ProteomeTools
training · DDA · 8 versions · 39 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:
HC-PT (NovoBench)
NovoBench’s ~10% subsample of the high-confidence InstaNovo split, 1 PTM, mean peptide length 12.53. Papers call it “the HC-PT dataset” and cite the InstaNovo paper for it, so a number reported on HC-PT is a number on a tenth of that corpus under NovoBench’s split.
train 213,284 · validation 25,718 · test 26,536 · introduced by NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics
No public address: this version is named in the literature but cannot be downloaded.
all-confidence (InstaNovo)
Every PSM of the ProteomeTools MaxQuant searches regardless of quality, about 28M spectra over the same 742k peptides as the high-confidence split. InstaNovo evaluates on it as AC-PT.
released 2023-08-30 · introduced by De novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experiments
Assembled from 3 third-party submissions:
Deposited by (2)
More than one paper here means one piece of work published twice, a preprint and its version of record; a deposit itself happens once.
- NovoBench: Benchmarking Deep Learning-based De Novo Sequencing Methods in Proteomics (2024) ML conference HC-PT (NovoBench)
- NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics (2024) preprint HC-PT (NovoBench)
Used by (37)
- MassNet: billion-scale AI-ready mass spectral corpus empowering scalable deep learning in proteomics (2026) peer-reviewed version not stated
- GyroNovo: Error-Guided Fragment Imputation with Mass-Aware Attention for De Novo Peptide Sequencing (2026) preprint HC-PT (NovoBench)
- 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
- Discrete Diffusion with Physical Mass Constraints for De Novo Peptide Sequencing (2026) ML conference HC-PT (NovoBench)
- LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing (2026) preprint HC-PT (NovoBench)
- 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
- Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control (2026) peer-reviewed HC-PT (NovoBench)
- A living proteomics benchmark for comprehensive evaluation of deep learning-based de novo peptide sequencing tools (2026) preprint 21-PTM subset, Bruker timsTOF, Parts I-III, TMT 6-plex, non-tryptic (Bruker)
- Generalizable Direct Protein Sequencing With InstaNexus (2026) peer-reviewed Parts I-III
- CausalNovo: Advancing De Novo Peptide Sequencing via a Causality-Informed Framework (2026) preprint HC-PT (NovoBench)
- 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
- Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing (2025) ML conference HC-PT (NovoBench)
- Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (2025) ML conference HC-PT (NovoBench)
- Zero-Shot De Novo Peptide Sequencing with Open Post-Translational Modification Discovery (2025) preprint 21-PTM subset
- MassNet: billion-scale AI-friendly mass spectral corpus enables robust de novo peptide sequencing (2025) preprint version not stated
- Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing (2025) preprint HC-PT (NovoBench)
- Advancing De Novo Glycopeptide Sequencing with InstaNovo in Glycoproteomics (2025) thesis Parts I-III
- Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (2025) preprint HC-PT (NovoBench)
- 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, all-confidence (InstaNovo), high-confidence (InstaNovo)
- NovoRank: Refinement for De Novo Peptide Sequencing Based on Spectral Clustering and Deep Learning (2025) peer-reviewed Parts I-III
- Bridging the Gap between Database Search and De Novo Peptide Sequencing with SearchNovo (2025) ML conference HC-PT (NovoBench)
- ReNovo: Retrieval-Based De Novo Mass Spectrometry Peptide Sequencing (2024) ML conference HC-PT (NovoBench)
- NovoBoard: A Comprehensive Framework for Evaluating the False Discovery Rate and Accuracy of De Novo Peptide Sequencing (2024) peer-reviewed Parts I-III
- Bridging the Gap between Database Search and De Novo Peptide Sequencing with SearchNovo (2024) preprint HC-PT (NovoBench)
- 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
- Mining Mass Spectra for Peptide Facts (2023) preprint Parts I-III
- De novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experiments (2023) preprint Parts I-III, all-confidence (InstaNovo), high-confidence (InstaNovo)
- 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
- Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning (2019) peer-reviewed Parts I-III
- De novo Peptide Sequencing (2016) peer-reviewed Parts I-III, high-confidence (InstaNovo)
Methods on these papers (28)
- CausalNovo algorithm
- De novo peptide sequencing (Proteome Informatics chapter) review
- DiffuNovo algorithm
- GyroNovo algorithm
- InstaNexus post-processor
- InstaNovo algorithm
- InstaNovo glycopeptide fine-tuning downstream-application
- InstaNovo+ algorithm
- InstaNovo-FM algorithm
- InstaNovo-P algorithm
- LIPNovo algorithm
- LIPNovo+ algorithm
- Living proteomics benchmark benchmark
- MARS downstream-application
- Modanovo algorithm
- NovoBench benchmark
- NovoBoard benchmark
- NovoRank post-processor
- Peptide facts mining adjacent
- PhysNovo algorithm
- Prosit adjacent
- RNovA algorithm
- ReNovo algorithm
- RefineNovo algorithm
- SearchNovo adjacent
- XuanjiNovo 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.