In this paper, we present a novel representation for dealing with this kind of data in the context of an area in molecular biology. Molecular biologists study the function and evolutionary relationships of genes and proteins in cells. The genetic data is represented as sequential strings of letters, or sequences . Biologists use the large amount of known sequence data that now exists to help determine the function of new sequences. The similarity search algorithms developed for this purpose currently produce textual output.
Similarity reports for some sequences produce hundreds or thousands of pages of text.
The similarity information produced in these textual reports is proportional to the size of the databases of known sequences, which are growing rapidly. Ironically, as more information about possible function becomes available, the task of exploring similarity results and determining function becomes increasingly difficult. Visualization methods are needed for biologists to effectively explore the enormous amount of information available in the databases. The similarity analysis information produced is multi-dimensional, because it contains several orthogonal pieces of information for each similar region found. This discrete, multi-dimensional data lacks a natural visual representation.