TY  - JOUR
A1  - Kosch, Robin
A1  - Limm, Katharina
A1  - Staiger, Annette M.
A1  - Kurz, Nadine S.
A1  - Seifert, Nicole
A1  - Oláh, Bence
A1  - Solbrig, Stefan
A1  - Poeschel, Viola
A1  - Held, Gerhard
A1  - Ziepert, Marita
A1  - Schmitz, Norbert
A1  - Chteinberg, Emil
A1  - Siebert, Reiner
A1  - Spang, Rainer
A1  - Zacharias, Helena U.
A1  - Ott, German
A1  - Oefner, Peter J.
A1  - Altenbuchinger, Michael
T1  - Integration of high-throughput proteomic data and complementary omics layers with PriOmics
Y1  - 2026/01/01 
JF  - Genome Research 
JO  - Genome Research 
SP  - 197 
EP  - 213 
DO  - 10.1101/gr.279487.124 
VL  - 36 
IS  - 1 
UR  - http://genome.cshlp.org/content/36/1/197.abstract 
N2  - 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. 
ER  -