| --- |
| 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: |
|
|
| ```python |
| # 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. |
|
|
