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license: mit |
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--- |
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# Dataset Card for NovoBench |
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Datasets used for the baseline comparison of deep learning-based de novo peptide sequencing method |
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## Dataset Description |
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- **Repository:** [NovoBench](https://github.com/jingbo02/NovoBench) |
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- **Paper:** [NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics](https://arxiv.org/pdf/2406.11906) |
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### Dataset Summary |
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- **Seven-species Dataset.** The Seven-species dataset contains low-resolution mass spectrum and |
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their peptide labels from 7 different species. The previous work DeepNovo has evaluated its |
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performance on these datasets with the leave-one-out method, i.e., training the model on 6 species and |
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testing on the left one species, to mimic the real-world challenging cases where we have to identify |
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the never-before-seen peptide sequences for the observed mass spectrum. In this paper, we conducted |
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testing on the yeast species and training on the remaining 6 species. |
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- **Nine-species Dataset.** The Nine-species dataset is the most widely-used dataset by previous works |
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such as DeepNovo, PointNovo, and Casanovo, which contains high-resolution mass |
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spectrum and their peptide labels from 9 different species. We adopt the Nine-species dataset |
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used by the original publication of DeepNovo (MassIVE dataset identifier: MSV000081382) for |
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benchmarking. Similar to Seven-species dataset, we train models on 8 species and evaluate the left |
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yeast species. Additionally, these datasets contain 3 PTMs (oxidation of methionine, deamidation of |
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asparagine or glutamine), enabling the fair evaluation of various models’ performance in terms of |
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identifying PTMs. |
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- **HC-PT Dataset.** The HC-PT dataset, as detailed in the InstaNovo paper, includes synthetic tryptic |
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peptides that span all canonical human proteins and isoforms. It also encompasses peptides generated |
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by alternative proteases and HLA peptides. The key feature of the HC-PT dataset is its high-resolution |
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spectrum for human-origin peptides and the peptide labels are derived from the high-confidence |
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search results of MaxQuant. |
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## Dataset Structure |
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The dataset is tabular, where each row corresponds to a labelled MS2 spectra. |
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- `sequence (string)` \ |
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The target peptide sequence excluding post-translational modifications |
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- `modified_sequence (string)` \ |
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The target peptide sequence including post-translational modifications |
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- `precursor_mz (float64)` \ |
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The mass-to-charge of the precursor (from MS1) |
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- `charge (int64)` \ |
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The charge of the precursor (from MS1) |
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- `mz_array (list[float64])` \ |
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The mass-to-charge values of the MS2 spectrum |
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- `mz_array (list[float32])` \ |
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The intensity values of the MS2 spectrum |
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## Citation Information |
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If you use this dataset, please cite the NovoBench: |
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```bibtex |
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@misc{zhou2024novobenchbenchmarkingdeeplearningbased, |
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title={NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics}, |
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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}, |
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year={2024}, |
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eprint={2406.11906}, |
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archivePrefix={arXiv}, |
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primaryClass={q-bio.QM}, |
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url={https://arxiv.org/abs/2406.11906}, |
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} |
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``` |
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