Predicted mouse interactome and network-based interpretation of differentially expressed genes

Area of Science:

• Genomics
• Bioinformatics
• Mammalian Genetics

Background:

• The house mouse (Mus musculus) is a key mammalian model for genetic research due to human similarities.
• It provides insights into human diseases, drug efficacy, toxicity, and patient response prediction.
• Facilitating molecular mechanism studies in mice is crucial for translational research.

Purpose of the Study:

• To present the Mouse Interactome Database (MID, Version 1) for mouse molecular mechanism studies.
• To introduce a Gene Set Linkage Analysis (GSLA) web tool for annotating differentially expressed genes.
• To offer a comprehensive resource for mouse gene functional associations and analysis.

Main Methods:

• Integrated functional association evidence from 9 public databases to build the Mouse Interactome Database (MID).
• MID Version 1 contains 155,887 putative functional associations between mouse protein-coding genes.
• Developed a Gene Set Linkage Analysis (GSLA) web tool integrated with MID.

Main Results:

• MID encompasses 19.32% of all putative mouse protein interactions, with 26.02% potentially representing direct protein interactions.
• The MID/GSLA system demonstrated precise and informative annotations in case studies.
• MID/GSLA outperformed widely used tools like PANTHER and DAVID in functional annotation.

Conclusions:

• The Mouse Interactome Database (MID) and GSLA web tool provide valuable resources for mouse genetic and molecular research.
• This system enhances the understanding of functional impacts from gene expression data in mouse models.
• MID and GSLA offer improved functional annotation capabilities compared to existing tools.