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] 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
[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:
@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.