Datasets:

InstaDeepAI
/

CompOmics_PRIDE

---
license: other
license_name: embl-ebi-terms-of-use
license_link: https://www.ebi.ac.uk/about/terms-of-use/
tags:
  - biology
  - mass-spectrometry
  - proteomics
  - de-novo-peptide-sequencing
size_categories:
  - 1M<n<10M
configs:
  - config_name: default
    data_files:
      - split: train
        path: "data/train/*.parquet"
      - split: validation
        path: "data/validation/*.parquet"
      - split: test
        path: "data/test/*.parquet"
dataset_info:
  features:
    - name: "sequence"
      dtype: string
    - name: "modified_sequence"
      dtype: string
    - name: "precursor_mz"
      dtype: float64
    - name: "precursor_mass"
      dtype: float64
    - name: "Mass (theoretical)"
      dtype: float64
    - name: "precursor_charge"
      dtype: int64
    - name: "Mass error"
      dtype: float64
    - name: "Retention time"
      dtype: float64
    - name: "Score"
      dtype: float64
    - name: "PEP"
      dtype: float64
    - name: "Qvalue"
      dtype: float64
    - name: "TD"
      dtype: string
    - name: "Scan number"
      dtype: int64
    - name: "mz_array"
      sequence: float64
    - name: "intensity_array"
      sequence: float64
    - name: "PXD_identifier"
      dtype: string
    - name: "modification_string"
      dtype: string
    - name: "localisation_options"
      dtype: int64
  splits:
    - name: train
      num_examples: 6992871
    - name: validation
      num_examples: 123307
    - name: test
      num_examples: 1172885
---

# Dataset Card for CompOmics PRIDE

## Overview

Data assembled from 18 public PRIDE proteomics projects, partitioned into train, validation, and test splits.
This dataset was part of the training data for the _de novo_ peptide sequencing model [InstaNovo](https://github.com/instadeepai/instanovo) ([Eloff, Kalogeropoulos et al., *Nature Machine Intelligence*, 2025](https://doi.org/10.1038/s42256-025-01019-5)).

## Dataset Details

### Description

- **Total rows**: 8,289,063
- **PRIDE projects**: 18

### Structure

| Split | Rows | Projects |
|-------|------|----------|
| train | 6,992,871 | 18 |
| validation | 123,307 | 18 |
| test | 1,172,885 | 18 |

### Feature Schema

| Column | Type |
|--------|------|
| `sequence` | `large_string` |
| `modified_sequence` | `large_string` |
| `precursor_mz` | `double` |
| `precursor_mass` | `double` |
| `Mass (theoretical)` | `double` |
| `precursor_charge` | `int64` |
| `Mass error` | `double` |
| `Retention time` | `double` |
| `Score` | `double` |
| `PEP` | `double` |
| `Qvalue` | `double` |
| `TD` | `large_string` |
| `Scan number` | `int64` |
| `mz_array` | `large_list<element: double>` |
| `intensity_array` | `large_list<element: double>` |
| `PXD_identifier` | `large_string` |
| `modification_string` | `large_string` |
| `localisation_options` | `int64` |

Each row carries its source PRIDE accession in the `PXD_identifier` column.

## Dataset Sources

Per-PRIDE-project breakdown with original publications:

