Basic and clinical research in the omics field generates an ever-growing amount of high-dimensional data which requires advanced bioinformatical and statistical techniques to produce reliable results.
Beyond the standard analysis of individual next-generation sequencing data sets, our lab is interested in integrating multi-omics data from different domains, including the transcriptome, proteome, epigenome, lipidome and microbiome to resolve complex biological and biomedical questions such as the molecular mechanisms of resilience, neurodegenerative disorders and healthy ageing. Furthermore, we contribute to developing novel machine learning solutions for denoising, clustering and feature selection, which facilitates investigating higher-order relations between datasets, for example SNP-SNP associations as predictors of 3D chromatin structure.
Recently, we established third-generation sequencing in our lab with the Oxford nanopore technology. Generating longer reads at a reduced cost has opened multiple avenues for exciting research topics, e.g. identifying genomic variants, detecting RNA modifications in real-time and optimised microbiome profiling. Nanopore sequencing has the potential to be used as a diagnostic tool in the clinic. Moreover, adaptive sampling can facilitate direct enrichment of sequences of interest, e.g. rare bacterial species or challenging genomic regions to increase the sensitivity of the analysis.
- Since 2020: W2 Professor tenure track for Clinical Genomics and Bioinformatics and Deputy Director of the Institute of Human Genetics, University Medical Center (UMC), Mainz
- 2016 - 2020: Assistant Professor for Bioinformatics, Faculty of Biology, Johannes Gutenberg University (JGU), Mainz - Centre for Computational Sciences in Mainz (CSM); Head of the junior research group “Computational Systems Genetics”
- 2011 - 2015: Postdoc, Faculty of informatics, Universita della Svizzera italiana (USI), Lugano, Institute of Computational Science
- 2011: PhD in Theoretical Biophysics, Humboldt University of Berlin
- 2007: MSc in Bioinformatics, Free University of Berlin and Konrad Zuse-Institute for Scientific Computing (ZIB)
- 2004: BSc in Bioinformatics, Free University of Berlin and MPI for Molecular Genetics
Selected publications by Susanne Gerber
Vennin C, Hewel C, Todorov H, Wendelmuth M, Radyushkin K, Heimbach A, Horenko I, Ayash S, Müller M, Schweiger S, Gerber S and Lutz B (2022) A resilience-related glial-neurovascular network is transcriptionally activated after chronic social defeat in male mice. Cells, 11(21):3405 Link
Ruffini N*, Klingenberg S*, Heese R, Schweiger S and Gerber S (2022) The big picture of neurodegeneration: a meta study to extract the essential evidence on neurodegenerative diseases in a network-based approach.Front Aging Neurosci, 14:866886 (*indicates joint contribution) Link
Ruffini N, Klingenberg S, Schweiger S and Gerber S (2020) Common factors in neurodegeneration: a meta-study revealing shared patterns on a multi-omics scale. Cells, 9:2642 Link
Gerber S, Pospisil L, Navandar M and Horenko I (2020) Low-cost scalable discretization, prediction, and feature selection for complex systems. Sci Adv, 6:eaaw0961 Link
Hewel C, Kaiser J, Wierczeiko A, Linke J, Reinhardt C, Endres K and Gerber S (2019) Common miRNA patterns of Alzheimers disease and Parkinsons disease and their putative impact on commensal gut microbiota. Front Neurosci, 13:113 Link