Dataset Information

Thermodynamic and phylogenetic prediction of RNA secondary structures in the coding region of hepatitis C virus.

ABSTRACT: The existence and functional importance of RNA secondary structure in the replication of positive-stranded RNA viruses is increasingly recognized. We applied several computational methods to detect RNA secondary structure in the coding region of hepatitis C virus (HCV), including thermodynamic prediction, calculation of free energy on folding, and a newly developed method to scan sequences for covariant sites and associated secondary structures using a parsimony-based algorithm. Each of the prediction methods provided evidence for complex RNA folding in the core- and NS5B-encoding regions of the genome. The positioning of covariant sites and associated predicted stem-loop structures coincided with thermodynamic predictions of RNA base pairing, and localized precisely in parts of the genome with marked suppression of variability at synonymous sites. Combined, there was evidence for a total of six evolutionarily conserved stem-loop structures in the NS5B-encoding region and two in the core gene. The virus most closely related to HCV, GB virus-B (GBV-B) also showed evidence for similar internal base pairing in its coding region, although predictions of secondary structures were limited by the absence of comparative sequence data for this virus. While the role(s) of stem-loops in the coding region of HCV and GBV-B are currently unknown, the structure predictions in this study could provide the starting point for functional investigations using recently developed self-replicating clones of HCV.

SUBMITTER: Tuplin A

PROVIDER: S-EPMC1370300 | biostudies-other | 2002 Jun

REPOSITORIES: biostudies-other

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Thermodynamic and phylogenetic prediction of RNA secondary structures in the coding region of hepatitis C virus.

Tuplin Andrew A Wood Jonny J Evans David J DJ Patel Arvind H AH Simmonds Peter P

RNA (New York, N.Y.) 20020601 6

The existence and functional importance of RNA secondary structure in the replication of positive-stranded RNA viruses is increasingly recognized. We applied several computational methods to detect RNA secondary structure in the coding region of hepatitis C virus (HCV), including thermodynamic prediction, calculation of free energy on folding, and a newly developed method to scan sequences for covariant sites and associated secondary structures using a parsimony-based algorithm. Each of the pred ...[more]

PMID: 12088154

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Project description:BackgroundAccurate structure prediction methods play an important role for the understanding of RNA function. Energy-based, pseudoknot-free secondary structure prediction is one of the most widely used and versatile approaches, and improved methods for this task have received much attention over the past five years. Despite the impressive progress that as been achieved in this area, existing evaluations of the prediction accuracy achieved by various algorithms do not provide a comprehensive, statistically sound assessment. Furthermore, while there is increasing evidence that no prediction algorithm consistently outperforms all others, no work has been done to exploit the complementary strengths of multiple approaches.ResultsIn this work, we present two contributions to the area of RNA secondary structure prediction. Firstly, we use state-of-the-art, resampling-based statistical methods together with a previously published and increasingly widely used dataset of high-quality RNA structures to conduct a comprehensive evaluation of existing RNA secondary structure prediction procedures. The results from this evaluation clarify the performance relationship between ten well-known existing energy-based pseudoknot-free RNA secondary structure prediction methods and clearly demonstrate the progress that has been achieved in recent years. Secondly, we introduce AveRNA, a generic and powerful method for combining a set of existing secondary structure prediction procedures into an ensemble-based method that achieves significantly higher prediction accuracies than obtained from any of its component procedures.ConclusionsOur new, ensemble-based method, AveRNA, improves the state of the art for energy-based, pseudoknot-free RNA secondary structure prediction by exploiting the complementary strengths of multiple existing prediction procedures, as demonstrated using a state-of-the-art statistical resampling approach. In addition, AveRNA allows an intuitive and effective control of the trade-off between false negative and false positive base pair predictions. Finally, AveRNA can make use of arbitrary sets of secondary structure prediction procedures and can therefore be used to leverage improvements in prediction accuracy offered by algorithms and energy models developed in the future. Our data, MATLAB software and a web-based version of AveRNA are publicly available at http://www.cs.ubc.ca/labs/beta/Software/AveRNA.

Dataset Information

Thermodynamic and phylogenetic prediction of RNA secondary structures in the coding region of hepatitis C virus.

Publications

Thermodynamic and phylogenetic prediction of RNA secondary structures in the coding region of hepatitis C virus.

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