Summary: Creating clear, visually pleasing 2D depictions of RNA and DNA strands and their interactions is important to facilitate and communicate insights related to nucleic acid structure. Here we present RiboSketch, a secondary structure image production application that enables the visualization of multistranded structures via layout algorithms, comprehensive editing capabilities, and a multitude of simulation modes. These interactive features allow RiboSketch to create publication quality diagrams for structures with a wide range of composition, size and complexity. The program may be run in any web browser without the need for installation, or as a standalone Java application. Availability and implementation: https://rnastructure.cancer.gov/ribosketch.

BACKGROUND: RNA pseudoknots play important roles in many biological processes. Previous methods for comparative pseudoknot analysis mainly focus on simultaneous folding and alignment of RNA sequences. Little work has been done to align two known RNA secondary structures with pseudoknots taking into account both sequence and structure information of the two RNAs. RESULTS: In this article we present a novel method for aligning two known RNA secondary structures with pseudoknots. We adopt the partition function methodology to calculate the posterior log-odds scores of the alignments between bases or base pairs of the two RNAs with a dynamic programming algorithm. The posterior log-odds scores are then used to calculate the expected accuracy of an alignment between the RNAs. The goal is to find an optimal alignment with the maximum expected accuracy. We present a heuristic to achieve this goal. The performance of our method is investigated and compared with existing tools for RNA structure alignment. An extension of the method to multiple alignment of pseudoknot structures is also discussed. CONCLUSIONS: The method described here has been implemented in a tool named RKalign, which is freely accessible on the Internet. As more and more pseudoknots are revealed, collected and stored in public databases, we anticipate a tool like RKalign will play a significant role in data comparison, annotation, analysis, and retrieval in these databases.

MicroRNAs (miRNAs) are non-coding RNAs with approximately 22 nucleotides (nt) that are derived from precursor molecules. These precursor molecules or pre-miRNAs often fold into stem-loop hairpin structures. However, a large number of sequences with pre-miRNA-like hairpins can be found in genomes. It is a challenge to distinguish the real pre-miRNAs from other hairpin sequences with similar stem-loops (referred to as pseudo pre-miRNAs). Several computational methods have been developed to tackle this challenge. In this paper we propose a new method, called MirlD, for identifying and classifying microRNA precursors. We collect 74 features from the sequences and secondary structures of pre-miRNAs; some of these features are taken from our previous studies on non-coding RNA prediction while others were suggested in the literature. We develop a combinatorial feature mining algorithm to identify suitable feature sets. These feature sets are then used to train support vector machines to obtain classification models, based on which classifier ensemble is constructed. Finally we use a boosting algorithm to further enhance the accuracy of the classifier ensemble. Experimental results on a variety of species demonstrate the good performance of the proposed method, and its superiority over existing tools.

Small regulatory RNAs are highly abundant noncoding RNAs (ncRNA) found in bacterial genomes. These small regulatory ncRNAs (sRNAs) can regulate the synthesis of proteins by mediating mRNA transcription, translation and stability. Furthermore, they also control the activity of specific proteins by binding to them. In this study, we present a general computational approach for identifying the distinct structure of sRNAs in the Escherichia coli (E. coli) genome by a quantitative measure, Ediffthat is the energy difference between the optimal structure folded from a sequence segment and its corresponding optimal restrained structure where all base pairings formed in the original optimal structure are excluded. Our results indicate that most of the known small ncRNAs in E. coli K12 have very high normalized Ediffscores with high statistical significance. These sRNAs have distinct well-ordered structures that are both thermodynamically stable and uniquely folded.

Presents a method for finding patterns in 3D graphs. Each node in a graph is an undecomposable or atomic unit and has a label. Edges are links between the atomic units. Patterns are rigid substructures that may occur in a graph after allowing for an arbitrary number of whole-structure rotations and translations as well as a small number (specified by the user) of edit operations in the patterns or in the graph. (When a pattern appears in a graph only after the graph has been modified, we call that appearance "approximate occurrence.") The edit operations include relabeling a node, deleting a node and inserting a node. The proposed method is based on the geometric hashing technique, which hashes node-triplets of the graphs into a 3D table and compresses the label-triplets in the table. To demonstrate the utility of our algorithms, we discuss two applications of them in scientific data mining. First, we apply the method to locating frequently occurring motifs in two families of proteins pertaining to RNA-directed DNA polymerase and thymidylate synthase and use the motifs to classify the proteins. Then, we apply the method to clustering chemical compounds pertaining to aromatic compounds, bicyclicalkanes and photosynthesis. Experimental results indicate the good performance of our algorithms and high recall and precision rates for both classification and clustering.

