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An efficient method for the prediction of deleterious multiple-point mutations in the secondary structure of RNAs using suboptimal folding solutions.

Churkin A, Barash D - BMC Bioinformatics (2008)

Bottom Line: The approach is best examined using the dot plot representation for RNA secondary structure.For an RNA sequence of about 100 nts and 3-point mutations (n = 100, m = 3), for example, the proposed method reduces the running time from several hours or even days to several minutes, thus enabling the practical application of RNAmute to the analysis of multiple-point mutations.A complete explanation of the application, called MultiRNAmute, is available at [1].

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, Ben-Gurion University, 84105 Beer Sheva, Israel. churkin@cs.bgu.ac.il

ABSTRACT

Background: RNAmute is an interactive Java application which, given an RNA sequence, calculates the secondary structure of all single point mutations and organizes them into categories according to their similarity to the predicted structure of the wild type. The secondary structure predictions are performed using the Vienna RNA package. A more efficient implementation of RNAmute is needed, however, to extend from the case of single point mutations to the general case of multiple point mutations, which may often be desired for computational predictions alongside mutagenesis experiments. But analyzing multiple point mutations, a process that requires traversing all possible mutations, becomes highly expensive since the running time is O(nm) for a sequence of length n with m-point mutations. Using Vienna's RNAsubopt, we present a method that selects only those mutations, based on stability considerations, which are likely to be conformational rearranging. The approach is best examined using the dot plot representation for RNA secondary structure.

Results: Using RNAsubopt, the suboptimal solutions for a given wild-type sequence are calculated once. Then, specific mutations are selected that are most likely to cause a conformational rearrangement. For an RNA sequence of about 100 nts and 3-point mutations (n = 100, m = 3), for example, the proposed method reduces the running time from several hours or even days to several minutes, thus enabling the practical application of RNAmute to the analysis of multiple-point mutations.

Conclusion: A highly efficient addition to RNAmute that is as user friendly as the original application but that facilitates the practical analysis of multiple-point mutations is presented. Such an extension can now be exploited prior to site-directed mutagenesis experiments by virologists, for example, who investigate the change of function in an RNA virus via mutations that disrupt important motifs in its secondary structure. A complete explanation of the application, called MultiRNAmute, is available at [1].

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Stabilizing Mutations in the Dot Plot. The stabilizing mutations found by applying the proposed method on the dot plot in Figure 1. The stabilizing mutations are highlighted in circles.
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Figure 3: Stabilizing Mutations in the Dot Plot. The stabilizing mutations found by applying the proposed method on the dot plot in Figure 1. The stabilizing mutations are highlighted in circles.

Mentions: For example, location P(5, 14) for a mutation in the dot plot of Figure 2 signifies that a mutation in either nucleotide 5 or 14 on the RNA sequence forms a base pair between nucleotides 5 and 14. Note that P(5, 14) extends both stem1 and stem2, even connecting them, and as such it stabilizes the suboptimal solution shown in Figure 2. Additionally, P(1, 18) and the double location P(1, 18), P(0, 19) are also stabilizing locations because they extend stem1. All the "stabilizing" mutations that we found on the dot plot (Figure 2) are highlighted by circles in Figure 3.


An efficient method for the prediction of deleterious multiple-point mutations in the secondary structure of RNAs using suboptimal folding solutions.

Churkin A, Barash D - BMC Bioinformatics (2008)

Stabilizing Mutations in the Dot Plot. The stabilizing mutations found by applying the proposed method on the dot plot in Figure 1. The stabilizing mutations are highlighted in circles.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2386494&req=5

Figure 3: Stabilizing Mutations in the Dot Plot. The stabilizing mutations found by applying the proposed method on the dot plot in Figure 1. The stabilizing mutations are highlighted in circles.
Mentions: For example, location P(5, 14) for a mutation in the dot plot of Figure 2 signifies that a mutation in either nucleotide 5 or 14 on the RNA sequence forms a base pair between nucleotides 5 and 14. Note that P(5, 14) extends both stem1 and stem2, even connecting them, and as such it stabilizes the suboptimal solution shown in Figure 2. Additionally, P(1, 18) and the double location P(1, 18), P(0, 19) are also stabilizing locations because they extend stem1. All the "stabilizing" mutations that we found on the dot plot (Figure 2) are highlighted by circles in Figure 3.

Bottom Line: The approach is best examined using the dot plot representation for RNA secondary structure.For an RNA sequence of about 100 nts and 3-point mutations (n = 100, m = 3), for example, the proposed method reduces the running time from several hours or even days to several minutes, thus enabling the practical application of RNAmute to the analysis of multiple-point mutations.A complete explanation of the application, called MultiRNAmute, is available at [1].

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, Ben-Gurion University, 84105 Beer Sheva, Israel. churkin@cs.bgu.ac.il

ABSTRACT

Background: RNAmute is an interactive Java application which, given an RNA sequence, calculates the secondary structure of all single point mutations and organizes them into categories according to their similarity to the predicted structure of the wild type. The secondary structure predictions are performed using the Vienna RNA package. A more efficient implementation of RNAmute is needed, however, to extend from the case of single point mutations to the general case of multiple point mutations, which may often be desired for computational predictions alongside mutagenesis experiments. But analyzing multiple point mutations, a process that requires traversing all possible mutations, becomes highly expensive since the running time is O(nm) for a sequence of length n with m-point mutations. Using Vienna's RNAsubopt, we present a method that selects only those mutations, based on stability considerations, which are likely to be conformational rearranging. The approach is best examined using the dot plot representation for RNA secondary structure.

Results: Using RNAsubopt, the suboptimal solutions for a given wild-type sequence are calculated once. Then, specific mutations are selected that are most likely to cause a conformational rearrangement. For an RNA sequence of about 100 nts and 3-point mutations (n = 100, m = 3), for example, the proposed method reduces the running time from several hours or even days to several minutes, thus enabling the practical application of RNAmute to the analysis of multiple-point mutations.

Conclusion: A highly efficient addition to RNAmute that is as user friendly as the original application but that facilitates the practical analysis of multiple-point mutations is presented. Such an extension can now be exploited prior to site-directed mutagenesis experiments by virologists, for example, who investigate the change of function in an RNA virus via mutations that disrupt important motifs in its secondary structure. A complete explanation of the application, called MultiRNAmute, is available at [1].

Show MeSH
Related in: MedlinePlus