Accurate multiple sequence-structure alignment of RNA sequences using combinatorial optimization
- Markus Bauer^{1, 2}Email author,
- Gunnar W Klau^{1, 3}Email author and
- Knut Reinert^{1}
https://doi.org/10.1186/1471-2105-8-271
© Bauer et al; licensee BioMed Central Ltd. 2007
Received: 30 March 2007
Accepted: 27 July 2007
Published: 27 July 2007
Abstract
Background
The discovery of functional non-coding RNA sequences has led to an increasing interest in algorithms related to RNA analysis. Traditional sequence alignment algorithms, however, fail at computing reliable alignments of low-homology RNA sequences. The spatial conformation of RNA sequences largely determines their function, and therefore RNA alignment algorithms have to take structural information into account.
Results
We present a graph-based representation for sequence-structure alignments, which we model as an integer linear program (ILP). We sketch how we compute an optimal or near-optimal solution to the ILP using methods from combinatorial optimization, and present results on a recently published benchmark set for RNA alignments.
Conclusion
The implementation of our algorithm yields better alignments in terms of two published scores than the other programs that we tested: This is especially the case with an increasing number of input sequences. Our program LARA is freely available for academic purposes from http://www.planet-lisa.net.
1 Background
In recent years, research in RNA sequences and structures has dramatically increased: the discovery of functionally important, not protein-coding, RNA sequences has challenged the traditional picture of the flow of genetic information from DNA via RNA to proteins as functional units. It is now well-established that RNA molecules introduce an additional layer in genetic information processing. They play a significant active role in cell and developmental biology and carry out many tasks that were previously attributed exclusively to proteins. One of the most eminent examples is the class of microRNAs [1, 2], an abundant class of small functional RNAs that regulate gene expression by binding to a target in the mRNA. Other examples include snoRNAs, which modify ribosomal RNA [3], signal recognition particle RNAs [4], cis-acting regulatory elements, and piRNAs [5], a novel class of ncRNAs whose function is still unclear. It is likely that only a small fraction of regulatory RNAs has been identified so far and that many more have yet to be discovered [6].
Computational analyses have contributed largely to the discovery and advancement of biological knowledge. Heuristic methods, such as BLAST [7], or exact approaches based on dynamic programming, such as the Smith-Waterman algorithm [8], are used as everyday tools to analyze DNA and protein sequences. In case of RNA sequences, sequence information alone is not sufficient anymore. An RNA sequence folds back onto itself and forms hydrogen bonds between nucleotides. These bonds lead to the distinctive secondary structure of an RNA sequence.
Unfortunately, considering structural information adds an additional level of complexity to the problem of aligning two or several sequences. In the remainder of this section, we present a classification of structural alignment problem variants including previous work. Section 2 describes our new approach to multiple sequence-structure alignment. We employ methods from mathematical programming and solve the problem as an integer linear program resulting from a graph-theoretical reformulation. Section 3 is dedicated to an extensive computational study. We describe LARA, the freely available implementation of our novel approach, and present detailed results of a comparative study including state-of-the-art programs on a recently published benchmark database of structural alignments. The results show that on average our software is currently the best program in terms of alignment quality, outperforming other programs with an increasing number of input sequences. Finally, we discuss our results and suggest future research directions in Sect. 4.
In contrast to previous work [13–15] this article describes a full integer linear programming (ILP) formulation that does include arbitrary gap costs and an extensive performance analysis of our implementation for the first time. Due to page limits the mathematical fundament and all proofs are omitted: the interested reader is referred to the companion paper [16] that focuses on an in-depth description of the mathematical properties of the intricate multiple case containing all proofs.
1.1 Previous approaches
- 1.
Structure-to-structure alignments align two known secondary structures, typically the minimum free energy structures. This scenario applies if one searches for common structural motifs that are shared by both structures and there is reason to believe that the secondary structures are correct.
- 2.
Structure-to-unknown alignments align a given structure to a sequence with unknown structure. Applications are finding homologous sequences by inferring a consensus structure to a sequence (this is done, for example, in the verification phase of the FASTR package [9]), or finding new family members of ncRNA families: This problem has recently sparked considerable interest in the context of searching homologous structures of noncoding-RNAs in large genomic sequences. See [17] for a survey.
- 3.
In the unknown-to-unknown alignment problem, no previous structural information is given. It applies when two RNA sequences are suspected to share a common, but still unknown, structure. We constrain the space of possible structures by the entire set of possible Watson-Crick and wobble pairs. A reduction of the size of this space is possible, for instance, by applying a folding algorithm to obtain the base pair probabilities [18] and then considering only those interactions whose probabilities are above a certain threshold.
Classification of previous work
tree-based | annotated sequences | probabilistic | graph-based | |
---|---|---|---|---|
structure-to-structure | [20-22,61] | [22,25,26,62] | [34] | [14,41,43] |
structure-to-unknown | -- | [22] | [34,38] | [14,41,43] |
unknown-to-unknown | -- | [22,27–29,31] | [37] | [14,41,43] |
Tree-based models
Tree-based structural alignment algorithms view an RNA secondary structure as a tree. Depending on the particular model (either tree-editing [19] or tree alignment [20]), one either searches for the minimal number of operations (node inserting, node deletion, and node substitution) to transform one tree into the other, or into a common supertree. Algorithms employing the model from [20] have time complexities in O(n^{4}), thus making the computation expensive. Here and in the following, n denotes the size of the longest sequence. Tree-alignment algorithms have complexities that are on average only slightly worse than conventional sequence alignment. More precisely, their running time is in O(n^{2}·Δ^{2}), where Δ denotes the maximum number of branches of a multiloop in the input structures.
