metadata
dataset_info:
features:
- name: sequence
dtype: large_string
- name: modified_sequence
dtype: large_string
- name: precursor_charge
dtype: int64
- name: precursor_mz
dtype: float64
- name: mz_array
large_list: float64
- name: intensity_array
large_list: float64
- name: experiment_name
dtype: large_string
- name: spectrum_id
dtype: large_string
splits:
- name: test
num_bytes: 89580538
num_examples: 41158
download_size: 59261700
dataset_size: 89580538
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
Dataset Card: 21PTM dataset PXD009449 for InstaNovo-P
To assess the model performance of InstaNovo-P on phosphorylated peptides, we used a subset of project PXD009449 as evaluation dataset.
Original data source:
| Field | Value |
|---|---|
| Title | Systematic characterization of 21 post-translational modification using synthetic peptides |
| Description | The data presented in this study in the - context of the ProteoemTools project - is based on the synthesis of about 5000 synthetic |
| HostingRepository | PRIDE |
| AnnounceDate | 2024-10-22 |
| AnnouncementXML | Submission_2024-10-22_04:44:20.696.xml |
| ReviewLevel | Peer-reviewed dataset |
| DatasetOrigin | Original dataset |
| RepositorySupport | Unsupported dataset by repository |
| PrimarySubmitter | Daniel Zolg |
| SpeciesList | scientific name: Homo sapiens (Human); NCBI TaxID: 9606; |
| ModificationList | monomethylated residue; 3'-nitro-L-tyrosine; N6-malonyl-L-lysine; biotinylated residue; phosphorylated residue; acetylated residue; |
| Instrument | Orbitrap Fusion Lumos |
| URL | https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD009449 |
Citation:
If you use InstaNovo-P in your research, please cite: InstaNovo-P: a de novo peptide sequencing model for phosphoproteomics
@article{lauridsen_kalogeropoulos_2026_instanovo-p,
title = {InstaNovo-P: a de novo peptide sequencing model for phosphoproteomics},
author = {Lauridsen, Jesper and Canbay, Vahap and Catzel, Rachel and Ramasamy, Pathmanaban and Mabona, Amandla and Eloff, Kevin and Fullwood, Paul and Ferguson, Jennifer and Kirketerp-M{\o}ller, Annekatrine and Goldschmidt, Ida Sofie and Claeys, Tine and van Puyenbroeck, Sam and Lopez Carranza, Nicolas and Schoof, Erwin M. and Martens, Lennart and Van Goey, Jeroen and Francavilla, Chiara and Jenkins, Timothy Patrick and Kalogeropoulos, Konstantinos},
year = {2026},
journal = {Nature Communications},
volume = {17},
number = {1},
pages = {9277},
doi = {10.1038/s41467-026-75138-x},
publisher = {Springer Nature},
url = {https://doi.org/10.1038/s41467-026-75138-x}
}
If you use this dataset, please cite
@misc{instadeep_ltd_2026,
author = { InstaDeep Ltd },
title = { PXD009449 (Revision 7676f2c) },
year = 2026,
url = { https://huggingface.co/datasets/InstaDeepAI/PXD009449 },
doi = { 10.57967/hf/7818 },
publisher = { Hugging Face }
}
If you use the InstaNovo model to generate predictions, please also cite: InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments
@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}
}