PointNovo

algorithm · CNN + RNN

PointNovo: algorithm · CNN + RNN. Point-set network

Point-set network

Kind	algorithm
Deep learning	yes
Acquisition	DDA
Family	CNN + RNN

Code

• https://github.com/irleader/PointNovo

Live stars, open issues and last-push figures are on the Code activity chart.

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.

Trained on

• ABRF PRG 2018 HeLa DDA (PRG 2018 runs) · 2020 thesis, 2021 peer-reviewed
• BALF proteomics - In-depth proteomic analysis of human bronchoalveolar lavage fluid towards the biomarker discovery for (as deposited) · 2020 thesis, 2021 peer-reviewed
• Diabetes causes marked inhibition of mitochondrial metabolism in pancreatic β-cells (as deposited) · 2020 thesis, 2021 peer-reviewed
• High-resolution spatially-resolved proteome mapping using automated, sacrificial liquid-mediated sample transfer from la (as deposited) · 2020 thesis, 2021 peer-reviewed
• Low-density lipoprotein receptor-related protein 1 (LRP1)-derived peptides protect against aggregation of LDL and choles (as deposited) · 2020 thesis, 2021 peer-reviewed
• Nine-species benchmark (original (DeepNovo, 2017)) · 2020 thesis, 2021 peer-reviewed
• Proteome of the rodent malaria parasite Plasmodium berghei liver stage merosomes (as deposited) · 2020 thesis, 2021 peer-reviewed
• Proteomic analysis of six different tissues from the Atlantic bottlenose dolphin (Tursiops truncatus) (as deposited) · 2020 thesis, 2021 peer-reviewed

Evaluated on

• ABRF PRG 2018 HeLa DDA (PRG 2018 runs) · 2020 thesis, 2021 peer-reviewed
• HeLa proteome of 12,250 protein-coding genes (as deposited) · 2021 peer-reviewed
• High-resolution spatially-resolved proteome mapping using automated, sacrificial liquid-mediated sample transfer from la (as deposited) · 2020 thesis, 2021 peer-reviewed
• Human pancreatic cancer LC-MSMS (as deposited) · 2020 thesis
• Nine-species benchmark (original (DeepNovo, 2017)) · 2020 thesis, 2021 peer-reviewed
• Proteome of the rodent malaria parasite Plasmodium berghei liver stage merosomes (as deposited) · 2020 thesis, 2021 peer-reviewed
• Proteomic Analysis of Human Liver Reference Material (as deposited) · 2020 thesis
• Proteomic analysis of human sclera tissue using high resolution mass spectrometry (as deposited) · 2020 thesis

Patents (1)

• US11644470B2 Systems and methods for de novo peptide sequencing using deep learning and spectrum pairs ↗ (Bioinformatics Solutions Inc., priority 2019-04-15, active)

Patent families that claim this method, matched by hand. A patent’s status is Google Patents’ reading of its legal events.

Compared against in other papers (18)

54 tables from 18 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.

GyroNovo: Error-Guided Fragment Imputation with Mass-Aware Attention for De Novo Peptide Sequencing (2026) · 6 tables

Table2 (Seven-species)

GyroNovo: Error-Guided Fragment Imputation with Mass-Aware Attention for De Novo Peptide Sequencing, page 7: PTM-levelperformanceontheNine-Species,SevenSpecies, and HC-PT datasets. Best results are shown in bold.

Seven-species benchmark NovoBench split	Ptm precision	Ptm recall
DeepNovo · quoted	0.391	0.373
PointNovo · quoted	0.117	0.094
InstaNovo · quoted	0.126	0.115
Casanovo · quoted	0.360	0.251
AdaNovo · quoted	0.448	0.321
LIPNovo · quoted	0.604	0.498
LIPNovo+ · quoted	0.648	0.561
LIPNovo reproduced · retrained	0.602	0.500
GyroNovo	0.665	0.565

Table2 (Nine-species)

GyroNovo: Error-Guided Fragment Imputation with Mass-Aware Attention for De Novo Peptide Sequencing, page 7: PTM-levelperformanceontheNine-Species,SevenSpecies, and HC-PT datasets. Best results are shown in bold.

Nine-species benchmark	Ptm precision	Ptm recall
DeepNovo · quoted	0.576	0.529
PointNovo · quoted	0.629	0.546
InstaNovo · quoted	0.443	0.294
Casanovo · quoted	0.706	0.566
AdaNovo · quoted	0.652	0.570
LIPNovo · quoted	0.765	0.656
LIPNovo+ · quoted	0.801	0.678
LIPNovo reproduced · retrained	0.773	0.646
GyroNovo	0.815	0.731

Table2 (HC-PT)

GyroNovo: Error-Guided Fragment Imputation with Mass-Aware Attention for De Novo Peptide Sequencing, page 7: PTM-levelperformanceontheNine-Species,SevenSpecies, and HC-PT datasets. Best results are shown in bold.

ProteomeTools HC-PT (NovoBench)	Ptm precision	Ptm recall
DeepNovo · quoted	0.626	0.615
PointNovo · quoted	0.676	0.740
InstaNovo · quoted	0.350	0.261
Casanovo · quoted	0.501	0.460
AdaNovo · quoted	0.552	0.482
LIPNovo · quoted	0.732	0.745
LIPNovo+ · quoted	0.757	0.805
LIPNovo reproduced · retrained	0.690	0.762
GyroNovo	0.790	0.817

Table1 (Seven-species)

GyroNovo: Error-Guided Fragment Imputation with Mass-Aware Attention for De Novo Peptide Sequencing, page 7: Amino-acid- and peptide-level performance on the Nine-Species, Seven-Species, and HC-PT datasets. Best results are shown in bold.

Seven-species benchmark NovoBench split	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
DeepNovo · quoted	0.492	0.433	0.204	0.136
PointNovo · quoted	0.196	0.169	0.022	0.007
Casanovo · quoted	0.322	0.327	0.119	0.084
AdaNovo · quoted	0.379	0.385	0.174	0.135
InstaNovo · quoted	0.192	0.176	0.031	0.009
LIPNovo · quoted	0.557	0.560	0.327	0.281
LIPNovo+ · quoted	0.619	0.622	0.383	0.337
LIPNovo reproduced · retrained	0.553	0.554	0.325	0.270
GyroNovo	0.632	0.635	0.421	0.378

Table1 (Nine-species)

GyroNovo: Error-Guided Fragment Imputation with Mass-Aware Attention for De Novo Peptide Sequencing, page 7: Amino-acid- and peptide-level performance on the Nine-Species, Seven-Species, and HC-PT datasets. Best results are shown in bold.

Nine-species benchmark	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
PEAKS · quoted	0.748		0.428
DeepNovo · quoted	0.696	0.638	0.428	0.376
PointNovo · quoted	0.740	0.671	0.480	0.436
Casanovo · quoted	0.697	0.696	0.481	0.439
AdaNovo · quoted	0.698	0.709	0.505	0.469
InstaNovo · quoted	0.420	0.395	0.164	0.123
LIPNovo · quoted	0.797	0.797	0.582	0.547
LIPNovo+ · quoted	0.831	0.831	0.637	0.612
LIPNovo reproduced · retrained	0.794	0.796	0.583	0.549
GyroNovo	0.848	0.848	0.674	0.646

Table1 (HC-PT)

GyroNovo: Error-Guided Fragment Imputation with Mass-Aware Attention for De Novo Peptide Sequencing, page 7: Amino-acid- and peptide-level performance on the Nine-Species, Seven-Species, and HC-PT datasets. Best results are shown in bold.

ProteomeTools HC-PT (NovoBench)	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
DeepNovo · quoted	0.531	0.534	0.313	0.255
PointNovo · quoted	0.623	0.622	0.419	0.373
Casanovo · quoted	0.442	0.453	0.211	0.177
AdaNovo · quoted	0.442	0.451	0.212	0.178
InstaNovo · quoted	0.289	0.285	0.057	0.034
LIPNovo · quoted	0.637	0.643	0.458	0.427
LIPNovo+ · quoted	0.698	0.694	0.519	0.490
LIPNovo reproduced · retrained	0.653	0.654	0.469	0.438
GyroNovo	0.704	0.702	0.532	0.502

Discrete Diffusion with Physical Mass Constraints for De Novo Peptide Sequencing (2026) · 6 tables

Table2 (Seven-species)

Discrete Diffusion with Physical Mass Constraints for De Novo Peptide Sequencing, page 7: Comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in PTM-level performance.

Seven-species benchmark NovoBench split	Ptm precision	Ptm recall
DeepNovo · retrained	0.391	0.373
PointNovo · retrained	0.117	0.094
InstaNovo · retrained	0.126	0.115
Casanovo · retrained	0.360	0.251
AdaNovo · retrained	0.448	0.321
π-HelixNovo · retrained	0.473	0.366
LIPNovo · retrained	0.604	0.498
PhysNovo	0.654	0.549

Table2 (Nine-species)

Discrete Diffusion with Physical Mass Constraints for De Novo Peptide Sequencing, page 7: Comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in PTM-level performance.

