Explore real-world examples of Golgi’s analytical methods applied to proteomics datasets. Each note walks through the experimental context, compares against standard pipelines, and demonstrates how improved treatment of measurement quality can reveal deeper biological insight.
Protein quantification in mass spectrometry depends on how peptide measurements are combined into protein ratios. Conventional roll-up algorithms, such as MaxLFQ, assume all peptides contribute equally—a flawed assumption in DIA proteomics where measurement quality varies widely. Here we introduce Error-Reduced (ER) Roll-up, Golgi’s weighted aggregation strategy that learns from variance, assigning data-driven weights that reflect peptide reliability. Benchmarking and simulation show up to a 40% reduction in variance and improved ratio fidelity, bringing proteomic quantification closer to ground truth and enabling more reproducible biological insight.