A tool that builds upon the tree paradigm is RNAFORESTER [21]. It computes multiple structure-to-structure alignments of RNA sequences by performing tree-alignment in a progressive fashion.
Annotated sequences
We call a sequence that is augmented by structural information an annotated sequence. Classical dynamic programming (DP) algorithms can be extended to annotated sequences. The DP solution for the structure-to-structure and structure-to-unknown problem then typically requires O(n^{4}) and O(n^{3}) in time and space, respectively. Bafna, Muthukrishnan, and Ravi describe an algorithm that simultaneously aligns the sequence and secondary structure of two RNA sequences [22]. Their method runs in time O(n^{4}), which still does not make it applicable to instances of realistic size. Eddy [23] proposes an algorithm that reduces the memory consumption to O(n^{2} log n). The STRAL tool [24] uses the values of the base pair probability matrices, as given by the partition function [18], to compute the maximal pairing probability of a single nucleotide and to align the sequences in a CLUSTALW-like fashion.
In the restricted structure-to-structure scenario, one can resort to more sophisticated edit-models like the one proposed by Jiang in [25] where the authors specify operations both on the sequence and the structure level. The dynamic programming algorithm is in O(n^{4}), making the computation rather tedious for longer sequences. A program that implements the Jiang model is MARNA [26]: it computes pairwise sequence-structure alignments, but is additionally able to compute multiple alignments. To this end, MARNA computes all pairwise structural alignment and uses T-COFFEE to compute the actual multiple alignment incorporating the structural information of the pairwise alignments.
The unknown-to-unknown scenario requires the simultaneous computation of the alignment and consensus structure. The computational problem of simultaneously considering sequence and structure of an RNA molecule was initially addressed by Sankoff in [27], where the author proposed a DP algorithm to align and fold a set of RNA sequences at the same time. The CPU and memory requirements of the original algorithm are O(n^{3k}) and O(n^{2k}), respectively, where k is the number of sequences and n is their maximal length. Current implementations modify Sankoff's algorithm by imposing limits on the size or shape of substructures, e.g., DYNALIGN [28, 29], or FOLDALIGN [30] that combine a sliding window and banded alignment approach. Hofacker, Bernhart, and Stadler [31] have presented the PMMULTI software to align base pair probability matrices. Their recursions are essentially the same as the ones given by Sankoff in [27] and subsequently used for sequence-structure alignment by Bafna et al. in [22] with the only difference that they consider probabilities instead of fixed structures. By banding the range of possible alignment positions they bring the time and space complexity of the pairwise case down to O(n^{4}) and O(n^{3}), respectively. For the multiple case, they align consensus base pair probability matrices in a progressive fashion. Similar in spirit are FOLDALIGNM [32] or LOCARNA [33], two recent reimplementations of the PMMULTI approach. FOLDALIGNM provides both several restrictions on the alignment and a two-stage procedure to fill the DP matrix: this further reduces the running time to O(n^{2}δ^{2}) where n is the length of the longer sequence and d is the maximal length difference of the alignment of two subsequences. LOCARNA on the other hand takes advantage of the sparse base pair probabilities matrices to reduce the running time.
Probabilistic models
Eddy and Durbin [34] describe covariance models for measuring the secondary structure and primary sequence consensus of RNA sequence families. They present algorithms for analyzing and comparing RNA sequences as well as database search techniques. Since the basic operation in their approach is an expensive dynamic programming algorithm, their algorithms cannot analyze sequences longer than 150–200 nucleotides. Therefore, recent approaches reduce the running time by incorporating additional information, e.g. Holmes et al.'s STEMLOC [35, 36] where the authors propose the concept of alignment/fold envelopes that constrain possible alignments. Along these lines, in [37] the authors keep a set of probabilistically derived alignment positions fixed: these alignment positions serve subsequently as anchors for the structural alignment which prune away large parts of the search space. The authors of [38] describe a method based on conditional random fields to align an RNA sequence with known structure to one with unknown structure. They estimate their parameters using conditional random fields and compute the alignment using the recursions from [39].
Graph-based models
Kececioglu [40] has introduced a graph-theoretical model for the classical primary sequence alignment problem. In [41] the authors present a first extension of this model to RNA structures and propose a branch-and-cut approach based on an integer linear programming formulation. Based on this formulation and inspired by the successful application of Lagrangian relaxation by Lancia and Caprara [42] to the related contact map overlap problem, in [43] the authors switch from branch-and-cut to the Lagrangian relaxation technique. They are able to solve instances a magnitude larger by simultaneously reducing the running time significantly. In [44] the authors give a graph-theoretic model for the computation of multiple sequence alignments with arbitrary gap costs. In the next section we will combine the formulations given in [43] and [44], resulting in a novel graph-based formulation for sequence-structure alignment with arbitrary gap costs.
Note that the graph-based model naturally deals with all three alignment scenarios. In addition, unlike other algorithmic approaches, the graph-based algorithms do not restrict the input in any way and hence can handle arbitrary pseudoknots: Pseudoknots have been shown to play important roles in a variety of biological processes, see [45] for a recent review. Most DP-based algorithms assume nested secondary structures to compute subproblems efficiently. Few exceptions exist, for example [46], but these algorithms are always restricted to certain classes of pseudoknots (like H-type pseudoknots) and do not handle the general case.
