Lilian Heil, Lilian is a Ph.D. student in Genome Sciences interested in developing methods for the acquisition and analysis of quantitative proteomics data. She joined the department in the fall of 2019 after graduating from the University of North Carolina in the winter of 2018 with a B.S. in Chemistry. Before graduate school, she worked in the Hicks Lab at UNC for 2 years as an undergraduate trainee and for several months as a full time research assistant using mass spectrometry to identify and characterize novel bioactive peptides. There, she helped to discover and sequence a novel antimicrobial peptide from Amaranthus tricolor and developed a passion for mass spectrometry.
One benefit of parallel reaction monitoring compared to selected reaction monitoring is the ability to refine transitions after data acquisition. Transition refinement is a critical piece of quantitative analysis, particularly in unit resolution data where interferences are common. While it is possible to manually select the best set of transitions for each peptide, the process can be highly inefficient for large-scale assays. Here, we demonstrate two transition refinement methods in Skyline: one uses calibration curves to select the set of transitions that yields the most sensitivity, and the second identifies the set of transitions with strong correlation.
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