Abstract
BackgroundTo aid in bioinformatics data processing and analysis, an increasing number of web-based applications are being deployed. Although this is a positive circumstance in general, the proliferation of tools makes it difficult to find the right tool, or more importantly, the right set of tools that can work together to solve real complex problems.ResultsMagallanes (Magellan) is a versatile, platform-independent Java library of algorithms aimed at discovering bioinformatics web services and associated data types. A second important feature of Magallanes is its ability to connect available and compatible web services into workflows that can process data sequentially to reach a desired output given a particular input. Magallanes' capabilities can be exploited both as an API or directly accessed through a graphic user interface.The Magallanes' API is freely available for academic use, and together with Magallanes application has been tested in MS-Windows™ XP and Unix-like operating systems. Detailed implementation information, including user manuals and tutorials, is available at .ConclusionDifferent implementations of the same client (web page, desktop applications, web services, etc.) have been deployed and are currently in use in real installations such as the National Institute of Bioinformatics (Spain) and the ACGT-EU project. This shows the potential utility and versatility of the software library, including the integration of novel tools in the domain and with strong evidences in the line of facilitate the automatic discovering and composition of workflows.
Highlights
To aid in bioinformatics data processing and analysis, an increasing number of webbased applications are being deployed
A discovery process aims to segregate a set of services or data-types that satisfy a given number of requirements from the larger pool of available resources; for example, what services are able to process my molecular sequence?
Scoring system: resource retrieval The rationale of the scoring system used to rank resources is to combine the learning rate based on traditional keyword-resource tuple (KR) voting systems with user's feedback
Summary
To aid in bioinformatics data processing and analysis, an increasing number of webbased applications are being deployed This is a positive circumstance in general, the proliferation of tools makes it difficult to find the right tool, or more importantly, the right set of tools that can work together to solve real complex problems. Some metadata repositories such as BioMoby [2] and FETA [3] recognize the importance of sharing data formats between tools, and make use of this strategy to implement integration architectures Such repository approaches have collected large sets of registered services and data types, making a manual discovery process difficult and time consuming.
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