---
dataset_info:
  features:
  - name: Sequences
    dtype: string
  - name: Classes
    dtype: int64
  - name: Proteins
    dtype: string
  splits:
  - name: train
    num_bytes: 6154112
    num_examples: 167882
  - name: val
    num_bytes: 683364
    num_examples: 18654
  - name: test
    num_bytes: 2205303
    num_examples: 60185
  download_size: 6270638
  dataset_size: 9042779
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: val
    path: data/val-*
  - split: test
    path: data/test-*
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.