Dataset Information

Protein identification false discovery rates for very large proteomics data sets generated by tandem mass spectrometry.

ABSTRACT: Comprehensive characterization of a proteome is a fundamental goal in proteomics. To achieve saturation coverage of a proteome or specific subproteome via tandem mass spectrometric identification of tryptic protein sample digests, proteomics data sets are growing dramatically in size and heterogeneity. The trend toward very large integrated data sets poses so far unsolved challenges to control the uncertainty of protein identifications going beyond well established confidence measures for peptide-spectrum matches. We present MAYU, a novel strategy that reliably estimates false discovery rates for protein identifications in large scale data sets. We validated and applied MAYU using various large proteomics data sets. The data show that the size of the data set has an important and previously underestimated impact on the reliability of protein identifications. We particularly found that protein false discovery rates are significantly elevated compared with those of peptide-spectrum matches. The function provided by MAYU is critical to control the quality of proteome data repositories and thereby to enhance any study relying on these data sources. The MAYU software is available as standalone software and also integrated into the Trans-Proteomic Pipeline.

SUBMITTER: Reiter L

PROVIDER: S-EPMC2773710 | biostudies-literature | 2009 Nov

REPOSITORIES: biostudies-literature

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Protein identification false discovery rates for very large proteomics data sets generated by tandem mass spectrometry.

Reiter Lukas L Claassen Manfred M Schrimpf Sabine P SP Jovanovic Marko M Schmidt Alexander A Buhmann Joachim M JM Hengartner Michael O MO Aebersold Ruedi R

Molecular & cellular proteomics : MCP 20090716 11

Comprehensive characterization of a proteome is a fundamental goal in proteomics. To achieve saturation coverage of a proteome or specific subproteome via tandem mass spectrometric identification of tryptic protein sample digests, proteomics data sets are growing dramatically in size and heterogeneity. The trend toward very large integrated data sets poses so far unsolved challenges to control the uncertainty of protein identifications going beyond well established confidence measures for peptid ...[more]

PMID: 19608599

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Project description:All large scale LC-MS/MS post-translational methylation site discovery experiments require methylpeptide spectrum matches (methyl-PSMs) to be identified at acceptably low false discovery rates (FDRs). To meet estimated methyl-PSM FDRs, methyl-PSM filtering criteria are often determined using the target-decoy approach. The efficacy of this methyl-PSM filtering approach has, however, yet to be thoroughly evaluated. Here, we conduct a systematic analysis of methyl-PSM FDRs across a range of sample preparation workflows (each differing in their exposure to the alcohols methanol and isopropyl alcohol) and mass spectrometric instrument platforms (each employing a different mode of MS/MS dissociation). Through (13)CD3-methionine labeling (heavy-methyl SILAC) of Saccharomyces cerevisiae cells and in-depth manual data inspection, accurate lists of true positive methyl-PSMs were determined, allowing methyl-PSM FDRs to be compared with target-decoy approach-derived methyl-PSM FDR estimates. These results show that global FDR estimates produce extremely unreliable methyl-PSM filtering criteria; we demonstrate that this is an unavoidable consequence of the high number of amino acid combinations capable of producing peptide sequences that are isobaric to methylated peptides of a different sequence. Separate methyl-PSM FDR estimates were also found to be unreliable due to prevalent sources of false positive methyl-PSMs that produce high peptide identity score distributions. Incorrect methylation site localizations, peptides containing cysteinyl-S-β-propionamide, and methylated glutamic or aspartic acid residues can partially, but not wholly, account for these false positive methyl-PSMs. Together, these results indicate that the target-decoy approach is an unreliable means of estimating methyl-PSM FDRs and methyl-PSM filtering criteria. We suggest that orthogonal methylpeptide validation (e.g. heavy-methyl SILAC or its offshoots) should be considered a prerequisite for obtaining high confidence methyl-PSMs in large scale LC-MS/MS methylation site discovery experiments and make recommendations on how to reduce methyl-PSM FDRs in samples not amenable to heavy isotope labeling. Data are available via ProteomeXchange with the data identifier PXD002857.

Dataset Information

Protein identification false discovery rates for very large proteomics data sets generated by tandem mass spectrometry.

Publications

Protein identification false discovery rates for very large proteomics data sets generated by tandem mass spectrometry.

Similar Datasets

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