Abstract A massively parallel Genetic Algorithm (GA) has been applied to RNA sequence folding on three different computer architectures. The GA, an evolution-like algorithm that is applied to a large population of RNA structures based on a pool of helical stems derived from an RNA sequence, evolves this population in parallel. The algorithm was originally designed and developed for a 16384 processor SIMD (Single Instruction Multiple Data) MasPar MP-2. More recently it has been adapted to a 64 processor MIMD (Multiple Instruction Multiple Data) SGI ORIGIN 2000, and a 512 processor MIMD CRAY T3E. The MIMD version of the algorithm raises issues concerning RNA structure data-layout and processor communication. In addition, the effects of population variation on the predicted results are discussed. Also presented are the scaling properties of the algorithm from the perspective of the number of physical processors utilized and the number of virtual processors (RNA structures) operated upon. Contact: [email protected]; [email protected]; [email protected]; [email protected] * To whom correspondence should be addressed. 4 Current address: HPC Applications Inc., 10080 Old Frederick Road, Ellicott City, MD 21042, USA.

A distance-mapping algorithm takes a set of objects and a distance metric and then maps those objects to a Euclidean or pseudo-Euclidean space in such a way that the distances among objects are approximately preserved. Distancemapping algorithms are a useful tool for clustering and visualization in data intensive applications, because they replace expensive distance calculations by sum-of-square calculations. This can make clustering in large databases with expensive distance metrics practical. In this paper we present five distance-mapping algorithms and conduct experiments to compare their performance in data clustering applications. These include two algorithms called FastMap and MetricMap, and three hybrid heuristics that combine the two algorithms in different ways. Experimental results on both synthetic and RNA data show the superiority of the hybrid algorithms. The results imply that FastMap and MetricMap capture complementary information about distance metrics and therefore ca...

MOTIVATION: Stem Trace is one of the latest tools available in STRUCTURELAB, an RNA structure analysis computer workbench. The paradigm used in STRUCTURELAB views RNA structure determination as a problem of dealing with a database of a large number of computationally generated structures. Stem Trace provides the capability to analyze this data set in a novel, visually driven, interactive and exploratory way. In addition to providing graphs at a high level of ion, it is also connected with complementary visualization tools which provide orthogonal views of the same data, as well as drawing of structures represented by a stem trace. Thus, on top of being an analysis tool, Stem Trace is a graphical user interface to an RNA structural information database. RESULTS: We illustrate Stem Trace's capabilities with several examples of the analysis of RNA folding data performed on 24 strains of HIV-1, HIV-2 and SIV sequences around the HIV dimerization region. This dimer linkage site has been found to play a role in encapsidation, reverse transcription, recombination, and inhibition of translation. Our examples show how Stem Trace elucidates preservation of structures in this region across the various strains of HIV. AVAILABILITY: The program can be made available upon request. It runs on SUN, SGI and DEC (Compaq) Unix workstations.

Ordered, labeled trees are trees in which each node has a label and the left-to-right order of its children (if it has any) is fixed. Such trees have many applications in vision, pattern recognition, molecular biology and natural language processing. We consider a substructure of an ordered labeled tree T to be a connected subgraph of T. Given two ordered labeled trees T/sub 1/ and T/sub 2/ and an integer d, the largest approximately common substructure problem is to find a substructure U/sub 1/ of T/sub 1/ and a substructure U/sub 2/ of T/sub 2/ such that U/sub 1/ is within edit distance d of U/sub 2/ and where there does not exist any other substructure V/sub 1/ of T/sub 1/ and V/sub 2/ of T/sub 2/ such that V/sub 1/ and V/sub 2/ satisfy the distance constraint and the sum of the sizes of V/sub 1/ and V/sub 2/ is greater than the sum of the sizes of U/sub 1/ and U/sub 2/. We present a dynamic programming algorithm to solve this problem, which runs as fast as the fastest known algorithm for computing the edit distance of two trees when the distance allowed in the common substructures is a constant independent of the input trees. To demonstrate the utility of our algorithm, we discuss its application to discovering motifs in multiple RNA secondary structures (which are ordered labeled trees).

