The PATIKA Project aims to provide the scientific community with an integrated environment composed of a central database and a visual editor, built around an extensive ontology and an integration framework. It also features tools for analyzing and clustering microarray data and inference of pathways.

Perceiving the increasing demand on easily accessible tools, PATIKAweb aims to supply a Web-based service with a user-friendly interface without requiring any registrations or installations. It focuses on providing a simple yet powerful interface for querying and visualizing the PATIKA database. The database currently contains human pathway data integrated from popular public pathway databases such as Reactome, and interfaces with certain other major databases and ontologies such as UniProt, Entrez Gene, and GO. It features a user-friendly interface, dynamic visualization, graph-theoretic queries for extracting biologically important phenomena and import/export facilities from and to various exchange formats such as BioPAX.

PATIKAweb fully supports the PATIKA ontology, including a multiple-view schema for bioentity and mechanistic levels, compartments and compound graphs for visualizing molecular complexes, pathways and black-box reactions. Ability to import pathway models in BioPAX (level 2) format makes analysis of one's private data possible.

Figure 1. Sample mechanistic, or state-transition, level (left) and bioentity level (right) views.

The advanced querying mechanisms of PATIKAweb are presented through user-friendly graphical interfaces. This facility enables access to data in both biological entity and mechanistic levels. Querying component both supports SQL-like queries and an array of graph-theoretic queries such as common targets and regulators, shortest paths, feedback loops, and “interesting subgraphs” based on user’s genes of interest.

The layout component uses a specialized layout algorithm, which can handle both compound graphs and compartments, to automatically layout pathways. Graph editing functionalities such as scrolling and zooming, do/undo and delete as well as advanced features such as expand/collapse nested abstractions are available to manage the current pathway model.

PATIKAweb has a comprehensive microarray data analysis component named PATIKAmad integrated into its powerful visualisation environment. The tool enables users to visualize microarray data mapped onto pathway models. It also provides mechanisms for clustering microarray data using popular methods.

Currently PATIKAweb version 2.1 is publicly available for non-profit use, and the implementation uses the JSP (JavaServer Pages technology) edition of the Tom Sawyer Visualization technology to handle this highly-dynamic and advanced visual content.