The GTfold package includes 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.

GTfold now includes related programs such as RNAStructProfiling, RNAStructViz, and NNTMParameterEditor. The details of these additional programs can be found on the Github page; details for compiling and running RNA profiling can be found on the Related page.

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


Alumni: Joshua Anderson, Andrew Ash, Rohit Banga, Prashant Gaurav, Sonny Hernandez, Neha Jatav, Sainath Mallidi, Amrita Mathuriya, Gregory Nou, George Johnston, Christopher Mize, Manoj Soni, M. Shel Swenson, and Zsuzsanna Sukosd


M.S. Swenson, J. Anderson, A. Ash, P. Gaurav, Z.Sukos, D.A. Bader, S.C. Harvey, and C.E. Heitsch. 2012. "GTfold: Enabling parallel RNA secondary structure prediction on multi-core desktops." BMC Research Notes. 5(1):341.

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.


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).