Predicts RNA secondary structures from sequence. MC-Fold is a part of an RNA-structure-prediction method based on nucleotide cyclic motifs (NCMs). This application is implemented as a pipeline with MC-Sym, an other computer program that predicts all-atoms RNA tertiary structure from sequence and secondary structure. MC-Fold takes into account the in-stem non-canonical base pairs, and suites of, into a unified energetic framework.
Generates a set of 3-D structure models from a syntactic definition of the modeled RNA molecule. MC-Sym is an RNA 3-D structure modeling system. It creates a conformational search space from this input into which conformations are built and validated according to a set of constraints. It includes sequence constitution and nucleotide-nucleotide interaction types.
Identifies cloverleaf structure for each sequence. MC-Cons computes a program that assigns an RNA secondary structure among sub-optimals for many sequences, such that the assigned secondary structures maximize the resemblance to all others. It assigns to each sequence the structure that maximizes the overall sum of pairwise structural similarities. This online application can also be seen as a filter between multiple-sequences and tertiary structure.
Allows RNA secondary structure prediction and analysis. RNAstructure is designed to make algorithms accessible for a variety of user needs. The software includes methods for predicting bimolecular structure, conserved structures in multiple homologs and siRNA design. Several methods are also available for predicting structures for a single sequence, including maximum expected accuracy, stochastic sampling, exhaustive traceback and pseudoknot prediction.
Gathers an assortment of methods for RNA secondary structure analyses. ViennaRNA provides a unified interface to a set of command-line programs dealing with: (i) noncoding RNA detection, (ii) three different algorithms for structure prediction, (iii) RNA folding kinetics, (iv) sequence design considering RNA-RNA hybridizations, and (v) utilities that mainly assist in processing input- and output data and more.
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.
Provides easy access to RNA and DNA folding and hybridization software to the scientific community at large. Detailed output, in the form of structure plots with or without reliability information, single strand frequency plots and 'energy dot plots', are available for the folding of single sequences.
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
Allows extraction of an RNA secondary structure from atom coordinate data collected and presentation of this structure it in both textual and graphical form. RNApdbee is a web server that provides two usage scenarios: the (1) 3D scenario (the basic one) to derive the secondary structure topology of RNA from the pdb data and the (2) 2D scenario to convert between the CT, BPSEQ and extended dot-bracket notations.
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.
Deals with RNA structure probing and post-transcriptional modifications mapping high-throughput data. RNA Framework is a modular toolkit. Its main features are (i) automatic reference transcriptome creation, (ii) automatic reads preprocessing (adapter clipping and trimming) and mapping, (iii) scoring and data normalization and (iv) accurate RNA folding prediction by incorporating structural probing data. It can perform not only RNA Structure analysis, but also analysis of RNA post-transcriptional modifications mapping experiments (such as m1A-seq, m6A-seq, 2OMe-seq, and Pseudo-seq).
Facilitates distributed access to bioinformatics command line programs. JABAWS provides functions for alignments and sequence analyses of up to 1000 sequences with up to 1000 residues. This tool assists users to recognize potential performance issues and aids with web services troubleshooting. It is able to determine amino acid alignment conservation.
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.
Establishes a central, redistributable workbench for scientists and programmers working with RNA-related data. The RNA workbench builds a sustainable community around it. This platform is unique in combining available tools, workflows and training material, as well as providing easy access for experimentalists. It serves as a central hub for programmers, which can easily integrate and deploy their existing or novel tools and workflows.
A method which includes information about homologous sequences into the prediction of the secondary structure of the target sequence, and showed that it substantially improved the performance of secondary structure predictions.
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.
Computes an aspect of the folding landscape of an RNA nucleotide sequence. RNALOSS supplies several details about the distribution of locally optimal secondary structures. This tool can be useful for designing RNA sequences with low bending energy, but whose distribution of secondary structures requires rapid and robust bending.
