InstaDeepAI/ms_ninespecies_benchmark

Dataset Card for Nine-Species excluding Yeast

Dataset used for the baseline comparison of InstaNovo to other models.

Dataset Summary

Dataset used in the original DeepNovo 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 with the identifier MSV000081382.

Please also cite InstaNovo:

@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}
}