VariantAnalysis |

Incorporating deep learning TF models into SNEEP

Thu, 13 Aug 2026 00:00:00 +0000

Project Description

In our lab, we developed a statistical approach (SNEEP, see ) that identifies regulatory single nucleotide variants (SNVs) affecting transcription factor (TF) binding. Currently, the approach uses position weight matrices (PWMs) as TF models. However, these models have disadvantages, and the community has been investigating more reliable models based on deep learning, such as BPNet. Recently, the TF database JASPAR extended its set of available motifs by adding a large collection of TF-specific BPNet models, which can be used as an alternative to the classical PWM approach.

This project focuses on improving and adapting our SNEEP approach by incorporating these deep learning models as TF models.

Research Goals

Explore how to derive a differential TF binding score and p-value using BPNet models per TF. Investigate whether the SNEEP statistic is still applicable. If not, figure out which changes need to be made.
Analyze how deletions and insertions can be handled
Adapt the SNEEP software accordingly
Test the new implementation
Evaluate and compare the results to the previous version of SNEEP

Prerequisites

C++ programming or motivation to learn it
Basic knowlegde of genomics is helpful, but not required

How to Apply

Send an Email with a short motivation to the corresponding supervisor .

Modifying SNEEP to account for averaged differential TF Binding

Thu, 13 Aug 2026 00:00:00 +0000

Project Description

In our lab, we developed a statistical approach (SNEEP, see ) that identifies regulatory single nucleotide variants (SNVs) affecting transcription factor (TF) binding. Currently, the approach uses a maximization to identify the position with the strongest differential TF binding score. However, recent studies show that TFs might also bind at sites where several medium-strength binding sites overlap.

This project focuses on improving and adapting our SNEEP approach by modifying the underlying statistical model such that the above-described binding behavior is taken into account.

Research Goals

Modify the SNEEP statistic to use an average differential TF binding score instead of the maximum
Adapt the SNEEP software accordingly
Test the new implementation
Evaluate and compare the results to the previous version of SNEEP

Prerequisites

C++ programming or motivation to learn it
Basic knowlegde of genomics is helpful, but not required

How to Apply

Send an Email with a short motivation to the corresponding supervisor .

Placing regulatory variants into tissue context

Wed, 12 Aug 2026 00:00:00 +0000

Project Description

With the rapid development of spatial transcriptomics techniques, the bioinformatics possibilities in this research area are growing as well. A recent approach (gsMAP) integrates spatial transcriptomics data with genome-wide association study (GWAS) summary statistics, enabling spatially resolved linking of genetic associations to individual cells.

In our lab, we developed a statistical approach (SNEEP, see ) that identifies regulatory single nucleotide variants that impact transcription factor binding in cis-regulatory elements, such as enhancer. This project aims to incorporate SNEEP into gsMAP in order to improve the inclusion of functionally important regulatory variants to the spatial mapping step.

Research Goals

Combine SNEEP and gsMAP
Test the new implementation
Evaluate and compare the results to the original method

Prerequisites

Python programming
Basic knowlegde of genomics is helpful, but not required

How to Apply

Send an Email with a short motivation to the corresponding supervisor .

Exploring the regulatory impact of non-coding genetic variants

Fri, 19 Jun 2026 00:00:00 +0000

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

SNEEP

Fri, 17 May 2024 00:00:00 +0000

SNEEP is a statistical approach to predict the effect of non-coding genetic variants on transcription factor (TF) binding sites. It scales to large genomic datasets and can integrate cell type specific genetic and epigenetic information such as open chromatin or expression data to prioritize functional non-coding variants. Further, by linking variants to target genes via enhancer-gene interactions, SNEEP enables mechanistic interpretation of genetic associations.