Development of software for better assessment of non-coding variants on gene expression - translating the results from the research unit to clinical application

• Applicant: Professor Dr. Dominik Seelow
• Subject Area: Human Genetics ; Bioinformatics and Theoretical Biology ; Cell Biology
• Term: since 2019
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 400728090

While much progress has been achieved in predicting the deleteriousness of coding variants, the assessment of non-coding variants still lags far behind. With only about a third of current Whole Exome Sequencing projects yielding a causal variant, it has become clear that the non-coding space may also play a role in genetic disease. For example, it is self-evident that the homozygous loss of a gene promoter can lead to the complete loss of protein expression. In the first funding period, we have focussed on predicting the effect of variants on transcription factor binding and demonstrated that neural networks can identify hitherto unknown binding motifs and predict the effects of DNA variants on binding affinity. In the upcoming funding period, we plan to develop software that combines deep learning-based models for transcription factor binding sites with information about regulatory regions in the genome, hence allowing us to predict whether or not DNA variants are located within promoters or enhancers of disease-relevant genes and to what extent they might affect transcription factor binding. We will also create a command-line tool to be integrated into genome sequencing pipelines, as well as a web-based application for researchers to test potentially regulatory variants for potential effects on functional candidate genes. We will integrate these functionalities into our mutation detection software, MutationDistiller, which ranks DNA variants based on their deleteriousness to the gene and the role of the affected gene in the patient's phenotype.

• DFG Programme: Research Units
• Subproject of: FOR 2841: Beyond the Exome – Identifying, Analyzing, and Predicting the Disease Potential of Non-Coding DNA Variants