Protein Identification False Discovery Rates for Very Large Proteomics Data Sets Generated by Tandem Mass Spectrometry*

By Lukas Reiter, Manfred Claassen, Sabine Schrimpf, M. Jovanović, A. Schmidt, Joachim M. Buhmann, Michael O. Hengartner, R. Aebersold. Molecular & Cellular Proteomics, 2009

317 citations recorded by the source corpus.

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.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/a99eade401bcbda62707d2fff0b52d69ff45f532

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Citations in the corpus

The MaxQuant computational platform for mass spectrometry-based shotgun proteomics (2016, background): “This is a very crucial step, because if search results for separate raw files are collected into one larger data set without any further higher-level FDR control, false-positive protein identifications will aggregate 42,43 .”
Data‐independent acquisition‐based SWATH‐MS for quantitative proteomics: a tutorial (2018, unknown): “If SWATH extraction results are summarized at the inferred protein level, the error rate can be further inflated by such false positives (Reiter et al , 2009; Rosen-berger et al , 2017a).”
iProphet: Multi-level Integrative Analysis of Shotgun Proteomic Data Improves Peptide and Protein Identification Rates and Error Estimates* (2011, background): “Although Peptide-Prophet and ProteinProphet have been shown to provide accurate estimates in the case of small to intermediate data sets, several simplifying assumptions in these tools limit their performance with increasing data set size (20).”
Extending the Limits of Quantitative Proteome Profiling with Data-Independent Acquisition and Application to Acetaminophen-Treated Three-Dimensional Liver Microtissues (2015, background): “The FDR was controlled at protein level (37).”
A Scalable Approach for Protein False Discovery Rate Estimation in Large Proteomic Data Sets (2015, background): “mation of false positive protein hits termed MAYU (27).”