PEAKS

algorithm · Graph / DP

PEAKS: algorithm · Graph / DP. Commercial DP-based de novo

Commercial DP-based de novo

Kind	algorithm
Deep learning	no
Acquisition	DDA
Family	Graph / DP

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

• Surpassing 10,000 identified and quantified proteins in a single run by optimizing current LC-MS instrumentation and dat (as deposited) · 2022 peer-reviewed

Compared against in other papers (14)

19 tables from 14 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) · 1 table

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

Discrete Diffusion with Physical Mass Constraints for De Novo Peptide Sequencing (2026) · 1 table

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

LIPNovo+: Self-Reflective Latent Imputation for Robust De Novo Peptide Sequencing (2026) · 2 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

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

CausalNovo: Advancing De Novo Peptide Sequencing via a Causality-Informed Framework (2026) · 1 table

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

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).

Latent Imputation before Prediction: A New Computational Paradigm for De Novo Peptide Sequencing (2025) · 1 table

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.

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

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

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

GA-Novo: De Novo Peptide Sequencing via Tandem Mass Spectrometry using Genetic Algorithm (2019) · 1 table

Table 5

GA-Novo: De Novo Peptide Sequencing via Tandem Mass Spectrometry using Genetic Algorithm, page 11: The results of sequencing 120 MS/MS spectra by GA-Novo and PEAKS.

120 spectra of the Wessels et al. 2012 full factorial LC-MS/MS benchmark (E. coli K12, LTQ-FT); no accession	Amino acid precision	Amino acid recall	Peptide recall
GA-Novo	0.89	0.88	0.64
PEAKS · released	0.85	0.84	0.56

Protein identification with deep learning: from abc to xyz (2017) · 1 table

Table 1

Protein identification with deep learning: from abc to xyz, page 10: The recall of PEAKS and DeepNovo at the amino acid level and the peptide level.

S. cerevisiae proteome of Hebert et al. 2014 (The one hour yeast proteome), Orbitrap Fusion HCD; no accession	Amino acid recall	Peptide recall
PEAKS de novo · released	0.574	0.257
DeepNovo de novo	0.743	0.617
DeepNovo database search	0.899	0.898

Papers describing it (7)

• An Effective Algorithm for the Peptide De Novo Sequencing from MS/MS Spectrum (2003, Lecture Notes in Computer Science, peer-reviewed)
• PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry (2003, Rapid Communications in Mass Spectrometry, peer-reviewed)
• An effective algorithm for peptide de novo sequencing from MS/MS spectra (2005, Journal of Computer and System Sciences, peer-reviewed)
• Complexity and scoring function of MS/MS peptide de novo sequencing (2006, Computational Systems Bioinformatics, peer-reviewed)
• Improving de novo sequencing accuracy for Ion Trap data in PEAKS software (2007, presentation)
• Better score function for peptide identification with ETD MS/MS spectra (2010, BMC Bioinformatics, peer-reviewed)
• A streamlined platform for analyzing tera-scale DDA and DIA mass spectrometry data enables highly sensitive immunopeptidomics (2022, Nature Communications, peer-reviewed)

Papers using it (142)

