Lee Tak, Yang Sunmo, Kim Eiru, Ko Younhee, Hwang Sohyun, Shin Junha, Shim Jung Eun, Shim Hongseok, Kim Hyojin, Kim Chanyoung, Lee Insuk
Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, Korea.
Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul, Korea Center for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, University of Texas at Austin, TX 78712, USA.
Nucleic Acids Res. 2015 Jan;43(Database issue):D996-1002. doi: 10.1093/nar/gku1053. Epub 2014 Oct 29.
Arabidopsis thaliana is a reference plant that has been studied intensively for several decades. Recent advances in high-throughput experimental technology have enabled the generation of an unprecedented amount of data from A. thaliana, which has facilitated data-driven approaches to unravel the genetic organization of plant phenotypes. We previously published a description of a genome-scale functional gene network for A. thaliana, AraNet, which was constructed by integrating multiple co-functional gene networks inferred from diverse data types, and we demonstrated the predictive power of this network for complex phenotypes. More recently, we have observed significant growth in the availability of omics data for A. thaliana as well as improvements in data analysis methods that we anticipate will further enhance the integrated database of co-functional networks. Here, we present an updated co-functional gene network for A. thaliana, AraNet v2 (available at http://www.inetbio.org/aranet), which covers approximately 84% of the coding genome. We demonstrate significant improvements in both genome coverage and accuracy. To enhance the usability of the network, we implemented an AraNet v2 web server, which generates functional predictions for A. thaliana and 27 nonmodel plant species using an orthology-based projection of nonmodel plant genes on the A. thaliana gene network.
拟南芥是一种已经被深入研究了几十年的参考植物。高通量实验技术的最新进展使得能够从拟南芥中生成前所未有的大量数据,这促进了以数据驱动的方法来揭示植物表型的遗传组织。我们之前发表了一篇关于拟南芥基因组规模功能基因网络AraNet的描述,该网络是通过整合从多种数据类型推断出的多个共功能基因网络构建而成的,并且我们证明了这个网络对复杂表型的预测能力。最近,我们观察到拟南芥组学数据的可用性显著增长,以及数据分析方法的改进,我们预计这将进一步增强共功能网络的综合数据库。在这里,我们展示了一个更新的拟南芥共功能基因网络AraNet v2(可在http://www.inetbio.org/aranet获取),它覆盖了大约84%的编码基因组。我们证明了在基因组覆盖范围和准确性方面都有显著提高。为了提高该网络的可用性,我们实现了一个AraNet v2网络服务器,它使用非模式植物基因在拟南芥基因网络上基于直系同源性的投影,为拟南芥和27种非模式植物物种生成功能预测。