Mirela Andronescu

RNA STRAND: The RNA Secondary Structure and Statistical Analysis Database

BackgroundThe ability to access, search and analyse secondary structures of a large set of known RNA molecules is very important for deriving improved RNA energy models, for evaluating computational predictions of RNA secondary structures and for a better understanding of RNA folding. Currently there is no database that can easily provide these capabilities for almost all RNA molecules with known secondary structures.ResultsIn this paper we describe RNA STRAND – the RNA secondary STRucture and statistical ANalysis Database, a curated database containing known secondary structures of any type and organism. Our new database provides a wide collection of known RNA secondary structures drawn from public databases, searchable and downloadable in a common format. Comprehensive statistical information on the secondary structures in our database is provided using the RNA Secondary Structure Analyser, a new tool we have developed to analyse RNA secondary structures. The information thus obtained is valuable for understanding to which extent and with which probability certain structural motifs can appear. We outline several ways in which the data provided in RNA STRAND can facilitate research on RNA structure, including the improvement of RNA energy models and evaluation of secondary structure prediction programs. In order to keep up-to-date with new RNA secondary structure experiments, we offer the necessary tools to add solved RNA secondary structures to our database and invite researchers to contribute to RNA STRAND.ConclusionRNA STRAND is a carefully assembled database of trusted RNA secondary structures, with easy on-line tools for searching, analyzing and downloading user selected entries, and is publicly available at http://www.rnasoft.ca/strand.

RNAsoft: A suite of RNA secondary structure prediction and design software tools.

DNA and RNA strands are employed in novel ways in the construction of nanostructures, as molecular tags in libraries of polymers and in therapeutics. New software tools for prediction and design of molecular structure will be needed in these applications. The RNAsoft suite of programs provides tools for predicting the secondary structure of a pair of DNA or RNA molecules, testing that combinatorial tag sets of DNA and RNA molecules have no unwanted secondary structure and designing RNA strands that fold to a given input secondary structure. The tools are based on standard thermodynamic models of RNA secondary structure formation. RNAsoft can be found online at http://www.RNAsoft.ca.

Efficient parameter estimation for RNA secondary structure prediction

MOTIVATION Accurate prediction of RNA secondary structure from the base sequence is an unsolved computational challenge. The accuracy of predictions made by free energy minimization is limited by the quality of the energy parameters in the underlying free energy model. The most widely used model, the Turner99 model, has hundreds of parameters, and so a robust parameter estimation scheme should efficiently handle large data sets with thousands of structures. Moreover, the estimation scheme should also be trained using available experimental free energy data in addition to structural data. RESULTS In this work, we present constraint generation (CG), the first computational approach to RNA free energy parameter estimation that can be efficiently trained on large sets of structural as well as thermodynamic data. Our CG approach employs a novel iterative scheme, whereby the energy values are first computed as the solution to a constrained optimization problem. Then the newly computed energy parameters are used to update the constraints on the optimization function, so as to better optimize the energy parameters in the next iteration. Using our method on biologically sound data, we obtain revised parameters for the Turner99 energy model. We show that by using our new parameters, we obtain significant improvements in prediction accuracy over current state of-the-art methods. AVAILABILITY Our CG implementation is available at http://www.rnasoft.ca/CG/.

Computational approaches for RNA energy parameter estimation.

Methods for efficient and accurate prediction of RNA structure are increasingly valuable, given the current rapid advances in understanding the diverse functions of RNA molecules in the cell. To enhance the accuracy of secondary structure predictions, we developed and refined optimization techniques for the estimation of energy parameters. We build on two previous approaches to RNA free-energy parameter estimation: (1) the Constraint Generation (CG) method, which iteratively generates constraints that enforce known structures to have energies lower than other structures for the same molecule; and (2) the Boltzmann Likelihood (BL) method, which infers a set of RNA free-energy parameters that maximize the conditional likelihood of a set of reference RNA structures. Here, we extend these approaches in two main ways: We propose (1) a max-margin extension of CG, and (2) a novel linear Gaussian Bayesian network that models feature relationships, which effectively makes use of sparse data by sharing statistical strength between parameters. We obtain significant improvements in the accuracy of RNA minimum free-energy pseudoknot-free secondary structure prediction when measured on a comprehensive set of 2518 RNA molecules with reference structures. Our parameters can be used in conjunction with software that predicts RNA secondary structures, RNA hybridization, or ensembles of structures. Our data, software, results, and parameter sets in various formats are freely available at http://www.cs.ubc.ca/labs/beta/Projects/RNA-Params.

