Integration of high-throughput proteomic data and complementary omics layers with PriOmics
- Robin Kosch1,12,
- Katharina Limm2,
- Annette M. Staiger3,
- Nadine S. Kurz4,
- Nicole Seifert4,
- Bence Oláh5,
- Stefan Solbrig2,
- Viola Poeschel6,
- Gerhard Held7,
- Marita Ziepert8,
- Norbert Schmitz9,
- Emil Chteinberg10,
- Reiner Siebert10,
- Rainer Spang2,
- Helena U. Zacharias5,
- German Ott11,
- Peter J. Oefner2 and
- Michael Altenbuchinger4
- 1 University Medical Center Gottingen, Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School;
- 2 University of Regensburg;
- 3 Robert-Bosch-Krankenhaus, Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, University of Tubingen;
- 4 University Medical Center Gottingen;
- 5 Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School;
- 6 Saarland University Medical School;
- 7 Westpfalz-Klinikum;
- 8 Leipzig University;
- 9 University Hospital Muenster;
- 10 Ulm University and Ulm University Medical Center;
- 11 Robert-Bosch-Krankenhaus
Abstract
High-throughput bottom-up proteomics data cover 1,000s of proteins and related co- and post-translational modifications (CTMs/PTMs). Yet, it remains an open question how to holistically explore such data and their relationship to complementary omics/phenotypical information. Graphical models are particularly suited to study molecular networks and underlying regulatory mechanisms, as they can distinguish direct from indirect relationships, aside from their generalizability to diverse data types. We propose PriOmics to integrate proteomics data with complementary omics and phenotypical data. PriOmics models intensities of individual proteotypic peptides and incorporates their protein affiliation as prior knowledge to resolve statistical relationships between proteins and CTMs/PTMs. This was verified in simulation studies, which also demonstrate that PriOmics can disentangle regulatory effects of protein modifications from those of respective protein abundances. These findings were substantiated in a Diffuse Large B-Cell Lymphoma (DLBCL) dataset where we integrated SWATH-MS-based proteomics with transcriptomic and phenotypic data.
- Received April 19, 2024.
- Accepted October 9, 2025.
- Published by Cold Spring Harbor Laboratory Press
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