Genomics and Bioinformatics of Infectious Diseases
Group head
Prof. Dr. Klaus Jung
Staff
Franz Leonard Böge, M.Phil., M.Sc.
Shamini Hemandhar Kumar, M.Sc.
Josefin Säurich, M.Sc.
Michael Selle, M.Sc.
Sergej Ruff, B.Sc.
Daulin Ashirov, B.Sc.-Student
Contents of our research
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.
Funded Projects
Running projects:
- 2024-2028: Initiative Research Data Management Lower Saxony - Pilar 2, funded by the MWK Niedersachen
- 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
- 2019-2028: GRK 2485 VIPER (Virus detection, pathogenesis and intervention), funded by the DFG
Finished projects:
- 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
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/
Job postings, doctoral oportunities, and topics for theses
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).
Study material
Statistics and Data Science (M.Sc. FPPE)
FPPE_StatisicsDataScience_JUNG_part1_ProbabilityTheory.zip
FPPE_StatisicsDataScience_JUNG_part2_DescriptiveStatistics.zip
Data Analysis and Bioinformatics with R
KJUNG_DataAnalysis_with_R_Part1_Basics.zip
KJUNG_DataAnalysis_with_R_Part2_DescriptiveStatistics_Graphics.zip
KJUNG_DataAnalysis_with_R_Part3_Pobability_Estimation_Confidence.zip
KJUNG_DataAnalysis_with_R_Part4_StatisticalTesting.zip
KJUNG_DataAnalysis_with_R_Part5_Correlation.zip
KJUNG_DataAnalysis_with_R_Part6_RegressionAnalysis.zip
KJUNG_DataAnalysis_with_R_Part7_StudyDesign_SampleSize.zip
KJUNG_DataAnalysis_with_R_Part8_ANOVA.zip
KJUNG_DataAnalysis_with_R_Part9_MultipleTesting.zip
KJUNG_DataAnalysis_with_R_Part10_GenexpressionAnalysis.zip