Jung Lab - Bioinformatics

Head:
Prof. Dr. Klaus Jung

Staff:
Dr. Max Hassenstein
Sergej Ruff, M.Sc.
Whitney Tam, M.Sc.
Bullerjahn, Cynthia, B.Sc.
Ronisha Shankar Rao, B.Sc.

Contents of our research

Our group conducts research on bioinformatics methods for the analysis of molecular data from high-throughput experiments. In such experiments, gene expression levels for thousands of genes (transcriptomics) or complete genomes of different species (genomics) are studied by means of next-generation sequencing. Our method development aims to obtain highly reproducible results or to quantify the uncertainty in the results. Hereby, we make use of statistical methods such as resampling or evidence synthesis (e.g. meta-analysis or merging of multiple independent data sets), and adapt these methods for bioinformatics purposes. Furthermore, we focus specifically on applications in infection research, e.g. viral meta genomics where the community of viruses in a biological sample of an infected host is determined. We also have collaborations with researchers from different areas of biology and medicine and also develop our computational methods in these projects. A list of third-party funded projects is given below. The list of publications can be found here.

Funded Projects

Running projects:

2024-2028: Initiative Research Data Management Lower Saxony - Pilar 2, funded by the MWK Niedersachen
2019-2028: GRK 2485 VIPER (Virus detection, pathogenesis and intervention), funded by the DFG

Finished projects:

2021-2024: EVOLECTION (System to Evolve productive sow herds by statistic, AI and sensor data driven selection of the tribal sows in criss-cross-breeding), funded by the BMEL
2020-2024: SMABEYOND (Spinal Muscular Atrophy (SMA) beyond motoneuron degeneration: multi-system aspects), funded by the EU
2019-2023: FibrOmics (Translating Omics studies into clinically relevant insights for lung fibrosis patients), funded by the MWK Niedersachen
2019-2021: DigiStep (Digitalisierungsschritte von Lehrinhalten im Tiermedizinstudium), funded by the MWK Niedersachsen
2017-2018: GlykoViroLectinTools (Etablierung von Lektin-Bibliotheken aus Mensch, Schaf und Stechmücken – eine neue Plattform für Bindungsstudien mit viralen Glykoproteinen am Beispiel des Rifttalfiebers), funded by the BMBF
2016-2018: N-RENNT (Niedersachsen-Research Network on Neuroinfectiology), funded by the MWK Niedersachsen

Software

Many molecular high-throughput experiments result in high-dimensional data matrices with the number of features (represented in the rows) being much larger than the number of samples (represented in the columns). Since multiple features are measured on the same experimental unit, the data can be regarded as repeated measurements.
The R-package 'RepeatedHighDim', developed by our group, comprises a selection of functions for different aspects of the analysis of high-dimensional repeated measurements. In particular, functions for 1) outlier detection, 2) differential expression analysis, 3) self contained gene-set tests, and 4) the generation of binary random data.

Download: https://cran.r-project.org/web/packages/RepeatedHighDim/index.html

Tutorial: https://software.klausjung-lab.de/