On the combinatorics of sparsification
© Huang and Reidys; licensee BioMed Central Ltd. 2012
Received: 31 December 2011
Accepted: 11 October 2012
Published: 22 October 2012
We study the sparsification of dynamic programming based on folding algorithms of RNA structures. Sparsification is a method that improves significantly the computation of minimum free energy (mfe) RNA structures.
We provide a quantitative analysis of the sparsification of a particular decomposition rule, Λ∗. This rule splits an interval of RNA secondary and pseudoknot structures of fixed topological genus. Key for quantifying sparsifications is the size of the so called candidate sets. Here we assume mfe-structures to be specifically distributed (see Assumption 1) within arbitrary and irreducible RNA secondary and pseudoknot structures of fixed topological genus. We then present a combinatorial framework which allows by means of probabilities of irreducible sub-structures to obtain the expectation of the Λ∗-candidate set w.r.t. a uniformly random input sequence. We compute these expectations for arc-based energy models via energy-filtered generating functions (GF) in case of RNA secondary structures as well as RNA pseudoknot structures. Furthermore, for RNA secondary structures we also analyze a simplified loop-based energy model. Our combinatorial analysis is then compared to the expected number of Λ∗-candidates obtained from the folding mfe-structures. In case of the mfe-folding of RNA secondary structures with a simplified loop-based energy model our results imply that sparsification provides a significant, constant improvement of 91% (theory) to be compared to an 96% (experimental, simplified arc-based model) reduction. However, we do not observe a linear factor improvement. Finally, in case of the “full” loop-energy model we can report a reduction of 98% (experiment).
Sparsification was initially attributed a linear factor improvement. This conclusion was based on the so called polymer-zeta property, which stems from interpreting polymer chains as self-avoiding walks. Subsequent findings however reveal that the O(n) improvement is not correct. The combinatorial analysis presented here shows that, assuming a specific distribution (see Assumption 1), of mfe-structures within irreducible and arbitrary structures, the expected number of Λ∗-candidates is Θ(n 2). However, the constant reduction is quite significant, being in the range of 96%. We furthermore show an analogous result for the sparsification of the Λ∗-decomposition rule for RNA pseudoknotted structures of genus one. Finally we observe that the effect of sparsification is sensitive to the employed energy model.
KeywordsSparsification Generating function Dynamic programming
RNA structures, diagrams and genus filtration
An RNA sequence is a linear, oriented sequence of the nucleotides (bases) A,U,G,C. These sequences “fold” by establishing bonds between pairs of nucleotides. In this paper, we only consider the Watson-Crick base pair A-U or G-C and wobble base pairs U-G. The global conformation of an RNA molecule is determined by topological constraints encoded at the level of secondary structure, i.e., by the mutual arrangements of the base pairs .
We shall consider diagrams as fatgraphs, , that is graphs G together with a collection of cyclic orderings, called fattenings. Each fatgraph determines an oriented surface [3, 4] which is connected if G is and has some associated genus g(G) ≥ 0 and number r(G)≥1 of boundary components. Clearly, contains G as a deformation retract . Fatgraphs were first applied to RNA secondary structures in [6, 7].
A diagram hence determines a unique surface (with boundary). Filling the boundary components with discs we can pass from to a surface without boundary. Euler characteristic, χ, and genus, g, of this surface is given by χ = v−e + r and , respectively, where v,e,r is the number of discs, ribbons and boundary components in , . The genus of a diagram is that of its associated surface without boundary and a diagram of genus g is referred to as g-diagram.
A g-diagram without arcs of the form (i,i + 1) (1-arcs) is called a g-structure. A g-diagram that contains only vertices of degree three, i.e. does not contain any vertices not incident to arcs in the upper half-plane, is called a g-matching. A diagram is called irreducible, if and only if it cannot be split into two by cutting the backbone without cutting an arc, see Figure 2.
Folded configurations are energetically somewhat optimal. Here energy is obtained by adding contributions of loops  contained in RNA secondary and pseudoknot structures. Any RNA structure has a unique and disjoint decomposition into such loops which are really stems from the fatgraph [9, 10] interpretation of such structures in which loops correspond to boundary components . Additional constraints imply further properties, like for instance certain minimum arc-length conditions  and the nonexistence of isolated bonds. An mfe-RNA structure can be predicted in polynomials time by means of dynamic programming (DP) routines [12, 13].