2 Results
This section deals with our novel graph-based approach to structural RNA alignment. We first give the problem definition and then describe the graph-theoretical model we use, which combines the models presented in [43] and [44]. We convert the nucleotides of the input sequences into vertices of a graph, and we add edges between the vertices that represent either structural information or possible alignments of pairs of nucleotides. Based on the graph model we develop an integer linear programming formulation. We find solutions using an algorithmic approach employing methods from combinatorial optimization. For sake of simplicity, we will limit the description to the two-sequence case. We want to stress, however, that the model can be extended to the multiple case without changing the core algorithms and ideas. The interested reader is referred to an extensive theoretical description including proofs and a computational complexity discussion appearing elsewhere [16].
2.1 Graph-theoretical model for structural RNA alignment
Problem definition
Basic model
Let s = s_{1}, ..., s_{ n }be a sequence of length n over the alphabet Σ = {A, C, G, U}. A pair (s_{ i }, s_{ j }) is called an interaction if i < j, and nucleotide i pairs with j. In most cases, these pairs will be Watson-Crick or wobble base pairs. The set p of interactions is called the annotation of sequence s. Two interactions (s_{ k }, s_{ l }) and (s_{ m }, s_{ o }) are said to be inconsistent, if they share one base; they form a pseudoknot if they "cross" each other, that is, if k <m <l <o or m <k <o <l. A pair (s, p) is called an annotated sequence. Note that a structure where no pair of interactions is inconsistent with each other forms a valid secondary structure of an RNA sequence, possibly with pseudoknots.
Gap edges
Interaction match
Gapped structural trace
- 1.
The vertices ${v}_{l}^{A}$ and ${v}_{k}^{B}$ of sequences A and B are either incident to exactly one alignment edge e ∈ $\mathcal{L}$ or spanned by a gap edge g ∈ $\mathcal{G}$. In other words, a nucleotide is either aligned or "aligned" to a gap.
- 2.
A line l can realize at most one interaction match (l, m), because a nucleotide can pair with at most one other nucleotide in a valid RNA secondary structure.
- 3.
There are no two lines k, l ∈ L that cross or touch each other: Crossing lines induce ordering conflicts in the alignment, whereas touching lines imply that two different nucleotides are mapped to the same nucleotide in the other sequence.
- 4.
There are no two gaps edges ${e}_{kl}^{A},{e}_{mn}^{A}\in \mathcal{G}$ such that ${e}_{kl}^{A}$ is in conflict with ${e}_{mn}^{A}$, and there are no two gaps edges ${e}_{kl}^{B},{e}_{mn}^{B}\in \mathcal{G}$ such that ${e}_{kl}^{B}$ is in conflict with ${e}_{mn}^{B}$.
We assign weights w_{ l }and w_{ kl }for each line l and interaction match (k, l) that represents the benefit of realizing l or (k, l). By default, we set these scores along the lines of standard scoring methods, e.g., BLOSUM matrices for the weight of the lines, base pair probabilities [18] for the interaction match scores, or by using the RIBOSUM scoring matrices derived from alignments of ribosomal RNAs [47]. Our model, however, is not limited to standard scoring schemes. Since we can set each (sequence or structure) weight separately, the user can assign completely arbitrary scores to each line or interaction match which makes the incorporation of expert knowledge into the computation of structural alignments easy. Furthermore, we assign negative weights to gap edges ${a}_{kl}^{A}$ with representing the gap penalty for aligning substring ${s}_{k}^{A},\mathrm{...},{s}_{l}^{A}$ with gap characters. Note that the model allows for arbitrary, position-dependent gap scoring.
Approaches for traditional sequence alignment aim at maximizing the score of edges in an alignment $\mathcal{L}$. Structural alignments, however, must also take the structural information encoded in the interaction edges into account. The problem of structurally aligning two annotated sequences (s^{ A }, p^{ A }) and (s^{ B }, p^{ B }) corresponds to finding an alignment such that the weight of the sequence part (i.e., the weight of selected lines plus gap penalties) plus the weight of the realized interaction matches is maximal. More formally, we seek to maximize $\sum}_{l\in \mathcal{L}}{w}_{l}}+{\displaystyle {\sum}_{g\in \mathcal{G}}{w}_{g}}+{\displaystyle {\sum}_{(i,j)\in \mathcal{I}}{w}_{ij$, where ($\mathcal{L}$, $\mathcal{G}$) represents an alignment with arbitrary gap costs, and $\mathcal{I}$ contains the interaction matches realized by $\mathcal{L}$. Observe that this graph-theoretical reformulation matches the problem statement given at the beginning of this section.
Biological aspects
The basic entities of our model are the alignment, interaction, and gap edges in the structural graph, which contribute to the objective function rather independently. Hence, one could argue that the model does not capture important features of RNA structures, like the incorporation of stacking energies or loop scores that depend on the actual size of the loop. We are aware of these limitations.
Nevertheless, the results of our computational experiments presented in Sect. 3 show that this approach yields high-quality structural alignments. In the pairwise case, our graph-based model is competitive with state-of-the-art approaches and develops its strength with an increasing number of sequences, outperforming all other programs that we tested (for details see Sect. 3). Additionally, the authors of [48] showed that models that do not capture stacking energies and loops are still competitive.
Beyond, our graph-based approach offers the possibility to change the model from nucleotides as the working entities to stems: Instead of taking single nucleotides as the vertices of the structural graph, we could search for candidate stems in the sequences and introduce a vertex for each half-stem. This would allow us to incorporate energy-based scoring into our model, which then, however, will have to be adapted to take into account overlapping stem candidates.
2.2 Integer linear program and Lagrangian relaxation
Given the graph-theoretical model it is straightforward to transform it to an integer linear program (ILP). We associate binary variables with each line, interaction match, and gap edge, and model the constraints of a valid gapped structural trace by adding inequalities to the linear program.
