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MapMan is a user-driven tool that displays large genomics datasets onto diagrams of metabolic pathways or other processes. Here, we present new developments, including improvements of the gene assignments and the user interface, a strategy to visualize multilayered datasets, the incorporation of statistics packages, and extensions of the software to incorporate more biological information including visualization of corresponding genes and horizontal searches for similar global responses across large numbers of arrays.

Improving the structure of code can help developers work with a software system more efficiently and more consistently. Aspect-oriented programming (AOP) offers additional ways to structure software by providing explicit means to modularize crosscutting concerns (CCCs) in modularity units called aspects. With the advent of AOP, a new kind of structural improvement of software needs to be considered: the refactoring of non-modularized CCCs into aspects. Refactorings have shown to be helpful for object-oriented software development and maintenance, but their application to aspect-oriented software is not yet well understood. In particular, since refactorings of non-modularized crosscutting concerns involve multiple program elements with potentially complicated relationships, they are considerably more complex than traditional refactorings; the lack of tool support to help plan, reason about and execute CCC refactorings impedes the improvement of code modularity. The thesis of this research is that the refactoring of crosscutting concerns can be supported by a role-based concern model. In this model, crosscutting concerns are described in terms of abstract roles, and instructions for refactoring the concerns are written in terms of those roles. To apply a refactoring, a developer maps a subset of the roles to concrete program elements; a tool can then help complete the mapping of roles to the existing program. Refactoring instructions are then applied to manipulate and modularize the concrete elements corresponding to the crosscutting concern. The abstract nature of such a role-based concern model allows the definition of a refactoring description separately from concrete systems it may be applied to, and allows using a single description to refactor multiple instances of the same concern. To aid developers in restructuring the implementation of crosscutting concerns using aspect-oriented programming, we introduce in this dissertation a refactoring approach and proof-of-concept tool founded on our role-based concern model. We show that abstract descriptions of crosscutting concerns can be applied to previously existing software and we describe the potential for expressing and executing a variety of new CCC refactorings.

The wide application of high-throughput transcriptomics using microarrays has generated a plethora of technical platforms, data repositories, and sophisticated statistical analysis methods, leaving the individual scientist with the problem of choosing the appropriate approach to address a biological question. Several software applications that provide a rich environment for microarray analysis and data storage are available (e.g. GeneSpring, EMMA2), but these are mostly commercial or require an advanced informatics infrastructure. There is a need for a noncommercial, easy-to-use graphical application that aids the lab researcher to find the proper method to analyze microarray data, without this requiring expert understanding of the complex underlying statistics, or programming skills. We have developed Robin, a Java-based graphical wizard application that harnesses the advanced statistical analysis functions of the R/BioConductor project. Robin implements streamlined workflows that guide the user through all steps of two-color, single-color, or Affymetrix microarray analysis. It provides functions for thorough quality assessment of the data and automatically generates warnings to notify the user of potential outliers, low-quality chips, or low statistical power. The results are generated in a standard format that allows ready use with both specialized analysis tools like MapMan and PageMan and generic spreadsheet applications. To further improve user friendliness, Robin includes both integrated help and comprehensive external documentation. To demonstrate the statistical power and ease of use of the workflows in Robin, we present a case study in which we apply Robin to analyze a two-color microarray experiment comparing gene expression in tomato (Solanum lycopersicum) leaves, flowers, and roots.