PepNet

algorithm · CNN

PepNet: algorithm · CNN. Temporal convolutional network

Temporal convolutional network

Kind algorithm
Deep learning yes
Acquisition DDA
Family CNN

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

Benchmarks

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

Compared against in other papers (4)

5 tables from 4 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.

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].”

π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing (2025) · 1 table

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
BiATNovo: An Attention-based Bidirectional De Novo Sequencing Framework for Data-Independent-Acquisition Mass Spectrometry (2024) · 1 table

Table 2

BiATNovo: An Attention-based Bidirectional De Novo Sequencing Framework for Data-Independent-Acquisition Mass Spectrometry, page 8: Comparison of DeepNovo-DIA, PepNet and BiATNovo

De novo sequencing of DIA data Amino acid precision Amino acid recall Peptide precision
OC UTI Plasma OC UTI Plasma OC UTI Plasma
DeepNovo-DIA 0.5562 0.6286 0.4354 0.5596 0.6243 0.4335 0.5098 0.5525 0.3322
BiATNovo 0.6817 0.7108 0.4525 0.6746 0.7076 0.4511 0.6439 0.6584 0.3691
PepNet 0.4008 0.4004 0.3064
Transformer-Based De Novo Peptide Sequencing for Data-Independent Acquisition Mass Spectrometry (2024) · 1 table

TABLE I

Transformer-Based De Novo Peptide Sequencing for Data-Independent Acquisition Mass Spectrometry, page 5: Precision comparison of Transformer-DIA, DeepNovo-DIA and PepNet.

De novo sequencing of DIA data Peptide precision Amino acid precision
UTI OC Plasma UTI OC Plasma
DiaTrans 0.663 0.798 0.672 0.731 0.829 0.78
DeepNovo-DIA 0.403 0.417 0.383 0.566 0.547 0.702
PepNet 0.296 0.368 0.351 0.396 0.455 0.509

Papers describing it (2)

Paper using it

An application or evaluation that ran this method. It is not counted among its authors below.

Authors (4)

Kaiyuan Liu, Yuzhen Ye, Haixu Tang, Sujun Li

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