DNA sequence clustering has become a valuable method in support of gene discovery and gene expression analysis. Our interest lies in leveraging the sequence diversity within clusters of expressed sequence tags (ESTs) to model gene structure for the study of gene variants that arise from, among other things, alternative mRNA splicing, polymorphism, and divergence after gene duplication, fusion, and translocation events. In previous work, CRAW was developed to discover gene variants from assembled clusters of ESTs. Most importantly, novel gene features (the differing units between gene variants, for example alternative exons, polymorphisms, transposable elements, etc.) that are specialized to tissue, disease, population, or developmental states can be identified when these tools collate DNA source information with gene variant discrimination. While the goal is complete automation of novel feature and gene variant detection, current methods are far from perfect and hence the development of effective tools for visualization and exploratory data analysis are of paramount importance in the process of sifting through candidate genes and validating targets.