Alternative splicing (AS) of genes is a key contributor to transcriptome variations and numerous disease. RNA-Seq experiments produce millions of short RNA reads and are commonly used to assess alternative splicing variations in one of two ways: Full gene isoform quantification, or relative abundance of binary AS events such as exon skipping. In this talk I will present a new framework we developed, based on gene splice graphs, to define, quantify and visualize splicing variations. The new formulation, termed LSV (local splice variations) captures previously defined binary AS events, but also much more complex variations. We show such complex variations are common across the metazoan, and can be accurately quantified. Next, I will discuss our current research into accurately capturing splicing variations when handling large heterogeneous datasets. Such data can involve hundreds or more human subjects and pose statistical and computational challenges.