ABRF PRG 2018 HeLa DDA
benchmark · DDA · 1 version · 8 papers
HeLa DDA runs from the ABRF Proteomics Research Group’s 2018 study, “Evaluation of Data-Independent Acquisition (DIA) for Protein Quantification in Academic and Core Facility Settings”. DeepNovoV2 and PointNovo split them into peptide-disjoint…
| Kind | benchmark |
| Acquisition | DDA |
| Organisms | Homo sapiens (HeLa) |
| Home | https://abrf.org/research-group/proteomics-research-group-prg |
HeLa DDA runs from the ABRF Proteomics Research Group’s 2018 study, “Evaluation of Data-Independent Acquisition (DIA) for Protein Quantification in Academic and Core Facility Settings”. DeepNovoV2 and PointNovo split them into peptide-disjoint training, validation and test sets, and later papers test on PointNovo’s.
Versions
PRG 2018 runs
The study’s HeLa DDA runs. No paper here gives them an accession; the address is π-HelixNovo’s processed copy of PointNovo’s test set, in a Zenodo record that also holds its code and weights.
Where it lives:
- DOI · 10.5281/zenodo.10405582 datasets.zip: π-HelixNovo’s processed copy of PointNovo’s test set
Trained on by (4)
- DePS: An improved deep learning model for de novo peptide sequencing (2022) preprint PRG 2018 runs
- Computationally instrument-resolution-independent de novo peptide sequencing for high-resolution devices (2021) peer-reviewed PRG 2018 runs
- Peptide Sequencing with Deep Learning (2020) thesis PRG 2018 runs
- DeepNovoV2: Better de novo peptide sequencing with deep learning (2019) preprint PRG 2018 runs
Evaluated on by (8)
- π-HelixNovo2: Making Accurate Online De Novo Peptide Sequencing Available to All (2026) peer-reviewed PRG 2018 runs
- A transformer-based semi-autoregressive framework for high-speed and accurate de novo peptide sequencing (2025) peer-reviewed PRG 2018 runs
- Introducing π-HelixNovo for practical large-scale de novo peptide sequencing (2024) peer-reviewed PRG 2018 runs
- Introducing PandaNovo for practical large-scale de novo peptide sequencing (2023) preprint PRG 2018 runs
- DePS: An improved deep learning model for de novo peptide sequencing (2022) preprint PRG 2018 runs
- Computationally instrument-resolution-independent de novo peptide sequencing for high-resolution devices (2021) peer-reviewed PRG 2018 runs
- Peptide Sequencing with Deep Learning (2020) thesis PRG 2018 runs
- DeepNovoV2: Better de novo peptide sequencing with deep learning (2019) preprint PRG 2018 runs
Methods on these papers (6)
- DePS algorithm
- DeepNovo V2 algorithm
- PointNovo algorithm
- TSARseqNovo algorithm
- π-HelixNovo algorithm
- π-HelixNovo2 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.