BACKGROUND The main research topic in this paper is how to compare multiple biological experiments using transcriptome data, where each experiment is measured and designed to compare control and treated samples. Comparison of multiple biological experiments is usually performed in terms of the number of DEGs in an arbitrary combination of biological experiments. This process is usually facilitated with Venn diagram but there are several issues when Venn diagram is used to compare and analyze multiple experiments in terms of DEGs. First, current Venn diagram tools do not provide systematic analysis to prioritize genes. Because that current tools generally do not fully focus to prioritize genes, genes that are located in the segments in the Venn diagram (especially, intersection) is usually difficult to rank. Second, elucidating the phenotypic difference only with the lists of DEGs and expression values is challenging when the experimental designs have the combination of treatments. Experiment designs that aim to find the synergistic effect of the combination of treatments are very difficult to find without an informative system. RESULTS We introduce Venn-diaNet, a Venn diagram based analysis framework that uses network propagation upon protein-protein interaction network to prioritizes genes from experiments that have multiple DEG lists. We suggest that the two issues can be effectively handled by ranking or prioritizing genes with segments of a Venn diagram. The user can easily compare multiple DEG lists with gene rankings, which is easy to understand and also can be coupled with additional analysis for their purposes. Our system provides a web-based interface to select seed genes in any of areas in a Venn diagram and then perform network propagation analysis to measure the influence of the selected seed genes in terms of ranked list of DEGs. CONCLUSIONS We suggest that our system can logically guide to select seed genes without additional prior knowledge that makes us free from the seed selection of network propagation issues. We showed that Venn-diaNet can reproduce the research findings reported in the original papers that have experiments that compare two, three and eight experiments. Venn-diaNet is freely available at: http://biohealth.snu.ac.kr/software/venndianet.

中文翻译：

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Venn-diaNet：基于维恩图的网络传播分析框架，用于比较多个生物学实验。

背景技术本文的主要研究主题是如何使用转录组数据比较多个生物学实验，其中测量和设计每个实验以比​​较对照样品和处理样品。多个生物学实验的比较通常是根据生物学实验的任意组合中的DEG数量进行的。通常使用Venn图来促进此过程，但是当使用Venn图来比较和分析DEG的多个实验时，存在多个问题。首先，当前的维恩图工具无法提供系统的分析来确定基因的优先级。因为当前的工具通常不能完全集中精力对基因进行优先排序，所以位于维恩图（尤其是交集）中的片段中的基因通常很难排名。第二，当实验设计结合多种治疗方法时，仅通过DEGs和表达值来阐明表型差异是具有挑战性的。没有信息系统，很难找到旨在发现多种治疗方法协同作用的实验设计。结果我们引入Venn-diaNet，这是一个基于Venn图的分析框架，该框架使用蛋白质-蛋白质相互作用网络上的网络传播对具有多个DEG列表的实验中的基因进行优先排序。我们建议可以通过对带有维恩图片段的基因进行排序或优先排序来有效地解决这两个问题。用户可以轻松地将多个DEG列表与基因排名进行比较，这很容易理解，并且还可以结合其他分析用于他们的目的。我们的系统提供了一个基于Web的界面，可以在Venn图的任何区域中选择种子基因，然后执行网络传播分析，以根据DEG的排名列表来衡量所选种子基因的影响。结论我们建议我们的系统可以在没有其他先验知识的情况下从逻辑上指导选择种子基因，这使我们摆脱了网络传播问题的种子选择。我们证明了Venn-diaNet可以重现原始论文中报告的研究结果，这些论文的实验将两个，三个和八个实验进行了比较。Venn-diaNet可从以下网址免费获得：http：//biohealth.snu.ac.kr/software/venndianet。结论我们建议我们的系统可以在没有其他先验知识的情况下从逻辑上指导选择种子基因，这使我们摆脱了网络传播问题的种子选择。我们证明了Venn-diaNet可以重现原始论文中报告的研究结果，该论文的实验将两个，三个和八个实验进行了比较。Venn-diaNet可从以下网址免费获得：http：//biohealth.snu.ac.kr/software/venndianet。结论我们建议我们的系统可以在没有其他先验知识的情况下从逻辑上指导选择种子基因，这使我们摆脱了网络传播问题的种子选择。我们证明了Venn-diaNet可以重现原始论文中报告的研究结果，该论文的实验将两个，三个和八个实验进行了比较。Venn-diaNet可从以下网址免费获得：http：//biohealth.snu.ac.kr/software/venndianet。