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

• PeptidomicsDB: a unified resource of endogenous peptidomes for peptide discovery and functional exploration across species and biological contexts (2026, Nucleic Acids Research, peer-reviewed)
• Proteomic profiling of Camelus dromedarius blood leukocytes using protein sequence database searches and de novo peptide sequencing (2026, Journal of King Saud University – Science, peer-reviewed)
• An integrated De novo sequencing and virtual screening strategy for the discovery of low-abundance natural peptides with potential bioactivities from Asini Corii Colla (2026, Journal of Chromatography B, peer-reviewed)
• Sequence-resolved discovery and cellular validation of immunomodulatory peptides from cricket protein hydrolysate (2026, Future Foods, peer-reviewed)
• Ancient proteins identify various Denisovan remains from Southwest China (2026, Nature, peer-reviewed)
• Towards the establishment of a comprehensive food inspection system: An approach to utilize de novo peptide sequencing from MS/MS spectra (2026, Food and Humanity, peer-reviewed)
• Early maturation of host antimicrobial peptide defences is associated with host–pathogen coexistence (2026, Nature Chemical Biology, peer-reviewed)
• Sequence-Resolved Discovery of Immunomodulatory Peptides from Cricket Protein Hydrolysate as a Functional Alternative Protein Ingredient (2026, SSRN Electronic Journal, preprint)
• Enamel proteins from six Homo erectus specimens across China (2026, Nature, peer-reviewed)
• N-Terminal Coumarin Derivatization-Aided De Novo Peptide Sequencing and Its Application to Peptidomics Using LC-Trapped Ion Mobility Spectrometry-qTOF/MS (2026, Analytical Chemistry, peer-reviewed)
• Screening and Identification of TRPV1-Targeting Peptides from Apis mellifera Venom with Inflammatory Modulatory Potential (2026, Journal of Natural Products, peer-reviewed)
• N-terminal Coumarin Derivatization-aided De Novo Peptide Sequencing and its Application to Peptidomics using LC-trapped Ion Mobility Spectrometry-qTOF/MS (2026, ChemRxiv, preprint)
• Towards the establishment of a comprehensive food inspection system: an approach to utilise de novo peptide sequencing from MS/MS spectra (2026, SSRN Electronic Journal, preprint)
• Systematic benchmarking of mass spectrometry-based antibody sequencing reveals methodological biases (2025, Cell Systems, peer-reviewed)
• Mass spectrometry-based analysis of rheumatoid factor (2025, Frontiers in Immunology, peer-reviewed)
• SequenceAssembler: A tool for protein sequence assembly from mass spectrometry data (2025, Journal of Proteomics, peer-reviewed)
• Expanding the repertoire of conopeptides from the venom of the vermivorous snail Conus regius (2025, Toxicon, peer-reviewed)
• The proteome of the late Middle Pleistocene Harbin individual (2025, Science, peer-reviewed)
• Structural Basis for Postfusion-Specific Binding to the Respiratory Syncytial Virus F Protein by the Canonical Antigenic Site I Antibody 131–2a (2025, ACS Infectious Diseases, peer-reviewed)
• Proteolytic signatures of coagulation identified by plasma peptidomics (2025, Journal of Thrombosis and Haemostasis, peer-reviewed)
• A green dichromophoric protein enabling foliage mimicry in arthropods (2025, Proceedings of the National Academy of Sciences, peer-reviewed)
• Enamel proteins reveal biological sex and genetic variability in southern African Paranthropus (2025, Science, peer-reviewed)
• Antidiabetic and Immunomodulatory Properties of Peptide Fractions from Sacha Inchi Oil Press-Cake (2025, Foods, peer-reviewed)
• Decoding Protein Glycosylation by an Integrative Mass Spectrometry-Based De Novo Sequencing Strategy (2025, JACS Au, peer-reviewed)
• Interferon-α promotes HLA-B-restricted presentation of conventional and alternative antigens in human pancreatic β-cells (2025, Nature Communications, peer-reviewed)
