Knowledge-guided analysis of "omics" data using the KnowEnG cloud platform

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Genomics data analysis requires sophisticated computational tools.
• Integrating prior biological knowledge with raw data can enhance discovery.
• Existing tools may lack accessibility or interoperability.

Purpose of the Study:

• To introduce Knowledge Engine for Genomics (KnowEnG), a novel computational system.
• To provide accessible and advanced tools for genomics data analysis.
• To accelerate biomedical discovery through knowledge-guided data mining.

Main Methods:

• Development of a "Knowledge Network" integrating prior gene information from multiple databases.
• Implementation of knowledge-guided data mining and machine learning algorithms.
• Adherence to FAIR principles for findability, accessibility, interoperability, and reusability.
• Cloud-based execution for scalability and cost-effectiveness.
• Provision of multiple access modes, including a web portal with visualization tools.

Main Results:

• KnowEnG offers tools for gene prioritization, sample clustering, gene set analysis, and expression signature analysis.
• The system successfully analyzed cancer datasets, recreating and expanding upon published results.
• Demonstrated potential for democratizing advanced genomics analysis tools.

Conclusions:

• KnowEnG provides a powerful, accessible platform for advanced genomics data analysis.
• The knowledge-guided approach enhances the utility of bioinformatics tools.
• KnowEnG facilitates broader participation and accelerates discovery in the genomics era.