Proteomics workshops

This hands-on course analyses mass-spectrometry measurements of biochemically fractionated cells to infer subcellular protein locations. Participants build reproducible R/Bioconductor workflows for preprocessing, quality assessment, annotation and machine-learning-based localization prediction. Guided examples connect protein profiling measurements to cellular maps and interpretation. The course targets proteomics researchers, bioinformaticians and data analysts; previous proteomics experience is helpful but introductory experimental context is provided. It covers fractionation-based measurements rather than antibody imaging or other spatial microscopy technologies. The dated programme lists online teaching from 12:00 to 16:00 Berlin time on November 10–12.

Genomics, proteomics and imaging now measure molecules and cells across many scales. This workshop examines how mechanistic models can integrate those measurements and biological dynamics to explain and engineer cellular behaviour. Researchers in single-cell analysis, dynamic proteomics, quantitative imaging, synthetic biology and theory will connect experimental and computational approaches. The programme links three themes: cell dynamics and interactions; molecular mechanisms and engineering; and rigorous quantitative models. Sessions address multiscale modelling, molecules over time, large-scale perturbations, prediction of cell states, dynamic processes, and stem-cell engineering. Virtual participation includes livestreamed talks, questions and discussion, plus available recordings for four weeks; individual speakers may decline recording. Virtual participants cannot submit presentation abstracts. Fees: PhD students €175, academia €200, industry €275; card payment is required. Virtual registration closes 10 November 2026. On-site registration is closed. Programme times are Europe/Berlin (CET, UTC+1): 17 November opening at 13:15 and talks from 13:30; 18 and 19 November sessions from 09:00; 20 November from 10:00, with closing remarks at 13:15–13:30.

Work through mass-spectrometry proteomics analysis in R and Bioconductor, from raw observations to statistical interpretation. Practical tutorials cover data formats, extraction and visualization, integration of identification results, quantitative data structures and preprocessing. The final session uses statistical analysis to identify proteins with different abundance between experimental groups. 2027-01-27–2027-01-29, 14:00–20:00 daily, Europe/Berlin (CET, UTC+01:00). Online course; fee €380. Working R knowledge is required. Mass-spectrometry or proteomics familiarity is useful but not essential; typical datasets and instruments are introduced during the course.

This hands-on course focuses on analysing, interpreting and statistically evaluating quantitative proteomics data. Researchers with basic mass-spectrometry knowledge learn how stable-isotope labelling and label-free approaches support biological experiments, and how to select suitable hardware, software and analysis strategies. Computer-based practicals and group exercises develop skills for handling quantitative data, while lectures connect protein regulation, cellular communication, functional and chemical proteomics, and post-translational modifications. A dedicated session examines practical uses of AI in proteomics and mass spectrometry. The course is relevant both to wet-lab researchers seeking computational experience and to researchers deepening their understanding of quantitative methods.

This course combines theoretical instruction with practical analysis of transcriptomic and proteomic data. It is designed for biologists and biochemists beginning to use sequencing and mass spectrometry, rather than specialists in either method. Participants study RNA-seq quantification and differential expression, protein identification and quantification, annotation and statistical evaluation. Software training includes SearchGUI, PeptideShaker and DiaNN; Cytoscape is used to integrate measurements and explore complementary information about gene and protein expression. The course connects primary-data processing with biological interpretation at the level of cells, tissues and organisms.

Process mass-spectrometry proteomics data from spectra through peptide identification and quantification to protein lists and functional interpretation. Practical training covers search and post-processing tools, quantitative and data-independent methods, public-data reuse, proteogenomics, PRIDE, ProteomeXchange, UniProt and interaction resources including IntAct and IMEX. Participants need a scientific degree; familiarity with proteomics mass spectrometry is helpful. There are 30 selected places, costing £975 for academia or £1,275 for industry. Applications open 14 December 2026 and close at 23:59 UK time on 14 March 2027, with decisions by 26 March. The confirmed 12–16 July dates replace the previously advertised 5–9 July schedule.