• SILAC-based quantification reveals modulation of the immunopeptidome in BRAF and MEK inhibitor sensitive and resistant melanoma cells (2025, Frontiers in Immunology, peer-reviewed)
• Identification, screening, and comprehensive evaluation of novel thrombin inhibitory peptides from the hirudo produced using pepsin (2024, Frontiers in Pharmacology, peer-reviewed)
• Systematic benchmarking of mass spectrometry-based antibody sequencing reveals methodological biases (2024, bioRxiv, preprint)
• Food processing drives the toxic lectin reduction and bioactive peptide enhancement in Pinellia ternata (2024, Current Research in Food Science, peer-reviewed)
• Proteomic Analysis of the Major Alkali-Soluble Inca Peanut (Plukenetia volubilis) Proteins (2024, Foods, peer-reviewed)
• NovoLign: metaproteomics by sequence alignment (2024, ISME Communications, peer-reviewed)
• Immunoglobulin repertoire sequencing and de novo sequencing – Powerful tools for identifying free light chains from patients with light chain cast nephropathy (2024, International Immunopharmacology, peer-reviewed)
• PeposX-Exhaust: A lightweight and efficient tool for identification of short peptides (2024, Food Chemistry: X, peer-reviewed)
• Improved detection of tryptic immunoglobulin variable region peptides by chromatographic and gas-phase fractionation techniques (2024, Cell Reports Methods, peer-reviewed)
• NovoLign: metaproteomics by sequence alignment (2024, bioRxiv, preprint)
• Olive Pomace Extract Contains Low Molecular Weight Peptides and Possesses ACE Inhibitory Activity (2024, International Journal of Molecular Sciences, peer-reviewed)
• Mirror-image trypsin digestion and sequencing of D-proteins (2024, Nature Chemistry, peer-reviewed)
• A novel metalloproteinase-derived cryptide from Bothrops cotiara venom inhibits angiotensin-converting enzyme activity (2024, Biochimie, peer-reviewed)
• Peposx-Exhaust: A Lightweight and Efficient Tool for Identification of Short Peptides (2023, SSRN Electronic Journal, preprint)
• Unveiling the peptidome diversity of Lachesis muta snake venom: Discovery of novel fragments of metalloproteinase, l-amino acid oxidase, and bradykinin potentiating peptides (2023, Biochemical and Biophysical Research Communications, peer-reviewed)
• Functional peptidomics analysis of Saccharomyces pastorianus protein hydrolysates based on different enzyme treatments (2023, Chinese Journal of Chromatography, peer-reviewed)
• Anti-methicillin-resistant Staphylococcus aureus and antibiofilm activity of new peptides produced by a Brevibacillus strain (2023, PeerJ, peer-reviewed)
• Direct Mass Spectrometry-Based Detection and Antibody Sequencing of Monoclonal Gammopathy of Undetermined Significance from Patient Serum: A Case Study (2023, Journal of Proteome Research, peer-reviewed)
• Characterization of natural peptides in Pheretima by integrating proteogenomics and label-free peptidomics (2023, Journal of Pharmaceutical Analysis, peer-reviewed)
• Exploring the Immunogenicity of Noncanonical HLA-I Tumor Ligands Identified through Proteogenomics (2023, Clinical Cancer Research, peer-reviewed)
• Seasonal and water restriction-related changes in Eucalyptus grandis leaf proteins: Shedding light on the dark proteome (2023, Current Plant Biology, peer-reviewed)
• Next-Generation Sequencing for Venomics: Application of Multi-Enzymatic Limited Digestion for Inventorying the Snake Venom Arsenal (2023, Toxins, peer-reviewed)
• De novo sequencing of multiple tandem mass spectra of peptide containing SILAC labeling (2023, thesis)
• Isolated anti-Ro52 identifies a severe subset of Sjögren’s syndrome patients (2023, Frontiers in Immunology, peer-reviewed)
• Complementarity determining regions in SARS-CoV-2 hybrid immunity (2023, Frontiers in Immunology, peer-reviewed)
