Kim Eiru, Lee Insuk
Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
Methods Mol Biol. 2017;1611:183-198. doi: 10.1007/978-1-4939-7015-5_14.
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.
小鼠(小家鼠)是一种常用的模式生物,用于研究参与发育、免疫学和疾病表型的人类基因。尽管小鼠基因敲除技术最近取得了重大进展,但鉴定感兴趣功能的候选基因可以进一步加速新基因功能的发现。基因功能的协同性质使得基于关联有罪原则推断基因功能成为可能。因此,基因组规模的共功能网络可以通过网络分析为基因提供功能预测。我们最近构建了这样一个小鼠网络(MouseNet),它通过788,080个功能关系将超过88%的蛋白质编码基因相互连接起来。配套的网络服务器(www.inetbio.org/mousenet)使没有生物信息学专业知识的研究人员能够生成有助于发现新基因功能的预测。在本章中,我们介绍了MouseNet的理论框架,以及对小鼠和其他模式脊椎动物的基因和通路进行功能预测的逐步说明和技术提示。