The main objective of this project is to aid genome researchers with the task of elucidating patterns and clusters in large amounts of biological data. For genome researchers who are interested in comparing gene or protein sequences to the sequences within one genome or across genomes, this task involves executing hundreds of thousands of similarity searches that produce text output. This project involves the development of two specific software tools for visualizing and exploring the similarity data in a database of biological sequence similarity results. The first tool will be an Interactive Categorization Tool. This tool will display attributes of selected similarity database objects in a 2D scatterplot and enable dynamic manipulation of the display. This will enable the genome researcher to explore the attributes of similarities and categorize the similarities based on those attributes. For example, the genome researcher will be able to vary the input parameters of a function for computing the strength of each detected similarity and display a plot with the strength of each similarity shown as the color of each point, and the points situated in the 2D space based on score and statistical significance as the X and Y axes. The tool will enable genome researchers to dynamically manipulate the generation of higher- level concepts or categories for detected similarities (strong, marginal, and weak similarities as opposed to individual similarities with particular values of score and statistical significance that are more difficult to compare). This will lead to their ability to categorize hits as orthologous or paralogous, based on various attributes of the detected similarities. Score and p-value are not the only attributes that can be used -- the system is general enough that other attributes, such as percent identity, percent conserved, and length of alignment, among others, could be used in functions. Thus, genome researchers can cond uct exploration at different stages of the genome comparison research process. The second tool will be a Cluster Exploration Tool. Using the results from data mining techniques that cluster like sequences together, genome researchers will be able to visualize the similarities among the sequences in the clusters. For example, the tool can be used for a cluster of new unknown sequences that were found similar to members of a group of known sequences. The new sequences can be positioned as nodes on the left in a bipartite graph, and the known sequences that they are similar to can be positioned along the right. Lines drawn between the nodes, colored differently based on the strength of the hits, will enable the researcher to visualize the connectedness of the sequences in the cluster. Details about each sequence and each similarity in the cluster can be obtained from the DBMS. This will enable genome researchers to study groups of orthologous or parologous sequences. A key feature of these tools is that they will be 'thin' clients (often referred to as applets) that communicate with the underlying DBMS via queries formulated visually by the genome researchers. The use of Java- based components for these tools will enable them to be easily used and shared by the bioinformatics community and the genome research community. The development of these tools will demonstrate the feasibility of the thin-client approach that is the hallmark of the network computing architecture philosophy.