• HyPep: An Open-Source Software for Identification and Discovery of Neuropeptides Using Sequence Homology Search (2023, Journal of Proteome Research, peer-reviewed)
• Antibody sequences assembly method based on weighted de Bruijn graph (2023, Mathematical Biosciences and Engineering, peer-reviewed)
• Enrichment and fragmentation approaches for enhanced detection and characterization of endogenous glycosylated neuropeptides (2023, PROTEOMICS, peer-reviewed)
• Identification, Screening, and Comprehensive Evaluation of Novel DPP-IV Inhibitory Peptides from the Tilapia Skin Gelatin Hydrolysate Produced Using Ginger Protease (2022, Biomolecules, peer-reviewed)
• Venomics survey of six myrmicine ants provides insights into the molecular and structural diversity of their peptide toxins (2022, Insect Biochemistry and Molecular Biology, peer-reviewed)
• De novo assisted AFB1-Specific monoclonal antibody sequence assembly and comprehensive molecular characterization (2022, Analytical Biochemistry, peer-reviewed)
• Identification of Daboia siamensis venome using integrated multi-omics data (2022, Scientific Reports, peer-reviewed)
• Interaction With Fungi Promotes the Accumulation of Specific Defense Molecules in Orchid Tubers and May Increase the Value of Tubers for Biotechnological and Medicinal Applications: The Case Study of Interaction Between Dactylorhiza sp. and Tulasnella calospora (2022, Frontiers in Plant Science, peer-reviewed)
• Characterization of Proteasome-Generated Spliced Peptides Detected by Mass Spectrometry (2022, The Journal of Immunology, peer-reviewed)
• Meta-proteomic analysis of two mammoth’s trunks by EVA technology and high-resolution mass spectrometry for an indirect picture of their habitat and the characterization of the collagen type I, alpha-1 and alpha-2 sequence (2022, Amino Acids, peer-reviewed)
• Peptidomes and Structures Illustrate How SLA-I Micropolymorphism Influences the Preference of Binding Peptide Length (2022, Frontiers in Immunology, peer-reviewed)
• Characterisation of Endogenous Peptides Present in Virgin Olive Oil (2022, International Journal of Molecular Sciences, peer-reviewed)
• Identification of vicilin, legumin and antimicrobial peptide 2a as macadamia nut allergens (2022, Food Chemistry, peer-reviewed)
• Protein cycling in the eastern tropical North Pacific oxygen‐deficient zone: A de novo‐discovery peptidomic approach (2022, Limnology and Oceanography, peer-reviewed)
• Venomics and antivenomics of Indian spectacled cobra (Naja naja) from the Western Ghats (2022, Acta Tropica, peer-reviewed)
• Rapid de novo discovery of peptidomimetic affinity reagents for human angiotensin converting enzyme 2 (2022, Communications Chemistry, peer-reviewed)
• Mass spectrometry profiling and quantitation of changes in circulating hormones secreted over time in Cancer borealis hemolymph due to feeding behavior (2022, Analytical and Bioanalytical Chemistry, peer-reviewed)
• Protein Identification Strategies for the Greenshell Mussel Perna canaliculus (2021, thesis)
• Digging deeper into the immunopeptidome: characterization of post-translationally modified peptides presented by MHC I (2021, Journal of Proteins and Proteomics, peer-reviewed)
• Proteogenomic Analysis Unveils the HLA Class I-Presented Immunopeptidome in Melanoma and EGFR-Mutant Lung Adenocarcinoma (2021, Molecular & Cellular Proteomics, peer-reviewed)
• Novel peptides contribute to the antimicrobial activity of camel milk fermented with Lactobacillus plantarum IS10 (2021, Food Control, peer-reviewed)
• The Choice of Search Engine Affects Sequencing Depth and HLA Class I Allele-Specific Peptide Repertoires (2021, Molecular & Cellular Proteomics, peer-reviewed)
• Quantitative proteomic comparison of salt stress in Chlamydomonas reinhardtii and the snow alga Chlamydomonas nivalis reveals mechanisms for salt-triggered fatty acid accumulation via reallocation of carbon resources (2021, Biotechnology for Biofuels, peer-reviewed)
