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Improving Peptide-Level Mass Spectrometry Analysis via Double Competition

Cite this: J. Proteome Res. 2022, 21, 10, 2412–2420
Publication Date (Web):September 27, 2022
https://doi.org/10.1021/acs.jproteome.2c00282
Copyright © 2022 American Chemical Society

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    Abstract

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    The analysis of shotgun proteomics data often involves generating lists of inferred peptide-spectrum matches (PSMs) and/or of peptides. The canonical approach for generating these discovery lists is by controlling the false discovery rate (FDR), most commonly through target-decoy competition (TDC). At the PSM level, TDC is implemented by competing each spectrum’s best-scoring target (real) peptide match with its best match against a decoy database. This PSM-level procedure can be adapted to the peptide level by selecting the top-scoring PSM per peptide prior to FDR estimation. Here, we first highlight and empirically augment a little known previous work by He et al., which showed that TDC-based PSM-level FDR estimates can be liberally biased. We thus propose that researchers instead focus on peptide-level analysis. We then 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. An alternative approach that carries out a double competition, first at the PSM and then at the peptide level (“PSM-and-peptide”), is the most powerful method, yielding an average increase of 17% more discovered peptides at 1% FDR threshold relative to the PSM-only method.

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jproteome.2c00282.

    • Notations used, details of algorithms, and performances of peptide-level FDR estimation procedures (PDF)

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    Cited By

    This article is cited by 6 publications.

    1. Jack Freestone, William Stafford Noble, Uri Keich. Reinvestigating the Correctness of Decoy-Based False Discovery Rate Control in Proteomics Tandem Mass Spectrometry. Journal of Proteome Research 2024, Article ASAP.
    2. Jack Freestone, William S. Noble, Uri Keich. Analysis of Tandem Mass Spectrometry Data with CONGA: Combining Open and Narrow Searches with Group-Wise Analysis. Journal of Proteome Research 2024, Article ASAP.
    3. Michael R. Lazear. Sage: An Open-Source Tool for Fast Proteomics Searching and Quantification at Scale. Journal of Proteome Research 2023, 22 (11) , 3652-3659. https://doi.org/10.1021/acs.jproteome.3c00486
    4. Arya Ebadi, Jack Freestone, William S. Noble, Uri Keich. Bridging the False Discovery Gap. Journal of Proteome Research 2023, 22 (7) , 2172-2178. https://doi.org/10.1021/acs.jproteome.3c00176
    5. Teeradon Phlairaharn, Zilu Ye, Elena Krismer, Anna-Kathrine Pedersen, Maik Pietzner, Jesper V. Olsen, Erwin M. Schoof, Brian C. Searle. Optimizing Linear Ion-Trap Data-Independent Acquisition toward Single-Cell Proteomics. Analytical Chemistry 2023, 95 (26) , 9881-9891. https://doi.org/10.1021/acs.analchem.3c00842
    6. Frank Lawrence Nii Adoquaye Acquaye, Attila Kertesz-Farkas, William Stafford Noble. Efficient Indexing of Peptides for Database Search Using Tide. Journal of Proteome Research 2023, 22 (2) , 577-584. https://doi.org/10.1021/acs.jproteome.2c00617

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