Exploring the regulatory impact of non-coding genetic variants

19 June 2026·
Nina Baumgarten
Nina Baumgarten
Dennis Hecker
Dennis Hecker
Laura Rumpf
Laura Rumpf
Marcel H. Schulz, PhD
Marcel H. Schulz, PhD
projects

Overview

SNEEP is a computational method to identify regulatory Single Nucleotide Variants (rSNVs) that alter the binding sites of Transcription Factors (TFs). It can efficiently analyze large collections of genetic variants, such as GWAS datasets. SNEEP combines its statistical approach with cell type-specific regulatory to prioritize functional non-coding variants and connect them to their target genes.

Applications

In a large-scale study of coronary artery disease, SNEEP was used to connect disease-associated variants to candidate target genes based on enhancer-gene interaction prediceted with the gABC-score across 45 cell types. In another cardiovascular disease studies, SNEEP identified thousands of regulatory SNPs and uncovered novel candidate disease genes, including non-coding RNAs with experimentally validated functions in cardiac disease models.

Associated Publications

Linked publications

Dennis Hecker, Xiaoning Song, Nina Baumgarten, Anastasiia Diagel, Nikoletta Katsaouni, Ling Li, Shuangyue Li, Ranjan Kumar Maji, Fatemeh Behjati Ardakani, Lijiang Ma, Zhaolong Li, Aldo Moggio, Daniel Tews, Hendrik Sager, Lars Maegdefessel, Martin Wabitsch, Johan L. M. Björkegren, Heribert Schunkert, Zhifen Chen, Marcel H. Schulz (2026). Cell type-specific epigenetic regulatory circuitry of coronary artery disease loci. Nature Communications.
DOI
Nina Baumgarten, Laura Rumpf, Thorsten Kessler, Marcel H. Schulz (2024). A statistical approach for identifying single nucleotide variants that affect transcription factor binding. iScience.
DOI
Chaonan Zhu, Nina Baumgarten, Meiqian Wu, Yue Wang, Arka Provo Das, Jaskiran Kaur, Fatemeh Behjati Ardakani, Thanh Thuy Duong, Minh Duc Pham, Maria Duda, Stefanie Dimmeler, Ting Yuan, Marcel H. Schulz, Jaya Krishnan (2023). CVD-associated SNPs with regulatory potential reveal novel non-coding disease genes. Human Genomics.
DOI
VariantAnalysis
Nina Baumgarten
Authors
Nina Baumgarten (she/her)
Postdoc
I am a bioinformatician with a diverse background in analyzing genetic and epigenetic data. I have had the chance to work on a variety of projects since I joined the lab, like decoding the effect of non-coding variations, analysing GWAS data, and studying TF binding. Recently, my focus has been shifted toward spatial transcriptomics, which allows me to map genetic findings directly onto tissue architecture. I am motivated by the possibility that these data-driven insights might lead to a better understanding of complex diseases. In my free time, I love spending time in nature for hiking—it’s where the best ideas come to me!
Dennis Hecker
Authors
Dennis Hecker (he/him)
Postdoc
I am a biologist by training that turned into a bioinformatician for the PhD. Since starting in the SchulzLab, I have been working on analysing a variety of data and their integration with a focus on the epignome, specifically interactions between regulatory regions and genes. I also have a love-hate relationship with chromatin contact data. What keeps me going is the variety of research questions that we work on to know more about human biology and its use in medicine. I am a big fan of Python and coffee.
Laura Rumpf
Authors
Laura Rumpf (she/her)
PhD student
I’m a computer scientist by training who found my way into bioinformatics during my master’s. My work focuses on understanding enhancer–gene regulation using machine learning and single-cell transcriptomic and epigenomic data. I’m particularly interested in how computational models can help uncover regulatory mechanisms and translate these insights into disease-related questions in computational medicine. Outside the lab, I’m usually fueled by coffee and a slightly too serious interest in latte art.
Marcel H. Schulz, PhD
Authors
Marcel H. Schulz, PhD (he/him)
Professor for Computational Biology
I am a Computational Biologist by education. I develop computational systems biology models and machine learning algorithms that further the understanding of gene regulation. We work on groundbreaking computational methods that enable novel approaches for personalized medicine applications in the context of different human diseases. We live and breathe open-source development.