https://cgm.uni-frankfurt.de/tags/bachelor/BachelorHugoBlox 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/bachelor/https://cgm.uni-frankfurt.de/theses/sneepaveragestatistic/Thu, 13 Aug 2026 00:00:00 +0000https://cgm.uni-frankfurt.de/theses/sneepaveragestatistic/<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 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.</p> <p>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.</p> <h2 id="research-goals">Research Goals</h2> <ul> <li>Modify the SNEEP statistic to use an average differential TF binding score instead of the maximum</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/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>https://cgm.uni-frankfurt.de/theses/schawas/Fri, 19 Jun 2026 00:00:00 +0000https://cgm.uni-frankfurt.de/theses/schawas/<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. The current implementation works entirely on bulk data.</p> <h2 id="research-goals">Research Goals</h2> <ul> <li>Adjust and expand the to also allow the usage of single-cell data.</li> <li>Add input processing of single-cell data, which can then be given to pre-trained single-cell models.</li> <li>Test the pipeline on additional data.</li> </ul> <h2 id="prerequisites">Prerequisites</h2> <ul> <li>Python/R 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>https://cgm.uni-frankfurt.de/theses/liverdiseaseanalysis/Fri, 19 Jun 2026 00:00:00 +0000https://cgm.uni-frankfurt.de/theses/liverdiseaseanalysis/<h2 id="project-description">Project Description</h2> <p>Do you want to learn how large transcriptomics datasets from patients are being analyzed? This project is about the analysis of RNA-seq samples from patients with liver disease. The experimental design of the study is involved and therefore needs to be tailored to generate informative results. This project combines statistical analysis with an interesting biological question on a novel dataset from collaborators.</p> <h2 id="research-goals">Research Goals</h2> <ul> <li>Conduct meta-data aware analysis of liver-disease specific genes</li> <li>Compare with other patient cohort data to corroborate results</li> </ul> <h2 id="prerequisites">Prerequisites</h2> <ul> <li>Python experience</li> <li>Basic understanding of RNA-seq analyses</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>