GyroNovo

algorithm · Transformer (AR)

GyroNovo: algorithm · Transformer (AR). Attacks missing b- and y-ion fragments on two fronts. Rather than treating imputation as a fixed reconstruction task, it uses the decoder errors seen during training to…

Attacks missing b- and y-ion fragments on two fronts. Rather than treating imputation as a fixed reconstruction task, it uses the decoder errors seen during training to steer the imputation objective toward the fragments that actually cause mistakes, and to build easy and hard augmented views of each spectrum so the decoder learns under varying spectral corruption. It also gives self-attention a mass-aware inductive bias, using rotary embeddings to encode pairwise mass differences between peaks. Inference needs no extra inputs or search. Reports about 9 points of peptide-level and 7 points of amino-acid-level precision over the previous best on NovoBench.

Kind algorithm
Deep learning yes
Acquisition DDA
Family Transformer (AR)

Reported comparisons (6)

The comparison tables this method’s own papers print, standardised: every value on a 0-1 scale, methods down the side, the measure and then the species across. These are numbers papers report about themselves and their baselines. They are not a leaderboard, and they do not compare across tables: each was produced by a different group, on the dataset named in its corner, with each baseline either retrained, run from released weights or quoted from another paper. Where the paper says which, it follows the method’s name (hover it for the sentence); most papers do not say. Bold is the best value in a column and underline the runner-up, our ranking rather than the paper’s own marks.

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

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 (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

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

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 (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

Paper describing it

Authors (3)

Abdellah El Mekki, Laks V.S. Lakshmanan, Muhammad Abdul-Mageed

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