Bioinformatics Program, Boston University, Boston, Massachusetts, USA.
PLoS One. 2012;7(9):e45211. doi: 10.1371/journal.pone.0045211. Epub 2012 Sep 13.
Curated gene sets from databases such as KEGG Pathway and Gene Ontology are often used to systematically organize lists of genes or proteins derived from high-throughput data. However, the information content inherent to some relationships between the interrogated gene sets, such as pathway crosstalk, is often underutilized. A gene set network, where nodes representing individual gene sets such as KEGG pathways are connected to indicate a functional dependency, is well suited to visualize and analyze global gene set relationships. Here we introduce a novel gene set network construction algorithm that integrates gene lists derived from high-throughput experiments with curated gene sets to construct co-enrichment gene set networks. Along with previously described co-membership and linkage algorithms, we apply the co-enrichment algorithm to eight gene set collections to construct integrated multi-evidence gene set networks with multiple edge types connecting gene sets. We demonstrate the utility of approach through examples of novel gene set networks such as the chromosome map co-differential expression gene set network. A total of twenty-four gene set networks are exposed via a web tool called MetaNet, where context-specific multi-edge gene set networks are constructed from enriched gene sets within user-defined gene lists. MetaNet is freely available at http://blaispathways.dfci.harvard.edu/metanet/.
从数据库（如 KEGG 通路和基因本体论）中精心挑选的基因集通常用于系统地组织从高通量数据中得出的基因或蛋白质列表。然而，一些被询问的基因集之间的关系所固有的信息内容，例如途径串扰，往往未被充分利用。基因集网络，其中代表单个基因集（如 KEGG 通路）的节点相互连接以指示功能依赖性，非常适合可视化和分析全局基因集关系。在这里，我们引入了一种新的基因集网络构建算法，该算法将来自高通量实验的基因列表与精心挑选的基因集集成在一起，以构建共富集基因集网络。与之前描述的共同成员和链接算法一起，我们将共富集算法应用于八个基因集集合，以构建具有连接基因集的多种边缘类型的集成多证据基因集网络。我们通过构建新颖的基因集网络（例如染色体图谱共差异表达基因集网络）的示例来证明该方法的实用性。总共通过称为 MetaNet 的网络工具公开了二十四个基因集网络，其中从用户定义的基因列表中富集的基因集中构建上下文特定的多边缘基因集网络。MetaNet 可免费在 http://blaispathways.dfci.harvard.edu/metanet/ 获得。
