Cite the published Nature Communications article

README.md

@@ -55,20 +55,20 @@ To assess the model performance of `InstaNovo-P` on phosphorylated peptides, we
 
 ## Citation:
 
-If you use `InstaNovo-P` in your research, please cite: `InstaNovo-P: A de novo peptide sequencing model for phosphoproteomics`
+If you use `InstaNovo-P` in your research, please cite: `InstaNovo-P: a de novo peptide sequencing model for phosphoproteomics`
 
 ```bibtex
-@article {Lauridsen2025.05.14.654049,
-	author = {Lauridsen, Jesper and Ramasamy, Pathmanaban and Catzel, Rachel and Canbay, Vahap and Mabona, Amandla and Eloff, Kevin and Fullwood, Paul and Ferguson, Jennifer and Kirketerp-M{\o}ller, Annekatrine and Goldschmidt, Ida Sofie and Claeys, Tine and van Puyenbroeck, Sam and Lopez Carranza, Nicolas and Schoof, Erwin M. and Martens, Lennart and Van Goey, Jeroen and Francavilla, Chiara and Jenkins, Timothy Patrick and Kalogeropoulos, Konstantinos},
-	title = {InstaNovo-P: A de novo peptide sequencing model for phosphoproteomics},
-	elocation-id = {2025.05.14.654049},
-	year = {2025},
-	doi = {10.1101/2025.05.14.654049},
-	publisher = {Cold Spring Harbor Laboratory},
-	abstract = {Phosphorylation, a crucial post-translational modification (PTM), plays a central role in cellular signaling and disease mechanisms. Mass spectrometry-based phosphoproteomics is widely used for system-wide characterization of phosphorylation events. However, traditional methods struggle with accurate phosphorylated site localization, complex search spaces, and detecting sequences outside the reference database. Advances in de novo peptide sequencing offer opportunities to address these limitations, but have yet to become integrated and adapted for phosphoproteomics datasets. Here, we present InstaNovo-P, a phosphorylation specific version of our transformer-based InstaNovo model, fine-tuned on extensive phosphoproteomics datasets. InstaNovo-P significantly surpasses existing methods in phosphorylated peptide detection and phosphorylated site localization accuracy across multiple datasets, including complex experimental scenarios. Our model robustly identifies peptides with single and multiple phosphorylated sites, effectively localizing phosphorylation events on serine, threonine, and tyrosine residues. We experimentally validate our model predictions by studying FGFR2 signaling, further demonstrating that InstaNovo-P uncovers phosphorylated sites previously missed by traditional database searches. These predictions align with critical biological processes, confirming the model{\textquoteright}s capacity to yield valuable biological insights. InstaNovo-P adds value to phosphoproteomics experiments by effectively identifying biologically relevant phosphorylation events without prior information, providing a powerful analytical tool for the dissection of signaling pathways.Competing Interest StatementR.C, A.M., K.E, N.L.C., and J.V.G. are employees of InstaDeep, 5 Merchant Square, London, UK. The other authors declare no competing interests.},
-	URL = {https://www.biorxiv.org/content/early/2025/05/18/2025.05.14.654049},
-	eprint = {https://www.biorxiv.org/content/early/2025/05/18/2025.05.14.654049.full.pdf},
-	journal = {bioRxiv}
+@article{lauridsen_kalogeropoulos_2026_instanovo-p,
+	title = {InstaNovo-P: a de novo peptide sequencing model for phosphoproteomics},
+	author = {Lauridsen, Jesper and Canbay, Vahap and Catzel, Rachel and Ramasamy, Pathmanaban and Mabona, Amandla and Eloff, Kevin and Fullwood, Paul and Ferguson, Jennifer and Kirketerp-M{\o}ller, Annekatrine and Goldschmidt, Ida Sofie and Claeys, Tine and van Puyenbroeck, Sam and Lopez Carranza, Nicolas and Schoof, Erwin M. and Martens, Lennart and Van Goey, Jeroen and Francavilla, Chiara and Jenkins, Timothy Patrick and Kalogeropoulos, Konstantinos},
+	year = {2026},
+	journal = {Nature Communications},
+	volume = {17},
+	number = {1},
+	pages = {9277},
+	doi = {10.1038/s41467-026-75138-x},
+	publisher = {Springer Nature},
+	url = {https://doi.org/10.1038/s41467-026-75138-x}
 }
 ```