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Wellcome Trust funds Sprint project for further two years

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The Wellcome Trust has agreed to fund the Sprint project for a further two years, ensuring further development for the software that allows scientists to concentrate on research problems rather than computer problems.

Gene analysis is becoming increasingly complex and can be greatly enhanced by exploiting the power of HPC, but the software can be difficult for researchers to use. To allow greater access to the benefits of HPC, EPCC and the Division of Pathway Medicine at the University of Edinburgh developed a prototype framework called Sprint, which allows biostatisticians to more easily exploit HPC systems.

The Wellcome Trust has now funded the Sprint project for a further two years. This will allow the development of the Sprint framework and for a number of commonly used functions to be added to enable its use by a wide community.

Sprint (Simple Parallel R INTerface) is an easy-to-use parallel version of R, a statistical language that processes the data gleaned from microarray analysis, a technique which allows the simultaneous measurement of thousands to millions of genes or sequences across tens to thousands of different samples.

Processing the data that is produced by microarray analysis tests the limits of existing bioinformatics computing infrastructure. A solution is to use HPC systems, which offer more processors and memory than desktop computer systems.

However, R must be able to utilise multiple processors if it is to fully exploit the power of HPC systems to analyse genomic data. There are existing modules that enable R to do this, but they are either difficult for HPC novices or cannot be used to solve certain classes of problem. Sprint allows parallelised functions to be added to R without the need to master parallel programming methods, enabling the easy exploitation of HPC systems.

Professor Peter Ghazal, director of the Division of Pathway Medicine, says: 'Sprint will greatly increase the computing power available to many researchers and is therefore a unique opportunity to accelerate the discovery of the genes linked to diseases.'