NovoBoard: A Comprehensive Framework for Evaluating the False Discovery Rate and Accuracy of De Novo Peptide Sequencing

peer-reviewed · Molecular & Cellular Proteomics · 2024

peer-reviewed · Molecular & Cellular Proteomics · 2024. Ngoc Hieu Tran et al. De novo peptide sequencing is one of the most fundamental research areas in mass spectrometry-based…
Date 2024-11-01
Type peer-reviewed
Venue Molecular & Cellular Proteomics
Publisher ASBMB
Contribution benchmark
DOI 10.1016/j.mcpro.2024.100849
Citations (OpenAlex) 13
Venue 2-year citedness 4.17

Preprint version: NovoBoard: a comprehensive framework for evaluating the false discovery rate and accuracy of de novo peptide sequencing (2024-04-16, bioRxiv)

Abstract

De novo peptide sequencing is one of the most fundamental research areas in mass spectrometry-based proteomics. Many 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) of de novo peptide-spectrum matches. 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 and validate their FDRs. Our FDR estimation provides valuable information to assess the reliability of new peptides identified by de novo sequencing tools, especially when no ground-truth information is available to evaluate their accuracy. The FDR estimation can also be used to evaluate the capability of de novo peptide sequencing tools to distinguish between de novo peptide-spectrum matches and random matches. 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.

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 (16)

Cited by (13)

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