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A widely used collection of programs for thermodynamic RNA secondary structure prediction. Over the years, many additional tools have been developed building on the core programs of the package to also address issues related to noncoding RNA detection, RNA folding kinetics, or efficient sequence design considering RNA-RNA hybridizations. The ViennaRNA web services provide easy and user-friendly web access to these tools.
A RNA secondary structure prediction tool based on the idea of combining small motifs, called nucleotide cyclic motifs (NCMs). The algorithm implemented here has polynomial runtime in O(n^3) and uses a (pseudo-energy) scoring scheme. This program uses the same database as MC-Fold (which has exponential run-time) and aims to be able to produce the same results. The underlying grammar of our implementation is unambiguous and allows the complete evaluation of all structures within an energy band above the ground state, presenting each unique structure just once. Alternatively, the grammar allows partition function calculations.
R-scape / RNA Structural Covariation Above Phylogenetic Expectation
A computational method that quantitatively tests whether covariation analysis supports the presence of a conserved RNA secondary structure. R-scape analyzes a multiple-RNA sequence alignment and quantitates the statistical support for evolutionary conservation of an RNA structure. For a given alignment, R-scape produces many simulated null alignments (default 20) and calculates an APC G-test statistic for each alignment column pair, thus collecting an expected null distribution conditioned on the input alignment’s characteristics, including its length in columns, sequence number, pairwise identity, base composition, substitution types, and phylogenetic correlation
Detects structured noncoding RNAs in comparative genomics data. RNAz is a program for predicting structurally conserved and thermodynamically stable RNA secondary structures in multiple sequence alignments. It can be used in genome wide screens to detect functional RNA structures, as found in noncoding RNAs (ncRNAs) and cis-acting regulatory elements of messenger RNAs (mRNAs). The algorithm allows to calculate thermodynamic stability scores based on a dinucleotide background model.
PHMMTS / Pair Hidden Markov Models on Tree Structures
Serves for structural alignment of a binary tree and a sequence. PHMMTSs can be applied to aligning RNA secondary structures, i.e. a pairwise alignment to align an unfolded RNA sequence into an RNA sequence of known secondary structure. This tool assists users to identify non-coding RNA regions in an unfolded RNA sequence by aligning it into non-coding RNAs of known secondary structure. Moreover, this tool can be used for calculating structural alignments of RNA sequences.
Takes an alignment of RNA sequences as input and predicts a common structure for all sequences. Pfold is based on the KH-99 algorithm, which was only useful for a limited number of sequences due to its large computation time. This work makes the algorithm practically useful for larger numbers of sequences. The main concerns are treatment of gaps, computational speed and robustness. A new version of this algorithm has been created, called PPfold. PPfold is a parallelized version of Pfold, and can predict the structure of much longer alignments without underflow errors.
SPuNC / Structure Prediction using Nucleotide Composition
Evaluates candidate structures for a set of homologous RNAs on their ability to reproduce the patterns exhibited by biological structures. SPuNC is a structure prediction method that consists of the following steps: (1) given a multiple sequence alignment, generate an ensemble of candidate structures; (2) score all structures in the ensemble with a scoring function; and (3) return top-scoring structure(s) or consensus structure.
SPARCS / Structural Profile Assignment of RNA Coding Sequences
Predicts structured, unstructured and disordered regions in coding RNA sequences. SPARCS enables to sample uniformly random sequences preserving the encoded protein sequence and the DFs. It provides a list of segments with predicted strongly and weakly structured segments. It also assists user with the calculation of accurate Z-scores and the prediction of strongly and weakly structured regions, along with disordered regions in exons.
CROSS / Computational Recognition of Secondary Structure
Calculates the structural profile of an RNA sequence (single- or double-stranded state) at single-nucleotide resolution and without sequence length restrictions. CROSS algorithm is an artificial neural network with one hidden layer and two adaptive weight matrices to predict the structural state of a nucleotide considering its flanking residues. CROSS produces a table with the propensity scores and a graphical representation of the profile.
Performs various secondary structural analyses on single RNA sequences by CentroidFold, CentroidHomfold, IPKnot, CapR, Rchange, Raccess and RintD. By just giving an RNA sequence to the web server, the user can get the different types of solutions of the secondary structures, the marginal probabilities such as base-paring probabilities, loop probabilities and accessibilities of the local bases, the energy changes by arbitrary base mutations as well as the measures for validations of the predicted secondary structures.
Predicts RNA secondary structure. Advanced_multiloops permits to avoid isolated base pairs. It includes the free energy change contributions of coaxial stacking, dangling ends, terminal mismatches, and end penalties fully. The tool examines the entire search space of possible structures. It is composed of a logarithmic model and an Aalberts and Nandagopal (AN) model. The linear model appears to be superior to the logarithmic and AN models for structure prediction, and as an energy model.
ILM / Iterative Loop Matching
Predicts RNA secondary structures including pseudoknots. ILM can not only predict consensus structures for aligned homologous sequences, using combined thermodynamic and covariance scores, but can also be applied to individual sequences, using thermodynamic information alone. The algorithm has been tested on a number of RNA families. Using 8–12 homologous sequences, the algorithm correctly identifies more than 90% of base-pairs for short sequences and 80% overall. It correctly predicts nearly all pseudoknots and produces very few spurious base-pairs for sequences without pseudoknots. Comparisons show that this algorithm is both more sensitive and more specific than the maximum weighted matching method. In addition, ILM has high-prediction accuracy on individual sequences, comparable with the PKNOTS algorithm, while using much less computational resources.
Computes local base pairing probabilities for long DNA sequences. Rfold implements an algorithm that exactly computes the base pairing probabilities associated with the energy model under the constraint on the maximal span W of base pairs. The complexity of this algorithm is given by O(NW²) in time and O(N + W²) in memory, where N is the sequence length. This algorithm has a higher sensitivity to the true base pairs as compared to that of RNAplfold. Rfold implements another algorithm that predicts a mutually consistent set of local secondary structures by maximizing the expected accuracy function. The comparison of the local secondary structure predictions with those of RNALfold indicates that this algorithm is more accurate. The source code of the Rfold software is freely available for download.
A theoretical model capturing both RNA pair families and extended secondary structure motifs with shared nucleotides using 2-diagrams. RNAwolf was compared to three state-of-the-art thermodynamic folding algorithms [RNAfold, UNAfold and RNAstructure] to assess the prediction quality of the model. The results show that, not unexpectedly, the ‘enhanced Nussinov’ algorithm cannot compete with state-of-the-art tools due to its simplified energy model. The ‘enhanced Nussinov-algorithm’, has to perform more work than classical secondary structure prediction programs when filling the dynamic programming matrices.
The super-n-motifs model
Provides an efficient way of comparing secondary structures from linear and circular RNA comprising pseudoknots and G-quadruplex (G4s). The super-n-motifs model is based on the idea that similar secondary structures share similar combinations of motifs. Since secondary structures can be decomposed into building blocks, i.e. basic motifs such as stems or hairpin loops, the secondary structures can be seen as being formed by multiple combinations of motifs. The super-n-motifs model can be particularly helpful for RNA annotation, structure-based phylogeny, homology search in databases and identification of new families in populations of RNA. Since this model efficiently handles pseudoknots and G4 motifs, it can also help in understanding their functional roles.
Aligns two sequences and finds a common structure, including multibranch loops. Dynalign is a dynamic programming algorithm that uses nearest-neighbor rules for predicting the free energies of secondary structures. This method improves the accuracy of secondary structure prediction relative to prediction for a single sequence by free energy minimization. It is directly applicable to many problems, such as determining the secondary structures of RNAs found by in vitro evolution.

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