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---
license: cc0-1.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: sequence
dtype: string
- name: modified_sequence
dtype: string
- name: precursor_mz
dtype: float64
- name: precursor_charge
dtype: int64
- name: mz_array
sequence: float64
- name: intensity_array
sequence: float32
splits:
- name: train
num_bytes: 839098224
num_examples: 499402
- name: validation
num_bytes: 49792990
num_examples: 28572
- name: test
num_bytes: 300840334
num_examples: 111312
download_size: 1414788031
dataset_size: 1189731548
---
# Dataset Card for Nine-Species excluding Yeast
Dataset used for the baseline comparison of InstaNovo to other models.
## Dataset Description
- **Repository:** [InstaNovo](https://github.com/instadeepai/InstaNovo)
- **Paper:** [InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments](https://www.nature.com/articles/s42256-025-01019-5)
### Dataset Summary
Dataset used in the original [DeepNovo](https://www.pnas.org/doi/full/10.1073/pnas.1705691114) paper.
- The training set contains 8 species excluding yeast
- The validation/test set contains the yeast species
## 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 original authors.
The original data is available on [MASSIVE](https://massive.ucsd.edu/ProteoSAFe/static/massive.jsp) with the identifier `MSV000081382`.
Please also cite InstaNovo:
```bibtex
@article{eloff_kalogeropoulos_2025_instanovo,
title = {InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale
proteomics experiments},
author = {Eloff, Kevin and Kalogeropoulos, Konstantinos and Mabona, Amandla and Morell,
Oliver and Catzel, Rachel and Rivera-de-Torre, Esperanza and Berg Jespersen,
Jakob and Williams, Wesley and van Beljouw, Sam P. B. and Skwark, Marcin J.
and Laustsen, Andreas Hougaard and Brouns, Stan J. J. and Ljungars,
Anne and Schoof, Erwin M. and Van Goey, Jeroen and auf dem Keller, Ulrich and
Beguir, Karim and Lopez Carranza, Nicolas and Jenkins, Timothy P.},
year = 2025,
month = {Mar},
day = 31,
journal = {Nature Machine Intelligence},
doi = {10.1038/s42256-025-01019-5},
issn = {2522-5839},
url = {https://doi.org/10.1038/s42256-025-01019-5}
}
``` |