Cascadia

algorithm · Transformer (AR)

Cascadia: algorithm · Transformer (AR). Transformer for DIA

Transformer for DIA

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

Code

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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

Evaluated on

Compared against in other papers (1)

2 tables from 1 paper 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.

DiffNovo: A Transformer-Diffusion Model for De Novo Peptide Sequencing (2025) · 2 tables

Table 2

DiffNovo: A Transformer-Diffusion Model for De Novo Peptide Sequencing, page 8: Comparison of DiffNovo and other state-of-the-art (SOTA) methods on peptide precision across three DIA datasets.

De novo sequencing of DIA data Peptide precision
UTI OC Plasma
DiffNovo 0.659 0.735 0.632
DeepNovo-DIA 0.403 0.417 0.351
PepNet 0.296 0.368 0.550
Cascadia 0.291 0.301 0.736

PepNet, Plasma: the printed cell reads “0.550/0.530*/0.664+”, and only 0.550 is in the table. The paper’s note: “+ Indicates the filtered peptide-level accuracy, and * indicates the peptide-level accuracy reported in [10].”

Table 1

DiffNovo: A Transformer-Diffusion Model for De Novo Peptide Sequencing, page 7: Comparison of DiffNovo and other state-of-the-art (SOTA) methods on amino acid recall and precision across three DIA datasets.

De novo sequencing of DIA data Amino acid recall Amino acid precision
UTI OC Plasma UTI OC Plasma
DiffNovo 0.665 0.749 0.663 0.675 0.743 0.675
DeepNovo-DIA 0.566 0.547 0.689 0.612 0.601 0.702
PepNet 0.396 0.455 0.509 0.391 0.451 0.491
Cascadia 0.293 0.286 0.704 0.341 0.349 0.782

PepNet, Plasma: the printed cell reads “0.491 / 0.725*”, and only 0.491 is in the table. The paper’s note: “* Indicates the positional accuracy reported in [10].”

Papers describing it (2)

Authors (9)

Justin Sanders, Bo Wen, Paul Rudnick, Rich Johnson, Christine C. Wu, Sewoong Oh, Michael J. MacCoss, William Stafford Noble, Michael Riffle

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