A guide to multi-omics data collection and integration for translational medicine

Area of Science:

• Translational Medicine
• Bioinformatics
• Computational Biology

Background:

• High-throughput technologies enable multi-omics patient sample collection for integrated analysis.
• Complexity of multi-omics data integration raises questions about current computational methods.
• Lack of consensus exists on optimal omics combinations and integration methodologies.

Purpose of the Study:

• To guide the design of multi-omics studies in translational medicine.
• To advise on selecting appropriate omics types and data integration methods.
• To provide a framework for choosing computational tools based on research objectives.

Main Methods:

• Systematic review of articles integrating multiple omics measurements from patient samples.
• Identification and categorization of five key translational medicine objectives.
• Description and comparison of computational methods and tools for multi-omics data integration.

Main Results:

• Five objectives identified: disease patterns, subtyping, diagnosis/prognosis, drug response, and regulatory processes.
• Common trends in omics type selection for different objectives and diseases are described.
• Data integration tools are grouped by scientific objectives, with computational challenges and evaluation criteria discussed.

Conclusions:

• The article provides a structured approach to designing multi-omics studies in translational medicine.
• It offers guidance on selecting omics types and computational methods tailored to specific research goals.
• Examples of tools for integration and downstream analysis are presented to facilitate novel insights.