The handling of lines and gap edges is straightforward: We associate a x and z variable to each line and gap edge, respectively. We set x_{ l }= 1 if and only if line l ∈ L is part of the alignment $\mathcal{L}$, and z_{ a }= 1 if and only if gap edge a ∈ G is part of the alignment.
As described in Sect. 2.1, the sets L, I, and G refer to lines, interaction edges, and gap edges, and the sets ${\mathcal{C}}_{L}$ and ${\mathcal{C}}_{G}$ contain subsets of mutually conflicting lines or gap edges.
y_{ lm }= y_{ ml } ∀l, m ∈ L (7)
x ∈ {0, 1}^{ L }y ∈ {0, 1}^{L×L}z ∈ {0, 1}^{ G } (8)
Lemma 2.1 (Proof in [16])
A feasible solution to the ILP (1)–(8) corresponds to a valid gapped structural trace of weight equal to the objective function and vice versa.
Observe that constraints (2)–(6) exactly correspond to the properties of a gapped structural trace as described in Sect. 2.1.
In [49] the authors show that the problem of computing an optimal gapped structural trace is already NP-hard, even without considering gap costs. Hence, we cannot hope to find an optimal solution to the problem in polynomial time.
Commonly used mathematical programming techniques for NP-hard problems therefore resort to various relaxation techniques that are the basis for further processing. A relaxation results from the removal of constraints from the original ILP formulation, and is often solvable in polynomial time. A popular relaxation is the so called LP relaxation where the integrality constraints on the variables are dropped, yielding a standard linear program, for which solutions can be found efficiently.
Another possible relaxation technique is Lagrangian relaxation: Instead of just dropping certain inequalities, we move them to the objective function, associated with a penalty term that becomes active if the dropped constraint is violated. By iteratively adapting those penalty terms using, for instance, subgradient optimization, we get better solutions with each iteration. A crucial parameter is therefore the number of iterations that we perform: the higher the number, the more likely it is to end up with an optimal or near-optimal solution.
Inspired by the successful approach of Lancia and Caprara for the contact map overlap problem, we consider the relaxation resulting from moving constraint (7) into the objective function.
Lemma 2.2 (Proof in [16])
The relaxed problem is equivalent to the pairwise sequence alignment problem with arbitrary gap costs.
2.3 Algorithms for the pairwise and multiple case
Our algorithm for the pairwise RNA structural alignment problem consists of iteratively solving the primary sequence alignment problem associated with the relaxation. The penalization of the relaxed inequality is reflected in an adapted scoring matrix for the primary alignment. Intuitively, these weights incorporate also the structural information. In each iteration we get a new lower bound for the problem by analyzing the primary sequence alignments and inferring the best structural completion of this alignment. In fact, this corresponds to solving a maximum weighted matching problem in a general graph. For details see [16]. In the course of the algorithm, these solutions get better and better. Furthermore, the value of the relaxation itself constitutes an upper bound on the problem, which decreases with an increasing number of iterations. When these bounds coincide, we have provably found an optimal solution, otherwise, we get near-optimal solutions with a quality guarantee. Assuming an upper bound on the number of interaction matches per line, which is typically the case with base pair probability matrices of RNA sequences, we get a running time of O(n^{2}) for each Lagrangian iteration. Since we fix the number of iterations, this leads to an overall time complexity of O(n^{2}).
For the multiple case, similar in spirit to the MARNA software, we combine our pairwise method with the popular progressive alignment software T-COFFEE [50]. Progressive methods build multiple alignments from pairwise alignments. The pairwise distances are usually used to compute a guide tree which in turn determines the order in which the sequences are aligned to the evolving multiple alignment.
Progressive approaches often suffer from their sensitivity to the order in which the sequences are chosen during the alignment process. T-COFFEE reduces this effect by making use of local alignment information from all pairwise sequence alignments during its progressive alignment phase. We supply such local alignment information based on all-against-all structural alignments computed with our pairwise approach, assigning a high score to conserved interaction matches. The structural information is subsequently passed on to T-COFFEE that computes a multiple alignment, taking into account the additional structural information.
3 Experiments
The basis of our computational experiments is the recently published benchmark set BRALIBASE 2.1 [51]. We compared our program to four other alignment programs (MARNA, FOLDALIGNM, MAFFT, and STRAL) using two established measures for the quality of structural alignments (Compalign and SCI score). We performed all experiments with default parameters.
3.1 BRAliBase 2.1
We chose this data set, which is available from [52], as our test set, since it covers a greater range of typical noncoding-RNA families than the original BRALIBASE data set [12]. BRALIBASE 2.1 contains 36 different RNA families, ranging from approximately 26 nucleotides long Histone 3'UTR stem-loop motifs to approximately 300 nucleotides long eukaryotic SRP RNAs. See [51] for a detailed listing of all instances. BRALIBASE 2.1 reference alignments are based on manually curated seed alignments of the Rfam 7.0 database [53]. Out of the pool of all ncRNA families that have more than 50 sequences in their seed alignment, either 2, 3, 5, 7, 10 or 15 sequences were randomly drawn considering constraints on the sequences (e.g., average pairwise sequence identity or structural conservation). These subsets of the original seed alignments form the instances of BRALIBASE: in the following we stick to the BRALIBASE naming convention and refer to the sets of instances by k 2, k 3, k 5, k 7, k 10, and k 15, depending on the number of sequences per instance.
3.2 Compalign and SCI
We use two different scores to measure the quality of the computed alignments: the Compalign value codes the degree of similarity to a given reference alignment as given by the percentage of columns that are identically aligned as in the reference alignment. A value of 1 states that the reference and test alignment are the same, whereas 0 denotes that no column was correctly aligned with respect to the reference alignment.
