PROSPECT: Proteomics Dataset for Machine Learning

PROSPECT (PROteometools SPECTrum compendium) is a large annotated dataset leveraging the raw data from ProteomeTools [1].

Features

Access to a large annotated Mass Spectrometry dataset.
Utilities for downloading and splitting data.

Installation

Install with:

pip install git+https://github.com/wilhelm-lab/PROSPECT

Usage

Downloading the Dataset

Using `prospectdataset`:

Install and import the package

import prospectdataset as prods

To download data for retention time prediction only:

prods.download_dataset('retention-time', SAVE_PATH)

To download data for MS/MS spectrum prediction (includes both metadata and spectra):

prods.download_dataset('all', SAVE_PATH)

To download only one of the 12 packages (for a faster download and a smaller dataset to experiment with), enter a substring from the package name, package names are in Zenodo. For example to download the package TUM_missing_first, the following would download the meta data file for the specfic package:

prods.download_dataset('retention-time', SAVE_PATH, 'missing')

All downloaded files are in the parquet format. They can be easily read using panda's pd.read_parquet(). For faster loading, we recommend using fastparquet as an engine, in case it fails for some reason, pyarrow can also be used.

df = pd.read_parquet(PARQUET_FILEPATH, engine='fastparquet')

From Zenodo:

Download and unzip from Zenodo.

Splitting and Filtering Scripts

The three following bash scripts can be used to perform data splitting, merge files, and to filter the retention time data (iRT). Once the package is installed, they are accessbile as bash commands on the system level. Use the option -h to see the arguments of each script.

shuffle-split-data: Shuffle and split data
merge-files: merge multiple files into one
filter-irt: filter iRT data to prepare for training.

License

The project is licensed under the MIT license.

Dataset Hosting

The dataset is hosted on Zenodo PROSPECT DOI and is licensed under Creative Commons Attribution 4.0 International.

Citation

If you use PROSPECT, please cite our paper PROSPECT: Labeled Tandem Mass Spectrometry Dataset for Machine Learning in Proteomics:

@inproceedings{shoumanprospect,
  title={PROSPECT: Labeled Tandem Mass Spectrometry Dataset for Machine Learning in Proteomics},
  author={Shouman, Omar and Gabriel, Wassim and Giurcoiu, Victor-George and Sternlicht, Vitor and Wilhelm, Mathias},
  booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track}
}

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.

liangzhendong123/PROSPECT