ArticleBMC bioinformatics2008
RNAalifold: improved consensus structure prediction for RNA alignments.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 343 papers.
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343 citing papers in PubMed.
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- Hepatitis C Virus 5'UTR Sequences That Bind eIF3 and Ribosomal 40S Subunits Confer Stimulation of Minus-Strand RNA Synthesis.International journal of molecular sciences · 2026Article
- Deep learning for RNA secondary structure determination: gauging generalizability and broadening the scope of traditional methods.RNA (New York, N.Y.) · 2026Review
- Machine learning for RNA secondary structure prediction: a review of current methods and challenges.RNA (New York, N.Y.) · 2026Review
- Conservation of extended sequence and structure in the branchpoint-to-3' splice site region upstream of neural microexons.bioRxiv : the preprint server for biology · 2026Article
- NTFold: Structure-Sensing Nucleotide Attention Learning for RNA Secondary Structure Prediction.Sensors (Basel, Switzerland) · 2026Article
- Binding of Glycyl-tRNA synthetase to Mengovirus RNA stimulates translation.Nucleic acids research · 2026Article
- DMS-informed secondary structure modeling of Epstein-Barr Virus LMP-1 pre-mRNA defines novel elements spanning introns.PloS one · 2026Article
- Genetic make-up and regulation of the L-lysine biosynthesis pathway inMicrobial cell (Graz, Austria) · 2026Article
- RNA Structure and Its Function.Methods in molecular biology (Clifton, N.J.) · 2026Article
283 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
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Abstract
backgroundThe prediction of a consensus structure for a set of related RNAs is an important first step for subsequent analyses. RNAalifold, which computes the minimum energy structure that is simultaneously formed by a set of aligned sequences, is one of the oldest and most widely used tools for this task. In recent years, several alternative approaches have been advocated, pointing to several shortcomings of the original RNAalifold approach.
resultsWe show that the accuracy of RNAalifold predictions can be improved substantially by introducing a different, more rational handling of alignment gaps, and by replacing the rather simplistic model of covariance scoring with more sophisticated RIBOSUM-like scoring matrices. These improvements are achieved without compromising the computational efficiency of the algorithm. We show here that the new version of RNAalifold not only outperforms the old one, but also several other tools recently developed, on different datasets.
conclusionThe new version of RNAalifold not only can replace the old one for almost any application but it is also competitive with other approaches including those based on SCFGs, maximum expected accuracy, or hierarchical nearest neighbor classifiers.
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