The second score is the so called structural conservation index [54] (or SCI in short). The SCI basically gives the degree of conservation of a consensus structure induced by a multiple alignment in relation to the minimum free energy structure of each sequence (to be more precise, not the actual structures are compared but their respective energy values). A SCI value of ≈ 1 indicates very high structural conservation, whereas a value around 0 indicates no structural conservation at all. Note that the SCI score can be greater than 1, because covariance information is additionally rewarded in the computation.
We have used the programs compalignp and scif to compute the Compalign and SCI score. Both tools are freely available from the BRAliBase website.
3.3 Other structural alignment programs
We implemented our approach called LARA in C++ within the LISA framework. LISA (Li brary of S tructural A lignment algorithms) contains various methods for aligning protein and RNA structures as well as biological networks.
Furthermore, we selected several other multiple structural alignment programs to compare the results. We used MARNA [26] (available from [55]) using an ensemble of three suboptimal structures as its input, STRAL [24] (a sequence based algorithm incorporating McCaskill's base pair probabilities, available from [56]), and a reimplementation of the PMCOMP approach called FOLDALIGNM [32] (a banded variant of Sankoff's algorithm that aligns base pair probability matrices, available from [57]). Furthermore, to compare the performance of the structure-based alignment programs to purely sequence-based ones, we performed the same tests with MAFFT [58], a recent multiple sequence alignment program which is available from [59]. We want to emphasize that we did not perform any parameter tuning for any program (this includes LARA), i.e., we downloaded the programs from the respective websites and performed the computations out of the box without specifying any optional parameters.
Since earlier studies [12, 51] showed that structural alignments only contribute an additional benefit – compared to sequence-based approaches – if the pairwise sequence identity drops below ≈ 50 – 60%, we restricted the test set to instances of low homology, i.e., instances having a pairwise sequence identity below 50%.
3.4 LaRA
A scoring system for structural alignments has to provide two different kinds of scores: scores for the sequence and the structure part (in case of LARA, these correspond to weights for the alignment and interaction edges, respectively). Since the structure is considered to contain the necessary information for "correct" alignments, we have to make sure that the structure scores contribute the major part to the overall score.
We do not generate the complete annotation for our input sequence, that is, an interaction edge between every possible interaction, but restrict interaction edges to those having base pair probabilities larger than a threshold p_{min}. For our experiments we resorted to a value of 0.003, similar as in PMCOMP. The impact of different p_{min} values is twofold: First, the lower the value is, the higher the structure scores are. Secondly, a high p_{min} value leads to a sparser structure graph.
where lg is the natural logarithm. For the sequence scoring, we take the entries from the RIBOSUM matrices [47] as the actual sequence scores (that is the scores for pairs of nucleotides) and multiply them by a user-specific adjustment factor τ. The default value for τ is 0.05, leading to a small sequence score contribution to the overall score. If one knows, however, that sequence is equally or more important than the structure (e.g., in case of riboswitches), one simply has to increase the value of τ.
The second scheme employs the RIBOSUM scoring matrices both for sequence and structure scoring: these matrices are based on given alignments of ribosomal RNAs from which log-odds scores were derived. They provide both sequence and structure scores, without rescaling the scores.
The scoring of gap edges follows the scheme of affine gap costs with an gap open and extension penalty of -6 and -2, respectively.
Score vs. alignment accuracy
Furthermore, we assumed that there should a correlation between the actual performance of our algorithm and, again, the quality of our alignments: Remember that each Lagrange iteration results in a new valid solution and a new upper bound for the problem instance. Dividing the value of the highest lower bound by the value of the lowest upper bound gives an optimality ratio, i.e., a measure of how close the best solution is to an optimal one. Assuming an inverse correlation between the gap between lower and upper bound and the quality of the alignment, we again took all k 10 BRALIBASE instances of low pairwise sequence identity and computed the arithmetic mean of the optimality ratios of all pairwise alignments. The right side of Fig. 13 shows the plot for all 189 k 10 instances with a sequence similarity lower than 50%. Most of the instances behave as expected: the higher the average optimality ratio is, the closer is the computed alignment to the reference alignment (and vice versa). There is, however, a group of 19 instances that behave differently (marked as red boxes in Fig. 13): Although their average optimality ratio is high (> 0.7), the corresponding Compalign value is rather low compared to instances of a similar average optimality ratio. A closer inspection revealed that all instances of the upper left corner (that is instances having a Compalign value lower than 0.65 and an average optimality ratio of greater than 0.7, represented by red boxes in Fig. 13) comprises almost all instances of either bacterial SRP RNAs or SECIS elements (just one SRP RNA instance is not among the 19 instances). We therefore increased the number of iterations for one SECIS instance to see whether this would positively influence the quality of the alignment. By setting the number of iterations to 500, 1000, and 2000 we got average optimality ratios of 0.83, 0.85, and 0.87, by simultaneously yielding Compalign values of 0.39, 0.38, and 0.36, respectively. Obviously, the better the computed alignments in terms of the optimality ratio are, the worse they got with respect to the reference alignment.
3.5 Comparison with other programs
As described in Sect. 3.2 we used two different scores to assess the quality of the computed alignments: the Compalign (the degree of similarity between the test alignment to a given reference alignment) and the SCI score (the degree of structural conservation induced by the test alignment).
FOLDALIGNM performs an alignment and clustering of the input sequences at the same time: in some instances, FOLDALIGNM splits the input sequences into two clusters. Since the scores that we use depend on the number of input sequences, we dropped those FOLDALIGNM alignments that did not contain all sequences in the final alignment: This leads to 43, 30, 11, 15, 19, and 6 instances that we did not consider in case of k 2, k 3, k 5, k 7, k 10, and k 15 instances.
