Background: Examining the distribution of variation has proven an extremely profitable technique in the effort to identify sequences of biological significance. Most approaches in the field, however, evaluate only the conserved portions of sequences -ignoring the biological significance of sequence differences. A suite of sophisticated likelihood based statistical models from the field of molecular evolution provides the basis for extracting the information from the full distribution of sequence variation. The number of different problems to which phylogeny-based maximum likelihood calculations can be applied is extensive. Available software packages that can perform likelihood calculations suffer from a lack of flexibility and scalability, or employ error-prone approaches to model parameterisation.

In this paper, we describe a graphical workflow management system called HyperThesis to address the challenges of integrating bioinformatics applications. HyperThesis is an integral component of the Genomics Research Network Architecture (gRNA). The gRNA was designed and developed to address the challenges of developing new bioinformatics applications. Specifically, HyperThesis makes constructing workflows (pipelines of execution of applications) in the gRNA fast and intuitive for biologists and bio-programmers alike. It provides a large repository of interconnectable, parameterized workflow components for processing and relating diverse biological data and software programs. It also enables us to add new workflow components as new algorithms develop in ones area of interest. HyperThesis has been fully implemented using Java.

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