387 research outputs found
Expected degree for RNA secondary structure networks
Consider the network of all secondary structures of a given RNA sequence,
where nodes are connected when the corresponding structures have base pair
distance one. The expected degree of the network is the average number of
neighbors, where average may be computed with respect to the either the uniform
or Boltzmann probability. Here we describe the first algorithm, RNAexpNumNbors,
that can compute the expected number of neighbors, or expected network degree,
of an input sequence. For RNA sequences from the Rfam database, the expected
degree is significantly less than the CMFE structure, defined to have minimum
free energy over all structures consistent with the Rfam consensus structure.
The expected degree of structural RNAs, such as purine riboswitches,
paradoxically appears to be smaller than that of random RNA, yet the difference
between the degree of the MFE structure and the expected degree is larger than
that of random RNA. Expected degree does not seem to correlate with standard
structural diversity measures of RNA, such as positional entropy, ensemble
defect, etc. The program {\tt RNAexpNumNbors} is written in C, runs in cubic
time and quadratic space, and is publicly available at
http://bioinformatics.bc.edu/clotelab/RNAexpNumNbors.Comment: 25 pages, 5 figures, 5 table
The weak pigeonhole principle for function classes in S^1_2
It is well known that S^1_2 cannot prove the injective weak pigeonhole
principle for polynomial time functions unless RSA is insecure. In this note we
investigate the provability of the surjective (dual) weak pigeonhole principle
in S^1_2 for provably weaker function classes.Comment: 11 page
RNALOSS: a web server for RNA locally optimal secondary structures
RNAomics, analogous to proteomics, concerns aspects of the secondary and tertiary structure, folding pathway, kinetics, comparison, function and regulation of all RNA in a living organism. Given recently discovered roles played by micro RNA, small interfering RNA, riboswitches, ribozymes, etc., it is important to gain insight into the folding process of RNA sequences. We describe the web server RNALOSS, which provides information about the distribution of locally optimal secondary structures, that possibly form kinetic traps in the folding process. The tool RNALOSS may be useful in designing RNA sequences which not only have low folding energy, but whose distribution of locally optimal secondary structures would suggest rapid and robust folding. Website:
Combinatorics of locally optimal RNA secondary structures
It is a classical result of Stein and Waterman that the asymptotic number of
RNA secondary structures is .
Motivated by the kinetics of RNA secondary structure formation, we are
interested in determining the asymptotic number of secondary structures that
are locally optimal, with respect to a particular energy model. In the Nussinov
energy model, where each base pair contributes -1 towards the energy of the
structure, locally optimal structures are exactly the saturated structures, for
which we have previously shown that asymptotically, there are many saturated structures for a sequence of length
. In this paper, we consider the base stacking energy model, a mild variant
of the Nussinov model, where each stacked base pair contributes -1 toward the
energy of the structure. Locally optimal structures with respect to the base
stacking energy model are exactly those secondary structures, whose stems
cannot be extended. Such structures were first considered by Evers and
Giegerich, who described a dynamic programming algorithm to enumerate all
locally optimal structures. In this paper, we apply methods from enumerative
combinatorics to compute the asymptotic number of such structures.
Additionally, we consider analogous combinatorial problems for secondary
structures with annotated single-stranded, stacking nucleotides (dangles).Comment: 27 page
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