https://cgm.uni-frankfurt.de/tags/master/MasterHugoBlox Kit (https://hugoblox.com)en-usThu, 13 Aug 2026 00:00:00 +0000https://cgm.uni-frankfurt.de/media/logo.svghttps://cgm.uni-frankfurt.de/tags/master/https://cgm.uni-frankfurt.de/theses/sneepbpnet/Thu, 13 Aug 2026 00:00:00 +0000https://cgm.uni-frankfurt.de/theses/sneepbpnet/<h2 id="project-description">Project Description</h2> <p>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.</p> <p>This project focuses on improving and adapting our SNEEP approach by incorporating these deep learning models as TF models.</p> <h2 id="research-goals">Research Goals</h2> <ul> <li>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.</li> <li>Analyze how deletions and insertions can be handled</li> <li>Adapt the SNEEP software accordingly</li> <li>Test the new implementation</li> <li>Evaluate and compare the results to the previous version of SNEEP</li> </ul> <h2 id="prerequisites">Prerequisites</h2> <ul> <li>C++ programming or motivation to learn it</li> <li>Basic knowlegde of genomics is helpful, but not required</li> </ul> <h2 id="how-to-apply">How to Apply</h2> <p>Send an Email with a short motivation to the corresponding supervisor .</p>https://cgm.uni-frankfurt.de/theses/gwastotissuecontextsneep/Wed, 12 Aug 2026 00:00:00 +0000https://cgm.uni-frankfurt.de/theses/gwastotissuecontextsneep/<h2 id="project-description">Project Description</h2> <p>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.</p> <p>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.</p> <h2 id="research-goals">Research Goals</h2> <ul> <li>Combine SNEEP and gsMAP</li> <li>Test the new implementation</li> <li>Evaluate and compare the results to the original method</li> </ul> <h2 id="prerequisites">Prerequisites</h2> <ul> <li>Python programming</li> <li>Basic knowlegde of genomics is helpful, but not required</li> </ul> <h2 id="how-to-apply">How to Apply</h2> <p>Send an Email with a short motivation to the corresponding supervisor .</p>https://cgm.uni-frankfurt.de/theses/allelespecificstare/Fri, 19 Jun 2026 00:00:00 +0000https://cgm.uni-frankfurt.de/theses/allelespecificstare/<h2 id="project-description">Project Description</h2> <p>Linking enhancers to genes is difficult. The state-of-the-art appraoch to link regulatory regions to genes is using the generalized Activity-by-Contact approach (gABC) see . The thesis is about enhancing the exisiting workflow to handle allele-specific epigenome data.</p> <h2 id="research-goals">Research Goals</h2> <ul> <li>Adjust the to accommodate allele-specific epigenome signals</li> <li>Test the new pipeline on novel heart related epigenome data</li> </ul> <h2 id="prerequisites">Prerequisites</h2> <ul> <li>Python programming</li> <li>ideally some knowledge about epigenomics</li> </ul> <h2 id="how-to-apply">How to Apply</h2> <p>Send a short Email with a short motivation to the corresponding supervisor .</p>https://cgm.uni-frankfurt.de/theses/hawasregion/Fri, 19 Jun 2026 00:00:00 +0000https://cgm.uni-frankfurt.de/theses/hawasregion/<h2 id="project-description">Project Description</h2> <p>The epigenome is the layer on top of the genome that regulates how genes are expressed. We developed a Nextflow pipeline that uses machine learning models, that were trained to predict gene expression from epigenome data, to find genes which are associated with specific conditions such as cancer.</p> <h2 id="research-goals">Research Goals</h2> <ul> <li>Adjust and expand the to find which regions differ between groups of samples.</li> <li>Add a test to find which transcription factors are enriched in those regions.</li> <li>Test the pipeline on heart related epigenome data.</li> </ul> <h2 id="prerequisites">Prerequisites</h2> <ul> <li>Python programming</li> <li>Ideally some knowledge about epigenomics</li> <li>Experience with Nextflow would help but not necessary</li> </ul> <h2 id="how-to-apply">How to Apply</h2> <p>Send a short Email with a short motivation to the corresponding supervisor .</p>