We have made several observations: First of all, in the pairwise case (i.e., the k 2 instances) LARA has a similar performance as the Sankoff variant FOLDALIGNM up to a sequence identity of ≈ 42%. For the range of ≈ 42 – 50% all programs (even sequence-based MAFFT) have comparable performance (except for MARNA). With an increasing number of input sequences per instance, especially for the k 10 and k 15 sequences, the results change tremendously: LARA outperforms the other programs, yielding average Compalign scores of ≈ 90%, whereas the other structure-based alignment programs have scores around ≈ 55 – 75%. This is quite remarkable, especially considering that FOLDALIGNM and LARA show a similar performance in the pairwise case: FOLDALIGNM, however, computes multiple alignments in a progressive fashion, whereas LARA computes all pairwise alignments and leaves it to T-COFFEE to compute an alignment that is highly consistent with all pairwise alignments. With an increasing number of input sequences, the consistency-based approach generates better alignments than the progressive methods (at least in the case of our experimental setup).
Another astonishing observation is the performance of MAFFT, a purely sequence-based program: the k 2 and k 3 instances show a comparable performance for instances above ≈ 42%, which is already surprising. With a growing number of input instances, the performance of MAFFT becomes even better: in case of 15 input instances, the program yields – on average – the second best results (behind LARA), outperforming even FOLDALIGNM and STRAL, which incorporate structural information. It has to be investigated whether the creation of the benchmark set has to be revisited, because these plots clearly contradict the hypothesis that sequence-based programs yields significantly worse results for input instances of a pairwise sequence identity below 50%.
Comparison of running times
Failed instances. Unsolved instances within a time limit of 20 minutes.
Program | k 2 | k 3 | k 5 | k 7 | k 10 | k 15 |
---|---|---|---|---|---|---|
LARA | 0 | 0 | 0 | 0 | 0 | 0 |
FOLDALIGNM | 0 | 0 | 0 | 0 | 0 | 0 |
STRAL | 0 | 0 | 0 | 0 | 0 | 0 |
MARNA | 0 | 49 | 23 | 17 | 12 | 6 |
MAFFT | 0 | 0 | 0 | 0 | 0 | 0 |
4 Conclusion
We have presented a novel method for computing high-quality pairwise structural RNA alignments. We approach the original problem using a flexible graph-based model, which naturally deals with pseudoknots.
We find solutions in our model by means of an integer linear programming formulation and the Lagrangian relaxation technique. For the multiple case, we compute all-against-all pairwise solutions and pass this information to T-COFFEE, a progressive alignment algorithm.
Our extensive computational experiments on a large set of benchmark alignments show that LARA, the implementation of our algorithm, is competitive with state-of-the art tools and outperforms alternative approaches with an increasing number of input sequences. The difference to other programs gets larger the more sequences that have to be aligned. In this context, we also find the performance of MAFFT, a purely sequence-based program, remarkable. MAFFT comes closer to manually curated reference alignments than all other structure-specific tools besides LARA for alignments of more than ten sequences.
Our plans for the future include a local version of our alignment algorithm. Furthermore, we are currently implementing an exact branch-and-bound framework around the Lagrangian approach and will develop a stem-based variant of LARA. Furthermore, the openness to pseudoknots is the main advantage of LARA over alternative approaches, and we plan to adapt our method to produce high-quality alignments of pseudoknotted structures.
Availability and requirements
LARA (Lagrangian relaxed alignments) is part of the C++ library LiSA and is freely available for academic purposes from http://www.planet-lisa.net. The binary runs under the Linux operating system.
All alignments that we computed and the scripts for generating the plots are also available from http://www.planet-lisa.net/.
Declarations
Acknowledgements
This work has been partly supported by the DFG grant KL 1390/2-1. MB is additionally supported by the International Max Planck Research School for Computational Biology and Scientific Computing. The authors thank the anonymous referees for valuable comments.
Authors’ Affiliations
References
- Lagos-Quintana M, Rauhut R, Lendeckel W, Tuschl T: Identification of novel genes coding for small expressed RNAs. Science. 2001, 294 (5543): 853-8.View ArticlePubMedGoogle Scholar
- Lau NC, Lim LP, Weinstein EG, Bartel DP: An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans. Science. 2001, 294 (5543): 858-62.View ArticlePubMedGoogle Scholar
- Samarsky DA, Fournier MJ: A comprehensive database for the small nucleolar RNAs from Saccharomyces cerevisiae. Nucleic Acids Res. 1999, 27: 161-164.PubMed CentralView ArticlePubMedGoogle Scholar
