RT Journal
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
JF Genome Research 
JO Genome Research 
YR 2026 
FD January 01 
VO 36 
IS 1 
SP 197 
OP 213 
DO 10.1101/gr.279487.124 
UL http://genome.cshlp.org/content/36/1/197.abstract 
AB 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.