We rely on a dynamic system to infer appropriate options, aiding maintenance.Ĭomparing this to the EpiGRAPH and Galaxy frameworks, which we believe are the closest existing systems, we find that both require substantial technical expertise when choosing the correct analysis and options.

This guided approach hides unnecessary complexities from the researcher, while confronting her with important design choices as needed.

In order to simplify the task of making choices, a step-wise approach has been implemented, displaying only the relevant options at each stage. However, the complex interdependencies between the large body of available tracks, a number of syntactically different analyses, and a range of choices for constructing null models, all pose challenges to the concepts of simplicity and ease of use. The system provides a web-based user interface with a low entry point.

A further speedup is achieved by memoizing intermediate results to disk, automatically retrieving them when needed for the same or different analyses on the same track(s) at any subsequent time, by any user. To reduce the memory footprint of analyses on genome-wide data, an iterative divide-and-conquer algorithm is automatically carried out when applicable.