可视化基因本体注释
Visualizing Gene Ontology annotations
摘要：
现代生物学技术可同时产生大量基因的读数，典型的例子是差异基因表达测量。此外，这些基因通常使用描述基因功能的GO术语进行丰富的注释，并可用于总结基因组规模实验的结果。然而，理解这种GO富集分析可能是具有挑战性的。例如，一组差异表达基因中过度表达的GO功能通常作为平面列表输出，这种格式不足以捕获GO注释标签的分层结构的复杂性。在本章中，我们将概述各种方法来可视化大型的、难以解释的GO术语列表。我们对它们的可用性（基于Web或独立）进行了分类，它们在总结大型GO术语列表时采用的主要原则，以及它们支持的可视化样式。这些对每个软件的简短评论旨在作为一个有用的清单，而不是对底层算法的全面描述。相反，我们展示了它们的使用的例子，并建议选择一个适当的可视化工具可能是至关重要的生物发现中的效用GO。
英文摘要：
Contemporary techniques in biology produce readouts for large numbers of genes simultaneously, the typical example being differential gene expression measurements. Moreover, those genes are often richly annotated using GO terms that describe gene function and that can be used to summarize the results of the genome-scale experiments. However, making sense of such GO enrichment analyses may be challenging. For instance, overrepresented GO functions in a set of differentially expressed genes are typically output as a flat list, a format not adequate to capture the complexities of the hierarchical structure of the GO annotation labels. In this chapter, we survey the various methods to visualize large, difficult-to-interpret lists of GO terms. We catalogue their availability (web-based or standalone), the main principles they employ in summarizing large lists of GO terms, and the visualization styles they support. These brief commentaries on each software are intended as a helpful inventory, rather than comprehensive descriptions of the underlying algorithms. Instead, we show examples of their use and suggest that the choice of an appropriate visualization tool may be crucial to the utility of GO in biological discovery.
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