metadata

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
Paper: NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics

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:

@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}, 
}