ProteomeTools
training · DDA · 6 versions · 20 papers
| Kind | training |
| Acquisition | DDA |
| Organisms | Homo sapiens (synthetic) |
| Home | https://www.proteometools.org/ |
Synthetic peptides covering the human proteome, measured deliberately rather than harvested, so the ground truth is the peptide that was synthesised. The main source of large labelled training sets in this field.
Versions
Parts I-III
The three PRIDE submissions as deposited.
released 2017-02-09
Where it lives:
high-confidence (InstaNovo)
The all-confidence search results reduced to best PSMs and published as a split parquet dataset with its own DOI.
spectra 2,655,403 · train 2,132,847 · validation 257,187 · test 265,369 · released 2024-12-01
Where it lives:
- DOI · 10.57967/hf/3822
- Hugging Face · InstaDeepAI/ms_proteometools
Assembled from 3 third-party submissions:
21-PTM subset
Synthetic peptides carrying 21 distinct post-translational modifications, the usual source for evaluating modified-peptide sequencing.
spectra 41,158 · released 2018-04-10
Where it lives:
- Hugging Face · InstaDeepAI/PXD009449
- PRIDE · PXD009449
Bruker timsTOF
The same peptide pools re-run on a Bruker timsTOF Pro, so a model trained on the Orbitrap runs can be tested for instrument transfer rather than only for sequence.
Where it lives:
- PRIDE · PXD019086
non-tryptic (Bruker)
HLA class I and II ligands plus Asp-N and Lys-N peptides on the Bruker instrument, the non-tryptic counterpart of Part III.
Where it lives:
- PRIDE · PXD043844
TMT 6-plex
Tryptic and non-tryptic sequences carrying TMT 6-plex labels, where the label itself changes the fragment ladder a de novo model has to read.
Where it lives:
Used by (20)
- Learning from tandem mass spectra at scale with a self-supervised foundation model for proteomics (2026) preprint 21-PTM subset, Parts I-III
- InstaNovo-P: a de novo peptide sequencing model for phosphoproteomics (2026) peer-reviewed 21-PTM subset, Parts I-III
- A large-scale unified deep learning model for peptide mass spectrum interpretation trained on multimodal data (2026) peer-reviewed Parts I-III
- Zero-shot de novo peptide sequencing with open posttranslational modification discovery (2026) peer-reviewed 21-PTM subset
- Generalizable Direct Protein Sequencing With InstaNexus (2026) peer-reviewed Parts I-III
- Modanovo: A Unified Model for Post-Translational Modification-Aware de Novo Sequencing Using Experimental Spectra from In Vivo and Synthetic Peptides (2025) preprint 21-PTM subset
- Generalizable direct protein sequencing with InstaNexus (2025) preprint Parts I-III
- Zero-Shot De Novo Peptide Sequencing with Open Post-Translational Modification Discovery (2025) preprint 21-PTM subset
- Advancing De Novo Glycopeptide Sequencing with InstaNovo in Glycoproteomics (2025) thesis Parts I-III
- InstaNovo-P: A de novo peptide sequencing model for phosphoproteomics (2025) preprint 21-PTM subset, Parts I-III
- InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments (2025) peer-reviewed Parts I-III, high-confidence (InstaNovo)
- NovoRank: Refinement for De Novo Peptide Sequencing Based on Spectral Clustering and Deep Learning (2025) peer-reviewed Parts I-III
- NovoBoard: A Comprehensive Framework for Evaluating the False Discovery Rate and Accuracy of De Novo Peptide Sequencing (2024) peer-reviewed Parts I-III
- π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (2024) preprint 21-PTM subset, Parts I-III
- NovoBoard: a comprehensive framework for evaluating the false discovery rate and accuracy of de novo peptide sequencing (2024) preprint Parts I-III
- MARS an improved de novo peptide candidate selection method for non-canonical antigen target discovery in cancer (2024) peer-reviewed Parts I-III
- De novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experiments (2023) preprint Parts I-III
- MARS: Improved De Novo Peptide Candidate Selection for Non-Canonical Antigen Target Discovery in Cancer (2022) preprint Parts I-III
- NovoRank: Machine Learning Based Post-processing for Performance Improvement in De Novo Peptide Sequencing (2022) thesis Parts I-III
- De novo Peptide Sequencing (2016) peer-reviewed Parts I-III
Methods on these papers (14)
- De novo peptide sequencing (Proteome Informatics chapter) review
- InstaNexus post-processor
- InstaNovo algorithm
- InstaNovo glycopeptide fine-tuning downstream-application
- InstaNovo+ algorithm
- InstaNovo-FM algorithm
- InstaNovo-P algorithm
- MARS downstream-application
- Modanovo algorithm
- NovoBoard benchmark
- NovoRank post-processor
- RNovA algorithm
- pUniFind algorithm
- π-PrimeNovo algorithm
Taken from the describing links only, so a paper that merely ran a tool on this data does not make that tool a method of it.