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A conditional random fields method for RNA sequence-structure relationship modeling and conformation sampling.

Wang Z, Xu J - Bioinformatics (2011)

Bottom Line: In addition, neither of these methods makes use of sequence information in sampling conformations.Experimental results show that our CRF method can model RNA sequence-structure relationship well and sequence information is important for conformation sampling.Our method, named as TreeFolder, generates a much higher percentage of native-like decoys than FARNA and BARNACLE, although we use the same simple energy function as BARNACLE. zywang@ttic.edu; j3xu@ttic.edu Supplementary data are available at Bioinformatics online.

View Article: PubMed Central - PubMed

Affiliation: Toyota Technological Institute at Chicago, IL, USA. zywang@ttic.edu

ABSTRACT

Unlabelled: Accurate tertiary structures are very important for the functional study of non-coding RNA molecules. However, predicting RNA tertiary structures is extremely challenging, because of a large conformation space to be explored and lack of an accurate scoring function differentiating the native structure from decoys. The fragment-based conformation sampling method (e.g. FARNA) bears shortcomings that the limited size of a fragment library makes it infeasible to represent all possible conformations well. A recent dynamic Bayesian network method, BARNACLE, overcomes the issue of fragment assembly. In addition, neither of these methods makes use of sequence information in sampling conformations. Here, we present a new probabilistic graphical model, conditional random fields (CRFs), to model RNA sequence-structure relationship, which enables us to accurately estimate the probability of an RNA conformation from sequence. Coupled with a novel tree-guided sampling scheme, our CRF model is then applied to RNA conformation sampling. Experimental results show that our CRF method can model RNA sequence-structure relationship well and sequence information is important for conformation sampling. Our method, named as TreeFolder, generates a much higher percentage of native-like decoys than FARNA and BARNACLE, although we use the same simple energy function as BARNACLE.

Contact: zywang@ttic.edu; j3xu@ttic.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

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Overlay representation of the best centroids (red) of 1q9a, 2a43 and 1xjr (from left to right) with their native structures (blue). These three RNA molecules have lengths of 27 nt, 26 nt and 49 nt.
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Figure 11: Overlay representation of the best centroids (red) of 1q9a, 2a43 and 1xjr (from left to right) with their native structures (blue). These three RNA molecules have lengths of 27 nt, 26 nt and 49 nt.

Mentions: Overlay examples: Figure 11 shows three overlay examples of 1q9a, 2a43 and 1xjr with length of 27 nt, 26 nt and 49 nt, respectively. Pictures in blue display native, while in red the best centroids produced by our algorithm. As shown in this figure, our algorithm recovered a pseudoknot for 2a43.Fig. 11.


A conditional random fields method for RNA sequence-structure relationship modeling and conformation sampling.

Wang Z, Xu J - Bioinformatics (2011)

Overlay representation of the best centroids (red) of 1q9a, 2a43 and 1xjr (from left to right) with their native structures (blue). These three RNA molecules have lengths of 27 nt, 26 nt and 49 nt.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117333&req=5

Figure 11: Overlay representation of the best centroids (red) of 1q9a, 2a43 and 1xjr (from left to right) with their native structures (blue). These three RNA molecules have lengths of 27 nt, 26 nt and 49 nt.
Mentions: Overlay examples: Figure 11 shows three overlay examples of 1q9a, 2a43 and 1xjr with length of 27 nt, 26 nt and 49 nt, respectively. Pictures in blue display native, while in red the best centroids produced by our algorithm. As shown in this figure, our algorithm recovered a pseudoknot for 2a43.Fig. 11.

Bottom Line: In addition, neither of these methods makes use of sequence information in sampling conformations.Experimental results show that our CRF method can model RNA sequence-structure relationship well and sequence information is important for conformation sampling.Our method, named as TreeFolder, generates a much higher percentage of native-like decoys than FARNA and BARNACLE, although we use the same simple energy function as BARNACLE. zywang@ttic.edu; j3xu@ttic.edu Supplementary data are available at Bioinformatics online.

View Article: PubMed Central - PubMed

Affiliation: Toyota Technological Institute at Chicago, IL, USA. zywang@ttic.edu

ABSTRACT

Unlabelled: Accurate tertiary structures are very important for the functional study of non-coding RNA molecules. However, predicting RNA tertiary structures is extremely challenging, because of a large conformation space to be explored and lack of an accurate scoring function differentiating the native structure from decoys. The fragment-based conformation sampling method (e.g. FARNA) bears shortcomings that the limited size of a fragment library makes it infeasible to represent all possible conformations well. A recent dynamic Bayesian network method, BARNACLE, overcomes the issue of fragment assembly. In addition, neither of these methods makes use of sequence information in sampling conformations. Here, we present a new probabilistic graphical model, conditional random fields (CRFs), to model RNA sequence-structure relationship, which enables us to accurately estimate the probability of an RNA conformation from sequence. Coupled with a novel tree-guided sampling scheme, our CRF model is then applied to RNA conformation sampling. Experimental results show that our CRF method can model RNA sequence-structure relationship well and sequence information is important for conformation sampling. Our method, named as TreeFolder, generates a much higher percentage of native-like decoys than FARNA and BARNACLE, although we use the same simple energy function as BARNACLE.

Contact: zywang@ttic.edu; j3xu@ttic.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Show MeSH