README.md

---
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  - name: Sequences
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  - name: Classes
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license: cc
---

# Detectability - Sinitcyn

This dataset contains bottom-up proteomics data from six different human cell lines (GM12878, HeLa S3, HepG2, hES1, HUVEC, and K562), deep fractioning (24–80 fractions) and three different fragmentation methods (HCD, CAD and ETD). All cell lines were digested with six different proteases (LysC, LysN, AspN, chymotrypsin, GluC and trypsin).

## Dataset Details

- **Curated by:** Aalborg University - Denmark, in collaboration with Wilhelmlab, TU Munich.
- **License:** CC0 1.0 Universal

### Dataset Sources

The data is based on the datasets introduced in [[1]](#ref1) and available at: https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD024364

## Uses

The dataset is intended to be used for training, fine-tuning, and evaluating detectability prediction models, given a peptide sequence.


## References

<a id="ref1"></a>[1] Sinitcyn, P., Richards, A. L., Weatheritt, R. J., Brademan, D. R., Marx, H., Shishkova, E., ... & Coon, J. J. (2023). Global detection of human variants and isoforms by deep proteome sequencing. Nature biotechnology, 41(12), 1776-1786.


## 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.