pwOmics: an R package for pathway-based integration of time-series omics data using public database knowledge

Area of Science:

• Bioinformatics
• Systems Biology
• Computational Biology

Background:

• Omics data generation is increasing, enabling detailed biological process characterization.
• Integrating multi-level signaling data is crucial for comprehensive biological understanding.

Purpose of the Study:

• To present a straightforward approach for integrative analysis of multi-omics data.
• To enable pathway-based comparison of coupled human proteomic and genomic/transcriptomic datasets.
• To facilitate combined interpretation of regulatory effects over time.

Main Methods:

• Utilizes pathway and interaction models from public databases.
• Performs pathway-based, level-specific data comparison using log fold changes.
• Employs network reconstruction and inference methods (Steiner tree, dynamic Bayesian network inference).

Main Results:

• Achieves cross-platform consensus analysis for downstream and upstream effects.
• Provides functional insights at the levels of pathways, transcription factors, and genes/transcripts.
• Generates consensus graphical networks for further analysis and visualization.

Conclusions:

• pwOmics offers a robust framework for integrative multi-omics data analysis.
• Facilitates a deeper understanding of biological regulatory mechanisms.
• Enables combined interpretation of dynamic regulatory effects across different data types.