Nine-species benchmark revised (balanced)	Ptm precision	Ptm recall
DeepNovo · retrained	0.576	0.529
PointNovo · retrained	0.629	0.546
InstaNovo · retrained	0.443	0.294
Casanovo · retrained	0.706	0.566
AdaNovo · retrained	0.652	0.570
π-HelixNovo · retrained	0.680	0.598
LIPNovo · retrained	0.765	0.656
PhysNovo	0.793	0.686

Table2 (HC-PT)

Discrete Diffusion with Physical Mass Constraints for De Novo Peptide Sequencing, page 7: Comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in PTM-level performance.

ProteomeTools HC-PT (NovoBench)	Ptm precision	Ptm recall
DeepNovo · retrained	0.626	0.615
PointNovo · retrained	0.676	0.740
InstaNovo · retrained	0.350	0.261
Casanovo · retrained	0.501	0.460
AdaNovo · retrained	0.552	0.482
π-HelixNovo · retrained	0.568	0.667
LIPNovo · retrained	0.732	0.745
PhysNovo	0.763	0.770

Table1 (Seven-species)

Discrete Diffusion with Physical Mass Constraints for De Novo Peptide Sequencing, page 7: Comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in amino acid-level and peptidelevel performance. The best results are marked in bold, and the second best are underlined.

Seven-species benchmark NovoBench split	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
DeepNovo · retrained	0.492	0.433	0.204	0.136
PointNovo · retrained	0.196	0.169	0.022	0.007
InstaNovo · retrained	0.192	0.176	0.031	0.009
Casanovo · retrained	0.322	0.327	0.119	0.084
AdaNovo · retrained	0.379	0.385	0.174	0.135
π-HelixNovo · retrained	0.481	0.472	0.234	0.173
LIPNovo · retrained	0.557	0.560	0.327	0.281
ReNovo	0.512	0.514	0.278	0.228
PhysNovo	0.568	0.569	0.346	0.290

Table1 (Nine-species)

Discrete Diffusion with Physical Mass Constraints for De Novo Peptide Sequencing, page 7: Comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in amino acid-level and peptidelevel performance. The best results are marked in bold, and the second best are underlined.

Nine-species benchmark revised (balanced)	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
PEAKS	0.748		0.428
DeepNovo · retrained	0.696	0.638	0.428	0.376
PointNovo · retrained	0.740	0.671	0.480	0.436
InstaNovo · retrained	0.420	0.395	0.164	0.123
Casanovo · retrained	0.697	0.696	0.481	0.439
AdaNovo · retrained	0.698	0.709	0.505	0.469
π-HelixNovo · retrained	0.765	0.758	0.517	0.453
LIPNovo · retrained	0.797	0.797	0.582	0.547
π-PrimeNovo	0.790
ReNovo	0.770	0.769	0.568	0.528
PhysNovo	0.815	0.814	0.588	0.553

Table1 (HC-PT)

Discrete Diffusion with Physical Mass Constraints for De Novo Peptide Sequencing, page 7: Comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in amino acid-level and peptidelevel performance. The best results are marked in bold, and the second best are underlined.

ProteomeTools HC-PT (NovoBench)	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
DeepNovo · retrained	0.531	0.534	0.313	0.255
PointNovo · retrained	0.623	0.622	0.419	0.373
InstaNovo · retrained	0.289	0.285	0.057	0.034
Casanovo · retrained	0.442	0.453	0.211	0.177
AdaNovo · retrained	0.442	0.451	0.212	0.178
π-HelixNovo · retrained	0.588	0.582	0.356	0.318
LIPNovo · retrained	0.637	0.643	0.458	0.427
ReNovo	0.651	0.648	0.467	0.436
PhysNovo	0.649	0.652	0.473	0.444

MemNovo: Look Back at the Spectrum for Balanced De Novo Peptide Sequencing from Mass Spectrometry (2026) · 1 table

Table2

MemNovo: Look Back at the Spectrum for Balanced De Novo Peptide Sequencing from Mass Spectrometry, page 7: Performance comparison on the Nine Species benchmark. We report amino acid precision (AA) and peptide precision (Pep.) for each method. For Casanovo and InstaNovo, we additionally show results with MemNovo applied and the corresponding relative improvement (Rel. Imp.). Best results for each species are shown in bold. Results marked with † are reported from original publications, which may use different model versions and evaluation protocols; direct numerical comparison with our evaluation should be interpreted with caution.

Nine-species benchmark revised (balanced)	Amino acid precision
	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
DeepNovo · quoted	0.6230	0.6100	0.7500	0.6940	0.6300	0.7310	0.6790	0.7420	0.6020	0.6734
PointNovo · quoted	0.6260	0.6060	0.7790	0.7120	0.6440	0.7330	0.7300	0.7680	0.5890	0.6874
Casanovo	0.6352	0.6885	0.7672	0.7253	0.6730	0.7302	0.7446	0.7701	0.6895	0.7137
AdaNovo · quoted	0.6460	0.6180	0.7930	0.7280	0.6500	0.7400	0.7190	0.7390	0.6420	0.6972
InstaNovo	0.7351	0.7934	0.8447	0.8214	0.7754	0.8181	0.8450	0.8367	0.7631	0.8037
MemNovo on Casanovo	0.6524	0.7031	0.7688	0.7287	0.7749	0.7442	0.7469	0.7941	0.7055	0.7354
MemNovo on InstaNovo	0.7614	0.8130	0.8553	0.8350	0.7934	0.8324	0.8566	0.8504	0.7912	0.8210

Nine-species benchmark revised (balanced)	Peptide precision
	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
DeepNovo · quoted	0.2860	0.2930	0.4620	0.4220	0.3300	0.4540	0.4360	0.4490	0.2530	0.3761
PointNovo · quoted	0.3550	0.3510	0.5340	0.4780	0.3960	0.5130	0.5110	0.5180	0.2980	0.4393
Casanovo	0.2404	0.3105	0.4089	0.3254	0.2875	0.3873	0.3314	0.3585	0.2274	0.3197
AdaNovo · quoted	0.4670	0.3730	0.5930	0.4960	0.4310	0.5300	0.5460	0.5280	0.3720	0.4818
InstaNovo	0.3485	0.4562	0.5559	0.4927	0.4318	0.5116	0.5177	0.5185	0.3435	0.4640
MemNovo on Casanovo	0.3372	0.4541	0.4968	0.4295	0.5226	0.5250	0.3530	0.5309	0.3529	0.4447
MemNovo on InstaNovo	0.3522	0.4675	0.5981	0.5051	0.4419	0.5305	0.5481	0.5298	0.3668	0.4822

LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing (2026) · 7 tables

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

Table3 (Seven-species)

LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing, page 16: Comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in PTM-level performance. The best and second-best results are marked in bold and underline, respectively.

Seven-species benchmark NovoBench split	Ptm precision	Ptm recall
DeepNovo	0.391	0.373
PointNovo	0.117	0.094
InstaNovo	0.126	0.115
Casanovo · retrained	0.360	0.251
AdaNovo	0.448	0.321
π-HelixNovo	0.473	0.366
SearchNovo	0.472	0.447
Casanovo retrained · retrained	0.368	0.292
LIPNovo	0.604	0.498
LIPNovo+	0.648	0.561

Table3 (Nine-species)

LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing, page 16: Comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in PTM-level performance. The best and second-best results are marked in bold and underline, respectively.

Nine-species benchmark	Ptm precision	Ptm recall
DeepNovo	0.576	0.529
PointNovo	0.629	0.546
InstaNovo	0.443	0.294
Casanovo · retrained	0.706	0.566
AdaNovo	0.652	0.570
π-HelixNovo	0.680	0.598
SearchNovo	0.764	0.599
Casanovo retrained · retrained	0.755	0.601
LIPNovo	0.765	0.656
LIPNovo+	0.801	0.678

Table3 (HC-PT)

LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing, page 16: Comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in PTM-level performance. The best and second-best results are marked in bold and underline, respectively.

ProteomeTools HC-PT (NovoBench)	Ptm precision	Ptm recall
DeepNovo	0.626	0.615
PointNovo	0.676	0.740
InstaNovo	0.350	0.261
Casanovo · retrained	0.501	0.460
AdaNovo	0.552	0.482
π-HelixNovo	0.568	0.667
SearchNovo	0.715	0.772
Casanovo retrained · retrained	0.550	0.582
LIPNovo	0.732	0.745
LIPNovo+	0.757	0.805

In the paper: column ptm recall: the original table underlined SearchNovo (0.772), LIPNovo (0.745).

Table2 (Seven-species)

LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing, page 15: Comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in amino acid-level and peptide-level performance. The best and second-best results are marked in bold and underline, respectively.

