Integration of high-throughput proteomic data and complementary omics layers with PriOmics
- Robin Kosch1,2,
- Katharina Limm3,
- Annette M. Staiger4,5,
- Nadine S. Kurz1,
- Nicole Seifert1,
- Bence Oláh2,
- Stefan Solbrig6,
- Viola Poeschel7,
- Gerhard Held8,
- Marita Ziepert9,
- Norbert Schmitz10,
- Emil Chteinberg11,
- Reiner Siebert11,
- Rainer Spang12,
- Helena U. Zacharias2,
- German Ott4,
- Peter J. Oefner3,13 and
- Michael Altenbuchinger1,13
- 1Department of Medical Bioinformatics, University Medical Center Göttingen, 37077 Göttingen, Germany;
- 2Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, 30625 Hannover, Germany;
- 3Chair and Institute of Functional Genomics, University of Regensburg, 93053 Regensburg, Germany;
- 4Department of Clinical Pathology, Robert-Bosch-Krankenhaus, 70376 Stuttgart, Germany;
- 5Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology, 70376 Stuttgart, and University of Tübingen, Germany;
- 6Institute of Theoretical Physics, University of Regensburg, 93040 Regensburg, Germany;
- 7Department of Internal Medicine 1 (Oncology, Hematology, Clinical Immunology, and Rheumatology), Saarland University Medical School, 66421 Homburg/Saar, Germany;
- 8Department of Internal Medicine 1, Westpfalz-Klinikum, 67655 Kaiserslautern, Germany;
- 9Institute for Medical Informatics, Statistics and Epidemiology, University Leipzig, 04107 Leipzig, Germany;
- 10Department of Medicine A (Hematology, Oncology, Pulmonology), University Hospital Münster, 48149 Münster, Germany;
- 11Institute of Human Genetics, Ulm University and Ulm University Medical Center, 89081 Ulm, Germany;
- 12Department of Statistical Bioinformatics, University of Regensburg, 93053 Regensburg, Germany
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↵13 These authors contributed equally to this work.
Abstract
High-throughput bottom-up proteomic data cover thousands 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/phenotypic 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. Here, we propose PriOmics to integrate proteomic data with complementary omics and phenotypic 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 is verified in simulation studies, which also demonstrate that PriOmics can disentangle regulatory effects of protein modifications from those of respective protein abundances. These findings are substantiated in a diffuse large B cell lymphoma (DLBCL) data set in which we integrate SWATH-MS-based proteomics with transcriptomic and phenotypic data.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.279487.124.
- Received April 19, 2024.
- Accepted October 9, 2025.
This article is distributed exclusively by Cold Spring Harbor Laboratory Press for the first six months after the full-issue publication date (see https://genome.cshlp.org/site/misc/terms.xhtml). After six months, it is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