The most commonly used tools predicting simple RNA secondary structure mfold and the Vienna RNA Package, require O(n 2) space and O(n 3) time. In the following we omit “simple” and refer to secondary structures containing crossing arcs as pseudoknot structures.
Generalizing the matrices of the DP-routines of secondary structure folding [13, 14] to gap-matrices , leads to a DP-folding of pseudoknotted structures  (pknot‐R&E) with O(n 4) space an O(n 6) time complexity. The following references provide a certainly incomplete list of DP-approaches to RNA pseudoknot structure prediction using various structure classes characterized in terms of recursion equations and/or stochastic grammars: [9, 15–26]. The most efficient algorithm for pseudoknot structures is  (pknotsRG) having O(n 2) space and O(n 4) time complexity. This algorithm however considers only a restricted class of pseudoknots.
Note that RNA secondary structures are exactly structures of topological genus zero . The topological classification of RNA structures [10, 11, 28] has recently been translated into an efficient DP-algorithm . Fixing the topological genus of RNA structures implies that there are only finitely many types, the so called irreducible shadows .
Sparsification has been also applied in the context of RNA-RNA interaction structures  as well as RNA pseudoknot structures . In difference to RNA secondary structures, however, not every decomposition rule in the DP-folding of RNA pseudoknot structures is amendable to sparsification. By construction, sparsification can only be applied for calculating mfe-energy structures. Since the computation of the partition function [20, 33] needs to take into account all sub-structures, sparsification does not work.
there exists an optimal solution for a sub-structure over where via Λ2 and L v is obtained from and via Λ1,
there exists an optimal solution for a sub-structure over where via Λ 3 and L v is obtained by and via Λ1.
Note that if Λ2 is s-compatible to Λ1then Λ3 is s-compatible to Λ1. To summarize
( s -compatible) Suppose L v is the optimal solution for S v over v, under decomposition rule Λ1. is obtained from two optimal solutions and under rule Λ2. Then Λ2 is called s-compatible to Λ1 if there exist some rule Λ3 such that and .
Figure 3B depicts two such ways that realize the same optimal solution L v . Sparsification prunes any such multiple computations of the same optimal value. Note that by symmetry, Λ2 and Λ3 are both s-compatible to Λ1.
We next come to the important concept of candidates. The latter mark the essential computation paths for the DP-routine.
and we shall denote the set of Λ-candidates set by Q Λ.
By construction a Λ-candidate v is a union of disjoint intervals such that its optimal solution L v cannot be obtained via a Λ-splitting. This optimal solution allows to construct a non-unique arc-configuration (sub-structure) over v[13, 14] and the above Λ-splitting consequently translates into a splitting of this sub-structure. This connects the notion of Λ-candidates with that of sub-structures and shows that a Λ-candidate implies a sub-structure that is Λ-irreducible.
In summary, as for the impact of sparsification,  claims that sparsification reduces the time complexity by a linear factor. This claim is based on the assumption that RNA molecules satisfy the polymer-zeta property. Subsequent studies draw a slightly different picture  concluding that that sparsification requires O(nZ) time, where n denotes the length of input sequence, and Z is a sparsity parameter satisfying n ≤ Z<n 2. Recently, it has been shown in  that an asymptotic time complexity of a sparsified RNA folding algorithm using standard energy parameters remains O(n 3) under a wide variety of condition.
Sparsification of RNA secondary structures
An interval [i,j] is a Λ∗-candidate if the optimal solution over [i,j] is given by L i,j =V i,j >W i,j . Indeed, [i,j] is a candidate iff [i,j] is in the candidate set of Λ∗, and we denote the set by Q. Suppose the optimal solution W i,j is given by W i,j = L i,q + L q + 1,j and suppose we have L i,q = L i,k + L k + 1,q . Then since [i,q] is not a candidate, Lemma 1 shows that we can compute W i,j = L i,k + L k + 1,j , where [i,k] is a candidate.
