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Genomics and Bioinformatics of Infectious Diseases

Head:

Prof. Dr. Klaus Jung


Staff:

Moritz Kohls, M.Sc.

Mr. Robin Kosch, M.Sc.

Mr. Ihsan Muchsin, M.Sc.

Mrs. Christine Winter, M.Sc.

Research

The research activities of our working group follow two directions. First, we employ existing bioinformatic tools to investigate infectious diseases on different 'omics' levels, for example to identify viral sequences in host samples. In this context, we collaborate closely with other groups at the University of Veterinary Medicine Hannover who are working in the area of infection research - one of the main research foci of the University. Second, we develop new computational and statistical methods for the analysis of data from high-throughput experiments (e.g. NGS, Microarrays, Proteomics).

Projects, Publications & Software

Niedersachsen-Research Network on Neuroinfectiology (N-RENNT)

Our group participates in the Niedersachsen-Research Network on Neuroinfectiology (N-RENNT), funded by the Ministry of Science and Culture of Lower Saxony. In the subproject Bioinformatics we aim to identify virus sequences in sequencing data from host samples. For that purpose we use mapping approaches as well as unsupervised classification methods. In addition, we work on new methods for analyzing high-throughput transcriptomic data from DNA microarrays or RNA-seq experiments.

  • References and Software:

[1] Kruppa J, van der Vries E, Jo WK, Postel A, Becher P, Osterhaus A and Jung K (2017) kmerPyramid: an interactive visualization tool for nucleobase and k-mer frequencies. Bioinformatics, published online first [abstract], [R-package kmerPyramid].

[2] Kruppa J and Jung K (2017) Automated multigroup outlier identification in molecular high-throughput data using bagplots and gemplots. BMC Bioinformatics, 18:232. [abstract], [R-package gemPlot]

[3] Kruppa J, Lepenies B and Jung K (2017) A genetic algorithm for simulating correlated binary data from biomedical research. Computers in Biology and Medicine, accepted for publication.

Analysis of Lectin-sequences (GlykoViroLectinTools)

GlykoViroLectinTools is a joint project with Prof. Stefanie Becker and Prof. Bernd Lepenies, funded by the German Research Platform for Zoonoses. The Bioinformatics part of this projects concentrates on analyzing the amino acid sequences of lectins. Lectins play an important role in immune defense.

Set-based analysis of expression data

Global test procedures for gene or protein expression data perform a single hypothesis test on a set of molecular features instead of an individual test per feature. This procedure can idenfity group effects in a molecular subset, defined for example by a molecular pathway or another category. Global tests can also be used for integrating expression data from different molecular domains (e.g. mRNA and miRNA).

  • References and Software:

[1] Kruppa J, Kramer K, Beißbarth T and Jung K (2016): A simulation framework for correlated count data of feature subsets in high-throughput sequencing or proteomics experiments. Statistical Applications in Genetics and Molecular Biology, 15, 401-414. [abstract].

[2] Bayerlová M, Jung K, Kramer K, Klemm F, Bleckmann A and Beißbarth T (2015): Comparative study on gene set and pathway topology-based enrichment methods. BMC Bioinformatics, 16: 334 [abstract].

[3] Jung K, Dihazi H, Bibi A, Dihazi GH and Beißbarth T (2014): Adaption of the Global Test Idea to Proteomics Data with Missing Values. Bioinformatics, 30, 1424-30. [abstract, R-package RepeatedHighDim]

[4] Artmann S*, Jung K*, Bleckmann A and Beißbarth T (2012): Detection of simultaneous Group Effects in microRNA Expression and related Target Gene Sets. PLoS ONE, 7, e38365. [abstract, R-package miRtest]

[5] Jung K, Becker B, Brunner E and Beißbarth T (2011): Comparison of Global Tests for Functional Gene Sets in Two-Group Designs and Selection of Potentially Effect-causing Genes. Bioinformatics, 27, 1377-1383. [abstract, R-package RepeatedHighDim]

Teaching material

Interactive learning tools for analysis methods of high-throughput gene expression data. These tools can be run in a web browser in combination with the free sotware environment R.

 

Gene set enrichment analysis, Differential analysis & vulcano plots, Microarray data normalization, Prinicple component analysis, Hierarchical clustering

Current Topics for Bachelor- and Master-Theses

Announcements of current topics for bachelor and master theses are provided here.

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