The Dundee led project, receiving around £700,000 from BBSRC, is headed by Professor Geoffrey Barton from Dundee College of Life Sciences, Resource for Protein Structure Prediction and Sequence Analysis will provide scientists with the software tools to take raw genetic sequence data and make prediction about the structure and function of the proteins that they encode, helping to make use of the enormous volumes of DNA sequence data that are generated in modern bioscience.
“We have developed tools at Dundee that are now used by researchers around the world thousands of times a month and across the scientific spectrum, from research into diseases like cancer to areas such as plant sciences,” said Professor Barton,
“Research now generates massive amounts of data which has to be handled, stored and analysed. The work we have done and which we continue to develop provides fundamental capabilities for researchers to use this data efficiently.”
Edinburgh for visualisation and parallel networking
At University of Edinburgh, Professor Thomas Freeman (left), with Dr Anton Enright, (right) EMBL European Bioinformatics Institute are awarded funding for the Development of a Rapid Processing Pipeline and Graph-based Visualization for the Analysis of Next Generation Sequencing Data .
Here the development of BioLayout Express3D has been specifically designed for visualisation, clustering, exploration and analysis of very large network graphs in two- and three-dimensional space derived primarily, but not exclusively, from biological data is a powerful tool for the visualisation and analysis of network graphs, primarily, but not exclusively, from biological data.
SPRINT for HPC micro analysis
Professor Peter Ghazal (RIGHT) at University of Edinburgh is also awarded for the SPRINT (Simple Parallel R INTerface) approach to network biology. This 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. An approach 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. 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.
The other awards were to: