Peptide Filtering and Quantification Methods

ABSTRACT: In bottom-up proteomics, data are acquired on peptides resulting from proteolysis. In XIC-based quantification, the quality of the protein abundance estimation depends on how peptide data are filtered and on which quantification method is used to sum up peptide intensities into protein abundances. So far, these two questions have been addressed independently. Here, we studied to which extent the relative performances of the quantification methods depend on the filters applied on peptide intensity data. To this end, we performed a spike-in experiment using Universal Protein Standard (UPS1) to evaluate the performances of five quantification methods, including TOP3, iBAQ, Average of all peptide intensities or log-intensities and intensity modeling, in five datasets obtained after application of four peptide filters based on peptide sharing between proteins, retention time variability, peptides occurrence and peptide intensity profiles. We showed that estimated protein abundances were not equally affected by filters depending on the computation mode (sum or average) and the type of data (intensity or log intensity) used in the quantification methods and that filters could have contrasting effects depending on the quantification objective (absolute or relative). Our results also indicate that intensity modeling was the most robust method, providing the best results in absence of any filter, but that the different quantification methods can reach similar performances when appropriate peptide filters are used. Altogether, our findings provide clues to best handle intensity data according to the quantification objective and to the experimental design.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Saccharomyces Cerevisiae (baker's Yeast)

TISSUE(S): Cell Suspension Culture

SUBMITTER: Mélisande Blein-Nicolas  

LAB HEAD: Michel Zivy

PROVIDER: PXD009740 | Pride | 2018-11-14

REPOSITORIES: pride