转录因子 (TF) 是协调基因表达的上游调节因子，因此是生物信息学研究的核心。虽然理解基因和蛋白质的生物学背景的核心策略包括注释富集分析，例如基因本体术语富集，但这些方法不太适合分析 TF 组。尤其如此，因为此类方法不旨在包括下游过程，并且给定一组 TF，预期的顶级本体将围绕转录过程。我们介绍了 TFTenricher，这是一个 Python 工具箱，专门用于识别基因本体术语、细胞通路和疾病，这些术语在用户定义的人类 TF 集下游的基因中过度表达。我们评估了下游基因目标关于假阳性注释的推断，并发现了基于共表达的推断，以最好地预测下游过程。基于这些下游基因，TFTenricher 使用一些最常见的基因功能数据库，包括 GO、KEGG 和 Reactome，来计算功能富集。通过将 TFTenricher 应用于 21 种疾病中 TF 的差异表达，我们发现了与疾病机制相关的重要术语，而同一数据集上的基因集富集分析主要确定了与转录相关的过程。TFTenricher 包使用户能够在任何一组 TF 及其下游基因中搜索生物学背景。TFTenricher 可在 https://github.com/rasma774/Tftenricher 上作为 Python 3 工具箱使用，
Transcription factors (TFs) are the upstream regulators that orchestrate gene expression, and therefore a centrepiece in bioinformatics studies. While a core strategy to understand the biological context of genes and proteins includes annotation enrichment analysis, such as Gene Ontology term enrichment, these methods are not well suited for analysing groups of TFs. This is particularly true since such methods do not aim to include downstream processes, and given a set of TFs, the expected top ontologies would revolve around transcription processes. We present the TFTenricher, a Python toolbox that focuses specifically at identifying gene ontology terms, cellular pathways, and diseases that are over-represented among genes downstream of user-defined sets of human TFs. We evaluated the inference of downstream gene targets with respect to false positive annotations, and found an inference based on co-expression to best predict downstream processes. Based on these downstream genes, the TFTenricher uses some of the most common databases for gene functionalities, including GO, KEGG and Reactome, to calculate functional enrichments. By applying the TFTenricher to differential expression of TFs in 21 diseases, we found significant terms associated with disease mechanism, while the gene set enrichment analysis on the same dataset predominantly identified processes related to transcription. The TFTenricher package enables users to search for biological context in any set of TFs and their downstream genes. The TFTenricher is available as a Python 3 toolbox at https://github.com/rasma774/Tftenricher , under a GNU GPL license and with minimal dependencies.
