Cross-species queries of large gene expression databases

Area of Science:

• Bioinformatics
• Computational Biology
• Systems Biology

Background:

• Gene expression databases (e.g., Gene Expression Omnibus, ArrayExpress) contain vast amounts of multi-species data.
• Comparing gene expression across species is crucial for understanding conserved biological pathways, especially for drug testing in model organisms.
• Existing methods struggle to effectively compare dynamic expression data across different species.

Purpose of the Study:

• To develop a robust method for comparing gene expression experiments across different species.
• To enable effective cross-species analysis of large-scale gene expression data.
• To facilitate the identification of conserved biological pathways and mechanisms.

Main Methods:

• A novel distance metric was defined to compare the ranking of orthologous genes between species.
• An optimization problem was solved to learn the parameters of this distance metric using known similar expression experiment pairs.
• The method was validated by comparing millions of mouse and human expression experiment pairs.

Main Results:

• The developed method outperforms previous approaches and simpler rank comparison techniques for cross-species expression analysis.
• The learned function effectively matches expression experiments between different species.
• Millions of mouse and human expression experiment pairs were successfully compared.

Conclusions:

• The new method significantly enhances the ability to perform cross-species gene expression analysis.
• This facilitates the discovery of functionally related genes and conserved biological responses.
• Applications include hypothesizing biological mechanisms and identifying similar pathways in human diseases and model organisms.