Author

Rachel Catzel

ORCID 0000-0002-2983-9833Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Research Focus:protein structure prediction· 3 Assay:proteomics· 3 Approach:transformer / foundation model· 3 Research Focus:deconvolution· 2 Research Focus:software / tool· 2 Research Focus:benchmarking· 1 Approach:contrastive learning· 1 Research Focus:database / resource· 1

Frequent Co-Authors

Jeroen Van GoeyiD· 3 shared Kevin EloffiD· 3 shared Konstantinos KalogeropoulosiD· 3 shared Nicolás López CarranzaiD· 3 shared Timothy P. JenkinsiD· 3 shared Amandla MabonaiD· 2 shared Erwin M. SchoofiD· 2 shared Alberto SantosiD· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 41 mutual co-author links

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3 shared papersBioinformatics

Jeroen Van GoeyORCID

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3 papers

Recent·Most cited

Learning from tandem mass spectra at scale with a self-supervised foundation model for proteomics

Mechiel Nieuwoudt, Marco Reverenna, Divanisha Patel, Rachel Catzel, Isaac H.J. Houngue, Jemma Daniel, Kevin Eloff, Alberto Santos, Nicolás López Carranza, Timothy P. Jenkins, Jeroen Van Goey, Konstantinos Kalogeropoulos

This paper introduces InstaNovo-FM, a self-supervised foundation model for proteomics that learns from tandem mass spectra to enable transferable applications across datasets and instruments.

bioRxiv · Sep 8, 2026 · PreprintBioinformatics

InstaNovo-P: A de novo peptide sequencing model for phosphoproteomics

Jesper Lauridsen, Vahap Canbay, Rachel Catzel, Pathmanaban Ramasamy, Amandla Mabona, Kevin Eloff, Paul Fullwood, Jennifer Ferguson, Annekatrine Kirketerp-Moller, Ida Sofie Goldschmidt, Tine Claeys, Sam van Puyenbroeck, Nicolás López Carranza, Erwin M. Schoof, Lennart Martens, , Konstantinos Kalogeropoulos

InstaNovo-P improves phosphorylated peptide detection and site localization in phosphoproteomics by using a transformer-based model fine-tuned on phosphoproteomics data.

Nature Communications · Jul 10, 2026Peer-reviewedBioinformatics

De novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experiments

Kevin Eloff, Konstantinos Kalogeropoulos, Amandla Mabona, Oliver Morell, Rachel Catzel, Esperanza Rivera‐de‐Torre, Jakob Berg Jespersen, Wesley Williams, Sam P. B. van Beljouw, Marcin J. Skwark, Andreas H. Laustsen, Stan J. J. Brouns, Anne Ljungars, Erwin M. Schoof, Jeroen Van Goey, , Timothy P. Jenkins

InstaNovo is a transformer model for de novo peptide sequencing that outperforms existing methods, enabling accurate identification of peptides and discovery of novel sequences and organisms.

Nature Machine Intelligence · Mar 31, 2025Peer-reviewedBioinformatics