Datasets:

Wilhelmlab
/

prospect-ptms-charge

license: cc-by-4.0
dataset_info:
  - config_name: default
    features:
      - name: modified_sequence
        dtype: string
      - name: raw_file
        sequence: string
      - name: scan_number
        sequence: int64
      - name: package
        sequence: string
      - name: most_abundant_charge_state
        sequence: int64
      - name: observed_charge_states
        sequence: int64
      - name: charge_state_dist
        sequence: float64
    splits:
      - name: train
        num_bytes: 771591307
        num_examples: 1142537
      - name: val
        num_bytes: 220810369
        num_examples: 328072
      - name: test
        num_bytes: 108923080
        num_examples: 161588
    download_size: 440448117
    dataset_size: 1101324756
  - config_name: holdout
    features:
      - name: modified_sequence
        dtype: string
      - name: raw_file
        sequence: string
      - name: scan_number
        sequence: int64
      - name: package
        sequence: string
      - name: most_abundant_charge_state
        sequence: int64
      - name: observed_charge_states
        sequence: int64
      - name: charge_state_dist
        sequence: float64
    splits:
      - name: test
        num_bytes: 29980852
        num_examples: 42028
    download_size: 9257686
    dataset_size: 29980852
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: val
        path: data/val-*
      - split: test
        path: data/test-*
  - config_name: holdout
    data_files:
      - split: test
        path: holdout/test-*

PROSPECT PTMs - Precursor Charge Prediction

A mass-spectrometry dataset for applied machine learning in proteomics, processed and split for the task of precursor charge prediction.

Dataset Details

Curated by: Wilhelmlab - Technical University of Munich - School of Life Sciences - Germany
License: CC-BY4.0

Dataset Sources

The data is based on the PROSPECT PTMs datasets hosted in Zenodo [3][4][5][6].

Repository: https://github.com/wilhelm-lab/PROSPECT

Uses

The dataset is intended to be used for precursor charge state prediction given a peptide sequence. Optionally, addiional inputs can be used as features to encode PTMs or features describing the experimental setup.

Dataset Structure

The dataset has two configurations:

default: this is the train/val/test data that is based on all the Zenodo PROSPECT PTMs datasets [3][4][5]
holdout: this is the holdout datasets that can be eventually used to evaluate a model capable of processing PTMs. It is based on the PROSPECT Test-PTM dataset on Zenodo [6].

Use one of the following lines to load the respective configuration:

# main data for training and evaluation; contains train, val, tesst splits
main_dataset = load_dataset("Wilhelmlab/prospect-ptms-charge")

# holdout data for final benchmarking and model comparison; contains test split only
holdout_dataset = load_dataset("Wilhelmlab/prospect-ptms-charge", "holdout")

Dataset Creation

Curation Rationale

The dataset is intended to serve as a reference benchmark dataset for precursor charge state prediction, processed, split, and ready-to-use for developing deep learning models for this specific task.

Source Data

The upstream source data is based on the ProteomeTools datasets available on PRIDE [1][2].

Data Collection and Processing

[More Information Needed]

Annotations

Three labels are aggregated from the original data to enable different formulation of the precursor charge state prediction.

most_abundant_charge_state one-hot encoded version of the most dominant charge for the sequence
observed_charge_states: binarized vector (k-hot encoded) with all possible charge states
charge_state_dist: the proportions for different charge states for a given sequence based on all occurences in the original data

Personal and Sensitive Information

The dataset does not contain any personal, sensitive, or private data.

Recommendations

We recommend using the holdout configuration for solely evaluation models at the end of the research iteration.

Citation

BibTeX:

[More Information Needed]

APA:

References

[1] Daniel P Zolg, Mathias Wilhelm, Karsten Schnatbaum, Johannes Zerweck, Tobias Knaute, Bernard Delanghe, Derek J Bailey, Siegfried Gessulat, Hans-Christian Ehrlich, Maximilian Weininger, et al. Building proteometools based on a complete synthetic human proteome. Nature methods, 14(3):259–262, 2017.

[2] Daniel Paul Zolg, Mathias Wilhelm, Tobias Schmidt, Guillaume Médard, Johannes Zerweck, Tobias Knaute, Holger Wenschuh, Ulf Reimer, Karsten Schnatbaum, and Bernhard Kuster. Pro- teometools: Systematic characterization of 21 post-translational protein modifications by liquid chromatography tandem mass spectrometry (lc-ms/ms) using synthetic peptides. Molecular & Cellular Proteomics, 17(9):1850–1863, 2018.

[3] Wassim Gabriel, Omar Shouman, and Mathias Wilhelm. PROSPECT PTMs Dataset - Multi- PTM. DOI:https://doi.org/10.5281/zenodo.11472525, 2024.

[4] Wassim Gabriel, Omar Shouman, and Mathias Wilhelm. PROSPECT PTMs Dataset - TMT. DOI:https://doi.org/10.5281/zenodo.8221499, 2023.

[5] Wassim Gabriel, Omar Shouman, and Mathias Wilhelm. PROSPECT PTMs Dataset - TMT- PTM. DOI:https://doi.org/10.5281/zenodo.11474099, 2024.

[6] Wassim Gabriel, Omar Shouman, and Mathias Wilhelm. PROSPECT PTMs Dataset - Test-PTM. DOI:https://doi.org/10.5281/zenodo.11477731, 2024.

Dataset Card Contact

mathias.wilhelm@tum.de

Wilhelmlab, TU Munich, School of Life Sciences, Germany.