GTfold: a scalable multicore code for RNA secondary structure prediction

The prediction of the correct secondary structures of large RNAs is one of the unsolved challenges of computational molecular biology. Among the major obstacles is the fact that accurate calculations scale as O(n4 ), so the computational requirements become prohibitive as the length increases. Existing folding programs implement heuristics and approximations to overcome these limitations. We present a new parallel multicore and scalable program called GTfold, which is one to two orders of magnitude faster than the de facto standard programs and achieves comparable accuracy of prediction. Development of GTfold opens up a new path for the algorithmic improvements and application of an improved thermodynamic model to increase the prediction accuracy. In this paper we analyze the algorithm’s concurrency and describe the parallelism for a shared memory environment such as a symmetric multiprocessor or multicore chip. In a remarkable demonstration, GTfold now optimally folds 11 picornavir...

• Type: Conference
• Year: 2009
• Where: SAC
• Authors: Amrita Mathuriya, David A. Bader, Christine E. Heitsch, Stephen C. Harvey
• Keywords: Applied Computing, Computational Molecular Biology, Georgia Institute, Multicore Chips, SAC 2009