Seven-species benchmark NovoBench split	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
DeepNovo	0.492	0.433	0.204	0.136
PointNovo	0.196	0.169	0.022	0.007
Casanovo · retrained	0.322	0.327	0.119	0.084
AdaNovo	0.379	0.385	0.174	0.135
π-HelixNovo	0.481	0.472	0.234	0.173
InstaNovo	0.192	0.176	0.031	0.009
SearchNovo	0.489	0.488	0.259	0.174
ReNovo	0.512	0.514	0.278	0.228
DiffuNovo	0.437	0.435	0.193	0.162
Casanovo retrained · retrained	0.357	0.366	0.159	0.119
LIPNovo	0.557	0.560	0.327	0.281
LIPNovo+	0.619	0.622	0.383	0.337

Table2 (Nine-species)

LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing, page 15: Comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in amino acid-level and peptide-level performance. The best and second-best results are marked in bold and underline, respectively.

Nine-species benchmark	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
PEAKS	0.748		0.428
DeepNovo	0.696	0.638	0.428	0.376
PointNovo	0.740	0.671	0.480	0.436
Casanovo · retrained	0.697	0.696	0.481	0.439
AdaNovo	0.698	0.709	0.505	0.469
π-HelixNovo	0.765	0.758	0.517	0.453
InstaNovo	0.420	0.395	0.164	0.123
SearchNovo	0.748	0.746	0.550	0.489
ReNovo	0.770	0.769	0.568	0.528
DiffuNovo	0.791	0.789	0.565	0.536
Casanovo retrained · retrained	0.741	0.740	0.529	0.493
LIPNovo	0.797	0.797	0.582	0.547
LIPNovo+	0.831	0.831	0.637	0.612

Table2 (HC-PT)

LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing, page 15: Comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in amino acid-level and peptide-level performance. The best and second-best results are marked in bold and underline, respectively.

ProteomeTools HC-PT (NovoBench)	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
DeepNovo	0.531	0.534	0.313	0.255
PointNovo	0.623	0.622	0.419	0.373
Casanovo · retrained	0.442	0.453	0.211	0.177
AdaNovo	0.442	0.451	0.212	0.178
π-HelixNovo	0.588	0.582	0.356	0.318
InstaNovo	0.289	0.285	0.057	0.034
SearchNovo	0.652	0.658	0.447	0.413
ReNovo	0.651	0.648	0.467	0.436
DiffuNovo	0.654	0.654	0.458	0.434
Casanovo retrained · retrained	0.525	0.530	0.324	0.290
LIPNovo	0.637	0.643	0.458	0.427
LIPNovo+	0.698	0.694	0.519	0.490

In the paper: column amino acid precision: the original table underlined LIPNovo (0.637).

Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control (2026) · 6 tables

Table2 (Seven-species)

Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control, page 7: The comparison of de novo peptide sequencing performance between our proposed model, DiffuNovo, and other stateof-the-art methods on the three benchmark datasets. We report precision and AUC at the peptide level, and precision and recall at the amino acid level. The best and the second best are highlighted with bold and underline, respectively.

Seven-species benchmark NovoBench split	Peptide precision	Peptide AUC	Amino acid precision	Amino acid recall
DeepNovo	0.204	0.136	0.492	0.433
PointNovo	0.022	0.007	0.196	0.169
Casanovo	0.119	0.084	0.322	0.327
AdaNovo	0.174	0.135	0.379	0.385
π-HelixNovo	0.234	0.173	0.481	0.472
DiffuNovo Logits	0.233	0.104	0.430	0.428
DiffuNovo MBR	0.193	0.162	0.437	0.435

Table2 (Nine-species)

Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control, page 7: The comparison of de novo peptide sequencing performance between our proposed model, DiffuNovo, and other stateof-the-art methods on the three benchmark datasets. We report precision and AUC at the peptide level, and precision and recall at the amino acid level. The best and the second best are highlighted with bold and underline, respectively.

Nine-species benchmark	Peptide precision	Peptide AUC	Amino acid precision	Amino acid recall
DeepNovo	0.428	0.376	0.696	0.638
PointNovo	0.480	0.436	0.740	0.671
Casanovo	0.481	0.439	0.697	0.696
AdaNovo	0.505	0.469	0.698	0.709
π-HelixNovo	0.517	0.453	0.765	0.758
DiffuNovo Logits	0.572	0.413	0.785	0.783
DiffuNovo MBR	0.565	0.536	0.791	0.789

In the paper: column amino acid precision: the original table underlined π-HelixNovo (0.765).
In the paper: column peptide precision: the original table underlined π-HelixNovo (0.517).
In the paper: column amino acid recall: the original table underlined π-HelixNovo (0.758).

Table2 (HC-PT)

Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control, page 7: The comparison of de novo peptide sequencing performance between our proposed model, DiffuNovo, and other stateof-the-art methods on the three benchmark datasets. We report precision and AUC at the peptide level, and precision and recall at the amino acid level. The best and the second best are highlighted with bold and underline, respectively.

ProteomeTools HC-PT (NovoBench)	Peptide precision	Peptide AUC	Amino acid precision	Amino acid recall
DeepNovo	0.313	0.255	0.531	0.534
PointNovo	0.419	0.373	0.623	0.622
Casanovo	0.211	0.177	0.442	0.453
AdaNovo	0.212	0.178	0.442	0.451
π-HelixNovo	0.356	0.318	0.588	0.582
DiffuNovo Logits	0.485	0.324	0.648	0.648
DiffuNovo MBR	0.458	0.434	0.654	0.654

In the paper: column amino acid precision: the original table underlined PointNovo (0.623).
In the paper: column peptide precision: the original table underlined PointNovo (0.419).
In the paper: column amino acid recall: the original table underlined PointNovo (0.622).

Table 3 (Seven-species)

Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control, page 7: Empirical comparison of PTM identification. We evaluate the ability of DiffuNovo and other models to identify Post-Translational Modifications (PTMs) on the benchmark datasets. The best results and the second best are highlighted with bold and underline, respectively.

Seven-species benchmark NovoBench split	Ptm precision
DeepNovo	0.391
PointNovo	0.117
AdaNovo	0.448
Casanovo	0.360
π-HelixNovo	0.473
DiffuNovo	0.515

Table 3 (Nine-species)

Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control, page 7: Empirical comparison of PTM identification. We evaluate the ability of DiffuNovo and other models to identify Post-Translational Modifications (PTMs) on the benchmark datasets. The best results and the second best are highlighted with bold and underline, respectively.

Nine-species benchmark	Ptm precision
DeepNovo	0.576
PointNovo	0.629
AdaNovo	0.652
Casanovo	0.706
π-HelixNovo	0.680
DiffuNovo	0.822

Table 3 (HC-PT)

Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control, page 7: Empirical comparison of PTM identification. We evaluate the ability of DiffuNovo and other models to identify Post-Translational Modifications (PTMs) on the benchmark datasets. The best results and the second best are highlighted with bold and underline, respectively.

ProteomeTools HC-PT (NovoBench)	Ptm precision
DeepNovo	0.626
PointNovo	0.676
AdaNovo	0.552
Casanovo	0.501
π-HelixNovo	0.568
DiffuNovo	0.705

CausalNovo: Advancing De Novo Peptide Sequencing via a Causality-Informed Framework (2026) · 6 tables

Table2 (Seven-species)

CausalNovo: Advancing De Novo Peptide Sequencing via a Causality-Informed Framework, page 6: Comparison with state-of-the-art models in PTM-level performance. † denotes our retrained results, and others are given by NovoBench. The best is marked in bold.

Seven-species benchmark NovoBench split	Ptm precision	Ptm recall
DeepNovo · quoted	0.391	0.373
PointNovo · quoted	0.117	0.094
InstaNovo · quoted	0.126	0.115
SearchNovo · quoted	0.472	0.447
Casanovo · quoted	0.360	0.251
Casanovo · retrained	0.368	0.292
CausalNovo on †CasaNovo	0.503	0.422
AdaNovo · quoted	0.448	0.321
AdaNovo · retrained	0.430	0.356
CausalNovo on †AdaNovo	0.469	0.398
π-HelixNovo · quoted	0.473	0.366
π-HelixNovo · retrained	0.362	0.370
CausalNovo on †π-HelixNovo	0.513	0.427

Table2 (Nine-species)

CausalNovo: Advancing De Novo Peptide Sequencing via a Causality-Informed Framework, page 6: Comparison with state-of-the-art models in PTM-level performance. † denotes our retrained results, and others are given by NovoBench. The best is marked in bold.

Nine-species benchmark	Ptm precision	Ptm recall
DeepNovo · quoted	0.576	0.529
PointNovo · quoted	0.629	0.546
InstaNovo · quoted	0.443	0.294
SearchNovo · quoted	0.764	0.599
Casanovo · quoted	0.706	0.566
Casanovo · retrained	0.755	0.601
CausalNovo on †CasaNovo	0.791	0.608
AdaNovo · quoted	0.652	0.570
AdaNovo · retrained	0.678	0.552
CausalNovo on †AdaNovo	0.769	0.607
π-HelixNovo · quoted	0.680	0.598
π-HelixNovo · retrained	0.723	0.593
CausalNovo on †π-HelixNovo	0.731	0.616

Table2 (HC-PT)

CausalNovo: Advancing De Novo Peptide Sequencing via a Causality-Informed Framework, page 6: Comparison with state-of-the-art models in PTM-level performance. † denotes our retrained results, and others are given by NovoBench. The best is marked in bold.

