Integration of high-throughput proteomic data and complementary omics layers with PriOmics.

Area of Science:

• Proteomics
• Systems Biology
• Bioinformatics

Background:

• High-throughput proteomic datasets contain extensive information on proteins and their modifications.
• Integrating proteomic data with other omics and phenotypic data remains a challenge.
• Graphical models offer a powerful framework for analyzing complex molecular networks and regulatory mechanisms.

Purpose of the Study:

• To develop a novel computational approach, PriOmics, for integrating proteomic data with complementary omics and phenotypic data.
• To model statistical relationships between proteins and co- and post-translational modifications (CTMs/PTMs) using proteomic peptide intensity data.
• To differentiate the regulatory effects of CTMs/PTMs from protein abundance variations.

Main Methods:

• PriOmics utilizes proteomic peptide intensities and incorporates protein affiliation as prior knowledge.
• The method employs graphical models to infer direct and indirect relationships within molecular networks.
• Simulation studies were conducted to validate the approach and its ability to disentangle modification and abundance effects.

Main Results:

• PriOmics successfully integrates diverse omics and phenotypic data.
• The method accurately models relationships between proteins and CTMs/PTMs.
• Simulation studies confirmed PriOmics' capability to distinguish regulatory effects of protein modifications from protein abundance.

Conclusions:

• PriOmics provides a robust framework for holistic exploration of high-throughput proteomic data.
• The approach enhances understanding of molecular regulatory mechanisms by integrating multi-omics information.
• Application to a diffuse large B cell lymphoma dataset demonstrates the utility of PriOmics in biological discovery.