- Gorodkin J, Knudsen B, Zwieb C, Samuelsson T: SRPDB (Signal Recognition Particle Database). Nucleic Acids Res. 2001, 29: 169-170.PubMed CentralView ArticlePubMedGoogle Scholar
- Kim VN: Small RNAs just got bigger: Piwi-interacting RNAs (piRNAs) in mammalian testes. Genes Dev. 2006, 20 (15): 1993-1997.View ArticlePubMedGoogle Scholar
- Mattick JS: The functional genomics of noncoding RNA. Science. 2005, 309 (5740): 1527-1528.View ArticlePubMedGoogle Scholar
- Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic Local Alignment Search Tool. J Mol Biol. 1990, 215: 403-410.View ArticlePubMedGoogle Scholar
- Smith TF, Waterman MS: Identification of Common Molecular Subsequences. J Mol Biol. 1981, 147: 195-197.View ArticlePubMedGoogle Scholar
- Zhang S, Haas B, Eskin E, Bafna V: Searching Genomes for Noncoding RNA Using FastR. IEEE/ACM Trans Comput Biol Bioinform. 2005, 2 (4): 366-379.View ArticlePubMedGoogle Scholar
- Wolf M, Achtziger M, Schultz J, Dandekar T, Müller T: Homology modeling revealed more than 20,000 rRNA internal transcribed spacer 2 (ITS2) secondary structures. RNA. 2005, 11 (11): 1616-1623.PubMed CentralView ArticlePubMedGoogle Scholar
- Hofacker IL, Fekete M, Stadler PF: Secondary Structure Prediction for Aligned RNA Sequences. J Mol Biol. 2002, 319: 1059-1066.View ArticlePubMedGoogle Scholar
- Gardner P, Wilm A, Washietl S: A benchmark of multiple sequence alignment programs upon structural RNAs. Nucl Acids Res. 2005, 33 (8): 2433-2439.PubMed CentralView ArticlePubMedGoogle Scholar
- Bauer M, Klau GW: Structural Alignment of Two RNA Sequences with Lagrangian Relaxation. Proc ISAAC'04, Volume 3341 of LNCS. 2004, Springer-Verlag, 113-125.Google Scholar
- Bauer M, Klau GW, Reinert K: Multiple Structural RNA Alignment with Lagrangian Relaxation. Proc WABI'05, Volume 3692 of LNBI. 2005, 303-314.Google Scholar
- Bauer M, Klau GW, Reinert K: Fast and Accurate Structural RNA Alignment by Progressive Lagrangian Relaxation. Proc CompLife'05, Volume 3695 of LNBI. 2005, 217-228.Google Scholar
- Bauer M, Klau GW, Reinert K: An Exact Mathematical Programming Approach to Multiple RNA Sequence-Structure Alignment. Tech Rep TR-B-07-07. 2007, Dept. of Mathematics and Computer Science, Free University Berlin, [Submitted to Algorithmic Operations Research], [http://www.inf.fu-berlin.de/inst/pubs]Google Scholar
- Freyhult EK, Bollback JP, Gardner PP: Exploring genomic dark matter: A critical assessment of the performance of homology search methods on noncoding RNA. Genome Research. 2007, 17: 117-125.PubMed CentralView ArticlePubMedGoogle Scholar
- McCaskill JS: The Equilibrium Partition Function and Base Pair Binding Probabilities for RNA Secondary Structure. Biopolymers. 1990, 29: 1105-1119.View ArticlePubMedGoogle Scholar
- Zhang K, Shasha D: Simple fast algorithms for the editing distance between trees and related problems. SIAM J Comput. 1989, 18 (6): 1245-1262.View ArticleGoogle Scholar
- Jiang T, Wang J, Zhang K: Alignment of Trees – An Alternative to Tree Edit. Theor Comput Sci. 1995, 143: 137-148.View ArticleGoogle Scholar
- Höchsmann M, Töller T, Giegerich R, Kurtz S: Local Similarity in RNA Secondary Structures. Proc IEEE Comput Soc Bioinform Conf. 2003, 2: 159-168.PubMedGoogle Scholar
- Bafna V, Muthukrishnan S, Ravi R: Computing similarity between RNA strings. Proc of CPM'95, no. 937 in LNCS. 1995, Springer, 1-16.Google Scholar
- Eddy SR: A memory-efficient dynamic programming algorithm for optimal alignment of a sequence to an RNA secondary structure. BMC Bioinformatics. 2002, 3: 18-PubMed CentralView ArticlePubMedGoogle Scholar
- Dalli D, Wilm A, Mainz I, Steger G: STRAL: progressive alignment of non-coding RNA using base pairing probability vectors in quadratic time. Bioinformatics. 2006, 22 (13): 1593-1599.View ArticlePubMedGoogle Scholar
- Jiang T, Lin GH, Ma B, Zhang K: A general edit distance between RNA structures. J Comput Biol. 2002, 9 (2): 371-388.View ArticlePubMedGoogle Scholar
- Siebert S, Backofen R: MARNA: Multiple alignment and consensus structure prediction of RNAs based on sequence structure comparisons. Bioinformatics. 2005, 21 (16): 3352-3359.View ArticlePubMedGoogle Scholar
- Sankoff D: Simultaneous solution of the RNA folding, alignment, and proto-sequence problems. SIAM J Appl Math. 1985, 45: 810-825.View ArticleGoogle Scholar
- Mathews DH, Turner DH: Dynalign: An Algorithm for Finding Secondary Structures Common to Two RNA Sequences. J Mol Biol. 2002, 317: 191-203.View ArticlePubMedGoogle Scholar
- Mathews D: Predicting a set of minimal free energy RNA secondary structures common to two sequences. Bioinformatics. 2005, 21: 2246-2253.View ArticlePubMedGoogle Scholar
- Hull Havgaard J, Lyngsø R, Stormo G, Gorodkin J: Pairwise local structural alignment of RNA sequences with sequence similarity less than 40%. Bioinformatics. 2005, 21: 1815-1824.View ArticleGoogle Scholar
- Hofacker IL, Bernhart SHF, Stadler PF: Alignment of RNABase Pairing Probability Matrices. Bioinformatics. 2004, 20: 2222-2227.View ArticlePubMedGoogle Scholar
