Duplicate from Wilhelmlab/detectability-proteometools

Co-authored-by: Omar Shouman <omsh@users.noreply.huggingface.co>

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README.md

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+---
+license: cc-by-4.0
+dataset_info:
+  features:
+  - name: Sequences
+    dtype: string
+  - name: Classes
+    dtype: int64
+  splits:
+  - name: train
+    num_bytes: 6087276
+    num_examples: 236758
+  - name: val
+    num_bytes: 1521645
+    num_examples: 59190
+  - name: test
+    num_bytes: 845949
+    num_examples: 32884
+  download_size: 5786856
+  dataset_size: 8454870
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: val
+    path: data/val-*
+  - split: test
+    path: data/test-*
+---
+
+# Detectability - ProteomeTools
+
+This data was collected from the PRIDE repository with the identifiers [PXD004732](https://www.ebi.ac.uk/pride/archive/projects/PXD004732), [PXD010595](https://www.ebi.ac.uk/pride/archive/projects/PXD010595), and [PXD021013](https://www.ebi.ac.uk/pride/archive/projects/PXD021013). The datasets were originally obtained by analyzing pools of approximately 1000 synthetic peptides. RAW data was analyzed using either specific, semi-specific, or unspecific in silico digestion settings in MaxQuant and with Trypsin, LysN, or AspN as specified protease. In all studies, peptide pools were subjected to liquid chromatography using a Dionex 3000 HPLC system (Thermo Fisher Scientific) coupled inline with an Orbitrap Fusion Lumos mass spectrometer (Thermo Fisher Scientific).
+
+
+## Dataset Details
+
+- **Curated by:** Aalborg University - Denmark, in collaboration with Wilhelmlab - TU Munich.
+
+### Dataset Sources
+
+The data is based on the ProteomeTools datasets introduced in [[1]](#ref1) and [[2]](#ref2) and available at:
+- https://www.ebi.ac.uk/pride/archive/projects/PXD004732
+- https://www.ebi.ac.uk/pride/archive/projects/PXD010595
+- https://www.ebi.ac.uk/pride/archive/projects/PXD021013
+
+## Uses
+
+The dataset is intended to be used for training, fine-tuning, and evaluating detectability prediction models, given a peptide sequence. Note that since the peptides were synthesized, training on the dataset will be somewhat biased to synthesizability, rathen than digestability.
+
+
+## References
+
+<a id="ref1"></a>[1] Zolg, D. P., Wilhelm, M., Schnatbaum, K., Zerweck, J., Knaute, T., Delanghe, B., ... & Kuster, B. (2017). Building ProteomeTools based on a complete synthetic human proteome. Nature methods, 14(3), 259-262.‏
+
+<a id="ref2"></a>[2] Gessulat, S., Schmidt, T., Zolg, D. P., Samaras, P., Schnatbaum, K., Zerweck, J., ... & Wilhelm, M. (2019). Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning. Nature methods, 16(6), 509-518.‏
+
+
+## Citation
+
+**BibTeX:**
+
+```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.

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