Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, the Netherlands.
Molecular Microbiology, Amsterdam Institute of Molecular and Life Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
Microbiol Spectr. 2024 Mar 5;12(3):e0345623. doi: 10.1128/spectrum.03456-23. Epub 2024 Jan 31.
To infer the biological meaning from transcriptome data, it is useful to focus on genes that are regulated by the same regulator, i.e., regulons. Unfortunately, current gene set enrichment analysis (GSEA) tools do not consider whether a gene is activated or repressed by a regulator. This distinction is crucial when analyzing regulons since a regulator can work as an activator of certain genes and as a repressor of other genes, yet both sets of genes belong to the same regulon. Therefore, simply averaging expression differences of the genes of such a regulon will not properly reflect the activity of the regulator. What makes it more complicated is the fact that many genes are regulated by different transcription factors, and current transcriptome analysis tools are unable to indicate which regulator is most likely responsible for the observed expression difference of a gene. To address these challenges, we developed the gene set enrichment analysis program GINtool. Additional features of GINtool are novel graphical representations to facilitate the visualization of gene set analyses of transcriptome data, the possibility to include functional categories as gene sets for analysis, and the option to analyze expression differences within operons, which is useful when analyzing prokaryotic transcriptome and also proteome data.IMPORTANCEMeasuring the activity of all genes in cells is a common way to elucidate the function and regulation of genes. These transcriptome analyses produce large amounts of data since genomes contain thousands of genes. The analysis of these large data sets is challenging. Therefore, we developed a new software tool called GINtool that can facilitate the analysis of transcriptome data by using prior knowledge of gene sets controlled by the same regulator, the so-called regulons. An important novelty of GINtool is that it can take into account the directionality of gene regulation in these analyses, i.e., whether a gene is activated or repressed, which is crucial to assess whether a regulon or functional category is affected. GINtool also includes new graphical methods to facilitate the visual inspection of regulation events in transcriptome data sets. These and additional analysis methods included in GINtool make it a powerful software tool to analyze transcriptome data.
从转录组数据中推断生物学意义，关注受同一调控因子调节的基因（即调控子）是很有用的。不幸的是，目前的基因集富集分析（GSEA）工具并没有考虑一个基因是被调控因子激活还是抑制。在分析调控子时，这种区别是至关重要的，因为一个调控因子可以作为某些基因的激活剂，也可以作为其他基因的抑制剂，但这两组基因都属于同一个调控子。因此，简单地平均调控子中基因的表达差异并不能正确反映调控因子的活性。更复杂的是，许多基因受到不同转录因子的调节，而目前的转录组分析工具无法指出哪个调控因子最有可能导致观察到的基因表达差异。为了解决这些挑战，我们开发了基因集富集分析程序 GINtool。GINtool 的其他特点是新颖的图形表示，以方便可视化转录组数据的基因集分析，有可能将功能类别作为基因集进行分析，以及在操纵子内分析表达差异的选项，这在分析原核转录组和蛋白质组数据时很有用。
重要性
测量细胞中所有基因的活性是阐明基因功能和调控的常用方法。这些转录组分析会产生大量数据，因为基因组包含数千个基因。分析这些大数据集具有挑战性。因此，我们开发了一个名为 GINtool 的新软件工具，该工具可以利用同一调控因子控制的基因集（即调控子）的先验知识来简化转录组数据的分析。GINtool 的一个重要新颖之处在于，它可以在这些分析中考虑基因调控的方向性，即一个基因是被激活还是被抑制，这对于评估调控子或功能类别是否受到影响至关重要。GINtool 还包括新的图形方法，以方便检查转录组数据集的调控事件。这些和 GINtool 中包含的其他分析方法使它成为一个强大的软件工具，可以分析转录组数据。
