The mouse, Mus musculus, is a popular model organism for the study of human genes involved in development, immunology, and disease phenotypes. Despite recent revolutions in gene-knockout technologies in mouse, identification of candidate genes for functions of interest can further accelerate the discovery of novel gene functions. The collaborative nature of genetic functions allows for the inference of gene functions based on the principle of guilt-by-association. Genome-scale co-functional networks could therefore provide functional predictions for genes via network analysis. We recently constructed such a network for mouse (MouseNet), which interconnects over 88% of protein-coding genes with 788,080 functional relationships. The companion web server (www.inetbio.org/mousenet) enables researchers with no bioinformatics expertise to generate predictions that facilitate discovery of novel gene functions. In this chapter, we present the theoretical framework for MouseNet, as well as step-by-step instructions and technical tips for functional prediction of genes and pathways in mouse and other model vertebrates.
|Title of host publication||Methods in Molecular Biology|
|Publisher||Humana Press Inc.|
|Number of pages||16|
|Publication status||Published - 2017|
|Name||Methods in Molecular Biology|
Bibliographical noteFunding Information:
This work was supported by grants from the National Research Foundation of Korea (2015R1A2A1A15055859, 2012M3A9B4028641, 2012M3A9C7050151).
© Springer Science+Business Media LLC 2017.
All Science Journal Classification (ASJC) codes
- Molecular Biology