Reijnders Maarten J M F, Waterhouse Robert M
Department of Ecology and Evolution, Swiss Institute of Bioinformatics, University of Lausanne, Lausanne, Switzerland.
Front Bioinform. 2021 Apr 1;1:638255. doi: 10.3389/fbinf.2021.638255. eCollection 2021.
The Gene Ontology (GO) is a cornerstone of functional genomics research that drives discoveries through knowledge-informed computational analysis of biological data from large-scale assays. Key to this success is how the GO can be used to support hypotheses or conclusions about the biology or evolution of a study system by identifying annotated functions that are overrepresented in subsets of genes of interest. Graphical visualizations of such GO term enrichment results are critical to aid interpretation and avoid biases by presenting researchers with intuitive visual data summaries. Amongst current visualization tools and resources there is a lack of standalone open-source software solutions that facilitate explorations of key features of multiple lists of GO terms. To address this we developed GO-Figure!, an open-source Python software for producing user-customisable semantic similarity scatterplots of redundancy-reduced GO term lists. The lists are simplified by grouping together terms with similar functions using their quantified information contents and semantic similarities, with user-control over grouping thresholds. Representatives are then selected for plotting in two-dimensional semantic space where similar terms are placed closer to each other on the scatterplot, with an array of user-customisable graphical attributes. GO-Figure! offers a simple solution for command-line plotting of informative summary visualizations of lists of GO terms, designed to support exploratory data analyses and dataset comparisons.
基因本体论(Gene Ontology,GO)是功能基因组学研究的基石,它通过对大规模实验中生物数据进行基于知识的计算分析来推动发现。这一成功的关键在于如何通过识别在感兴趣的基因子集中过度富集的注释功能,利用GO来支持关于研究系统的生物学或进化的假设或结论。此类GO术语富集结果的图形可视化对于帮助解释以及通过向研究人员呈现直观的视觉数据摘要来避免偏差至关重要。在当前的可视化工具和资源中,缺乏便于探索多个GO术语列表关键特征的独立开源软件解决方案。为了解决这一问题,我们开发了GO-Figure!,这是一款用于生成用户可定制的、对冗余减少的GO术语列表进行语义相似性散点图的开源Python软件。通过使用量化的信息内容和语义相似性将功能相似的术语分组在一起,简化这些列表,并由用户控制分组阈值。然后选择代表在二维语义空间中进行绘图,在散点图上,相似的术语彼此靠得更近,同时具有一系列用户可定制的图形属性。GO-Figure!为GO术语列表的信息性摘要可视化的命令行绘图提供了一个简单的解决方案,旨在支持探索性数据分析和数据集比较。