ArticleNucleic acids research2010
Tfold: efficient in silico prediction of non-coding RNA secondary structures.
Article in Nucleic acids research, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- CRISPR-Cas13b mediated gene knockdowns in Leishmania infantum.International journal for parasitology. Drugs and drug resistance · 2025Article
- A divide-and-conquer approach based on deep learning for long RNA secondary structure prediction: Focus on pseudoknots identification.PloS one · 2025Article
- Review of machine learning methods for RNA secondary structure prediction.PLoS computational biology · 2021Review
- Bi-objective integer programming for RNA secondary structure prediction with pseudoknots.BMC bioinformatics · 2018Article
- Quantifying variances in comparative RNA secondary structure prediction.BMC bioinformatics · 2013Article
- Predicting pseudoknotted structures across two RNA sequences.Bioinformatics (Oxford, England) · 2012Article
- SAVoR: a server for sequencing annotation and visualization of RNA structures.Nucleic acids research · 2012Article
- A fast ab-initio method for predicting miRNA precursors in genomes.Nucleic acids research · 2012Article
- Article
- Prediction of RNA secondary structure by maximizing pseudo-expected accuracy.BMC bioinformatics · 2010Article
- A predictive model for secondary RNA structure using graph theory and a neural network.BMC bioinformatics · 2010Article
- RNA secondary structure prediction by using discrete mathematics: an interdisciplinary research experience for undergraduate students.CBE life sciences education · 2010Article
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Authors and funding
2 authors.
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Abstract
Predicting RNA secondary structures is a very important task, and continues to be a challenging problem, even though several methods and algorithms are proposed in the literature. In this article, we propose an algorithm called Tfold, for predicting non-coding RNA secondary structures. Tfold takes as input a RNA sequence for which the secondary structure is searched and a set of aligned homologous sequences. It combines criteria of stability, conservation and covariation in order to search for stems and pseudoknots (whatever their type). Stems are searched recursively, from the most to the least stable. Tfold uses an algorithm called SSCA for selecting the most appropriate sequences from a large set of homologous sequences (taken from a database for example) to use for the prediction. Tfold can take into account one or several stems considered by the user as belonging to the secondary structure. Tfold can return several structures (if requested by the user) when 'rival' stems are found. Tfold has a complexity of O(n(2)), with n the sequence length. The developed software, which offers several different uses, is available on the web site: http://tfold.ibisc.univ-evry.fr/TFold.
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