VisANT 3.5: multi-scale network visualization, analysis and inference based on the gene ontology.
VisANT 3.5: multi-scale network visualization, analysis and inference based on the gene ontology.
复制标题
DOI：
10.1093/nar/gkp406
复制
发表时间：
2009-07
影响因子：
14.9
通讯作者：
DeLisi C
中科院分区：
文献类型：
--
作者：
Hu Z;Hung JH;Wang Y;Chang YC;Huang CL;Huyck M;DeLisi C
尽管基因本体（GO）在生物数据库和应用中有着广泛的应用，但GO在网络分析中的作用通常仅限于对基因或基因集进行功能注释，而忽略了相关性的辅助信息。在这里，我们报告的新功能VisANT的可视化，挖掘，分析和建模的生物网络的综合软件平台，扩展GO在网络可视化，分析和推理的应用。新的VisANT功能可分为三类。(i)可视化：一个新的基于树的浏览器允许GO层次的可视化。GO术语可以很容易地放入网络中，以将在该术语下注释的基因分组，从而将分层本体与网络集成。这有助于多尺度可视化和分析。(ii)灵活的注释模式：除了用可用的最具体的功能描述来注释网络节点的常规方法之外，VisANT还提供了在任何定制的抽象级别上注释基因的功能。(iii)查找过度表示的GO术语和表达式丰富的GO模块：两种新算法已被实现为VisANT插件。一个检测任何给定子网络中过度表示的GO注释，另一个找到在指定表型或扰动数据集中富集的GO类别。这两种算法都考虑了网络拓扑（即基于各种证据来源的基因之间的相关性）。VisANT可在http://visant.bu.edu上免费获得。
Despite its wide usage in biological databases and applications, the role of the gene ontology (GO) in network analysis is usually limited to functional annotation of genes or gene sets with auxiliary information on correlations ignored. Here, we report on new capabilities of VisANT—an integrative software platform for the visualization, mining, analysis and modeling of the biological networks—which extend the application of GO in network visualization, analysis and inference. The new VisANT functions can be classified into three categories. (i) Visualization: a new tree-based browser allows visualization of GO hierarchies. GO terms can be easily dropped into the network to group genes annotated under the term, thereby integrating the hierarchical ontology with the network. This facilitates multi-scale visualization and analysis. (ii) Flexible annotation schema: in addition to conventional methods for annotating network nodes with the most specific functional descriptions available, VisANT also provides functions to annotate genes at any customized level of abstraction. (iii) Finding over-represented GO terms and expression-enriched GO modules: two new algorithms have been implemented as VisANT plugins. One detects over-represented GO annotations in any given sub-network and the other finds the GO categories that are enriched in a specified phenotype or perturbed dataset. Both algorithms take account of network topology (i.e. correlations between genes based on various sources of evidence). VisANT is freely available at http://visant.bu.edu.
登录
查看更多内容
影响因子：
12.3
作者：
Peña-Castillo L;Tasan M;Myers CL;Lee H;Joshi T;Zhang C;Guan Y;Leone M;Pagnani A;Kim WK;Krumpelman C;Tian W;Obozinski G;Qi Y;Mostafavi S;Lin GN;Berriz GF;Gibbons FD;Lanckriet G;Qiu J;Grant C;Barutcuoglu Z;Hill DP;Warde-Farley D;Grouios C;Ray D;Blake JA;Deng M;Jordan MI;Noble WS;Morris Q;Klein-Seetharaman J;Bar-Joseph Z;Chen T;Sun F;Troyanskaya OG;Marcotte EM;Xu D;Hughes TR;Roth FP
通讯作者：
Roth FP
影响因子：
14.9
作者：
Hu Z;Mellor J;Wu J;Yamada T;Holloway D;Delisi C
通讯作者：
Delisi C
影响因子：
14.9
作者：
Brohée S;Faust K;Lima-Mendez G;Sand O;Janky R;Vanderstocken G;Deville Y;van Helden J
通讯作者：
van Helden J
影响因子：
14.9
作者：
Khatri P;Voichita C;Kattan K;Ansari N;Khatri A;Georgescu C;Tarca AL;Draghici S
通讯作者：
Draghici S
影响因子：
5.8
作者：
Garcia, Olivier;Saveanu, Cosmin;Aittokallio, Tero
通讯作者：
Aittokallio, Tero