Datasets:

Wilhelmlab
/

prospect-ptms-ms2

license: cc-by-4.0
dataset_info:
  - config_name: default
    features:
      - name: raw_file
        dtype: string
      - name: scan_number
        dtype: int64
      - name: method_nbr
        dtype: int64
      - name: precursor_charge_onehot
        sequence: int32
      - name: collision_energy_aligned_normed
        dtype: float64
      - name: intensities_raw
        sequence: float64
      - name: package
        dtype: string
      - name: modified_sequence
        dtype: string
    splits:
      - name: train
        num_bytes: 32998969225
        num_examples: 21294649
      - name: val
        num_bytes: 9419944436
        num_examples: 6078851
      - name: test
        num_bytes: 4654383726
        num_examples: 3003623
    download_size: 3706447347
    dataset_size: 47073297387
  - config_name: holdout
    features:
      - name: raw_file
        dtype: string
      - name: scan_number
        dtype: int64
      - name: modified_sequence
        dtype: string
      - name: collision_energy_aligned_normed
        dtype: float64
      - name: intensities_raw
        sequence: float64
      - name: precursor_charge_onehot
        sequence: int32
      - name: method_nbr
        dtype: int64
    splits:
      - name: test
        num_bytes: 1208775037
        num_examples: 783150
    download_size: 50335479
    dataset_size: 1208775037
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 - Fragment Ion Intensity Prediction (MS2)

A mass-spectrometry dataset for applied machine learning in proteomics, annotated, processed and split for the task of fragment ion intensity 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 fragment ion intensity prediction given a peptide sequence. Optionally, addiional inputs can be used as features to encode PTMs or other 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-ms2")

# holdout data for final benchmarking and model comparison; contains test split only
holdout_dataset = load_dataset("Wilhelmlab/prospect-ptms-ms2", "holdout")

Dataset Creation

Curation Rationale

The dataset is intended to serve as a reference benchmark dataset for fragment ion intensity 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

The annotations are based on an expert system [7] with a set of rules listed in the PROSPECT paper [8]. The vector of intensities is collected in one column named intensities_raw.

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.

[7] Nadin Neuhauser, Annette Michalski, Jürgen Cox, and Matthias Mann. Expert system for computer-assisted annotation of ms/ms spectra. Molecular & Cellular Proteomics, 11(11):1500– 1509, 2012.

[8] Omar Shouman, Wassim Gabriel, Victor-George Giurcoiu, Vitor Sternlicht, and Mathias Wil- helm. PROSPECT: Labeled tandem mass spectrometry dataset for machine learning in pro- teomics. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, Advances in Neural Information Processing Systems, volume 35, pages 32882–32896. Curran Associates, Inc., 2022.

Dataset Card Contact

mathias.wilhelm@tum.de

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