Quality Control for the Target Decoy Approach for Peptide Identification

J Proteome Res. 2023 Feb 3;22(2):350-358. doi: 10.1021/acs.jproteome.2c00423. Epub 2023 Jan 17.

Abstract

Reliable peptide identification is key in mass spectrometry (MS) based proteomics. To this end, the target decoy approach (TDA) has become the cornerstone for extracting a set of reliable peptide-to-spectrum matches (PSMs) that will be used in downstream analysis. Indeed, TDA is now the default method to estimate the false discovery rate (FDR) for a given set of PSMs, and users typically view it as a universal solution for assessing the FDR in the peptide identification step. However, the TDA also relies on a minimal set of assumptions, which are typically never verified in practice. We argue that a violation of these assumptions can lead to poor FDR control, which can be detrimental to any downstream data analysis. We here therefore first clearly spell out these TDA assumptions, and introduce TargetDecoy, a Bioconductor package with all the necessary functionality to control the TDA quality and its underlying assumptions for a given set of PSMs.

Keywords: Bioconductor; TDA assumptions; diagnostic plots; false discovery rate; mass spectrometry; peptide identification; peptide-to-spectrum match; proteomics data analysis; quality control; target decoy approach.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Data Analysis
  • Databases, Protein
  • Peptides* / analysis
  • Proteomics / methods
  • Quality Control
  • Tandem Mass Spectrometry* / methods

Substances

  • Peptides