Modes of Inference for Evaluating the Confidence of Peptide Identifications

Fitzgibbon, Matt; Li, Qunhua; McIntosh, Martin W.

doi:10.1021/pr7007303

Cited by 37 publications

(54 citation statements)

References 6 publications

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“…After employing decoy analysis to ensure false discovery rates (FDRs) below 5%, 4,5 we used the convention that even if a peptide were detected with multiple spectra, each peptide was counted only once per LC–MS/MS run. (The same peptide was counted more than once if detected in multiple runs, which in some cases were from gel slices with different size ranges.)…”

Section: Resultsmentioning

confidence: 99%

Assessment of MS/MS Search Algorithms with Parent-Protein Profiling

et al. 2014

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Peptide mass spectrometry relies crucially on algorithms that match peptides to spectra. We describe a method to evaluate the accuracy of these algorithms based on the masses of parent proteins before trypsin endoprotease digestion. Measurement of conformance to parent proteins provides a score for comparison of the performances of different algorithms as well as alternative parameter settings for a given algorithm. Tracking of conformance scores for spectrum matches to proteins with progressively lower expression levels revealed that conformance scores are not uniform within data sets but are significantly lower for less abundant proteins. Similarly peptides with lower algorithm peptide-spectrum match scores have lower conformance. Although peptide mass spectrometry data is typically filtered through decoy analysis to ensure a low false discovery rate, this analysis confirms that the filtered data should not be considered as having a uniform confidence. The analysis suggests that use of different algorithms and multiple standardized parameter settings of these algorithms can increase significantly the numbers of peptides identified. This data set can be used as a resource for future algorithm assessment.

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Section: Resultsmentioning

confidence: 99%

Assessment of MS/MS Search Algorithms with Parent-Protein Profiling

et al. 2014

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“…The false discovery rate (FDR) was calculated as described. 21 The raw and identification data have been deposited to the PeptideAtlas with the data set URL identifier http://www. peptideatlas.org/PASS/PASS00529.…”

Section: ■ Experimental Sectionmentioning

confidence: 99%

Revisiting Peptide Identification by High-Accuracy Mass Spectrometry: Problems Associated with the Use of Narrow Mass Precursor Windows

et al. 2014

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Peptide identification is increasingly achieved through database searches in which mass precursor tolerance is set in the ppm range. This trend is driven by the high resolution and accuracy of modern mass spectrometers and the belief that the quality of peptide identification is fully controlled by estimating the false discovery rate (FDR) using the decoy-target approach. However, narrowing mass tolerance decreases the number of sequence candidates, and several authors have raised concerns that these search conditions can introduce inaccuracies. Here, we demonstrate that when scores that only depend on one sequence candidate are used, decoy-based estimates of the number of false positive identifications are accurate even with an average number of candidates of just 200, to the point that remarkably accurate FDR predictions can be made in completely different search conditions. However, when scores that are constructed taking information from additional sequence candidates are used together with low precursor mass tolerances, the proportion of peptides incorrectly identified may become significantly higher than the FDR estimated by the target-decoy approach. Our results suggest that with this kind of score the high mass accuracy of modern mass spectrometers should be exploited by using wide mass windows followed by postscoring mass filtering algorithms.

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“…CID spectra will match to a predicted spectra with varying degrees of fidelity and a variety of open-source and commercial tools exist to select peptides that match with low overall error rates. 16–20 …”

Section: Selected Properties Of Ms and Rna-seq Assaysmentioning

confidence: 99%

Identifying Abundant Immunotherapy and Other Targets in Solid Tumors

et al. 2017

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RNA-seq and mass-spectrometry proteomics combined with growing data repositories have greatly increased the capacity to identify candidate proteins or protein sequence variants that share properties of ideal therapy targets, which include being abundant in cancer cells, absent or rare in adult organs (especially vital organs), and shared by many patient tumors. RNA-seq and fixed content arrays can identify genes that are over-expressed or mis-expressed in cancer. RNA-seq is uniquely suited to identifying-cancer specific sequence variants. We review factors relevant for determining whether products of genes that are abundant or differentially abundant in RNA-seq are concordant or discordant with proteins that are identified as abundant or differentially abundant in mass-spectrometry proteomics assays.

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Modes of Inference for Evaluating the Confidence of Peptide Identifications

Cited by 37 publications

References 6 publications

Assessment of MS/MS Search Algorithms with Parent-Protein Profiling

Assessment of MS/MS Search Algorithms with Parent-Protein Profiling

Revisiting Peptide Identification by High-Accuracy Mass Spectrometry: Problems Associated with the Use of Narrow Mass Precursor Windows

Identifying Abundant Immunotherapy and Other Targets in Solid Tumors

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