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Computational RNA secondary structure design: empirical complexity and improved methods.

Aguirre-Hernández R, Hoos HH, Condon A - BMC Bioinformatics (2007)

Bottom Line: Such understanding provides the basis for improving the performance of one of the best algorithms for this problem, RNA-SSD, and for characterising its limitations.We also found that the algorithms are in general faster when constraints are placed only on paired bases in the structure.Our analysis helps to better understand the strengths and limitations of both the RNA-SSD and RNAinverse algorithms, and suggests ways in which the performance of these algorithms can be further improved.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Applied Mathematics, University of British Columbia, Vancouver, BC, Canada. rosalia@cs.ubc.ca <rosalia@cs.ubc.ca>

ABSTRACT

Background: We investigate the empirical complexity of the RNA secondary structure design problem, that is, the scaling of the typical difficulty of the design task for various classes of RNA structures as the size of the target structure is increased. The purpose of this work is to understand better the factors that make RNA structures hard to design for existing, high-performance algorithms. Such understanding provides the basis for improving the performance of one of the best algorithms for this problem, RNA-SSD, and for characterising its limitations.

Results: To gain insights into the practical complexity of the problem, we present a scaling analysis on random and biologically motivated structures using an improved version of the RNA-SSD algorithm, and also the RNAinverse algorithm from the Vienna package. Since primary structure constraints are relevant for designing RNA structures, we also investigate the correlation between the number and the location of the primary structure constraints when designing structures and the performance of the RNA-SSD algorithm. The scaling analysis on random and biologically motivated structures supports the hypothesis that the running time of both algorithms scales polynomially with the size of the structure. We also found that the algorithms are in general faster when constraints are placed only on paired bases in the structure. Furthermore, we prove that, according to the standard thermodynamic model, for some structures that the RNA-SSD algorithm was unable to design, there exists no sequence whose minimum free energy structure is the target structure.

Conclusion: Our analysis helps to better understand the strengths and limitations of both the RNA-SSD and RNAinverse algorithms, and suggests ways in which the performance of these algorithms can be further improved.

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Examples of structures not designed by RNA-SSD. Structures not designed by RNA-SSD have short stems separated by loops, indicated by arrows in the Figure. (a) Random structure of length 450 (RND-450-n84). This is the only random structure in our data set that RNA-SSD did not design. Note that it has two internal loops separated only by one base pair. (b) Biologically motivated structure of length 74 (BIOM-50-n262).
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Figure 5: Examples of structures not designed by RNA-SSD. Structures not designed by RNA-SSD have short stems separated by loops, indicated by arrows in the Figure. (a) Random structure of length 450 (RND-450-n84). This is the only random structure in our data set that RNA-SSD did not design. Note that it has two internal loops separated only by one base pair. (b) Biologically motivated structure of length 74 (BIOM-50-n262).

Mentions: The random structures are designable by construction since they were obtained by folding a set of random sequences with the RNAfold function from the Vienna package (see Section 5). RNA-SSD was able to design all of these structures except one of length 450 (Figure 3a). This structure has several short stems separated by loops (Figure 5a). In particular, it has two internal loops next to each other; one of these is slightly asymmetric with seven unpaired bases, while the other is symmetric with four unpaired bases. Although allowed by the thermodynamic model, this motif is hard to design. (This will be discussed in more detail in the later section on undesignable structures.) RNAinverse failed to design 1.16% (i.e., 28/2400) of the random structures of length 200 or less and was not evaluated on larger structures because of excessive run time requirements.


Computational RNA secondary structure design: empirical complexity and improved methods.

Aguirre-Hernández R, Hoos HH, Condon A - BMC Bioinformatics (2007)

Examples of structures not designed by RNA-SSD. Structures not designed by RNA-SSD have short stems separated by loops, indicated by arrows in the Figure. (a) Random structure of length 450 (RND-450-n84). This is the only random structure in our data set that RNA-SSD did not design. Note that it has two internal loops separated only by one base pair. (b) Biologically motivated structure of length 74 (BIOM-50-n262).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1808480&req=5

Figure 5: Examples of structures not designed by RNA-SSD. Structures not designed by RNA-SSD have short stems separated by loops, indicated by arrows in the Figure. (a) Random structure of length 450 (RND-450-n84). This is the only random structure in our data set that RNA-SSD did not design. Note that it has two internal loops separated only by one base pair. (b) Biologically motivated structure of length 74 (BIOM-50-n262).
Mentions: The random structures are designable by construction since they were obtained by folding a set of random sequences with the RNAfold function from the Vienna package (see Section 5). RNA-SSD was able to design all of these structures except one of length 450 (Figure 3a). This structure has several short stems separated by loops (Figure 5a). In particular, it has two internal loops next to each other; one of these is slightly asymmetric with seven unpaired bases, while the other is symmetric with four unpaired bases. Although allowed by the thermodynamic model, this motif is hard to design. (This will be discussed in more detail in the later section on undesignable structures.) RNAinverse failed to design 1.16% (i.e., 28/2400) of the random structures of length 200 or less and was not evaluated on larger structures because of excessive run time requirements.

Bottom Line: Such understanding provides the basis for improving the performance of one of the best algorithms for this problem, RNA-SSD, and for characterising its limitations.We also found that the algorithms are in general faster when constraints are placed only on paired bases in the structure.Our analysis helps to better understand the strengths and limitations of both the RNA-SSD and RNAinverse algorithms, and suggests ways in which the performance of these algorithms can be further improved.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Applied Mathematics, University of British Columbia, Vancouver, BC, Canada. rosalia@cs.ubc.ca <rosalia@cs.ubc.ca>

ABSTRACT

Background: We investigate the empirical complexity of the RNA secondary structure design problem, that is, the scaling of the typical difficulty of the design task for various classes of RNA structures as the size of the target structure is increased. The purpose of this work is to understand better the factors that make RNA structures hard to design for existing, high-performance algorithms. Such understanding provides the basis for improving the performance of one of the best algorithms for this problem, RNA-SSD, and for characterising its limitations.

Results: To gain insights into the practical complexity of the problem, we present a scaling analysis on random and biologically motivated structures using an improved version of the RNA-SSD algorithm, and also the RNAinverse algorithm from the Vienna package. Since primary structure constraints are relevant for designing RNA structures, we also investigate the correlation between the number and the location of the primary structure constraints when designing structures and the performance of the RNA-SSD algorithm. The scaling analysis on random and biologically motivated structures supports the hypothesis that the running time of both algorithms scales polynomially with the size of the structure. We also found that the algorithms are in general faster when constraints are placed only on paired bases in the structure. Furthermore, we prove that, according to the standard thermodynamic model, for some structures that the RNA-SSD algorithm was unable to design, there exists no sequence whose minimum free energy structure is the target structure.

Conclusion: Our analysis helps to better understand the strengths and limitations of both the RNA-SSD and RNAinverse algorithms, and suggests ways in which the performance of these algorithms can be further improved.

Show MeSH