Comparison of the secondary structure of the 5' non-coding region of poliovirus 3 RNA derived from the genetic algorithm with the model of Skinner et al. (J. Mol. Biol., 207, 379-392, 1989) demonstrates many of the confirmed structural elements. The genetic algorithm (Shapiro and Navetta, J. Supercomput., 8, 195-201, 1994) generates a population of all possible stems, then mixes, combines, and recombines these stems in multiple iterations on a massively parallel computer, ultimately selecting a most fit structure based on its energy. The secondary structure of the region containing the determinants of neurovirulence was better predicted using the genetic algorithm, whereas the dynamic programming algorithm (Zuker, Science, 244, 48-52, 1989) required phylogenetic comparative sequence analysis to arrive at the correct conclusion. In addition, artificial mutations were introduced throughout this region of the genome and although rearrangements in structure may occur, many structures persisted, suggesting that the given structures thus selected may have evolved to withstand isolated mutations. The genetic algorithm-derived structure for the 5' non-coding region compares favorably with the biological data and functions previously described, and contains all of the 'persistent' structures, suggesting also that the persistence factor may be an aid to validating structures.

MOTIVATION: Using the genetic algorithm (GA) for RNA folding on a massively parallel supercomputer, MasPar MP-2 with 16,384 processors, we successfully predicted the existence of H-type pseudoknots in several sequences. RESULTS: The GA is applied to folding the tRNA-like 3' end of turnip yellow mosaic virus (TYMV) RNA sequence with 82 nucleotides, the 3' UTRs of satellite tobacco necrosis virus (STNV)-2 RNA sequence with 619 nucleotides and STNV-I RNA sequence with 622 nucleotides, and the bacteriophage T2, T4 and T6 gene 32 mRNA sequences with 946, 1340 and 946 nucleotides, respectively. The GA's results match the phylogenetically supported tertiary structures of these sequences.

An annealing mutation operator in the genetic algorithms (GA) for RNA folding on a MasPar MP-2 has been designed. The mutation probability descends along a hyperbola with respect to the size of the secondary structure, hence the total number of mutations at each generation drops linearly. Especially for long sequences with thousands of nucleotides as opposed to hundreds of nucleotides, the new mutation operator can make the distribution of free energies over all processors on MasPar MP-2 converge only after hundreds of generations. Based upon this new mutation operator, a technique to terminate the GA is also developed. The new mutation operator runs very efficiently. Some variations of the annealing mutation operator are also discussed.

Suppose you are given a set of natural entities (e.g., proteins, organisms, weather patterns, etc.) that possess some important common externally observable properties. You also have a structural description of the entities (e.g., sequence, topological, or geometrical data) and a distance metric. Combinatorial pattern discovery is the activity of finding patterns in the structural data that might explain these common properties based on the metric.

In our recent series of papers, we have used the structures of statistical significance from Monte Carlo simulations to improve the predictions of secondary structure of RNA and to analyze the possible role of locally significant structures in the life cycle of human immunodeficiency virus. Because of intensive computational requirements for Monte Carlo simulation, it becomes impractical even using a supercomputer to assess the significance of a structure with a window size greater than 200 along an RNA sequence of 1000 bases or more. In this paper, we have developed a new procedure that drastically reduces the time needed to assess the significance of structures. In fact, the efficiency of this new method allows us to assess structures on the VAX as well as the CRAY.

In a previous paper, an algorithm was presented for analyzing multiple RNA secondary structures utilizing a multiple string alignment algorithm. In this paper we present another approach to the problem of comparing many secondary structures by utilizing a very efficient tree-matching algorithm that will compare two trees in O([T1] X [T2] X L1 X L2) in the worst case and very close to O([T1] X [T2]) for average trees representing secondary structures. The result of the pairwise comparison algorithm is then used with a cluster algorithm to produce a multiple structure clustering which can be displayed in a taxonomy tree to show related structures.

A method for assessing the preserved stem-loops of RNA secondary structures is presented. Frequently recurring helical stems in a set of secondary structures resulting from the simulated folding process of a given RNA are assessed and consensus structural motifs can then be selected to construct a secondary structure of the RNA. Alternatively, it can be applied to a series of 'optimal' and 'suboptimal' secondary structures computed using the dynamic program developed by Williams and Tinoco. To demonstrate the power and the usefulness of the program we give examples of this procedure.

A new distributed computational procedure is presented for rapidly determining the similarity of multiple conformations of RNA secondary structures. A data abstraction scheme is utilized to reduce the quantity of data that must be handled to determine the degree of similarity among multiple structures. The method has been used to compare 200 structures with easy visualization of both those structures and substructures that are similar and those that are vastly different. It has the capability of processing many more conformations as a function of research requirements. The algorithm is described as well as some suggestions for future uses and extensions.