DeepNovo
algorithm · CNN + RNN
The model that started the deep-learning wave (CNN+LSTM)
| Kind | algorithm |
| Deep learning | yes |
| Acquisition | DDA |
| Family | CNN + RNN |
Code
Live stars, open issues and last-push figures are on the Code activity chart.
Checkpoints
| Version | Trained on | Host | Licence | Size | Checked | Backup |
|---|---|---|---|---|---|---|
| train.example | — | Google Drive | Non-commercial use (custom) | 286 MB | gated 2026-10-02 | copy |
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. Where a copy is linked, it is a backup of someone else’s weights kept in case the original link goes stale; the original is the link to cite and to prefer.
Benchmarks
- denovo_benchmarks: median peptide-level average precision 0.200 over 86 datasets, median rank 14 of 14 (version bm-1.0.0).
- ProteoBench, on the nine-species benchmark, ProteoBench selection: peptide-level AUC 0.586; precision 0.346 at 89% coverage; amino-acid AUC 0.613 (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.
Table 1
Protein identification with deep learning: from abc to xyz, page 10: The recall of PEAKS and DeepNovo at the amino acid level and the peptide level.
Paper describing it
- De novo peptide sequencing by deep learning (2017, Proceedings of the National Academy of Sciences, peer-reviewed)
Papers using it (3)
Applications and evaluations that ran this method. They are not counted among its authors below.
- Application of a Novel Hybrid CNN-GNN for Peptide Ion Encoding (2023, Journal of Proteome Research, peer-reviewed)
- Parallel Factor Analysis Enables Quantification and Identification of Highly Convolved Data-Independent-Acquired Protein Spectra (2020, Patterns, peer-reviewed)
- Parallel factor analysis enables quantification and identification of highly-convolved data independent-acquired protein spectra (2020, bioRxiv, preprint)