From 6e705e56183e960c65962532954e154eed7cbbee Mon Sep 17 00:00:00 2001 From: Melih Yilmaz <32707537+melihyilmaz@users.noreply.github.com> Date: Sat, 24 Feb 2024 10:33:57 -0800 Subject: [PATCH] Add 9-species model weights link to FAQ (#303) * Add model weights link * Generate new screengrabs with rich-codex * Clarify that these weights should only be used for benchmarking --------- Co-authored-by: github-actions[bot] Co-authored-by: Wout Bittremieux --- docs/faq.md | 7 ++ docs/images/help.svg | 164 ++++++++++++++++++++++++++++++++++++++++--- 2 files changed, 160 insertions(+), 11 deletions(-) diff --git a/docs/faq.md b/docs/faq.md index 8a46feb8..c37fc901 100644 --- a/docs/faq.md +++ b/docs/faq.md @@ -145,6 +145,13 @@ You can use any of the scheduler classes available in [`torch.optim.lr_scheduler ## Miscellaneous +**Where can I find Casanovo model weights trained on the nine-species benchmark?** + +You can find the Casanovo weights corresponding to the nine-species benchmark [on Zenodo](https://doi.org/10.5281/zenodo.10694984), compatible with Casanovo v4.x.x. +These weights correspond to training and validation on eight species using the default configurations, with the remaining species held out for testing, as indicated by the file names. +Note that these weights are only intended for evaluation purposes on this specific benchmark dataset. +For general-purpose usage of Casanovo, use its [default weights](https://casanovo.readthedocs.io/en/latest/getting_started.html#download-model-weights) instead, as these will give significantly improved performance. + **How can I generate a precision–coverage curve?** You can evaluate a trained Casanovo model compared to ground-truth peptide labels using a precision–coverage curve. diff --git a/docs/images/help.svg b/docs/images/help.svg index baf2e237..42180a3f 100644 --- a/docs/images/help.svg +++ b/docs/images/help.svg @@ -1,4 +1,4 @@ - + - - + + - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - + - + - - $ casanovo --help + + $ casanovo --help + +Usage:casanovo [OPTIONSCOMMAND [ARGS]...                                     + + ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓  + ┃                                  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.                                                            + + 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.                                               + +╭─ Options ────────────────────────────────────────────────────────────────────╮ +--help-h    Show this message and exit.                                     +╰──────────────────────────────────────────────────────────────────────────────╯ +╭─ Commands ───────────────────────────────────────────────────────────────────╮ +configure Generate a Casanovo configuration file to customize.               +evaluate  Evaluate de novo peptide sequencing performance.                   +sequence  De novo sequence peptides from tandem mass spectra.                +train     Train a Casanovo model on your own data.                           +version   Get the Casanovo version information                               +╰──────────────────────────────────────────────────────────────────────────────╯ +