Sparsification on RNA pseudoknot structures
In the following we will study RNA pseudoknot structures of fixed topological genus, see RNA structures, diagrams and genus filtration for details. An algorithm folding such pseudoknot structures, gfold, has been presented in . The decomposition rules that appear in gfold are reminiscent to those of pknot‐R&E but as they restrict the genus of sub-structures, the iteration of gap-matrices is severely restricted and the effect of sparsification of these decompositions is significantly smaller.
In the following, we restrict our analysis in pseudoknotted structures to only the decomposition rule Λ∗, which splices an interval into two subsequent intervals. Put differently, Λ∗ cuts the backbone of an RNA pseudoknot structure of fixed genus g over one interval without cutting a bond.
Efficiency of sparsification
By construction, the fewer candidates the DP-routine encounters, the more efficient the sparsification. Thus it is of utmost importance to analyze the number of candidates. In the case of sparsification of RNA secondary structures we have one basic decomposition rule Λ∗ acting on intervals, namely Λ∗splices an interval into two disjoint, subsequent intervals. The implied notion of a Λ∗-irreducible sub-structure is that of a sub-structure nested in a maximal arc, where maximal refers to the partial order of two arcs (i j) ≤ (i ′ j ′ ) iff i ′ ≤ i∧j ≤ j ′ . This observation relates irreducibility to nesting of arcs and following this line of thought  identifies a specific property of polymer-chains introduced in [35, 36] to be of relevance for the size of candidate sets:
(Polymer-zeta property) Let denote the probability of a structure over an interval [i,j] under some decomposition rule Λ. Then we say Λ follows the polymer-zeta property if for some constant b, c > 0 and m = j−i.
Polymer-zeta comes from modeling the 2D-folding of a polymer chain as a self-avoiding walk (SAW) in a 2D lattice . It implies that the probability of a base pair (i j) depends only on the length of the arc, i.e. , where m = j−i. In  stipulate that RNA molecules satisfy the polymer-zeta property and approximate by  using 50,000 mRNA sequences of an average length of 1992 nucleotides . They find b≈2.11 and c≈1.47. The average probability is displayed in Figure 4, Page 865  for increasing m. Furthermore, it is implied via Figure six, Page 867  that the average number of candidates converges to a constant, implying that sparsification of DP-routine folding secondary structure takes Λ(n 2) time complexity.
These findings have been questioned by , where it has been observed that the time complexity of a sparsified RNA folding algorithm based on energy minimization remains O(n 3) independently of the energy function used and the base composition of the RNA sequence.  argues that the significant effect of sparsification on the DP-routine is largely a finite-size effect. Namely, when the sequence length is below some threshold, the algorithm is dominated by the quadratic time factor. In this context, it may be worth pointing out that In  noticed that the improvement of a sparsified base-pairing maximization algorithm depends heavily on the base composition of the input. Backofen parameterizes explicitly the cardinality of candidate sets in .
In this paper we study the sparsification of the decomposition rule Λ∗[31, 32] for RNA secondary and RNA pseudoknot structures of fixed topological genus. Based on Assumption 1 below our paper provides a combinatorial framework for quantifying the effects of sparsification of the Λ∗ rule.
We shall prove that the candidate set [29, 31, 32] is indeed small. We compute the probability of an interval being a candidate for two different energy models. For both models, this is facilitated via computing the generating function (GF) of structures and the generating function of irreducible structures. By studying the asymptotics of coefficients in these generating functions, we can compute the expected number of candidates of a uniformly random input sequence for large n. We show similar results for RNA pseudoknot structures of fixed topological genus. This provides new insights into the improvements of the sparsification of the concatenation-rule Λ∗ in the presence of cross serial interactions. Our observations complement the detailed analysis of Backofen [31, 32]. We show that although for pseudoknot structures of fixed topological genus [10, 11] the effect of sparsification on the global time complexity is still unclear, the decomposition rule that splits an interval can be sped up significantly.
The concept of a partition function is close to that of a generating function. In case of , i.e., each structure contributes equally regardless the underlying sequence and the partition function equals [z n ]G(z), where G is the generating function and [z n ]G is the coefficient of the term z n .
Two important energy models are arc-based  and loop-based , respectively. The loop-based energy-filtration is different from the notion of “stickiness” . The compatibility of two positions by folding random sequences is considered to be 6/16, reminiscent of the probability of two given positions to be compatible by Watson-Crick and Wobble base pairs rules.
