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RNA global alignment in the joint sequence-structure space using elastic shape analysis.

Laborde J, Robinson D, Srivastava A, Klassen E, Zhang J - Nucleic Acids Res. (2013)

Bottom Line: Comparison/alignment of RNA molecules provides an effective means to predict their functions and understand their evolutionary relationships.Based on ESA, a rigorous mathematical framework can be built for RNA structure comparison.Means and covariances of full structures can be defined and computed, and probability distributions on spaces of such structures can be constructed for a group of RNAs.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics, Florida State University, FL, USA.

ABSTRACT
The functions of RNAs, like proteins, are determined by their structures, which, in turn, are determined by their sequences. Comparison/alignment of RNA molecules provides an effective means to predict their functions and understand their evolutionary relationships. For RNA sequence alignment, most methods developed for protein and DNA sequence alignment can be directly applied. RNA 3-dimensional structure alignment, on the other hand, tends to be more difficult than protein structure alignment due to the lack of regular secondary structures as observed in proteins. Most of the existing RNA 3D structure alignment methods use only the backbone geometry and ignore the sequence information. Using both the sequence and backbone geometry information in RNA alignment may not only produce more accurate classification, but also deepen our understanding of the sequence-structure-function relationship of RNA molecules. In this study, we developed a new RNA alignment method based on elastic shape analysis (ESA). ESA treats RNA structures as three dimensional curves with sequence information encoded on additional dimensions so that the alignment can be performed in the joint sequence-structure space. The similarity between two RNA molecules is quantified by a formal distance, geodesic distance. Based on ESA, a rigorous mathematical framework can be built for RNA structure comparison. Means and covariances of full structures can be defined and computed, and probability distributions on spaces of such structures can be constructed for a group of RNAs. Our method was further applied to predict functions of RNA molecules and showed superior performance compared with previous methods when tested on benchmark datasets. The programs are available at http://stat.fsu.edu/ ∼jinfeng/ESA.html.

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(a) Distribution of the lengths of RNAs in FSCOR dataset; and (b) Number of members in each of the classes in the FSCOR dataset.
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gkt187-F4: (a) Distribution of the lengths of RNAs in FSCOR dataset; and (b) Number of members in each of the classes in the FSCOR dataset.

Mentions: To test the performance of ESA and compare it with previous methods, we use a benchmark dataset, FSCOR (8, 9), compiled from the SCOR database (30). FSCOR contains 419 RNA structures in 168 functional classes. The histograms of chain lengths for the 419 RNA molecules are plotted in Figure 4a, and the histogram of number of members in each class is plotted in Figure 4b. We can see that most of the RNA molecules have fairly small sizes, with <200 residues, and the class frequencies are very unbalanced, with many classes containing only one member.Figure 4.


RNA global alignment in the joint sequence-structure space using elastic shape analysis.

Laborde J, Robinson D, Srivastava A, Klassen E, Zhang J - Nucleic Acids Res. (2013)

(a) Distribution of the lengths of RNAs in FSCOR dataset; and (b) Number of members in each of the classes in the FSCOR dataset.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3675459&req=5

gkt187-F4: (a) Distribution of the lengths of RNAs in FSCOR dataset; and (b) Number of members in each of the classes in the FSCOR dataset.
Mentions: To test the performance of ESA and compare it with previous methods, we use a benchmark dataset, FSCOR (8, 9), compiled from the SCOR database (30). FSCOR contains 419 RNA structures in 168 functional classes. The histograms of chain lengths for the 419 RNA molecules are plotted in Figure 4a, and the histogram of number of members in each class is plotted in Figure 4b. We can see that most of the RNA molecules have fairly small sizes, with <200 residues, and the class frequencies are very unbalanced, with many classes containing only one member.Figure 4.

Bottom Line: Comparison/alignment of RNA molecules provides an effective means to predict their functions and understand their evolutionary relationships.Based on ESA, a rigorous mathematical framework can be built for RNA structure comparison.Means and covariances of full structures can be defined and computed, and probability distributions on spaces of such structures can be constructed for a group of RNAs.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics, Florida State University, FL, USA.

ABSTRACT
The functions of RNAs, like proteins, are determined by their structures, which, in turn, are determined by their sequences. Comparison/alignment of RNA molecules provides an effective means to predict their functions and understand their evolutionary relationships. For RNA sequence alignment, most methods developed for protein and DNA sequence alignment can be directly applied. RNA 3-dimensional structure alignment, on the other hand, tends to be more difficult than protein structure alignment due to the lack of regular secondary structures as observed in proteins. Most of the existing RNA 3D structure alignment methods use only the backbone geometry and ignore the sequence information. Using both the sequence and backbone geometry information in RNA alignment may not only produce more accurate classification, but also deepen our understanding of the sequence-structure-function relationship of RNA molecules. In this study, we developed a new RNA alignment method based on elastic shape analysis (ESA). ESA treats RNA structures as three dimensional curves with sequence information encoded on additional dimensions so that the alignment can be performed in the joint sequence-structure space. The similarity between two RNA molecules is quantified by a formal distance, geodesic distance. Based on ESA, a rigorous mathematical framework can be built for RNA structure comparison. Means and covariances of full structures can be defined and computed, and probability distributions on spaces of such structures can be constructed for a group of RNAs. Our method was further applied to predict functions of RNA molecules and showed superior performance compared with previous methods when tested on benchmark datasets. The programs are available at http://stat.fsu.edu/ ∼jinfeng/ESA.html.

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