NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics
ML conference · NeurIPS 2024 · 2024
| Date | 2024-06-16 |
| Type | ML conference |
| Venue | NeurIPS 2024 |
| Publisher | arXiv |
| Contribution | benchmark |
| DOI | 10.48550/arXiv.2406.11906 |
| Citations (OpenAlex) | 2 |
Abstract
Tandem mass spectrometry has played a pivotal role in advancing proteomics, enabling the high-throughput analysis of protein composition in biological tissues. Many deep learning methods have been developed for \emph{de novo} peptide sequencing task, i.e., predicting the peptide sequence for the observed mass spectrum. However, two key challenges seriously hinder the further advancement of this important task. Firstly, since there is no consensus for the evaluation datasets, the empirical results in different research papers are often not comparable, leading to unfair comparison. Secondly, the current methods are usually limited to amino acid-level or peptide-level precision and recall metrics. In this work, we present the first unified benchmark NovoBench for \emph{de novo} peptide sequencing, which comprises diverse mass spectrum data, integrated models, and comprehensive evaluation metrics. Recent impressive methods, including DeepNovo, PointNovo, Casanovo, InstaNovo, AdaNovo and \(π\)-HelixNovo are integrated into our framework. In addition to amino acid-level and peptide-level precision and recall, we evaluate the models’ performance in terms of identifying post-tranlational modifications (PTMs), efficiency and robustness to peptide length, noise peaks and missing fragment ratio, which are important influencing factors while seldom be considered. Leveraging this benchmark, we conduct a large-scale study of current methods, report many insightful findings that open up new possibilities for future development.
Methods and tools
- NovoBench: NeurIPS benchmark for DL de novo
Cites (21)
- AdaNovo: Adaptive De Novo Peptide Sequencing with Conditional Mutual Information (2024) semanticscholar
- Transformer-Based De Novo Peptide Sequencing for Data-Independent Acquisition Mass Spectrometry (2023) semanticscholar
- Accurate de novo peptide sequencing using fully convolutional neural networks (2023) semanticscholar
- Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model (2023) semanticscholar
- De novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experiments (2023) semanticscholar
- Introducing PandaNovo for practical large-scale de novo peptide sequencing (2023) semanticscholar
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- NovoHMM: A Hidden Markov Model for de Novo Peptide Sequencing (2005) semanticscholar
- AUDENS: A Tool for Automated Peptide de Novo Sequencing (2005) semanticscholar
- PepNovo: de novo peptide sequencing via probabilistic network modeling (2005) semanticscholar
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Cited by (4)
- Limitations of de novo sequencing in resolving sequence ambiguity (2025) crossref
- Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing (2025) semanticscholar
- InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments (2025) semanticscholar
- Bridging the Gap between Database Search and De Novo Peptide Sequencing with SearchNovo (2024) both