Running PeptideProphet Separately on Replicates Improves Peptide Identification Results

TL;DR

Running PeptideProphet separately on each replicate and then combining results enhances peptide identification accuracy in shotgun proteomics, leveraging the Bagging principle for better spectrum coverage and statistical power.

Contribution

This paper introduces a novel approach of applying PeptideProphet separately on replicates and combining results, improving peptide identification over the traditional merging method.

Findings

Consistent improvement on standard protein dataset

Enhanced results on Human and Yeast datasets

Demonstrates effectiveness of replicate-specific analysis

Abstract

Limited spectrum coverage is a problem in shotgun proteomics. Replicates are generated to improve the spectrum coverage. When integrating peptide identification results obtained from replicates, the state-of-the-art algorithm PeptideProphet combines Peptide-Spectrum Matches (PSMs) before building the statistical model to calculate peptide probabilities. In this paper, we find the connection between merging results of replicates and Bagging, which is a standard routine to improve the power of statistical methods. Following Bagging's philosophy, we propose to run PeptideProphet separately on each replicate and combine the outputs to obtain the final peptide probabilities. In our experiments, we show that the proposed routine can improve PeptideProphet consistently on a standard protein dataset, a Human dataset and a Yeast dataset.