Casanovo
algorithm · Transformer (AR)
First Transformer
| Kind | algorithm |
| Deep learning | yes |
| Acquisition | DDA |
| Family | Transformer (AR) |
Code
Live stars, open issues and last-push figures are on the Code activity chart.
Checkpoints
| Version | Trained on | Host | Licence | Size | Checked |
|---|---|---|---|---|---|
| nine-species | — | Zenodo archival | CC-BY-4.0 | 5.1 GB | live 2026-10-02 |
| data set and weights | — | Zenodo archival | Apache-2.0 | 3.9 GB | live 2026-10-02 |
| MassIVE-KB splits | — | institutional | not stated | — | live 2026-10-02 |
| v4.0.0 | MassIVE-KB v1 | GitHub release | Apache-2.0 | — | live 2026-10-02 |
| v4.2.0 | ~2M PSMs from MassIVE-KB v1 + v2.0.15 | GitHub release | Apache-2.0 | — | live 2026-10-02 |
| v5.0.0 | — | GitHub release | Apache-2.0 | — | live 2026-10-02 |
| v5.2.0 | the default –model orbitrap selector from v5.2.0 onward | GitHub release | Apache-2.0 | — | live 2026-10-02 |
| v5.2.0 | the –model timstof selector from v5.2.0 onward | GitHub release | Apache-2.0 | 575 MB | live 2026-10-06 |
A host marked archival has a DOI and keeps what it is given. The others can move or disappear, which is why they are checked rather than merely listed. verified means the bytes were fetched and hashed on the date shown; live means only that the host answered when last asked.
Benchmarks
- denovo_benchmarks: median peptide-level average precision 0.777 over 86 datasets, median rank 7 of 14 (version 5.0.0).
- ProteoBench, on the nine-species benchmark, ProteoBench selection: peptide-level AUC 0.901; precision 0.650 at 100% coverage; amino-acid AUC 0.951 (version 4.0, beam search, submitted 2026-08-07).
Both are mass-based matches on the tool’s most recent run. What these numbers mean.
Reported comparison
The comparison table this method’s own papers print, standardised: every value on a 0-1 scale, methods down the side, the measure and then the species across. These are numbers papers report about themselves and their baselines. They are not a leaderboard, and they do not compare across tables: each was produced by a different group, on the dataset named in its corner, with each baseline either retrained, run from released weights or quoted from another paper. Where the paper says which, it follows the method’s name (hover it for the sentence); most papers do not say. Bold is the best value in a column and underline the runner-up, our ranking rather than the paper’s own marks.
Table2
De novo mass spectrometry peptide sequencing with a transformer model, page 6: Empirical comparison of Casanovo, DeepNovo and PointNovo. The table lists the peptide-level and amino acid-level precision of three competing models and coverage of Casanovo with precursor m/z filtering on all nine benchmark cross-validation folds. Each fold’s test set contains spectra from a single species, with nearly disjoint sets of peptides between species. For cross-validation folds corresponding to mouse and human, five models were trained with different random initializations. For these species, we report standard deviation of the performance measures.
