RT Journal Article
SR Electronic
T1 Detecting more peptides from bottom-up mass spectrometry data via peptide-level target-decoy competition
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.05.11.491571
DO 10.1101/2022.05.11.491571
A1 Andy Lin
A1 Temana Short
A1 William Stafford Noble
A1 Uri Keich
YR 2022
UL http://biorxiv.org/content/early/2022/05/11/2022.05.11.491571.abstract
AB A critical statistical task in the analysis of shotgun proteomics data involves controlling the false discovery rate (FDR) among the reported set of discoveries. This task is most commonly solved at the peptide-spectrum match (PSM) level by using target-decoy competition (TDC), in which a set of observed spectra are searched against a database containing a mixture of real (target) and decoy peptides. The PSM-level procedure can be adapted to the peptide level by selecting the top-scoring PSM per peptide prior to FDR estimation. Here, we investigate both PSM-level and peptide-level FDR control methods and come to two conclusions. First, although the TDC procedure is provably correct under certain assumptions, we observe that one of these assumptions—that incorrect PSMs are independent of one another—is frequently violated. Hence, we empirically demonstrate that TDC-based PSM-level FDR estimates can be liberally biased. We propose that researchers avoid PSM-level results and instead focus on peptide-level analysis. Second, we investigate three ways to carry out peptide level TDC and show that the most common method (“PSM-only”) offers the lowest statistical power in practice. The most powerful method, peptide-level FDR with PSM competition (“PSM-and-peptide”), carries out competition first at the PSM level and then again at the peptide level. In our experiments, this approach yields an average increase of 17% more discovered peptides at a 1% FDR threshold relative to the PSM-only method.Competing Interest StatementThe authors have declared no competing interest.