A comprehensive comparison of comparative RNA structure prediction approaches

Paul P Gardner,Robert Giegerich

doi:10.1186/1471-2105-5-140

Paul P Gardner, Robert Giegerich

Open Access

https://doi.org/10.1186/1471-2105-5-140

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Journal: BMC bioinformatics	Publication Date: Jan 1, 2004
Citations: 423	License type: CC BY 2.0

Affiliation: Bielefeld University, University of Copenhagen

Abstract

BackgroundAn increasing number of researchers have released novel RNA structure analysis and prediction algorithms for comparative approaches to structure prediction. Yet, independent benchmarking of these algorithms is rarely performed as is now common practice for protein-folding, gene-finding and multiple-sequence-alignment algorithms.ResultsHere we evaluate a number of RNA folding algorithms using reliable RNA data-sets and compare their relative performance.ConclusionsWe conclude that comparative data can enhance structure prediction but structure-prediction-algorithms vary widely in terms of both sensitivity and selectivity across different lengths and homologies. Furthermore, we outline some directions for future research.

Highlights

An increasing number of researchers have released novel RNA structure analysis and prediction algorithms for comparative approaches to structure prediction
We conclude that comparative data can enhance structure prediction but structure-prediction-algorithms vary widely in terms of both sensitivity and selectivity across different lengths and homologies
Single sequence methods The accuracy of the Minimum Free Energy (MFE) single sequence method has been evaluated elsewhere and was found to have an accuracy of 73% when averaged over many different RNAs and "basepair slippage" was tolerated in the evaluation [68]

Summary

Introduction

An increasing number of researchers have released novel RNA structure analysis and prediction algorithms for comparative approaches to structure prediction. A fundamental tenet of biology is that a stable tertiary structure is essential for biological function. The experimental determination of RNA structure remains difficult [11]; Researchers increasingly turn to computational methods. To date the most popular structure prediction algorithm is the Minimum Free Energy (MFE) method for folding a single sequence, this has been implemented by two packages: Mfold [12] and RNAfold [13]. There are several independent reasons why the accuracy of MFE structure prediction is limited in practise (see discussion below). The best accuracy can be achieved by employing comparative (page number not for citation purposes)

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Discussion

Conclusion