ProteomeTools HC-PT (NovoBench)	Ptm precision	Ptm recall
DeepNovo · quoted	0.626	0.615
PointNovo · quoted	0.676	0.740
InstaNovo · quoted	0.350	0.261
SearchNovo · quoted	0.715	0.772
Casanovo · quoted	0.501	0.460
Casanovo · retrained	0.550	0.582
CausalNovo on †CasaNovo	0.671	0.741
AdaNovo · quoted	0.552	0.482
AdaNovo · retrained	0.562	0.532
CausalNovo on †AdaNovo	0.652	0.743
π-HelixNovo · quoted	0.568	0.667
π-HelixNovo · retrained	0.632	0.566
CausalNovo on †π-HelixNovo	0.737	0.746

Table1 (Seven-species)

CausalNovo: Advancing De Novo Peptide Sequencing via a Causality-Informed Framework, page 6: Comparison with state-of-the-art models in amino acid-level and peptide-level performance. † denotes our retrained results, and others are provided by NovoBench. The best results are marked in bold, and the second-best results are underlined.

Seven-species benchmark NovoBench split	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
DeepNovo · quoted	0.492	0.433	0.204	0.136
PointNovo · quoted	0.196	0.169	0.022	0.007
InstaNovo · quoted	0.192	0.176	0.031	0.009
SearchNovo · quoted	0.489	0.488	0.259	0.174
Casanovo · quoted	0.322	0.327	0.119	0.084
Casanovo · retrained	0.357	0.366	0.159	0.119
CausalNovo on †CasaNovo	0.477	0.478	0.245	0.200
AdaNovo · quoted	0.379	0.385	0.174	0.135
AdaNovo · retrained	0.403	0.405	0.189	0.149
CausalNovo on †AdaNovo	0.453	0.453	0.233	0.192
π-HelixNovo · quoted	0.481	0.472	0.234	0.173
π-HelixNovo · retrained	0.465	0.462	0.218	0.156
CausalNovo on †π-HelixNovo	0.536	0.534	0.282	0.229

Table1 (Nine-species)

CausalNovo: Advancing De Novo Peptide Sequencing via a Causality-Informed Framework, page 6: Comparison with state-of-the-art models in amino acid-level and peptide-level performance. † denotes our retrained results, and others are provided by NovoBench. The best results are marked in bold, and the second-best results are underlined.

Nine-species benchmark	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
PEAKS · quoted	0.748		0.428
DeepNovo · quoted	0.696	0.638	0.428	0.376
PointNovo · quoted	0.740	0.671	0.480	0.436
InstaNovo · quoted	0.420	0.395	0.164	0.123
SearchNovo · quoted	0.748	0.746	0.550	0.489
Casanovo · quoted	0.697	0.696	0.481	0.439
Casanovo · retrained	0.741	0.740	0.529	0.493
CausalNovo on †CasaNovo	0.765	0.766	0.564	0.528
AdaNovo · quoted	0.698	0.709	0.505	0.469
AdaNovo · retrained	0.681	0.681	0.473	0.439
CausalNovo on †AdaNovo	0.744	0.746	0.542	0.507
π-HelixNovo · quoted	0.765	0.758	0.517	0.453
π-HelixNovo · retrained	0.765	0.752	0.509	0.431
CausalNovo on †π-HelixNovo	0.787	0.784	0.543	0.483

Table1 (HC-PT)

CausalNovo: Advancing De Novo Peptide Sequencing via a Causality-Informed Framework, page 6: Comparison with state-of-the-art models in amino acid-level and peptide-level performance. † denotes our retrained results, and others are provided by NovoBench. The best results are marked in bold, and the second-best results are underlined.

ProteomeTools HC-PT (NovoBench)	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
DeepNovo · quoted	0.531	0.534	0.313	0.255
PointNovo · quoted	0.623	0.622	0.419	0.373
InstaNovo · quoted	0.289	0.285	0.057	0.034
SearchNovo · quoted	0.652	0.658	0.447	0.413
Casanovo · quoted	0.442	0.453	0.211	0.177
Casanovo · retrained	0.525	0.530	0.324	0.290
CausalNovo on †CasaNovo	0.635	0.639	0.459	0.426
AdaNovo · quoted	0.442	0.451	0.212	0.178
AdaNovo · retrained	0.492	0.496	0.289	0.254
CausalNovo on †AdaNovo	0.634	0.637	0.453	0.420
π-HelixNovo · quoted	0.588	0.582	0.356	0.318
π-HelixNovo · retrained	0.532	0.537	0.301	0.261
CausalNovo on †π-HelixNovo	0.656	0.658	0.450	0.415

Bidirectional Representations Augmented Autoregressive Biological Sequence Generation (2025) · 1 table

Table1

Bidirectional Representations Augmented Autoregressive Biological Sequence Generation, page 8: Comparison of the performance of CROSSNOVO and baseline methods on the 9-species-v1 test set.

The same table is also printed in Bidirectional Representations Augmented Autoregressive Biological Sequence Generation (Table1, page 8).

Nine-species benchmark original (DeepNovo, 2017)	Amino acid precision
	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
PEAKS	0.600	0.639	0.748	0.673	0.633	0.728	0.644	0.719	0.586	0.663
π-PrimeNovo	0.784	0.729	0.802	0.801	0.763	0.815	0.822	0.846	0.734	0.788
DeepNovo	0.623	0.610	0.750	0.694	0.630	0.731	0.679	0.742	0.602	0.673
PointNovo	0.626	0.606	0.779	0.712	0.644	0.733	0.730	0.768	0.589	0.687
Casanovo	0.689	0.586	0.684	0.679	0.629	0.721	0.668	0.749	0.603	0.667
InstaNovo	0.703	0.636	0.691	0.712	0.660	0.732	0.711	0.739	0.619	0.689
Casanovo V2	0.760	0.676	0.752	0.755	0.706	0.785	0.748	0.790	0.681	0.739
π-HelixNovo	0.765	0.665	0.768	0.784	0.757	0.721	0.793	0.816	0.681	0.750
ContraNovo	0.798	0.771	0.797	0.799	0.745	0.810	0.807	0.828	0.711	0.785
CrossNovo	0.816	0.800	0.814	0.826	0.785	0.830	0.831	0.856	0.740	0.811

Nine-species benchmark original (DeepNovo, 2017)	Peptide recall
	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
PEAKS	0.197	0.277	0.428	0.356	0.287	0.403	0.362	0.387	0.203	0.322
π-PrimeNovo	0.567	0.574	0.697	0.650	0.603	0.697	0.702	0.721	0.531	0.638
DeepNovo	0.286	0.293	0.462	0.422	0.330	0.454	0.436	0.449	0.253	0.376
PointNovo	0.355	0.351	0.534	0.478	0.396	0.513	0.511	0.518	0.298	0.439
Casanovo	0.426	0.341	0.490	0.478	0.406	0.521	0.506	0.537	0.330	0.448
InstaNovo	0.471	0.455	0.559	0.528	0.466	0.732	0.564	0.576	0.416	0.530
Casanovo V2	0.483	0.446	0.599	0.557	0.493	0.618	0.589	0.622	0.446	0.539
π-HelixNovo	0.483	0.392	0.568	0.560	0.473	0.560	0.623	0.596	0.388	0.517
ContraNovo	0.567	0.622	0.674	0.630	0.576	0.672	0.677	0.688	0.486	0.621
CrossNovo	0.596	0.661	0.698	0.660	0.610	0.695	0.716	0.726	0.518	0.654

In the paper: column peptide recall, Mouse: the original table only underlined ContraNovo (Contra.) (0.567); π-PrimeNovo (Prime.) (0.567) ties with it and is underlined here too.
In the paper: column peptide recall, Tomato: the original table bolded π-PrimeNovo (Prime.) (0.697), InstaNovo (Insta) (0.732) and underlined CrossNovo (Ours) (0.695).

Protein Language Model-Aligned Spectra Embeddings for De Novo Peptide Sequencing (2025) · 1 table

Table 2

Protein Language Model-Aligned Spectra Embeddings for De Novo Peptide Sequencing, page 7: Performance comparison results in terms of amino acid precision and peptide recall on the ninespecies dataset [55]. The performance of the baseline methods, except for Casanovo v4.2, is reported from [65]. Bolded and underlined results represent the first and second best performance per species (or averaged across species in the last column), respectively.

