NovoBoard: a comprehensive framework for evaluating the false discovery rate and accuracy of de novo peptide sequencing

preprint · bioRxiv · 2024

preprint · bioRxiv · 2024. Ngoc Hieu Tran et al. De novo peptide sequencing is a fundamental research area in mass spectrometry (MS) based proteomics…
Date 2024-04-16
Type preprint
Venue bioRxiv
Publisher Cold Spring Harbor Laboratory
Contribution benchmark
DOI 10.1101/2024.04.16.589668
Citations (OpenAlex) 3

Peer-reviewed version: NovoBoard: A Comprehensive Framework for Evaluating the False Discovery Rate and Accuracy of De Novo Peptide Sequencing (2024-11-01, Molecular & Cellular Proteomics)

Abstract

De novo peptide sequencing is a fundamental research area in mass spectrometry (MS) based proteomics. However, those methods have often been evaluated using a couple of simple metrics that do not fully reflect their overall performance. Moreover, there has not been an established method to estimate the false discovery rate (FDR) and the significance of de novo peptide-spectrum matches (PSMs). Here we propose NovoBoard, a comprehensive framework to evaluate the performance of de novo peptide sequencing methods. The framework consists of diverse benchmark datasets (including tryptic, nontryptic, immunopeptidomics, and different species), and a standard set of accuracy metrics to evaluate the fragment ions, amino acids, and peptides of the de novo results. More importantly, a new approach is designed to evaluate de novo peptide sequencing methods on target-decoy spectra and to estimate their FDRs. Our results thoroughly reveal the strengths and weaknesses of different de novo peptide sequencing methods, and how their performances depend on specific applications and the types of data. Our FDR estimation also shows that some tools may perform better than the others in distinguishing between de novo PSMs and random matches, and can be used to assess the significance of de novo PSMs.

Authors

  1. Ngoc Hieu Tran · Bioinformatics Solutions Inc., University of Waterloo
  2. Rui Qiao · Bioinformatics Solutions Inc., University of Waterloo
  3. Zeping Mao · Bioinformatics Solutions Inc., University of Waterloo
  4. Shengying Pan · Bioinformatics Solutions Inc.
  5. Qing Zhang · Bioinformatics Solutions Inc.
  6. Wenting Li · Bioinformatics Solutions Inc.
  7. Lei Xin · Bioinformatics Solutions Inc.
  8. Ming Li · Bioinformatics Solutions Inc., Peng Cheng Laboratory, University of Waterloo, University of Western Ontario
  9. Baozhen Shan · Bioinformatics Solutions Inc.

Methods and tools

  • NovoBoard: Decoy-based FDR + accuracy framework

Cites (10)

Cited by (6)

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