Abstract
BackgroundIn systems biology, and many other areas of research, there is a need for the interoperability of tools and data sources that were not originally designed to be integrated. Due to the interdisciplinary nature of systems biology, and its association with high throughput experimental platforms, there is an additional need to continually integrate new technologies. As scientists work in isolated groups, integration with other groups is rarely a consideration when building the required software tools.ResultsWe illustrate an approach, through the discussion of a purpose built software architecture, which allows disparate groups to reuse tools and access data sources in a common manner. The architecture allows for: the rapid development of distributed applications; interoperability, so it can be used by a wide variety of developers and computational biologists; development using standard tools, so that it is easy to maintain and does not require a large development effort; extensibility, so that new technologies and data types can be incorporated; and non intrusive development, insofar as researchers need not to adhere to a pre-existing object model.ConclusionBy using a relatively simple integration strategy, based upon a common identity system and dynamically discovered interoperable services, a light-weight software architecture can become the focal point through which scientists can both get access to and analyse the plethora of experimentally derived data.
Highlights
In systems biology, and many other areas of research, there is a need for the interoperability of tools and data sources that were not originally designed to be integrated
This paper discusses the requirements and design of the ISB Informatics Infrastructure, which is abbreviated as I3
The architecture needed to be flexible enough to integrate research data gathered using a varied collection of technologies and platforms, including: microfluidics based cell growth analysis involving both FACS and specially built LabView controlled 'lab on a chip' platforms; proteomics mass spectrometry results generated via the TransProteomics Pipeline (TPP) [14], which were generated from over 10 different mass spectrometry platforms; genomics data from both microarray expression and ChIP-chip platforms; and cellular imaging experiments involving a large number of different microscopy based platforms including confocal microscopy, automated software controlled microscopy environments (e.g. IPLab controlled Leicas) and automated machinery (e.g. LEAP system from Cyntellect)
Summary
Many other areas of research, there is a need for the interoperability of tools and data sources that were not originally designed to be integrated. Within the life sciences we continually see a steady increase in the volume and complexity of data being generated by experiments The management of this data requires rapid development of high quality integration and analysis tools. Systems biology has the same data management requirements as many other fields of science, but due to its interdisciplinary nature and strong association with high throughput techniques these needs are more apparent These requirements are to allow for: the rapid introduction of new data sources derived from new and emerging technologies; the interoperability between analysis tools written in a variety of different languages; a means to integrate data sources to support data mining and searching operations; and the ability to directly access the experi-. Designs based on enterprise systems can provide solutions to these problems within an organisation, but they have to be tailored to suit the specific needs of researchers
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