---
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: experiment_name
    dtype: string
  - name: evidence_index
    dtype: int64
  - name: scan_number
    dtype: int64
  - name: sequence
    dtype: string
  - name: modified_sequence
    dtype: string
  - name: precursor_mz
    dtype: float64
  - name: precursor_recalibrated_mz
    dtype: float64
  - name: precursor_mass
    dtype: float64
  - name: precursor_charge
    dtype: int64
  - name: retention_time
    dtype: float64
  - name: mz_array
    sequence: float32
  - name: intensity_array
    sequence: float32
  splits:
  - name: train
    num_bytes: 3370985593
    num_examples: 2132847
  - name: validation
    num_bytes: 413243959
    num_examples: 257187
  - name: test
    num_bytes: 421581021
    num_examples: 265369
  download_size: 3944832530
  dataset_size: 4205810573
---


# Dataset Card for High-Confidence ProteomeTools
Dataset used to train, validate and test InstaNovo and InstaNovo+.


## 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

This dataset consists of the highest-confidence peptide-spectral matches from three parts of the [ProteomeTools](https://www.proteometools.org/) datasets. The original datasets may be found in the PRIDE repository with identifiers: 
- `PXD004732` (Part I)
- `PXD010595` (Part II)
- `PXD021013` (Part III)

The dataset has been split on unique peptides with the following ratio:
- 80% train
- 10% validation
- 10% test

## 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

MaxQuant additional columns:
- `experiment_name (string)`
- `evidence_index (in64)`
- `scan_number (in64)`
- `precursor_recalibrated_mz (float64)`

## Citation Information

If you use this dataset, please cite the original authors.
The original [ProteomeTools](https://www.proteometools.org/) data is available on [PRIDE](https://www.ebi.ac.uk/pride/) with identifiers `PXD004732` (Part I), `PXD010595` (Part II), and `PXD021013` (Part III).

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}
}
```