README.md

---
license: cc-by-4.0
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  - name: val
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configs:
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  data_files:
  - split: train
    path: data/train-*
  - split: val
    path: data/val-*
  - split: test
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---

# Detectability - ProteomeTools

This data was collected from the PRIDE repository with the identifiers [PXD004732](https://www.ebi.ac.uk/pride/archive/projects/PXD004732), [PXD010595](https://www.ebi.ac.uk/pride/archive/projects/PXD010595), and [PXD021013](https://www.ebi.ac.uk/pride/archive/projects/PXD021013). The datasets were originally obtained by analyzing pools of approximately 1000 synthetic peptides. RAW data was analyzed using either specific, semi-specific, or unspecific in silico digestion settings in MaxQuant and with Trypsin, LysN, or AspN as specified protease. In all studies, peptide pools were subjected to liquid chromatography using a Dionex 3000 HPLC system (Thermo Fisher Scientific) coupled inline with an Orbitrap Fusion Lumos mass spectrometer (Thermo Fisher Scientific).


## Dataset Details

- **Curated by:** Aalborg University - Denmark, in collaboration with Wilhelmlab - TU Munich.

### Dataset Sources

The data is based on the ProteomeTools datasets introduced in [[1]](#ref1) and [[2]](#ref2) and available at:
- https://www.ebi.ac.uk/pride/archive/projects/PXD004732
- https://www.ebi.ac.uk/pride/archive/projects/PXD010595
- https://www.ebi.ac.uk/pride/archive/projects/PXD021013

## Uses

The dataset is intended to be used for training, fine-tuning, and evaluating detectability prediction models, given a peptide sequence. Note that since the peptides were synthesized, training on the dataset will be somewhat biased to synthesizability, rathen than digestability.


## References

<a id="ref1"></a>[1] Zolg, D. P., Wilhelm, M., Schnatbaum, K., Zerweck, J., Knaute, T., Delanghe, B., ... & Kuster, B. (2017). Building ProteomeTools based on a complete synthetic human proteome. Nature methods, 14(3), 259-262.‏

<a id="ref2"></a>[2] Gessulat, S., Schmidt, T., Zolg, D. P., Samaras, P., Schnatbaum, K., Zerweck, J., ... & Wilhelm, M. (2019). Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning. Nature methods, 16(6), 509-518.‏


## Citation

**BibTeX:**

```bibtex
@article {Abdul-Khalek2024.10.28.620610,
	author = {Abdul-Khalek, Naim and Picciani, Mario and Wimmer, Reinhard and Overgaard, Michael Toft and Wilhelm, Mathias and Echers, Simon Gregersen},
	title = {To fly, or not to fly, that is the question: A deep learning model for peptide detectability prediction in mass spectrometry},
	elocation-id = {2024.10.28.620610},
	year = {2024},
	doi = {10.1101/2024.10.28.620610},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2024/10/31/2024.10.28.620610},
	eprint = {https://www.biorxiv.org/content/early/2024/10/31/2024.10.28.620610.full.pdf},
	journal = {bioRxiv}
}
```

**APA:**

Abdul-Khalek, N., Picciani, M., Wimmer, R., Overgaard, M. T., Wilhelm, M., & Gregersen Echers, S. (2024). To fly, or not to fly, that is the question: A deep learning model for peptide detectability prediction in mass spectrometry. bioRxiv, 2024-10.‏

## Dataset Card Contact

Simon Gregersen, sgr@bio.aau.dk, Department of Chemistry and Biosciences, Aalborg University.

Mathias Wilhelm, mathias.wilhelm@tum.de, Wilhelmlab, TU Munich, School of Life Sciences, Germany.