Nine-species benchmark	Amino acid precision
	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Saccharomyces cerevisiae	Average
DeepNovo	0.742	0.602	0.630	0.610	0.694	0.623	0.679	0.731	0.750	0.673
PointNovo	0.768	0.589	0.644	0.606	0.712	0.626	0.730	0.733	0.779	0.687
Casanovo · retrained	0.749	0.603	0.629	0.586	0.679	0.689	0.668	0.721	0.684	0.667
AdaNovo	0.739	0.642	0.650	0.618	0.728	0.646	0.719	0.740	0.793	0.697
Casanovo V2 · retrained	0.790	0.681	0.706	0.676	0.755	0.760	0.748	0.785	0.752	0.739
Casanovo v4.2 · retrained	0.793	0.678	0.705	0.668	0.687	0.756	0.753	0.791	0.768	0.733
PLMNovo ESM-2 8M	0.794	0.684	0.707	0.670	0.755	0.759	0.764	0.790	0.755	0.742
PLMNovo ESM-2 650M	0.797	0.688	0.709	0.676	0.755	0.760	0.766	0.795	0.771	0.746

Nine-species benchmark	Peptide recall
	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Saccharomyces cerevisiae	Average
DeepNovo	0.449	0.253	0.330	0.293	0.422	0.286	0.436	0.454	0.462	0.376
PointNovo	0.518	0.298	0.396	0.351	0.478	0.355	0.511	0.513	0.534	0.439
Casanovo · retrained	0.537	0.330	0.406	0.341	0.478	0.426	0.506	0.521	0.490	0.448
AdaNovo	0.528	0.372	0.431	0.373	0.496	0.467	0.546	0.530	0.593	0.481
Casanovo V2 · retrained	0.622	0.446	0.493	0.446	0.557	0.483	0.589	0.618	0.599	0.539
Casanovo v4.2 · retrained	0.603	0.421	0.478	0.437	0.498	0.468	0.558	0.608	0.584	0.517
PLMNovo ESM-2 8M	0.602	0.423	0.484	0.434	0.544	0.470	0.565	0.606	0.571	0.522
PLMNovo ESM-2 650M	0.601	0.425	0.483	0.432	0.544	0.464	0.576	0.606	0.579	0.523

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (2025) · 6 tables

Table2 (Seven-species)

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing, page 6: Empirical comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in PTM-level performance. † denotes our retrained results, and other results are given by NovoBench. The best results are marked in bold.

The same table is also printed in Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (Table2, page 6).

Seven-species benchmark NovoBench split	Ptm precision	Ptm recall
DeepNovo · quoted	0.391	0.373
PointNovo · quoted	0.117	0.094
InstaNovo · quoted	0.126	0.115
Casanovo · quoted	0.360	0.251
AdaNovo · quoted	0.448	0.321
AdaNovo · retrained	0.430	0.356
π-HelixNovo · quoted	0.473	0.366
π-HelixNovo · retrained	0.362	0.370
Casanovo · retrained	0.368	0.292
LIPNovo	0.604	0.498

Table2 (Nine-species)

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing, page 6: Empirical comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in PTM-level performance. † denotes our retrained results, and other results are given by NovoBench. The best results are marked in bold.

The same table is also printed in Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (Table2, page 6).

Nine-species benchmark	Ptm precision	Ptm recall
DeepNovo · quoted	0.576	0.529
PointNovo · quoted	0.629	0.546
InstaNovo · quoted	0.443	0.294
Casanovo · quoted	0.706	0.566
AdaNovo · quoted	0.652	0.570
AdaNovo · retrained	0.678	0.552
π-HelixNovo · quoted	0.680	0.598
π-HelixNovo · retrained	0.723	0.593
Casanovo · retrained	0.755	0.601
LIPNovo	0.765	0.656

Table2 (HC-PT)

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing, page 6: Empirical comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in PTM-level performance. † denotes our retrained results, and other results are given by NovoBench. The best results are marked in bold.

The same table is also printed in Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (Table2, page 6).

ProteomeTools HC-PT (NovoBench)	Ptm precision	Ptm recall
DeepNovo · quoted	0.626	0.615
PointNovo · quoted	0.676	0.740
InstaNovo · quoted	0.350	0.261
Casanovo · quoted	0.501	0.460
AdaNovo · quoted	0.552	0.482
AdaNovo · retrained	0.562	0.532
π-HelixNovo · quoted	0.568	0.667
π-HelixNovo · retrained	0.632	0.566
Casanovo · retrained	0.550	0.582
LIPNovo	0.732	0.745

Table1 (Seven-species)

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing, page 6: Empirical comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in amino acid-level and peptide-level performance. † denotes our retrained results, and other results are provided by NovoBench. The best is marked in bold.

The same table is also printed in Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (Table1, page 6).

Seven-species benchmark NovoBench split	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
DeepNovo · quoted	0.492	0.433	0.204	0.136
PointNovo · quoted	0.196	0.169	0.022	0.007
InstaNovo · quoted	0.192	0.176	0.031	0.009
Casanovo · quoted	0.322	0.327	0.119	0.084
AdaNovo · quoted	0.379	0.385	0.174	0.135
AdaNovo · retrained	0.403	0.405	0.189	0.149
π-HelixNovo · quoted	0.481	0.472	0.234	0.173
π-HelixNovo · retrained	0.465	0.462	0.218	0.156
Casanovo · retrained	0.357	0.366	0.159	0.119
LIPNovo	0.557	0.560	0.327	0.281

Table1 (Nine-species)

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing, page 6: Empirical comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in amino acid-level and peptide-level performance. † denotes our retrained results, and other results are provided by NovoBench. The best is marked in bold.

The same table is also printed in Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (Table1, page 6).

Nine-species benchmark	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
PEAKS · quoted	0.748		0.428
DeepNovo · quoted	0.696	0.638	0.428	0.376
PointNovo · quoted	0.740	0.671	0.480	0.436
InstaNovo · quoted	0.420	0.395	0.164	0.123
Casanovo · quoted	0.697	0.696	0.481	0.439
AdaNovo · quoted	0.698	0.709	0.505	0.469
AdaNovo · retrained	0.681	0.681	0.473	0.439
π-HelixNovo · quoted	0.765	0.758	0.517	0.453
π-HelixNovo · retrained	0.765	0.752	0.509	0.431
Casanovo · retrained	0.741	0.740	0.529	0.493
LIPNovo	0.797	0.797	0.582	0.547

In the paper: column amino acid precision: the original table only underlined π-HelixNovo (π-HelixNovo† (Yang et al., 2024)) (0.765); π-HelixNovo (π-HelixNovo (Yang et al., 2024)) (0.765) ties with it and is underlined here too.

Table1 (HC-PT)

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing, page 6: Empirical comparison with state-of-the-art methods on Nine-species, Seven-species, and HC-PT datasets in amino acid-level and peptide-level performance. † denotes our retrained results, and other results are provided by NovoBench. The best is marked in bold.

The same table is also printed in Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (Table1, page 6).

ProteomeTools HC-PT (NovoBench)	Amino acid precision	Amino acid recall	Peptide precision	Peptide AUC
DeepNovo · quoted	0.531	0.534	0.313	0.255
PointNovo · quoted	0.623	0.622	0.419	0.373
InstaNovo · quoted	0.289	0.285	0.057	0.034
Casanovo · quoted	0.442	0.453	0.211	0.177
AdaNovo · quoted	0.442	0.451	0.212	0.178
AdaNovo · retrained	0.492	0.496	0.289	0.254
π-HelixNovo · quoted	0.588	0.582	0.356	0.318
π-HelixNovo · retrained	0.532	0.537	0.301	0.261
Casanovo · retrained	0.525	0.530	0.324	0.290
LIPNovo	0.637	0.643	0.458	0.427

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing (2025) · 1 table

Table1

Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing, page 7: Comparison of the performance on the 9-species-V1 benchmark datasets. The models are categorized by their architecture type: DB represents Database, AR stands for Autoregressive Generation, and NAR denotes Non-Autoregressive Generation. The bold font indicates the best performance.

The same table is also printed in Curriculum Learning for Biological Sequence Prediction: The Case of De Novo Peptide Sequencing (Table1, page 7).

Nine-species benchmark original (DeepNovo, 2017)	Amino acid precision
	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
PEAKS	0.600	0.639	0.748	0.673	0.633	0.728	0.644	0.719	0.586	0.663
DeepNovo	0.623	0.610	0.750	0.694	0.630	0.731	0.679	0.742	0.602	0.673
PointNovo	0.626	0.606	0.779	0.712	0.644	0.733	0.730	0.768	0.589	0.687
Casanovo · released	0.689	0.586	0.684	0.679	0.629	0.721	0.668	0.749	0.603	0.667
AdaNovo	0.646	0.618	0.793	0.728	0.650	0.740	0.719	0.739	0.642	0.697
Casanovo V2 · released	0.760	0.676	0.752	0.755	0.706	0.785	0.748	0.790	0.681	0.739
π-PrimeNovo · released	0.784	0.729	0.802	0.801	0.763	0.815	0.822	0.846	0.734	0.788
RefineNovo	0.800	0.730	0.818	0.819	0.780	0.825	0.835	0.854	0.742	0.800

Nine-species benchmark original (DeepNovo, 2017)	Peptide recall
	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
PEAKS	0.197	0.277	0.428	0.356	0.287	0.403	0.362	0.387	0.203	0.322
DeepNovo	0.286	0.293	0.462	0.422	0.330	0.454	0.436	0.449	0.253	0.376
PointNovo	0.355	0.351	0.534	0.478	0.396	0.513	0.511	0.518	0.298	0.439
Casanovo · released	0.426	0.341	0.490	0.478	0.406	0.521	0.506	0.537	0.330	0.448
AdaNovo	0.467	0.373	0.593	0.496	0.431	0.530	0.546	0.528	0.372	0.481
Casanovo V2 · released	0.483	0.446	0.599	0.557	0.493	0.618	0.589	0.622	0.446	0.539
π-PrimeNovo · released	0.567	0.574	0.697	0.650	0.603	0.697	0.702	0.721	0.531	0.638
RefineNovo	0.583	0.581	0.709	0.667	0.616	0.705	0.720	0.736	0.549	0.653

Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing (2025) · 2 tables

Table6

Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing, page 17: Evaluation of RankNovo in comparison to baseline and base methods on the 9-species-V1 test set. Bolded entries indicate the best-performing models. The symbol “†” indicates that the model serves as both a baseline and a base model. “⋆” signifies that the base models were developed and trained by us.

