Visual Analytics Of 3D Macro Molecular Structure

Abstract

Currently, molecular biology research is dominated by the rapid advances in DNA sequencing and related technologies. However, interpreting these data, and gaining insight into underlying molecular processes, remains challenging. Of the many strategies being pursued in this effort to understand the biomolecular machinery of life, one of the most enduring involves considering three-dimensional structure. Increasingly, experimental data is providing information on this 3D structure and dynamics of chromosomes in living cells –– these data, in turn, promise new insight into fundamental genomic processes. Similarly, the flood of DNA and protein sequence data provides a vast resource that can be used to greatly extend our ability to predict the 3D structure of proteins. In this thesis I describe Score, a visual analytics framework I have created to help biologists understand the 3D structure of chromosomes and proteins, and to explore how this structure relates to the underlying biological function. Usage of the Score framework is demonstrated through two case studies: Aquaria and Rondo. Aquaria is a web-based protein sequence structure viewer allowing users to find and navigate through structures for any given protein sequence. Rondo is a web-based chromosome structure viewer that can superimpose feature track information.