Our group's research currently focuses on the following topics:


Application of nanopore-sequencing for genomic and epigenomic studies

Nanopore-sequencing (Nanopore-seq) is a rising long-read sequencing technology that provides relatively cost-friendly and rapid products to sequence nucleic acids. Currently, our lab is working in close collaboration with the human genetic diagnostic facility ( and has applied nanopore-seq for a variety of applications such as SARS-CoV-2 variant detection in line with our Covid-19 Sequencing Initiative, human genome sequencing and cancer panel analyses for identification of SVPs of risk. Additionally, we use nanopore-seq in a variety of research projects such as bacterial genome sequencing and identification of epigenetic marks including RNA and DNA modifications. Our focus is to establish novel pipelines and working strategies to sequence and analyse long-reads data and apply them routinly in a variety of ongoing and upcoming research projects.

Lab members involved: Tamer Butto, Anna Wierczeiko, Charlotte Hewel, Stefan Mündnich,

Funding: Reality Initiative 


Deciphering the epigenetic basis of resilience

This study examines the molecular mechanisms regulating neuronal function in stress-susceptible and resilient mice following chronic social defeat (CSD) by comparing the transcriptomes of activated cells in selected brain regions using RNA-seq. In addition, using the assay for transposase-accessible chromatin followed by high-throughput sequencing (ATAC-seq) and bisulfite sequencing (Bis-seq), we will monitor the global changes in chromatin accessibility and DNA methylation signatures that alter the functional states of neurons in response to stress.

Lab members involved: Tamer Butto, Kanak Mungikar

Collaboration partners: Dr. Jennifer Winter, Prof. Dr. Susann Schweiger

Funding: CRC 1193 Resilience  (


Using multi-omics integration to explore the molecular background of stress resilience

Resilience is the ability to cope with stress or to quickly recover to a pre-crisis state after being exposed to extreme stress. As the human mental status is highly diverse, there are presumably many different molecular mechanisms underlying resilience. The main goal of this project is the identification of common molecular patterns between resilient individuals by analyzing and integrating various omics levels, e.g. transcriptomics, proteomics, methylomics and metagenomics. By subsequently selecting important features within the large amount of data, we aim to give a less complex view of the dynamic process of resilience. This work is in collaboration with the Leibniz Institute for Resilience Research (LIR) ( The majority of the underlying data sets originate from the MARP ( and LORA ( studies from the LIR.

Lab members involved: Nicolas Ruffini, Anna Wierczeiko

Collaboration partners: Prof. Dr. Raffael Kalisch
Funding: Reality Initiative 


Uncovering neural subpopulations and transcriptional networks underlying stress resilience

Chronic stress and traumatic events have a major impact on human psychological health and the manifestation of stress-related mental disorders. The majority of individuals encountering such stress are able to surmount it and therefore are resilient, while a small portion of the population manifests mood disorders, such as anxiety, social dysfunction and depression. However, very little is known about the rewiring of gene regulatory programs underlying these processes.

Chronic social defeat (CSD) is known to evoke responses in distinct brain areas, especially the hippocampus, prefrontal cortex, and amygdala. In this project, we conduct single-cell RNA-seq from the hippocampus and basolateral amygdala of socially defeated mice from resilient and non-resilient groups and use this approach not only to reveal distinct neural subpopulations in response to CSD, but also to uncover whether these subpopulations behave differently in resilient and non-resilient mice. Using extensive computational analysis, we aim at characterizing gene expression programs that operate at the single-cell level in response to stress and how such networks are differentially (re)programmed between resilient and non-resilient mice.

Lab members involved: Mohit Navandar,  Hristo Todorov

Collaboration partners: Prof. Dr. Beat Lutz


BIG data integration of genetic and epigenetic variations in neurodegenerative diseases

To gain a better understanding of the global mechanisms underlying neurodegeneration we use supercomputing facilities and recently developed High-Performance Computing methods in multivariate Genome Wide Association Studies
(GWAS) for the extraction of global patterns. Analysis includes genetic as well as epigenetic and transcriptional aspects, underlying neurodegenerative diseases i.e. Alzheimer’s, Parkinson’s and Huntington’s disease. Via a trans-Omics evaluation followed by in silico modeling we hope to extract core (biochemical) networks across multiple omic-layers (Genome, Transcriptome, Methylome).

