Abstract 5275: An integrated pipeline for TCGA data analysis

The accumulation of publicly available high-throughput experimental data has made analyzing the data a bottleneck in scientific discovery. In this study, we explore a computational, high-throughtput approach for identifying cancer related biological factors using the data available at the Cancer Genome Atlas (TCGA). We developed an integrative genomic analysis (IGA) tool, which performs multiple analysis tasks, including gene/protein level differential expression analysis, gene set level enrichment analysis and network/pathway level comparison. Several data types can be analyzed, including RNA-seq, DNA methylation, miRNA-seq, and proteomics. By varying cancer types, race groups, and data types, we are able to generate a large set of novel findings, which may serve as experimental targets for biologists and biochemists. Interactive reports are generated to facilitate further exploration of the biological significance of findings. With the high-throughput approach and intgrative tools, the bottlenect for us now shifts to publishing these results. We invite researchers in cancer research community to collaborate with us to publish the findings. Citation Format: Kaixian Yu, Yun Xu, Ke Tang, Albert Steppi, Jun Zhou, Zheng Ouyang, Jinfeng Zhang. An integrated pipeline for TCGA data analysis. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5275.