OrthoClust: an orthology-based network framework for clustering data across multiple species

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• High-dimensional genomics data are increasingly available across diverse organisms.
• Analyzing data across species presents challenges due to evolutionary divergence and data integration complexities.

Purpose of the Study:

• To develop a computational framework, OrthoClust, for simultaneous clustering of genomics data across multiple species.
• To leverage gene orthology relationships for integrating cross-species co-association networks.

Main Methods:

• OrthoClust integrates co-association networks from individual species using inter-species gene orthology.
• The framework performs simultaneous clustering to identify cross-species gene modules.
• Applied to RNA-Seq expression profiles of Caenorhabditis elegans and Drosophila melanogaster from the modENCODE consortium.

Main Results:

• OrthoClust successfully identified optimized gene modules that are fundamentally cross-species.
• These modules represent both conserved and species-specific gene expression patterns.
• Demonstrated application on C. elegans and D. melanogaster RNA-Seq data.

Conclusions:

• OrthoClust provides a robust method for cross-species genomics data analysis.
• The identified cross-species modules can facilitate functional inference of uncharacterized elements, such as non-coding RNAs, via guilt-by-association.
• This approach enhances the understanding of conserved and divergent biological functions across species.