Related Experiment Video
Updated: May 29, 2026

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
Published on: September 20, 2022
Target-decoy approach and false discovery rate: when things may go wrong
Nitin Gupta1, Nuno Bandeira, Uri Keich
1Bioinformatics Program, University of California San Diego, La Jolla, CA, USA.
Abstract:
The target-decoy approach (TDA) has done the field of proteomics a great service by filling in the need to estimate the false discovery rates (FDR) of peptide identifications. While TDA is often viewed as a universal solution to the problem of FDR evaluation, we argue that the time has come to critically re-examine TDA and to acknowledge not only its merits but also its demerits. We demonstrate that some popular MS/MS search tools are not TDA-compliant and that it is easy to develop a non-TDA compliant tool that outperforms all TDA-compliant tools. Since the distinction between TDA-compliant and non-TDA compliant tools remains elusive, we are concerned about a possible proliferation of non-TDA-compliant tools in the future (developed with the best intentions). We are also concerned that estimation of the FDR by TDA awkwardly depends on a virtual coin toss and argue that it is important to take the coin toss factor out of our estimation of the FDR. Since computing FDR via TDA suffers from various restrictions, we argue that TDA is not needed when accurate p-values of individual Peptide-Spectrum Matches are available.
Related Concept Videos
Understanding Deception
False Memories
One primary source of false memories is misattribution, where individuals incorrectly associate external information with...
Errors In Hypothesis Tests
Accuracy and Errors in Hypothesis Testing
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
Types of Errors: Detection and Minimization
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
Detection of Gross Error: The Q Test

