Author

David Bouyssié

Centre National de la Recherche ScientifiqueORCID 0000-0002-0847-4759Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 3 Approach:benchmark study· 2 Assay:proteomics· 2 Approach:statistical model· 2 Research Focus:database / resource· 1 Assay:metagenomics· 1

Frequent Co-Authors

Juan Antonio VizcaínoiD· 2 shared Julian UszkoreitiD· 2 shared Veit SchwämmleiD· 2 shared Yasset Pérez‐RiveroliD· 2 shared Alvis BrāzmaiD· 1 shared Anja FüllgrabeiD· 1 shared Anne Gonzalez de PeredoiD· 1 shared Balázs KunkliiD· 1 shared

Activity by Year

2021-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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2 shared papersBioinformatics

Juan Antonio VizcaínoORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

WOMBAT-P: Benchmarking Label-Free Proteomics Data Analysis Workflows

David Bouyssié, Pınar Altıner, Salvador Capella-Gutiérrez, José M. Fernández, Yanick Paco Hagemeijer, Peter L. Horvatovich, Martin Hubálek, Fredrik Levander, Pierluigi Mauri, Magnus Palmblad, Wolfgang Raffelsberger, Laura Rodríguez‐Navas, Dario Di Silvestre, Balázs Kunkli, Julian Uszkoreit, , Veit Schwämmle

WOMBAT-P benchmarks label-free proteomics workflows by comparing four analysis pipelines using standardized metrics and datasets.

Journal of Proteome Research · Dec 1, 2023Peer-reviewedBioinformatics

A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics

Bart Van Puyvelde, Simon Daled, Sander Willems, Ralf Gabriels, Anne Gonzalez de Peredo, Karima Chaoui, Emmanuelle Mouton‐Barbosa, David Bouyssié, Kurt Boonen, Christopher J. Hughes, Lee A. Gethings, Yasset Pérez‐Riverol, Nic Bloomfield, Stephen A. Tate, Odile Burlet‐Schiltz, , Maarten Dhaenens

The paper presents a comprehensive benchmark dataset for evaluating label-free quantification methods across various proteomics acquisition strategies.

Scientific Data · Mar 30, 2022Peer-reviewedBioinformatics

A proteomics sample metadata representation for multiomics integration, and big data analysis.

Chengxin Dai, Anja Füllgrabe, Julianus Pfeuffer, Elizaveta M. Solovyeva, Jingwen Deng, Pablo A Moreno, Selvakumar Kamatchinathan, Deepti J Kundu, Nancy George, Silvie Korena Fexova, Björn Andreas Grüning, Melanie Christine Föll, Johannes Griss, Marc Vaudel, Enrique Audain, , Yasset Pérez‐Riverol

The paper introduces a standardized metadata format for proteomics datasets to improve reusability and integration across public repositories.

Nature Communications · Oct 6, 2021Peer-reviewedBioinformatics