title: Decoy methods for assessing false positives and false discovery rates in shotgun proteomics.
abstract: The potential of getting a significant number of false positives (FPs) in peptide-spectrum matches (PSMs) obtained by proteomic database search has been well-recognized. Among the attempts to assess FPs, the concomitant use of target and decoy databases is widely practiced. By adjusting filtering criteria, FPs and false discovery rate (FDR) can be controlled at a desired level. Although the target-decoy approach is gaining in popularity, subtle differences in decoy construction (e.g., reversing vs stochastic methods), rate calculation (e.g., total vs unique PSMs), or searching (separate vs composite) do exist among various implementations. In the present study, we evaluated the effects of these differences on FP and FDR estimations using a rat kidney protein sample and the SEQUEST search engine as an example. On the effects of decoy construction, we found that, when a single scoring filter (XCorr) was used, stochastic methods generated a higher estimation of FPs and FDR than sequence reversing methods, likely due to an increase in unique peptides. This higher estimation could largely be attenuated by creating decoy databases similar in effective size but not by a simple normalization with a unique-peptide coefficient. When multiple filters were applied, the differences seen between reversing and stochastic methods significantly diminished, suggesting multiple filterings reduce the dependency on how a decoy is constructed. For a fixed set of filtering criteria, FDR and FPs estimated by using unique PSMs were almost twice those using total PSMs. The higher estimation seemed to be dependent on data acquisition setup. As to the differences between performing separate or composite searches, in general, FDR estimated from the separate search was about three times that from the composite search. The degree of difference gradually decreased as the filtering criteria became more stringent. Paradoxically, the estimated true positives in separate search were higher when multiple filters were used. By analyzing a standard protein mixture, we demonstrated that the higher estimation of FDR and FPs in the separate search likely reflected an overestimation, which could be corrected with a simple merging procedure. Our study illustrates the relative merits of different implementations of the target-decoy strategy, which should be worth contemplating when large-scale proteomic biomarker discovery is to be attempted.
authors.affiliation: Proteomics Core Facility, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland 20892, USA.
authors.forename: Guanghui
authors.initials: G
authors.lastname: Wang
authors.forename: Wells W
authors.initials: WW
authors.lastname: Wu
authors.forename: Zheng
authors.initials: Z
authors.lastname: Zhang
authors.forename: Shyama
authors.initials: S
authors.lastname: Masilamani
authors.forename: Rong-Fong
authors.initials: RF
authors.lastname: Shen
mesh_terms: D000465:Algorithms; D000818:Animals; D002853:Chromatography, Liquid / Q000379:methods; D030562:Databases, Protein; D005189:False Positive Reactions; D007668:Kidney / Q000737:chemistry* / Q000378:metabolism; D008297:Male; D011506:Proteins / Q000032:analysis*; D040901:Proteomics / Q000379:methods* / Q000592:standards; D051381:Rats; D013269:Stochastic Processes; D053719:Tandem Mass Spectrometry
chemical_list: D011506:Proteins