In other words, the goal of this project is to integrate data from different sources (genomes, transcription datasets, DNA methylation and others) in order to determine which information is most relevant in order to explain the phenotypes observed in neurodegenerative diseases such as Alzheimer’s Disease. This will hopefully lead to the identification of genes or pathways which are involved in disease formation. Available genomes of control and Alzheimer patients (Alzheimer sequencing project) will help to find variations in coding regions, promoters and regulatory regions (whether SNP or larger variants). RNA-seq will provide both
information about expression and possible mutations in structural expressed regions of the genome. As such, noncoding RNA could be of special focus as mutations in protein-coding regions are rare. Epigenetic data will finally bring information on possible regulatory mechanisms of genes.

Lab members involved: Susanne Klingenberg, Nicolas Ruffini, Kanak Mungikar

Collaboration partners: Prof. Dr. Susann Schweiger


Discovery of polygenic adaptation patterns in Chironomus riparius

In cooperation with the Senckenberg Biodiversity and Climate Research Center Institute Frankfurt am Main, we are currently researching the genetic effects of rapid adaptation caused by selection. In an ongoing experiment, a C. Riparius population is set under selection pressure by only using the early emerging midges for continuing the line. We then employ sequencing analysis of these adapted individuals to test the polygenic adaptation. Ws are aiming at uncovering the pattern of those genes that have changed together under selection pressure.

To this end, it is of great importance to secure a solid method for variant calling, which mostly makes use of machine learning. this, we are testing and comparing different variant calling tools for both individual and pooled sequencing data such as GATK, DeepVariant, Population and CRISP.

Secondly, with the help of unsupervised machine learning (also better known as data mining), we are interested in finding the pattern of polygenic adaptation by means of pattern recognition and clustering.

Lab members involved: Cosima Caliendo

Collaboration partners: Prof. Dr. Markus Pfenninger 
Funding: Research Center for Emergent Algorithmic Intelligence (Emergent AI Center)


Reliability of next-generation sequencing machines and bioinformatic processing pipelines

Next-Generation Sequencing (NGS) is commonly used to gain insights into various questions from biology and medicine. This approach depends heavily on reliable and accurate data. In this project, we investigated the impact of different sequencing machines and their respective bioinformatic processing pipelines on the resulting variant call data.

We examined 99 individuals, which were sequenced three times with three different sequencers and were post-processed with different bioinformatic processing pipelines. The analysis showed considerable heterogeneity between the three sequencing cohorts. Even after applying non-specific filters like MAF and HWE, which are the standard for quality filtering and noise reduction in NGS cohorts, differences of several thousand variants per individual remained. This illustrates the need for continued refinement of both laboratory and computational analysis methods in order to achieve bias-free data.

Lab members involved: Stanislav Sys, Stephan Weißbach, Charlotte Hewel, Hristo Todorov


Optimization of the calcium imaging analysis pipeline

Calcium Imaging Analysis enables researchers to track the activity of hundreds and thousands of neurons within the brains of living animals. However, analyzing this huge amount of data raises some difficulties. Together with the group of Prof. Dr. Albrecht Stroh ( and in collaboration with the Fraunhofer ITWM, we are following the goal of improving and accelerating the analysis of calcium imaging data, starting with the automatic marking of neurons in the generated image files using deep learning. By optimizing and simplifying the application through user-friendly interfaces of the entire analysis pipeline, the effort and time required for Calcium Imaging Analyses can be drastically reduced and will also be less dependent on personal decisions.

Lab members involved: Anna Wierczeiko, Nicolas Ruffini,

Collaboration partners: Prof. Dr. Albrecht Stroh


Characterization of gut microbiota composition using 16S rRNA sequencing

Sequencing of the 16S ribosomal RNA marker gene (16S rRNA) provides a cost-effective method to characterize the bacterial composition of biological or ecological samples. In experimental animal studies, this approach allows investigating the impact of factors such as pharmacological treatment or diet on commensal microbiota. Another crucial research question is whether shifts in gut bacterial composition are associated with disease phenotypes. In ongoing collaborations with experimental groups, we focus on the bioinformatical and biostatistical analysis of 16S sequencing data. This requires applying ordination techniques that can handle distance measures appropriate for ecological data such as principal coordinates analysis or correspondence analysis. Furthermore, no consensus on the optimal statistical technique for differential abundance analysis exists. Therefore, this decision needs to be based on considerations such as sample size, experimental setup, and specific research question.

Lab members involved: Hristo Todorov, Anna Wierczeiko, Tamer Butto

Collaboration partners: Prof. Dr. Beat Lutz, Kristina Endres, Prof. Dr. Christoph Reinhardt