GTfold: Enabling parallel RNA secondary structure prediction on multi-core desktops

Abstract

BackgroundAccurate and efficient RNA secondary structure prediction remains an important open problem in computational molecular biology. Historically, advances in computing technology have enabled faster and more accurate RNA secondary structure predictions. Previous parallelized prediction programs achieved significant improvements in runtime, but their implementations were not portable from niche high-performance computers or easily accessible to most RNA researchers. With the increasing prevalence of multi-core desktop machines, a new parallel prediction program is needed to take full advantage of today’s computing technology.FindingsWe present here the first implementation of RNA secondary structure prediction by thermodynamic optimization for modern multi-core computers. We show that GTfold predicts secondary structure in less time than UNAfold and RNAfold, without sacrificing accuracy, on machines with four or more cores.ConclusionsGTfold supports advances in RNA structural biology by reducing the timescales for secondary structure prediction. The difference will be particularly valuable to researchers working with lengthy RNA sequences, such as RNA viral genomes.

Evaluating the accuracy of SHAPE-directed RNA secondary structure predictions

“…To minimize simulation run times, secondary structures were predicted using GTfold (37), a parallelized multi-core thermodynamic optimization program. Like UNAfold (10) and RNAfold (11), GTfold implements the standard Turner NNTM energy model (22,23).…”

RNA secondary structure prediction based on SHAPE data in helix regions

“…investigated experimental errors with bootstrap and jackknife models. Both models showed that we can recover errors by removing some SHAPE data.Jackknife approach demonstrated when 35% of the data are left out; the confidence levels of SHAPE-directed secondary structure prediction are significantly closer to the native structure.In this work, we affirm that SHAPE data are more informative in some specific parts of RNA.To show that how to employ SHAPE data in helix regions for improving RSSP, RNAstructure[11] and GTfold[12] software are employed. At first, RNA sequence and SHAPE data are given as inputs to each of software for RSSP based on MFE with considering SHAPE data.…”

“…Some algorithms, such as RNAstructure 2 [11] and GTfold [12] can predict RNA secondary structure based on MFE with SHAPE data or without SHAPE data.…”

New insights from cluster analysis methods for RNA secondary structure prediction

“…Although these cluster analysis methods have been developed more recently, we show that their run times do not suffer much in comparison. We use the runtime of GTfold [50], a parallelized implementation of the MFE method, for comparison. We measure the time it takes to generate and analyze a Boltzmann sample for a given sequence using a high resolution timer.…”