PriOmics: integration of high-throughput proteomic data with complementary omics layers using mixed graphical modeling with group priors
ABSTRACT Mass spectrometry (MS)-based high-throughput proteomics data cover abundances of 1,000s of proteins and facilitate the study of co- and post-translational modifications (CTMs/PTMs) such as acetylation, ubiquitination, and phosphorylation. Yet, it remains an open question how to holistically explore such data and their relationship to complementary omics layers or phenotypical information. Network inference methods aim for a holistic analysis of data to reveal relationships between molecular variables and to resolve underlying regulatory mechanisms. Among those, graphical models have received increased attention as they can distinguish direct from indirect relationships, aside from their generalizability to diverse data types. We propose PriOmics as a graphical modeling approach to integrate proteomics data with complementary omics layers and pheno- and genotypical information. PriOmics models intensities of individual peptides and incorporates their protein affiliation as prior knowledge in order to resolve statistical relationships between proteins and CTMs/PTMs. We show in simulation studies that PriOmics improves the recovery of statistical associations compared to the state of the art and demonstrate that it can disentangle regulatory effects of protein modifications from those of respective protein abundances. These findings are substantiated in a dataset of Diffuse Large B-Cell Lymphomas (DLBCLs) where we integrate SWATH-MS-based proteomics data with transcriptomic and phenotypic information.GRAPHICAL ABSTRACT <jats:fig id="ufig1" position="float" orientation="portrait" fig-type="figure"><jats:graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="566517v1_ufig1" position="float" orientation="portrait" /></jats:fig>.
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Preprint| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
bioRxiv.org - (2023) vom: 17. Nov. Zur Gesamtaufnahme - year:2023 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Kosch, Robin [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2023.11.10.566517 |
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| PPN (Katalog-ID): |
XBI041540158 |
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| 245 | 1 | 0 | |a PriOmics: integration of high-throughput proteomic data with complementary omics layers using mixed graphical modeling with group priors |
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| 520 | |a ABSTRACT Mass spectrometry (MS)-based high-throughput proteomics data cover abundances of 1,000s of proteins and facilitate the study of co- and post-translational modifications (CTMs/PTMs) such as acetylation, ubiquitination, and phosphorylation. Yet, it remains an open question how to holistically explore such data and their relationship to complementary omics layers or phenotypical information. Network inference methods aim for a holistic analysis of data to reveal relationships between molecular variables and to resolve underlying regulatory mechanisms. Among those, graphical models have received increased attention as they can distinguish direct from indirect relationships, aside from their generalizability to diverse data types. We propose PriOmics as a graphical modeling approach to integrate proteomics data with complementary omics layers and pheno- and genotypical information. PriOmics models intensities of individual peptides and incorporates their protein affiliation as prior knowledge in order to resolve statistical relationships between proteins and CTMs/PTMs. We show in simulation studies that PriOmics improves the recovery of statistical associations compared to the state of the art and demonstrate that it can disentangle regulatory effects of protein modifications from those of respective protein abundances. These findings are substantiated in a dataset of Diffuse Large B-Cell Lymphomas (DLBCLs) where we integrate SWATH-MS-based proteomics data with transcriptomic and phenotypic information.GRAPHICAL ABSTRACT <jats:fig id="ufig1" position="float" orientation="portrait" fig-type="figure"><jats:graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="566517v1_ufig1" position="float" orientation="portrait" /></jats:fig> | ||
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| 650 | 4 | |a 570 |7 (dpeaa)DE-84 | |
| 700 | 1 | |a Limm, Katharina |4 aut | |
| 700 | 1 | |a Staiger, Annette M. |4 aut | |
| 700 | 1 | |a Kurz, Nadine S. |4 aut | |
| 700 | 1 | |a Seifert, Nicole |4 aut | |
| 700 | 1 | |a Oláh, Bence |4 aut | |
| 700 | 1 | |a Solbrig, Stefan |4 aut | |
| 700 | 1 | |a Ziepert, Marita |4 aut | |
| 700 | 1 | |a Chteinberg, Emil |4 aut | |
| 700 | 1 | |a Spang, Rainer |4 aut | |
| 700 | 1 | |a Siebert, Reiner |4 aut | |
| 700 | 1 | |a Zacharias, Helena U. |4 aut | |
| 700 | 1 | |a Ott, German |4 aut | |
| 700 | 1 | |a Oefner, Peter J. |4 aut | |
| 700 | 1 | |a Altenbuchinger, Michael |0 (orcid)0000-0003-1102-6532 |4 aut | |
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