| Nine-species benchmark | Peptide precision | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Mus musculus | Homo sapiens | Saccharomyces cerevisiae | Methanosarcina mazei | Apis mellifera | Solanum lycopersicum | Vigna mungo | Bacillus subtilis | Candidatus Thiodiazotropha endoloripes | |
| DeepNovo · released | 0.286 | 0.293 | 0.462 | 0.422 | 0.330 | 0.454 | 0.436 | 0.449 | 0.253 |
| PointNovo · quoted | 0.355 | 0.351 | 0.534 | 0.478 | 0.396 | 0.513 | 0.511 | 0.518 | 0.298 |
| Casanovo | 0.665 | 0.683 | 0.824 | 0.771 | 0.732 | 0.771 | 0.798 | 0.805 | 0.695 |
| Nine-species benchmark | Peptide coverage | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Mus musculus | Homo sapiens | Saccharomyces cerevisiae | Methanosarcina mazei | Apis mellifera | Solanum lycopersicum | Vigna mungo | Bacillus subtilis | Candidatus Thiodiazotropha endoloripes | |
| Casanovo | 0.666 | 0.537 | 0.681 | 0.630 | 0.557 | 0.557 | 0.547 | 0.671 | 0.534 |
| Nine-species benchmark | Peptide precision at coverage 1 | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Mus musculus | Homo sapiens | Saccharomyces cerevisiae | Methanosarcina mazei | Apis mellifera | Solanum lycopersicum | Vigna mungo | Bacillus subtilis | Candidatus Thiodiazotropha endoloripes | |
| Casanovo | 0.443 | 0.367 | 0.561 | 0.486 | 0.408 | 0.460 | 0.437 | 0.540 | 0.371 |
| Nine-species benchmark | Amino acid precision | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Mus musculus | Homo sapiens | Saccharomyces cerevisiae | Methanosarcina mazei | Apis mellifera | Solanum lycopersicum | Vigna mungo | Bacillus subtilis | Candidatus Thiodiazotropha endoloripes | |
| DeepNovo · released | 0.623 | 0.610 | 0.750 | 0.694 | 0.630 | 0.731 | 0.679 | 0.742 | 0.602 |
| PointNovo · quoted | 0.626 | 0.606 | 0.779 | 0.712 | 0.644 | 0.733 | 0.730 | 0.768 | 0.589 |
| Casanovo | 0.899 | 0.898 | 0.952 | 0.935 | 0.920 | 0.929 | 0.920 | 0.943 | 0.908 |
| Nine-species benchmark | Amino acid precision at coverage 1 | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Mus musculus | Homo sapiens | Saccharomyces cerevisiae | Methanosarcina mazei | Apis mellifera | Solanum lycopersicum | Vigna mungo | Bacillus subtilis | Candidatus Thiodiazotropha endoloripes | |
| Casanovo | 0.562 | 0.424 | 0.591 | 0.518 | 0.461 | 0.471 | 0.442 | 0.573 | 0.405 |
Papers describing it (7)
- De novo mass spectrometry peptide sequencing with a transformer model (2022, bioRxiv, preprint)
- De novo mass spectrometry peptide sequencing with a transformer model (2022, ICML 2022, ML conference)
- Sequence-to-sequence translation from mass spectra to peptides with a transformer model (2023, bioRxiv, preprint)
- Sequence-to-sequence translation from mass spectra to peptides with a transformer model (2024, Nature Communications, peer-reviewed)
- Accounting for Digestion Enzyme Bias in Casanovo (2024, Journal of Proteome Research, peer-reviewed)
- Improvements to CasaNovo, a deep learning de novo peptide sequencer (2025, bioRxiv, preprint)
- Improvements to Casanovo, a Deep Learning De Novo Peptide Sequencer (2025, Journal of Proteome Research, peer-reviewed)
Papers using it (9)
Applications and evaluations that ran this method. They are not counted among its authors below.
- Can we Confidently Sequence the „Peptide Dark Matter“? Reaching a Consensus for De Novo Peptide Sequencing (2026, Proceedings of the 38th European Peptide Symposium, presentation)
- Spectra Fragment-Ion and Amino Acid Probability Prediction for Peptide Sequencing (2026, thesis)
- Reference-free protein sequencing by consensus assembly of redundant de novo peptide reads (2026, bioRxiv, preprint)
- Tandem Mass Spectra Representation Design for Transformer-Based De Novo Peptide Sequencing (2026, thesis)
- CasanovoGUI: a cross-platform desktop application for deep learning-based de novo peptide sequencing with Casanovo (2026, bioRxiv, preprint)
- Self-assembling proteins compose the chemically resistant shell biomaterial of planktonic tintinnid ciliates (2026, Nature Communications, peer-reviewed)
- NovoTax: prokaryotic strain identification from mass spectrometry-based proteomics data (2026, bioRxiv, preprint)
- Systematic benchmarking of mass spectrometry-based antibody sequencing reveals methodological biases (2025, Cell Systems, peer-reviewed)
- Systematic benchmarking of mass spectrometry-based antibody sequencing reveals methodological biases (2024, bioRxiv, preprint)