Han Seonggyun, Kim Dongwook, Kim Youngjun, Choi Kanghoon, Miller Jason E, Kim Dokyoon, Lee Younghee
Department of Biomedical Informatics, University of Utah, University of Utah School of Medicine, Salt Lake City, UT, 84108, USA.
University of Utah, School of Computing, University of Utah, Salt Lake City, UT, 84108, USA.
BMC Med Genomics. 2018 Apr 20;11(Suppl 2):25. doi: 10.1186/s12920-018-0348-8.
The Cancer Genome Atlas (TCGA) project is a public resource that provides transcriptomic, DNA sequence, methylation, and clinical data for 33 cancer types. Transforming the large size and high complexity of TCGA cancer genome data into integrated knowledge can be useful to promote cancer research. Alternative splicing (AS) is a key regulatory mechanism of genes in human cancer development and in the interaction with epigenetic factors. Therefore, AS-guided integration of existing TCGA data sets will make it easier to gain insight into the genetic architecture of cancer risk and related outcomes. There are already existing tools analyzing and visualizing alternative mRNA splicing patterns for large-scale RNA-seq experiments. However, these existing web-based tools are limited to the analysis of individual TCGA data sets at a time, such as only transcriptomic information.
We implemented CAS-viewer (integrative analysis of Cancer genome data based on Alternative Splicing), a web-based tool leveraging multi-cancer omics data from TCGA. It illustrates alternative mRNA splicing patterns along with methylation, miRNAs, and SNPs, and then provides an analysis tool to link differential transcript expression ratio to methylation, miRNA, and splicing regulatory elements for 33 cancer types. Moreover, one can analyze AS patterns with clinical data to identify potential transcripts associated with different survival outcome for each cancer.
CAS-viewer is a web-based application for transcript isoform-driven integration of multi-omics data in multiple cancer types and will aid in the visualization and possible discovery of biomarkers for cancer by integrating multi-omics data from TCGA.
癌症基因组图谱（TCGA）项目是一项公共资源，它提供了33种癌症类型的转录组学、DNA序列、甲基化和临床数据。将TCGA癌症基因组数据的大尺寸和高复杂性转化为综合知识，有助于推动癌症研究。可变剪接（AS）是人类癌症发展以及与表观遗传因子相互作用中基因的关键调控机制。因此，基于AS对现有TCGA数据集进行整合，将更易于深入了解癌症风险的遗传结构及相关结果。已有工具可用于分析和可视化大规模RNA测序实验中的可变mRNA剪接模式。然而，这些现有的基于网络的工具一次仅限于分析单个TCGA数据集，比如仅分析转录组信息。
我们开发了CAS-viewer（基于可变剪接的癌症基因组数据综合分析），这是一种基于网络的工具，利用来自TCGA的多癌种组学数据。它展示了可变mRNA剪接模式以及甲基化、微小RNA（miRNA）和单核苷酸多态性（SNP），然后提供一个分析工具，将差异转录本表达比率与33种癌症类型的甲基化、miRNA和剪接调控元件联系起来。此外，用户可以结合临床数据分析可变剪接模式，以识别每种癌症中与不同生存结果相关的潜在转录本。
CAS-viewer是一个基于网络的应用程序，用于在多种癌症类型中进行转录本异构体驱动的多组学数据整合，通过整合来自TCGA的多组学数据，将有助于癌症生物标志物的可视化及可能的发现。
