The biological interpretation of gene lists with interesting shared properties, such as up- or down-regulation in a particular experiment, is typically accomplished using gene ontology enrichment analysis tools. Given a list of genes, a gene ontology (GO) enrichment analysis may return hundreds of statistically significant GO results in a "flat" list, which can be challenging to summarize. It can also be difficult to keep pace with rapidly expanding biological knowledge, which often results in daily changes to any of the over 47,000 gene ontologies that describe biological knowledge. GOATOOLS, a Python-based library, makes it more efficient to stay current with the latest ontologies and annotations, perform gene ontology enrichment analyses to determine over- and under-represented terms, and organize results for greater clarity and easier interpretation using a novel GOATOOLS GO grouping method. We performed functional analyses on both stochastic simulation data and real data from a published RNA-seq study to compare the enrichment results from GOATOOLS to two other popular tools: DAVID and GOstats. GOATOOLS is freely available through GitHub: https://github.com/tanghaibao/goatools .

中文翻译：

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山羊：用于基因本体分析的Python库。

具有有趣的共有属性（例如在特定实验中上调或下调）的基因列表的生物学解释通常是使用基因本体论富集分析工具来完成的。给定一个基因列表，基因本体（GO）富集分析可能会在“平面”列表中返回数百个具有统计意义的GO结果，这可能很难进行总结。跟上快速增长的生物知识的步伐也很困难，这经常导致描述生物知识的超过47,000种基因本体中的任何一种每天发生变化。GOATOOLS是一个基于Python的库，可让您更有效地了解最新的本体论和注释，执行基因本体论富集分析以确定过多或不足的术语，并使用新颖的GOATOOLS GO分组方法整理结果，以提高清晰度和解释难度。我们对随机模拟数据和来自已发表的RNA-seq研究的真实数据进行了功能分析，以比较GOATOOLS与其他两种流行工具DAVID和GOstats的富集结果。GOATOOLS可通过GitHub免费获得：https：//github.com/tanghaibao/goatools。