The same table is also printed in RankNovo: A Universal Reranking Approach for Robust De Novo Peptide Sequencing (Table1, page 8).

Nine-species benchmark original (DeepNovo, 2017)	Amino acid precision
	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Saccharomyces cerevisiae	Average
PEAKS	0.719	0.586	0.633	0.639	0.673	0.600	0.644	0.728	0.748	0.663
DeepNovo	0.742	0.602	0.630	0.610	0.694	0.623	0.679	0.731	0.750	0.673
PointNovo	0.768	0.589	0.644	0.606	0.712	0.626	0.730	0.733	0.779	0.687
Casanovo	0.749	0.603	0.629	0.586	0.679	0.689	0.668	0.721	0.684	0.667
Casanovo V2 · released	0.806	0.685	0.727	0.69	0.774	0.768	0.769	0.799	0.762	0.753
ContraNovo · released	0.828	0.706	0.761	0.771	0.798	0.799	0.804	0.808	0.782	0.784
ByNovo	0.858	0.723	0.791	0.767	0.823	0.803	0.836	0.828	0.804	0.804
Casanovo reverse decoding · retrained	0.804	0.699	0.728	0.719	0.769	0.776	0.782	0.795	0.762	0.759
ContraNovo reverse decoding · retrained	0.839	0.716	0.775	0.782	0.806	0.811	0.816	0.822	0.798	0.796
ByNovo reverse decoding	0.855	0.724	0.794	0.762	0.821	0.81	0.835	0.831	0.762	0.799
RankNovo	0.874	0.746	0.81	0.802	0.84	0.828	0.859	0.844	0.816	0.824

Nine-species benchmark original (DeepNovo, 2017)	Peptide recall
	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Saccharomyces cerevisiae	Average
PEAKS	0.387	0.203	0.287	0.277	0.356	0.197	0.362	0.403	0.428	0.322
DeepNovo	0.449	0.253	0.330	0.293	0.422	0.286	0.436	0.454	0.462	0.376
PointNovo	0.518	0.298	0.396	0.351	0.478	0.355	0.511	0.513	0.534	0.439
Casanovo	0.537	0.330	0.406	0.341	0.478	0.426	0.506	0.521	0.490	0.448
Casanovo V2 · released	0.646	0.46	0.527	0.492	0.592	0.493	0.628	0.637	0.629	0.567
ContraNovo · released	0.684	0.487	0.576	0.624	0.628	0.563	0.676	0.655	0.669	0.618
ByNovo	0.708	0.499	0.597	0.584	0.639	0.545	0.696	0.667	0.676	0.623
Casanovo reverse decoding · retrained	0.628	0.467	0.515	0.511	0.57	0.505	0.611	0.611	0.601	0.558
ContraNovo reverse decoding · retrained	0.682	0.499	0.583	0.606	0.621	0.566	0.673	0.654	0.664	0.616
ByNovo reverse decoding	0.703	0.493	0.59	0.554	0.637	0.543	0.685	0.659	0.629	0.610
RankNovo	0.738	0.539	0.63	0.642	0.672	0.583	0.733	0.691	0.703	0.660

RankNovo reranks the candidates of six base models. Casanovo V2 and ContraNovo are the released models; ByNovo is RankNovo's own in-house model, with an auxiliary b/y ion-annotation head (its Appendix B.4); R-Casanovo, R-ContraNovo and R-ByNovo are reverse-decoding versions of the three, trained by RankNovo's authors and recorded as the 'reverse decoding' variant. All six were trained on MassIVE-KB and evaluated zero-shot. '†' marks a model that is both baseline and base model, '⋆' one the authors trained. The plain Casanovo row repeats the values other papers print for Casanovo (0.749 and 0.537 on Bacillus) and the paper does not say where they come from. THE TEXT AND THE TABLE DISAGREE on V1: the prose gives RankNovo an amino-acid precision of 0.829 and a peptide recall '6.1%' above ByNovo; the table prints 0.824, and 0.660 against ByNovo's 0.623. The table's values are recorded.

Table1

Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing, page 7: Evaluation of RankNovo in comparison to baseline and base models on the 9-species-V1 and 9-species-V2 test sets. Bolded entries indicate the best-performing models. The symbol “†” indicates that the model serves as both a baseline and a base model. “⋆” signifies that the base models were developed and trained by us. Here, ByNovo is the best base model. So the performance of three self-trained base models (R-Casa, R-Contra, R-By) is only provided in Appendix D.1.

Nine-species benchmark	Amino acid precision
	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Saccharomyces cerevisiae	Average
PEAKS	0.719	0.586	0.633	0.639	0.673	0.600	0.644	0.728	0.748	0.663
DeepNovo	0.742	0.602	0.630	0.610	0.694	0.623	0.679	0.731	0.750	0.673
PointNovo	0.768	0.589	0.644	0.606	0.712	0.626	0.730	0.733	0.779	0.687
Casanovo	0.749	0.603	0.629	0.586	0.679	0.689	0.668	0.721	0.684	0.667
Casanovo V2 · released	0.888	0.791	0.823	0.872	0.877	0.813	0.891	0.891	0.915	0.862
ContraNovo · released	0.901	0.807	0.848	0.920	0.896	0.839	0.913	0.898	0.919	0.882
ByNovo	0.920	0.823	0.876	0.917	0.914	0.841	0.932	0.912	0.934	0.897
RankNovo	0.926	0.838	0.885	0.929	0.920	0.860	0.938	0.918	0.938	0.906

Nine-species benchmark	Peptide recall
	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Apis mellifera	Homo sapiens	Methanosarcina mazei	Mus musculus	Vigna mungo	Solanum lycopersicum	Saccharomyces cerevisiae	Average
PEAKS	0.387	0.203	0.287	0.277	0.356	0.197	0.362	0.403	0.428	0.322
DeepNovo	0.449	0.253	0.330	0.293	0.422	0.286	0.436	0.454	0.462	0.376
PointNovo	0.518	0.298	0.396	0.351	0.478	0.355	0.511	0.513	0.534	0.439
Casanovo	0.537	0.330	0.406	0.341	0.478	0.426	0.506	0.521	0.490	0.448
Casanovo V2 · released	0.793	0.558	0.669	0.712	0.754	0.555	0.772	0.783	0.837	0.714
ContraNovo · released	0.815	0.575	0.711	0.820	0.780	0.616	0.799	0.794	0.854	0.752
ByNovo	0.833	0.582	0.731	0.789	0.799	0.596	0.814	0.807	0.871	0.758
RankNovo	0.851	0.620	0.752	0.820	0.813	0.629	0.836	0.822	0.885	0.781

