Nina L. Soltero, Graeme C. McAlister, Derek J. Bailey, Vlad Zabrouskov
Bottom-up experiments for peptide identification are a common part of proteomics studies. Often, the objective of these experiments is to identify as many peptides as possible from a particular sample of interest. For samples of high concentration, complexity, and dynamic range, effective instrument control is essential. Utilization of dynamic exclusion (DE) lists is a key aspect of efficient data acquisition in data-dependent (DDA) workflows when analyzing samples of such complexity. These method filters determine on-line which peaks to exclude from subsequent MS2 interrogation. The filter settings - such as the exclusion duration, repeat count, and the choice of algorithm - can have a significant impact on the results of the experiment. Here, we explore the effects of these parameters on the results of a bottom-up peptide identification experiment
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