Update README.md

README.md

@@ -31,7 +31,7 @@ configs:
 
 # 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 with equimolar concentrations. 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).
+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
@@ -48,12 +48,13 @@ The data is based on the ProteomeTools datasets introduced in [1] and [2] and av
 
 ## Uses
 
-The dataset is intended to be used for training, fine-tuning, and evaluating detectability prediction models, given a peptide sequence.
+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
 
 [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.‏
+
 [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.‏
 
 
@@ -61,10 +62,24 @@ The dataset is intended to be used for training, fine-tuning, and evaluating det
 
 **BibTeX:**
 
-[More Information Needed]
+```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.