Submitted to: Analytical Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: October 25, 2012
Publication Date: October 29, 2012
Repository URL: http://handle.nal.usda.gov/10113/56697
Citation: Cooper, B. 2012. The problem with peptide presumption and the downfall of target-decoy false discovery rates. Analytical Chemistry. 84:9663-9667. Interpretive Summary: Mass spectrometry is used to identify proteins in biological samples. Given a mixture of peptides derived from the proteins, the mass spectrometer produces a spectrum which can be interpreted by software. The software assigns a score that says if the spectrum is a particular peptide versus some other peptide or some other substance like a sugar, fatty acid or oil. It is necessary to apply a scientific-based threshold to determine the lower end of acceptability for scores. However, some scientists are now accepting lower than normal scores. This study shows how the acceptance of low scores leads to questionable scientific data. This study also shows that the reasoning used to justify low scores is based on whim. The purpose of this research is to encourage the development of standards that lead to better scientific data quality. These findings are important to scientists at universities, institutes, government agencies and companies who want to assign better confidence to the peptides and proteins they discover by mass spectrometry.
Technical Abstract: In proteomics, peptide-tandem mass spectrum match scores and target-decoy database derived false discovery rates (FDR) are confidence indicators describing the quality of individual and sets of tandem mass spectrum matches. A user can impose a standard by prescribing a limit to these values, equivalent to drawing a line that separates better from poorer quality matches. As a result of setting narrower parent ion mass tolerances to reflect the better resolution of modern mass spectrometers, target-decoy derived FDRs can diminish. FDRs lowered this way consequently drive down the lower-limit for peptide-spectrum match score acceptance. Hence, data quality confidence appears to improve even while fragmentation evidence for some spectra remains weak. One negative outcome can be the presumed identification of peptides that do not exist. The options researchers have to improve proteomics data confidence are not panaceas, and there may be no satisfying solution as long as peptides are identified from a circumscribed list of proteins scientists wish to find.