Jung Lab - Bioinformatics

Our research group explores bioinformatics methods for analysing biological data from high-throughput experiments. In these experiments, e.g. next-generation sequencing or DNA microarray technology is used to determine the expression of thousands of genes simultaneously or genome sequences of entire organisms. In our method development, we particularly consider robust procedures or those that aim at a high reproducibility of results. We use classical statistical methods, such as resampling methods or methods of evidence synthesis (e.g. meta-analyses or the fusion of several independent data sets), and adapt them for bioinformatics purposes. Our methods focus in particular on applications in infection research. One main application is viral metagenomics, i.e. the detection of viral sequences in biological samples of infected hosts. In addition, we maintain further collaborations with scientists from various fields of biology and medicine, and develop our bioinformatics methods further in these projects. A list of externally funded projects can be found at the bottom of this page. The list of publications can be found here.

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

For further information in the projects please look at the TiHo database for research projects

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/

We regularly offer positions for employees who have a degree in bioinformatics or a comparable subject. Please feel free to contact us at any time regarding open positions and doctoral opportunities (also for veterinarians), as well as topics for bachelor's or master's theses. (E-Mail: klaus.jung@tiho-hannover.de or Telephone: 0511 953-8878).

Training Course RNA-seq (VIPER)

Kurs_2024.zip

Statistics and Data Science (M.Sc. FPPE)

Master_FPPE_part1.zip

Data Analysis and Bioinformatics with R

DataAnalysisR.zip