De Novo multi-omics pathway analysis (DMPA) designed for prior data independent inference of cell signaling pathways

ABSTRACT: New tools for cell signaling pathway inference from multi-omics data that are independent of previous knowledge are needed. Here we propose a new de novo method, the de novo multi-omics pathway analysis (DMPA), to model and combine omics data into network modules and pathways. DMPA was validated with published omics data and was found accurate in discovering published molecular associations in transcriptome, interactome, phosphoproteome, methylome, and metabolomics data and signaling pathways in multi-omics data. DMPA was benchmarked against module discovery and multi-omics integration methods and outperformed previous methods in module and pathway discovery especially when applied to datasets with low sample sizes. Transcription factor, kinase, subcellular location and function prediction algorithms were devised for transcriptome, phosphoproteome and interactome regulatory complexes and pathways, respectively. To apply DMPA in a biologically relevant context, interactome, phosphoproteome, transcriptome and proteome data were collected from analyses carried out using melanoma cells to address gamma-secretase cleavage-dependent signaling characteristics of the receptor tyrosine kinase TYRO3. The pathways modeled with DMPA reflected the predicted function and its direction in validation experiments.

INSTRUMENT(S): Orbitrap Fusion Lumos

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Johannes Merilahti  

LAB HEAD: Klaus Elenius

PROVIDER: PXD046503 | Pride | 2024-02-21

REPOSITORIES: Pride