Allows prediction of secondary structures of single stranded RNA or DNA sequences. RNAfold allows users to interact with the way the prediction is done. It can return an interactive RNA secondary structure plot, RNA secondary structure plots containing reliability annotation or a mountain plot. This tool is based on the Vienna RNA Package that produces a base pairing probability matrix.
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.
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.
A web-based tool for fast and accurate prediction of RNA 2D complex structures. Rtips comprises two computational tools based on integer programming, IPknot for predicting RNA secondary structures with pseudoknots and RactIP for predicting RNA-RNA interactions with kissing hairpins. Both servers can run much faster than existing services with the same purpose on large data sets as well as being at least comparable in prediction accuracy.
Allows determination of perturbation energies that minimizes the differences between predicted and observed pairing probabilities. RNApbfold is an online application that requires a sequence in FASTA format or shape reactivities for performing. For a sequence, this tool accepts until 1000nt. Furthermore, users can be notified via e-mail upon completion of the job.
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.
Aims to parse base pair data into detailed structure “maps” providing relevant contextual data for: stems, internal loops, bulges, multi-branched loops (multiloops), external loops, hairpin loops, and pseudoknots. bpRNA aims to produce secondary structure annotations from base pairing data. It supplies solutions to represent RNA structural data such as the structure array, which makes the structure easier to read and visualize by providing a character label for each nucleotide of the dot-bracket representation.
Predicts the structure common to most of the sequences in an alignment. RNAalifold combines a thermodynamic energy minimization with a simple scoring model to assess evolutionary conservation. It modifies the energy model by introducing a base pair conservation score that evaluates the alignment columns evidence for base pairing. This method has been used for the prediction of thermodynamically stable and/or evolutionary conserved RNAs.
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.
A global structural alignment algorithm to predict consensus secondary structures for unaligned sequences. RNAG uses a blocked Gibbs sampling algorithm, which has a theoretical advantage in convergence time. This algorithm iteratively samples from the conditional probability distributions P(Structure | Alignment) and P(Alignment | Structure).
Assists users in performing and evaluating simulations in the field of structure-based models (SBMs). eSBMTools is organized in modules that can be loaded into Python projects. It can be used at all stages in the context of SBM simulations: from generating the SBM itself, over manipulations of the model and configuration file generation, to extensive post-processing of simulation data. It also provides interfaces with a standard build of the GROMACS software suite.
Allows users to determine secondary structures for a given sequence. RNANR provides a software for investigating kinetics landscapes by using a set of algorithms including a non-redundant sampling algorithm for revealing locally optimal secondary structures. The program permits users to perform sampling according to three different parameters and contains optional functionalities for counting, generates realistic structures or specifies sets of base pairs.
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.
Determines RNA secondary structure from a collection of unaligned homologous RNA sequences. RAF simultaneously aligns and folds RNA sequences by optimizing a pair of unaligned RNA sequences for both sequence homology and structural conservation. It implements: a fast Sankoff-style inference engine, a simple progressive strategy and a max-margin framework. This tool includes new basis scoring functions.
A genetic algorithm to explore the very large search space of RNA secondary structure conformations for an optimal solution. MPGAfold operates on a population of thousands of possible solution structures, evolving them toward thermodynamic fitness. It may be run multiple times and in multiple phases.
Offers a method for predicting RNA secondary structures from primary sequences. FledFold is a standalone software, based on the nearest neighbor (NN) model, that intends to increase accuracy in detection, especially for investigating RNAs without pseudoknots. The method couples thermodynamic and kinetic factors of RNA secondary structure to perform its predictions.
Compares RNA secondary structures. RNAforester supports the computation of pairwise and multiple alignment of structures based on the tree alignment model. The user interface follows the philosophy of the Vienna RNA Package. RNAforester is available as a web application or can be download for local use as a command line based tool. RNAforester is a purely structure-based approach for comparing multiple RNA secondary structures.
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.