The increasing number of single cell and bulk RNAseq data sets describing complex gene expression profiles in different organisms, organs or cell types calls for an intuitive tool allowing rapid comparative analysis. Here we present Swift Profiling Of Transcriptomes (SPOT) as a web tool that allows not only differential expression analysis but also fast ranking of genes fitting transcription profiles of interest. Based on a heuristic approach the spot algorithm ranks the genes according to their proximity to the user-defined gene expression profile of interest. The best hits are visualized as a table, bar chart or dot plot and can be exported as an Excel file. While the tool is generally applicable, we tested it on RNAseq data from malaria parasites that undergo multiple stage transformations during their complex life cycle as well as on data from multiple human organs during development and cell lines infected by the SARS-CoV-2 virus. SPOT should enable non-bioinformaticians to easily analyse their own and any available dataset.

中文翻译：

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SPOT：一种启用转录组快速分析的网络工具

越来越多的单细胞和大量RNAseq数据集描述了不同生物体，器官或细胞类型中的复杂基因表达谱，因此需要一种直观的工具来进行快速的比较分析。在这里，我们介绍了转录组快速剖析（SPOT）作为网络工具，它不仅允许差异表达分析，而且允许对符合目标转录谱的基因进行快速排名。基于启发式方法，斑点算法根据基因与用户定义的目标基因表达谱的接近程度对基因进行排名。最佳匹配可以显示为表格，条形图或点状图，并且可以导出为Excel文件。虽然该工具普遍适用，我们对疟疾寄生虫在其复杂的生命周期中经历了多阶段转化的RNAseq数据，以及在SARS-CoV-2病毒感染的细胞和发育过程中来自多个人体器官的数据进行了测试。SPOT应该使非生物信息学家能够轻松地分析他们自己和任何可用的数据集。