Yip Kevin Y, Yu Haiyuan, Kim Philip M, Schultz Martin, Gerstein Mark
Department of Computer Science, Yale University, New Haven, CT 06511, USA.
Bioinformatics. 2006 Dec 1;22(23):2968-70. doi: 10.1093/bioinformatics/btl488. Epub 2006 Oct 4.
Biological processes involve complex networks of interactions between molecules. Various large-scale experiments and curation efforts have led to preliminary versions of complete cellular networks for a number of organisms. To grapple with these networks, we developed TopNet-like Yale Network Analyzer (tYNA), a Web system for managing, comparing and mining multiple networks, both directed and undirected. tYNA efficiently implements methods that have proven useful in network analysis, including identifying defective cliques, finding small network motifs (such as feed-forward loops), calculating global statistics (such as the clustering coefficient and eccentricity), and identifying hubs and bottlenecks. It also allows one to manage a large number of private and public networks using a flexible tagging system, to filter them based on a variety of criteria, and to visualize them through an interactive graphical interface. A number of commonly used biological datasets have been pre-loaded into tYNA, standardized and grouped into different categories.
The tYNA system can be accessed at http://networks.gersteinlab.org/tyna. The source code, JavaDoc API and WSDL can also be downloaded from the website. tYNA can also be accessed from the Cytoscape software using a plugin.
生物过程涉及分子间复杂的相互作用网络。各种大规模实验和整理工作已产生了许多生物体完整细胞网络的初步版本。为应对这些网络，我们开发了类似TopNet的耶鲁网络分析仪（tYNA），这是一个用于管理、比较和挖掘有向和无向多种网络的网络系统。tYNA有效地实现了已被证明在网络分析中有用的方法，包括识别有缺陷的团、寻找小网络基序（如前馈环）、计算全局统计量（如聚类系数和离心率）以及识别枢纽和瓶颈。它还允许用户使用灵活的标签系统管理大量私有和公共网络，根据各种标准对其进行筛选，并通过交互式图形界面进行可视化。一些常用的生物数据集已预先加载到tYNA中，进行了标准化并分组到不同类别。
可通过http://networks.gersteinlab.org/tyna访问tYNA系统。源代码、JavaDoc API和WSDL也可从该网站下载。也可使用插件从Cytoscape软件访问tYNA。