As for g ≥ 1 we have the following situation 
Suppose g ≥ 1. Then the following assertions hold
In the context of Λ∗-candidates we observed that irreducible sub-structures are of key importance. It is accordingly of relevance to understand the combinatorics of these structures. To this end let denote the GF of irreducible g-structures.
For a proof of Lemma 2, see Section Proofs.
For g ≥ 1 we have
We shall postpone the proof of Theorem 2 to Section Proofs.
The main result
Nussinov-like energy model
In the following we mimic some form of mfe-g-structures: inspired by the Nussinov energy model  we consider the weight of a g-structure over n vertices σ g,n to be given by w(σ g,n ) = cℓ, where c is a constant contribution of a single arc and ℓ is the number of arcs in σ g,n . Then by Assumption 1, we have the weight function . Note that the case (6/16)η c =1 corresponds to the uniform distribution, i.e. all g-structure have identical weight.
This approach requires to keep track of the number of arcs, i.e. we need to employ bivariate GF. In Theorem 1(b) we computed this bivariate GF and in Theorem 2(b) we derived from this bivariate GF , the GF of irreducible g-structures over n vertices containing ℓ arcs.
is smaller than any singularity of . In this situation τ affects the constant a g,τ and the exponential growth rate γ τ but not the sub-exponential factor . The latter stems from the singular expansion of C g (z). Analogously, we derive the τ-parameterized family of GF . We set the contribution of a single arc c=1 and the constant η = e, where e is the Euler number. Then we have the parameter τ = (6/16)e 1≈1.0125. By abuse of notation we will omit the subscript τassuming τ = (6/16)e 1.
where b g > 0 is a constant.
We proof the theorem by quantifying the probability of [i,j] being a Λ∗-candidate. In this case any (not necessarily unique) sub-structure, realizing the optimal solution L i,j , is Λ∗-irreducible, and therefore an irreducible structure over [i,j].
from which we can conclude and the theorem is proved. □
Loop-based energy model
In this section we discuss the loop-based energy model of RNA secondary structure folding. To be precise we evoke here trivariate GFs F(z t v) and F ∗(z t v) whose coefficients counting the numbers of secondary structures and irreducible secondary structures over n vertices having ℓ arcs and energy j, respectively. This becomes necessary since the loop-based model distinguishes between arcs and energy. The “cancelation” effect or reparameterization of stickiness  to which we referred to before does not appear in this context. Thus we need both an arc- as well as an energy-filtration.
A further complication emerges. In difference to the GFs E g (z,t) and the new GFs are not simply obtained by formally substituting into the power series D g (z) and as bivariate terms. The more complicated energy model requires a specific recursion for irreducible secondary structures.
The energy model used in prediction of secondary structure is more complicated than the simple arc-based energy model. Loops which are formed by arcs as well as isolated vertices between the arcs are considered to give energy contribution. Loops are categorized as hairpin loops (no nested arcs), interior loops (including bulge loops and stacks) and multi-loops (more than two arcs nested), see Figure 8. An arbitrary secondary structure can be uniquely decomposed into a collection of mutually disjoint loops. A result of the particular energy parameters  is that the energy model prefers interior loops, in particular stacks (no isolated vertex between two parallel arc), and disfavors multi-loops. Based on this observation, we give a simplified energy model for a loop λcontained in secondary structure which only depends on the loop types by
w(λ)=0.5 if λ is a hairpin loop,
w(λ)=1 if λ is an interior loop,
w(λ)=−5 if λ is a multi-loop,
where σ is an arbitary and σ ′ is an irreducible secondary structure. Along these lines, ℓ, ℓ ′ denote the number of arcs in σand σ ′ . In other words, what happens here is that we find a suitable parameterization which brings us back to a simple univariate GF whose coefficients count the sum of weights of structures over n vertices.
where p denotes the minimum number of isolated vertices to be inserted. Depending on the types of loops formed by (i,n), we have
hairpin loops: ,
interior loops: ,
multi-loops: there are at least two irreducible sub-structures, as well as isolated vertices, thus
which establishes the recursion. The uniqueness of the solution as a power series follows from the fact that each coefficient can evidently be recursively computed.
