Detecting outlier peptides in quantitative high-throughput mass spectrometry data

Quantitative high-throughput mass spectrometry has become an established tool to measure relative gene expression proteome-wide. The output of such an experiment usually consists of a list of expression ratios (fold changes) for several thousand proteins between two conditions. However, we observed that individual peptide fold changes may show a significantly different behavior than other peptides from the same protein and that these differences cannot be explained by imprecise measurements.Such outlier peptides can be the consequence of several technical (misidentifications, misquantifications) or biological (post-translational modifications, differential regulation of isoforms) reasons. We developed a method to detect outlier peptides in mass spectrometry data which is able to delineate imprecise measurements from real outlier peptides with high accuracy when the true difference is as small as 1.4 fold.We applied our method to experimental data and investigated the different technical and biological effects that result in outlier peptides. Our method will assist future research to reduce technical bias and can help to identify genes with differentially regulated protein isoforms in high throughput mass spectrometry data.

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► Quantitative LC-MS/MS experiments produce expression ratios of peptides.
► Individual peptide ratios from the same protein often differ substantially.
► There are factors other than noise for differing peptides.
► ANOVA can separate such outlier peptides from measurement noise.
► Our method allowed us to analyze and identify factors that lead to outlier peptides.