From 8f6fecec71b5687cdfb7192b52d756350412b397 Mon Sep 17 00:00:00 2001 From: Wout Bittremieux Date: Tue, 19 Nov 2024 19:12:26 +0100 Subject: [PATCH] Collect all Casanovo references on a dedicated citation page --- README.md | 15 +++++++++------ casanovo/casanovo.py | 20 ++++++++++++-------- docs/cite.md | 35 +++++++++++++++++++++++++++++++++++ docs/index.md | 12 +++++++++--- 4 files changed, 65 insertions(+), 17 deletions(-) create mode 100644 docs/cite.md diff --git a/README.md b/README.md index 35b7c646..ec12d0a3 100644 --- a/README.md +++ b/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. +Achieving state-of-the-art performance, Casanovo is a versatile and powerful tool for discovery, with impactful applications in bottom-up proteomics, immunopeptidomics, metaproteomics, and beyond. -- 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) diff --git a/casanovo/casanovo.py b/casanovo/casanovo.py index 8bdfa58f..f0e0d2f2 100644 --- a/casanovo/casanovo.py +++ b/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., 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 + + For more information on how to cite different versions of Casanovo, + please see [https://casanovo.readthedocs.io/en/latest/cite.html](). """ return diff --git a/docs/cite.md b/docs/cite.md new file mode 100644 index 00000000..6b92bab8 --- /dev/null +++ b/docs/cite.md @@ -0,0 +1,35 @@ +# 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. + +### Main Reference: Casanovo v4.x + +For general use of Casanovo, please cite the following paper: + +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) + +### Casanovo v2.x: Spectrum Transformer Neural Network + +For research involving the spectrum transformer neural network architecture introduced in Casanovo v2.x: + +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) + +### Casanovo v4.2.x: Accounting for Digestion Enzyme Bias + +For work involving Casanovo's enhanced performance on tryptic and non-tryptic data: + +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) + +### Casanovo for Database Searching + +For using Casanovo as a learned score function for sequence database searching: + +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! diff --git a/docs/index.md b/docs/index.md index 3dd77622..1d202e69 100644 --- a/docs/index.md +++ b/docs/index.md @@ -4,10 +4,15 @@ ![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. +Achieving state-of-the-art performance, Casanovo is a versatile and powerful tool for discovery, with impactful applications in bottom-up proteomics, immunopeptidomics, metaproteomics, and beyond. -- 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? + +- 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. ```{toctree} --- @@ -17,6 +22,7 @@ maxdepth: 1 Getting Started File Formats FAQs +Citing Contributing Code of Conduct Changelog