Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2010 Apr;38(7):2453-66.
doi: 10.1093/nar/gkp1067. Epub 2010 Jan 4.

Tfold: efficient in silico prediction of non-coding RNA secondary structures

Stéfan Engelen  1 , Fariza Tahi
Affiliations

Tfold: efficient in silico prediction of non-coding RNA secondary structures

Stéfan Engelen et al. Nucleic Acids Res. 2010 Apr.

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.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.
When two stems are comparable (i.e. they do not share nucleotides), they can be: disjoint (A), included (B) or interlaced (C). When two comparable stems are interlaced, they form a pseudoknot; otherwise they are compatible.
Figure 2.
Figure 2.
The ‘divide and conquer’ approach applied on a sequence S with a set of selected stems (anchoring points): S is subdivided into several subsequences [S2, S5, S4S6 (concatenation of S4 and S6), S8 and S1S3S7S9] where the search for other stems could be performed.
Figure 3.
Figure 3.
Tfold software interface: page allowing to set the values of the different parameters; the values by default are the recommended values.
Figure 4.
Figure 4.
Results obtained by Tfold and several other RNA secondary structure prediction software that do not predict for pseudoknots. (A) sensitivity results. (B) PPV results.
Figure 5.
Figure 5.
Correlation (MCC) results obtained by Tfold and several other RNA secondary structure prediction software that do not predict for pseudoknots.
Figure 6.
Figure 6.
Results obtained by Tfold and several other RNA secondary structure prediction software that predict pseudoknots. (A) sensitivity results. (B) PPV results.
Figure 7.
Figure 7.
Correlation (MCC) results obtained by Tfold and several other RNA secondary structure prediction software that predict for pseudoknots.
Figure 8.
Figure 8.
Correlation (MCC) results obtained by Tfold and by software tested in (50) on sets of sequences with high identity (A) and on sets of sequences with average identity (B).
Figure 9.
Figure 9.
Adjusted MCC results obtained by Tfold and by software tested in (50) on sets of sequences with high identity (A) and on sets of sequences with average identity (B).
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Jonikas M, Radmer R, Laederach A, Das R, Pearlman S, Herschlag D, Altman R. Coarse-grained modeling of large RNA molecules with knowledge-based potentials and structural filters. RNA. 2009;15:189–199. - PMC - PubMed
    1. Laederach A, Chan J, Schwartzman A, Willgohs E, Altman R. Modeling RNA tertiary structure motifs by graph-grammars. RNA. 2007;13:643–650. - PubMed
    1. St-Onge K, Thibault P, Hamel S, Major F. Modeling RNA tertiary structure motifs by graph-grammars. Nucleic Acids Res. 2007;35:1726–1736. - PMC - PubMed
    1. Mathews D, Sabina J, Zuker M, Turner D. Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J. Mol. Biol. 1999;288:911–940. - PubMed
    1. Andronescu M, Condon A, Hoos H, Mathews D, Murphy K. Efficient parameter estimation for RNA secondary structure prediction. Bioinformatics. 2007;23:19–28. - PubMed

Publication types

Substances