RankNovo reranks the candidates of six base models. Casanovo V2 and ContraNovo are the released models; ByNovo is RankNovo's own in-house model, with an auxiliary b/y ion-annotation head (its Appendix B.4); R-Casanovo, R-ContraNovo and R-ByNovo are reverse-decoding versions of the three, trained by RankNovo's authors and recorded as the 'reverse decoding' variant. All six were trained on MassIVE-KB and evaluated zero-shot. '†' marks a model that is both baseline and base model, '⋆' one the authors trained. The plain Casanovo row repeats the values other papers print for Casanovo (0.749 and 0.537 on Bacillus) and the paper does not say where they come from. THE TEXT AND THE TABLE DISAGREE on V1: the prose gives RankNovo an amino-acid precision of 0.829 and a peptide recall '6.1%' above ByNovo; the table prints 0.824, and 0.660 against ByNovo's 0.623. The table's values are recorded.
In the paper: column amino acid precision, Average: the original table bolded RankNovo (0.824), RankNovo (0.906).
In the paper: column amino acid precision, Bacillus: the original table bolded RankNovo (0.874), RankNovo (0.926).
In the paper: column amino acid precision, C. bacteria: the original table bolded RankNovo (0.746), RankNovo (0.838).
In the paper: column amino acid precision, Honeybee: the original table bolded RankNovo (0.810), RankNovo (0.885).
In the paper: column amino acid precision, Human: the original table bolded RankNovo (0.802), RankNovo (0.929).
In the paper: column amino acid precision, M. mazei: the original table bolded RankNovo (0.840), RankNovo (0.920).
In the paper: column amino acid precision, Mouse: the original table bolded RankNovo (0.828), RankNovo (0.860).
In the paper: column amino acid precision, Ricebean: the original table bolded RankNovo (0.859), RankNovo (0.938).
In the paper: column amino acid precision, Tomato: the original table bolded RankNovo (0.844), RankNovo (0.918).
In the paper: column amino acid precision, Yeast: the original table bolded RankNovo (0.816), RankNovo (0.938).
In the paper: column peptide recall, Average: the original table bolded RankNovo (0.660), RankNovo (0.781).
In the paper: column peptide recall, Bacillus: the original table bolded RankNovo (0.738), RankNovo (0.851).
In the paper: column peptide recall, C. bacteria: the original table bolded RankNovo (0.539), RankNovo (0.620).
In the paper: column peptide recall, Honeybee: the original table bolded RankNovo (0.630), RankNovo (0.752).
In the paper: column peptide recall, Human: the original table bolded RankNovo (0.642), RankNovo (0.820).
In the paper: column peptide recall, M. mazei: the original table bolded RankNovo (0.672), RankNovo (0.813).
In the paper: column peptide recall, Mouse: the original table bolded RankNovo (0.583), RankNovo (0.629).
In the paper: column peptide recall, Ricebean: the original table bolded RankNovo (0.733), RankNovo (0.836).
In the paper: column peptide recall, Tomato: the original table bolded RankNovo (0.691), RankNovo (0.822).
In the paper: column peptide recall, Yeast: the original table bolded RankNovo (0.703), RankNovo (0.885).

Distilling Non-Autoregressive Model Knowledge for Autoregressive De Novo Peptide Sequencing (2025) · 1 table

Table1

Distilling Non-Autoregressive Model Knowledge for Autoregressive De Novo Peptide Sequencing, page 7: Comparison of the performance of CrossNovo and baseline methods on 9-species-v1 test set. The bold font indicates the best performance.

Nine-species benchmark original (DeepNovo, 2017)	Amino acid precision
	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
PEAKS	0.600	0.639	0.748	0.673	0.633	0.728	0.644	0.719	0.586	0.663
π-PrimeNovo	0.784	0.729	0.802	0.801	0.763	0.815	0.822	0.846	0.734	0.788
DeepNovo	0.623	0.610	0.750	0.694	0.630	0.731	0.679	0.742	0.602	0.673
PointNovo	0.626	0.606	0.779	0.712	0.644	0.733	0.730	0.768	0.589	0.687
Casanovo	0.689	0.586	0.684	0.679	0.629	0.721	0.668	0.749	0.603	0.667
Casanovo V2	0.760	0.676	0.752	0.755	0.706	0.785	0.748	0.790	0.681	0.739
ContraNovo	0.798	0.771	0.797	0.799	0.745	0.810	0.807	0.828	0.711	0.785
CrossNovo	0.816	0.800	0.814	0.826	0.785	0.830	0.831	0.856	0.740	0.811

Nine-species benchmark original (DeepNovo, 2017)	Peptide recall
	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
PEAKS	0.197	0.277	0.428	0.356	0.287	0.403	0.362	0.387	0.203	0.322
π-PrimeNovo	0.567	0.574	0.697	0.650	0.603	0.697	0.702	0.721	0.531	0.638
DeepNovo	0.286	0.293	0.462	0.422	0.330	0.454	0.436	0.449	0.253	0.376
PointNovo	0.355	0.351	0.534	0.478	0.396	0.513	0.511	0.518	0.298	0.439
Casanovo	0.426	0.341	0.490	0.478	0.406	0.521	0.506	0.537	0.330	0.448
Casanovo V2	0.483	0.446	0.599	0.557	0.493	0.618	0.589	0.622	0.446	0.539
ContraNovo	0.567	0.622	0.674	0.630	0.576	0.672	0.677	0.688	0.486	0.621
CrossNovo	0.596	0.661	0.698	0.660	0.610	0.695	0.716	0.726	0.518	0.654

In the paper: column peptide recall, Mouse: the original table only underlined ContraNovo (Contra.) (0.567); π-PrimeNovo (Prime.) (0.567) ties with it and is underlined here too.

π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (2025) · 2 tables

SupplementaryTable4

π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing, page 27: Amino acid precision on the nine-species benchmark dataset. Note: the bold text indicates the highest performance in each row.

The same table is also printed in π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (SupplementaryTable4, page 33).

Nine-species benchmark	Amino acid precision
	Apis mellifera	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Homo sapiens	Methanosarcina mazei	Mus musculus	Saccharomyces cerevisiae	Solanum lycopersicum	Vigna mungo	Average
PEAKS	0.63	0.72	0.59	0.64	0.67	0.60	0.75	0.73	0.64	0.66
DeepNovo	0.63	0.74	0.60	0.61	0.69	0.62	0.75	0.73	0.68	0.67
PointNovo	0.64	0.77	0.59	0.61	0.71	0.63	0.78	0.73	0.73	0.69
Casanovo	0.63	0.75	0.60	0.59	0.68	0.69	0.68	0.72	0.67	0.67
Casanovo V2	0.71	0.79	0.68	0.68	0.76	0.76	0.75	0.79	0.75	0.74
π-PrimeNovo	0.76	0.84	0.72	0.72	0.80	0.78	0.80	0.82	0.82	0.79

Supplementary Table 3

π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing, page 26: Peptide recall on the nine-species benchmark dataset. Note: the bold text indicates the highest performance in each row.

The same table is also printed in π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (Supplementary Table 3, page 31).

Nine-species benchmark	Peptide recall
	Apis mellifera	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Homo sapiens	Methanosarcina mazei	Mus musculus	Saccharomyces cerevisiae	Solanum lycopersicum	Vigna mungo	Average
PEAKS	0.29	0.39	0.20	0.28	0.36	0.20	0.43	0.40	0.36	0.32
PepNet	0.28	0.42	0.24	0.23	0.38	0.28	0.37	0.43	0.32	0.33
DeepNovo	0.33	0.45	0.25	0.29	0.42	0.29	0.46	0.45	0.44	0.38
PointNovo	0.40	0.52	0.30	0.35	0.48	0.36	0.53	0.51	0.51	0.44
Casanovo	0.41	0.54	0.33	0.34	0.48	0.43	0.49	0.52	0.51	0.45
Casanovo V2	0.49	0.62	0.45	0.45	0.56	0.48	0.60	0.62	0.59	0.54
π-PrimeNovo	0.60	0.72	0.53	0.57	0.65	0.57	0.70	0.70	0.70	0.64

AdaNovo: Towards Robust De Novo Peptide Sequencing in Proteomics against Data Biases (2024) · 1 table

Table1

AdaNovo: Towards Robust De Novo Peptide Sequencing in Proteomics against Data Biases, page 7: Empirical comparison of previous models on 9-species benchmark. The best and the second best results are highlighted with bold and underlined, respectively. The reproduced performance of Casanovo is denoted as Casanovo (rep.). Following Casanovo [35], we train 5 models with different random initializations and report the standard deviation when the test dataset is Mouse or Human. The other standard deviations are not reported because it would be too computationally expensive.

Nine-species benchmark	Peptide precision
	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
DeepNovo · quoted	0.286	0.293	0.462	0.422	0.330	0.454	0.436	0.449	0.253	0.376
PointNovo · quoted	0.355	0.351	0.534	0.478	0.396	0.513	0.511	0.518	0.298	0.439
Casanovo · quoted	0.443	0.367	0.561	0.486	0.408	0.460	0.437	0.540	0.371	0.453
Casanovo rep. · retrained	0.449	0.343	0.568	0.474	0.422	0.463	0.549	0.513	0.347	0.459
AdaNovo	0.493	0.373	0.612	0.523	0.431	0.552	0.546	0.561	0.397	0.499

Nine-species benchmark	Amino acid precision
	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
DeepNovo · quoted	0.623	0.610	0.750	0.694	0.630	0.731	0.679	0.742	0.602	0.673
PointNovo · quoted	0.626	0.606	0.779	0.712	0.644	0.733	0.730	0.768	0.589	0.687
Casanovo · quoted	0.562	0.424	0.591	0.518	0.461	0.471	0.442	0.573	0.405	0.494
Casanovo rep. · retrained	0.612	0.585	0.753	0.686	0.640	0.720	0.727	0.711	0.617	0.672
AdaNovo	0.667	0.618	0.825	0.757	0.650	0.767	0.719	0.788	0.656	0.716

The plain Casanovo column is quoted: each value is Casanovo's own Table 2 precision at coverage 1, set here under AdaNovo's precision. AdaNovo's own run of Casanovo is 'Casanovo (rep.)', the column the arXiv version prints as plain 'Casanovo'.
In the paper: column amino acid precision, Bacillus: the original table bolded PointNovo (0.768), AdaNovo (0.788) and underlined DeepNovo (0.742).