• Exploring the Sexual Dimorphism of Crustacean Neuropeptide Expression Using Callinectes sapidus as a Model Organism (2021, Journal of Proteome Research, peer-reviewed)
• Database-independent de novo metaproteomics of complex microbial communities (2021, Cell Systems, peer-reviewed)
• Enzymatic valorization process of yellow cocoon waste for production of antioxidative sericin and fibroin film (2021, Journal of Chemical Technology & Biotechnology, peer-reviewed)
• Identification of novel protein-coding sequences in Eucalyptus grandis plants by high-resolution mass spectrometry (2021, Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics, peer-reviewed)
• Mass Spectrometry Quantification, Localization, and Discovery of Feeding-Related Neuropeptides in Cancer borealis (2021, ACS Chemical Neuroscience, peer-reviewed)
• Untangling the fibre ball: Proteomic characterization of South American camelid hair fibres by untargeted multivariate analysis and molecular networking (2021, Journal of Proteomics, peer-reviewed)
• ProAlanase is an Effective Alternative to Trypsin for Proteomics Applications and Disulfide Bond Mapping (2020, Molecular & Cellular Proteomics, peer-reviewed)
• Rubisco activase requires residues in the large subunit N terminus to remodel inhibited plant Rubisco (2020, Journal of Biological Chemistry, peer-reviewed)
• Comparative gender peptidomics of Bothrops atrox venoms: are there differences between them? (2020, Journal of Venomous Animals and Toxins including Tropical Diseases, peer-reviewed)
• Resistant starch slows the progression of CKD in the 5/6 nephrectomy mouse model (2020, Physiological Reports, peer-reviewed)
• Elucidation of the Signatures of Proteasome-Catalyzed Peptide Splicing (2020, Frontiers in Immunology, peer-reviewed)
• Palaeoproteomics gives new insight into early southern African pastoralism (2020, Scientific Reports, peer-reviewed)
• Quantitative profiling of microbial communities by de novo metaproteomics (2020, bioRxiv, preprint)
• The shell matrix of the european thorny oyster, Spondylus gaederopus: microstructural and molecular characterization (2020, Journal of Structural Biology, peer-reviewed)
• Venom Peptide Repertoire of the European Myrmicine Ant Manica rubida: Identification of Insecticidal Toxins (2020, Journal of Proteome Research, peer-reviewed)
• Proteogenomic analysis of granulocyte macrophage colony- stimulating factor autoantibodies in the blood of a patient with autoimmune pulmonary alveolar proteinosis (2020, Scientific Reports, peer-reviewed)
• A Decomposition Based Multi-objective Genetic Programming Algorithm for Classification of Highly Imbalanced Tandem Mass Spectrometry (2020, Lecture Notes in Computer Science, peer-reviewed)
• Delineating the venom toxin arsenal of Malabar pit viper (Trimeresurus malabaricus) from the Western Ghats of India and evaluating its immunological cross-reactivity and in vitro cytotoxicity (2020, International Journal of Biological Macromolecules, peer-reviewed)
• Antihypertensive Effects of Virgin Olive Oil (Unfiltered) Low Molecular Weight Peptides with ACE Inhibitory Activity in Spontaneously Hypertensive Rats (2020, Nutrients, peer-reviewed)
• The Peptide Venom Composition of the Fierce Stinging Ant Tetraponera aethiops (Formicidae: Pseudomyrmecinae) (2019, Toxins, peer-reviewed)
• PepSAVI-MS Reveals a Proline-rich Antimicrobial Peptide in Amaranthus tricolor (2019, Journal of Natural Products, peer-reviewed)
• Preprocessing Tandem Mass Spectra Using Genetic Programming for Peptide Identification (2019, Journal of the American Society for Mass Spectrometry, peer-reviewed)
• Learning to Rank Peptide-Spectrum Matches Using Genetic Programming (2019, 2019 IEEE Congress on Evolutionary Computation (CEC), peer-reviewed)
• ’Palaeoshellomics’ reveals the use of freshwater mother-of-pearl in prehistory (2019, eLife, peer-reviewed)
