ArticleNucleic acids research2011
Improving the accuracy of predicting secondary structure for aligned RNA sequences.
Article in Nucleic acids research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 71 citations in OpenAlex.
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- ECSFinder: optimized prediction of evolutionarily conserved RNA secondary structures from genome sequences.Nucleic acids research · 2025Article
- RiNALMo: general-purpose RNA language models can generalize well on structure prediction tasks.Nature communications · 2025Article
- Comprehensive benchmarking of large language models for RNA secondary structure prediction.Briefings in bioinformatics · 2025Article
- Multiple sequence alignment-based RNA language model and its application to structural inference.Nucleic acids research · 2024Article
- Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery.Briefings in bioinformatics · 2023Review
- ConsAlign: simultaneous RNA structural aligner based on rich transfer learning and thermodynamic ensemble model of alignment scoring.Bioinformatics (Oxford, England) · 2023Article
- KnotAli: informed energy minimization through the use of evolutionary information.BMC bioinformatics · 2022Article
- Prediction of RNA secondary structure including pseudoknots for long sequences.Briefings in bioinformatics · 2022Article
- LinAliFold and CentroidLinAliFold: fast RNA consensus secondary structure prediction for aligned sequences using beam search methods.Bioinformatics advances · 2022Article
- aliFreeFoldMulti: alignment-free method to predict secondary structures of multiple RNA homologs.NAR genomics and bioinformatics · 2020Article
- aliFreeFold: an alignment-free approach to predict secondary structure from homologous RNA sequences.Bioinformatics (Oxford, England) · 2018Article
- RNA Structure Duplications and Flavivirus Host Adaptation.Trends in microbiology · 2016Review
- Small molecule compounds targeting miRNAs for cancer therapy.Advanced drug delivery reviews · 2015Review
- A fast and robust iterative algorithm for prediction of RNA pseudoknotted secondary structures.BMC bioinformatics · 2014Article
- Finding consensus stable local optimal structures for aligned RNA sequences and its application to discovering riboswitch elements.International journal of bioinformatics research and applications · 2014Article
- Computational modeling of RNA 3D structures, with the aid of experimental restraints.RNA biology · 2014Review
- CompaRNA: a server for continuous benchmarking of automated methods for RNA secondary structure prediction.Nucleic acids research · 2013Article
- CentroidAlign-Web: A Fast and Accurate Multiple Aligner for Long Non-Coding RNAs.International journal of molecular sciences · 2013Article
- Activity-regulated RNA editing in select neuronal subfields in hippocampus.Nucleic acids research · 2013Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Considerable attention has been focused on predicting the secondary structure for aligned RNA sequences since it is useful not only for improving the limiting accuracy of conventional secondary structure prediction but also for finding non-coding RNAs in genomic sequences. Although there exist many algorithms of predicting secondary structure for aligned RNA sequences, further improvement of the accuracy is still awaited. In this article, toward improving the accuracy, a theoretical classification of state-of-the-art algorithms of predicting secondary structure for aligned RNA sequences is presented. The classification is based on the viewpoint of maximum expected accuracy (MEA), which has been successfully applied in various problems in bioinformatics. The classification reveals several disadvantages of the current algorithms but we propose an improvement of a previously introduced algorithm (CentroidAlifold). Finally, computational experiments strongly support the theoretical classification and indicate that the improved CentroidAlifold substantially outperforms other algorithms.
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