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# Global Spectrum Interpretation (GSI): A global, integrative approach for the protein inference problem

Aurélien Berthier, Emile Benoist, Guillaume Fertin, Géraldine Jean, Hélène Rogniaux

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Aurélien Berthier, Emile Benoist, Guillaume Fertin, Géraldine Jean, Hélène Rogniaux. Global Spectrum Interpretation (GSI): A global, integrative approach for the protein inference problem. EuPA 2025, Jun 2025, Saint-Malo, France. . [hal-05411345](https://hal.science/hal-05411345v1)

HAL Id: hal-05411345

https://hal.science/hal-05411345v1

Submitted on 11 Dec 2025

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Distributed under a Creative Commons Attribution 4.0 International License
Aurélien Berthier<fnref n="1" />, Emile Benoist<fnref n="1" />, Guillaume Fertin<fnref n="1" />, Géraldine Jean<fnref n="1" />,
Hélène Rogniaux<fnref n="2" />

## Background

### Main issues in Protein Inference :

* Performing spectra assignment to peptides and protein inference successively dismiss potentially correct identifications.

* Distinguishing proteins sharing peptides can be difficult without additional information.

### Hypotheses :

* Integrating biological information can help distinguish proteins.

* Lower scored peptide identifications should be kept for protein inference.

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## Comparison with other methods

**X!TandemPipeline** (Langella et al., 2017)
**HeLa Dataset** (10 155 spectra, 20 422 proteins)

### Number of proteins identifications :

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**EPIFANY** (Pfeuffer et al., 2020)
**iPRG2016 Dataset** (27 363 spectra, 5 592 proteins)

### Number of proteins identifications :

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## Model

### 3 types of nodes :

* **Proteins**, associated to **abundance**

* **Peptides**, associated to 2 **quantities** (expected and observed)

* **Spectra**, associated to a **selected** Spectra-Peptides edge

### 2 types of edges :

* **Proteins-Peptides**, associated to detectability

* **Spectra-Peptides**, associated to a normalized score

We use Linear Programming to find the optimal solution (**Abundances** + **Selected Spectra-Peptides** **Edges** + peptides **quantities**)

## Take Home Message

**We developed a new protein inference model able to simultaneously assign spectra to peptides and infer proteins.**

**We obtained state-of-the-art results on complex proteomics datasets.**

### Future work :

* Convert to Multi-Objective Linear Programming

* Test the model on new datasets

Contact : aurelien.berthier@univ-nantes.fr

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anr®

<footnote n="1">¹ Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000 Nantes, France</footnote>

<footnote n="2">² INRAE, UR1268 Biopolymères Interactions Assemblages, F-44316 Nantes, France
Supported by the French National Research Agency (ANR-24-CE45-3296 PEPTIDOMS)</footnote>