pNovo 3

algorithm · Learning-to-rank

pNovo 3: algorithm · Learning-to-rank. Learning-to-rank + pDeep

Learning-to-rank + pDeep

Kind	algorithm
Deep learning	no
Acquisition	DDA
Family	Learning-to-rank

Code

http://pfind.org/software/pNovo/index.html

Not tracked on the Code activity chart: those figures come from the GitHub API, and this link is not a GitHub repository.

Reported comparisons (3)

The comparison tables 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 (Nine-species)

pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework, page 5: Recall of top-1 peptides identified by different de novo sequencing algorithms

Nine-species benchmark	Peptide recall
Nine-species benchmark	Vigna mungo	Mus musculus	Methanosarcina mazei	Saccharomyces cerevisiae	Apis mellifera
pNovo 3	0.646	0.504	0.660	0.647	0.625
pNovo · quoted	0.429	0.257	0.424	0.477	0.367
PEAKS · quoted	0.443	0.249	0.424	0.500	0.380
Novor	0.174	0.097	0.191	0.191	0.137

Table2 (QE_HF_X1)

pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework, page 5: Recall of top-1 peptides identified by different de novo sequencing algorithms

HeLa Q Exactive HF runs (pNovo 3) QE_HF_X1	Peptide recall
HeLa Q Exactive HF runs (pNovo 3) QE_HF_X1	QE_HF_X1
pNovo 3	0.478
pNovo · quoted	0.298
PEAKS · quoted	0.322
Novor	0.109

Table2 (QE_HF_X2)

pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework, page 5: Recall of top-1 peptides identified by different de novo sequencing algorithms

HeLa Q Exactive HF runs (pNovo 3) QE_HF_X2	Peptide recall
HeLa Q Exactive HF runs (pNovo 3) QE_HF_X2	QE_HF_X2
pNovo 3	0.383
pNovo · quoted	0.214
PEAKS · quoted	0.246
Novor	0.093

Paper describing it

pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework (2019, Bioinformatics, peer-reviewed)

Papers using it (3)

Applications and evaluations that ran this method. They are not counted among its authors below.

Identification of Unknown Biological Toxin Proteins Using Mass Spectrometry: A Case Study on De Novo Sequencing of Ricin (2025, Toxins, peer-reviewed)
PeposX-Exhaust: A lightweight and efficient tool for identification of short peptides (2024, Food Chemistry: X, peer-reviewed)
Peposx-Exhaust: A Lightweight and Efficient Tool for Identification of Short Peptides (2023, SSRN Electronic Journal, preprint)

Authors (5)

Hao Yang, Hao Chi, Wen-Feng Zeng, Wen-Jing Zhou, Si-Min He

Seen in the charts

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