• TagGraph reveals vast protein modification landscapes from large tandem mass spectrometry datasets (2019, Nature Biotechnology, peer-reviewed)
• Math, science, history, unraveling the mystery—That all started with de novo! (2019, EuPA Open Proteomics, peer-reviewed)
• Metaproteomics reveals potential mechanisms by which dietary resistant starch supplementation attenuates chronic kidney disease progression in rats (2019, PLOS ONE, peer-reviewed)
• Estimating the Contribution of Proteasomal Spliced Peptides to the HLA-I Ligandome* (2018, Molecular & Cellular Proteomics, peer-reviewed)
• Deciphering the Molecular Diversity of an Ant Venom Peptidome through a Venomics Approach (2018, Journal of Proteome Research, peer-reviewed)
• A recombinant isoform of the Ole e 7 olive pollen allergen assembled by de novo mass spectrometry retains the allergenic ability of the natural allergen (2018, Journal of Proteomics, peer-reviewed)
• Mass spectrometry-assisted venom profiling of Hypnale hypnale found in the Western Ghats of India incorporating de novo sequencing approaches (2018, International Journal of Biological Macromolecules, peer-reviewed)
• Genetic Programming for Preprocessing Tandem Mass Spectra to Improve the Reliability of Peptide Identification (2018, 2018 IEEE Congress on Evolutionary Computation (CEC), peer-reviewed)
• Identification of <10 KD peptides in the water extraction of Venenum Bufonis from Bufo gargarizans using Nano LC-MS/MS and De novo sequencing (2018, Journal of Pharmaceutical and Biomedical Analysis, peer-reviewed)
• Evolutionary instability of CUG-Leu in the genetic code of budding yeasts (2018, Nature Communications, peer-reviewed)
• First report of the characterization of a snake venom apyrase (Ruviapyrase) from Indian Russell’s viper (Daboia russelii) venom (2018, International Journal of Biological Macromolecules, peer-reviewed)
• Estimating the Contribution of Proteasomal Spliced Peptides to the HLA-I Ligandome (2018, bioRxiv, preprint)
• Disentangling thermal stress responses in a reef-calcifier and its photosymbionts by shotgun proteomics (2018, Scientific Reports, peer-reviewed)
• Library Design-Facilitated High-Throughput Sequencing of Synthetic Peptide Libraries (2017, ACS Combinatorial Science, peer-reviewed)
• The sequence coverage in different methods of mass spectrometry data analysis obtained on model proteins (2017, Biomeditsinskaya Khimiya, peer-reviewed)
• Glareosin: a novel sexually dimorphic urinary lipocalin in the bank vole, Myodes glareolus (2017, Open Biology, peer-reviewed)
• Analysis of skin derived peptides from the Cuyaba Dwarf Frog Physalaemus nattereri by off-line LC MALDI MS/MS (2017, International Journal of Mass Spectrometry, peer-reviewed)
• Discovery of Novel Antimicrobial Peptides from Varanus komodoensis (Komodo Dragon) by Large-Scale Analyses and De-Novo-Assisted Sequencing Using Electron-Transfer Dissociation Mass Spectrometry (2017, Journal of Proteome Research, peer-reviewed)
• Characterisation of urinary WFDC12 in small nocturnal basal primates, mouse lemurs (Microcebus spp.) (2017, Scientific Reports, peer-reviewed)
• Peptidomics of Acanthoscurria gomesiana spider venom reveals new toxins with potential antimicrobial activity (2017, Journal of Proteomics, peer-reviewed)
• Rescuing discarded spectra: Full comprehensive analysis of a minimal proteome (2016, PROTEOMICS, peer-reviewed)
• Revealing the functional structure of a new PLA2 K49 from Bothriopsis taeniata snake venom employing automatic “de novo” sequencing using CID/HCD/ETD MS/MS analyses (2015, Journal of Proteomics, peer-reviewed)
• Large Scale Discovery and De Novo-Assisted Sequencing of Cationic Antimicrobial Peptides (CAMPs) by Microparticle Capture and Electron-Transfer Dissociation (ETD) Mass Spectrometry (2015, Journal of Proteome Research, peer-reviewed)
