ArticleMolecular & cellular proteomics : MCP2024
NovoBoard: A Comprehensive Framework for Evaluating the False Discovery Rate and Accuracy of De Novo Peptide Sequencing.
Article in Molecular & cellular proteomics : MCP, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- pXg: Comprehensive and Reliable Identification of MHC-I Antigens Using De Novo Peptide Sequencing and Immunotranscriptomic Datasets.Methods in molecular biology (Clifton, N.J.) · 2027Article
- AI proteomics: from protein identification to virtual cells.Nature methods · 2026Review
- Zero-shot de novo peptide sequencing with open posttranslational modification discovery.Nature biotechnology · 2026Article
- MegaPX: fast and space-efficient peptide assignment method using IBF-based multi-indexing.Bioinformatics (Oxford, England) · 2026Article
- PowerNovo2: A generative flow-based approach to non-autoregressive de novo peptide sequencing.PLoS computational biology · 2026Article
- Clinical peptidomics for respiratory diseases: matrices, workflows, and translation towards treatable traits, with a focus on COPD.Clinical proteomics · 2026Review
- Changes in the proteomic profile of athletes' plasma associated with exercise intensity.Scientific reports · 2026Article
- DiNovo enables high-coverage and high-confidence de novo peptide sequencing via mirror proteases and deep learning.Nature communications · 2026Article
- De novo peptide databases enable protein-based stable isotope probing of microbial communities with up to species-level resolution.Environmental microbiome · 2025Article
- Pairwise Attention: Leveraging Mass Differences to Enhance De Novo Sequencing of Mass Spectra.Journal of proteome research · 2025Article
- SWAPS: A Modular Deep-Learning Empowered Peptide Identity Propagation Framework Beyond Match-Between-Run.Journal of proteome research · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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