Motivation: Pseudoknots have generally been excluded from the prediction of RNA secondary structures due to its difficulty in modeling. Although, several dynamic programming algorithms exist for the prediction of pseudoknots using thermodynamic approaches, they are neither reliable nor efficient. On the other hand, comparative methods are more reliable, but are often done in an ad hoc manner and require expert intervention. Maximum weighted matching, an algorithm for pseudoknot prediction with comparative analysis, suffers from low-prediction accuracy in many cases.

The design of DNA and RNA sequences is critical for many endeavors, from DNA nanotechnology, to PCR-based applications, to DNA hybridization arrays. Results in the literature rely on a wide variety of design criteria adapted to the particular requirements of each application. Using an extensively-studied thermodynamic model, we perform a detailed study of several criteria for designing sequences intended to adopt a target secondary structure. We conclude that superior design methods should explicitly implement both a positive design paradigm (optimize affinity for the target structure) and a negative design paradigm (optimize specificity for the target structure). The commonly used approaches of sequence symmetry minimization and minimum free energy satisfaction primarily implement negative design and can be strengthened by introducing a positive design component. Surprisingly, our findings hold for a wide range of secondary structures and are robust to modest perturbation of the thermodynamic parameters used for evaluating sequence quality, suggesting the feasibility and ongoing utility of a unified approach to nucleic acid design as parameter sets are further refined. Finally, we observe that designing for thermodynamic stability does not determine folding kinetics, emphasizing the opportunity for extending design criteria to target kinetic features of the energy landscape.

Abstract. Dynamic programming is a classical programming technique, applicable in a wide variety of domains such as stochastic systems analysis, operations research, combinatorics of discrete structures, flow problems, parsing of ambiguous languages, and biosequence analysis. Little methodology has hitherto been available to guide the design of such algorithms. The matrix recurrences that typically describe a dynamic programming algorithm are difficult to construct, error-prone to implement, and, in nontrivial applications, almost impossible to debug completely. This article introduces a discipline designed to alleviate this problem. We describe an algebraic style of dynamic programming over sequence data. We define its formal framework, based on a combination of grammars and algebras, and including a formalization of Bellman’s Principle. We suggest a language used for algorithm design on a convenient level of abstraction. We outline three ways of implementing this language, including an embedding in a lazy functional language. The workings of the

Abstract. Motivated by the analysis of natural and engineered DNA and RNA systems, we present the first algorithm for calculating the partition function of an unpseudoknotted complex of multiple interacting nucleic acid strands. This dynamic program is based on a rigorous extension of secondary structure models to the multistranded case, addressing representation and distinguishability issues that do not arise for single-stranded structures. We then derive the form of the partition function for a fixed volume containing a dilute solution of nucleic acid complexes. This expression can be evaluated explicitly for small numbers of strands, allowing the calculation of the equilibrium population distribution for each species of complex. Alternatively, for large systems (e.g., a test tube), we show that the unique complex concentrations corresponding to thermodynamic equilibrium can be obtained by solving a convex programming problem. Partition function and concentration information can then be used to calculate equilibrium base-pairing observables. The underlying physics and mathematical formulation of these problems lead to an interesting blend of approaches, including ideas from graph theory, group theory, dynamic programming, combinatorics, convex optimization, and Lagrange duality.

Abstract. In this paper we derive the generating function of RNA structures with pseudoknots. We enumerate all k-noncrossing RNA pseudoknot structures categorized by their maximal sets of mutually intersecting arcs. In addition we enumerate pseudoknot structures over circular RNA. For 3-noncrossing RNA structures and RNA secondary structures we present a novel 4-term recursion formula and a 2-term recursion, respectively. Furthermore we enumerate for arbitrary k all k-noncrossing, restricted RNA structures i.e. k-noncrossing RNA structures without 2-arcs i.e. arcs of the form (i, i + 2), for 1 ≤ i ≤ n − 2. 1.

The Kinefold web server provides a web interface for stochastic folding simulations of nucleic acids on second to minute molecular time scales. Renaturation or co-transcriptional folding paths are simulated at the level of helix formation and dissociation in agreement with the seminal experimental results. Pseudoknots and topologically ‘entangled ’ helices (i.e. knots) are efficiently predicted taking into account simple geometrical and topological constraints. To encourage interactivity, simulations launched as immediate jobs are automatically stopped after a few seconds and return adapted recommendations. Users can then choose to continue incomplete simulations using the batch queuing system or go back and modify suggested options in their initial query. Detailed output provide (i) a series of low free energy structures, (ii) an online animated folding path and (iii) a programmable trajectory plot focusing on a few helices of interest to each user. The service can be accessed at

Computational prediction of the minimum free energy (mfe) secondary structure of an RNA molecule from its base sequence is valuable in understanding the structure and function of the molecule. Since the general problem of predicting pseudoknotted secondary structures is NP-hard, several algorithms have been proposed that find the mfe secondary structure from a restricted class of secondary structures. In this work, we order the algorithms by generality of the structure classes that they handle. We provide simple characterizations of the classes of structures handled by four algorithms, as well as linear time methods to test whether a given secondary structure is in three of these classes. We report on the percentage of biological structures from the PseudoBase and Gutell databases that are handled by these two algorithms.

Abstract. We address the problem of approximating the 2-Interval Pattern problem over its various models and restrictions. This problem, which is motivated by RNA secondary structure prediction, asks to find a maximum cardinality subset of a 2-interval set with respect to some prespecified model. For each such model, we give varying approximation quality depending on the different possible restrictions imposed on the input 2-interval set. 1

RNA can fold into a topological structure called a pseudoknot, composed of non-nested doublestranded stems connected by single-stranded loops. Our examination of the PseudoBase database of pseudoknotted RNA structures reveals asymmetries in the stem and loop lengths and provocative composition differences between the loops. By taking into account differences between major and minor grooves of the RNA double helix, we explain much of the asymmetry with a simple polymer physics model and statistical mechanical theory, with only one adjustable parameter.

Abstract. Efficient exact algorithms for finding optimal secondary structures of RNA sequences have been known for a quarter of a century. However, these algorithms are restricted to structures without overlapping base pairs, or pseudoknots. The ability to include pseudoknots has gained increased attention over the last five years, but three recent publications indicate that this might leave the problem intractable. In this paper we further investigate the complexity of the pseudoknot prediction problem in two simple models based on base pair stacking. We confirm the intractability of pseudoknot prediction by proving it  ¢¡ hard for binary strings in one model, and for strings over an unbounded alphabet in the other model. Conversely, we are also able to present a polynomial time algorithm for pseudoknot prediction for strings over a fixed size alphabet in the second model and a polynomial time approximation scheme for pseudoknot prediction for strings over a fixed size alphabet in the first model. 1