Novor

algorithm · Decision tree

Novor: algorithm · Decision tree. Real-time decision-tree scoring

Real-time decision-tree scoring

Kind algorithm
Deep learning no
Acquisition DDA
Family Decision tree

Data

What the papers describing this method trained it on and reported its results on, as each paper states it. A version is named only where the paper names one.

Also used, purpose not recorded

Benchmarks

  • denovo_benchmarks: median peptide-level average precision 0.825 over 86 datasets, median rank 4 of 15 (version v5.1.5-b302).

Both are mass-based matches on the tool’s most recent run. What these numbers mean.

Compared against in other papers (2)

4 tables from 2 papers that report this method as a baseline, newest first. The caveats of the comparisons above hold twice over: each paper ran, retrained or quoted this method its own way, so its number here is that paper’s claim, not a measurement of the method. Where a paper says which it did, the word follows the method’s name.

LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing (2026) · 1 table

Table5

LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing, page 18: Comparison with state-of-the-art methods on GraphNovo dataset [11].

GraphNovo dataset and checkpoint Peptide recall Peptide AUC Amino acid recall Amino acid precision
PEAKS 0.475 0.758 0.775
Novor 0.431 0.740 0.753
DeepNovo 0.497 0.769 0.771
PointNovo 0.601 0.815 0.816
Casanovo · retrained 0.605 0.684 0.684
GraphNovo 0.712 0.876 0.874
LIPNovo 0.729 0.707 0.876 0.882
LIPNovo+ 0.740 0.720 0.889 0.891
pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework (2019) · 3 tables

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
pNovo 3 0.383
pNovo · quoted 0.214
PEAKS · quoted 0.246
Novor 0.093

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
pNovo 3 0.478
pNovo · quoted 0.298
PEAKS · quoted 0.322
Novor 0.109

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
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

Papers describing it (2)

Papers using it (12)

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

Authors (2)

Bin Ma, Tiancong Wang

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