ITools: A Framework for Classification, Categorization and Integration of Computational Biology Resources

Ivo D Dinov,Michael Sherman,Arthur W Toga,Aris Floratos,H V Jagadish,Beth Kirschner,Ron Kikinis,D Stott Parker,Andrea Califano,Scott L Delp ,William J Bug ,Shawn N Murphy ,David B Kennedy ,Daniel L Rubin ,Jeanette P Schmidt ,Russ B Altman ,Isaac S Kohane ,Junxia Ma ,Brian D Athey ,William E Lorensen ,Jonathan M Dugan ,Mark A Musen

doi:10.1371/journal.pone.0002265

Abstract

The advancement of the computational biology field hinges on progress in three fundamental directions – the development of new computational algorithms, the availability of informatics resource management infrastructures and the capability of tools to interoperate and synergize. There is an explosion in algorithms and tools for computational biology, which makes it difficult for biologists to find, compare and integrate such resources. We describe a new infrastructure, iTools, for managing the query, traversal and comparison of diverse computational biology resources. Specifically, iTools stores information about three types of resources–data, software tools and web-services. The iTools design, implementation and resource meta - data content reflect the broad research, computational, applied and scientific expertise available at the seven National Centers for Biomedical Computing. iTools provides a system for classification, categorization and integration of different computational biology resources across space-and-time scales, biomedical problems, computational infrastructures and mathematical foundations. A large number of resources are already iTools-accessible to the community and this infrastructure is rapidly growing. iTools includes human and machine interfaces to its resource meta-data repository. Investigators or computer programs may utilize these interfaces to search, compare, expand, revise and mine meta-data descriptions of existent computational biology resources. We propose two ways to browse and display the iTools dynamic collection of resources. The first one is based on an ontology of computational biology resources, and the second one is derived from hyperbolic projections of manifolds or complex structures onto planar discs. iTools is an open source project both in terms of the source code development as well as its meta-data content. iTools employs a decentralized, portable, scalable and lightweight framework for long-term resource management. We demonstrate several applications of iTools as a framework for integrated bioinformatics. iTools and the complete details about its specifications, usage and interfaces are available at the iTools web page http://iTools.ccb.ucla.edu.

Highlights

Bioinformatics is quickly becoming the central core that integrates the many disparate bodies of data, scientific knowledge and computational infrastructure from fields as diverse as genetics, structural biology, medical and animal models of disease, imaging, engineering, etc
Time, function or interaction modeling techniques apply across the vast spectrum of scales from genotypes to phenotypes, from the small scale of microarray imaging for genomics, to the larger in vivo neuroimaging scale
Types of Computational Biology Resources At the highest level, we distinguish three types of computational biology resources – data, tools and services. (Downloadable) Data Resources type includes observed biomedical data, which is typically fed as input in different computational tools; model data, processed data resulting as an output from various tools; and textual data, spread sheets, web-pages

Summary

Introduction

Bioinformatics is quickly becoming the central core that integrates the many disparate bodies of data, scientific knowledge and computational infrastructure from fields as diverse as genetics, structural biology, medical and animal models of disease, imaging, engineering, etc. Time, function or interaction modeling techniques apply across the vast spectrum of scales from genotypes to phenotypes, from the small scale of microarray imaging for genomics, to the larger in vivo neuroimaging scale. In this manuscript, we discuss challenges of development, maintenance and dissemination of robust computational biology resources (data, tools and services, see below). There is an increasing need in the bioinformatics community for accurate accounting of data and resources, e.g., the bioinformatics Resourceome effort [7]

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