# Query Mix-Max Method for FDR Estimation Supported by Entrapment Queries

## TL;DR

This paper introduces a new method called QMM for estimating false discovery rates in proteomics without using decoy databases.

## Contribution

The QMM method is a decoy-free approach for FDR estimation using entrapment queries in shotgun proteomics.

## Key findings

- The QMM method provides reasonably accurate FDR estimation across various scenarios.
- The method tends to be conservatively biased at higher FDR values, ensuring stringent error control.
- Effectiveness depends on evolutionary distance and requires sufficient entrapment queries for stability.

## Abstract

Estimating the false discovery rate (FDR) is one of the
key steps
in ensuring appropriate error control in the analysis of shotgun proteomics
data. Traditional estimation methods typically rely on decoy sequence
databases or spectral libraries, which may not always provide satisfactory
results due to limitations of decoy construction methods. This study
introduces the query mix-max (QMM) method, a decoy-free alternative
for FDR estimation in proteomics. The QMM framework builds upon the
existing mix-max procedure but replaces decoy matches with entrapment
queries to estimate the number of false positive discoveries. Through
simulations and real data set analyses, the QMM method was demonstrated
to provide reasonably accurate FDR estimation across various scenarios,
particularly when smaller sample-to-entrapment spectra ratios were
achieved. The QMM method tends to be conservatively biased, particularly
at higher FDR values, which can ensure stringent FDR control. While
flexible, the protocol’s effectiveness may vary depending on
the evolutionary distance between the sample and entrapment organisms.
It also requires a sufficient number of entrapment queries to provide
stable FDR estimates, especially for low FDR values. Despite these
limitations, the QMM method is a promising alternative as one of the
first query-based FDR estimation approaches in shotgun proteomics.