MOTIVATION: Heterogeneity of databases and software resources continues to hamper the integration of biological information. Top-down solutions are not feasible for the full-scale problem of integration across biological species and data types. Bottom-up solutions so far have not integrated, in a maximally flexible way, dynamic and interactive graphical-user-interface components with data repositories and analysis tools. RESULTS: We present a component-based approach that relies on a generalized platform for component integration. The platform enables independently-developed components to synchronize their behavior and exchange services, without direct knowledge of one another. An interface-based data model allows the exchange of information with minimal component interdependency. From these interactions an integrated system results, which we call ISYSf1.gif" BORDER="0">. By allowing services to be discovered dynamically based on selected objects, ISYS encourages a kind of exploratory navigation that we believe to be well-suited for applications in genomic research.
MOTIVATION: Heterogeneity of databases and software resources continues to hamper the integration of biological information. Top-down solutions are not feasible for the full-scale problem of integration across biological species and data types. Bottom-up solutions so far have not integrated, in a maximally flexible way, dynamic and interactive graphical-user-interface components with data repositories and analysis tools. RESULTS: We present a component-based approach that relies on a generalized platform for component integration. The platform enables independently-developed components to synchronize their behavior and exchange services, without direct knowledge of one another. An interface-based data model allows the exchange of information with minimal component interdependency. From these interactions an integrated system results, which we call ISYSf1.gif" BORDER="0">. By allowing services to be discovered dynamically based on selected objects, ISYS encourages a kind of exploratory navigation that we believe to be well-suited for applications in genomic research.
Authors: Belinda Giardine; Cathy Riemer; Ross C Hardison; Richard Burhans; Laura Elnitski; Prachi Shah; Yi Zhang; Daniel Blankenberg; Istvan Albert; James Taylor; Webb Miller; W James Kent; Anton Nekrutenko Journal: Genome Res Date: 2005-09-16 Impact factor: 9.043
Authors: Manpreet S Katari; Steve D Nowicki; Felipe F Aceituno; Damion Nero; Jonathan Kelfer; Lee Parnell Thompson; Juan M Cabello; Rebecca S Davidson; Arthur P Goldberg; Dennis E Shasha; Gloria M Coruzzi; Rodrigo A Gutiérrez Journal: Plant Physiol Date: 2009-12-09 Impact factor: 8.340
Authors: Joshua Orvis; Jonathan Crabtree; Kevin Galens; Aaron Gussman; Jason M Inman; Eduardo Lee; Sreenath Nampally; David Riley; Jaideep P Sundaram; Victor Felix; Brett Whitty; Anup Mahurkar; Jennifer Wortman; Owen White; Samuel V Angiuoli Journal: Bioinformatics Date: 2010-04-22 Impact factor: 6.937
Authors: Ola Spjuth; Tobias Helmus; Egon L Willighagen; Stefan Kuhn; Martin Eklund; Johannes Wagener; Peter Murray-Rust; Christoph Steinbeck; Jarl E S Wikberg Journal: BMC Bioinformatics Date: 2007-02-22 Impact factor: 3.169