| Accession | Title | Reference |
|-----------|-------|-----------|
| [PXD000666](https://www.ebi.ac.uk/pride/archive/projects/PXD000666) | Deep proteomic evaluation of primary and cell line motoneuron disease models delineates major differences in neuronal characteristics | Hornburg D, Drepper C, Butter F, Meissner F, Sendtner M, Mann M. Deep proteomic evaluation of primary and cell line motoneuron disease models delineates major differences in neuronal characteristics. Mol Cell Proteomics. 2014 Sep 5. pii: [doi:10.1074/mcp.M113.037291](https://doi.org/10.1074/mcp.m113.037291) |
| [PXD000867](https://www.ebi.ac.uk/pride/archive/projects/PXD000867) | Cell type-resolved quantitative proteomics of mouse liver | Azimifar SB, Nagaraj N, Cox J, Mann M. Cell-type-resolved quantitative proteomics of murine liver. Cell Metab. 2014 Dec 2;20(6):1076-87<br>[doi:10.1016/j.cmet.2014.11.002](https://doi.org/10.1016/j.cmet.2014.11.002) |
| [PXD001839](https://www.ebi.ac.uk/pride/archive/projects/PXD001839) | Vinculin-network mediated cytoskeletal remodeling regulates contractile function in the aging heart - Rat Data | Kaushik G, Spenlehauer A, Sessions AO, Trujillo AS, Fuhrmann A, Fu Z, Venkatraman V, Pohl D, Tuler J, Wang M, Lakatta EG, Ocorr K, Bodmer R, Bernstein SI, Van Eyk JE, Cammarato A, Engler AJ. Vinculin network-mediated cytoskeletal remodeling regulates contractile function in the aging heart. Sci Transl Med. 2015 Jun 17;7(292):292ra99<br>[doi:10.1126/scitranslmed.aaa5843](https://doi.org/10.1126/scitranslmed.aaa5843) |
| [PXD003155](https://www.ebi.ac.uk/pride/archive/projects/PXD003155) | Atxn2-Knock-Out mice show branched chain amino acids and fatty acids pathway alterations | Meierhofer D, Halbach M, Sen NE, Gispert S, Auburger G. Atxn2-Knock-Out mice show branched chain amino acids and fatty acids pathway alterations. Mol Cell Proteomics. 2016 Feb 5. pii: mcp.M115.056770 |
| [PXD004364](https://www.ebi.ac.uk/pride/archive/projects/PXD004364) | Rat testis LC-MS/MS -  Integrated proteomics and metabolomics analysis of rat testis: Mechanism of arsenic-induced male reproductive toxicity | Huang Q, Luo L, Alamdar A, Zhang J, Liu L, Tian M, Eqani SA, Shen H. Integrated proteomics and metabolomics analysis of rat testis: Mechanism of arsenic-induced male reproductive toxicity. Sci Rep. 2016 6:32518<br>[doi:10.1038/srep32518](https://doi.org/10.1038/srep32518) |
| [PXD004612](https://www.ebi.ac.uk/pride/archive/projects/PXD004612) | Proteomic comparison study between female and male tendon | Sarver DC, Kharaz YA, Sugg KB, Gumucio JP, Comerford E, Mendias CL. Sex differences in tendon structure and function. J Orthop Res. 2017 Jan 10<br>[doi:10.1002/jor.23516](https://doi.org/10.1002/jor.23516) |
| [PXD005230](https://www.ebi.ac.uk/pride/archive/projects/PXD005230) | Proteome changes during mouse brain aging | Duda P, Wójcicka O, Wiśniewski JR, Rakus D. Global quantitative TPA-based proteomics of mouse brain structures reveals significant alterations in expression of proteins involved in neuronal plasticity during aging. Aging (Albany NY). 2018<br>[doi:10.18632/aginh.101501](https://doi.org/10.18632/aginh.101501) |
| [PXD006692](https://www.ebi.ac.uk/pride/archive/projects/PXD006692) | Proteomic Investigation of EVLP Tissue Lung Lobes | Roffia V, De Palma A, Lonati C, Silvestre DD, Rossi R, Mantero M, Gatti S, Dondossola D, Valenza F, Mauri P, Blasi F. Proteome Investigation of Rat Lungs subjected to Ex Vivo Perfusion (EVLP). Molecules. 2018 23(12)<br>[doi:10.3390/molecules23123061](https://doi.org/10.3390/molecules23123061) |
| [PXD011360](https://www.ebi.ac.uk/pride/archive/projects/PXD011360) | Bacterial and host proteins along and across the porcine GIT | Tröscher-Mußotter J, Tilocca B, Stefanski V, Seifert J. Analysis of the Bacterial and Host Proteins along and across the Porcine Gastrointestinal Tract. Proteomes. 2019 7(1)<br>[doi:10.3390/proteomes7010004](https://doi.org/10.3390/proteomes7010004) |
| [PXD011536](https://www.ebi.ac.uk/pride/archive/projects/PXD011536) | Proteomic analysis of liver tissue from a pig model of mutant INS gene induced diabetes of youth (MIDY) | Backman M, Flenkenthaler F, Blutke A, Dahlhoff M, Ländström E, Renner S, Philippou-Massier J, Krebs S, Rathkolb B, Prehn C, Grzybek M, Coskun Ü, Rothe M, Adamski J, de Angelis MH, Wanke R, Fröhlich T, Arnold GJ, Blum H, Wolf E. Multi-omics insights into functional alterations of the liver in insulin-deficient diabetes mellitus. Mol Metab. 2019 26:30-44<br>[doi:10.1016/j.molmet.2019.05.011](https://doi.org/10.1016/j.molmet.2019.05.011) |
