PDF(Pubmed)
Abstract：
Cluster analysis is one of the most widely used exploratory methods for visualization and grouping of gene expression patterns across multiple samples or treatment groups. Although several existing online tools can annotate clusters with functional terms, there is no all-in-one webserver to effectively prioritize genes/clusters using gene essentiality as well as congruency of mRNA-protein expression. Hence, we developed CAP-RNAseq that makes possible (1) upload and clustering of bulk RNA-seq data followed by identification, annotation and network visualization of all or selected clusters; and (2) prioritization using DepMap gene essentiality and/or dependency scores as well as the degree of correlation between mRNA and protein levels of genes within an expression cluster. In addition, CAP-RNAseq has an integrated primer design tool for the prioritized genes. Herein, we showed using comparisons with the existing tools and multiple case studies that CAP-RNAseq can uniquely aid in the discovery of co-expression clusters enriched with essential genes and prioritization of novel biomarker genes that exhibit high correlations between their mRNA and protein expression levels. CAP-RNAseq is applicable to RNA-seq data from different contexts including cancer and available at http://konulabapps.bilkent.edu.tr:3838/CAPRNAseq/ and the docker image is downloadable from https://hub.docker.com/r/konulab/caprnaseq.
摘要：
聚类分析是用于对多个样品或处理组的基因表达模式进行可视化和分组的最广泛使用的探索性方法之一。尽管一些现有的在线工具可以用功能术语注释集群，没有一体化的网络服务器可以使用基因必要性以及mRNA-蛋白质表达的一致性来有效地对基因/簇进行优先级排序。因此，我们开发了CAP-RNAseq，使（1）批量RNA-seq数据的上传和聚类，然后进行识别，所有或选定簇的注释和网络可视化;以及(2)使用DepMap基因必要性和/或依赖性评分以及表达簇内基因的mRNA和蛋白质水平之间的相关性程度进行优先级排序。此外,CAP-RNAseq具有针对优先基因的整合引物设计工具。在这里，我们使用现有工具和多个案例研究的比较表明,CAP-RNAseq可以独特地帮助发现富含必需基因的共表达簇,并优先考虑在mRNA和蛋白质表达水平之间表现出高度相关性的新型生物标志物基因.CAP-RNAseq适用于来自包括癌症在内的不同背景的RNA-seq数据，可在http://konulabapps获得。Bilkent.edu.tr:3838/CAPRNAseq/和docker镜像可从https://hub下载。docker.com/r/konulab/caprnaseq.
