Pathway Analysis Interpretation in the Multi-Omic Era.

Area of Science:

• Bioinformatics
• Systems Biology
• Genomics

Background:

• Pathway analysis is crucial for interpreting large-scale omics data.
• Common issues include database limitations and misinterpretation of pathway relevance, leading to "pathway fails."
• The Tumor Necrosis Factor (TNF) pathway exemplifies multifunctionality beyond its original annotation.

Purpose of the Study:

• To broadly evaluate pathway analysis interpretation methods.
• To clarify ideal use-case scenarios for embedding-based, semantic similarity-based, and network-based approaches.
• To provide guidance for aligning research objectives with appropriate pathway analysis methods.

Main Methods:

• Review and evaluation of different pathway analysis interpretation approaches.
• Assessment of strengths (e.g., visualization, ease of use) and limitations (e.g., data redundancy, database compatibility).
• Analysis of contextual examples, such as the TNF pathway.

Main Results:

• Different interpretation methods have distinct strengths and weaknesses.
• Input quality and method selection are critical for biologically meaningful results ("garbage in, garbage out").
• Areas for development include standardization, scalability, and data integration.

Conclusions:

• Choosing the correct pathway analysis interpretation method is vital for reliable biological insights.
• Addressing limitations and advancing interpretation techniques will enhance the utility of pathway analysis.
• Improved pathway analysis supports progress in systems biology and personalized medicine.