| [PXD013543](https://www.ebi.ac.uk/pride/archive/projects/PXD013543) | Myocardial proteome from obese rats with cardiac dysfunction C4PR_LIV | Vileigas DF, Harman VM, Freire PP, Marciano CLC, Sant'Ana PG, de Souza SLB, Mota GAF, da Silva VL, Campos DHS, Padovani CR, Okoshi K, Beynon RJ, Santos LD, Cicogna AC. Landscape of heart proteome changes in a diet-induced obesity model. Sci Rep. 2019 9(1):18050<br>[doi:10.1038/s41598-019-54522-2](https://doi.org/10.1038/s41598-019-54522-2) |
| [PXD015928](https://www.ebi.ac.uk/pride/archive/projects/PXD015928) | Heterogeneity of proteome dynamics between connective tissue phases of adult tendon | Choi H, Simpson D, Wang D, Prescott M, Pitsillides AA, Dudhia J, Clegg PD, Ping P, Thorpe CT. Heterogeneity of proteome dynamics between connective tissue phases of adult tendon. Elife. 2020 9<br>[doi:10.7554/elife.55262](https://doi.org/10.7554/elife.55262) |
| [PXD016793](https://www.ebi.ac.uk/pride/archive/projects/PXD016793) | Proteomics analysis of rat liver after disulfiram treatment | Bernier M, Harney D, Koay YC, Diaz A, Singh A, Wahl D, Pulpitel T, Ali A, Guiterrez V, Mitchell SJ, Kim EY, Mach J, Price NL, Aon MA, LeCouteur DG, Cogger VC, Fernandez-Hernando C, O'Sullivan J, Larance M, Cuervo AM, de Cabo R. Elucidating the mechanisms by which disulfiram protects against obesity and metabolic syndrome. NPJ Aging Mech Dis. 2020 6:8<br>[doi:10.1038/s41514-020-0046-6](https://doi.org/10.1038/s41514-020-0046-6) |
| [PXD017671](https://www.ebi.ac.uk/pride/archive/projects/PXD017671) | Proteomic changes of the liver in the absence of growth hormone (GH) actions | Riedel EO, Hinrichs A, Kemter E, Dahlhoff M, Backman M, Rathkolb B, Prehn C, Adamski J, Renner S, Blutke A, de Angelis MH, Bidlingmaier M, Schopohl J, Arnold GJ, Fröhlich T, Wolf E. Functional changes of the liver in the absence of growth hormone (GH) action - Proteomic and metabolomic insights from a GH receptor deficient pig model. Mol Metab. 2020 36:100978<br>[doi:10.1016/j.molmet.2020.100978](https://doi.org/10.1016/j.molmet.2020.100978) |
| [PXD019431](https://www.ebi.ac.uk/pride/archive/projects/PXD019431) | Circadian Mouse Articular Cartilage Proteomics | Dudek M, Angelucci C, Pathiranage D, Wang P, Mallikarjun V, Lawless C, Swift J, Kadler KE, Boot-Handford RP, Hoyland J, Lamande SR, Bateman JF, Meng QJ. Circadian time series proteomics reveals daily dynamics in cartilage physiology. Osteoarthritis Cartilage. 2021<br>[doi:10.1016/j.joca.2021.02.008](https://doi.org/10.1016/j.joca.2021.02.008) |
| [PXD019852](https://www.ebi.ac.uk/pride/archive/projects/PXD019852) | Holistic proteome analyses of heart samples from GHR-deficient (GHR-KO) pigs | Hinrichs A, Riedel EO, Klymiuk N, Blutke A, Kemter E, Längin M, Dahlhoff M, Keßler B, Kurome M, Zakhartchenko V, Jemiller EM, Ayares D, Bidlingmaier M, Flenkenthaler F, Hrabĕ de Angelis M, Arnold GJ, Reichart B, Fröhlich T, Wolf E. Growth hormone receptor knockout to reduce the size of donor pigs for preclinical xenotransplantation studies. Xenotransplantation. 2020:e12664<br>[doi:10.1111/xen.12664](https://doi.org/10.1111/xen.12664) |
| [PXD026910](https://www.ebi.ac.uk/pride/archive/projects/PXD026910) | Proteomic analysis of adipose tissue from a pig model of mutant INS gene induced diabetes of youth (MIDY) | Flenkenthaler F, Ländström E, Shashikadze B, Backman M, Blutke A, Philippou-Massier J, Renner S, Hrabe de Angelis M, Wanke R, Blum H, Arnold GJ, Wolf E, Fröhlich T. Differential Effects of Insulin-Deficient Diabetes Mellitus on Visceral vs. Subcutaneous Adipose Tissue-Multi-omics Insights From the Munich MIDY Pig Model. Front Med (Lausanne). 2021 8:751277<br>[doi:10.3389/fmed.2021.751277](https://doi.org/10.3389/fmed.2021.751277) |
| [PXD027772](https://www.ebi.ac.uk/pride/archive/projects/PXD027772) | A scalable, clinically severe pig model for Duchenne muscular dystrophy | Stirm M, Fonteyne LM, Shashikadze B, Lindner M, Chirivi M, Lange A, Kaufhold C, Mayer C, Medugorac I, Kessler B, Kurome M, Zakhartchenko V, Hinrichs A, Kemter E, Krause S, Wanke R, Arnold GJ, Wess G, Nagashima H, de Angelis MH, Flenkenthaler F, Kobelke LA, Bearzi C, Rizzi R, Bähr A, Reese S, Matiasek K, Walter MC, Kupatt C, Ziegler S, Bartenstein P, Fröhlich T, Klymiuk N, Blutke A, Wolf E. A scalable, clinically severe pig model for Duchenne muscular dystrophy. Dis Model Mech. 2021<br>[doi:10.1242/dmm.049285](https://doi.org/10.1242/dmm.049285) |

## Acknowledgements

This HuggingFace dataset was created by Jeroen Van Goey (InstaDeep).

Big thanks to Pathmanaban Ramasamy, Tine Claeys, and Lennart Martens of the [CompOmics research group](https://www.compomics.com/) for providing us with this training data.

## Citation

If you use this dataset, please cite the InstaNovo paper:

```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}
}
```

Please also cite the original PRIDE project publications listed in [Dataset Sources](#dataset-sources).