Karlebach Guy, Hansen Peter, Köhler Kristin, Robinson Peter N
The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT 06032, USA.
Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany.
NAR Genom Bioinform. 2024 Dec 5;6(4):lqae165. doi: 10.1093/nargab/lqae165. eCollection 2024 Dec.
Gene Ontology overrepresentation analysis (GO-ORA) is a standard approach towards characterizing salient functional characteristics of sets of differentially expressed genes (DGE) in RNA sequencing (RNA-seq) experiments. GO-ORA compares the distribution of GO annotations of the DGE to that of all genes or all expressed genes. This approach has not been available to characterize differential alternative splicing (DAS). Here, we introduce a desktop application called isopretGO for visualizing the functional implications of DGE and DAS that leverages our previously published machine-learning predictions of GO annotations for individual isoforms. We show based on an analysis of 100 RNA-seq datasets that DAS and DGE frequently have starkly different functional profiles. We present an example that shows how isopretGO can be used to identify functional shifts in RNA-seq data that can be attributed to differential splicing.
基因本体过表达分析（GO-ORA）是一种用于表征RNA测序（RNA-seq）实验中差异表达基因（DGE）集显著功能特征的标准方法。GO-ORA将DGE的GO注释分布与所有基因或所有表达基因的分布进行比较。这种方法尚未用于表征差异可变剪接（DAS）。在这里，我们引入了一个名为isopretGO的桌面应用程序，用于可视化DGE和DAS的功能影响，该应用程序利用了我们之前发表的针对单个异构体的GO注释的机器学习预测。基于对100个RNA-seq数据集的分析，我们表明DAS和DGE通常具有截然不同的功能谱。我们给出一个示例，展示了如何使用isopretGO来识别RNA-seq数据中可归因于差异剪接的功能变化。
