Introduction

GTfold is a fast, scalable multicore code for predicting RNA secondary structure that is one to two orders of magnitude faster than the de facto standard programs and achieves comparable accuracy of prediction.

We are seeing a paradigm shift to multicore chips and parallelism must be explicitly addressed to continue gaining performance with each new generation of systems. GTfold, which is implemented in C/C++ and uses OpenMP primitives for parallelization of the algorithm, opens up a new path for the algorithmic improvements and the application of improved thermodynamic models to increase prediction accuracy.

Downloads

We have binaries available for 32 and 64 bit Linux machines, Mac OSX, and Windows. Please see Guide page for usage details.

OS	binary
SUSE Linux (64-bit)	gtfold-2.0_x86-64_suse.tar.gz
Fedora (32-bit)	gtfold-2.0_x86_fc.tar.gz
Ubuntu (32-bit)	gtfold-2.0_x86_ubuntu.tar.gz
Mac OSX (64-bit)	gtfold-2.0_x86-64_osx.tar.gz
Cygwin (32-bit)	gtfold-2.0_x86_cygwin.tar.gz

If you wish to download and compile GTfold from the latest source code, please visit the Develop page.

People

Alumni: Joshua Anderson, Andrew Ash, Sonny Hernandez, Neha Jatav, Sainath Mallidi, Amrita Mathuriya, Gregory Nou, Manoj Soni, and Zsuzsanna Sukosd

Publications

A. Mathuriya, D.A. Bader, C.E. Heitsch, and S.C. Harvey, "GTfold: A Scalable Multicore Code for RNA Secondary Structure Prediction", 24th Annual ACM Symposium on Applied Computing (SAC), Computational Sciences Track, Honolulu, HI, March 8-12, 2009. (PDF)

Acknowledgements

This work was supported in part by National Institutes of Health grant NIH NIGMS R01 GM083621, and by NSF Grants CNS-0614915 and DBI-04-20513. Additionally, the research of Christine E. Heitsch, Ph.D., is supported in part by a Career Award at the Scientific Interface (CASI) from the Burroughs Wellcome Fund (BWF).