilie, Laura; Kafitz, Karl W.; Neu, Louis A.; Heiduschka, Sonja; Le, Stephanie; Prigione, Alessandro; Rose R., Christine
Three-dimensional brain organoids from human pluripotent stem cells are a powerful tool for studying human neural networks. This article presents a refined protocol for generating robust brain organoid slices derived from regionalized cortical organoids and grown at the air-liquid interphase. The procedures for slicing organoids and maintaining them in long-term culture are described. We then detail approaches for quality control including evaluation of cell death and cellular identity. Finally, we describe procedures for expression of a genetically-encoded nanosensor for ATP.