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Graphical interface for real-time monitoring of cosmic-ray neutron sensor data.

Contains a python package to compute kernel-density-estimated likelihood

Wrapper to control MinIO commands that are not covered by the MinIO Python SDK

Jupyter Notebooks zu den Übungen der Lehrveranstaltung

My guide to using OGS. It contains some overview and tips.

Pytest course

Streamflow traffic light

Provided are functions suitable for the analysis of mass spectrometry-based proteomics data, providing global information on protein abundance and abundances of post-translational modifications (PTMs). However, at least in parts, this package can be also used to analyse other omics, e.g. metabolomics and transcriptomics. The package includes processing of fold changes (e.g. from TMT or SILAC experiments) and intensities (e.g. from LFQ experiments). Processing includes checking reproducibility (PCAs, correlation bubble plots, sample-2-sample distance plots), identification of outliers (based on Mahalanobis distance), log2 transformation, median normalization, filtering for reliably identified proteins/sites, variance stabilization (based on the 'DEP' package), imputation (based on the 'DEP' package), calculation of average log2(fold changes), p-values, and adjusted p-values. These basic results are visualized (volcano plots, stacked bar charts of significant changes, numbers of identified proteins). Enrichment analyses (using 'clusterProfiler') are conducted based on the gene sets stored in the MSigDB (using 'msigdbr') and are visualized subsequently. Also enrichment results obtained using the 'Ingenuity Pathway Analysis' tool (Qiagen) are visualized. Weighted Gene Correlation Network Analysis ('WGCNA', including module formation, module-trait correlation, identification of hub genes/key drivers, visualization of results) is conducted. For PTM data, site intensities are extracted based on Proteome Discoverer's Peptide Isoform table. Furthermore, kinase enrichment for phosphoproteomics data using 'KinSwingR' can be conducted.