Cytoscape is often used for visualization and analysis of metabolic pathways. For example, based on KEGG data, a reader for KEGG Markup Language (KGML) is used to load files into Cytoscape. However, although multiple genes can be responsible for the same reaction, the KGML-reader KEGGScape only presents the first listed gene in a network node for a given reaction. This can lead to incorrect interpretations of the pathways. Our new method, FunHoP, shows all possible genes in each node, making the pathways more complete. FunHoP collapses all genes in a node into one measurement using read counts from RNA-seq. Assuming that activity for an enzymatic reaction mainly depends upon the gene with the highest number of reads, and weighting the reads on gene length and ratio, a new expression value is calculated for the node as a whole. Differential expression at node level is then applied to the networks. Using prostate cancer as model, we integrate RNA-seq data from two patient cohorts with metabolism data from literature. Here we show that FunHoP gives more consistent pathways that are easier to interpret biologically. Code and documentation for running FunHoP can be found at https://github.com/kjerstirise/FunHoP.

Cytoscape通常用于代谢途径的可视化和分析。例如，基于KEGG数据，使用KEGG标记语言（KGML）的阅读器将文件加载到Cytoscape中。然而，尽管多个基因可能导致相同的反应，但 KGML 阅读器 KEGGScape 仅针对给定反应呈现网络节点中第一个列出的基因。这可能会导致对路径的错误解释。我们的新方法 FunHoP 显示了每个节点中所有可能的基因，使路径更加完整。FunHoP 使用RNA-seq的读取计数将节点中的所有基因折叠为一个测量值。假设酶反应的活性主要取决于具有最高读数的基因，并根据基因长度和比率对读数进行加权，为整个节点计算新的表达值。然后将节点级别的差异表达应用于网络。使用前列腺癌作为模型，我们将两个患者队列的 RNA-seq 数据与文献中的代谢数据整合起来。在这里，我们证明 FunHoP 提供了更一致的途径，更容易从生物学角度解释。运行 FunHoP 的代码和文档可以在 https://github.com/kjerstirise/FunHoP 找到。