where α≈0.24 and β≈2.88 are constants and γ≈2.1673
Solving eq. 14 we obtain a unique solution for whose coefficient are all positive. Observing the dominant singularity of is ρ≈0.4614. F 0(z) is a function of and we examine the real root of minimal modulus of is bigger than ρ. Then by the supercritical paradigm  applying, F 0(z) and have identical exponential growth rates. Furthermore, and F 0(z) have the same sub-exponential factor , hence the lemma. □
By Lemma 4 we have where b is a constant. The proof is completely analogous to that of Theorem 3. □
In this paper we quantify the effect of sparsification of the rule Λ∗. This rule splits intervals and separates concatenated sub-structures. The sparsification of Λ∗ alone is claimed to provide a speed up of up to a linear factor of the DP-folding of RNA secondary structures . A similar conclusion is drawn in  where the sparsification of RNA-RNA interaction structures is shown to experience also a linear reduction in time complexity. Both papers [29, 30] base their conclusion on the validity of the polymer-zeta property. However,  comes to a different conclusion reporting a mere constant reduction in time complexity. While Λ∗is the key for the time complexity reduction of secondary structure folding, it is conceivable that for pseudoknot structures there may exist non-sparsifiable rules in which case the overall time complexity is not reduced.
In any case, the key is the set of candidates and we provide an analysis of Λ∗-candidates by combinatorial means. In general, the connection between candidates, i.e. unions of disjoint intervals and the combinatorics of structures is actually established by the algorithm itself via backtracking: at the end of the DP-algorithm a structure is being generated that realizes the previously computed energy as mfe-structure. This connects intervals and sub-structures.
So, does the condition c>1 in polymer-zeta apply in the context of RNA structures? In fact this condition would follow if the intervals in question are distributed as in uniformly sampled structures. This however, is far from reasonable, due to the fact that the mfe-algorithm deliberately designs some mfe-structure over the given interval. What the algorithm produces is in fact antagonistic to uniform sampling. We here wish to acknowledge the help of one anonymous referee in clarifying this point.
Our results imply that polymer-zeta does not hold. Our framework critically depends on a specific distribution of mfe structures within irreducible and arbitrary structures, explicated in Assumption 1. We have cross-checked Assumption 1 with the number of candidates in DP-programs (using the same energy model), see Figure 7 and Figure 9. With this conclusion we are in accord with [31, 34] but provide an entirely different approach.
The non validity of polymer-zeta has also been observed in the context of the limit distribution of the 5’-3’ distances of RNA secondary structures . Here it is observed that long arcs, to be precise arcs of lengths O(n) always exist. This is of course a contradiction to the polymer-zeta property in case of c>1.
The key to quantification of the expected number of candidates is the singularity analysis of a pair of energy-filtered GF, namely that of a class of structures and that of the subclass of all such structures that are irreducible. We show that for various energy models the singular expansions of both these functions are essentially equal–modulo some constant. This implies that the expected number of candidates is Λ(n 2) and all constants can explicitly be computed from a detailed singularity analysis. The good news is that depending on the energy model, a significant constant reduction, around 96% can be obtained. This is in accordance with data produced in  for the mfe-folding of random sequences. There a reduction by 98% is reported for sequences of length ≥ 500.
Our findings are of relevance for numerous results, that are formulated in terms of sizes of candidate sets . These can now be quantified. It is certainly of interest to devise a full fledged analysis of the loop-based energy model. While these computations are far from easy our framework shows how to perform such an analysis.
Using the paradigm of gap-matrices Backofen has shown  that the sparsification of the DP-folding of RNA pseudoknot structures exhibits additional instances, where sparsification can be applied, see Figure 5B. Our results show that the expected number of candidates is Λ(n 2), where the constant reduction is around 90%. This is in fact very good new since the sequence length in the context of RNA pseudoknot structure folding is in the order of hundreds of nucleotides. So sparsification of further instances does have an significant impact on the time complexity of the folding.
In this section, we prove Lemma 2 and Theorem 2.
Consequently, the Claim holds for any g ≥ 1.
We want to thank Thomas J.X. Li for discussions and comments. We want to thank an anonymous referee for pointing out an incorrect assumption of first version of this paper.
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