Zebrafish, Danio rerio, is one of the most qualified laboratory animals for the function-driven discovery of human disease genes for several reasons.

1. Zebrafish has been a long-standing model of vertebrate developmental biology in the virtue of high fecundity, rapid development, easy laboratory maintenance, efficient gene perturbation using morpholino, and allowance of noninvasive in vivo examination of organ development through the translucent embryo and body.
2. The organ development in zebrafish is highly similar to that in humans and the zebrafish genome contains more than 70% of human genes. Indeed, zebrafish has already become a common laboratory animal for in vivo validation of candidate disease genes identified from genome-wide association studies (GWAS) as well as WES.
3. Zebrafish embryos containing genetic defects of human diseases enable high throughput drug screening and toxicity tests in vivo and facilitate the investigation of disease mechanisms through various molecular and genomics techniques suitable for the organism.

Recently, many disease genes have been identified through network-based gene prioritization, in which the principle of guilt-by-association is applied to predict new genes for a disease based on their closeness to the genes implicated in the same disease in functional networks. To facilitate the identification of candidate disease genes in zebrafish by taking advantage of the massive amount of genomics big data, we developed a genome-scale co-functional network of zebrafish genes, DanioNet, which was constructed by Bayesian integration of 18 distinct types of genomics data. DanioNet contains 817,547 co-functional links between 16,063 zebrafish genes.
DanioNet has two prediction options:

1. Gene-centric prediction that infer functions of a gene
2. Pathway-centric prediction that prioritize genes for a pathway

2014.12.10 Danioet website launched.

This server development was supported by the National Research Foundation of Korea grant (2012M3A9B4028641, 2012M3A9C7050151, 2015R1A2A1A15055859).

Insuk Lee (insuklee (at) yonsei (dot) ac (dot) kr)