license:mit arxiv:2406.11906 region:us
---
license: mit
---

# Dataset Card for NovoBench
Datasets used for the baseline comparison of deep learning-based de novo peptide sequencing method

## Dataset Description

- **Repository:** [NovoBench](https://github.com/jingbo02/NovoBench)
- **Paper:** [NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics](https://arxiv.org/pdf/2406.11906) 


### Dataset Summary

- **Seven-species Dataset.** The Seven-species dataset contains low-resolution mass spectrum and
 their peptide labels from 7 different species. The previous work DeepNovo has evaluated its
 performance on these datasets with the leave-one-out method, i.e., training the model on 6 species and
 testing on the left one species, to mimic the real-world challenging cases where we have to identify
 the never-before-seen peptide sequences for the observed mass spectrum. In this paper, we conducted
 testing on the yeast species and training on the remaining 6 species.

- **Nine-species Dataset.** The Nine-species dataset is the most widely-used dataset by previous works
 such as DeepNovo, PointNovo, and Casanovo, which contains high-resolution mass
 spectrum and their peptide labels from 9 different species. We adopt the Nine-species dataset
 used by the original publication of DeepNovo (MassIVE dataset identifier: MSV000081382) for
 benchmarking. Similar to Seven-species dataset, we train models on 8 species and evaluate the left
 yeast species. Additionally, these datasets contain 3 PTMs (oxidation of methionine, deamidation of
 asparagine or glutamine), enabling the fair evaluation of various models’ performance in terms of
 identifying PTMs.
 - **HC-PT Dataset.** The HC-PT dataset, as detailed in the InstaNovo paper, includes synthetic tryptic
 peptides that span all canonical human proteins and isoforms. It also encompasses peptides generated
 by alternative proteases and HLA peptides. The key feature of the HC-PT dataset is its high-resolution
 spectrum for human-origin peptides and the peptide labels are derived from the high-confidence
 search results of MaxQuant.


## Dataset Structure

The dataset is tabular, where each row corresponds to a labelled MS2 spectra.
- `sequence (string)` \
  The target peptide sequence excluding post-translational modifications
- `modified_sequence (string)` \
  The target peptide sequence including post-translational modifications
- `precursor_mz (float64)` \
  The mass-to-charge of the precursor (from MS1)
- `charge (int64)` \
  The charge of the precursor (from MS1)
- `mz_array (list[float64])` \
  The mass-to-charge values of the MS2 spectrum
- `mz_array (list[float32])` \
  The intensity values of the MS2 spectrum



## Citation Information

If you use this dataset, please cite the NovoBench:

```bibtex
@misc{zhou2024novobenchbenchmarkingdeeplearningbased,
      title={NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics}, 
      author={Jingbo Zhou and Shaorong Chen and Jun Xia and Sizhe Liu and Tianze Ling and Wenjie Du and Yue Liu and Jianwei Yin and Stan Z. Li},
      year={2024},
      eprint={2406.11906},
      archivePrefix={arXiv},
      primaryClass={q-bio.QM},
      url={https://arxiv.org/abs/2406.11906}, 
}
```
