Collect all Casanovo references on a dedicated citation page (#403)

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README.md

@@ -4,13 +4,16 @@
 
 ![image](https://user-images.githubusercontent.com/32707537/152622912-ca87da20-a64c-4e3f-9ca1-721c6b0d9c64.png)
 
-If you use Casanovo in your work, please cite the following publications:
+Casanovo is a state-of-the-art deep learning tool designed for _de novo_ peptide sequencing.
+Powered by a transformer neural network, Casanovo "translates" peaks in MS/MS spectra into amino acid sequences with remarkable precision.
+Casanovo can be used for to find unexpected peptide sequences in any data-dependent acquisition, bottom-up tandem mass spectrometry dataset, and is particularly useful for immunopeptidomics, metaproteomics, paleoproteomics, venomics, or any setting in which you are interested in identifying peptides that may not be in your protein database.
 
-- Yilmaz, M., Fondrie, W. E., Bittremieux, W., Oh, S. & Noble, W. S. *De novo* mass spectrometry peptide sequencing with a transformer model. in *Proceedings of the 39th International Conference on Machine Learning - ICML '22* vol. 162 25514–25522 (PMLR, 2022). [https://proceedings.mlr.press/v162/yilmaz22a.html](https://proceedings.mlr.press/v162/yilmaz22a.html)
-- Yilmaz, M., Fondrie, W. E., Bittremieux, W., Melendez, C.F., Nelson, R., Ananth, V., Oh, S. & Noble, W. S. Sequence-to-sequence translation from mass spectra to peptides with a transformer model. in *Nature Communications* **15**, 6427 (2024). [doi:10.1038/s41467-024-49731-x](https://doi.org/10.1038/s41467-024-49731-x)
+Why choose Casanovo?
 
-## Documentation
-
-#### https://casanovo.readthedocs.io/en/latest/
+- Unmatched accuracy: Cutting-edge AI ensures precise and reliable peptide sequencing, even in challenging datasets.
+- Open-source innovation: Freely available and easy to integrate into existing visualization workflows.
+- Actively maintained: Join a growing network of researchers and developers to stay at the forefront of technology.
 
+## [Documentation](https://casanovo.readthedocs.io/en/latest/)
 
+## [Citation information](https://casanovo.readthedocs.io/en/latest/cite.html)

casanovo/casanovo.py

@@ -95,20 +95,24 @@ def __init__(self, *args, **kwargs) -> None:
 def main() -> None:
     """# Casanovo
 
-    Casanovo de novo sequences peptides from tandem mass spectra using a
-    Transformer model. Casanovo currently supports mzML, mzXML, and MGF files
-    for de novo sequencing and annotated MGF files, such as those from
-    MassIVE-KB, for training new models.
+    Casanovo is a state-of-the-art deep learning tool designed for de
+    novo peptide sequencing. Powered by a transformer neural network,
+    Casanovo "translates" peaks in MS/MS spectra into amino acid
+    sequences.
 
     Links:
     - Documentation: [https://casanovo.readthedocs.io]()
     - Official code repository: [https://github.com/Noble-Lab/casanovo]()
 
     If you use Casanovo in your work, please cite:
-    - Yilmaz, M., Fondrie, W. E., Bittremieux, W., Oh, S. & Noble, W. S. De novo
-    mass spectrometry peptide sequencing with a transformer model. Proceedings
-    of the 39th International Conference on Machine Learning - ICML '22 (2022)
-    doi:10.1101/2022.02.07.479481.
+    - Yilmaz, M., Fondrie, W. E., Bittremieux, W., Oh, S. & Noble, W. S.
+    De novo mass spectrometry peptide sequencing with a transformer
+    model. Proceedings of the 39th International Conference on Machine
+    Learning - ICML '22 (2022).
+    [https://proceedings.mlr.press/v162/yilmaz22a.html]().
+
+    For more information on how to cite different versions of Casanovo,
+    please see [https://casanovo.readthedocs.io/en/latest/cite.html]().
 
     """
     return

docs/cite.md

@@ -0,0 +1,27 @@
+# How to Cite Casanovo
+
+When using Casanovo in your research, please cite the relevant scientific publications to acknowledge the work and contributions behind the tool.
+Below, you will find detailed information on how to cite Casanovo, including citations for its various versions and functionalities.
+
+For general use of Casanovo, please cite the following paper:
+
+Yilmaz, M., Fondrie, W. E., Bittremieux, W., Oh, S. & Noble, W. S. *De novo* mass spectrometry peptide sequencing with a transformer model. in *Proceedings of the 39th International Conference on Machine Learning - ICML '22* vol. 162 25514–25522 (PMLR, 2022). [https://proceedings.mlr.press/v162/yilmaz22a.html](https://proceedings.mlr.press/v162/yilmaz22a.html)
+
+In addition, you may wish to cite one or more of these additional publications, depending on relevance to your work.
+
+- For improved performance of Casanovo by training on the MassIVE-KB data and applications in immunopeptidomics, metaproteomics, and the dark human proteome (Casanovo v4.x):  
+Yilmaz, M., Fondrie, W. E., Bittremieux, W., Melendez, C.F., Nelson, R., Ananth, V., Oh, S. & Noble, W. S. Sequence-to-sequence translation from mass spectra to peptides with a transformer model. in *Nature Communications* **15**, 6427 (2024). [doi:10.1038/s41467-024-49731-x](https://doi.org/10.1038/s41467-024-49731-x)
+
+- For work involving Casanovo's enhanced performance on tryptic and non-tryptic data (Casanovo v4.2.x):  
+Melendez, C., Sanders, J., Yilmaz, M., Bittremieux, W., Fondrie, W. E., Oh, S. & Noble, W. S. Accounting for digestion enzyme bias in Casanovo. in *Journal of Proteome Research* **23**, 4761–4769 (2024). [doi:10.1021/acs.jproteome.4c00422](https://doi.org/10.1021/acs.jproteome.4c00422)
+
+- For using Casanovo as a learned score function for sequence database searching (Casanovo-DB):  
+Ananth, V., Sanders, J., Yilmaz, M., Wen, B., Oh, S. & Noble, W. S. A learned score function improves the power of mass spectrometry database search. in *Bioinformatics* **40**, i410–i417 (2024). [doi:10.1093/bioinformatics/btae218](https://doi.org/10.1093/bioinformatics/btae218)
+
+## Notes for Citation
+
+- Always ensure you are citing the correct version or functionality of Casanovo relevant to your use case.
+- If you have questions about how to cite Casanovo in specific scenarios, feel free to reach out to the Casanovo community or maintainers.
+
+By citing Casanovo appropriately, you help support the ongoing development and innovation of this open-source tool.
+Thank you for contributing to the community.