ReNovo: Retrieval-Based De Novo Mass Spectrometry Peptide Sequencing (2024) · 3 tables

Table1 (Seven-species)

ReNovo: Retrieval-Based De Novo Mass Spectrometry Peptide Sequencing, page 7: Empirical comparison of de novo peptide sequencing models. The best and the second best are highlighted with bold and underline, respectively.

Seven-species benchmark NovoBench split	Peptide precision	Peptide AUC	Amino acid precision	Amino acid recall
DeepNovo	0.204	0.136	0.492	0.433
PointNovo	0.022	0.007	0.196	0.169
Casanovo	0.119	0.084	0.322	0.327
π-HelixNovo	0.234	0.173	0.481	0.472
AdaNovo	0.174	0.135	0.379	0.385
ReNovo	0.278	0.228	0.512	0.514

Table1 (Nine-species)

ReNovo: Retrieval-Based De Novo Mass Spectrometry Peptide Sequencing, page 7: Empirical comparison of de novo peptide sequencing models. The best and the second best are highlighted with bold and underline, respectively.

Nine-species benchmark	Peptide precision	Peptide AUC	Amino acid precision	Amino acid recall
DeepNovo	0.428	0.376	0.696	0.638
PointNovo	0.480	0.436	0.740	0.671
Casanovo	0.481	0.439	0.697	0.696
π-HelixNovo	0.517	0.453	0.765	0.758
AdaNovo	0.505	0.469	0.698	0.709
ReNovo	0.568	0.528	0.770	0.769

Table1 (HC-PT)

ReNovo: Retrieval-Based De Novo Mass Spectrometry Peptide Sequencing, page 7: Empirical comparison of de novo peptide sequencing models. The best and the second best are highlighted with bold and underline, respectively.

ProteomeTools HC-PT (NovoBench)	Peptide precision	Peptide AUC	Amino acid precision	Amino acid recall
DeepNovo	0.313	0.255	0.531	0.534
PointNovo	0.419	0.373	0.623	0.622
Casanovo	0.211	0.177	0.442	0.453
π-HelixNovo	0.356	0.318	0.588	0.582
AdaNovo	0.212	0.178	0.442	0.451
ReNovo	0.467	0.436	0.651	0.648

ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing (2024) · 1 table

Table 1

ContraNovo: A Contrastive Learning Approach to Enhance De Novo Peptide Sequencing, page 6: Comparison of the performance of ContraNovo and five baseline methods on 9-species-V1 test set. The bold font indicates the best performance.

Nine-species benchmark original (DeepNovo, 2017)	Amino acid precision
	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
PEAKS	0.600	0.639	0.748	0.673	0.633	0.728	0.644	0.719	0.586	0.663
DeepNovo	0.623	0.610	0.750	0.694	0.630	0.731	0.679	0.742	0.602	0.673
PointNovo	0.626	0.606	0.779	0.712	0.644	0.733	0.730	0.768	0.589	0.687
Casanovo	0.689	0.586	0.684	0.679	0.629	0.721	0.668	0.749	0.603	0.667
Casanovo V2	0.760	0.676	0.752	0.755	0.706	0.785	0.748	0.790	0.681	0.739
ContraNovo	0.798	0.771	0.797	0.799	0.745	0.810	0.807	0.828	0.711	0.785

Nine-species benchmark original (DeepNovo, 2017)	Peptide precision
	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes	Average
PEAKS	0.197	0.277	0.428	0.356	0.287	0.403	0.362	0.387	0.203	0.322
DeepNovo	0.286	0.293	0.462	0.422	0.330	0.454	0.436	0.449	0.253	0.376
PointNovo	0.355	0.351	0.534	0.478	0.396	0.513	0.511	0.518	0.298	0.439
Casanovo	0.426	0.341	0.490	0.478	0.406	0.521	0.506	0.537	0.330	0.448
Casanovo V2	0.483	0.446	0.599	0.557	0.493	0.618	0.589	0.622	0.446	0.539
ContraNovo	0.567	0.622	0.674	0.630	0.576	0.672	0.677	0.688	0.486	0.621

AdaNovo: Adaptive De Novo Peptide Sequencing with Conditional Mutual Information (2024) · 2 tables

Table2

AdaNovo: Adaptive De Novo Peptide Sequencing with Conditional Mutual Information, page 7: Empirical comparison of de novo sequencing models in terms of identifying amino acids with PTMs. The best and the second best results are highlighted bold and underlined, respectively.

Nine-species benchmark	Ptm precision
	Homo sapiens	Vigna mungo	Candidatus Thiodiazotropha endoloripes	Bacillus subtilis
DeepNovo · quoted	0.369	0.644	0.510	0.483
PointNovo · quoted	0.415	0.653	0.526	0.524
Casanovo · retrained	0.398	0.646	0.508	0.470
AdaNovo	0.483	0.689	0.575	0.565

Table1

AdaNovo: Adaptive De Novo Peptide Sequencing with Conditional Mutual Information, page 7: Empirical comparison of de novo sequencing models. The table lists the peptide-level and amino acid-level precision of three competing models on all nine benchmark cross-validation folds. Each fold’s test set contains spectra from a single species. Kindly note that peptide-level performance measures are the primary quantifier of the model’s practical utility because the goal is to assign a complete peptide sequence to each spectrum. The best and the second best results are highlighted bold and underlined, respectively.

Nine-species benchmark	Peptide precision
	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes
DeepNovo · quoted	0.286	0.293	0.462	0.422	0.330	0.454	0.436	0.449	0.253
PointNovo · quoted	0.355	0.351	0.534	0.478	0.396	0.513	0.511	0.518	0.298
Casanovo · retrained	0.449	0.343	0.568	0.474	0.422	0.463	0.549	0.513	0.347
AdaNovo	0.467	0.373	0.593	0.496	0.431	0.530	0.546	0.528	0.372

Nine-species benchmark	Amino acid precision
	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes
DeepNovo · quoted	0.623	0.610	0.750	0.694	0.630	0.731	0.679	0.742	0.602
PointNovo · quoted	0.626	0.606	0.779	0.712	0.644	0.733	0.730	0.768	0.589
Casanovo · retrained	0.612	0.585	0.753	0.686	0.640	0.720	0.727	0.718	0.617
AdaNovo	0.646	0.618	0.793	0.728	0.650	0.740	0.719	0.739	0.642

De novo mass spectrometry peptide sequencing with a transformer model (2022) · 1 table

Table2

De novo mass spectrometry peptide sequencing with a transformer model, page 6: Empirical comparison of Casanovo, DeepNovo and PointNovo. The table lists the peptide-level and amino acid-level precision of three competing models and coverage of Casanovo with precursor m/z filtering on all nine benchmark cross-validation folds. Each fold’s test set contains spectra from a single species, with nearly disjoint sets of peptides between species. For cross-validation folds corresponding to mouse and human, five models were trained with different random initializations. For these species, we report standard deviation of the performance measures.

Nine-species benchmark	Peptide precision
	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes
DeepNovo · released	0.286	0.293	0.462	0.422	0.330	0.454	0.436	0.449	0.253
PointNovo · quoted	0.355	0.351	0.534	0.478	0.396	0.513	0.511	0.518	0.298
Casanovo	0.665	0.683	0.824	0.771	0.732	0.771	0.798	0.805	0.695

Nine-species benchmark	Peptide coverage
	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes
Casanovo	0.666	0.537	0.681	0.630	0.557	0.557	0.547	0.671	0.534

Nine-species benchmark	Peptide precision at coverage 1
	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes
Casanovo	0.443	0.367	0.561	0.486	0.408	0.460	0.437	0.540	0.371

Nine-species benchmark	Amino acid precision
	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes
DeepNovo · released	0.623	0.610	0.750	0.694	0.630	0.731	0.679	0.742	0.602
PointNovo · quoted	0.626	0.606	0.779	0.712	0.644	0.733	0.730	0.768	0.589
Casanovo	0.899	0.898	0.952	0.935	0.920	0.929	0.920	0.943	0.908

Nine-species benchmark	Amino acid precision at coverage 1
	Mus musculus	Homo sapiens	Saccharomyces cerevisiae	Methanosarcina mazei	Apis mellifera	Solanum lycopersicum	Vigna mungo	Bacillus subtilis	Candidatus Thiodiazotropha endoloripes
Casanovo	0.562	0.424	0.591	0.518	0.461	0.471	0.442	0.573	0.405

Papers describing it (2)

• Peptide Sequencing with Deep Learning (2020, thesis)
• Computationally instrument-resolution-independent de novo peptide sequencing for high-resolution devices (2021, Nature Machine Intelligence, peer-reviewed)

Papers using it (2)

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

• The Immunopeptidome from a Genomic Perspective: Establishing the Noncanonical Landscape of MHC Class I–Associated Peptides (2023, Cancer Immunology Research, peer-reviewed)
• Application of a Novel Hybrid CNN-GNN for Peptide Ion Encoding (2023, Journal of Proteome Research, peer-reviewed)

Authors (7)

Rui Qiao, Ngoc Hieu Tran, Lei Xin, Xin Chen (Waterloo), Ming Li, Baozhen Shan, Ali Ghodsi

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