- Torarinsson E, Havgaard JH, Gorodkin J: Multiple structural alignment and clustering of RNA sequences. Bioinformatics. 2007, 23 (8): 926-932.View ArticlePubMedGoogle Scholar
- Will S, Reiche K, Hofacker IL, Stadler PF, Backofen R: Inferring Noncoding RNA Families and Classes by Means of Genome-Scale Structure-Based Clustering. PLoS Comput Biol. 2007, 3 (4): e65-[http://dx.doi.org/10.1371/journal.pcbi.0030065]PubMed CentralView ArticlePubMedGoogle Scholar
- Eddy SP, Durbin R: RNA sequence analysis using covariance models. Nucleic Acids Res. 1994, 22 (11): 2079-2088.PubMed CentralView ArticlePubMedGoogle Scholar
- Holmes I: A probabilistic model for the evolution of RNA structure. BMC Bioinformatics. 2004, 5: 166-PubMed CentralView ArticlePubMedGoogle Scholar
- Holmes I: Accelerated probabilistic inference of RNAstructure evolution. BMC Bioinformatics. 2004, 5: 73-View ArticleGoogle Scholar
- Dowell R, Eddy S: Efficient pairwise RNA structure prediction and alignment using sequence alignment constraints. BMC Bioinformatics. 2006, 7: 400-PubMed CentralView ArticlePubMedGoogle Scholar
- Sato K, Sakakibara Y: RNA secondary structural alignment with conditional random fields. Bioinformatics. 2005, 21 (suppl 2): 237-242.View ArticleGoogle Scholar
- Sakakibara Y: Pair hidden Markov models on tree structures. Bioinformatics. 2003, 19: i232-240.View ArticlePubMedGoogle Scholar
- Kececioglu J: The maximum weight trace problem inmultiple sequence alignment. Proc CPM'93, Volume 684 of LNCS. 1993, 106-119.Google Scholar
- Lenhof HP, Reinert K, Vingron M: A Polyhedral Approach to RNA Sequence Structure Alignment. J Comput Biol. 1998, 5 (3): 517-530.View ArticlePubMedGoogle Scholar
- Caprara A, Lancia G: Structural Alignment of Large-Size Proteins via Lagrangian Relaxation. Proc of RECOMB'02. 2002, ACM Press, 100-108.View ArticleGoogle Scholar
- Bauer M, Klau GW: Structural Alignment of Two RNA Sequences with Lagrangian Relaxation. Proc of ISAAC'04, no 3341 in LNCS. 2004, Springer, 113-123.Google Scholar
- Althaus E, Caprara A, Lenhof HP, Reinert K: A Branch-and-Cut Algorithm for Multiple Sequence Alignment. Mathematical Programming. 2006, 105 (2–3): 387-425.View ArticleGoogle Scholar
- Staple DW, Butcher SE: Pseudoknots: RNA Structures with Diverse Functions. PLoS Biology. 2005, 3 (6): e213-PubMed CentralView ArticlePubMedGoogle Scholar
- Dost B, Han B, Zhang S, Bafna V: Structural Alignment of Pseudoknotted RNA. Proceedings of RECOMB. 2006, 143-158.Google Scholar
- Klein R, Eddy SR: RSEARCH: Finding homologs of single structured RNA sequences. BMC Bioinformatics. 2003, 4: 44-PubMed CentralView ArticlePubMedGoogle Scholar
- Dowell RD, Eddy SR: Evaluation of several lightweight stochastic context-free grammars for RNA secondary structure prediction. BMC Bioinformatics. 2004, 5: 71-PubMed CentralView ArticlePubMedGoogle Scholar
- Goldman D, Papadimitriou CH, Istrail S: Algorithmic Aspects of Protein Structure Similarity. Proc FOCS'99. 1999, 512-522.Google Scholar
- Notredame C, Higgins DG, Heringa J: T-Coffee: A novel method for fast and accurate multiple sequence alignment. Journal of Molecular Biology. 2000Google Scholar
- Wilm A, Mainz I, Steger G: An enhanced RNA alignment benchmark for sequence alignment programs. Algorithms for Molecular Biology. 2006, 1: 19-PubMed CentralView ArticlePubMedGoogle Scholar
- BRAliBase 2.1. [http://www.biophys.uni-duesseldorf.de/bralibase/]
- Griffiths-Jones S, Moxon S, Marshall M, Khanna A, Eddy SR, Bateman A: Rfam: annotating non-coding RNAs in complete genomes. Nucl Acids Res. 2005, 33: D121-124.PubMed CentralView ArticlePubMedGoogle Scholar
- Washietl S, Hofacker I, Lukasser M, Hüttenhofer A, Stadler P: Mapping of conserved RNA secondary structures predicts thousands of functional noncoding RNAs in the human genome. Nature Biotechnology. 2005, 23 (11): 1383-1390.View ArticlePubMedGoogle Scholar
- MARNA. [http://biwww2.informatik.uni-freiburg.de/Software/MARNA/index.html]
- StrAl Webservice. [http://www.biophys.uni-duesseldorf.de/stral/]
- Foldalign. [http://foldalign.ku.dk/software/index.html]
- Katoh K, Kuma Ki, Toh H, Miyata T: MAFFT version 5: improvement in accuracy of multiple sequence alignment. Nucl Acids Res. 2005, 33 (2): 511-518.PubMed CentralView ArticlePubMedGoogle Scholar
- MAFFT – a multiple sequence alignment program. [http://align.bmr.kyushu-u.ac.jp/mafft/software/source.html]
- Thompson JD, Higgins DG, Gibson TJ: CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucl Acids Res. 1994, 22 (22): 4673-4680.PubMed CentralView ArticlePubMedGoogle Scholar
- Shapiro BA, Zhang K: Comparing Multiple RNA Secondary Structures Using Tree Comparisons. CABIOS. 1990, 6: 309-318.PubMedGoogle Scholar
- Evans P: Finding Common Subsequences with Arcs and Pseudoknots. Proc of CPM'99, no 1645 in LNCS. 1999, Springer, 270-280.Google Scholar
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