• De novo sequencing of disulphide rich peptides using tandem mass spectrometry (2015, thesis)
• Novel Antifungal Peptides Produced by Leuconostoc mesenteroides DU15 Effectively Inhibit Growth of Aspergillus niger (2015, Journal of Food Science, peer-reviewed)
• Antiproliferative effect of Dendrobium catenatum Lindley polypeptides against human liver, gastric and breast cancer cell lines (2015, Food & Function, peer-reviewed)
• PepExplorer: A Similarity-driven Tool for Analyzing de Novo Sequencing Results (2014, Molecular & Cellular Proteomics, peer-reviewed)
• Practical Ring-Opening Strategy for the Sequence Determination of Cyclic Peptides from One-Bead-One-Compound Libraries (2013, ACS Combinatorial Science, peer-reviewed)
• Moving Pieces in a Venomic Puzzle: Unveiling Post-translationally Modified Toxins from Tityus serrulatus (2013, Journal of Proteome Research, peer-reviewed)
• Integrating de novo Sequencing and Database Search for Monoclonal Antibody Sequencing (2013, Europe PMC (PubMed Central), abstract)
• Proteomic studies highlight outer‐membrane proteins related to biofilm development in the marine bacterium Pseudoalteromonas sp. D41 (2012, PROTEOMICS, peer-reviewed)
• Partial De Novo Sequencing and Unusual CID Fragmentation of a 7 kDa, Disulfide-Bridged Toxin (2012, Journal of the American Society for Mass Spectrometry, peer-reviewed)
• Introducing an Asp-Pro Linker in the Synthesis of Random One-Bead-One-Compound Hexapeptide Libraries Compatible with ESI-MS Analysis (2012, ACS Combinatorial Science, peer-reviewed)
• Extensive Characterization of Tupaia belangeri Neuropeptidome Using an Integrated Mass Spectrometric Approach (2012, Journal of Proteome Research, peer-reviewed)
• Isolation and determination of the primary structure of a lectin protein from the serum of the American alligator (Alligator mississippiensis) (2012, Comparative Biochemistry and Physiology Part B: Biochemistry and Molecular Biology, peer-reviewed)
• Integrating de Novo Sequencing and Database Search for Peptide Identification (2012, Europe PMC (PubMed Central), abstract)
• Extensive De Novo Sequencing of New Parvalbumin Isoforms Using a Novel Combination of Bottom-Up Proteomics, Accurate Molecular Mass Measurement by FTICR−MS, and Selected MS/MS Ion Monitoring (2010, Journal of Proteome Research, peer-reviewed)
• Immune Responses Are Characterized by Specific Shared Immunoglobulin Peptides That Can Be Detected by Proteomic Techniques (2010, Journal of Biological Chemistry, peer-reviewed)
• Plant‐extract‐induced changes in the proteome of the soil‐borne pathogenic fungus Thielaviopsis basicola (2010, PROTEOMICS, peer-reviewed)
• De Novo Interpretation of Tandem Mass Spectra (2009, Current Protocols in Bioinformatics, peer-reviewed)
• A comparative study of the accuracy of several de novo sequencing software packages for datasets derived by matrix‐assisted laser desorption/ionisation and electrospray (2008, Rapid Communications in Mass Spectrometry, peer-reviewed)
• A proteomic‐based approach for the characterization of some major structural proteins involved in host–parasite relationships from the silkworm parasite Nosema bombycis (Microsporidia) (2007, PROTEOMICS, peer-reviewed)
• De novo protein sequence analysis of Macaca mulatta (2007, BMC Genomics, peer-reviewed)
• Molecular Diversity in Venom from the Australian Brown Snake, Pseudonaja textilis (2006, Molecular & Cellular Proteomics, peer-reviewed)
• Defining parameters for homology-tolerant database searching (2004, PubMed, peer-reviewed)

Authors (21)

Bin Ma, Kaizhong Zhang, Chengzhi Liang, Christopher Hendrie, Ming Li, Amanda Doherty-Kirby, Gilles Lajoie, Changjiang Xu, Denis Yuen, Iain Rogers, Xiaowen Liu, Baozhen Shan, Lei Xin, Rui Qiao, Xin Chen (Waterloo), Ngoc Hieu Tran, Shengying Pan, Sahar Rabinoviz, Haibo Bian, Xianliang He, Brenton Morse

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