Thermodynamically based DNA strand design

We describe a new algorithm for design of strand sets, for use in DNA computations or universal microarrays. Our algorithm can design sets that satisfy any of several thermodynamic and combinatorial constraints, which aim to maximize desired hybridizations between strands and their complements, while minimizing undesired cross-hybridizations. To heuristically search for good strand sets, our algorithm uses a conflict-driven stochastic local search approach, which is known to be effective in solving comparable search problems. The PairFold program of Andronescu et al. [M. Andronescu, Z. C. Zhang and A. Condon (2005) J. Mol. Biol., 345, 987–1001; M. Andronescu, R. Aguirre-Hernandez, A. Condon, and H. Hoos (2003) Nucleic Acids Res., 31, 3416–3422.] is used to calculate the minimum free energy of hybridization between two mismatched strands. We describe new thermodynamic measures of the quality of strand sets. With respect to these measures of quality, our algorithm consistently finds, within reasonable time, sets that are significantly better than previously published sets in the literature.

Algorithms for predicting the secondary structure of pairs and combinatorial sets of nucleic acid strands

Secondary structure prediction of nucleic acid molecules is a very important problem in computational molecular biology. In this thesis we introduce two new algorithms for: (1) secondary structure prediction of pairs of nucleic acid molecules (PairFold), and (2) finding which sequences, formed from a combinatorial set of nucleic acid strands, have the most stable secondary structures (CombFold). Our algorithms run in polynomial time in the sequences lengths and are extensions of the free energy minimization algorithm [72] for secondary structure prediction without pseudoknots, using the nearest neighbour thermodynamic model. Predicting hybridization of pairs of molecules is motivated by important applications such as ribozyme - mRNA target duplexes, primer binding prediction and DNA code design. Finding the most stable concatenations in combinatorial sets of strands is useful for SELEX experiments and for testing whether sets in DNA computing or tag libraries concatenate without secondary structure. Our results for PairFold predictions show over 80% accuracy for sequences of up to 100 nucleotides. The performance goes down as the sequences increase in length and as the number of non-canonical base pairs, pseudoknots and tertiary interactions, none of these considered here, increases. The accuracy of CombFold is similar to that of the free energy minimization algorithm for single strands, being just a polynomial method for structure prediction of a combinatorial set of strands. We show that although complex, CombFold can quickly predict large concatenations of sets drawn from the literature. In the future, these two algorithms can be combined to predict the most stable duplexes formed by two combinatorial sets.

Algorithms for testing that sets of DNA words concatenate without secondary structure

We present an efficient algorithm for determining whether all moleculesin a combinatorial set of DNA or RNA strandsare structure free, and thus availablefor bonding to their Watson-Crick complements.This work is motivated by the goalof testing whether strands used in DNAcomputations or as molecular bar-codesare structure free, where the strands areconcatenations of short words. We alsopresent an algorithm for determining whetherall words in S*, for some finite setS of equi-length words, are structure free.

The Determination of RNA Folding Nearest Neighbor Parameters

The stability of RNA secondary structure can be predicted using a set of nearest neighbor parameters. These parameters are widely used by algorithms that predict secondary structure. This contribution introduces the UV optical melting experiments that are used to determine the folding stability of short RNA strands. It explains how the nearest neighbor parameters are chosen and how the values are fit to the data. A sample nearest neighbor calculation is provided. The contribution concludes with new methods that use the database of sequences with known structures to determine parameter values.

Algorithms for Testing That Sets of DNA Words Concatenate without Secondary Structure

We present an efficient algorithm for determining whether all molecules in a combinatorial set of DNA or RNA strands are structure free, and thus available for bonding to their Watson-Crick complements. This work is motivated by the goal of testing whether strands used in DNA computations or as molecular bar-codes are structure free, where the strands are concatenations of short words. We also present an algorithm for determining whether all words in S*, for some finite set S of equi-length words, are structure-free.

A genome-wide 3C-method for characterizing the three-dimensional architectures of genomes

Accumulating evidence demonstrates that the three-dimensional (3D) organization of chromosomes within the eukaryotic nucleus reflects and influences genomic activities, including transcription, DNA replication, recombination and DNA repair. In order to uncover structure-function relationships, it is necessary first to understand the principles underlying the folding and the 3D arrangement of chromosomes. Chromosome conformation capture (3C) provides a powerful tool for detecting interactions within and between chromosomes. A high throughput derivative of 3C, chromosome conformation capture on chip (4C), executes a genome-wide interrogation of interaction partners for a given locus. We recently developed a new method, a derivative of 3C and 4C, which, similar to Hi-C, is capable of comprehensively identifying long-range chromosome interactions throughout a genome in an unbiased fashion. Hence, our method can be applied to decipher the 3D architectures of genomes. Here, we provide a detailed protocol for this method.