报告简介：Eukaryotic cells generate remarkable regulatory and functional complexity from a finite set of genes. Production of mRNA isoforms through alternative processing and modification of RNA is essential for generating this complexity. The rapid accumulation of clinical cancer RNA-seq datasets has provided the opportunity to comprehensively elucidate the landscape and functional significance of mRNA isoform variation in cancer. However, the enormous potential of these large-scale datasets cannot be fully realized without the development of methods for discovering patterns and generating biological insights from massive cancer transcriptome data. To address this critical need in cancer research, we have developed a series of novel computational and statistical methods (rMATS, GLiMMPS, SURVIV) for detecting and characterizing alternative isoform variation in cancer transcriptomes. We have applied these methods to TCGA RNA-seq data across multiple cancer types. These studies have revealed mRNA isoforms and their associated regulatory networks that play crucial roles in cancer.