Omic space: coordinate-based integration and analysis of genomic phenomic interactions

Area of Science:

• Bioinformatics
• Systems Biology
• Genomics

Background:

• Advancements in genomics provide extensive omic interaction data.
• Integrating diverse omic datasets is crucial for generating reliable biological hypotheses.
• A coordinate-based approach is needed for omic interaction analysis.

Purpose of the Study:

• Introduce the concept of omic space for integrating omic interactions.
• Facilitate coordinate-based integration and analysis of omic data.
• Develop a framework for comprehensive biological data integration and hypothesis generation.

Main Methods:

• Define omic space using orthogonal genomic-coordinate axes for various omic planes (genomic, transcriptomic, proteomic, metabolomic, phenomic).
• Develop a ranking system for correspondences among interactions within the omic space.
• Implement the 'Genome <==> Phenome Superhighway' (GPS) system for database integration and visualization.

Main Results:

• The omic space concept enables assimilation of biological findings into hypotheses, linking higher-order phenomena with lower-order mechanisms.
• A ranking of interaction correspondences effectively identifies candidate gene pairs for epistatic interactions in mouse tumor models.
• The omic space provides a convenient framework for database integration, demonstrated by the GPS system.

Conclusions:

• The omic space framework facilitates comprehensive integration and analysis of omic interactions.
• This approach aids in generating robust biological hypotheses by connecting multi-level biological data.
• The GPS system offers a practical tool for visualizing and integrating omic data across multiple species.