Ron Schweßinger

The paper identifies a de novo enhancer mutation in KCNB1 causing ST Depression Syndrome, a cardiac arrhythmia, and shows it can be predicted by a deep neural network.

This paper introduces seq2cells, a model predicting single-cell gene expression from DNA sequence, revealing cell-type-specific variant effects and heterogeneity.

The paper examines how dynamic chromatin boundaries around the Runx1 gene influence its expression during hematopoietic development in mice.

DeepC predicts chromatin interactions from DNA sequence using deep learning and transfer learning, generalizing across cell types and resolutions.

The paper introduces a new method, NuTi Capture-C, that improves high-resolution chromosome conformation capture by reducing noise and increasing sequencing content, enabling reproducible genome-wide interaction profiles.

CaptureCompendium provides software tools to simplify the design, analysis, and visualization of 3C experiments, addressing the need for specialized bioinformatics skills in genome folding studies.

The paper develops a platform combining ATAC-seq, DNaseI footprints, NG Capture-C, and machine learning to identify causal variants and genes for polygenic human traits.

This study shows that enhancers primarily regulate gene expression by promoting transcription initiation rather than by affecting RNA polymerase II pausing.

This study models erythropoiesis in CDA-I by analyzing patient-derived erythroblasts, identifying abnormalities in DNA accessibility, proliferation, and enucleation, with genotype-phenotype correlations.

The paper develops Tri-C, a single-cell 3C method, to reveal dynamic chromatin interactions and regulatory hubs in mammalian cells.