EPCC and the Pathway Medicine division at Edinburgh University have developed a prototype framework called Sprint in order to allow greater access to the benefits of high-performance computing (HPC).
Gene analysis is becoming increasingly complex and can be enhanced by exploiting the power of HPC, but the software can be difficult for researchers to use.
The Sprint software, however, enables biostatisticians to 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 - which stands for 'simple parallel R interface' - is a parallel version of R, a statistical language that processes the data gleaned from microarray analysis: a technique that allows the simultaneous measurement of thousands to millions of genes or sequences across tens or 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 too difficult for HPC novices or they 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 exploitation of HPC systems.
Prof Peter Ghazal, director of the division of Pathway Medicine, said: '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.'
