NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916462794375168 |
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| author | Zhou, Jingbo Chen, Shaorong Xia, Jun Liu, Sizhe Ling, Tianze Du, Wenjie Liu, Yue Yin, Jianwei Li, Stan Z. |
| author_facet | Zhou, Jingbo Chen, Shaorong Xia, Jun Liu, Sizhe Ling, Tianze Du, Wenjie Liu, Yue Yin, Jianwei Li, Stan Z. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_11906 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics Zhou, Jingbo Chen, Shaorong Xia, Jun Liu, Sizhe Ling, Tianze Du, Wenjie Liu, Yue Yin, Jianwei Li, Stan Z. Quantitative Methods Artificial Intelligence 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. |
| title | NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics |
| topic | Quantitative Methods Artificial Intelligence |
| url | https://arxiv.org/abs/2406.11906 |