PWA - 138 Statistical Significance in LC-MS based Label-free Protein Quantification Analysis

Label-free MS-based quantification of peptides from LC-MS data is a valuable complement to MS-based quantification technologies such as SILAC, ICPL, or gel based quantification. However, statistically valid labelfree quantification of peptides and proteins from a digest of a proteomics sample in up to hundreds of LC-MS experiments is a challenge, as it requires excellent sensitivity, mass accuracy, and reproducibility of retention time and signal intensity. Therefore we developed a method to measure LC-MS data quality in respect to the amount of usable statistically valid quantification information. Additionally, we evaluate the effect of quality improvement algorithms like retention time alignment and intensity normalization within this method.