Timeseries analysis of -omics data can be carried out by fitting spline curves to the data and using limma for hypothesis testing. For this, the right spline freedom and further hyperparameters must be identified, and the obtained hits clustered based on the spline shape.The R package SplineOmics streamlines this whole process and generates reports

Student resources for UE Bioinformatische Übungen I

Systematic functional screening of chromatin factors identifies strong lineage and disease dependencies in normal and malignant haematopoiesis.

Epigenetic regulators of fibrotic transformation in cardiac fibroblasts

R scripts for ATAC-seq analysis of eosinophil chromatin accessibility in allergic skin-lung crosstalk research.

Practical exercises for the course "Hands-on Biomedical Data - Resources and Analysis Tools"

Stores the HTML reports that are the output of the functions of the SplineOmics package, so that they can be linked in the documentation of the actual SplineOmics package, without placing those big files there.

Single-cell transcriptomics and epigenomics unravel the role of monocytes in neuroblastoma bone marrow metastasis

Reliable interpretability of biology-inspired deep neural networks